Identify_Customer_Segments.html

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<title>Identify_Customer_Segments</title>

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}
.glyphicon-triangle-top:before {
  content: "\e253";
}
.glyphicon-console:before {
  content: "\e254";
}
.glyphicon-superscript:before {
  content: "\e255";
}
.glyphicon-subscript:before {
  content: "\e256";
}
.glyphicon-menu-left:before {
  content: "\e257";
}
.glyphicon-menu-right:before {
  content: "\e258";
}
.glyphicon-menu-down:before {
  content: "\e259";
}
.glyphicon-menu-up:before {
  content: "\e260";
}
* {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
*:before,
*:after {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
html {
  font-size: 10px;
  -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
}
body {
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-size: 13px;
  line-height: 1.42857143;
  color: #000;
  background-color: #fff;
}
input,
button,
select,
textarea {
  font-family: inherit;
  font-size: inherit;
  line-height: inherit;
}
a {
  color: #337ab7;
  text-decoration: none;
}
a:hover,
a:focus {
  color: #23527c;
  text-decoration: underline;
}
a:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
figure {
  margin: 0;
}
img {
  vertical-align: middle;
}
.img-responsive,
.thumbnail > img,
.thumbnail a > img,
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  display: block;
  max-width: 100%;
  height: auto;
}
.img-rounded {
  border-radius: 3px;
}
.img-thumbnail {
  padding: 4px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: all 0.2s ease-in-out;
  -o-transition: all 0.2s ease-in-out;
  transition: all 0.2s ease-in-out;
  display: inline-block;
  max-width: 100%;
  height: auto;
}
.img-circle {
  border-radius: 50%;
}
hr {
  margin-top: 18px;
  margin-bottom: 18px;
  border: 0;
  border-top: 1px solid #eeeeee;
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  margin: -1px;
  padding: 0;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
[role="button"] {
  cursor: pointer;
}
h1,
h2,
h3,
h4,
h5,
h6,
.h1,
.h2,
.h3,
.h4,
.h5,
.h6 {
  font-family: inherit;
  font-weight: 500;
  line-height: 1.1;
  color: inherit;
}
h1 small,
h2 small,
h3 small,
h4 small,
h5 small,
h6 small,
.h1 small,
.h2 small,
.h3 small,
.h4 small,
.h5 small,
.h6 small,
h1 .small,
h2 .small,
h3 .small,
h4 .small,
h5 .small,
h6 .small,
.h1 .small,
.h2 .small,
.h3 .small,
.h4 .small,
.h5 .small,
.h6 .small {
  font-weight: normal;
  line-height: 1;
  color: #777777;
}
h1,
.h1,
h2,
.h2,
h3,
.h3 {
  margin-top: 18px;
  margin-bottom: 9px;
}
h1 small,
.h1 small,
h2 small,
.h2 small,
h3 small,
.h3 small,
h1 .small,
.h1 .small,
h2 .small,
.h2 .small,
h3 .small,
.h3 .small {
  font-size: 65%;
}
h4,
.h4,
h5,
.h5,
h6,
.h6 {
  margin-top: 9px;
  margin-bottom: 9px;
}
h4 small,
.h4 small,
h5 small,
.h5 small,
h6 small,
.h6 small,
h4 .small,
.h4 .small,
h5 .small,
.h5 .small,
h6 .small,
.h6 .small {
  font-size: 75%;
}
h1,
.h1 {
  font-size: 33px;
}
h2,
.h2 {
  font-size: 27px;
}
h3,
.h3 {
  font-size: 23px;
}
h4,
.h4 {
  font-size: 17px;
}
h5,
.h5 {
  font-size: 13px;
}
h6,
.h6 {
  font-size: 12px;
}
p {
  margin: 0 0 9px;
}
.lead {
  margin-bottom: 18px;
  font-size: 14px;
  font-weight: 300;
  line-height: 1.4;
}
@media (min-width: 768px) {
  .lead {
    font-size: 19.5px;
  }
}
small,
.small {
  font-size: 92%;
}
mark,
.mark {
  background-color: #fcf8e3;
  padding: .2em;
}
.text-left {
  text-align: left;
}
.text-right {
  text-align: right;
}
.text-center {
  text-align: center;
}
.text-justify {
  text-align: justify;
}
.text-nowrap {
  white-space: nowrap;
}
.text-lowercase {
  text-transform: lowercase;
}
.text-uppercase {
  text-transform: uppercase;
}
.text-capitalize {
  text-transform: capitalize;
}
.text-muted {
  color: #777777;
}
.text-primary {
  color: #337ab7;
}
a.text-primary:hover,
a.text-primary:focus {
  color: #286090;
}
.text-success {
  color: #3c763d;
}
a.text-success:hover,
a.text-success:focus {
  color: #2b542c;
}
.text-info {
  color: #31708f;
}
a.text-info:hover,
a.text-info:focus {
  color: #245269;
}
.text-warning {
  color: #8a6d3b;
}
a.text-warning:hover,
a.text-warning:focus {
  color: #66512c;
}
.text-danger {
  color: #a94442;
}
a.text-danger:hover,
a.text-danger:focus {
  color: #843534;
}
.bg-primary {
  color: #fff;
  background-color: #337ab7;
}
a.bg-primary:hover,
a.bg-primary:focus {
  background-color: #286090;
}
.bg-success {
  background-color: #dff0d8;
}
a.bg-success:hover,
a.bg-success:focus {
  background-color: #c1e2b3;
}
.bg-info {
  background-color: #d9edf7;
}
a.bg-info:hover,
a.bg-info:focus {
  background-color: #afd9ee;
}
.bg-warning {
  background-color: #fcf8e3;
}
a.bg-warning:hover,
a.bg-warning:focus {
  background-color: #f7ecb5;
}
.bg-danger {
  background-color: #f2dede;
}
a.bg-danger:hover,
a.bg-danger:focus {
  background-color: #e4b9b9;
}
.page-header {
  padding-bottom: 8px;
  margin: 36px 0 18px;
  border-bottom: 1px solid #eeeeee;
}
ul,
ol {
  margin-top: 0;
  margin-bottom: 9px;
}
ul ul,
ol ul,
ul ol,
ol ol {
  margin-bottom: 0;
}
.list-unstyled {
  padding-left: 0;
  list-style: none;
}
.list-inline {
  padding-left: 0;
  list-style: none;
  margin-left: -5px;
}
.list-inline > li {
  display: inline-block;
  padding-left: 5px;
  padding-right: 5px;
}
dl {
  margin-top: 0;
  margin-bottom: 18px;
}
dt,
dd {
  line-height: 1.42857143;
}
dt {
  font-weight: bold;
}
dd {
  margin-left: 0;
}
@media (min-width: 541px) {
  .dl-horizontal dt {
    float: left;
    width: 160px;
    clear: left;
    text-align: right;
    overflow: hidden;
    text-overflow: ellipsis;
    white-space: nowrap;
  }
  .dl-horizontal dd {
    margin-left: 180px;
  }
}
abbr[title],
abbr[data-original-title] {
  cursor: help;
  border-bottom: 1px dotted #777777;
}
.initialism {
  font-size: 90%;
  text-transform: uppercase;
}
blockquote {
  padding: 9px 18px;
  margin: 0 0 18px;
  font-size: inherit;
  border-left: 5px solid #eeeeee;
}
blockquote p:last-child,
blockquote ul:last-child,
blockquote ol:last-child {
  margin-bottom: 0;
}
blockquote footer,
blockquote small,
blockquote .small {
  display: block;
  font-size: 80%;
  line-height: 1.42857143;
  color: #777777;
}
blockquote footer:before,
blockquote small:before,
blockquote .small:before {
  content: '\2014 \00A0';
}
.blockquote-reverse,
blockquote.pull-right {
  padding-right: 15px;
  padding-left: 0;
  border-right: 5px solid #eeeeee;
  border-left: 0;
  text-align: right;
}
.blockquote-reverse footer:before,
blockquote.pull-right footer:before,
.blockquote-reverse small:before,
blockquote.pull-right small:before,
.blockquote-reverse .small:before,
blockquote.pull-right .small:before {
  content: '';
}
.blockquote-reverse footer:after,
blockquote.pull-right footer:after,
.blockquote-reverse small:after,
blockquote.pull-right small:after,
.blockquote-reverse .small:after,
blockquote.pull-right .small:after {
  content: '\00A0 \2014';
}
address {
  margin-bottom: 18px;
  font-style: normal;
  line-height: 1.42857143;
}
code,
kbd,
pre,
samp {
  font-family: monospace;
}
code {
  padding: 2px 4px;
  font-size: 90%;
  color: #c7254e;
  background-color: #f9f2f4;
  border-radius: 2px;
}
kbd {
  padding: 2px 4px;
  font-size: 90%;
  color: #888;
  background-color: transparent;
  border-radius: 1px;
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25);
}
kbd kbd {
  padding: 0;
  font-size: 100%;
  font-weight: bold;
  box-shadow: none;
}
pre {
  display: block;
  padding: 8.5px;
  margin: 0 0 9px;
  font-size: 12px;
  line-height: 1.42857143;
  word-break: break-all;
  word-wrap: break-word;
  color: #333333;
  background-color: #f5f5f5;
  border: 1px solid #ccc;
  border-radius: 2px;
}
pre code {
  padding: 0;
  font-size: inherit;
  color: inherit;
  white-space: pre-wrap;
  background-color: transparent;
  border-radius: 0;
}
.pre-scrollable {
  max-height: 340px;
  overflow-y: scroll;
}
.container {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
@media (min-width: 768px) {
  .container {
    width: 768px;
  }
}
@media (min-width: 992px) {
  .container {
    width: 940px;
  }
}
@media (min-width: 1200px) {
  .container {
    width: 1140px;
  }
}
.container-fluid {
  margin-right: auto;
  margin-left: auto;
  padding-left: 0px;
  padding-right: 0px;
}
.row {
  margin-left: 0px;
  margin-right: 0px;
}
.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 {
  position: relative;
  min-height: 1px;
  padding-left: 0px;
  padding-right: 0px;
}
.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 {
  float: left;
}
.col-xs-12 {
  width: 100%;
}
.col-xs-11 {
  width: 91.66666667%;
}
.col-xs-10 {
  width: 83.33333333%;
}
.col-xs-9 {
  width: 75%;
}
.col-xs-8 {
  width: 66.66666667%;
}
.col-xs-7 {
  width: 58.33333333%;
}
.col-xs-6 {
  width: 50%;
}
.col-xs-5 {
  width: 41.66666667%;
}
.col-xs-4 {
  width: 33.33333333%;
}
.col-xs-3 {
  width: 25%;
}
.col-xs-2 {
  width: 16.66666667%;
}
.col-xs-1 {
  width: 8.33333333%;
}
.col-xs-pull-12 {
  right: 100%;
}
.col-xs-pull-11 {
  right: 91.66666667%;
}
.col-xs-pull-10 {
  right: 83.33333333%;
}
.col-xs-pull-9 {
  right: 75%;
}
.col-xs-pull-8 {
  right: 66.66666667%;
}
.col-xs-pull-7 {
  right: 58.33333333%;
}
.col-xs-pull-6 {
  right: 50%;
}
.col-xs-pull-5 {
  right: 41.66666667%;
}
.col-xs-pull-4 {
  right: 33.33333333%;
}
.col-xs-pull-3 {
  right: 25%;
}
.col-xs-pull-2 {
  right: 16.66666667%;
}
.col-xs-pull-1 {
  right: 8.33333333%;
}
.col-xs-pull-0 {
  right: auto;
}
.col-xs-push-12 {
  left: 100%;
}
.col-xs-push-11 {
  left: 91.66666667%;
}
.col-xs-push-10 {
  left: 83.33333333%;
}
.col-xs-push-9 {
  left: 75%;
}
.col-xs-push-8 {
  left: 66.66666667%;
}
.col-xs-push-7 {
  left: 58.33333333%;
}
.col-xs-push-6 {
  left: 50%;
}
.col-xs-push-5 {
  left: 41.66666667%;
}
.col-xs-push-4 {
  left: 33.33333333%;
}
.col-xs-push-3 {
  left: 25%;
}
.col-xs-push-2 {
  left: 16.66666667%;
}
.col-xs-push-1 {
  left: 8.33333333%;
}
.col-xs-push-0 {
  left: auto;
}
.col-xs-offset-12 {
  margin-left: 100%;
}
.col-xs-offset-11 {
  margin-left: 91.66666667%;
}
.col-xs-offset-10 {
  margin-left: 83.33333333%;
}
.col-xs-offset-9 {
  margin-left: 75%;
}
.col-xs-offset-8 {
  margin-left: 66.66666667%;
}
.col-xs-offset-7 {
  margin-left: 58.33333333%;
}
.col-xs-offset-6 {
  margin-left: 50%;
}
.col-xs-offset-5 {
  margin-left: 41.66666667%;
}
.col-xs-offset-4 {
  margin-left: 33.33333333%;
}
.col-xs-offset-3 {
  margin-left: 25%;
}
.col-xs-offset-2 {
  margin-left: 16.66666667%;
}
.col-xs-offset-1 {
  margin-left: 8.33333333%;
}
.col-xs-offset-0 {
  margin-left: 0%;
}
@media (min-width: 768px) {
  .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 {
    float: left;
  }
  .col-sm-12 {
    width: 100%;
  }
  .col-sm-11 {
    width: 91.66666667%;
  }
  .col-sm-10 {
    width: 83.33333333%;
  }
  .col-sm-9 {
    width: 75%;
  }
  .col-sm-8 {
    width: 66.66666667%;
  }
  .col-sm-7 {
    width: 58.33333333%;
  }
  .col-sm-6 {
    width: 50%;
  }
  .col-sm-5 {
    width: 41.66666667%;
  }
  .col-sm-4 {
    width: 33.33333333%;
  }
  .col-sm-3 {
    width: 25%;
  }
  .col-sm-2 {
    width: 16.66666667%;
  }
  .col-sm-1 {
    width: 8.33333333%;
  }
  .col-sm-pull-12 {
    right: 100%;
  }
  .col-sm-pull-11 {
    right: 91.66666667%;
  }
  .col-sm-pull-10 {
    right: 83.33333333%;
  }
  .col-sm-pull-9 {
    right: 75%;
  }
  .col-sm-pull-8 {
    right: 66.66666667%;
  }
  .col-sm-pull-7 {
    right: 58.33333333%;
  }
  .col-sm-pull-6 {
    right: 50%;
  }
  .col-sm-pull-5 {
    right: 41.66666667%;
  }
  .col-sm-pull-4 {
    right: 33.33333333%;
  }
  .col-sm-pull-3 {
    right: 25%;
  }
  .col-sm-pull-2 {
    right: 16.66666667%;
  }
  .col-sm-pull-1 {
    right: 8.33333333%;
  }
  .col-sm-pull-0 {
    right: auto;
  }
  .col-sm-push-12 {
    left: 100%;
  }
  .col-sm-push-11 {
    left: 91.66666667%;
  }
  .col-sm-push-10 {
    left: 83.33333333%;
  }
  .col-sm-push-9 {
    left: 75%;
  }
  .col-sm-push-8 {
    left: 66.66666667%;
  }
  .col-sm-push-7 {
    left: 58.33333333%;
  }
  .col-sm-push-6 {
    left: 50%;
  }
  .col-sm-push-5 {
    left: 41.66666667%;
  }
  .col-sm-push-4 {
    left: 33.33333333%;
  }
  .col-sm-push-3 {
    left: 25%;
  }
  .col-sm-push-2 {
    left: 16.66666667%;
  }
  .col-sm-push-1 {
    left: 8.33333333%;
  }
  .col-sm-push-0 {
    left: auto;
  }
  .col-sm-offset-12 {
    margin-left: 100%;
  }
  .col-sm-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-sm-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-sm-offset-9 {
    margin-left: 75%;
  }
  .col-sm-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-sm-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-sm-offset-6 {
    margin-left: 50%;
  }
  .col-sm-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-sm-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-sm-offset-3 {
    margin-left: 25%;
  }
  .col-sm-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-sm-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-sm-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 992px) {
  .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 {
    float: left;
  }
  .col-md-12 {
    width: 100%;
  }
  .col-md-11 {
    width: 91.66666667%;
  }
  .col-md-10 {
    width: 83.33333333%;
  }
  .col-md-9 {
    width: 75%;
  }
  .col-md-8 {
    width: 66.66666667%;
  }
  .col-md-7 {
    width: 58.33333333%;
  }
  .col-md-6 {
    width: 50%;
  }
  .col-md-5 {
    width: 41.66666667%;
  }
  .col-md-4 {
    width: 33.33333333%;
  }
  .col-md-3 {
    width: 25%;
  }
  .col-md-2 {
    width: 16.66666667%;
  }
  .col-md-1 {
    width: 8.33333333%;
  }
  .col-md-pull-12 {
    right: 100%;
  }
  .col-md-pull-11 {
    right: 91.66666667%;
  }
  .col-md-pull-10 {
    right: 83.33333333%;
  }
  .col-md-pull-9 {
    right: 75%;
  }
  .col-md-pull-8 {
    right: 66.66666667%;
  }
  .col-md-pull-7 {
    right: 58.33333333%;
  }
  .col-md-pull-6 {
    right: 50%;
  }
  .col-md-pull-5 {
    right: 41.66666667%;
  }
  .col-md-pull-4 {
    right: 33.33333333%;
  }
  .col-md-pull-3 {
    right: 25%;
  }
  .col-md-pull-2 {
    right: 16.66666667%;
  }
  .col-md-pull-1 {
    right: 8.33333333%;
  }
  .col-md-pull-0 {
    right: auto;
  }
  .col-md-push-12 {
    left: 100%;
  }
  .col-md-push-11 {
    left: 91.66666667%;
  }
  .col-md-push-10 {
    left: 83.33333333%;
  }
  .col-md-push-9 {
    left: 75%;
  }
  .col-md-push-8 {
    left: 66.66666667%;
  }
  .col-md-push-7 {
    left: 58.33333333%;
  }
  .col-md-push-6 {
    left: 50%;
  }
  .col-md-push-5 {
    left: 41.66666667%;
  }
  .col-md-push-4 {
    left: 33.33333333%;
  }
  .col-md-push-3 {
    left: 25%;
  }
  .col-md-push-2 {
    left: 16.66666667%;
  }
  .col-md-push-1 {
    left: 8.33333333%;
  }
  .col-md-push-0 {
    left: auto;
  }
  .col-md-offset-12 {
    margin-left: 100%;
  }
  .col-md-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-md-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-md-offset-9 {
    margin-left: 75%;
  }
  .col-md-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-md-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-md-offset-6 {
    margin-left: 50%;
  }
  .col-md-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-md-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-md-offset-3 {
    margin-left: 25%;
  }
  .col-md-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-md-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-md-offset-0 {
    margin-left: 0%;
  }
}
@media (min-width: 1200px) {
  .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 {
    float: left;
  }
  .col-lg-12 {
    width: 100%;
  }
  .col-lg-11 {
    width: 91.66666667%;
  }
  .col-lg-10 {
    width: 83.33333333%;
  }
  .col-lg-9 {
    width: 75%;
  }
  .col-lg-8 {
    width: 66.66666667%;
  }
  .col-lg-7 {
    width: 58.33333333%;
  }
  .col-lg-6 {
    width: 50%;
  }
  .col-lg-5 {
    width: 41.66666667%;
  }
  .col-lg-4 {
    width: 33.33333333%;
  }
  .col-lg-3 {
    width: 25%;
  }
  .col-lg-2 {
    width: 16.66666667%;
  }
  .col-lg-1 {
    width: 8.33333333%;
  }
  .col-lg-pull-12 {
    right: 100%;
  }
  .col-lg-pull-11 {
    right: 91.66666667%;
  }
  .col-lg-pull-10 {
    right: 83.33333333%;
  }
  .col-lg-pull-9 {
    right: 75%;
  }
  .col-lg-pull-8 {
    right: 66.66666667%;
  }
  .col-lg-pull-7 {
    right: 58.33333333%;
  }
  .col-lg-pull-6 {
    right: 50%;
  }
  .col-lg-pull-5 {
    right: 41.66666667%;
  }
  .col-lg-pull-4 {
    right: 33.33333333%;
  }
  .col-lg-pull-3 {
    right: 25%;
  }
  .col-lg-pull-2 {
    right: 16.66666667%;
  }
  .col-lg-pull-1 {
    right: 8.33333333%;
  }
  .col-lg-pull-0 {
    right: auto;
  }
  .col-lg-push-12 {
    left: 100%;
  }
  .col-lg-push-11 {
    left: 91.66666667%;
  }
  .col-lg-push-10 {
    left: 83.33333333%;
  }
  .col-lg-push-9 {
    left: 75%;
  }
  .col-lg-push-8 {
    left: 66.66666667%;
  }
  .col-lg-push-7 {
    left: 58.33333333%;
  }
  .col-lg-push-6 {
    left: 50%;
  }
  .col-lg-push-5 {
    left: 41.66666667%;
  }
  .col-lg-push-4 {
    left: 33.33333333%;
  }
  .col-lg-push-3 {
    left: 25%;
  }
  .col-lg-push-2 {
    left: 16.66666667%;
  }
  .col-lg-push-1 {
    left: 8.33333333%;
  }
  .col-lg-push-0 {
    left: auto;
  }
  .col-lg-offset-12 {
    margin-left: 100%;
  }
  .col-lg-offset-11 {
    margin-left: 91.66666667%;
  }
  .col-lg-offset-10 {
    margin-left: 83.33333333%;
  }
  .col-lg-offset-9 {
    margin-left: 75%;
  }
  .col-lg-offset-8 {
    margin-left: 66.66666667%;
  }
  .col-lg-offset-7 {
    margin-left: 58.33333333%;
  }
  .col-lg-offset-6 {
    margin-left: 50%;
  }
  .col-lg-offset-5 {
    margin-left: 41.66666667%;
  }
  .col-lg-offset-4 {
    margin-left: 33.33333333%;
  }
  .col-lg-offset-3 {
    margin-left: 25%;
  }
  .col-lg-offset-2 {
    margin-left: 16.66666667%;
  }
  .col-lg-offset-1 {
    margin-left: 8.33333333%;
  }
  .col-lg-offset-0 {
    margin-left: 0%;
  }
}
table {
  background-color: transparent;
}
caption {
  padding-top: 8px;
  padding-bottom: 8px;
  color: #777777;
  text-align: left;
}
th {
  text-align: left;
}
.table {
  width: 100%;
  max-width: 100%;
  margin-bottom: 18px;
}
.table > thead > tr > th,
.table > tbody > tr > th,
.table > tfoot > tr > th,
.table > thead > tr > td,
.table > tbody > tr > td,
.table > tfoot > tr > td {
  padding: 8px;
  line-height: 1.42857143;
  vertical-align: top;
  border-top: 1px solid #ddd;
}
.table > thead > tr > th {
  vertical-align: bottom;
  border-bottom: 2px solid #ddd;
}
.table > caption + thead > tr:first-child > th,
.table > colgroup + thead > tr:first-child > th,
.table > thead:first-child > tr:first-child > th,
.table > caption + thead > tr:first-child > td,
.table > colgroup + thead > tr:first-child > td,
.table > thead:first-child > tr:first-child > td {
  border-top: 0;
}
.table > tbody + tbody {
  border-top: 2px solid #ddd;
}
.table .table {
  background-color: #fff;
}
.table-condensed > thead > tr > th,
.table-condensed > tbody > tr > th,
.table-condensed > tfoot > tr > th,
.table-condensed > thead > tr > td,
.table-condensed > tbody > tr > td,
.table-condensed > tfoot > tr > td {
  padding: 5px;
}
.table-bordered {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > tbody > tr > th,
.table-bordered > tfoot > tr > th,
.table-bordered > thead > tr > td,
.table-bordered > tbody > tr > td,
.table-bordered > tfoot > tr > td {
  border: 1px solid #ddd;
}
.table-bordered > thead > tr > th,
.table-bordered > thead > tr > td {
  border-bottom-width: 2px;
}
.table-striped > tbody > tr:nth-of-type(odd) {
  background-color: #f9f9f9;
}
.table-hover > tbody > tr:hover {
  background-color: #f5f5f5;
}
table col[class*="col-"] {
  position: static;
  float: none;
  display: table-column;
}
table td[class*="col-"],
table th[class*="col-"] {
  position: static;
  float: none;
  display: table-cell;
}
.table > thead > tr > td.active,
.table > tbody > tr > td.active,
.table > tfoot > tr > td.active,
.table > thead > tr > th.active,
.table > tbody > tr > th.active,
.table > tfoot > tr > th.active,
.table > thead > tr.active > td,
.table > tbody > tr.active > td,
.table > tfoot > tr.active > td,
.table > thead > tr.active > th,
.table > tbody > tr.active > th,
.table > tfoot > tr.active > th {
  background-color: #f5f5f5;
}
.table-hover > tbody > tr > td.active:hover,
.table-hover > tbody > tr > th.active:hover,
.table-hover > tbody > tr.active:hover > td,
.table-hover > tbody > tr:hover > .active,
.table-hover > tbody > tr.active:hover > th {
  background-color: #e8e8e8;
}
.table > thead > tr > td.success,
.table > tbody > tr > td.success,
.table > tfoot > tr > td.success,
.table > thead > tr > th.success,
.table > tbody > tr > th.success,
.table > tfoot > tr > th.success,
.table > thead > tr.success > td,
.table > tbody > tr.success > td,
.table > tfoot > tr.success > td,
.table > thead > tr.success > th,
.table > tbody > tr.success > th,
.table > tfoot > tr.success > th {
  background-color: #dff0d8;
}
.table-hover > tbody > tr > td.success:hover,
.table-hover > tbody > tr > th.success:hover,
.table-hover > tbody > tr.success:hover > td,
.table-hover > tbody > tr:hover > .success,
.table-hover > tbody > tr.success:hover > th {
  background-color: #d0e9c6;
}
.table > thead > tr > td.info,
.table > tbody > tr > td.info,
.table > tfoot > tr > td.info,
.table > thead > tr > th.info,
.table > tbody > tr > th.info,
.table > tfoot > tr > th.info,
.table > thead > tr.info > td,
.table > tbody > tr.info > td,
.table > tfoot > tr.info > td,
.table > thead > tr.info > th,
.table > tbody > tr.info > th,
.table > tfoot > tr.info > th {
  background-color: #d9edf7;
}
.table-hover > tbody > tr > td.info:hover,
.table-hover > tbody > tr > th.info:hover,
.table-hover > tbody > tr.info:hover > td,
.table-hover > tbody > tr:hover > .info,
.table-hover > tbody > tr.info:hover > th {
  background-color: #c4e3f3;
}
.table > thead > tr > td.warning,
.table > tbody > tr > td.warning,
.table > tfoot > tr > td.warning,
.table > thead > tr > th.warning,
.table > tbody > tr > th.warning,
.table > tfoot > tr > th.warning,
.table > thead > tr.warning > td,
.table > tbody > tr.warning > td,
.table > tfoot > tr.warning > td,
.table > thead > tr.warning > th,
.table > tbody > tr.warning > th,
.table > tfoot > tr.warning > th {
  background-color: #fcf8e3;
}
.table-hover > tbody > tr > td.warning:hover,
.table-hover > tbody > tr > th.warning:hover,
.table-hover > tbody > tr.warning:hover > td,
.table-hover > tbody > tr:hover > .warning,
.table-hover > tbody > tr.warning:hover > th {
  background-color: #faf2cc;
}
.table > thead > tr > td.danger,
.table > tbody > tr > td.danger,
.table > tfoot > tr > td.danger,
.table > thead > tr > th.danger,
.table > tbody > tr > th.danger,
.table > tfoot > tr > th.danger,
.table > thead > tr.danger > td,
.table > tbody > tr.danger > td,
.table > tfoot > tr.danger > td,
.table > thead > tr.danger > th,
.table > tbody > tr.danger > th,
.table > tfoot > tr.danger > th {
  background-color: #f2dede;
}
.table-hover > tbody > tr > td.danger:hover,
.table-hover > tbody > tr > th.danger:hover,
.table-hover > tbody > tr.danger:hover > td,
.table-hover > tbody > tr:hover > .danger,
.table-hover > tbody > tr.danger:hover > th {
  background-color: #ebcccc;
}
.table-responsive {
  overflow-x: auto;
  min-height: 0.01%;
}
@media screen and (max-width: 767px) {
  .table-responsive {
    width: 100%;
    margin-bottom: 13.5px;
    overflow-y: hidden;
    -ms-overflow-style: -ms-autohiding-scrollbar;
    border: 1px solid #ddd;
  }
  .table-responsive > .table {
    margin-bottom: 0;
  }
  .table-responsive > .table > thead > tr > th,
  .table-responsive > .table > tbody > tr > th,
  .table-responsive > .table > tfoot > tr > th,
  .table-responsive > .table > thead > tr > td,
  .table-responsive > .table > tbody > tr > td,
  .table-responsive > .table > tfoot > tr > td {
    white-space: nowrap;
  }
  .table-responsive > .table-bordered {
    border: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:first-child,
  .table-responsive > .table-bordered > tbody > tr > th:first-child,
  .table-responsive > .table-bordered > tfoot > tr > th:first-child,
  .table-responsive > .table-bordered > thead > tr > td:first-child,
  .table-responsive > .table-bordered > tbody > tr > td:first-child,
  .table-responsive > .table-bordered > tfoot > tr > td:first-child {
    border-left: 0;
  }
  .table-responsive > .table-bordered > thead > tr > th:last-child,
  .table-responsive > .table-bordered > tbody > tr > th:last-child,
  .table-responsive > .table-bordered > tfoot > tr > th:last-child,
  .table-responsive > .table-bordered > thead > tr > td:last-child,
  .table-responsive > .table-bordered > tbody > tr > td:last-child,
  .table-responsive > .table-bordered > tfoot > tr > td:last-child {
    border-right: 0;
  }
  .table-responsive > .table-bordered > tbody > tr:last-child > th,
  .table-responsive > .table-bordered > tfoot > tr:last-child > th,
  .table-responsive > .table-bordered > tbody > tr:last-child > td,
  .table-responsive > .table-bordered > tfoot > tr:last-child > td {
    border-bottom: 0;
  }
}
fieldset {
  padding: 0;
  margin: 0;
  border: 0;
  min-width: 0;
}
legend {
  display: block;
  width: 100%;
  padding: 0;
  margin-bottom: 18px;
  font-size: 19.5px;
  line-height: inherit;
  color: #333333;
  border: 0;
  border-bottom: 1px solid #e5e5e5;
}
label {
  display: inline-block;
  max-width: 100%;
  margin-bottom: 5px;
  font-weight: bold;
}
input[type="search"] {
  -webkit-box-sizing: border-box;
  -moz-box-sizing: border-box;
  box-sizing: border-box;
}
input[type="radio"],
input[type="checkbox"] {
  margin: 4px 0 0;
  margin-top: 1px \9;
  line-height: normal;
}
input[type="file"] {
  display: block;
}
input[type="range"] {
  display: block;
  width: 100%;
}
select[multiple],
select[size] {
  height: auto;
}
input[type="file"]:focus,
input[type="radio"]:focus,
input[type="checkbox"]:focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
output {
  display: block;
  padding-top: 7px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
}
.form-control {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
}
.form-control:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.form-control::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.form-control:-ms-input-placeholder {
  color: #999;
}
.form-control::-webkit-input-placeholder {
  color: #999;
}
.form-control::-ms-expand {
  border: 0;
  background-color: transparent;
}
.form-control[disabled],
.form-control[readonly],
fieldset[disabled] .form-control {
  background-color: #eeeeee;
  opacity: 1;
}
.form-control[disabled],
fieldset[disabled] .form-control {
  cursor: not-allowed;
}
textarea.form-control {
  height: auto;
}
input[type="search"] {
  -webkit-appearance: none;
}
@media screen and (-webkit-min-device-pixel-ratio: 0) {
  input[type="date"].form-control,
  input[type="time"].form-control,
  input[type="datetime-local"].form-control,
  input[type="month"].form-control {
    line-height: 32px;
  }
  input[type="date"].input-sm,
  input[type="time"].input-sm,
  input[type="datetime-local"].input-sm,
  input[type="month"].input-sm,
  .input-group-sm input[type="date"],
  .input-group-sm input[type="time"],
  .input-group-sm input[type="datetime-local"],
  .input-group-sm input[type="month"] {
    line-height: 30px;
  }
  input[type="date"].input-lg,
  input[type="time"].input-lg,
  input[type="datetime-local"].input-lg,
  input[type="month"].input-lg,
  .input-group-lg input[type="date"],
  .input-group-lg input[type="time"],
  .input-group-lg input[type="datetime-local"],
  .input-group-lg input[type="month"] {
    line-height: 45px;
  }
}
.form-group {
  margin-bottom: 15px;
}
.radio,
.checkbox {
  position: relative;
  display: block;
  margin-top: 10px;
  margin-bottom: 10px;
}
.radio label,
.checkbox label {
  min-height: 18px;
  padding-left: 20px;
  margin-bottom: 0;
  font-weight: normal;
  cursor: pointer;
}
.radio input[type="radio"],
.radio-inline input[type="radio"],
.checkbox input[type="checkbox"],
.checkbox-inline input[type="checkbox"] {
  position: absolute;
  margin-left: -20px;
  margin-top: 4px \9;
}
.radio + .radio,
.checkbox + .checkbox {
  margin-top: -5px;
}
.radio-inline,
.checkbox-inline {
  position: relative;
  display: inline-block;
  padding-left: 20px;
  margin-bottom: 0;
  vertical-align: middle;
  font-weight: normal;
  cursor: pointer;
}
.radio-inline + .radio-inline,
.checkbox-inline + .checkbox-inline {
  margin-top: 0;
  margin-left: 10px;
}
input[type="radio"][disabled],
input[type="checkbox"][disabled],
input[type="radio"].disabled,
input[type="checkbox"].disabled,
fieldset[disabled] input[type="radio"],
fieldset[disabled] input[type="checkbox"] {
  cursor: not-allowed;
}
.radio-inline.disabled,
.checkbox-inline.disabled,
fieldset[disabled] .radio-inline,
fieldset[disabled] .checkbox-inline {
  cursor: not-allowed;
}
.radio.disabled label,
.checkbox.disabled label,
fieldset[disabled] .radio label,
fieldset[disabled] .checkbox label {
  cursor: not-allowed;
}
.form-control-static {
  padding-top: 7px;
  padding-bottom: 7px;
  margin-bottom: 0;
  min-height: 31px;
}
.form-control-static.input-lg,
.form-control-static.input-sm {
  padding-left: 0;
  padding-right: 0;
}
.input-sm {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-sm {
  height: 30px;
  line-height: 30px;
}
textarea.input-sm,
select[multiple].input-sm {
  height: auto;
}
.form-group-sm .form-control {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.form-group-sm select.form-control {
  height: 30px;
  line-height: 30px;
}
.form-group-sm textarea.form-control,
.form-group-sm select[multiple].form-control {
  height: auto;
}
.form-group-sm .form-control-static {
  height: 30px;
  min-height: 30px;
  padding: 6px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.input-lg {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-lg {
  height: 45px;
  line-height: 45px;
}
textarea.input-lg,
select[multiple].input-lg {
  height: auto;
}
.form-group-lg .form-control {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.form-group-lg select.form-control {
  height: 45px;
  line-height: 45px;
}
.form-group-lg textarea.form-control,
.form-group-lg select[multiple].form-control {
  height: auto;
}
.form-group-lg .form-control-static {
  height: 45px;
  min-height: 35px;
  padding: 11px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.has-feedback {
  position: relative;
}
.has-feedback .form-control {
  padding-right: 40px;
}
.form-control-feedback {
  position: absolute;
  top: 0;
  right: 0;
  z-index: 2;
  display: block;
  width: 32px;
  height: 32px;
  line-height: 32px;
  text-align: center;
  pointer-events: none;
}
.input-lg + .form-control-feedback,
.input-group-lg + .form-control-feedback,
.form-group-lg .form-control + .form-control-feedback {
  width: 45px;
  height: 45px;
  line-height: 45px;
}
.input-sm + .form-control-feedback,
.input-group-sm + .form-control-feedback,
.form-group-sm .form-control + .form-control-feedback {
  width: 30px;
  height: 30px;
  line-height: 30px;
}
.has-success .help-block,
.has-success .control-label,
.has-success .radio,
.has-success .checkbox,
.has-success .radio-inline,
.has-success .checkbox-inline,
.has-success.radio label,
.has-success.checkbox label,
.has-success.radio-inline label,
.has-success.checkbox-inline label {
  color: #3c763d;
}
.has-success .form-control {
  border-color: #3c763d;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-success .form-control:focus {
  border-color: #2b542c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168;
}
.has-success .input-group-addon {
  color: #3c763d;
  border-color: #3c763d;
  background-color: #dff0d8;
}
.has-success .form-control-feedback {
  color: #3c763d;
}
.has-warning .help-block,
.has-warning .control-label,
.has-warning .radio,
.has-warning .checkbox,
.has-warning .radio-inline,
.has-warning .checkbox-inline,
.has-warning.radio label,
.has-warning.checkbox label,
.has-warning.radio-inline label,
.has-warning.checkbox-inline label {
  color: #8a6d3b;
}
.has-warning .form-control {
  border-color: #8a6d3b;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-warning .form-control:focus {
  border-color: #66512c;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b;
}
.has-warning .input-group-addon {
  color: #8a6d3b;
  border-color: #8a6d3b;
  background-color: #fcf8e3;
}
.has-warning .form-control-feedback {
  color: #8a6d3b;
}
.has-error .help-block,
.has-error .control-label,
.has-error .radio,
.has-error .checkbox,
.has-error .radio-inline,
.has-error .checkbox-inline,
.has-error.radio label,
.has-error.checkbox label,
.has-error.radio-inline label,
.has-error.checkbox-inline label {
  color: #a94442;
}
.has-error .form-control {
  border-color: #a94442;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
}
.has-error .form-control:focus {
  border-color: #843534;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483;
}
.has-error .input-group-addon {
  color: #a94442;
  border-color: #a94442;
  background-color: #f2dede;
}
.has-error .form-control-feedback {
  color: #a94442;
}
.has-feedback label ~ .form-control-feedback {
  top: 23px;
}
.has-feedback label.sr-only ~ .form-control-feedback {
  top: 0;
}
.help-block {
  display: block;
  margin-top: 5px;
  margin-bottom: 10px;
  color: #404040;
}
@media (min-width: 768px) {
  .form-inline .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .form-inline .form-control-static {
    display: inline-block;
  }
  .form-inline .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .form-inline .input-group .input-group-addon,
  .form-inline .input-group .input-group-btn,
  .form-inline .input-group .form-control {
    width: auto;
  }
  .form-inline .input-group > .form-control {
    width: 100%;
  }
  .form-inline .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio,
  .form-inline .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .form-inline .radio label,
  .form-inline .checkbox label {
    padding-left: 0;
  }
  .form-inline .radio input[type="radio"],
  .form-inline .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .form-inline .has-feedback .form-control-feedback {
    top: 0;
  }
}
.form-horizontal .radio,
.form-horizontal .checkbox,
.form-horizontal .radio-inline,
.form-horizontal .checkbox-inline {
  margin-top: 0;
  margin-bottom: 0;
  padding-top: 7px;
}
.form-horizontal .radio,
.form-horizontal .checkbox {
  min-height: 25px;
}
.form-horizontal .form-group {
  margin-left: 0px;
  margin-right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .control-label {
    text-align: right;
    margin-bottom: 0;
    padding-top: 7px;
  }
}
.form-horizontal .has-feedback .form-control-feedback {
  right: 0px;
}
@media (min-width: 768px) {
  .form-horizontal .form-group-lg .control-label {
    padding-top: 11px;
    font-size: 17px;
  }
}
@media (min-width: 768px) {
  .form-horizontal .form-group-sm .control-label {
    padding-top: 6px;
    font-size: 12px;
  }
}
.btn {
  display: inline-block;
  margin-bottom: 0;
  font-weight: normal;
  text-align: center;
  vertical-align: middle;
  touch-action: manipulation;
  cursor: pointer;
  background-image: none;
  border: 1px solid transparent;
  white-space: nowrap;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  border-radius: 2px;
  -webkit-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
}
.btn:focus,
.btn:active:focus,
.btn.active:focus,
.btn.focus,
.btn:active.focus,
.btn.active.focus {
  outline: 5px auto -webkit-focus-ring-color;
  outline-offset: -2px;
}
.btn:hover,
.btn:focus,
.btn.focus {
  color: #333;
  text-decoration: none;
}
.btn:active,
.btn.active {
  outline: 0;
  background-image: none;
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn.disabled,
.btn[disabled],
fieldset[disabled] .btn {
  cursor: not-allowed;
  opacity: 0.65;
  filter: alpha(opacity=65);
  -webkit-box-shadow: none;
  box-shadow: none;
}
a.btn.disabled,
fieldset[disabled] a.btn {
  pointer-events: none;
}
.btn-default {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.btn-default:focus,
.btn-default.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.btn-default:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.btn-default:active:hover,
.btn-default.active:hover,
.open > .dropdown-toggle.btn-default:hover,
.btn-default:active:focus,
.btn-default.active:focus,
.open > .dropdown-toggle.btn-default:focus,
.btn-default:active.focus,
.btn-default.active.focus,
.open > .dropdown-toggle.btn-default.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.btn-default:active,
.btn-default.active,
.open > .dropdown-toggle.btn-default {
  background-image: none;
}
.btn-default.disabled:hover,
.btn-default[disabled]:hover,
fieldset[disabled] .btn-default:hover,
.btn-default.disabled:focus,
.btn-default[disabled]:focus,
fieldset[disabled] .btn-default:focus,
.btn-default.disabled.focus,
.btn-default[disabled].focus,
fieldset[disabled] .btn-default.focus {
  background-color: #fff;
  border-color: #ccc;
}
.btn-default .badge {
  color: #fff;
  background-color: #333;
}
.btn-primary {
  color: #fff;
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary:focus,
.btn-primary.focus {
  color: #fff;
  background-color: #286090;
  border-color: #122b40;
}
.btn-primary:hover {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  color: #fff;
  background-color: #286090;
  border-color: #204d74;
}
.btn-primary:active:hover,
.btn-primary.active:hover,
.open > .dropdown-toggle.btn-primary:hover,
.btn-primary:active:focus,
.btn-primary.active:focus,
.open > .dropdown-toggle.btn-primary:focus,
.btn-primary:active.focus,
.btn-primary.active.focus,
.open > .dropdown-toggle.btn-primary.focus {
  color: #fff;
  background-color: #204d74;
  border-color: #122b40;
}
.btn-primary:active,
.btn-primary.active,
.open > .dropdown-toggle.btn-primary {
  background-image: none;
}
.btn-primary.disabled:hover,
.btn-primary[disabled]:hover,
fieldset[disabled] .btn-primary:hover,
.btn-primary.disabled:focus,
.btn-primary[disabled]:focus,
fieldset[disabled] .btn-primary:focus,
.btn-primary.disabled.focus,
.btn-primary[disabled].focus,
fieldset[disabled] .btn-primary.focus {
  background-color: #337ab7;
  border-color: #2e6da4;
}
.btn-primary .badge {
  color: #337ab7;
  background-color: #fff;
}
.btn-success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success:focus,
.btn-success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.btn-success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.btn-success:active:hover,
.btn-success.active:hover,
.open > .dropdown-toggle.btn-success:hover,
.btn-success:active:focus,
.btn-success.active:focus,
.open > .dropdown-toggle.btn-success:focus,
.btn-success:active.focus,
.btn-success.active.focus,
.open > .dropdown-toggle.btn-success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.btn-success:active,
.btn-success.active,
.open > .dropdown-toggle.btn-success {
  background-image: none;
}
.btn-success.disabled:hover,
.btn-success[disabled]:hover,
fieldset[disabled] .btn-success:hover,
.btn-success.disabled:focus,
.btn-success[disabled]:focus,
fieldset[disabled] .btn-success:focus,
.btn-success.disabled.focus,
.btn-success[disabled].focus,
fieldset[disabled] .btn-success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.btn-success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.btn-info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info:focus,
.btn-info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.btn-info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.btn-info:active:hover,
.btn-info.active:hover,
.open > .dropdown-toggle.btn-info:hover,
.btn-info:active:focus,
.btn-info.active:focus,
.open > .dropdown-toggle.btn-info:focus,
.btn-info:active.focus,
.btn-info.active.focus,
.open > .dropdown-toggle.btn-info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.btn-info:active,
.btn-info.active,
.open > .dropdown-toggle.btn-info {
  background-image: none;
}
.btn-info.disabled:hover,
.btn-info[disabled]:hover,
fieldset[disabled] .btn-info:hover,
.btn-info.disabled:focus,
.btn-info[disabled]:focus,
fieldset[disabled] .btn-info:focus,
.btn-info.disabled.focus,
.btn-info[disabled].focus,
fieldset[disabled] .btn-info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.btn-info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.btn-warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning:focus,
.btn-warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.btn-warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.btn-warning:active:hover,
.btn-warning.active:hover,
.open > .dropdown-toggle.btn-warning:hover,
.btn-warning:active:focus,
.btn-warning.active:focus,
.open > .dropdown-toggle.btn-warning:focus,
.btn-warning:active.focus,
.btn-warning.active.focus,
.open > .dropdown-toggle.btn-warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.btn-warning:active,
.btn-warning.active,
.open > .dropdown-toggle.btn-warning {
  background-image: none;
}
.btn-warning.disabled:hover,
.btn-warning[disabled]:hover,
fieldset[disabled] .btn-warning:hover,
.btn-warning.disabled:focus,
.btn-warning[disabled]:focus,
fieldset[disabled] .btn-warning:focus,
.btn-warning.disabled.focus,
.btn-warning[disabled].focus,
fieldset[disabled] .btn-warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.btn-warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.btn-danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger:focus,
.btn-danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.btn-danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.btn-danger:active:hover,
.btn-danger.active:hover,
.open > .dropdown-toggle.btn-danger:hover,
.btn-danger:active:focus,
.btn-danger.active:focus,
.open > .dropdown-toggle.btn-danger:focus,
.btn-danger:active.focus,
.btn-danger.active.focus,
.open > .dropdown-toggle.btn-danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.btn-danger:active,
.btn-danger.active,
.open > .dropdown-toggle.btn-danger {
  background-image: none;
}
.btn-danger.disabled:hover,
.btn-danger[disabled]:hover,
fieldset[disabled] .btn-danger:hover,
.btn-danger.disabled:focus,
.btn-danger[disabled]:focus,
fieldset[disabled] .btn-danger:focus,
.btn-danger.disabled.focus,
.btn-danger[disabled].focus,
fieldset[disabled] .btn-danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.btn-danger .badge {
  color: #d9534f;
  background-color: #fff;
}
.btn-link {
  color: #337ab7;
  font-weight: normal;
  border-radius: 0;
}
.btn-link,
.btn-link:active,
.btn-link.active,
.btn-link[disabled],
fieldset[disabled] .btn-link {
  background-color: transparent;
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn-link,
.btn-link:hover,
.btn-link:focus,
.btn-link:active {
  border-color: transparent;
}
.btn-link:hover,
.btn-link:focus {
  color: #23527c;
  text-decoration: underline;
  background-color: transparent;
}
.btn-link[disabled]:hover,
fieldset[disabled] .btn-link:hover,
.btn-link[disabled]:focus,
fieldset[disabled] .btn-link:focus {
  color: #777777;
  text-decoration: none;
}
.btn-lg,
.btn-group-lg > .btn {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
.btn-sm,
.btn-group-sm > .btn {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-xs,
.btn-group-xs > .btn {
  padding: 1px 5px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
.btn-block {
  display: block;
  width: 100%;
}
.btn-block + .btn-block {
  margin-top: 5px;
}
input[type="submit"].btn-block,
input[type="reset"].btn-block,
input[type="button"].btn-block {
  width: 100%;
}
.fade {
  opacity: 0;
  -webkit-transition: opacity 0.15s linear;
  -o-transition: opacity 0.15s linear;
  transition: opacity 0.15s linear;
}
.fade.in {
  opacity: 1;
}
.collapse {
  display: none;
}
.collapse.in {
  display: block;
}
tr.collapse.in {
  display: table-row;
}
tbody.collapse.in {
  display: table-row-group;
}
.collapsing {
  position: relative;
  height: 0;
  overflow: hidden;
  -webkit-transition-property: height, visibility;
  transition-property: height, visibility;
  -webkit-transition-duration: 0.35s;
  transition-duration: 0.35s;
  -webkit-transition-timing-function: ease;
  transition-timing-function: ease;
}
.caret {
  display: inline-block;
  width: 0;
  height: 0;
  margin-left: 2px;
  vertical-align: middle;
  border-top: 4px dashed;
  border-top: 4px solid \9;
  border-right: 4px solid transparent;
  border-left: 4px solid transparent;
}
.dropup,
.dropdown {
  position: relative;
}
.dropdown-toggle:focus {
  outline: 0;
}
.dropdown-menu {
  position: absolute;
  top: 100%;
  left: 0;
  z-index: 1000;
  display: none;
  float: left;
  min-width: 160px;
  padding: 5px 0;
  margin: 2px 0 0;
  list-style: none;
  font-size: 13px;
  text-align: left;
  background-color: #fff;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.15);
  border-radius: 2px;
  -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175);
  background-clip: padding-box;
}
.dropdown-menu.pull-right {
  right: 0;
  left: auto;
}
.dropdown-menu .divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.dropdown-menu > li > a {
  display: block;
  padding: 3px 20px;
  clear: both;
  font-weight: normal;
  line-height: 1.42857143;
  color: #333333;
  white-space: nowrap;
}
.dropdown-menu > li > a:hover,
.dropdown-menu > li > a:focus {
  text-decoration: none;
  color: #262626;
  background-color: #f5f5f5;
}
.dropdown-menu > .active > a,
.dropdown-menu > .active > a:hover,
.dropdown-menu > .active > a:focus {
  color: #fff;
  text-decoration: none;
  outline: 0;
  background-color: #337ab7;
}
.dropdown-menu > .disabled > a,
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  color: #777777;
}
.dropdown-menu > .disabled > a:hover,
.dropdown-menu > .disabled > a:focus {
  text-decoration: none;
  background-color: transparent;
  background-image: none;
  filter: progid:DXImageTransform.Microsoft.gradient(enabled = false);
  cursor: not-allowed;
}
.open > .dropdown-menu {
  display: block;
}
.open > a {
  outline: 0;
}
.dropdown-menu-right {
  left: auto;
  right: 0;
}
.dropdown-menu-left {
  left: 0;
  right: auto;
}
.dropdown-header {
  display: block;
  padding: 3px 20px;
  font-size: 12px;
  line-height: 1.42857143;
  color: #777777;
  white-space: nowrap;
}
.dropdown-backdrop {
  position: fixed;
  left: 0;
  right: 0;
  bottom: 0;
  top: 0;
  z-index: 990;
}
.pull-right > .dropdown-menu {
  right: 0;
  left: auto;
}
.dropup .caret,
.navbar-fixed-bottom .dropdown .caret {
  border-top: 0;
  border-bottom: 4px dashed;
  border-bottom: 4px solid \9;
  content: "";
}
.dropup .dropdown-menu,
.navbar-fixed-bottom .dropdown .dropdown-menu {
  top: auto;
  bottom: 100%;
  margin-bottom: 2px;
}
@media (min-width: 541px) {
  .navbar-right .dropdown-menu {
    left: auto;
    right: 0;
  }
  .navbar-right .dropdown-menu-left {
    left: 0;
    right: auto;
  }
}
.btn-group,
.btn-group-vertical {
  position: relative;
  display: inline-block;
  vertical-align: middle;
}
.btn-group > .btn,
.btn-group-vertical > .btn {
  position: relative;
  float: left;
}
.btn-group > .btn:hover,
.btn-group-vertical > .btn:hover,
.btn-group > .btn:focus,
.btn-group-vertical > .btn:focus,
.btn-group > .btn:active,
.btn-group-vertical > .btn:active,
.btn-group > .btn.active,
.btn-group-vertical > .btn.active {
  z-index: 2;
}
.btn-group .btn + .btn,
.btn-group .btn + .btn-group,
.btn-group .btn-group + .btn,
.btn-group .btn-group + .btn-group {
  margin-left: -1px;
}
.btn-toolbar {
  margin-left: -5px;
}
.btn-toolbar .btn,
.btn-toolbar .btn-group,
.btn-toolbar .input-group {
  float: left;
}
.btn-toolbar > .btn,
.btn-toolbar > .btn-group,
.btn-toolbar > .input-group {
  margin-left: 5px;
}
.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) {
  border-radius: 0;
}
.btn-group > .btn:first-child {
  margin-left: 0;
}
.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn:last-child:not(:first-child),
.btn-group > .dropdown-toggle:not(:first-child) {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group > .btn-group {
  float: left;
}
.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.btn-group .dropdown-toggle:active,
.btn-group.open .dropdown-toggle {
  outline: 0;
}
.btn-group > .btn + .dropdown-toggle {
  padding-left: 8px;
  padding-right: 8px;
}
.btn-group > .btn-lg + .dropdown-toggle {
  padding-left: 12px;
  padding-right: 12px;
}
.btn-group.open .dropdown-toggle {
  -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
  box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125);
}
.btn-group.open .dropdown-toggle.btn-link {
  -webkit-box-shadow: none;
  box-shadow: none;
}
.btn .caret {
  margin-left: 0;
}
.btn-lg .caret {
  border-width: 5px 5px 0;
  border-bottom-width: 0;
}
.dropup .btn-lg .caret {
  border-width: 0 5px 5px;
}
.btn-group-vertical > .btn,
.btn-group-vertical > .btn-group,
.btn-group-vertical > .btn-group > .btn {
  display: block;
  float: none;
  width: 100%;
  max-width: 100%;
}
.btn-group-vertical > .btn-group > .btn {
  float: none;
}
.btn-group-vertical > .btn + .btn,
.btn-group-vertical > .btn + .btn-group,
.btn-group-vertical > .btn-group + .btn,
.btn-group-vertical > .btn-group + .btn-group {
  margin-top: -1px;
  margin-left: 0;
}
.btn-group-vertical > .btn:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.btn-group-vertical > .btn:first-child:not(:last-child) {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn:last-child:not(:first-child) {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn {
  border-radius: 0;
}
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child,
.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle {
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.btn-group-justified {
  display: table;
  width: 100%;
  table-layout: fixed;
  border-collapse: separate;
}
.btn-group-justified > .btn,
.btn-group-justified > .btn-group {
  float: none;
  display: table-cell;
  width: 1%;
}
.btn-group-justified > .btn-group .btn {
  width: 100%;
}
.btn-group-justified > .btn-group .dropdown-menu {
  left: auto;
}
[data-toggle="buttons"] > .btn input[type="radio"],
[data-toggle="buttons"] > .btn-group > .btn input[type="radio"],
[data-toggle="buttons"] > .btn input[type="checkbox"],
[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] {
  position: absolute;
  clip: rect(0, 0, 0, 0);
  pointer-events: none;
}
.input-group {
  position: relative;
  display: table;
  border-collapse: separate;
}
.input-group[class*="col-"] {
  float: none;
  padding-left: 0;
  padding-right: 0;
}
.input-group .form-control {
  position: relative;
  z-index: 2;
  float: left;
  width: 100%;
  margin-bottom: 0;
}
.input-group .form-control:focus {
  z-index: 3;
}
.input-group-lg > .form-control,
.input-group-lg > .input-group-addon,
.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
  border-radius: 3px;
}
select.input-group-lg > .form-control,
select.input-group-lg > .input-group-addon,
select.input-group-lg > .input-group-btn > .btn {
  height: 45px;
  line-height: 45px;
}
textarea.input-group-lg > .form-control,
textarea.input-group-lg > .input-group-addon,
textarea.input-group-lg > .input-group-btn > .btn,
select[multiple].input-group-lg > .form-control,
select[multiple].input-group-lg > .input-group-addon,
select[multiple].input-group-lg > .input-group-btn > .btn {
  height: auto;
}
.input-group-sm > .form-control,
.input-group-sm > .input-group-addon,
.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
}
select.input-group-sm > .form-control,
select.input-group-sm > .input-group-addon,
select.input-group-sm > .input-group-btn > .btn {
  height: 30px;
  line-height: 30px;
}
textarea.input-group-sm > .form-control,
textarea.input-group-sm > .input-group-addon,
textarea.input-group-sm > .input-group-btn > .btn,
select[multiple].input-group-sm > .form-control,
select[multiple].input-group-sm > .input-group-addon,
select[multiple].input-group-sm > .input-group-btn > .btn {
  height: auto;
}
.input-group-addon,
.input-group-btn,
.input-group .form-control {
  display: table-cell;
}
.input-group-addon:not(:first-child):not(:last-child),
.input-group-btn:not(:first-child):not(:last-child),
.input-group .form-control:not(:first-child):not(:last-child) {
  border-radius: 0;
}
.input-group-addon,
.input-group-btn {
  width: 1%;
  white-space: nowrap;
  vertical-align: middle;
}
.input-group-addon {
  padding: 6px 12px;
  font-size: 13px;
  font-weight: normal;
  line-height: 1;
  color: #555555;
  text-align: center;
  background-color: #eeeeee;
  border: 1px solid #ccc;
  border-radius: 2px;
}
.input-group-addon.input-sm {
  padding: 5px 10px;
  font-size: 12px;
  border-radius: 1px;
}
.input-group-addon.input-lg {
  padding: 10px 16px;
  font-size: 17px;
  border-radius: 3px;
}
.input-group-addon input[type="radio"],
.input-group-addon input[type="checkbox"] {
  margin-top: 0;
}
.input-group .form-control:first-child,
.input-group-addon:first-child,
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group > .btn,
.input-group-btn:first-child > .dropdown-toggle,
.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle),
.input-group-btn:last-child > .btn-group:not(:last-child) > .btn {
  border-bottom-right-radius: 0;
  border-top-right-radius: 0;
}
.input-group-addon:first-child {
  border-right: 0;
}
.input-group .form-control:last-child,
.input-group-addon:last-child,
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group > .btn,
.input-group-btn:last-child > .dropdown-toggle,
.input-group-btn:first-child > .btn:not(:first-child),
.input-group-btn:first-child > .btn-group:not(:first-child) > .btn {
  border-bottom-left-radius: 0;
  border-top-left-radius: 0;
}
.input-group-addon:last-child {
  border-left: 0;
}
.input-group-btn {
  position: relative;
  font-size: 0;
  white-space: nowrap;
}
.input-group-btn > .btn {
  position: relative;
}
.input-group-btn > .btn + .btn {
  margin-left: -1px;
}
.input-group-btn > .btn:hover,
.input-group-btn > .btn:focus,
.input-group-btn > .btn:active {
  z-index: 2;
}
.input-group-btn:first-child > .btn,
.input-group-btn:first-child > .btn-group {
  margin-right: -1px;
}
.input-group-btn:last-child > .btn,
.input-group-btn:last-child > .btn-group {
  z-index: 2;
  margin-left: -1px;
}
.nav {
  margin-bottom: 0;
  padding-left: 0;
  list-style: none;
}
.nav > li {
  position: relative;
  display: block;
}
.nav > li > a {
  position: relative;
  display: block;
  padding: 10px 15px;
}
.nav > li > a:hover,
.nav > li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.nav > li.disabled > a {
  color: #777777;
}
.nav > li.disabled > a:hover,
.nav > li.disabled > a:focus {
  color: #777777;
  text-decoration: none;
  background-color: transparent;
  cursor: not-allowed;
}
.nav .open > a,
.nav .open > a:hover,
.nav .open > a:focus {
  background-color: #eeeeee;
  border-color: #337ab7;
}
.nav .nav-divider {
  height: 1px;
  margin: 8px 0;
  overflow: hidden;
  background-color: #e5e5e5;
}
.nav > li > a > img {
  max-width: none;
}
.nav-tabs {
  border-bottom: 1px solid #ddd;
}
.nav-tabs > li {
  float: left;
  margin-bottom: -1px;
}
.nav-tabs > li > a {
  margin-right: 2px;
  line-height: 1.42857143;
  border: 1px solid transparent;
  border-radius: 2px 2px 0 0;
}
.nav-tabs > li > a:hover {
  border-color: #eeeeee #eeeeee #ddd;
}
.nav-tabs > li.active > a,
.nav-tabs > li.active > a:hover,
.nav-tabs > li.active > a:focus {
  color: #555555;
  background-color: #fff;
  border: 1px solid #ddd;
  border-bottom-color: transparent;
  cursor: default;
}
.nav-tabs.nav-justified {
  width: 100%;
  border-bottom: 0;
}
.nav-tabs.nav-justified > li {
  float: none;
}
.nav-tabs.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-tabs.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-tabs.nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs.nav-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs.nav-justified > .active > a,
.nav-tabs.nav-justified > .active > a:hover,
.nav-tabs.nav-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs.nav-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs.nav-justified > .active > a,
  .nav-tabs.nav-justified > .active > a:hover,
  .nav-tabs.nav-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.nav-pills > li {
  float: left;
}
.nav-pills > li > a {
  border-radius: 2px;
}
.nav-pills > li + li {
  margin-left: 2px;
}
.nav-pills > li.active > a,
.nav-pills > li.active > a:hover,
.nav-pills > li.active > a:focus {
  color: #fff;
  background-color: #337ab7;
}
.nav-stacked > li {
  float: none;
}
.nav-stacked > li + li {
  margin-top: 2px;
  margin-left: 0;
}
.nav-justified {
  width: 100%;
}
.nav-justified > li {
  float: none;
}
.nav-justified > li > a {
  text-align: center;
  margin-bottom: 5px;
}
.nav-justified > .dropdown .dropdown-menu {
  top: auto;
  left: auto;
}
@media (min-width: 768px) {
  .nav-justified > li {
    display: table-cell;
    width: 1%;
  }
  .nav-justified > li > a {
    margin-bottom: 0;
  }
}
.nav-tabs-justified {
  border-bottom: 0;
}
.nav-tabs-justified > li > a {
  margin-right: 0;
  border-radius: 2px;
}
.nav-tabs-justified > .active > a,
.nav-tabs-justified > .active > a:hover,
.nav-tabs-justified > .active > a:focus {
  border: 1px solid #ddd;
}
@media (min-width: 768px) {
  .nav-tabs-justified > li > a {
    border-bottom: 1px solid #ddd;
    border-radius: 2px 2px 0 0;
  }
  .nav-tabs-justified > .active > a,
  .nav-tabs-justified > .active > a:hover,
  .nav-tabs-justified > .active > a:focus {
    border-bottom-color: #fff;
  }
}
.tab-content > .tab-pane {
  display: none;
}
.tab-content > .active {
  display: block;
}
.nav-tabs .dropdown-menu {
  margin-top: -1px;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar {
  position: relative;
  min-height: 30px;
  margin-bottom: 18px;
  border: 1px solid transparent;
}
@media (min-width: 541px) {
  .navbar {
    border-radius: 2px;
  }
}
@media (min-width: 541px) {
  .navbar-header {
    float: left;
  }
}
.navbar-collapse {
  overflow-x: visible;
  padding-right: 0px;
  padding-left: 0px;
  border-top: 1px solid transparent;
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1);
  -webkit-overflow-scrolling: touch;
}
.navbar-collapse.in {
  overflow-y: auto;
}
@media (min-width: 541px) {
  .navbar-collapse {
    width: auto;
    border-top: 0;
    box-shadow: none;
  }
  .navbar-collapse.collapse {
    display: block !important;
    height: auto !important;
    padding-bottom: 0;
    overflow: visible !important;
  }
  .navbar-collapse.in {
    overflow-y: visible;
  }
  .navbar-fixed-top .navbar-collapse,
  .navbar-static-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    padding-left: 0;
    padding-right: 0;
  }
}
.navbar-fixed-top .navbar-collapse,
.navbar-fixed-bottom .navbar-collapse {
  max-height: 340px;
}
@media (max-device-width: 540px) and (orientation: landscape) {
  .navbar-fixed-top .navbar-collapse,
  .navbar-fixed-bottom .navbar-collapse {
    max-height: 200px;
  }
}
.container > .navbar-header,
.container-fluid > .navbar-header,
.container > .navbar-collapse,
.container-fluid > .navbar-collapse {
  margin-right: 0px;
  margin-left: 0px;
}
@media (min-width: 541px) {
  .container > .navbar-header,
  .container-fluid > .navbar-header,
  .container > .navbar-collapse,
  .container-fluid > .navbar-collapse {
    margin-right: 0;
    margin-left: 0;
  }
}
.navbar-static-top {
  z-index: 1000;
  border-width: 0 0 1px;
}
@media (min-width: 541px) {
  .navbar-static-top {
    border-radius: 0;
  }
}
.navbar-fixed-top,
.navbar-fixed-bottom {
  position: fixed;
  right: 0;
  left: 0;
  z-index: 1030;
}
@media (min-width: 541px) {
  .navbar-fixed-top,
  .navbar-fixed-bottom {
    border-radius: 0;
  }
}
.navbar-fixed-top {
  top: 0;
  border-width: 0 0 1px;
}
.navbar-fixed-bottom {
  bottom: 0;
  margin-bottom: 0;
  border-width: 1px 0 0;
}
.navbar-brand {
  float: left;
  padding: 6px 0px;
  font-size: 17px;
  line-height: 18px;
  height: 30px;
}
.navbar-brand:hover,
.navbar-brand:focus {
  text-decoration: none;
}
.navbar-brand > img {
  display: block;
}
@media (min-width: 541px) {
  .navbar > .container .navbar-brand,
  .navbar > .container-fluid .navbar-brand {
    margin-left: 0px;
  }
}
.navbar-toggle {
  position: relative;
  float: right;
  margin-right: 0px;
  padding: 9px 10px;
  margin-top: -2px;
  margin-bottom: -2px;
  background-color: transparent;
  background-image: none;
  border: 1px solid transparent;
  border-radius: 2px;
}
.navbar-toggle:focus {
  outline: 0;
}
.navbar-toggle .icon-bar {
  display: block;
  width: 22px;
  height: 2px;
  border-radius: 1px;
}
.navbar-toggle .icon-bar + .icon-bar {
  margin-top: 4px;
}
@media (min-width: 541px) {
  .navbar-toggle {
    display: none;
  }
}
.navbar-nav {
  margin: 3px 0px;
}
.navbar-nav > li > a {
  padding-top: 10px;
  padding-bottom: 10px;
  line-height: 18px;
}
@media (max-width: 540px) {
  .navbar-nav .open .dropdown-menu {
    position: static;
    float: none;
    width: auto;
    margin-top: 0;
    background-color: transparent;
    border: 0;
    box-shadow: none;
  }
  .navbar-nav .open .dropdown-menu > li > a,
  .navbar-nav .open .dropdown-menu .dropdown-header {
    padding: 5px 15px 5px 25px;
  }
  .navbar-nav .open .dropdown-menu > li > a {
    line-height: 18px;
  }
  .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-nav .open .dropdown-menu > li > a:focus {
    background-image: none;
  }
}
@media (min-width: 541px) {
  .navbar-nav {
    float: left;
    margin: 0;
  }
  .navbar-nav > li {
    float: left;
  }
  .navbar-nav > li > a {
    padding-top: 6px;
    padding-bottom: 6px;
  }
}
.navbar-form {
  margin-left: 0px;
  margin-right: 0px;
  padding: 10px 0px;
  border-top: 1px solid transparent;
  border-bottom: 1px solid transparent;
  -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1);
  margin-top: -1px;
  margin-bottom: -1px;
}
@media (min-width: 768px) {
  .navbar-form .form-group {
    display: inline-block;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .form-control {
    display: inline-block;
    width: auto;
    vertical-align: middle;
  }
  .navbar-form .form-control-static {
    display: inline-block;
  }
  .navbar-form .input-group {
    display: inline-table;
    vertical-align: middle;
  }
  .navbar-form .input-group .input-group-addon,
  .navbar-form .input-group .input-group-btn,
  .navbar-form .input-group .form-control {
    width: auto;
  }
  .navbar-form .input-group > .form-control {
    width: 100%;
  }
  .navbar-form .control-label {
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio,
  .navbar-form .checkbox {
    display: inline-block;
    margin-top: 0;
    margin-bottom: 0;
    vertical-align: middle;
  }
  .navbar-form .radio label,
  .navbar-form .checkbox label {
    padding-left: 0;
  }
  .navbar-form .radio input[type="radio"],
  .navbar-form .checkbox input[type="checkbox"] {
    position: relative;
    margin-left: 0;
  }
  .navbar-form .has-feedback .form-control-feedback {
    top: 0;
  }
}
@media (max-width: 540px) {
  .navbar-form .form-group {
    margin-bottom: 5px;
  }
  .navbar-form .form-group:last-child {
    margin-bottom: 0;
  }
}
@media (min-width: 541px) {
  .navbar-form {
    width: auto;
    border: 0;
    margin-left: 0;
    margin-right: 0;
    padding-top: 0;
    padding-bottom: 0;
    -webkit-box-shadow: none;
    box-shadow: none;
  }
}
.navbar-nav > li > .dropdown-menu {
  margin-top: 0;
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu {
  margin-bottom: 0;
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
  border-bottom-right-radius: 0;
  border-bottom-left-radius: 0;
}
.navbar-btn {
  margin-top: -1px;
  margin-bottom: -1px;
}
.navbar-btn.btn-sm {
  margin-top: 0px;
  margin-bottom: 0px;
}
.navbar-btn.btn-xs {
  margin-top: 4px;
  margin-bottom: 4px;
}
.navbar-text {
  margin-top: 6px;
  margin-bottom: 6px;
}
@media (min-width: 541px) {
  .navbar-text {
    float: left;
    margin-left: 0px;
    margin-right: 0px;
  }
}
@media (min-width: 541px) {
  .navbar-left {
    float: left !important;
    float: left;
  }
  .navbar-right {
    float: right !important;
    float: right;
    margin-right: 0px;
  }
  .navbar-right ~ .navbar-right {
    margin-right: 0;
  }
}
.navbar-default {
  background-color: #f8f8f8;
  border-color: #e7e7e7;
}
.navbar-default .navbar-brand {
  color: #777;
}
.navbar-default .navbar-brand:hover,
.navbar-default .navbar-brand:focus {
  color: #5e5e5e;
  background-color: transparent;
}
.navbar-default .navbar-text {
  color: #777;
}
.navbar-default .navbar-nav > li > a {
  color: #777;
}
.navbar-default .navbar-nav > li > a:hover,
.navbar-default .navbar-nav > li > a:focus {
  color: #333;
  background-color: transparent;
}
.navbar-default .navbar-nav > .active > a,
.navbar-default .navbar-nav > .active > a:hover,
.navbar-default .navbar-nav > .active > a:focus {
  color: #555;
  background-color: #e7e7e7;
}
.navbar-default .navbar-nav > .disabled > a,
.navbar-default .navbar-nav > .disabled > a:hover,
.navbar-default .navbar-nav > .disabled > a:focus {
  color: #ccc;
  background-color: transparent;
}
.navbar-default .navbar-toggle {
  border-color: #ddd;
}
.navbar-default .navbar-toggle:hover,
.navbar-default .navbar-toggle:focus {
  background-color: #ddd;
}
.navbar-default .navbar-toggle .icon-bar {
  background-color: #888;
}
.navbar-default .navbar-collapse,
.navbar-default .navbar-form {
  border-color: #e7e7e7;
}
.navbar-default .navbar-nav > .open > a,
.navbar-default .navbar-nav > .open > a:hover,
.navbar-default .navbar-nav > .open > a:focus {
  background-color: #e7e7e7;
  color: #555;
}
@media (max-width: 540px) {
  .navbar-default .navbar-nav .open .dropdown-menu > li > a {
    color: #777;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #333;
    background-color: transparent;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #555;
    background-color: #e7e7e7;
  }
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #ccc;
    background-color: transparent;
  }
}
.navbar-default .navbar-link {
  color: #777;
}
.navbar-default .navbar-link:hover {
  color: #333;
}
.navbar-default .btn-link {
  color: #777;
}
.navbar-default .btn-link:hover,
.navbar-default .btn-link:focus {
  color: #333;
}
.navbar-default .btn-link[disabled]:hover,
fieldset[disabled] .navbar-default .btn-link:hover,
.navbar-default .btn-link[disabled]:focus,
fieldset[disabled] .navbar-default .btn-link:focus {
  color: #ccc;
}
.navbar-inverse {
  background-color: #222;
  border-color: #080808;
}
.navbar-inverse .navbar-brand {
  color: #9d9d9d;
}
.navbar-inverse .navbar-brand:hover,
.navbar-inverse .navbar-brand:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-text {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a {
  color: #9d9d9d;
}
.navbar-inverse .navbar-nav > li > a:hover,
.navbar-inverse .navbar-nav > li > a:focus {
  color: #fff;
  background-color: transparent;
}
.navbar-inverse .navbar-nav > .active > a,
.navbar-inverse .navbar-nav > .active > a:hover,
.navbar-inverse .navbar-nav > .active > a:focus {
  color: #fff;
  background-color: #080808;
}
.navbar-inverse .navbar-nav > .disabled > a,
.navbar-inverse .navbar-nav > .disabled > a:hover,
.navbar-inverse .navbar-nav > .disabled > a:focus {
  color: #444;
  background-color: transparent;
}
.navbar-inverse .navbar-toggle {
  border-color: #333;
}
.navbar-inverse .navbar-toggle:hover,
.navbar-inverse .navbar-toggle:focus {
  background-color: #333;
}
.navbar-inverse .navbar-toggle .icon-bar {
  background-color: #fff;
}
.navbar-inverse .navbar-collapse,
.navbar-inverse .navbar-form {
  border-color: #101010;
}
.navbar-inverse .navbar-nav > .open > a,
.navbar-inverse .navbar-nav > .open > a:hover,
.navbar-inverse .navbar-nav > .open > a:focus {
  background-color: #080808;
  color: #fff;
}
@media (max-width: 540px) {
  .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header {
    border-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu .divider {
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a {
    color: #9d9d9d;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus {
    color: #fff;
    background-color: transparent;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus {
    color: #fff;
    background-color: #080808;
  }
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover,
  .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus {
    color: #444;
    background-color: transparent;
  }
}
.navbar-inverse .navbar-link {
  color: #9d9d9d;
}
.navbar-inverse .navbar-link:hover {
  color: #fff;
}
.navbar-inverse .btn-link {
  color: #9d9d9d;
}
.navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link:focus {
  color: #fff;
}
.navbar-inverse .btn-link[disabled]:hover,
fieldset[disabled] .navbar-inverse .btn-link:hover,
.navbar-inverse .btn-link[disabled]:focus,
fieldset[disabled] .navbar-inverse .btn-link:focus {
  color: #444;
}
.breadcrumb {
  padding: 8px 15px;
  margin-bottom: 18px;
  list-style: none;
  background-color: #f5f5f5;
  border-radius: 2px;
}
.breadcrumb > li {
  display: inline-block;
}
.breadcrumb > li + li:before {
  content: "/\00a0";
  padding: 0 5px;
  color: #5e5e5e;
}
.breadcrumb > .active {
  color: #777777;
}
.pagination {
  display: inline-block;
  padding-left: 0;
  margin: 18px 0;
  border-radius: 2px;
}
.pagination > li {
  display: inline;
}
.pagination > li > a,
.pagination > li > span {
  position: relative;
  float: left;
  padding: 6px 12px;
  line-height: 1.42857143;
  text-decoration: none;
  color: #337ab7;
  background-color: #fff;
  border: 1px solid #ddd;
  margin-left: -1px;
}
.pagination > li:first-child > a,
.pagination > li:first-child > span {
  margin-left: 0;
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.pagination > li:last-child > a,
.pagination > li:last-child > span {
  border-bottom-right-radius: 2px;
  border-top-right-radius: 2px;
}
.pagination > li > a:hover,
.pagination > li > span:hover,
.pagination > li > a:focus,
.pagination > li > span:focus {
  z-index: 2;
  color: #23527c;
  background-color: #eeeeee;
  border-color: #ddd;
}
.pagination > .active > a,
.pagination > .active > span,
.pagination > .active > a:hover,
.pagination > .active > span:hover,
.pagination > .active > a:focus,
.pagination > .active > span:focus {
  z-index: 3;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
  cursor: default;
}
.pagination > .disabled > span,
.pagination > .disabled > span:hover,
.pagination > .disabled > span:focus,
.pagination > .disabled > a,
.pagination > .disabled > a:hover,
.pagination > .disabled > a:focus {
  color: #777777;
  background-color: #fff;
  border-color: #ddd;
  cursor: not-allowed;
}
.pagination-lg > li > a,
.pagination-lg > li > span {
  padding: 10px 16px;
  font-size: 17px;
  line-height: 1.3333333;
}
.pagination-lg > li:first-child > a,
.pagination-lg > li:first-child > span {
  border-bottom-left-radius: 3px;
  border-top-left-radius: 3px;
}
.pagination-lg > li:last-child > a,
.pagination-lg > li:last-child > span {
  border-bottom-right-radius: 3px;
  border-top-right-radius: 3px;
}
.pagination-sm > li > a,
.pagination-sm > li > span {
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
}
.pagination-sm > li:first-child > a,
.pagination-sm > li:first-child > span {
  border-bottom-left-radius: 1px;
  border-top-left-radius: 1px;
}
.pagination-sm > li:last-child > a,
.pagination-sm > li:last-child > span {
  border-bottom-right-radius: 1px;
  border-top-right-radius: 1px;
}
.pager {
  padding-left: 0;
  margin: 18px 0;
  list-style: none;
  text-align: center;
}
.pager li {
  display: inline;
}
.pager li > a,
.pager li > span {
  display: inline-block;
  padding: 5px 14px;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 15px;
}
.pager li > a:hover,
.pager li > a:focus {
  text-decoration: none;
  background-color: #eeeeee;
}
.pager .next > a,
.pager .next > span {
  float: right;
}
.pager .previous > a,
.pager .previous > span {
  float: left;
}
.pager .disabled > a,
.pager .disabled > a:hover,
.pager .disabled > a:focus,
.pager .disabled > span {
  color: #777777;
  background-color: #fff;
  cursor: not-allowed;
}
.label {
  display: inline;
  padding: .2em .6em .3em;
  font-size: 75%;
  font-weight: bold;
  line-height: 1;
  color: #fff;
  text-align: center;
  white-space: nowrap;
  vertical-align: baseline;
  border-radius: .25em;
}
a.label:hover,
a.label:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.label:empty {
  display: none;
}
.btn .label {
  position: relative;
  top: -1px;
}
.label-default {
  background-color: #777777;
}
.label-default[href]:hover,
.label-default[href]:focus {
  background-color: #5e5e5e;
}
.label-primary {
  background-color: #337ab7;
}
.label-primary[href]:hover,
.label-primary[href]:focus {
  background-color: #286090;
}
.label-success {
  background-color: #5cb85c;
}
.label-success[href]:hover,
.label-success[href]:focus {
  background-color: #449d44;
}
.label-info {
  background-color: #5bc0de;
}
.label-info[href]:hover,
.label-info[href]:focus {
  background-color: #31b0d5;
}
.label-warning {
  background-color: #f0ad4e;
}
.label-warning[href]:hover,
.label-warning[href]:focus {
  background-color: #ec971f;
}
.label-danger {
  background-color: #d9534f;
}
.label-danger[href]:hover,
.label-danger[href]:focus {
  background-color: #c9302c;
}
.badge {
  display: inline-block;
  min-width: 10px;
  padding: 3px 7px;
  font-size: 12px;
  font-weight: bold;
  color: #fff;
  line-height: 1;
  vertical-align: middle;
  white-space: nowrap;
  text-align: center;
  background-color: #777777;
  border-radius: 10px;
}
.badge:empty {
  display: none;
}
.btn .badge {
  position: relative;
  top: -1px;
}
.btn-xs .badge,
.btn-group-xs > .btn .badge {
  top: 0;
  padding: 1px 5px;
}
a.badge:hover,
a.badge:focus {
  color: #fff;
  text-decoration: none;
  cursor: pointer;
}
.list-group-item.active > .badge,
.nav-pills > .active > a > .badge {
  color: #337ab7;
  background-color: #fff;
}
.list-group-item > .badge {
  float: right;
}
.list-group-item > .badge + .badge {
  margin-right: 5px;
}
.nav-pills > li > a > .badge {
  margin-left: 3px;
}
.jumbotron {
  padding-top: 30px;
  padding-bottom: 30px;
  margin-bottom: 30px;
  color: inherit;
  background-color: #eeeeee;
}
.jumbotron h1,
.jumbotron .h1 {
  color: inherit;
}
.jumbotron p {
  margin-bottom: 15px;
  font-size: 20px;
  font-weight: 200;
}
.jumbotron > hr {
  border-top-color: #d5d5d5;
}
.container .jumbotron,
.container-fluid .jumbotron {
  border-radius: 3px;
  padding-left: 0px;
  padding-right: 0px;
}
.jumbotron .container {
  max-width: 100%;
}
@media screen and (min-width: 768px) {
  .jumbotron {
    padding-top: 48px;
    padding-bottom: 48px;
  }
  .container .jumbotron,
  .container-fluid .jumbotron {
    padding-left: 60px;
    padding-right: 60px;
  }
  .jumbotron h1,
  .jumbotron .h1 {
    font-size: 59px;
  }
}
.thumbnail {
  display: block;
  padding: 4px;
  margin-bottom: 18px;
  line-height: 1.42857143;
  background-color: #fff;
  border: 1px solid #ddd;
  border-radius: 2px;
  -webkit-transition: border 0.2s ease-in-out;
  -o-transition: border 0.2s ease-in-out;
  transition: border 0.2s ease-in-out;
}
.thumbnail > img,
.thumbnail a > img {
  margin-left: auto;
  margin-right: auto;
}
a.thumbnail:hover,
a.thumbnail:focus,
a.thumbnail.active {
  border-color: #337ab7;
}
.thumbnail .caption {
  padding: 9px;
  color: #000;
}
.alert {
  padding: 15px;
  margin-bottom: 18px;
  border: 1px solid transparent;
  border-radius: 2px;
}
.alert h4 {
  margin-top: 0;
  color: inherit;
}
.alert .alert-link {
  font-weight: bold;
}
.alert > p,
.alert > ul {
  margin-bottom: 0;
}
.alert > p + p {
  margin-top: 5px;
}
.alert-dismissable,
.alert-dismissible {
  padding-right: 35px;
}
.alert-dismissable .close,
.alert-dismissible .close {
  position: relative;
  top: -2px;
  right: -21px;
  color: inherit;
}
.alert-success {
  background-color: #dff0d8;
  border-color: #d6e9c6;
  color: #3c763d;
}
.alert-success hr {
  border-top-color: #c9e2b3;
}
.alert-success .alert-link {
  color: #2b542c;
}
.alert-info {
  background-color: #d9edf7;
  border-color: #bce8f1;
  color: #31708f;
}
.alert-info hr {
  border-top-color: #a6e1ec;
}
.alert-info .alert-link {
  color: #245269;
}
.alert-warning {
  background-color: #fcf8e3;
  border-color: #faebcc;
  color: #8a6d3b;
}
.alert-warning hr {
  border-top-color: #f7e1b5;
}
.alert-warning .alert-link {
  color: #66512c;
}
.alert-danger {
  background-color: #f2dede;
  border-color: #ebccd1;
  color: #a94442;
}
.alert-danger hr {
  border-top-color: #e4b9c0;
}
.alert-danger .alert-link {
  color: #843534;
}
@-webkit-keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
@keyframes progress-bar-stripes {
  from {
    background-position: 40px 0;
  }
  to {
    background-position: 0 0;
  }
}
.progress {
  overflow: hidden;
  height: 18px;
  margin-bottom: 18px;
  background-color: #f5f5f5;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
  box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1);
}
.progress-bar {
  float: left;
  width: 0%;
  height: 100%;
  font-size: 12px;
  line-height: 18px;
  color: #fff;
  text-align: center;
  background-color: #337ab7;
  -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15);
  -webkit-transition: width 0.6s ease;
  -o-transition: width 0.6s ease;
  transition: width 0.6s ease;
}
.progress-striped .progress-bar,
.progress-bar-striped {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-size: 40px 40px;
}
.progress.active .progress-bar,
.progress-bar.active {
  -webkit-animation: progress-bar-stripes 2s linear infinite;
  -o-animation: progress-bar-stripes 2s linear infinite;
  animation: progress-bar-stripes 2s linear infinite;
}
.progress-bar-success {
  background-color: #5cb85c;
}
.progress-striped .progress-bar-success {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-info {
  background-color: #5bc0de;
}
.progress-striped .progress-bar-info {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-warning {
  background-color: #f0ad4e;
}
.progress-striped .progress-bar-warning {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.progress-bar-danger {
  background-color: #d9534f;
}
.progress-striped .progress-bar-danger {
  background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
  background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);
}
.media {
  margin-top: 15px;
}
.media:first-child {
  margin-top: 0;
}
.media,
.media-body {
  zoom: 1;
  overflow: hidden;
}
.media-body {
  width: 10000px;
}
.media-object {
  display: block;
}
.media-object.img-thumbnail {
  max-width: none;
}
.media-right,
.media > .pull-right {
  padding-left: 10px;
}
.media-left,
.media > .pull-left {
  padding-right: 10px;
}
.media-left,
.media-right,
.media-body {
  display: table-cell;
  vertical-align: top;
}
.media-middle {
  vertical-align: middle;
}
.media-bottom {
  vertical-align: bottom;
}
.media-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.media-list {
  padding-left: 0;
  list-style: none;
}
.list-group {
  margin-bottom: 20px;
  padding-left: 0;
}
.list-group-item {
  position: relative;
  display: block;
  padding: 10px 15px;
  margin-bottom: -1px;
  background-color: #fff;
  border: 1px solid #ddd;
}
.list-group-item:first-child {
  border-top-right-radius: 2px;
  border-top-left-radius: 2px;
}
.list-group-item:last-child {
  margin-bottom: 0;
  border-bottom-right-radius: 2px;
  border-bottom-left-radius: 2px;
}
a.list-group-item,
button.list-group-item {
  color: #555;
}
a.list-group-item .list-group-item-heading,
button.list-group-item .list-group-item-heading {
  color: #333;
}
a.list-group-item:hover,
button.list-group-item:hover,
a.list-group-item:focus,
button.list-group-item:focus {
  text-decoration: none;
  color: #555;
  background-color: #f5f5f5;
}
button.list-group-item {
  width: 100%;
  text-align: left;
}
.list-group-item.disabled,
.list-group-item.disabled:hover,
.list-group-item.disabled:focus {
  background-color: #eeeeee;
  color: #777777;
  cursor: not-allowed;
}
.list-group-item.disabled .list-group-item-heading,
.list-group-item.disabled:hover .list-group-item-heading,
.list-group-item.disabled:focus .list-group-item-heading {
  color: inherit;
}
.list-group-item.disabled .list-group-item-text,
.list-group-item.disabled:hover .list-group-item-text,
.list-group-item.disabled:focus .list-group-item-text {
  color: #777777;
}
.list-group-item.active,
.list-group-item.active:hover,
.list-group-item.active:focus {
  z-index: 2;
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.list-group-item.active .list-group-item-heading,
.list-group-item.active:hover .list-group-item-heading,
.list-group-item.active:focus .list-group-item-heading,
.list-group-item.active .list-group-item-heading > small,
.list-group-item.active:hover .list-group-item-heading > small,
.list-group-item.active:focus .list-group-item-heading > small,
.list-group-item.active .list-group-item-heading > .small,
.list-group-item.active:hover .list-group-item-heading > .small,
.list-group-item.active:focus .list-group-item-heading > .small {
  color: inherit;
}
.list-group-item.active .list-group-item-text,
.list-group-item.active:hover .list-group-item-text,
.list-group-item.active:focus .list-group-item-text {
  color: #c7ddef;
}
.list-group-item-success {
  color: #3c763d;
  background-color: #dff0d8;
}
a.list-group-item-success,
button.list-group-item-success {
  color: #3c763d;
}
a.list-group-item-success .list-group-item-heading,
button.list-group-item-success .list-group-item-heading {
  color: inherit;
}
a.list-group-item-success:hover,
button.list-group-item-success:hover,
a.list-group-item-success:focus,
button.list-group-item-success:focus {
  color: #3c763d;
  background-color: #d0e9c6;
}
a.list-group-item-success.active,
button.list-group-item-success.active,
a.list-group-item-success.active:hover,
button.list-group-item-success.active:hover,
a.list-group-item-success.active:focus,
button.list-group-item-success.active:focus {
  color: #fff;
  background-color: #3c763d;
  border-color: #3c763d;
}
.list-group-item-info {
  color: #31708f;
  background-color: #d9edf7;
}
a.list-group-item-info,
button.list-group-item-info {
  color: #31708f;
}
a.list-group-item-info .list-group-item-heading,
button.list-group-item-info .list-group-item-heading {
  color: inherit;
}
a.list-group-item-info:hover,
button.list-group-item-info:hover,
a.list-group-item-info:focus,
button.list-group-item-info:focus {
  color: #31708f;
  background-color: #c4e3f3;
}
a.list-group-item-info.active,
button.list-group-item-info.active,
a.list-group-item-info.active:hover,
button.list-group-item-info.active:hover,
a.list-group-item-info.active:focus,
button.list-group-item-info.active:focus {
  color: #fff;
  background-color: #31708f;
  border-color: #31708f;
}
.list-group-item-warning {
  color: #8a6d3b;
  background-color: #fcf8e3;
}
a.list-group-item-warning,
button.list-group-item-warning {
  color: #8a6d3b;
}
a.list-group-item-warning .list-group-item-heading,
button.list-group-item-warning .list-group-item-heading {
  color: inherit;
}
a.list-group-item-warning:hover,
button.list-group-item-warning:hover,
a.list-group-item-warning:focus,
button.list-group-item-warning:focus {
  color: #8a6d3b;
  background-color: #faf2cc;
}
a.list-group-item-warning.active,
button.list-group-item-warning.active,
a.list-group-item-warning.active:hover,
button.list-group-item-warning.active:hover,
a.list-group-item-warning.active:focus,
button.list-group-item-warning.active:focus {
  color: #fff;
  background-color: #8a6d3b;
  border-color: #8a6d3b;
}
.list-group-item-danger {
  color: #a94442;
  background-color: #f2dede;
}
a.list-group-item-danger,
button.list-group-item-danger {
  color: #a94442;
}
a.list-group-item-danger .list-group-item-heading,
button.list-group-item-danger .list-group-item-heading {
  color: inherit;
}
a.list-group-item-danger:hover,
button.list-group-item-danger:hover,
a.list-group-item-danger:focus,
button.list-group-item-danger:focus {
  color: #a94442;
  background-color: #ebcccc;
}
a.list-group-item-danger.active,
button.list-group-item-danger.active,
a.list-group-item-danger.active:hover,
button.list-group-item-danger.active:hover,
a.list-group-item-danger.active:focus,
button.list-group-item-danger.active:focus {
  color: #fff;
  background-color: #a94442;
  border-color: #a94442;
}
.list-group-item-heading {
  margin-top: 0;
  margin-bottom: 5px;
}
.list-group-item-text {
  margin-bottom: 0;
  line-height: 1.3;
}
.panel {
  margin-bottom: 18px;
  background-color: #fff;
  border: 1px solid transparent;
  border-radius: 2px;
  -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05);
}
.panel-body {
  padding: 15px;
}
.panel-heading {
  padding: 10px 15px;
  border-bottom: 1px solid transparent;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel-heading > .dropdown .dropdown-toggle {
  color: inherit;
}
.panel-title {
  margin-top: 0;
  margin-bottom: 0;
  font-size: 15px;
  color: inherit;
}
.panel-title > a,
.panel-title > small,
.panel-title > .small,
.panel-title > small > a,
.panel-title > .small > a {
  color: inherit;
}
.panel-footer {
  padding: 10px 15px;
  background-color: #f5f5f5;
  border-top: 1px solid #ddd;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .list-group,
.panel > .panel-collapse > .list-group {
  margin-bottom: 0;
}
.panel > .list-group .list-group-item,
.panel > .panel-collapse > .list-group .list-group-item {
  border-width: 1px 0;
  border-radius: 0;
}
.panel > .list-group:first-child .list-group-item:first-child,
.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child {
  border-top: 0;
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .list-group:last-child .list-group-item:last-child,
.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child {
  border-bottom: 0;
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child {
  border-top-right-radius: 0;
  border-top-left-radius: 0;
}
.panel-heading + .list-group .list-group-item:first-child {
  border-top-width: 0;
}
.list-group + .panel-footer {
  border-top-width: 0;
}
.panel > .table,
.panel > .table-responsive > .table,
.panel > .panel-collapse > .table {
  margin-bottom: 0;
}
.panel > .table caption,
.panel > .table-responsive > .table caption,
.panel > .panel-collapse > .table caption {
  padding-left: 15px;
  padding-right: 15px;
}
.panel > .table:first-child,
.panel > .table-responsive:first-child > .table:first-child {
  border-top-right-radius: 1px;
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child {
  border-top-left-radius: 1px;
  border-top-right-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child {
  border-top-left-radius: 1px;
}
.panel > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child,
.panel > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child,
.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child,
.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child {
  border-top-right-radius: 1px;
}
.panel > .table:last-child,
.panel > .table-responsive:last-child > .table:last-child {
  border-bottom-right-radius: 1px;
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child {
  border-bottom-left-radius: 1px;
  border-bottom-right-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child {
  border-bottom-left-radius: 1px;
}
.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child,
.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child,
.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child,
.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child {
  border-bottom-right-radius: 1px;
}
.panel > .panel-body + .table,
.panel > .panel-body + .table-responsive,
.panel > .table + .panel-body,
.panel > .table-responsive + .panel-body {
  border-top: 1px solid #ddd;
}
.panel > .table > tbody:first-child > tr:first-child th,
.panel > .table > tbody:first-child > tr:first-child td {
  border-top: 0;
}
.panel > .table-bordered,
.panel > .table-responsive > .table-bordered {
  border: 0;
}
.panel > .table-bordered > thead > tr > th:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:first-child,
.panel > .table-bordered > tbody > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child,
.panel > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child,
.panel > .table-bordered > thead > tr > td:first-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:first-child,
.panel > .table-bordered > tbody > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child,
.panel > .table-bordered > tfoot > tr > td:first-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child {
  border-left: 0;
}
.panel > .table-bordered > thead > tr > th:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > th:last-child,
.panel > .table-bordered > tbody > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child,
.panel > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child,
.panel > .table-bordered > thead > tr > td:last-child,
.panel > .table-responsive > .table-bordered > thead > tr > td:last-child,
.panel > .table-bordered > tbody > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child,
.panel > .table-bordered > tfoot > tr > td:last-child,
.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child {
  border-right: 0;
}
.panel > .table-bordered > thead > tr:first-child > td,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > td,
.panel > .table-bordered > tbody > tr:first-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td,
.panel > .table-bordered > thead > tr:first-child > th,
.panel > .table-responsive > .table-bordered > thead > tr:first-child > th,
.panel > .table-bordered > tbody > tr:first-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th {
  border-bottom: 0;
}
.panel > .table-bordered > tbody > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td,
.panel > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td,
.panel > .table-bordered > tbody > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th,
.panel > .table-bordered > tfoot > tr:last-child > th,
.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th {
  border-bottom: 0;
}
.panel > .table-responsive {
  border: 0;
  margin-bottom: 0;
}
.panel-group {
  margin-bottom: 18px;
}
.panel-group .panel {
  margin-bottom: 0;
  border-radius: 2px;
}
.panel-group .panel + .panel {
  margin-top: 5px;
}
.panel-group .panel-heading {
  border-bottom: 0;
}
.panel-group .panel-heading + .panel-collapse > .panel-body,
.panel-group .panel-heading + .panel-collapse > .list-group {
  border-top: 1px solid #ddd;
}
.panel-group .panel-footer {
  border-top: 0;
}
.panel-group .panel-footer + .panel-collapse .panel-body {
  border-bottom: 1px solid #ddd;
}
.panel-default {
  border-color: #ddd;
}
.panel-default > .panel-heading {
  color: #333333;
  background-color: #f5f5f5;
  border-color: #ddd;
}
.panel-default > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ddd;
}
.panel-default > .panel-heading .badge {
  color: #f5f5f5;
  background-color: #333333;
}
.panel-default > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ddd;
}
.panel-primary {
  border-color: #337ab7;
}
.panel-primary > .panel-heading {
  color: #fff;
  background-color: #337ab7;
  border-color: #337ab7;
}
.panel-primary > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #337ab7;
}
.panel-primary > .panel-heading .badge {
  color: #337ab7;
  background-color: #fff;
}
.panel-primary > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #337ab7;
}
.panel-success {
  border-color: #d6e9c6;
}
.panel-success > .panel-heading {
  color: #3c763d;
  background-color: #dff0d8;
  border-color: #d6e9c6;
}
.panel-success > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #d6e9c6;
}
.panel-success > .panel-heading .badge {
  color: #dff0d8;
  background-color: #3c763d;
}
.panel-success > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #d6e9c6;
}
.panel-info {
  border-color: #bce8f1;
}
.panel-info > .panel-heading {
  color: #31708f;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.panel-info > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #bce8f1;
}
.panel-info > .panel-heading .badge {
  color: #d9edf7;
  background-color: #31708f;
}
.panel-info > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #bce8f1;
}
.panel-warning {
  border-color: #faebcc;
}
.panel-warning > .panel-heading {
  color: #8a6d3b;
  background-color: #fcf8e3;
  border-color: #faebcc;
}
.panel-warning > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #faebcc;
}
.panel-warning > .panel-heading .badge {
  color: #fcf8e3;
  background-color: #8a6d3b;
}
.panel-warning > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #faebcc;
}
.panel-danger {
  border-color: #ebccd1;
}
.panel-danger > .panel-heading {
  color: #a94442;
  background-color: #f2dede;
  border-color: #ebccd1;
}
.panel-danger > .panel-heading + .panel-collapse > .panel-body {
  border-top-color: #ebccd1;
}
.panel-danger > .panel-heading .badge {
  color: #f2dede;
  background-color: #a94442;
}
.panel-danger > .panel-footer + .panel-collapse > .panel-body {
  border-bottom-color: #ebccd1;
}
.embed-responsive {
  position: relative;
  display: block;
  height: 0;
  padding: 0;
  overflow: hidden;
}
.embed-responsive .embed-responsive-item,
.embed-responsive iframe,
.embed-responsive embed,
.embed-responsive object,
.embed-responsive video {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  height: 100%;
  width: 100%;
  border: 0;
}
.embed-responsive-16by9 {
  padding-bottom: 56.25%;
}
.embed-responsive-4by3 {
  padding-bottom: 75%;
}
.well {
  min-height: 20px;
  padding: 19px;
  margin-bottom: 20px;
  background-color: #f5f5f5;
  border: 1px solid #e3e3e3;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05);
}
.well blockquote {
  border-color: #ddd;
  border-color: rgba(0, 0, 0, 0.15);
}
.well-lg {
  padding: 24px;
  border-radius: 3px;
}
.well-sm {
  padding: 9px;
  border-radius: 1px;
}
.close {
  float: right;
  font-size: 19.5px;
  font-weight: bold;
  line-height: 1;
  color: #000;
  text-shadow: 0 1px 0 #fff;
  opacity: 0.2;
  filter: alpha(opacity=20);
}
.close:hover,
.close:focus {
  color: #000;
  text-decoration: none;
  cursor: pointer;
  opacity: 0.5;
  filter: alpha(opacity=50);
}
button.close {
  padding: 0;
  cursor: pointer;
  background: transparent;
  border: 0;
  -webkit-appearance: none;
}
.modal-open {
  overflow: hidden;
}
.modal {
  display: none;
  overflow: hidden;
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1050;
  -webkit-overflow-scrolling: touch;
  outline: 0;
}
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, -25%);
  -ms-transform: translate(0, -25%);
  -o-transform: translate(0, -25%);
  transform: translate(0, -25%);
  -webkit-transition: -webkit-transform 0.3s ease-out;
  -moz-transition: -moz-transform 0.3s ease-out;
  -o-transition: -o-transform 0.3s ease-out;
  transition: transform 0.3s ease-out;
}
.modal.in .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
.modal-open .modal {
  overflow-x: hidden;
  overflow-y: auto;
}
.modal-dialog {
  position: relative;
  width: auto;
  margin: 10px;
}
.modal-content {
  position: relative;
  background-color: #fff;
  border: 1px solid #999;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5);
  background-clip: padding-box;
  outline: 0;
}
.modal-backdrop {
  position: fixed;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  z-index: 1040;
  background-color: #000;
}
.modal-backdrop.fade {
  opacity: 0;
  filter: alpha(opacity=0);
}
.modal-backdrop.in {
  opacity: 0.5;
  filter: alpha(opacity=50);
}
.modal-header {
  padding: 15px;
  border-bottom: 1px solid #e5e5e5;
}
.modal-header .close {
  margin-top: -2px;
}
.modal-title {
  margin: 0;
  line-height: 1.42857143;
}
.modal-body {
  position: relative;
  padding: 15px;
}
.modal-footer {
  padding: 15px;
  text-align: right;
  border-top: 1px solid #e5e5e5;
}
.modal-footer .btn + .btn {
  margin-left: 5px;
  margin-bottom: 0;
}
.modal-footer .btn-group .btn + .btn {
  margin-left: -1px;
}
.modal-footer .btn-block + .btn-block {
  margin-left: 0;
}
.modal-scrollbar-measure {
  position: absolute;
  top: -9999px;
  width: 50px;
  height: 50px;
  overflow: scroll;
}
@media (min-width: 768px) {
  .modal-dialog {
    width: 600px;
    margin: 30px auto;
  }
  .modal-content {
    -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
    box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5);
  }
  .modal-sm {
    width: 300px;
  }
}
@media (min-width: 992px) {
  .modal-lg {
    width: 900px;
  }
}
.tooltip {
  position: absolute;
  z-index: 1070;
  display: block;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 12px;
  opacity: 0;
  filter: alpha(opacity=0);
}
.tooltip.in {
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.tooltip.top {
  margin-top: -3px;
  padding: 5px 0;
}
.tooltip.right {
  margin-left: 3px;
  padding: 0 5px;
}
.tooltip.bottom {
  margin-top: 3px;
  padding: 5px 0;
}
.tooltip.left {
  margin-left: -3px;
  padding: 0 5px;
}
.tooltip-inner {
  max-width: 200px;
  padding: 3px 8px;
  color: #fff;
  text-align: center;
  background-color: #000;
  border-radius: 2px;
}
.tooltip-arrow {
  position: absolute;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.tooltip.top .tooltip-arrow {
  bottom: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-left .tooltip-arrow {
  bottom: 0;
  right: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.top-right .tooltip-arrow {
  bottom: 0;
  left: 5px;
  margin-bottom: -5px;
  border-width: 5px 5px 0;
  border-top-color: #000;
}
.tooltip.right .tooltip-arrow {
  top: 50%;
  left: 0;
  margin-top: -5px;
  border-width: 5px 5px 5px 0;
  border-right-color: #000;
}
.tooltip.left .tooltip-arrow {
  top: 50%;
  right: 0;
  margin-top: -5px;
  border-width: 5px 0 5px 5px;
  border-left-color: #000;
}
.tooltip.bottom .tooltip-arrow {
  top: 0;
  left: 50%;
  margin-left: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-left .tooltip-arrow {
  top: 0;
  right: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.tooltip.bottom-right .tooltip-arrow {
  top: 0;
  left: 5px;
  margin-top: -5px;
  border-width: 0 5px 5px;
  border-bottom-color: #000;
}
.popover {
  position: absolute;
  top: 0;
  left: 0;
  z-index: 1060;
  display: none;
  max-width: 276px;
  padding: 1px;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
  font-style: normal;
  font-weight: normal;
  letter-spacing: normal;
  line-break: auto;
  line-height: 1.42857143;
  text-align: left;
  text-align: start;
  text-decoration: none;
  text-shadow: none;
  text-transform: none;
  white-space: normal;
  word-break: normal;
  word-spacing: normal;
  word-wrap: normal;
  font-size: 13px;
  background-color: #fff;
  background-clip: padding-box;
  border: 1px solid #ccc;
  border: 1px solid rgba(0, 0, 0, 0.2);
  border-radius: 3px;
  -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
  box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2);
}
.popover.top {
  margin-top: -10px;
}
.popover.right {
  margin-left: 10px;
}
.popover.bottom {
  margin-top: 10px;
}
.popover.left {
  margin-left: -10px;
}
.popover-title {
  margin: 0;
  padding: 8px 14px;
  font-size: 13px;
  background-color: #f7f7f7;
  border-bottom: 1px solid #ebebeb;
  border-radius: 2px 2px 0 0;
}
.popover-content {
  padding: 9px 14px;
}
.popover > .arrow,
.popover > .arrow:after {
  position: absolute;
  display: block;
  width: 0;
  height: 0;
  border-color: transparent;
  border-style: solid;
}
.popover > .arrow {
  border-width: 11px;
}
.popover > .arrow:after {
  border-width: 10px;
  content: "";
}
.popover.top > .arrow {
  left: 50%;
  margin-left: -11px;
  border-bottom-width: 0;
  border-top-color: #999999;
  border-top-color: rgba(0, 0, 0, 0.25);
  bottom: -11px;
}
.popover.top > .arrow:after {
  content: " ";
  bottom: 1px;
  margin-left: -10px;
  border-bottom-width: 0;
  border-top-color: #fff;
}
.popover.right > .arrow {
  top: 50%;
  left: -11px;
  margin-top: -11px;
  border-left-width: 0;
  border-right-color: #999999;
  border-right-color: rgba(0, 0, 0, 0.25);
}
.popover.right > .arrow:after {
  content: " ";
  left: 1px;
  bottom: -10px;
  border-left-width: 0;
  border-right-color: #fff;
}
.popover.bottom > .arrow {
  left: 50%;
  margin-left: -11px;
  border-top-width: 0;
  border-bottom-color: #999999;
  border-bottom-color: rgba(0, 0, 0, 0.25);
  top: -11px;
}
.popover.bottom > .arrow:after {
  content: " ";
  top: 1px;
  margin-left: -10px;
  border-top-width: 0;
  border-bottom-color: #fff;
}
.popover.left > .arrow {
  top: 50%;
  right: -11px;
  margin-top: -11px;
  border-right-width: 0;
  border-left-color: #999999;
  border-left-color: rgba(0, 0, 0, 0.25);
}
.popover.left > .arrow:after {
  content: " ";
  right: 1px;
  border-right-width: 0;
  border-left-color: #fff;
  bottom: -10px;
}
.carousel {
  position: relative;
}
.carousel-inner {
  position: relative;
  overflow: hidden;
  width: 100%;
}
.carousel-inner > .item {
  display: none;
  position: relative;
  -webkit-transition: 0.6s ease-in-out left;
  -o-transition: 0.6s ease-in-out left;
  transition: 0.6s ease-in-out left;
}
.carousel-inner > .item > img,
.carousel-inner > .item > a > img {
  line-height: 1;
}
@media all and (transform-3d), (-webkit-transform-3d) {
  .carousel-inner > .item {
    -webkit-transition: -webkit-transform 0.6s ease-in-out;
    -moz-transition: -moz-transform 0.6s ease-in-out;
    -o-transition: -o-transform 0.6s ease-in-out;
    transition: transform 0.6s ease-in-out;
    -webkit-backface-visibility: hidden;
    -moz-backface-visibility: hidden;
    backface-visibility: hidden;
    -webkit-perspective: 1000px;
    -moz-perspective: 1000px;
    perspective: 1000px;
  }
  .carousel-inner > .item.next,
  .carousel-inner > .item.active.right {
    -webkit-transform: translate3d(100%, 0, 0);
    transform: translate3d(100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.prev,
  .carousel-inner > .item.active.left {
    -webkit-transform: translate3d(-100%, 0, 0);
    transform: translate3d(-100%, 0, 0);
    left: 0;
  }
  .carousel-inner > .item.next.left,
  .carousel-inner > .item.prev.right,
  .carousel-inner > .item.active {
    -webkit-transform: translate3d(0, 0, 0);
    transform: translate3d(0, 0, 0);
    left: 0;
  }
}
.carousel-inner > .active,
.carousel-inner > .next,
.carousel-inner > .prev {
  display: block;
}
.carousel-inner > .active {
  left: 0;
}
.carousel-inner > .next,
.carousel-inner > .prev {
  position: absolute;
  top: 0;
  width: 100%;
}
.carousel-inner > .next {
  left: 100%;
}
.carousel-inner > .prev {
  left: -100%;
}
.carousel-inner > .next.left,
.carousel-inner > .prev.right {
  left: 0;
}
.carousel-inner > .active.left {
  left: -100%;
}
.carousel-inner > .active.right {
  left: 100%;
}
.carousel-control {
  position: absolute;
  top: 0;
  left: 0;
  bottom: 0;
  width: 15%;
  opacity: 0.5;
  filter: alpha(opacity=50);
  font-size: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
  background-color: rgba(0, 0, 0, 0);
}
.carousel-control.left {
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1);
}
.carousel-control.right {
  left: auto;
  right: 0;
  background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%);
  background-repeat: repeat-x;
  filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1);
}
.carousel-control:hover,
.carousel-control:focus {
  outline: 0;
  color: #fff;
  text-decoration: none;
  opacity: 0.9;
  filter: alpha(opacity=90);
}
.carousel-control .icon-prev,
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-left,
.carousel-control .glyphicon-chevron-right {
  position: absolute;
  top: 50%;
  margin-top: -10px;
  z-index: 5;
  display: inline-block;
}
.carousel-control .icon-prev,
.carousel-control .glyphicon-chevron-left {
  left: 50%;
  margin-left: -10px;
}
.carousel-control .icon-next,
.carousel-control .glyphicon-chevron-right {
  right: 50%;
  margin-right: -10px;
}
.carousel-control .icon-prev,
.carousel-control .icon-next {
  width: 20px;
  height: 20px;
  line-height: 1;
  font-family: serif;
}
.carousel-control .icon-prev:before {
  content: '\2039';
}
.carousel-control .icon-next:before {
  content: '\203a';
}
.carousel-indicators {
  position: absolute;
  bottom: 10px;
  left: 50%;
  z-index: 15;
  width: 60%;
  margin-left: -30%;
  padding-left: 0;
  list-style: none;
  text-align: center;
}
.carousel-indicators li {
  display: inline-block;
  width: 10px;
  height: 10px;
  margin: 1px;
  text-indent: -999px;
  border: 1px solid #fff;
  border-radius: 10px;
  cursor: pointer;
  background-color: #000 \9;
  background-color: rgba(0, 0, 0, 0);
}
.carousel-indicators .active {
  margin: 0;
  width: 12px;
  height: 12px;
  background-color: #fff;
}
.carousel-caption {
  position: absolute;
  left: 15%;
  right: 15%;
  bottom: 20px;
  z-index: 10;
  padding-top: 20px;
  padding-bottom: 20px;
  color: #fff;
  text-align: center;
  text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6);
}
.carousel-caption .btn {
  text-shadow: none;
}
@media screen and (min-width: 768px) {
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-prev,
  .carousel-control .icon-next {
    width: 30px;
    height: 30px;
    margin-top: -10px;
    font-size: 30px;
  }
  .carousel-control .glyphicon-chevron-left,
  .carousel-control .icon-prev {
    margin-left: -10px;
  }
  .carousel-control .glyphicon-chevron-right,
  .carousel-control .icon-next {
    margin-right: -10px;
  }
  .carousel-caption {
    left: 20%;
    right: 20%;
    padding-bottom: 30px;
  }
  .carousel-indicators {
    bottom: 20px;
  }
}
.clearfix:before,
.clearfix:after,
.dl-horizontal dd:before,
.dl-horizontal dd:after,
.container:before,
.container:after,
.container-fluid:before,
.container-fluid:after,
.row:before,
.row:after,
.form-horizontal .form-group:before,
.form-horizontal .form-group:after,
.btn-toolbar:before,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:before,
.btn-group-vertical > .btn-group:after,
.nav:before,
.nav:after,
.navbar:before,
.navbar:after,
.navbar-header:before,
.navbar-header:after,
.navbar-collapse:before,
.navbar-collapse:after,
.pager:before,
.pager:after,
.panel-body:before,
.panel-body:after,
.modal-header:before,
.modal-header:after,
.modal-footer:before,
.modal-footer:after,
.item_buttons:before,
.item_buttons:after {
  content: " ";
  display: table;
}
.clearfix:after,
.dl-horizontal dd:after,
.container:after,
.container-fluid:after,
.row:after,
.form-horizontal .form-group:after,
.btn-toolbar:after,
.btn-group-vertical > .btn-group:after,
.nav:after,
.navbar:after,
.navbar-header:after,
.navbar-collapse:after,
.pager:after,
.panel-body:after,
.modal-header:after,
.modal-footer:after,
.item_buttons:after {
  clear: both;
}
.center-block {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.pull-right {
  float: right !important;
}
.pull-left {
  float: left !important;
}
.hide {
  display: none !important;
}
.show {
  display: block !important;
}
.invisible {
  visibility: hidden;
}
.text-hide {
  font: 0/0 a;
  color: transparent;
  text-shadow: none;
  background-color: transparent;
  border: 0;
}
.hidden {
  display: none !important;
}
.affix {
  position: fixed;
}
@-ms-viewport {
  width: device-width;
}
.visible-xs,
.visible-sm,
.visible-md,
.visible-lg {
  display: none !important;
}
.visible-xs-block,
.visible-xs-inline,
.visible-xs-inline-block,
.visible-sm-block,
.visible-sm-inline,
.visible-sm-inline-block,
.visible-md-block,
.visible-md-inline,
.visible-md-inline-block,
.visible-lg-block,
.visible-lg-inline,
.visible-lg-inline-block {
  display: none !important;
}
@media (max-width: 767px) {
  .visible-xs {
    display: block !important;
  }
  table.visible-xs {
    display: table !important;
  }
  tr.visible-xs {
    display: table-row !important;
  }
  th.visible-xs,
  td.visible-xs {
    display: table-cell !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-block {
    display: block !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline {
    display: inline !important;
  }
}
@media (max-width: 767px) {
  .visible-xs-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm {
    display: block !important;
  }
  table.visible-sm {
    display: table !important;
  }
  tr.visible-sm {
    display: table-row !important;
  }
  th.visible-sm,
  td.visible-sm {
    display: table-cell !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-block {
    display: block !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline {
    display: inline !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .visible-sm-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md {
    display: block !important;
  }
  table.visible-md {
    display: table !important;
  }
  tr.visible-md {
    display: table-row !important;
  }
  th.visible-md,
  td.visible-md {
    display: table-cell !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-block {
    display: block !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline {
    display: inline !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .visible-md-inline-block {
    display: inline-block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg {
    display: block !important;
  }
  table.visible-lg {
    display: table !important;
  }
  tr.visible-lg {
    display: table-row !important;
  }
  th.visible-lg,
  td.visible-lg {
    display: table-cell !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-block {
    display: block !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline {
    display: inline !important;
  }
}
@media (min-width: 1200px) {
  .visible-lg-inline-block {
    display: inline-block !important;
  }
}
@media (max-width: 767px) {
  .hidden-xs {
    display: none !important;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  .hidden-sm {
    display: none !important;
  }
}
@media (min-width: 992px) and (max-width: 1199px) {
  .hidden-md {
    display: none !important;
  }
}
@media (min-width: 1200px) {
  .hidden-lg {
    display: none !important;
  }
}
.visible-print {
  display: none !important;
}
@media print {
  .visible-print {
    display: block !important;
  }
  table.visible-print {
    display: table !important;
  }
  tr.visible-print {
    display: table-row !important;
  }
  th.visible-print,
  td.visible-print {
    display: table-cell !important;
  }
}
.visible-print-block {
  display: none !important;
}
@media print {
  .visible-print-block {
    display: block !important;
  }
}
.visible-print-inline {
  display: none !important;
}
@media print {
  .visible-print-inline {
    display: inline !important;
  }
}
.visible-print-inline-block {
  display: none !important;
}
@media print {
  .visible-print-inline-block {
    display: inline-block !important;
  }
}
@media print {
  .hidden-print {
    display: none !important;
  }
}
/*!
*
* Font Awesome
*
*/
/*!
 *  Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome
 *  License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License)
 */
/* FONT PATH
 * -------------------------- */
@font-face {
  font-family: 'FontAwesome';
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0');
  src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg');
  font-weight: normal;
  font-style: normal;
}
.fa {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
}
/* makes the font 33% larger relative to the icon container */
.fa-lg {
  font-size: 1.33333333em;
  line-height: 0.75em;
  vertical-align: -15%;
}
.fa-2x {
  font-size: 2em;
}
.fa-3x {
  font-size: 3em;
}
.fa-4x {
  font-size: 4em;
}
.fa-5x {
  font-size: 5em;
}
.fa-fw {
  width: 1.28571429em;
  text-align: center;
}
.fa-ul {
  padding-left: 0;
  margin-left: 2.14285714em;
  list-style-type: none;
}
.fa-ul > li {
  position: relative;
}
.fa-li {
  position: absolute;
  left: -2.14285714em;
  width: 2.14285714em;
  top: 0.14285714em;
  text-align: center;
}
.fa-li.fa-lg {
  left: -1.85714286em;
}
.fa-border {
  padding: .2em .25em .15em;
  border: solid 0.08em #eee;
  border-radius: .1em;
}
.fa-pull-left {
  float: left;
}
.fa-pull-right {
  float: right;
}
.fa.fa-pull-left {
  margin-right: .3em;
}
.fa.fa-pull-right {
  margin-left: .3em;
}
/* Deprecated as of 4.4.0 */
.pull-right {
  float: right;
}
.pull-left {
  float: left;
}
.fa.pull-left {
  margin-right: .3em;
}
.fa.pull-right {
  margin-left: .3em;
}
.fa-spin {
  -webkit-animation: fa-spin 2s infinite linear;
  animation: fa-spin 2s infinite linear;
}
.fa-pulse {
  -webkit-animation: fa-spin 1s infinite steps(8);
  animation: fa-spin 1s infinite steps(8);
}
@-webkit-keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
@keyframes fa-spin {
  0% {
    -webkit-transform: rotate(0deg);
    transform: rotate(0deg);
  }
  100% {
    -webkit-transform: rotate(359deg);
    transform: rotate(359deg);
  }
}
.fa-rotate-90 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)";
  -webkit-transform: rotate(90deg);
  -ms-transform: rotate(90deg);
  transform: rotate(90deg);
}
.fa-rotate-180 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)";
  -webkit-transform: rotate(180deg);
  -ms-transform: rotate(180deg);
  transform: rotate(180deg);
}
.fa-rotate-270 {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)";
  -webkit-transform: rotate(270deg);
  -ms-transform: rotate(270deg);
  transform: rotate(270deg);
}
.fa-flip-horizontal {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)";
  -webkit-transform: scale(-1, 1);
  -ms-transform: scale(-1, 1);
  transform: scale(-1, 1);
}
.fa-flip-vertical {
  -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)";
  -webkit-transform: scale(1, -1);
  -ms-transform: scale(1, -1);
  transform: scale(1, -1);
}
:root .fa-rotate-90,
:root .fa-rotate-180,
:root .fa-rotate-270,
:root .fa-flip-horizontal,
:root .fa-flip-vertical {
  filter: none;
}
.fa-stack {
  position: relative;
  display: inline-block;
  width: 2em;
  height: 2em;
  line-height: 2em;
  vertical-align: middle;
}
.fa-stack-1x,
.fa-stack-2x {
  position: absolute;
  left: 0;
  width: 100%;
  text-align: center;
}
.fa-stack-1x {
  line-height: inherit;
}
.fa-stack-2x {
  font-size: 2em;
}
.fa-inverse {
  color: #fff;
}
/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen
   readers do not read off random characters that represent icons */
.fa-glass:before {
  content: "\f000";
}
.fa-music:before {
  content: "\f001";
}
.fa-search:before {
  content: "\f002";
}
.fa-envelope-o:before {
  content: "\f003";
}
.fa-heart:before {
  content: "\f004";
}
.fa-star:before {
  content: "\f005";
}
.fa-star-o:before {
  content: "\f006";
}
.fa-user:before {
  content: "\f007";
}
.fa-film:before {
  content: "\f008";
}
.fa-th-large:before {
  content: "\f009";
}
.fa-th:before {
  content: "\f00a";
}
.fa-th-list:before {
  content: "\f00b";
}
.fa-check:before {
  content: "\f00c";
}
.fa-remove:before,
.fa-close:before,
.fa-times:before {
  content: "\f00d";
}
.fa-search-plus:before {
  content: "\f00e";
}
.fa-search-minus:before {
  content: "\f010";
}
.fa-power-off:before {
  content: "\f011";
}
.fa-signal:before {
  content: "\f012";
}
.fa-gear:before,
.fa-cog:before {
  content: "\f013";
}
.fa-trash-o:before {
  content: "\f014";
}
.fa-home:before {
  content: "\f015";
}
.fa-file-o:before {
  content: "\f016";
}
.fa-clock-o:before {
  content: "\f017";
}
.fa-road:before {
  content: "\f018";
}
.fa-download:before {
  content: "\f019";
}
.fa-arrow-circle-o-down:before {
  content: "\f01a";
}
.fa-arrow-circle-o-up:before {
  content: "\f01b";
}
.fa-inbox:before {
  content: "\f01c";
}
.fa-play-circle-o:before {
  content: "\f01d";
}
.fa-rotate-right:before,
.fa-repeat:before {
  content: "\f01e";
}
.fa-refresh:before {
  content: "\f021";
}
.fa-list-alt:before {
  content: "\f022";
}
.fa-lock:before {
  content: "\f023";
}
.fa-flag:before {
  content: "\f024";
}
.fa-headphones:before {
  content: "\f025";
}
.fa-volume-off:before {
  content: "\f026";
}
.fa-volume-down:before {
  content: "\f027";
}
.fa-volume-up:before {
  content: "\f028";
}
.fa-qrcode:before {
  content: "\f029";
}
.fa-barcode:before {
  content: "\f02a";
}
.fa-tag:before {
  content: "\f02b";
}
.fa-tags:before {
  content: "\f02c";
}
.fa-book:before {
  content: "\f02d";
}
.fa-bookmark:before {
  content: "\f02e";
}
.fa-print:before {
  content: "\f02f";
}
.fa-camera:before {
  content: "\f030";
}
.fa-font:before {
  content: "\f031";
}
.fa-bold:before {
  content: "\f032";
}
.fa-italic:before {
  content: "\f033";
}
.fa-text-height:before {
  content: "\f034";
}
.fa-text-width:before {
  content: "\f035";
}
.fa-align-left:before {
  content: "\f036";
}
.fa-align-center:before {
  content: "\f037";
}
.fa-align-right:before {
  content: "\f038";
}
.fa-align-justify:before {
  content: "\f039";
}
.fa-list:before {
  content: "\f03a";
}
.fa-dedent:before,
.fa-outdent:before {
  content: "\f03b";
}
.fa-indent:before {
  content: "\f03c";
}
.fa-video-camera:before {
  content: "\f03d";
}
.fa-photo:before,
.fa-image:before,
.fa-picture-o:before {
  content: "\f03e";
}
.fa-pencil:before {
  content: "\f040";
}
.fa-map-marker:before {
  content: "\f041";
}
.fa-adjust:before {
  content: "\f042";
}
.fa-tint:before {
  content: "\f043";
}
.fa-edit:before,
.fa-pencil-square-o:before {
  content: "\f044";
}
.fa-share-square-o:before {
  content: "\f045";
}
.fa-check-square-o:before {
  content: "\f046";
}
.fa-arrows:before {
  content: "\f047";
}
.fa-step-backward:before {
  content: "\f048";
}
.fa-fast-backward:before {
  content: "\f049";
}
.fa-backward:before {
  content: "\f04a";
}
.fa-play:before {
  content: "\f04b";
}
.fa-pause:before {
  content: "\f04c";
}
.fa-stop:before {
  content: "\f04d";
}
.fa-forward:before {
  content: "\f04e";
}
.fa-fast-forward:before {
  content: "\f050";
}
.fa-step-forward:before {
  content: "\f051";
}
.fa-eject:before {
  content: "\f052";
}
.fa-chevron-left:before {
  content: "\f053";
}
.fa-chevron-right:before {
  content: "\f054";
}
.fa-plus-circle:before {
  content: "\f055";
}
.fa-minus-circle:before {
  content: "\f056";
}
.fa-times-circle:before {
  content: "\f057";
}
.fa-check-circle:before {
  content: "\f058";
}
.fa-question-circle:before {
  content: "\f059";
}
.fa-info-circle:before {
  content: "\f05a";
}
.fa-crosshairs:before {
  content: "\f05b";
}
.fa-times-circle-o:before {
  content: "\f05c";
}
.fa-check-circle-o:before {
  content: "\f05d";
}
.fa-ban:before {
  content: "\f05e";
}
.fa-arrow-left:before {
  content: "\f060";
}
.fa-arrow-right:before {
  content: "\f061";
}
.fa-arrow-up:before {
  content: "\f062";
}
.fa-arrow-down:before {
  content: "\f063";
}
.fa-mail-forward:before,
.fa-share:before {
  content: "\f064";
}
.fa-expand:before {
  content: "\f065";
}
.fa-compress:before {
  content: "\f066";
}
.fa-plus:before {
  content: "\f067";
}
.fa-minus:before {
  content: "\f068";
}
.fa-asterisk:before {
  content: "\f069";
}
.fa-exclamation-circle:before {
  content: "\f06a";
}
.fa-gift:before {
  content: "\f06b";
}
.fa-leaf:before {
  content: "\f06c";
}
.fa-fire:before {
  content: "\f06d";
}
.fa-eye:before {
  content: "\f06e";
}
.fa-eye-slash:before {
  content: "\f070";
}
.fa-warning:before,
.fa-exclamation-triangle:before {
  content: "\f071";
}
.fa-plane:before {
  content: "\f072";
}
.fa-calendar:before {
  content: "\f073";
}
.fa-random:before {
  content: "\f074";
}
.fa-comment:before {
  content: "\f075";
}
.fa-magnet:before {
  content: "\f076";
}
.fa-chevron-up:before {
  content: "\f077";
}
.fa-chevron-down:before {
  content: "\f078";
}
.fa-retweet:before {
  content: "\f079";
}
.fa-shopping-cart:before {
  content: "\f07a";
}
.fa-folder:before {
  content: "\f07b";
}
.fa-folder-open:before {
  content: "\f07c";
}
.fa-arrows-v:before {
  content: "\f07d";
}
.fa-arrows-h:before {
  content: "\f07e";
}
.fa-bar-chart-o:before,
.fa-bar-chart:before {
  content: "\f080";
}
.fa-twitter-square:before {
  content: "\f081";
}
.fa-facebook-square:before {
  content: "\f082";
}
.fa-camera-retro:before {
  content: "\f083";
}
.fa-key:before {
  content: "\f084";
}
.fa-gears:before,
.fa-cogs:before {
  content: "\f085";
}
.fa-comments:before {
  content: "\f086";
}
.fa-thumbs-o-up:before {
  content: "\f087";
}
.fa-thumbs-o-down:before {
  content: "\f088";
}
.fa-star-half:before {
  content: "\f089";
}
.fa-heart-o:before {
  content: "\f08a";
}
.fa-sign-out:before {
  content: "\f08b";
}
.fa-linkedin-square:before {
  content: "\f08c";
}
.fa-thumb-tack:before {
  content: "\f08d";
}
.fa-external-link:before {
  content: "\f08e";
}
.fa-sign-in:before {
  content: "\f090";
}
.fa-trophy:before {
  content: "\f091";
}
.fa-github-square:before {
  content: "\f092";
}
.fa-upload:before {
  content: "\f093";
}
.fa-lemon-o:before {
  content: "\f094";
}
.fa-phone:before {
  content: "\f095";
}
.fa-square-o:before {
  content: "\f096";
}
.fa-bookmark-o:before {
  content: "\f097";
}
.fa-phone-square:before {
  content: "\f098";
}
.fa-twitter:before {
  content: "\f099";
}
.fa-facebook-f:before,
.fa-facebook:before {
  content: "\f09a";
}
.fa-github:before {
  content: "\f09b";
}
.fa-unlock:before {
  content: "\f09c";
}
.fa-credit-card:before {
  content: "\f09d";
}
.fa-feed:before,
.fa-rss:before {
  content: "\f09e";
}
.fa-hdd-o:before {
  content: "\f0a0";
}
.fa-bullhorn:before {
  content: "\f0a1";
}
.fa-bell:before {
  content: "\f0f3";
}
.fa-certificate:before {
  content: "\f0a3";
}
.fa-hand-o-right:before {
  content: "\f0a4";
}
.fa-hand-o-left:before {
  content: "\f0a5";
}
.fa-hand-o-up:before {
  content: "\f0a6";
}
.fa-hand-o-down:before {
  content: "\f0a7";
}
.fa-arrow-circle-left:before {
  content: "\f0a8";
}
.fa-arrow-circle-right:before {
  content: "\f0a9";
}
.fa-arrow-circle-up:before {
  content: "\f0aa";
}
.fa-arrow-circle-down:before {
  content: "\f0ab";
}
.fa-globe:before {
  content: "\f0ac";
}
.fa-wrench:before {
  content: "\f0ad";
}
.fa-tasks:before {
  content: "\f0ae";
}
.fa-filter:before {
  content: "\f0b0";
}
.fa-briefcase:before {
  content: "\f0b1";
}
.fa-arrows-alt:before {
  content: "\f0b2";
}
.fa-group:before,
.fa-users:before {
  content: "\f0c0";
}
.fa-chain:before,
.fa-link:before {
  content: "\f0c1";
}
.fa-cloud:before {
  content: "\f0c2";
}
.fa-flask:before {
  content: "\f0c3";
}
.fa-cut:before,
.fa-scissors:before {
  content: "\f0c4";
}
.fa-copy:before,
.fa-files-o:before {
  content: "\f0c5";
}
.fa-paperclip:before {
  content: "\f0c6";
}
.fa-save:before,
.fa-floppy-o:before {
  content: "\f0c7";
}
.fa-square:before {
  content: "\f0c8";
}
.fa-navicon:before,
.fa-reorder:before,
.fa-bars:before {
  content: "\f0c9";
}
.fa-list-ul:before {
  content: "\f0ca";
}
.fa-list-ol:before {
  content: "\f0cb";
}
.fa-strikethrough:before {
  content: "\f0cc";
}
.fa-underline:before {
  content: "\f0cd";
}
.fa-table:before {
  content: "\f0ce";
}
.fa-magic:before {
  content: "\f0d0";
}
.fa-truck:before {
  content: "\f0d1";
}
.fa-pinterest:before {
  content: "\f0d2";
}
.fa-pinterest-square:before {
  content: "\f0d3";
}
.fa-google-plus-square:before {
  content: "\f0d4";
}
.fa-google-plus:before {
  content: "\f0d5";
}
.fa-money:before {
  content: "\f0d6";
}
.fa-caret-down:before {
  content: "\f0d7";
}
.fa-caret-up:before {
  content: "\f0d8";
}
.fa-caret-left:before {
  content: "\f0d9";
}
.fa-caret-right:before {
  content: "\f0da";
}
.fa-columns:before {
  content: "\f0db";
}
.fa-unsorted:before,
.fa-sort:before {
  content: "\f0dc";
}
.fa-sort-down:before,
.fa-sort-desc:before {
  content: "\f0dd";
}
.fa-sort-up:before,
.fa-sort-asc:before {
  content: "\f0de";
}
.fa-envelope:before {
  content: "\f0e0";
}
.fa-linkedin:before {
  content: "\f0e1";
}
.fa-rotate-left:before,
.fa-undo:before {
  content: "\f0e2";
}
.fa-legal:before,
.fa-gavel:before {
  content: "\f0e3";
}
.fa-dashboard:before,
.fa-tachometer:before {
  content: "\f0e4";
}
.fa-comment-o:before {
  content: "\f0e5";
}
.fa-comments-o:before {
  content: "\f0e6";
}
.fa-flash:before,
.fa-bolt:before {
  content: "\f0e7";
}
.fa-sitemap:before {
  content: "\f0e8";
}
.fa-umbrella:before {
  content: "\f0e9";
}
.fa-paste:before,
.fa-clipboard:before {
  content: "\f0ea";
}
.fa-lightbulb-o:before {
  content: "\f0eb";
}
.fa-exchange:before {
  content: "\f0ec";
}
.fa-cloud-download:before {
  content: "\f0ed";
}
.fa-cloud-upload:before {
  content: "\f0ee";
}
.fa-user-md:before {
  content: "\f0f0";
}
.fa-stethoscope:before {
  content: "\f0f1";
}
.fa-suitcase:before {
  content: "\f0f2";
}
.fa-bell-o:before {
  content: "\f0a2";
}
.fa-coffee:before {
  content: "\f0f4";
}
.fa-cutlery:before {
  content: "\f0f5";
}
.fa-file-text-o:before {
  content: "\f0f6";
}
.fa-building-o:before {
  content: "\f0f7";
}
.fa-hospital-o:before {
  content: "\f0f8";
}
.fa-ambulance:before {
  content: "\f0f9";
}
.fa-medkit:before {
  content: "\f0fa";
}
.fa-fighter-jet:before {
  content: "\f0fb";
}
.fa-beer:before {
  content: "\f0fc";
}
.fa-h-square:before {
  content: "\f0fd";
}
.fa-plus-square:before {
  content: "\f0fe";
}
.fa-angle-double-left:before {
  content: "\f100";
}
.fa-angle-double-right:before {
  content: "\f101";
}
.fa-angle-double-up:before {
  content: "\f102";
}
.fa-angle-double-down:before {
  content: "\f103";
}
.fa-angle-left:before {
  content: "\f104";
}
.fa-angle-right:before {
  content: "\f105";
}
.fa-angle-up:before {
  content: "\f106";
}
.fa-angle-down:before {
  content: "\f107";
}
.fa-desktop:before {
  content: "\f108";
}
.fa-laptop:before {
  content: "\f109";
}
.fa-tablet:before {
  content: "\f10a";
}
.fa-mobile-phone:before,
.fa-mobile:before {
  content: "\f10b";
}
.fa-circle-o:before {
  content: "\f10c";
}
.fa-quote-left:before {
  content: "\f10d";
}
.fa-quote-right:before {
  content: "\f10e";
}
.fa-spinner:before {
  content: "\f110";
}
.fa-circle:before {
  content: "\f111";
}
.fa-mail-reply:before,
.fa-reply:before {
  content: "\f112";
}
.fa-github-alt:before {
  content: "\f113";
}
.fa-folder-o:before {
  content: "\f114";
}
.fa-folder-open-o:before {
  content: "\f115";
}
.fa-smile-o:before {
  content: "\f118";
}
.fa-frown-o:before {
  content: "\f119";
}
.fa-meh-o:before {
  content: "\f11a";
}
.fa-gamepad:before {
  content: "\f11b";
}
.fa-keyboard-o:before {
  content: "\f11c";
}
.fa-flag-o:before {
  content: "\f11d";
}
.fa-flag-checkered:before {
  content: "\f11e";
}
.fa-terminal:before {
  content: "\f120";
}
.fa-code:before {
  content: "\f121";
}
.fa-mail-reply-all:before,
.fa-reply-all:before {
  content: "\f122";
}
.fa-star-half-empty:before,
.fa-star-half-full:before,
.fa-star-half-o:before {
  content: "\f123";
}
.fa-location-arrow:before {
  content: "\f124";
}
.fa-crop:before {
  content: "\f125";
}
.fa-code-fork:before {
  content: "\f126";
}
.fa-unlink:before,
.fa-chain-broken:before {
  content: "\f127";
}
.fa-question:before {
  content: "\f128";
}
.fa-info:before {
  content: "\f129";
}
.fa-exclamation:before {
  content: "\f12a";
}
.fa-superscript:before {
  content: "\f12b";
}
.fa-subscript:before {
  content: "\f12c";
}
.fa-eraser:before {
  content: "\f12d";
}
.fa-puzzle-piece:before {
  content: "\f12e";
}
.fa-microphone:before {
  content: "\f130";
}
.fa-microphone-slash:before {
  content: "\f131";
}
.fa-shield:before {
  content: "\f132";
}
.fa-calendar-o:before {
  content: "\f133";
}
.fa-fire-extinguisher:before {
  content: "\f134";
}
.fa-rocket:before {
  content: "\f135";
}
.fa-maxcdn:before {
  content: "\f136";
}
.fa-chevron-circle-left:before {
  content: "\f137";
}
.fa-chevron-circle-right:before {
  content: "\f138";
}
.fa-chevron-circle-up:before {
  content: "\f139";
}
.fa-chevron-circle-down:before {
  content: "\f13a";
}
.fa-html5:before {
  content: "\f13b";
}
.fa-css3:before {
  content: "\f13c";
}
.fa-anchor:before {
  content: "\f13d";
}
.fa-unlock-alt:before {
  content: "\f13e";
}
.fa-bullseye:before {
  content: "\f140";
}
.fa-ellipsis-h:before {
  content: "\f141";
}
.fa-ellipsis-v:before {
  content: "\f142";
}
.fa-rss-square:before {
  content: "\f143";
}
.fa-play-circle:before {
  content: "\f144";
}
.fa-ticket:before {
  content: "\f145";
}
.fa-minus-square:before {
  content: "\f146";
}
.fa-minus-square-o:before {
  content: "\f147";
}
.fa-level-up:before {
  content: "\f148";
}
.fa-level-down:before {
  content: "\f149";
}
.fa-check-square:before {
  content: "\f14a";
}
.fa-pencil-square:before {
  content: "\f14b";
}
.fa-external-link-square:before {
  content: "\f14c";
}
.fa-share-square:before {
  content: "\f14d";
}
.fa-compass:before {
  content: "\f14e";
}
.fa-toggle-down:before,
.fa-caret-square-o-down:before {
  content: "\f150";
}
.fa-toggle-up:before,
.fa-caret-square-o-up:before {
  content: "\f151";
}
.fa-toggle-right:before,
.fa-caret-square-o-right:before {
  content: "\f152";
}
.fa-euro:before,
.fa-eur:before {
  content: "\f153";
}
.fa-gbp:before {
  content: "\f154";
}
.fa-dollar:before,
.fa-usd:before {
  content: "\f155";
}
.fa-rupee:before,
.fa-inr:before {
  content: "\f156";
}
.fa-cny:before,
.fa-rmb:before,
.fa-yen:before,
.fa-jpy:before {
  content: "\f157";
}
.fa-ruble:before,
.fa-rouble:before,
.fa-rub:before {
  content: "\f158";
}
.fa-won:before,
.fa-krw:before {
  content: "\f159";
}
.fa-bitcoin:before,
.fa-btc:before {
  content: "\f15a";
}
.fa-file:before {
  content: "\f15b";
}
.fa-file-text:before {
  content: "\f15c";
}
.fa-sort-alpha-asc:before {
  content: "\f15d";
}
.fa-sort-alpha-desc:before {
  content: "\f15e";
}
.fa-sort-amount-asc:before {
  content: "\f160";
}
.fa-sort-amount-desc:before {
  content: "\f161";
}
.fa-sort-numeric-asc:before {
  content: "\f162";
}
.fa-sort-numeric-desc:before {
  content: "\f163";
}
.fa-thumbs-up:before {
  content: "\f164";
}
.fa-thumbs-down:before {
  content: "\f165";
}
.fa-youtube-square:before {
  content: "\f166";
}
.fa-youtube:before {
  content: "\f167";
}
.fa-xing:before {
  content: "\f168";
}
.fa-xing-square:before {
  content: "\f169";
}
.fa-youtube-play:before {
  content: "\f16a";
}
.fa-dropbox:before {
  content: "\f16b";
}
.fa-stack-overflow:before {
  content: "\f16c";
}
.fa-instagram:before {
  content: "\f16d";
}
.fa-flickr:before {
  content: "\f16e";
}
.fa-adn:before {
  content: "\f170";
}
.fa-bitbucket:before {
  content: "\f171";
}
.fa-bitbucket-square:before {
  content: "\f172";
}
.fa-tumblr:before {
  content: "\f173";
}
.fa-tumblr-square:before {
  content: "\f174";
}
.fa-long-arrow-down:before {
  content: "\f175";
}
.fa-long-arrow-up:before {
  content: "\f176";
}
.fa-long-arrow-left:before {
  content: "\f177";
}
.fa-long-arrow-right:before {
  content: "\f178";
}
.fa-apple:before {
  content: "\f179";
}
.fa-windows:before {
  content: "\f17a";
}
.fa-android:before {
  content: "\f17b";
}
.fa-linux:before {
  content: "\f17c";
}
.fa-dribbble:before {
  content: "\f17d";
}
.fa-skype:before {
  content: "\f17e";
}
.fa-foursquare:before {
  content: "\f180";
}
.fa-trello:before {
  content: "\f181";
}
.fa-female:before {
  content: "\f182";
}
.fa-male:before {
  content: "\f183";
}
.fa-gittip:before,
.fa-gratipay:before {
  content: "\f184";
}
.fa-sun-o:before {
  content: "\f185";
}
.fa-moon-o:before {
  content: "\f186";
}
.fa-archive:before {
  content: "\f187";
}
.fa-bug:before {
  content: "\f188";
}
.fa-vk:before {
  content: "\f189";
}
.fa-weibo:before {
  content: "\f18a";
}
.fa-renren:before {
  content: "\f18b";
}
.fa-pagelines:before {
  content: "\f18c";
}
.fa-stack-exchange:before {
  content: "\f18d";
}
.fa-arrow-circle-o-right:before {
  content: "\f18e";
}
.fa-arrow-circle-o-left:before {
  content: "\f190";
}
.fa-toggle-left:before,
.fa-caret-square-o-left:before {
  content: "\f191";
}
.fa-dot-circle-o:before {
  content: "\f192";
}
.fa-wheelchair:before {
  content: "\f193";
}
.fa-vimeo-square:before {
  content: "\f194";
}
.fa-turkish-lira:before,
.fa-try:before {
  content: "\f195";
}
.fa-plus-square-o:before {
  content: "\f196";
}
.fa-space-shuttle:before {
  content: "\f197";
}
.fa-slack:before {
  content: "\f198";
}
.fa-envelope-square:before {
  content: "\f199";
}
.fa-wordpress:before {
  content: "\f19a";
}
.fa-openid:before {
  content: "\f19b";
}
.fa-institution:before,
.fa-bank:before,
.fa-university:before {
  content: "\f19c";
}
.fa-mortar-board:before,
.fa-graduation-cap:before {
  content: "\f19d";
}
.fa-yahoo:before {
  content: "\f19e";
}
.fa-google:before {
  content: "\f1a0";
}
.fa-reddit:before {
  content: "\f1a1";
}
.fa-reddit-square:before {
  content: "\f1a2";
}
.fa-stumbleupon-circle:before {
  content: "\f1a3";
}
.fa-stumbleupon:before {
  content: "\f1a4";
}
.fa-delicious:before {
  content: "\f1a5";
}
.fa-digg:before {
  content: "\f1a6";
}
.fa-pied-piper-pp:before {
  content: "\f1a7";
}
.fa-pied-piper-alt:before {
  content: "\f1a8";
}
.fa-drupal:before {
  content: "\f1a9";
}
.fa-joomla:before {
  content: "\f1aa";
}
.fa-language:before {
  content: "\f1ab";
}
.fa-fax:before {
  content: "\f1ac";
}
.fa-building:before {
  content: "\f1ad";
}
.fa-child:before {
  content: "\f1ae";
}
.fa-paw:before {
  content: "\f1b0";
}
.fa-spoon:before {
  content: "\f1b1";
}
.fa-cube:before {
  content: "\f1b2";
}
.fa-cubes:before {
  content: "\f1b3";
}
.fa-behance:before {
  content: "\f1b4";
}
.fa-behance-square:before {
  content: "\f1b5";
}
.fa-steam:before {
  content: "\f1b6";
}
.fa-steam-square:before {
  content: "\f1b7";
}
.fa-recycle:before {
  content: "\f1b8";
}
.fa-automobile:before,
.fa-car:before {
  content: "\f1b9";
}
.fa-cab:before,
.fa-taxi:before {
  content: "\f1ba";
}
.fa-tree:before {
  content: "\f1bb";
}
.fa-spotify:before {
  content: "\f1bc";
}
.fa-deviantart:before {
  content: "\f1bd";
}
.fa-soundcloud:before {
  content: "\f1be";
}
.fa-database:before {
  content: "\f1c0";
}
.fa-file-pdf-o:before {
  content: "\f1c1";
}
.fa-file-word-o:before {
  content: "\f1c2";
}
.fa-file-excel-o:before {
  content: "\f1c3";
}
.fa-file-powerpoint-o:before {
  content: "\f1c4";
}
.fa-file-photo-o:before,
.fa-file-picture-o:before,
.fa-file-image-o:before {
  content: "\f1c5";
}
.fa-file-zip-o:before,
.fa-file-archive-o:before {
  content: "\f1c6";
}
.fa-file-sound-o:before,
.fa-file-audio-o:before {
  content: "\f1c7";
}
.fa-file-movie-o:before,
.fa-file-video-o:before {
  content: "\f1c8";
}
.fa-file-code-o:before {
  content: "\f1c9";
}
.fa-vine:before {
  content: "\f1ca";
}
.fa-codepen:before {
  content: "\f1cb";
}
.fa-jsfiddle:before {
  content: "\f1cc";
}
.fa-life-bouy:before,
.fa-life-buoy:before,
.fa-life-saver:before,
.fa-support:before,
.fa-life-ring:before {
  content: "\f1cd";
}
.fa-circle-o-notch:before {
  content: "\f1ce";
}
.fa-ra:before,
.fa-resistance:before,
.fa-rebel:before {
  content: "\f1d0";
}
.fa-ge:before,
.fa-empire:before {
  content: "\f1d1";
}
.fa-git-square:before {
  content: "\f1d2";
}
.fa-git:before {
  content: "\f1d3";
}
.fa-y-combinator-square:before,
.fa-yc-square:before,
.fa-hacker-news:before {
  content: "\f1d4";
}
.fa-tencent-weibo:before {
  content: "\f1d5";
}
.fa-qq:before {
  content: "\f1d6";
}
.fa-wechat:before,
.fa-weixin:before {
  content: "\f1d7";
}
.fa-send:before,
.fa-paper-plane:before {
  content: "\f1d8";
}
.fa-send-o:before,
.fa-paper-plane-o:before {
  content: "\f1d9";
}
.fa-history:before {
  content: "\f1da";
}
.fa-circle-thin:before {
  content: "\f1db";
}
.fa-header:before {
  content: "\f1dc";
}
.fa-paragraph:before {
  content: "\f1dd";
}
.fa-sliders:before {
  content: "\f1de";
}
.fa-share-alt:before {
  content: "\f1e0";
}
.fa-share-alt-square:before {
  content: "\f1e1";
}
.fa-bomb:before {
  content: "\f1e2";
}
.fa-soccer-ball-o:before,
.fa-futbol-o:before {
  content: "\f1e3";
}
.fa-tty:before {
  content: "\f1e4";
}
.fa-binoculars:before {
  content: "\f1e5";
}
.fa-plug:before {
  content: "\f1e6";
}
.fa-slideshare:before {
  content: "\f1e7";
}
.fa-twitch:before {
  content: "\f1e8";
}
.fa-yelp:before {
  content: "\f1e9";
}
.fa-newspaper-o:before {
  content: "\f1ea";
}
.fa-wifi:before {
  content: "\f1eb";
}
.fa-calculator:before {
  content: "\f1ec";
}
.fa-paypal:before {
  content: "\f1ed";
}
.fa-google-wallet:before {
  content: "\f1ee";
}
.fa-cc-visa:before {
  content: "\f1f0";
}
.fa-cc-mastercard:before {
  content: "\f1f1";
}
.fa-cc-discover:before {
  content: "\f1f2";
}
.fa-cc-amex:before {
  content: "\f1f3";
}
.fa-cc-paypal:before {
  content: "\f1f4";
}
.fa-cc-stripe:before {
  content: "\f1f5";
}
.fa-bell-slash:before {
  content: "\f1f6";
}
.fa-bell-slash-o:before {
  content: "\f1f7";
}
.fa-trash:before {
  content: "\f1f8";
}
.fa-copyright:before {
  content: "\f1f9";
}
.fa-at:before {
  content: "\f1fa";
}
.fa-eyedropper:before {
  content: "\f1fb";
}
.fa-paint-brush:before {
  content: "\f1fc";
}
.fa-birthday-cake:before {
  content: "\f1fd";
}
.fa-area-chart:before {
  content: "\f1fe";
}
.fa-pie-chart:before {
  content: "\f200";
}
.fa-line-chart:before {
  content: "\f201";
}
.fa-lastfm:before {
  content: "\f202";
}
.fa-lastfm-square:before {
  content: "\f203";
}
.fa-toggle-off:before {
  content: "\f204";
}
.fa-toggle-on:before {
  content: "\f205";
}
.fa-bicycle:before {
  content: "\f206";
}
.fa-bus:before {
  content: "\f207";
}
.fa-ioxhost:before {
  content: "\f208";
}
.fa-angellist:before {
  content: "\f209";
}
.fa-cc:before {
  content: "\f20a";
}
.fa-shekel:before,
.fa-sheqel:before,
.fa-ils:before {
  content: "\f20b";
}
.fa-meanpath:before {
  content: "\f20c";
}
.fa-buysellads:before {
  content: "\f20d";
}
.fa-connectdevelop:before {
  content: "\f20e";
}
.fa-dashcube:before {
  content: "\f210";
}
.fa-forumbee:before {
  content: "\f211";
}
.fa-leanpub:before {
  content: "\f212";
}
.fa-sellsy:before {
  content: "\f213";
}
.fa-shirtsinbulk:before {
  content: "\f214";
}
.fa-simplybuilt:before {
  content: "\f215";
}
.fa-skyatlas:before {
  content: "\f216";
}
.fa-cart-plus:before {
  content: "\f217";
}
.fa-cart-arrow-down:before {
  content: "\f218";
}
.fa-diamond:before {
  content: "\f219";
}
.fa-ship:before {
  content: "\f21a";
}
.fa-user-secret:before {
  content: "\f21b";
}
.fa-motorcycle:before {
  content: "\f21c";
}
.fa-street-view:before {
  content: "\f21d";
}
.fa-heartbeat:before {
  content: "\f21e";
}
.fa-venus:before {
  content: "\f221";
}
.fa-mars:before {
  content: "\f222";
}
.fa-mercury:before {
  content: "\f223";
}
.fa-intersex:before,
.fa-transgender:before {
  content: "\f224";
}
.fa-transgender-alt:before {
  content: "\f225";
}
.fa-venus-double:before {
  content: "\f226";
}
.fa-mars-double:before {
  content: "\f227";
}
.fa-venus-mars:before {
  content: "\f228";
}
.fa-mars-stroke:before {
  content: "\f229";
}
.fa-mars-stroke-v:before {
  content: "\f22a";
}
.fa-mars-stroke-h:before {
  content: "\f22b";
}
.fa-neuter:before {
  content: "\f22c";
}
.fa-genderless:before {
  content: "\f22d";
}
.fa-facebook-official:before {
  content: "\f230";
}
.fa-pinterest-p:before {
  content: "\f231";
}
.fa-whatsapp:before {
  content: "\f232";
}
.fa-server:before {
  content: "\f233";
}
.fa-user-plus:before {
  content: "\f234";
}
.fa-user-times:before {
  content: "\f235";
}
.fa-hotel:before,
.fa-bed:before {
  content: "\f236";
}
.fa-viacoin:before {
  content: "\f237";
}
.fa-train:before {
  content: "\f238";
}
.fa-subway:before {
  content: "\f239";
}
.fa-medium:before {
  content: "\f23a";
}
.fa-yc:before,
.fa-y-combinator:before {
  content: "\f23b";
}
.fa-optin-monster:before {
  content: "\f23c";
}
.fa-opencart:before {
  content: "\f23d";
}
.fa-expeditedssl:before {
  content: "\f23e";
}
.fa-battery-4:before,
.fa-battery:before,
.fa-battery-full:before {
  content: "\f240";
}
.fa-battery-3:before,
.fa-battery-three-quarters:before {
  content: "\f241";
}
.fa-battery-2:before,
.fa-battery-half:before {
  content: "\f242";
}
.fa-battery-1:before,
.fa-battery-quarter:before {
  content: "\f243";
}
.fa-battery-0:before,
.fa-battery-empty:before {
  content: "\f244";
}
.fa-mouse-pointer:before {
  content: "\f245";
}
.fa-i-cursor:before {
  content: "\f246";
}
.fa-object-group:before {
  content: "\f247";
}
.fa-object-ungroup:before {
  content: "\f248";
}
.fa-sticky-note:before {
  content: "\f249";
}
.fa-sticky-note-o:before {
  content: "\f24a";
}
.fa-cc-jcb:before {
  content: "\f24b";
}
.fa-cc-diners-club:before {
  content: "\f24c";
}
.fa-clone:before {
  content: "\f24d";
}
.fa-balance-scale:before {
  content: "\f24e";
}
.fa-hourglass-o:before {
  content: "\f250";
}
.fa-hourglass-1:before,
.fa-hourglass-start:before {
  content: "\f251";
}
.fa-hourglass-2:before,
.fa-hourglass-half:before {
  content: "\f252";
}
.fa-hourglass-3:before,
.fa-hourglass-end:before {
  content: "\f253";
}
.fa-hourglass:before {
  content: "\f254";
}
.fa-hand-grab-o:before,
.fa-hand-rock-o:before {
  content: "\f255";
}
.fa-hand-stop-o:before,
.fa-hand-paper-o:before {
  content: "\f256";
}
.fa-hand-scissors-o:before {
  content: "\f257";
}
.fa-hand-lizard-o:before {
  content: "\f258";
}
.fa-hand-spock-o:before {
  content: "\f259";
}
.fa-hand-pointer-o:before {
  content: "\f25a";
}
.fa-hand-peace-o:before {
  content: "\f25b";
}
.fa-trademark:before {
  content: "\f25c";
}
.fa-registered:before {
  content: "\f25d";
}
.fa-creative-commons:before {
  content: "\f25e";
}
.fa-gg:before {
  content: "\f260";
}
.fa-gg-circle:before {
  content: "\f261";
}
.fa-tripadvisor:before {
  content: "\f262";
}
.fa-odnoklassniki:before {
  content: "\f263";
}
.fa-odnoklassniki-square:before {
  content: "\f264";
}
.fa-get-pocket:before {
  content: "\f265";
}
.fa-wikipedia-w:before {
  content: "\f266";
}
.fa-safari:before {
  content: "\f267";
}
.fa-chrome:before {
  content: "\f268";
}
.fa-firefox:before {
  content: "\f269";
}
.fa-opera:before {
  content: "\f26a";
}
.fa-internet-explorer:before {
  content: "\f26b";
}
.fa-tv:before,
.fa-television:before {
  content: "\f26c";
}
.fa-contao:before {
  content: "\f26d";
}
.fa-500px:before {
  content: "\f26e";
}
.fa-amazon:before {
  content: "\f270";
}
.fa-calendar-plus-o:before {
  content: "\f271";
}
.fa-calendar-minus-o:before {
  content: "\f272";
}
.fa-calendar-times-o:before {
  content: "\f273";
}
.fa-calendar-check-o:before {
  content: "\f274";
}
.fa-industry:before {
  content: "\f275";
}
.fa-map-pin:before {
  content: "\f276";
}
.fa-map-signs:before {
  content: "\f277";
}
.fa-map-o:before {
  content: "\f278";
}
.fa-map:before {
  content: "\f279";
}
.fa-commenting:before {
  content: "\f27a";
}
.fa-commenting-o:before {
  content: "\f27b";
}
.fa-houzz:before {
  content: "\f27c";
}
.fa-vimeo:before {
  content: "\f27d";
}
.fa-black-tie:before {
  content: "\f27e";
}
.fa-fonticons:before {
  content: "\f280";
}
.fa-reddit-alien:before {
  content: "\f281";
}
.fa-edge:before {
  content: "\f282";
}
.fa-credit-card-alt:before {
  content: "\f283";
}
.fa-codiepie:before {
  content: "\f284";
}
.fa-modx:before {
  content: "\f285";
}
.fa-fort-awesome:before {
  content: "\f286";
}
.fa-usb:before {
  content: "\f287";
}
.fa-product-hunt:before {
  content: "\f288";
}
.fa-mixcloud:before {
  content: "\f289";
}
.fa-scribd:before {
  content: "\f28a";
}
.fa-pause-circle:before {
  content: "\f28b";
}
.fa-pause-circle-o:before {
  content: "\f28c";
}
.fa-stop-circle:before {
  content: "\f28d";
}
.fa-stop-circle-o:before {
  content: "\f28e";
}
.fa-shopping-bag:before {
  content: "\f290";
}
.fa-shopping-basket:before {
  content: "\f291";
}
.fa-hashtag:before {
  content: "\f292";
}
.fa-bluetooth:before {
  content: "\f293";
}
.fa-bluetooth-b:before {
  content: "\f294";
}
.fa-percent:before {
  content: "\f295";
}
.fa-gitlab:before {
  content: "\f296";
}
.fa-wpbeginner:before {
  content: "\f297";
}
.fa-wpforms:before {
  content: "\f298";
}
.fa-envira:before {
  content: "\f299";
}
.fa-universal-access:before {
  content: "\f29a";
}
.fa-wheelchair-alt:before {
  content: "\f29b";
}
.fa-question-circle-o:before {
  content: "\f29c";
}
.fa-blind:before {
  content: "\f29d";
}
.fa-audio-description:before {
  content: "\f29e";
}
.fa-volume-control-phone:before {
  content: "\f2a0";
}
.fa-braille:before {
  content: "\f2a1";
}
.fa-assistive-listening-systems:before {
  content: "\f2a2";
}
.fa-asl-interpreting:before,
.fa-american-sign-language-interpreting:before {
  content: "\f2a3";
}
.fa-deafness:before,
.fa-hard-of-hearing:before,
.fa-deaf:before {
  content: "\f2a4";
}
.fa-glide:before {
  content: "\f2a5";
}
.fa-glide-g:before {
  content: "\f2a6";
}
.fa-signing:before,
.fa-sign-language:before {
  content: "\f2a7";
}
.fa-low-vision:before {
  content: "\f2a8";
}
.fa-viadeo:before {
  content: "\f2a9";
}
.fa-viadeo-square:before {
  content: "\f2aa";
}
.fa-snapchat:before {
  content: "\f2ab";
}
.fa-snapchat-ghost:before {
  content: "\f2ac";
}
.fa-snapchat-square:before {
  content: "\f2ad";
}
.fa-pied-piper:before {
  content: "\f2ae";
}
.fa-first-order:before {
  content: "\f2b0";
}
.fa-yoast:before {
  content: "\f2b1";
}
.fa-themeisle:before {
  content: "\f2b2";
}
.fa-google-plus-circle:before,
.fa-google-plus-official:before {
  content: "\f2b3";
}
.fa-fa:before,
.fa-font-awesome:before {
  content: "\f2b4";
}
.fa-handshake-o:before {
  content: "\f2b5";
}
.fa-envelope-open:before {
  content: "\f2b6";
}
.fa-envelope-open-o:before {
  content: "\f2b7";
}
.fa-linode:before {
  content: "\f2b8";
}
.fa-address-book:before {
  content: "\f2b9";
}
.fa-address-book-o:before {
  content: "\f2ba";
}
.fa-vcard:before,
.fa-address-card:before {
  content: "\f2bb";
}
.fa-vcard-o:before,
.fa-address-card-o:before {
  content: "\f2bc";
}
.fa-user-circle:before {
  content: "\f2bd";
}
.fa-user-circle-o:before {
  content: "\f2be";
}
.fa-user-o:before {
  content: "\f2c0";
}
.fa-id-badge:before {
  content: "\f2c1";
}
.fa-drivers-license:before,
.fa-id-card:before {
  content: "\f2c2";
}
.fa-drivers-license-o:before,
.fa-id-card-o:before {
  content: "\f2c3";
}
.fa-quora:before {
  content: "\f2c4";
}
.fa-free-code-camp:before {
  content: "\f2c5";
}
.fa-telegram:before {
  content: "\f2c6";
}
.fa-thermometer-4:before,
.fa-thermometer:before,
.fa-thermometer-full:before {
  content: "\f2c7";
}
.fa-thermometer-3:before,
.fa-thermometer-three-quarters:before {
  content: "\f2c8";
}
.fa-thermometer-2:before,
.fa-thermometer-half:before {
  content: "\f2c9";
}
.fa-thermometer-1:before,
.fa-thermometer-quarter:before {
  content: "\f2ca";
}
.fa-thermometer-0:before,
.fa-thermometer-empty:before {
  content: "\f2cb";
}
.fa-shower:before {
  content: "\f2cc";
}
.fa-bathtub:before,
.fa-s15:before,
.fa-bath:before {
  content: "\f2cd";
}
.fa-podcast:before {
  content: "\f2ce";
}
.fa-window-maximize:before {
  content: "\f2d0";
}
.fa-window-minimize:before {
  content: "\f2d1";
}
.fa-window-restore:before {
  content: "\f2d2";
}
.fa-times-rectangle:before,
.fa-window-close:before {
  content: "\f2d3";
}
.fa-times-rectangle-o:before,
.fa-window-close-o:before {
  content: "\f2d4";
}
.fa-bandcamp:before {
  content: "\f2d5";
}
.fa-grav:before {
  content: "\f2d6";
}
.fa-etsy:before {
  content: "\f2d7";
}
.fa-imdb:before {
  content: "\f2d8";
}
.fa-ravelry:before {
  content: "\f2d9";
}
.fa-eercast:before {
  content: "\f2da";
}
.fa-microchip:before {
  content: "\f2db";
}
.fa-snowflake-o:before {
  content: "\f2dc";
}
.fa-superpowers:before {
  content: "\f2dd";
}
.fa-wpexplorer:before {
  content: "\f2de";
}
.fa-meetup:before {
  content: "\f2e0";
}
.sr-only {
  position: absolute;
  width: 1px;
  height: 1px;
  padding: 0;
  margin: -1px;
  overflow: hidden;
  clip: rect(0, 0, 0, 0);
  border: 0;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
.sr-only-focusable:active,
.sr-only-focusable:focus {
  position: static;
  width: auto;
  height: auto;
  margin: 0;
  overflow: visible;
  clip: auto;
}
/*!
*
* IPython base
*
*/
.modal.fade .modal-dialog {
  -webkit-transform: translate(0, 0);
  -ms-transform: translate(0, 0);
  -o-transform: translate(0, 0);
  transform: translate(0, 0);
}
code {
  color: #000;
}
pre {
  font-size: inherit;
  line-height: inherit;
}
label {
  font-weight: normal;
}
/* Make the page background atleast 100% the height of the view port */
/* Make the page itself atleast 70% the height of the view port */
.border-box-sizing {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.corner-all {
  border-radius: 2px;
}
.no-padding {
  padding: 0px;
}
/* Flexible box model classes */
/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */
/* This file is a compatability layer.  It allows the usage of flexible box 
model layouts accross multiple browsers, including older browsers.  The newest,
universal implementation of the flexible box model is used when available (see
`Modern browsers` comments below).  Browsers that are known to implement this 
new spec completely include:

    Firefox 28.0+
    Chrome 29.0+
    Internet Explorer 11+ 
    Opera 17.0+

Browsers not listed, including Safari, are supported via the styling under the
`Old browsers` comments below.
*/
.hbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
.hbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.vbox {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
.vbox > * {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
}
.hbox.reverse,
.vbox.reverse,
.reverse {
  /* Old browsers */
  -webkit-box-direction: reverse;
  -moz-box-direction: reverse;
  box-direction: reverse;
  /* Modern browsers */
  flex-direction: row-reverse;
}
.hbox.box-flex0,
.vbox.box-flex0,
.box-flex0 {
  /* Old browsers */
  -webkit-box-flex: 0;
  -moz-box-flex: 0;
  box-flex: 0;
  /* Modern browsers */
  flex: none;
  width: auto;
}
.hbox.box-flex1,
.vbox.box-flex1,
.box-flex1 {
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex,
.vbox.box-flex,
.box-flex {
  /* Old browsers */
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
.hbox.box-flex2,
.vbox.box-flex2,
.box-flex2 {
  /* Old browsers */
  -webkit-box-flex: 2;
  -moz-box-flex: 2;
  box-flex: 2;
  /* Modern browsers */
  flex: 2;
}
.box-group1 {
  /*  Deprecated */
  -webkit-box-flex-group: 1;
  -moz-box-flex-group: 1;
  box-flex-group: 1;
}
.box-group2 {
  /* Deprecated */
  -webkit-box-flex-group: 2;
  -moz-box-flex-group: 2;
  box-flex-group: 2;
}
.hbox.start,
.vbox.start,
.start {
  /* Old browsers */
  -webkit-box-pack: start;
  -moz-box-pack: start;
  box-pack: start;
  /* Modern browsers */
  justify-content: flex-start;
}
.hbox.end,
.vbox.end,
.end {
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
}
.hbox.center,
.vbox.center,
.center {
  /* Old browsers */
  -webkit-box-pack: center;
  -moz-box-pack: center;
  box-pack: center;
  /* Modern browsers */
  justify-content: center;
}
.hbox.baseline,
.vbox.baseline,
.baseline {
  /* Old browsers */
  -webkit-box-pack: baseline;
  -moz-box-pack: baseline;
  box-pack: baseline;
  /* Modern browsers */
  justify-content: baseline;
}
.hbox.stretch,
.vbox.stretch,
.stretch {
  /* Old browsers */
  -webkit-box-pack: stretch;
  -moz-box-pack: stretch;
  box-pack: stretch;
  /* Modern browsers */
  justify-content: stretch;
}
.hbox.align-start,
.vbox.align-start,
.align-start {
  /* Old browsers */
  -webkit-box-align: start;
  -moz-box-align: start;
  box-align: start;
  /* Modern browsers */
  align-items: flex-start;
}
.hbox.align-end,
.vbox.align-end,
.align-end {
  /* Old browsers */
  -webkit-box-align: end;
  -moz-box-align: end;
  box-align: end;
  /* Modern browsers */
  align-items: flex-end;
}
.hbox.align-center,
.vbox.align-center,
.align-center {
  /* Old browsers */
  -webkit-box-align: center;
  -moz-box-align: center;
  box-align: center;
  /* Modern browsers */
  align-items: center;
}
.hbox.align-baseline,
.vbox.align-baseline,
.align-baseline {
  /* Old browsers */
  -webkit-box-align: baseline;
  -moz-box-align: baseline;
  box-align: baseline;
  /* Modern browsers */
  align-items: baseline;
}
.hbox.align-stretch,
.vbox.align-stretch,
.align-stretch {
  /* Old browsers */
  -webkit-box-align: stretch;
  -moz-box-align: stretch;
  box-align: stretch;
  /* Modern browsers */
  align-items: stretch;
}
div.error {
  margin: 2em;
  text-align: center;
}
div.error > h1 {
  font-size: 500%;
  line-height: normal;
}
div.error > p {
  font-size: 200%;
  line-height: normal;
}
div.traceback-wrapper {
  text-align: left;
  max-width: 800px;
  margin: auto;
}
div.traceback-wrapper pre.traceback {
  max-height: 600px;
  overflow: auto;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
body {
  background-color: #fff;
  /* This makes sure that the body covers the entire window and needs to
       be in a different element than the display: box in wrapper below */
  position: absolute;
  left: 0px;
  right: 0px;
  top: 0px;
  bottom: 0px;
  overflow: visible;
}
body > #header {
  /* Initially hidden to prevent FLOUC */
  display: none;
  background-color: #fff;
  /* Display over codemirror */
  position: relative;
  z-index: 100;
}
body > #header #header-container {
  display: flex;
  flex-direction: row;
  justify-content: space-between;
  padding: 5px;
  padding-bottom: 5px;
  padding-top: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
body > #header .header-bar {
  width: 100%;
  height: 1px;
  background: #e7e7e7;
  margin-bottom: -1px;
}
@media print {
  body > #header {
    display: none !important;
  }
}
#header-spacer {
  width: 100%;
  visibility: hidden;
}
@media print {
  #header-spacer {
    display: none;
  }
}
#ipython_notebook {
  padding-left: 0px;
  padding-top: 1px;
  padding-bottom: 1px;
}
[dir="rtl"] #ipython_notebook {
  margin-right: 10px;
  margin-left: 0;
}
[dir="rtl"] #ipython_notebook.pull-left {
  float: right !important;
  float: right;
}
.flex-spacer {
  flex: 1;
}
#noscript {
  width: auto;
  padding-top: 16px;
  padding-bottom: 16px;
  text-align: center;
  font-size: 22px;
  color: red;
  font-weight: bold;
}
#ipython_notebook img {
  height: 28px;
}
#site {
  width: 100%;
  display: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  overflow: auto;
}
@media print {
  #site {
    height: auto !important;
  }
}
/* Smaller buttons */
.ui-button .ui-button-text {
  padding: 0.2em 0.8em;
  font-size: 77%;
}
input.ui-button {
  padding: 0.3em 0.9em;
}
span#kernel_logo_widget {
  margin: 0 10px;
}
span#login_widget {
  float: right;
}
[dir="rtl"] span#login_widget {
  float: left;
}
span#login_widget > .button,
#logout {
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button:focus,
#logout:focus,
span#login_widget > .button.focus,
#logout.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
span#login_widget > .button:hover,
#logout:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
span#login_widget > .button:active:hover,
#logout:active:hover,
span#login_widget > .button.active:hover,
#logout.active:hover,
.open > .dropdown-togglespan#login_widget > .button:hover,
.open > .dropdown-toggle#logout:hover,
span#login_widget > .button:active:focus,
#logout:active:focus,
span#login_widget > .button.active:focus,
#logout.active:focus,
.open > .dropdown-togglespan#login_widget > .button:focus,
.open > .dropdown-toggle#logout:focus,
span#login_widget > .button:active.focus,
#logout:active.focus,
span#login_widget > .button.active.focus,
#logout.active.focus,
.open > .dropdown-togglespan#login_widget > .button.focus,
.open > .dropdown-toggle#logout.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
span#login_widget > .button:active,
#logout:active,
span#login_widget > .button.active,
#logout.active,
.open > .dropdown-togglespan#login_widget > .button,
.open > .dropdown-toggle#logout {
  background-image: none;
}
span#login_widget > .button.disabled:hover,
#logout.disabled:hover,
span#login_widget > .button[disabled]:hover,
#logout[disabled]:hover,
fieldset[disabled] span#login_widget > .button:hover,
fieldset[disabled] #logout:hover,
span#login_widget > .button.disabled:focus,
#logout.disabled:focus,
span#login_widget > .button[disabled]:focus,
#logout[disabled]:focus,
fieldset[disabled] span#login_widget > .button:focus,
fieldset[disabled] #logout:focus,
span#login_widget > .button.disabled.focus,
#logout.disabled.focus,
span#login_widget > .button[disabled].focus,
#logout[disabled].focus,
fieldset[disabled] span#login_widget > .button.focus,
fieldset[disabled] #logout.focus {
  background-color: #fff;
  border-color: #ccc;
}
span#login_widget > .button .badge,
#logout .badge {
  color: #fff;
  background-color: #333;
}
.nav-header {
  text-transform: none;
}
#header > span {
  margin-top: 10px;
}
.modal_stretch .modal-dialog {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  min-height: 80vh;
}
.modal_stretch .modal-dialog .modal-body {
  max-height: calc(100vh - 200px);
  overflow: auto;
  flex: 1;
}
.modal-header {
  cursor: move;
}
@media (min-width: 768px) {
  .modal .modal-dialog {
    width: 700px;
  }
}
@media (min-width: 768px) {
  select.form-control {
    margin-left: 12px;
    margin-right: 12px;
  }
}
/*!
*
* IPython auth
*
*/
.center-nav {
  display: inline-block;
  margin-bottom: -4px;
}
[dir="rtl"] .center-nav form.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] .center-nav .navbar-text {
  float: right;
}
[dir="rtl"] .navbar-inner {
  text-align: right;
}
[dir="rtl"] div.text-left {
  text-align: right;
}
/*!
*
* IPython tree view
*
*/
/* We need an invisible input field on top of the sentense*/
/* "Drag file onto the list ..." */
.alternate_upload {
  background-color: none;
  display: inline;
}
.alternate_upload.form {
  padding: 0;
  margin: 0;
}
.alternate_upload input.fileinput {
  position: absolute;
  display: block;
  width: 100%;
  height: 100%;
  overflow: hidden;
  cursor: pointer;
  opacity: 0;
  z-index: 2;
}
.alternate_upload .btn-xs > input.fileinput {
  margin: -1px -5px;
}
.alternate_upload .btn-upload {
  position: relative;
  height: 22px;
}
::-webkit-file-upload-button {
  cursor: pointer;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
ul#tabs {
  margin-bottom: 4px;
}
ul#tabs a {
  padding-top: 6px;
  padding-bottom: 4px;
}
[dir="rtl"] ul#tabs.nav-tabs > li {
  float: right;
}
[dir="rtl"] ul#tabs.nav.nav-tabs {
  padding-right: 0;
}
ul.breadcrumb a:focus,
ul.breadcrumb a:hover {
  text-decoration: none;
}
ul.breadcrumb i.icon-home {
  font-size: 16px;
  margin-right: 4px;
}
ul.breadcrumb span {
  color: #5e5e5e;
}
.list_toolbar {
  padding: 4px 0 4px 0;
  vertical-align: middle;
}
.list_toolbar .tree-buttons {
  padding-top: 1px;
}
[dir="rtl"] .list_toolbar .tree-buttons .pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .list_toolbar .col-sm-4,
[dir="rtl"] .list_toolbar .col-sm-8 {
  float: right;
}
.dynamic-buttons {
  padding-top: 3px;
  display: inline-block;
}
.list_toolbar [class*="span"] {
  min-height: 24px;
}
.list_header {
  font-weight: bold;
  background-color: #EEE;
}
.list_placeholder {
  font-weight: bold;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
}
.list_container {
  margin-top: 4px;
  margin-bottom: 20px;
  border: 1px solid #ddd;
  border-radius: 2px;
}
.list_container > div {
  border-bottom: 1px solid #ddd;
}
.list_container > div:hover .list-item {
  background-color: red;
}
.list_container > div:last-child {
  border: none;
}
.list_item:hover .list_item {
  background-color: #ddd;
}
.list_item a {
  text-decoration: none;
}
.list_item:hover {
  background-color: #fafafa;
}
.list_header > div,
.list_item > div {
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
.list_header > div input,
.list_item > div input {
  margin-right: 7px;
  margin-left: 14px;
  vertical-align: text-bottom;
  line-height: 22px;
  position: relative;
  top: -1px;
}
.list_header > div .item_link,
.list_item > div .item_link {
  margin-left: -1px;
  vertical-align: baseline;
  line-height: 22px;
}
[dir="rtl"] .list_item > div input {
  margin-right: 0;
}
.new-file input[type=checkbox] {
  visibility: hidden;
}
.item_name {
  line-height: 22px;
  height: 24px;
}
.item_icon {
  font-size: 14px;
  color: #5e5e5e;
  margin-right: 7px;
  margin-left: 7px;
  line-height: 22px;
  vertical-align: baseline;
}
.item_modified {
  margin-right: 7px;
  margin-left: 7px;
}
[dir="rtl"] .item_modified.pull-right {
  float: left !important;
  float: left;
}
.item_buttons {
  line-height: 1em;
  margin-left: -5px;
}
.item_buttons .btn,
.item_buttons .btn-group,
.item_buttons .input-group {
  float: left;
}
.item_buttons > .btn,
.item_buttons > .btn-group,
.item_buttons > .input-group {
  margin-left: 5px;
}
.item_buttons .btn {
  min-width: 13ex;
}
.item_buttons .running-indicator {
  padding-top: 4px;
  color: #5cb85c;
}
.item_buttons .kernel-name {
  padding-top: 4px;
  color: #5bc0de;
  margin-right: 7px;
  float: left;
}
[dir="rtl"] .item_buttons.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .item_buttons .kernel-name {
  margin-left: 7px;
  float: right;
}
.toolbar_info {
  height: 24px;
  line-height: 24px;
}
.list_item input:not([type=checkbox]) {
  padding-top: 3px;
  padding-bottom: 3px;
  height: 22px;
  line-height: 14px;
  margin: 0px;
}
.highlight_text {
  color: blue;
}
#project_name {
  display: inline-block;
  padding-left: 7px;
  margin-left: -2px;
}
#project_name > .breadcrumb {
  padding: 0px;
  margin-bottom: 0px;
  background-color: transparent;
  font-weight: bold;
}
.sort_button {
  display: inline-block;
  padding-left: 7px;
}
[dir="rtl"] .sort_button.pull-right {
  float: left !important;
  float: left;
}
#tree-selector {
  padding-right: 0px;
}
#button-select-all {
  min-width: 50px;
}
[dir="rtl"] #button-select-all.btn {
  float: right ;
}
#select-all {
  margin-left: 7px;
  margin-right: 2px;
  margin-top: 2px;
  height: 16px;
}
[dir="rtl"] #select-all.pull-left {
  float: right !important;
  float: right;
}
.menu_icon {
  margin-right: 2px;
}
.tab-content .row {
  margin-left: 0px;
  margin-right: 0px;
}
.folder_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f114";
}
.folder_icon:before.fa-pull-left {
  margin-right: .3em;
}
.folder_icon:before.fa-pull-right {
  margin-left: .3em;
}
.folder_icon:before.pull-left {
  margin-right: .3em;
}
.folder_icon:before.pull-right {
  margin-left: .3em;
}
.notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
}
.notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.notebook_icon:before.pull-left {
  margin-right: .3em;
}
.notebook_icon:before.pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f02d";
  position: relative;
  top: -1px;
  color: #5cb85c;
}
.running_notebook_icon:before.fa-pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.fa-pull-right {
  margin-left: .3em;
}
.running_notebook_icon:before.pull-left {
  margin-right: .3em;
}
.running_notebook_icon:before.pull-right {
  margin-left: .3em;
}
.file_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f016";
  position: relative;
  top: -2px;
}
.file_icon:before.fa-pull-left {
  margin-right: .3em;
}
.file_icon:before.fa-pull-right {
  margin-left: .3em;
}
.file_icon:before.pull-left {
  margin-right: .3em;
}
.file_icon:before.pull-right {
  margin-left: .3em;
}
#notebook_toolbar .pull-right {
  padding-top: 0px;
  margin-right: -1px;
}
ul#new-menu {
  left: auto;
  right: 0;
}
#new-menu .dropdown-header {
  font-size: 10px;
  border-bottom: 1px solid #e5e5e5;
  padding: 0 0 3px;
  margin: -3px 20px 0;
}
.kernel-menu-icon {
  padding-right: 12px;
  width: 24px;
  content: "\f096";
}
.kernel-menu-icon:before {
  content: "\f096";
}
.kernel-menu-icon-current:before {
  content: "\f00c";
}
#tab_content {
  padding-top: 20px;
}
#running .panel-group .panel {
  margin-top: 3px;
  margin-bottom: 1em;
}
#running .panel-group .panel .panel-heading {
  background-color: #EEE;
  padding-top: 4px;
  padding-bottom: 4px;
  padding-left: 7px;
  padding-right: 7px;
  line-height: 22px;
}
#running .panel-group .panel .panel-heading a:focus,
#running .panel-group .panel .panel-heading a:hover {
  text-decoration: none;
}
#running .panel-group .panel .panel-body {
  padding: 0px;
}
#running .panel-group .panel .panel-body .list_container {
  margin-top: 0px;
  margin-bottom: 0px;
  border: 0px;
  border-radius: 0px;
}
#running .panel-group .panel .panel-body .list_container .list_item {
  border-bottom: 1px solid #ddd;
}
#running .panel-group .panel .panel-body .list_container .list_item:last-child {
  border-bottom: 0px;
}
.delete-button {
  display: none;
}
.duplicate-button {
  display: none;
}
.rename-button {
  display: none;
}
.move-button {
  display: none;
}
.download-button {
  display: none;
}
.shutdown-button {
  display: none;
}
.dynamic-instructions {
  display: inline-block;
  padding-top: 4px;
}
/*!
*
* IPython text editor webapp
*
*/
.selected-keymap i.fa {
  padding: 0px 5px;
}
.selected-keymap i.fa:before {
  content: "\f00c";
}
#mode-menu {
  overflow: auto;
  max-height: 20em;
}
.edit_app #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.edit_app #menubar .navbar {
  /* Use a negative 1 bottom margin, so the border overlaps the border of the
    header */
  margin-bottom: -1px;
}
.dirty-indicator {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator.pull-left {
  margin-right: .3em;
}
.dirty-indicator.pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-dirty.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-dirty.pull-left {
  margin-right: .3em;
}
.dirty-indicator-dirty.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  width: 20px;
}
.dirty-indicator-clean.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean.pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f00c";
}
.dirty-indicator-clean:before.fa-pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.fa-pull-right {
  margin-left: .3em;
}
.dirty-indicator-clean:before.pull-left {
  margin-right: .3em;
}
.dirty-indicator-clean:before.pull-right {
  margin-left: .3em;
}
#filename {
  font-size: 16pt;
  display: table;
  padding: 0px 5px;
}
#current-mode {
  padding-left: 5px;
  padding-right: 5px;
}
#texteditor-backdrop {
  padding-top: 20px;
  padding-bottom: 20px;
}
@media not print {
  #texteditor-backdrop {
    background-color: #EEE;
  }
}
@media print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container .CodeMirror-gutter,
  #texteditor-backdrop #texteditor-container .CodeMirror-gutters {
    background-color: #fff;
  }
}
@media not print {
  #texteditor-backdrop #texteditor-container {
    padding: 0px;
    background-color: #fff;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
.CodeMirror-dialog {
  background-color: #fff;
}
/*!
*
* IPython notebook
*
*/
/* CSS font colors for translated ANSI escape sequences */
/* The color values are a mix of
   http://www.xcolors.net/dl/baskerville-ivorylight and
   http://www.xcolors.net/dl/euphrasia */
.ansi-black-fg {
  color: #3E424D;
}
.ansi-black-bg {
  background-color: #3E424D;
}
.ansi-black-intense-fg {
  color: #282C36;
}
.ansi-black-intense-bg {
  background-color: #282C36;
}
.ansi-red-fg {
  color: #E75C58;
}
.ansi-red-bg {
  background-color: #E75C58;
}
.ansi-red-intense-fg {
  color: #B22B31;
}
.ansi-red-intense-bg {
  background-color: #B22B31;
}
.ansi-green-fg {
  color: #00A250;
}
.ansi-green-bg {
  background-color: #00A250;
}
.ansi-green-intense-fg {
  color: #007427;
}
.ansi-green-intense-bg {
  background-color: #007427;
}
.ansi-yellow-fg {
  color: #DDB62B;
}
.ansi-yellow-bg {
  background-color: #DDB62B;
}
.ansi-yellow-intense-fg {
  color: #B27D12;
}
.ansi-yellow-intense-bg {
  background-color: #B27D12;
}
.ansi-blue-fg {
  color: #208FFB;
}
.ansi-blue-bg {
  background-color: #208FFB;
}
.ansi-blue-intense-fg {
  color: #0065CA;
}
.ansi-blue-intense-bg {
  background-color: #0065CA;
}
.ansi-magenta-fg {
  color: #D160C4;
}
.ansi-magenta-bg {
  background-color: #D160C4;
}
.ansi-magenta-intense-fg {
  color: #A03196;
}
.ansi-magenta-intense-bg {
  background-color: #A03196;
}
.ansi-cyan-fg {
  color: #60C6C8;
}
.ansi-cyan-bg {
  background-color: #60C6C8;
}
.ansi-cyan-intense-fg {
  color: #258F8F;
}
.ansi-cyan-intense-bg {
  background-color: #258F8F;
}
.ansi-white-fg {
  color: #C5C1B4;
}
.ansi-white-bg {
  background-color: #C5C1B4;
}
.ansi-white-intense-fg {
  color: #A1A6B2;
}
.ansi-white-intense-bg {
  background-color: #A1A6B2;
}
.ansi-default-inverse-fg {
  color: #FFFFFF;
}
.ansi-default-inverse-bg {
  background-color: #000000;
}
.ansi-bold {
  font-weight: bold;
}
.ansi-underline {
  text-decoration: underline;
}
/* The following styles are deprecated an will be removed in a future version */
.ansibold {
  font-weight: bold;
}
.ansi-inverse {
  outline: 0.5px dotted;
}
/* use dark versions for foreground, to improve visibility */
.ansiblack {
  color: black;
}
.ansired {
  color: darkred;
}
.ansigreen {
  color: darkgreen;
}
.ansiyellow {
  color: #c4a000;
}
.ansiblue {
  color: darkblue;
}
.ansipurple {
  color: darkviolet;
}
.ansicyan {
  color: steelblue;
}
.ansigray {
  color: gray;
}
/* and light for background, for the same reason */
.ansibgblack {
  background-color: black;
}
.ansibgred {
  background-color: red;
}
.ansibggreen {
  background-color: green;
}
.ansibgyellow {
  background-color: yellow;
}
.ansibgblue {
  background-color: blue;
}
.ansibgpurple {
  background-color: magenta;
}
.ansibgcyan {
  background-color: cyan;
}
.ansibggray {
  background-color: gray;
}
div.cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  border-radius: 2px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  border-width: 1px;
  border-style: solid;
  border-color: transparent;
  width: 100%;
  padding: 5px;
  /* This acts as a spacer between cells, that is outside the border */
  margin: 0px;
  outline: none;
  position: relative;
  overflow: visible;
}
div.cell:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: transparent;
}
div.cell.jupyter-soft-selected {
  border-left-color: #E3F2FD;
  border-left-width: 1px;
  padding-left: 5px;
  border-right-color: #E3F2FD;
  border-right-width: 1px;
  background: #E3F2FD;
}
@media print {
  div.cell.jupyter-soft-selected {
    border-color: transparent;
  }
}
div.cell.selected,
div.cell.selected.jupyter-soft-selected {
  border-color: #ababab;
}
div.cell.selected:before,
div.cell.selected.jupyter-soft-selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #42A5F5;
}
@media print {
  div.cell.selected,
  div.cell.selected.jupyter-soft-selected {
    border-color: transparent;
  }
}
.edit_mode div.cell.selected {
  border-color: #66BB6A;
}
.edit_mode div.cell.selected:before {
  position: absolute;
  display: block;
  top: -1px;
  left: -1px;
  width: 5px;
  height: calc(100% +  2px);
  content: '';
  background: #66BB6A;
}
@media print {
  .edit_mode div.cell.selected {
    border-color: transparent;
  }
}
.prompt {
  /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */
  min-width: 14ex;
  /* This padding is tuned to match the padding on the CodeMirror editor. */
  padding: 0.4em;
  margin: 0px;
  font-family: monospace;
  text-align: right;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
  /* Don't highlight prompt number selection */
  -webkit-touch-callout: none;
  -webkit-user-select: none;
  -khtml-user-select: none;
  -moz-user-select: none;
  -ms-user-select: none;
  user-select: none;
  /* Use default cursor */
  cursor: default;
}
@media (max-width: 540px) {
  .prompt {
    text-align: left;
  }
}
div.inner_cell {
  min-width: 0;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_area {
  border: 1px solid #cfcfcf;
  border-radius: 2px;
  background: #f7f7f7;
  line-height: 1.21429em;
}
/* This is needed so that empty prompt areas can collapse to zero height when there
   is no content in the output_subarea and the prompt. The main purpose of this is
   to make sure that empty JavaScript output_subareas have no height. */
div.prompt:empty {
  padding-top: 0;
  padding-bottom: 0;
}
div.unrecognized_cell {
  padding: 5px 5px 5px 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.unrecognized_cell .inner_cell {
  border-radius: 2px;
  padding: 5px;
  font-weight: bold;
  color: red;
  border: 1px solid #cfcfcf;
  background: #eaeaea;
}
div.unrecognized_cell .inner_cell a {
  color: inherit;
  text-decoration: none;
}
div.unrecognized_cell .inner_cell a:hover {
  color: inherit;
  text-decoration: none;
}
@media (max-width: 540px) {
  div.unrecognized_cell > div.prompt {
    display: none;
  }
}
div.code_cell {
  /* avoid page breaking on code cells when printing */
}
@media print {
  div.code_cell {
    page-break-inside: avoid;
  }
}
/* any special styling for code cells that are currently running goes here */
div.input {
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.input {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
/* input_area and input_prompt must match in top border and margin for alignment */
div.input_prompt {
  color: #303F9F;
  border-top: 1px solid transparent;
}
div.input_area > div.highlight {
  margin: 0.4em;
  border: none;
  padding: 0px;
  background-color: transparent;
}
div.input_area > div.highlight > pre {
  margin: 0px;
  border: none;
  padding: 0px;
  background-color: transparent;
}
/* The following gets added to the <head> if it is detected that the user has a
 * monospace font with inconsistent normal/bold/italic height.  See
 * notebookmain.js.  Such fonts will have keywords vertically offset with
 * respect to the rest of the text.  The user should select a better font.
 * See: https://github.com/ipython/ipython/issues/1503
 *
 * .CodeMirror span {
 *      vertical-align: bottom;
 * }
 */
.CodeMirror {
  line-height: 1.21429em;
  /* Changed from 1em to our global default */
  font-size: 14px;
  height: auto;
  /* Changed to auto to autogrow */
  background: none;
  /* Changed from white to allow our bg to show through */
}
.CodeMirror-scroll {
  /*  The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/
  /*  We have found that if it is visible, vertical scrollbars appear with font size changes.*/
  overflow-y: hidden;
  overflow-x: auto;
}
.CodeMirror-lines {
  /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */
  /* we have set a different line-height and want this to scale with that. */
  /* Note that this should set vertical padding only, since CodeMirror assumes
       that horizontal padding will be set on CodeMirror pre */
  padding: 0.4em 0;
}
.CodeMirror-linenumber {
  padding: 0 8px 0 4px;
}
.CodeMirror-gutters {
  border-bottom-left-radius: 2px;
  border-top-left-radius: 2px;
}
.CodeMirror pre {
  /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only,
    use .CodeMirror-lines for vertical */
  padding: 0 0.4em;
  border: 0;
  border-radius: 0;
}
.CodeMirror-cursor {
  border-left: 1.4px solid black;
}
@media screen and (min-width: 2138px) and (max-width: 4319px) {
  .CodeMirror-cursor {
    border-left: 2px solid black;
  }
}
@media screen and (min-width: 4320px) {
  .CodeMirror-cursor {
    border-left: 4px solid black;
  }
}
/*

Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>
Adapted from GitHub theme

*/
.highlight-base {
  color: #000;
}
.highlight-variable {
  color: #000;
}
.highlight-variable-2 {
  color: #1a1a1a;
}
.highlight-variable-3 {
  color: #333333;
}
.highlight-string {
  color: #BA2121;
}
.highlight-comment {
  color: #408080;
  font-style: italic;
}
.highlight-number {
  color: #080;
}
.highlight-atom {
  color: #88F;
}
.highlight-keyword {
  color: #008000;
  font-weight: bold;
}
.highlight-builtin {
  color: #008000;
}
.highlight-error {
  color: #f00;
}
.highlight-operator {
  color: #AA22FF;
  font-weight: bold;
}
.highlight-meta {
  color: #AA22FF;
}
/* previously not defined, copying from default codemirror */
.highlight-def {
  color: #00f;
}
.highlight-string-2 {
  color: #f50;
}
.highlight-qualifier {
  color: #555;
}
.highlight-bracket {
  color: #997;
}
.highlight-tag {
  color: #170;
}
.highlight-attribute {
  color: #00c;
}
.highlight-header {
  color: blue;
}
.highlight-quote {
  color: #090;
}
.highlight-link {
  color: #00c;
}
/* apply the same style to codemirror */
.cm-s-ipython span.cm-keyword {
  color: #008000;
  font-weight: bold;
}
.cm-s-ipython span.cm-atom {
  color: #88F;
}
.cm-s-ipython span.cm-number {
  color: #080;
}
.cm-s-ipython span.cm-def {
  color: #00f;
}
.cm-s-ipython span.cm-variable {
  color: #000;
}
.cm-s-ipython span.cm-operator {
  color: #AA22FF;
  font-weight: bold;
}
.cm-s-ipython span.cm-variable-2 {
  color: #1a1a1a;
}
.cm-s-ipython span.cm-variable-3 {
  color: #333333;
}
.cm-s-ipython span.cm-comment {
  color: #408080;
  font-style: italic;
}
.cm-s-ipython span.cm-string {
  color: #BA2121;
}
.cm-s-ipython span.cm-string-2 {
  color: #f50;
}
.cm-s-ipython span.cm-meta {
  color: #AA22FF;
}
.cm-s-ipython span.cm-qualifier {
  color: #555;
}
.cm-s-ipython span.cm-builtin {
  color: #008000;
}
.cm-s-ipython span.cm-bracket {
  color: #997;
}
.cm-s-ipython span.cm-tag {
  color: #170;
}
.cm-s-ipython span.cm-attribute {
  color: #00c;
}
.cm-s-ipython span.cm-header {
  color: blue;
}
.cm-s-ipython span.cm-quote {
  color: #090;
}
.cm-s-ipython span.cm-link {
  color: #00c;
}
.cm-s-ipython span.cm-error {
  color: #f00;
}
.cm-s-ipython span.cm-tab {
  background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=);
  background-position: right;
  background-repeat: no-repeat;
}
div.output_wrapper {
  /* this position must be relative to enable descendents to be absolute within it */
  position: relative;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
  z-index: 1;
}
/* class for the output area when it should be height-limited */
div.output_scroll {
  /* ideally, this would be max-height, but FF barfs all over that */
  height: 24em;
  /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */
  width: 100%;
  overflow: auto;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8);
  display: block;
}
/* output div while it is collapsed */
div.output_collapsed {
  margin: 0px;
  padding: 0px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
div.out_prompt_overlay {
  height: 100%;
  padding: 0px 0.4em;
  position: absolute;
  border-radius: 2px;
}
div.out_prompt_overlay:hover {
  /* use inner shadow to get border that is computed the same on WebKit/FF */
  -webkit-box-shadow: inset 0 0 1px #000;
  box-shadow: inset 0 0 1px #000;
  background: rgba(240, 240, 240, 0.5);
}
div.output_prompt {
  color: #D84315;
}
/* This class is the outer container of all output sections. */
div.output_area {
  padding: 0px;
  page-break-inside: avoid;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
div.output_area .MathJax_Display {
  text-align: left !important;
}
div.output_area .rendered_html table {
  margin-left: 0;
  margin-right: 0;
}
div.output_area .rendered_html img {
  margin-left: 0;
  margin-right: 0;
}
div.output_area img,
div.output_area svg {
  max-width: 100%;
  height: auto;
}
div.output_area img.unconfined,
div.output_area svg.unconfined {
  max-width: none;
}
div.output_area .mglyph > img {
  max-width: none;
}
/* This is needed to protect the pre formating from global settings such
   as that of bootstrap */
.output {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: vertical;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: vertical;
  -moz-box-align: stretch;
  display: box;
  box-orient: vertical;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: column;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.output_area {
    /* Old browsers */
    display: -webkit-box;
    -webkit-box-orient: vertical;
    -webkit-box-align: stretch;
    display: -moz-box;
    -moz-box-orient: vertical;
    -moz-box-align: stretch;
    display: box;
    box-orient: vertical;
    box-align: stretch;
    /* Modern browsers */
    display: flex;
    flex-direction: column;
    align-items: stretch;
  }
}
div.output_area pre {
  margin: 0;
  padding: 1px 0 1px 0;
  border: 0;
  vertical-align: baseline;
  color: black;
  background-color: transparent;
  border-radius: 0;
}
/* This class is for the output subarea inside the output_area and after
   the prompt div. */
div.output_subarea {
  overflow-x: auto;
  padding: 0.4em;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
  max-width: calc(100% - 14ex);
}
div.output_scroll div.output_subarea {
  overflow-x: visible;
}
/* The rest of the output_* classes are for special styling of the different
   output types */
/* all text output has this class: */
div.output_text {
  text-align: left;
  color: #000;
  /* This has to match that of the the CodeMirror class line-height below */
  line-height: 1.21429em;
}
/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */
div.output_stderr {
  background: #fdd;
  /* very light red background for stderr */
}
div.output_latex {
  text-align: left;
}
/* Empty output_javascript divs should have no height */
div.output_javascript:empty {
  padding: 0;
}
.js-error {
  color: darkred;
}
/* raw_input styles */
div.raw_input_container {
  line-height: 1.21429em;
  padding-top: 5px;
}
pre.raw_input_prompt {
  /* nothing needed here. */
}
input.raw_input {
  font-family: monospace;
  font-size: inherit;
  color: inherit;
  width: auto;
  /* make sure input baseline aligns with prompt */
  vertical-align: baseline;
  /* padding + margin = 0.5em between prompt and cursor */
  padding: 0em 0.25em;
  margin: 0em 0.25em;
}
input.raw_input:focus {
  box-shadow: none;
}
p.p-space {
  margin-bottom: 10px;
}
div.output_unrecognized {
  padding: 5px;
  font-weight: bold;
  color: red;
}
div.output_unrecognized a {
  color: inherit;
  text-decoration: none;
}
div.output_unrecognized a:hover {
  color: inherit;
  text-decoration: none;
}
.rendered_html {
  color: #000;
  /* any extras will just be numbers: */
}
.rendered_html em {
  font-style: italic;
}
.rendered_html strong {
  font-weight: bold;
}
.rendered_html u {
  text-decoration: underline;
}
.rendered_html :link {
  text-decoration: underline;
}
.rendered_html :visited {
  text-decoration: underline;
}
.rendered_html h1 {
  font-size: 185.7%;
  margin: 1.08em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h2 {
  font-size: 157.1%;
  margin: 1.27em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h3 {
  font-size: 128.6%;
  margin: 1.55em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h4 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
}
.rendered_html h5 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h6 {
  font-size: 100%;
  margin: 2em 0 0 0;
  font-weight: bold;
  line-height: 1.0;
  font-style: italic;
}
.rendered_html h1:first-child {
  margin-top: 0.538em;
}
.rendered_html h2:first-child {
  margin-top: 0.636em;
}
.rendered_html h3:first-child {
  margin-top: 0.777em;
}
.rendered_html h4:first-child {
  margin-top: 1em;
}
.rendered_html h5:first-child {
  margin-top: 1em;
}
.rendered_html h6:first-child {
  margin-top: 1em;
}
.rendered_html ul:not(.list-inline),
.rendered_html ol:not(.list-inline) {
  padding-left: 2em;
}
.rendered_html ul {
  list-style: disc;
}
.rendered_html ul ul {
  list-style: square;
  margin-top: 0;
}
.rendered_html ul ul ul {
  list-style: circle;
}
.rendered_html ol {
  list-style: decimal;
}
.rendered_html ol ol {
  list-style: upper-alpha;
  margin-top: 0;
}
.rendered_html ol ol ol {
  list-style: lower-alpha;
}
.rendered_html ol ol ol ol {
  list-style: lower-roman;
}
.rendered_html ol ol ol ol ol {
  list-style: decimal;
}
.rendered_html * + ul {
  margin-top: 1em;
}
.rendered_html * + ol {
  margin-top: 1em;
}
.rendered_html hr {
  color: black;
  background-color: black;
}
.rendered_html pre {
  margin: 1em 2em;
  padding: 0px;
  background-color: #fff;
}
.rendered_html code {
  background-color: #eff0f1;
}
.rendered_html p code {
  padding: 1px 5px;
}
.rendered_html pre code {
  background-color: #fff;
}
.rendered_html pre,
.rendered_html code {
  border: 0;
  color: #000;
  font-size: 100%;
}
.rendered_html blockquote {
  margin: 1em 2em;
}
.rendered_html table {
  margin-left: auto;
  margin-right: auto;
  border: none;
  border-collapse: collapse;
  border-spacing: 0;
  color: black;
  font-size: 12px;
  table-layout: fixed;
}
.rendered_html thead {
  border-bottom: 1px solid black;
  vertical-align: bottom;
}
.rendered_html tr,
.rendered_html th,
.rendered_html td {
  text-align: right;
  vertical-align: middle;
  padding: 0.5em 0.5em;
  line-height: normal;
  white-space: normal;
  max-width: none;
  border: none;
}
.rendered_html th {
  font-weight: bold;
}
.rendered_html tbody tr:nth-child(odd) {
  background: #f5f5f5;
}
.rendered_html tbody tr:hover {
  background: rgba(66, 165, 245, 0.2);
}
.rendered_html * + table {
  margin-top: 1em;
}
.rendered_html p {
  text-align: left;
}
.rendered_html * + p {
  margin-top: 1em;
}
.rendered_html img {
  display: block;
  margin-left: auto;
  margin-right: auto;
}
.rendered_html * + img {
  margin-top: 1em;
}
.rendered_html img,
.rendered_html svg {
  max-width: 100%;
  height: auto;
}
.rendered_html img.unconfined,
.rendered_html svg.unconfined {
  max-width: none;
}
.rendered_html .alert {
  margin-bottom: initial;
}
.rendered_html * + .alert {
  margin-top: 1em;
}
[dir="rtl"] .rendered_html p {
  text-align: right;
}
div.text_cell {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
}
@media (max-width: 540px) {
  div.text_cell > div.prompt {
    display: none;
  }
}
div.text_cell_render {
  /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/
  outline: none;
  resize: none;
  width: inherit;
  border-style: none;
  padding: 0.5em 0.5em 0.5em 0.4em;
  color: #000;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
a.anchor-link:link {
  text-decoration: none;
  padding: 0px 20px;
  visibility: hidden;
}
h1:hover .anchor-link,
h2:hover .anchor-link,
h3:hover .anchor-link,
h4:hover .anchor-link,
h5:hover .anchor-link,
h6:hover .anchor-link {
  visibility: visible;
}
.text_cell.rendered .input_area {
  display: none;
}
.text_cell.rendered .rendered_html {
  overflow-x: auto;
  overflow-y: hidden;
}
.text_cell.rendered .rendered_html tr,
.text_cell.rendered .rendered_html th,
.text_cell.rendered .rendered_html td {
  max-width: none;
}
.text_cell.unrendered .text_cell_render {
  display: none;
}
.text_cell .dropzone .input_area {
  border: 2px dashed #bababa;
  margin: -1px;
}
.cm-header-1,
.cm-header-2,
.cm-header-3,
.cm-header-4,
.cm-header-5,
.cm-header-6 {
  font-weight: bold;
  font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;
}
.cm-header-1 {
  font-size: 185.7%;
}
.cm-header-2 {
  font-size: 157.1%;
}
.cm-header-3 {
  font-size: 128.6%;
}
.cm-header-4 {
  font-size: 110%;
}
.cm-header-5 {
  font-size: 100%;
  font-style: italic;
}
.cm-header-6 {
  font-size: 100%;
  font-style: italic;
}
/*!
*
* IPython notebook webapp
*
*/
@media (max-width: 767px) {
  .notebook_app {
    padding-left: 0px;
    padding-right: 0px;
  }
}
#ipython-main-app {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook_panel {
  margin: 0px;
  padding: 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  height: 100%;
}
div#notebook {
  font-size: 14px;
  line-height: 20px;
  overflow-y: hidden;
  overflow-x: auto;
  width: 100%;
  /* This spaces the page away from the edge of the notebook area */
  padding-top: 20px;
  margin: 0px;
  outline: none;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  min-height: 100%;
}
@media not print {
  #notebook-container {
    padding: 15px;
    background-color: #fff;
    min-height: 0;
    -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
    box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  }
}
@media print {
  #notebook-container {
    width: 100%;
  }
}
div.ui-widget-content {
  border: 1px solid #ababab;
  outline: none;
}
pre.dialog {
  background-color: #f7f7f7;
  border: 1px solid #ddd;
  border-radius: 2px;
  padding: 0.4em;
  padding-left: 2em;
}
p.dialog {
  padding: 0.2em;
}
/* Word-wrap output correctly.  This is the CSS3 spelling, though Firefox seems
   to not honor it correctly.  Webkit browsers (Chrome, rekonq, Safari) do.
 */
pre,
code,
kbd,
samp {
  white-space: pre-wrap;
}
#fonttest {
  font-family: monospace;
}
p {
  margin-bottom: 0;
}
.end_space {
  min-height: 100px;
  transition: height .2s ease;
}
.notebook_app > #header {
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
@media not print {
  .notebook_app {
    background-color: #EEE;
  }
}
kbd {
  border-style: solid;
  border-width: 1px;
  box-shadow: none;
  margin: 2px;
  padding-left: 2px;
  padding-right: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
.jupyter-keybindings {
  padding: 1px;
  line-height: 24px;
  border-bottom: 1px solid gray;
}
.jupyter-keybindings input {
  margin: 0;
  padding: 0;
  border: none;
}
.jupyter-keybindings i {
  padding: 6px;
}
.well code {
  background-color: #ffffff;
  border-color: #ababab;
  border-width: 1px;
  border-style: solid;
  padding: 2px;
  padding-top: 1px;
  padding-bottom: 1px;
}
/* CSS for the cell toolbar */
.celltoolbar {
  border: thin solid #CFCFCF;
  border-bottom: none;
  background: #EEE;
  border-radius: 2px 2px 0px 0px;
  width: 100%;
  height: 29px;
  padding-right: 4px;
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  /* Old browsers */
  -webkit-box-pack: end;
  -moz-box-pack: end;
  box-pack: end;
  /* Modern browsers */
  justify-content: flex-end;
  display: -webkit-flex;
}
@media print {
  .celltoolbar {
    display: none;
  }
}
.ctb_hideshow {
  display: none;
  vertical-align: bottom;
}
/* ctb_show is added to the ctb_hideshow div to show the cell toolbar.
   Cell toolbars are only shown when the ctb_global_show class is also set.
*/
.ctb_global_show .ctb_show.ctb_hideshow {
  display: block;
}
.ctb_global_show .ctb_show + .input_area,
.ctb_global_show .ctb_show + div.text_cell_input,
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border-top-right-radius: 0px;
  border-top-left-radius: 0px;
}
.ctb_global_show .ctb_show ~ div.text_cell_render {
  border: 1px solid #cfcfcf;
}
.celltoolbar {
  font-size: 87%;
  padding-top: 3px;
}
.celltoolbar select {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  width: inherit;
  font-size: inherit;
  height: 22px;
  padding: 0px;
  display: inline-block;
}
.celltoolbar select:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.celltoolbar select::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.celltoolbar select:-ms-input-placeholder {
  color: #999;
}
.celltoolbar select::-webkit-input-placeholder {
  color: #999;
}
.celltoolbar select::-ms-expand {
  border: 0;
  background-color: transparent;
}
.celltoolbar select[disabled],
.celltoolbar select[readonly],
fieldset[disabled] .celltoolbar select {
  background-color: #eeeeee;
  opacity: 1;
}
.celltoolbar select[disabled],
fieldset[disabled] .celltoolbar select {
  cursor: not-allowed;
}
textarea.celltoolbar select {
  height: auto;
}
select.celltoolbar select {
  height: 30px;
  line-height: 30px;
}
textarea.celltoolbar select,
select[multiple].celltoolbar select {
  height: auto;
}
.celltoolbar label {
  margin-left: 5px;
  margin-right: 5px;
}
.tags_button_container {
  width: 100%;
  display: flex;
}
.tag-container {
  display: flex;
  flex-direction: row;
  flex-grow: 1;
  overflow: hidden;
  position: relative;
}
.tag-container > * {
  margin: 0 4px;
}
.remove-tag-btn {
  margin-left: 4px;
}
.tags-input {
  display: flex;
}
.cell-tag:last-child:after {
  content: "";
  position: absolute;
  right: 0;
  width: 40px;
  height: 100%;
  /* Fade to background color of cell toolbar */
  background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE);
}
.tags-input > * {
  margin-left: 4px;
}
.cell-tag,
.tags-input input,
.tags-input button {
  display: block;
  width: 100%;
  height: 32px;
  padding: 6px 12px;
  font-size: 13px;
  line-height: 1.42857143;
  color: #555555;
  background-color: #fff;
  background-image: none;
  border: 1px solid #ccc;
  border-radius: 2px;
  -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075);
  -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s;
  height: 30px;
  padding: 5px 10px;
  font-size: 12px;
  line-height: 1.5;
  border-radius: 1px;
  box-shadow: none;
  width: inherit;
  font-size: inherit;
  height: 22px;
  line-height: 22px;
  padding: 0px 4px;
  display: inline-block;
}
.cell-tag:focus,
.tags-input input:focus,
.tags-input button:focus {
  border-color: #66afe9;
  outline: 0;
  -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
  box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6);
}
.cell-tag::-moz-placeholder,
.tags-input input::-moz-placeholder,
.tags-input button::-moz-placeholder {
  color: #999;
  opacity: 1;
}
.cell-tag:-ms-input-placeholder,
.tags-input input:-ms-input-placeholder,
.tags-input button:-ms-input-placeholder {
  color: #999;
}
.cell-tag::-webkit-input-placeholder,
.tags-input input::-webkit-input-placeholder,
.tags-input button::-webkit-input-placeholder {
  color: #999;
}
.cell-tag::-ms-expand,
.tags-input input::-ms-expand,
.tags-input button::-ms-expand {
  border: 0;
  background-color: transparent;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
.cell-tag[readonly],
.tags-input input[readonly],
.tags-input button[readonly],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  background-color: #eeeeee;
  opacity: 1;
}
.cell-tag[disabled],
.tags-input input[disabled],
.tags-input button[disabled],
fieldset[disabled] .cell-tag,
fieldset[disabled] .tags-input input,
fieldset[disabled] .tags-input button {
  cursor: not-allowed;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button {
  height: auto;
}
select.cell-tag,
select.tags-input input,
select.tags-input button {
  height: 30px;
  line-height: 30px;
}
textarea.cell-tag,
textarea.tags-input input,
textarea.tags-input button,
select[multiple].cell-tag,
select[multiple].tags-input input,
select[multiple].tags-input button {
  height: auto;
}
.cell-tag,
.tags-input button {
  padding: 0px 4px;
}
.cell-tag {
  background-color: #fff;
  white-space: nowrap;
}
.tags-input input[type=text]:focus {
  outline: none;
  box-shadow: none;
  border-color: #ccc;
}
.completions {
  position: absolute;
  z-index: 110;
  overflow: hidden;
  border: 1px solid #ababab;
  border-radius: 2px;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  line-height: 1;
}
.completions select {
  background: white;
  outline: none;
  border: none;
  padding: 0px;
  margin: 0px;
  overflow: auto;
  font-family: monospace;
  font-size: 110%;
  color: #000;
  width: auto;
}
.completions select option.context {
  color: #286090;
}
#kernel_logo_widget .current_kernel_logo {
  display: none;
  margin-top: -1px;
  margin-bottom: -1px;
  width: 32px;
  height: 32px;
}
[dir="rtl"] #kernel_logo_widget {
  float: left !important;
  float: left;
}
.modal .modal-body .move-path {
  display: flex;
  flex-direction: row;
  justify-content: space;
  align-items: center;
}
.modal .modal-body .move-path .server-root {
  padding-right: 20px;
}
.modal .modal-body .move-path .path-input {
  flex: 1;
}
#menubar {
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
  margin-top: 1px;
}
#menubar .navbar {
  border-top: 1px;
  border-radius: 0px 0px 2px 2px;
  margin-bottom: 0px;
}
#menubar .navbar-toggle {
  float: left;
  padding-top: 7px;
  padding-bottom: 7px;
  border: none;
}
#menubar .navbar-collapse {
  clear: left;
}
[dir="rtl"] #menubar .navbar-toggle {
  float: right;
}
[dir="rtl"] #menubar .navbar-collapse {
  clear: right;
}
[dir="rtl"] #menubar .navbar-nav {
  float: right;
}
[dir="rtl"] #menubar .nav {
  padding-right: 0px;
}
[dir="rtl"] #menubar .navbar-nav > li {
  float: right;
}
[dir="rtl"] #menubar .navbar-right {
  float: left !important;
}
[dir="rtl"] ul.dropdown-menu {
  text-align: right;
  left: auto;
}
[dir="rtl"] ul#new-menu.dropdown-menu {
  right: auto;
  left: 0;
}
.nav-wrapper {
  border-bottom: 1px solid #e7e7e7;
}
i.menu-icon {
  padding-top: 4px;
}
[dir="rtl"] i.menu-icon.pull-right {
  float: left !important;
  float: left;
}
ul#help_menu li a {
  overflow: hidden;
  padding-right: 2.2em;
}
ul#help_menu li a i {
  margin-right: -1.2em;
}
[dir="rtl"] ul#help_menu li a {
  padding-left: 2.2em;
}
[dir="rtl"] ul#help_menu li a i {
  margin-right: 0;
  margin-left: -1.2em;
}
[dir="rtl"] ul#help_menu li a i.pull-right {
  float: left !important;
  float: left;
}
.dropdown-submenu {
  position: relative;
}
.dropdown-submenu > .dropdown-menu {
  top: 0;
  left: 100%;
  margin-top: -6px;
  margin-left: -1px;
}
[dir="rtl"] .dropdown-submenu > .dropdown-menu {
  right: 100%;
  margin-right: -1px;
}
.dropdown-submenu:hover > .dropdown-menu {
  display: block;
}
.dropdown-submenu > a:after {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  display: block;
  content: "\f0da";
  float: right;
  color: #333333;
  margin-top: 2px;
  margin-right: -10px;
}
.dropdown-submenu > a:after.fa-pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.fa-pull-right {
  margin-left: .3em;
}
.dropdown-submenu > a:after.pull-left {
  margin-right: .3em;
}
.dropdown-submenu > a:after.pull-right {
  margin-left: .3em;
}
[dir="rtl"] .dropdown-submenu > a:after {
  float: left;
  content: "\f0d9";
  margin-right: 0;
  margin-left: -10px;
}
.dropdown-submenu:hover > a:after {
  color: #262626;
}
.dropdown-submenu.pull-left {
  float: none;
}
.dropdown-submenu.pull-left > .dropdown-menu {
  left: -100%;
  margin-left: 10px;
}
#notification_area {
  float: right !important;
  float: right;
  z-index: 10;
}
[dir="rtl"] #notification_area {
  float: left !important;
  float: left;
}
.indicator_area {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] .indicator_area {
  float: left !important;
  float: left;
}
#kernel_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  border-left: 1px solid;
}
#kernel_indicator .kernel_indicator_name {
  padding-left: 5px;
  padding-right: 5px;
}
[dir="rtl"] #kernel_indicator {
  float: left !important;
  float: left;
  border-left: 0;
  border-right: 1px solid;
}
#modal_indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
}
[dir="rtl"] #modal_indicator {
  float: left !important;
  float: left;
}
#readonly-indicator {
  float: right !important;
  float: right;
  color: #777;
  margin-left: 5px;
  margin-right: 5px;
  width: 11px;
  z-index: 10;
  text-align: center;
  width: auto;
  margin-top: 2px;
  margin-bottom: 0px;
  margin-left: 0px;
  margin-right: 0px;
  display: none;
}
.modal_indicator:before {
  width: 1.28571429em;
  text-align: center;
}
.edit_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f040";
}
.edit_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.edit_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.edit_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: ' ';
}
.command_mode .modal_indicator:before.fa-pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.fa-pull-right {
  margin-left: .3em;
}
.command_mode .modal_indicator:before.pull-left {
  margin-right: .3em;
}
.command_mode .modal_indicator:before.pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f10c";
}
.kernel_idle_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_idle_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_idle_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f111";
}
.kernel_busy_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_busy_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_busy_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f1e2";
}
.kernel_dead_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_dead_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_dead_icon:before.pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before {
  display: inline-block;
  font: normal normal normal 14px/1 FontAwesome;
  font-size: inherit;
  text-rendering: auto;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  content: "\f127";
}
.kernel_disconnected_icon:before.fa-pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.fa-pull-right {
  margin-left: .3em;
}
.kernel_disconnected_icon:before.pull-left {
  margin-right: .3em;
}
.kernel_disconnected_icon:before.pull-right {
  margin-left: .3em;
}
.notification_widget {
  color: #777;
  z-index: 10;
  background: rgba(240, 240, 240, 0.5);
  margin-right: 4px;
  color: #333;
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget:focus,
.notification_widget.focus {
  color: #333;
  background-color: #e6e6e6;
  border-color: #8c8c8c;
}
.notification_widget:hover {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  color: #333;
  background-color: #e6e6e6;
  border-color: #adadad;
}
.notification_widget:active:hover,
.notification_widget.active:hover,
.open > .dropdown-toggle.notification_widget:hover,
.notification_widget:active:focus,
.notification_widget.active:focus,
.open > .dropdown-toggle.notification_widget:focus,
.notification_widget:active.focus,
.notification_widget.active.focus,
.open > .dropdown-toggle.notification_widget.focus {
  color: #333;
  background-color: #d4d4d4;
  border-color: #8c8c8c;
}
.notification_widget:active,
.notification_widget.active,
.open > .dropdown-toggle.notification_widget {
  background-image: none;
}
.notification_widget.disabled:hover,
.notification_widget[disabled]:hover,
fieldset[disabled] .notification_widget:hover,
.notification_widget.disabled:focus,
.notification_widget[disabled]:focus,
fieldset[disabled] .notification_widget:focus,
.notification_widget.disabled.focus,
.notification_widget[disabled].focus,
fieldset[disabled] .notification_widget.focus {
  background-color: #fff;
  border-color: #ccc;
}
.notification_widget .badge {
  color: #fff;
  background-color: #333;
}
.notification_widget.warning {
  color: #fff;
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning:focus,
.notification_widget.warning.focus {
  color: #fff;
  background-color: #ec971f;
  border-color: #985f0d;
}
.notification_widget.warning:hover {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  color: #fff;
  background-color: #ec971f;
  border-color: #d58512;
}
.notification_widget.warning:active:hover,
.notification_widget.warning.active:hover,
.open > .dropdown-toggle.notification_widget.warning:hover,
.notification_widget.warning:active:focus,
.notification_widget.warning.active:focus,
.open > .dropdown-toggle.notification_widget.warning:focus,
.notification_widget.warning:active.focus,
.notification_widget.warning.active.focus,
.open > .dropdown-toggle.notification_widget.warning.focus {
  color: #fff;
  background-color: #d58512;
  border-color: #985f0d;
}
.notification_widget.warning:active,
.notification_widget.warning.active,
.open > .dropdown-toggle.notification_widget.warning {
  background-image: none;
}
.notification_widget.warning.disabled:hover,
.notification_widget.warning[disabled]:hover,
fieldset[disabled] .notification_widget.warning:hover,
.notification_widget.warning.disabled:focus,
.notification_widget.warning[disabled]:focus,
fieldset[disabled] .notification_widget.warning:focus,
.notification_widget.warning.disabled.focus,
.notification_widget.warning[disabled].focus,
fieldset[disabled] .notification_widget.warning.focus {
  background-color: #f0ad4e;
  border-color: #eea236;
}
.notification_widget.warning .badge {
  color: #f0ad4e;
  background-color: #fff;
}
.notification_widget.success {
  color: #fff;
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success:focus,
.notification_widget.success.focus {
  color: #fff;
  background-color: #449d44;
  border-color: #255625;
}
.notification_widget.success:hover {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  color: #fff;
  background-color: #449d44;
  border-color: #398439;
}
.notification_widget.success:active:hover,
.notification_widget.success.active:hover,
.open > .dropdown-toggle.notification_widget.success:hover,
.notification_widget.success:active:focus,
.notification_widget.success.active:focus,
.open > .dropdown-toggle.notification_widget.success:focus,
.notification_widget.success:active.focus,
.notification_widget.success.active.focus,
.open > .dropdown-toggle.notification_widget.success.focus {
  color: #fff;
  background-color: #398439;
  border-color: #255625;
}
.notification_widget.success:active,
.notification_widget.success.active,
.open > .dropdown-toggle.notification_widget.success {
  background-image: none;
}
.notification_widget.success.disabled:hover,
.notification_widget.success[disabled]:hover,
fieldset[disabled] .notification_widget.success:hover,
.notification_widget.success.disabled:focus,
.notification_widget.success[disabled]:focus,
fieldset[disabled] .notification_widget.success:focus,
.notification_widget.success.disabled.focus,
.notification_widget.success[disabled].focus,
fieldset[disabled] .notification_widget.success.focus {
  background-color: #5cb85c;
  border-color: #4cae4c;
}
.notification_widget.success .badge {
  color: #5cb85c;
  background-color: #fff;
}
.notification_widget.info {
  color: #fff;
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info:focus,
.notification_widget.info.focus {
  color: #fff;
  background-color: #31b0d5;
  border-color: #1b6d85;
}
.notification_widget.info:hover {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  color: #fff;
  background-color: #31b0d5;
  border-color: #269abc;
}
.notification_widget.info:active:hover,
.notification_widget.info.active:hover,
.open > .dropdown-toggle.notification_widget.info:hover,
.notification_widget.info:active:focus,
.notification_widget.info.active:focus,
.open > .dropdown-toggle.notification_widget.info:focus,
.notification_widget.info:active.focus,
.notification_widget.info.active.focus,
.open > .dropdown-toggle.notification_widget.info.focus {
  color: #fff;
  background-color: #269abc;
  border-color: #1b6d85;
}
.notification_widget.info:active,
.notification_widget.info.active,
.open > .dropdown-toggle.notification_widget.info {
  background-image: none;
}
.notification_widget.info.disabled:hover,
.notification_widget.info[disabled]:hover,
fieldset[disabled] .notification_widget.info:hover,
.notification_widget.info.disabled:focus,
.notification_widget.info[disabled]:focus,
fieldset[disabled] .notification_widget.info:focus,
.notification_widget.info.disabled.focus,
.notification_widget.info[disabled].focus,
fieldset[disabled] .notification_widget.info.focus {
  background-color: #5bc0de;
  border-color: #46b8da;
}
.notification_widget.info .badge {
  color: #5bc0de;
  background-color: #fff;
}
.notification_widget.danger {
  color: #fff;
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger:focus,
.notification_widget.danger.focus {
  color: #fff;
  background-color: #c9302c;
  border-color: #761c19;
}
.notification_widget.danger:hover {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  color: #fff;
  background-color: #c9302c;
  border-color: #ac2925;
}
.notification_widget.danger:active:hover,
.notification_widget.danger.active:hover,
.open > .dropdown-toggle.notification_widget.danger:hover,
.notification_widget.danger:active:focus,
.notification_widget.danger.active:focus,
.open > .dropdown-toggle.notification_widget.danger:focus,
.notification_widget.danger:active.focus,
.notification_widget.danger.active.focus,
.open > .dropdown-toggle.notification_widget.danger.focus {
  color: #fff;
  background-color: #ac2925;
  border-color: #761c19;
}
.notification_widget.danger:active,
.notification_widget.danger.active,
.open > .dropdown-toggle.notification_widget.danger {
  background-image: none;
}
.notification_widget.danger.disabled:hover,
.notification_widget.danger[disabled]:hover,
fieldset[disabled] .notification_widget.danger:hover,
.notification_widget.danger.disabled:focus,
.notification_widget.danger[disabled]:focus,
fieldset[disabled] .notification_widget.danger:focus,
.notification_widget.danger.disabled.focus,
.notification_widget.danger[disabled].focus,
fieldset[disabled] .notification_widget.danger.focus {
  background-color: #d9534f;
  border-color: #d43f3a;
}
.notification_widget.danger .badge {
  color: #d9534f;
  background-color: #fff;
}
div#pager {
  background-color: #fff;
  font-size: 14px;
  line-height: 20px;
  overflow: hidden;
  display: none;
  position: fixed;
  bottom: 0px;
  width: 100%;
  max-height: 50%;
  padding-top: 8px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  /* Display over codemirror */
  z-index: 100;
  /* Hack which prevents jquery ui resizable from changing top. */
  top: auto !important;
}
div#pager pre {
  line-height: 1.21429em;
  color: #000;
  background-color: #f7f7f7;
  padding: 0.4em;
}
div#pager #pager-button-area {
  position: absolute;
  top: 8px;
  right: 20px;
}
div#pager #pager-contents {
  position: relative;
  overflow: auto;
  width: 100%;
  height: 100%;
}
div#pager #pager-contents #pager-container {
  position: relative;
  padding: 15px 0px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
div#pager .ui-resizable-handle {
  top: 0px;
  height: 8px;
  background: #f7f7f7;
  border-top: 1px solid #cfcfcf;
  border-bottom: 1px solid #cfcfcf;
  /* This injects handle bars (a short, wide = symbol) for 
        the resize handle. */
}
div#pager .ui-resizable-handle::after {
  content: '';
  top: 2px;
  left: 50%;
  height: 3px;
  width: 30px;
  margin-left: -15px;
  position: absolute;
  border-top: 1px solid #cfcfcf;
}
.quickhelp {
  /* Old browsers */
  display: -webkit-box;
  -webkit-box-orient: horizontal;
  -webkit-box-align: stretch;
  display: -moz-box;
  -moz-box-orient: horizontal;
  -moz-box-align: stretch;
  display: box;
  box-orient: horizontal;
  box-align: stretch;
  /* Modern browsers */
  display: flex;
  flex-direction: row;
  align-items: stretch;
  line-height: 1.8em;
}
.shortcut_key {
  display: inline-block;
  width: 21ex;
  text-align: right;
  font-family: monospace;
}
.shortcut_descr {
  display: inline-block;
  /* Old browsers */
  -webkit-box-flex: 1;
  -moz-box-flex: 1;
  box-flex: 1;
  /* Modern browsers */
  flex: 1;
}
span.save_widget {
  height: 30px;
  margin-top: 4px;
  display: flex;
  justify-content: flex-start;
  align-items: baseline;
  width: 50%;
  flex: 1;
}
span.save_widget span.filename {
  height: 100%;
  line-height: 1em;
  margin-left: 16px;
  border: none;
  font-size: 146.5%;
  text-overflow: ellipsis;
  overflow: hidden;
  white-space: nowrap;
  border-radius: 2px;
}
span.save_widget span.filename:hover {
  background-color: #e6e6e6;
}
[dir="rtl"] span.save_widget.pull-left {
  float: right !important;
  float: right;
}
[dir="rtl"] span.save_widget span.filename {
  margin-left: 0;
  margin-right: 16px;
}
span.checkpoint_status,
span.autosave_status {
  font-size: small;
  white-space: nowrap;
  padding: 0 5px;
}
@media (max-width: 767px) {
  span.save_widget {
    font-size: small;
    padding: 0 0 0 5px;
  }
  span.checkpoint_status,
  span.autosave_status {
    display: none;
  }
}
@media (min-width: 768px) and (max-width: 991px) {
  span.checkpoint_status {
    display: none;
  }
  span.autosave_status {
    font-size: x-small;
  }
}
.toolbar {
  padding: 0px;
  margin-left: -5px;
  margin-top: 2px;
  margin-bottom: 5px;
  box-sizing: border-box;
  -moz-box-sizing: border-box;
  -webkit-box-sizing: border-box;
}
.toolbar select,
.toolbar label {
  width: auto;
  vertical-align: middle;
  margin-right: 2px;
  margin-bottom: 0px;
  display: inline;
  font-size: 92%;
  margin-left: 0.3em;
  margin-right: 0.3em;
  padding: 0px;
  padding-top: 3px;
}
.toolbar .btn {
  padding: 2px 8px;
}
.toolbar .btn-group {
  margin-top: 0px;
  margin-left: 5px;
}
.toolbar-btn-label {
  margin-left: 6px;
}
#maintoolbar {
  margin-bottom: -3px;
  margin-top: -8px;
  border: 0px;
  min-height: 27px;
  margin-left: 0px;
  padding-top: 11px;
  padding-bottom: 3px;
}
#maintoolbar .navbar-text {
  float: none;
  vertical-align: middle;
  text-align: right;
  margin-left: 5px;
  margin-right: 0px;
  margin-top: 0px;
}
.select-xs {
  height: 24px;
}
[dir="rtl"] .btn-group > .btn,
.btn-group-vertical > .btn {
  float: right;
}
.pulse,
.dropdown-menu > li > a.pulse,
li.pulse > a.dropdown-toggle,
li.pulse.open > a.dropdown-toggle {
  background-color: #F37626;
  color: white;
}
/**
 * Primary styles
 *
 * Author: Jupyter Development Team
 */
/** WARNING IF YOU ARE EDITTING THIS FILE, if this is a .css file, It has a lot
 * of chance of beeing generated from the ../less/[samename].less file, you can
 * try to get back the less file by reverting somme commit in history
 **/
/*
 * We'll try to get something pretty, so we
 * have some strange css to have the scroll bar on
 * the left with fix button on the top right of the tooltip
 */
@-moz-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-webkit-keyframes fadeOut {
  from {
    opacity: 1;
  }
  to {
    opacity: 0;
  }
}
@-moz-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
@-webkit-keyframes fadeIn {
  from {
    opacity: 0;
  }
  to {
    opacity: 1;
  }
}
/*properties of tooltip after "expand"*/
.bigtooltip {
  overflow: auto;
  height: 200px;
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
}
/*properties of tooltip before "expand"*/
.smalltooltip {
  -webkit-transition-property: height;
  -webkit-transition-duration: 500ms;
  -moz-transition-property: height;
  -moz-transition-duration: 500ms;
  transition-property: height;
  transition-duration: 500ms;
  text-overflow: ellipsis;
  overflow: hidden;
  height: 80px;
}
.tooltipbuttons {
  position: absolute;
  padding-right: 15px;
  top: 0px;
  right: 0px;
}
.tooltiptext {
  /*avoid the button to overlap on some docstring*/
  padding-right: 30px;
}
.ipython_tooltip {
  max-width: 700px;
  /*fade-in animation when inserted*/
  -webkit-animation: fadeOut 400ms;
  -moz-animation: fadeOut 400ms;
  animation: fadeOut 400ms;
  -webkit-animation: fadeIn 400ms;
  -moz-animation: fadeIn 400ms;
  animation: fadeIn 400ms;
  vertical-align: middle;
  background-color: #f7f7f7;
  overflow: visible;
  border: #ababab 1px solid;
  outline: none;
  padding: 3px;
  margin: 0px;
  padding-left: 7px;
  font-family: monospace;
  min-height: 50px;
  -moz-box-shadow: 0px 6px 10px -1px #adadad;
  -webkit-box-shadow: 0px 6px 10px -1px #adadad;
  box-shadow: 0px 6px 10px -1px #adadad;
  border-radius: 2px;
  position: absolute;
  z-index: 1000;
}
.ipython_tooltip a {
  float: right;
}
.ipython_tooltip .tooltiptext pre {
  border: 0;
  border-radius: 0;
  font-size: 100%;
  background-color: #f7f7f7;
}
.pretooltiparrow {
  left: 0px;
  margin: 0px;
  top: -16px;
  width: 40px;
  height: 16px;
  overflow: hidden;
  position: absolute;
}
.pretooltiparrow:before {
  background-color: #f7f7f7;
  border: 1px #ababab solid;
  z-index: 11;
  content: "";
  position: absolute;
  left: 15px;
  top: 10px;
  width: 25px;
  height: 25px;
  -webkit-transform: rotate(45deg);
  -moz-transform: rotate(45deg);
  -ms-transform: rotate(45deg);
  -o-transform: rotate(45deg);
}
ul.typeahead-list i {
  margin-left: -10px;
  width: 18px;
}
[dir="rtl"] ul.typeahead-list i {
  margin-left: 0;
  margin-right: -10px;
}
ul.typeahead-list {
  max-height: 80vh;
  overflow: auto;
}
ul.typeahead-list > li > a {
  /** Firefox bug **/
  /* see https://github.com/jupyter/notebook/issues/559 */
  white-space: normal;
}
ul.typeahead-list  > li > a.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .typeahead-list {
  text-align: right;
}
.cmd-palette .modal-body {
  padding: 7px;
}
.cmd-palette form {
  background: white;
}
.cmd-palette input {
  outline: none;
}
.no-shortcut {
  min-width: 20px;
  color: transparent;
}
[dir="rtl"] .no-shortcut.pull-right {
  float: left !important;
  float: left;
}
[dir="rtl"] .command-shortcut.pull-right {
  float: left !important;
  float: left;
}
.command-shortcut:before {
  content: "(command mode)";
  padding-right: 3px;
  color: #777777;
}
.edit-shortcut:before {
  content: "(edit)";
  padding-right: 3px;
  color: #777777;
}
[dir="rtl"] .edit-shortcut.pull-right {
  float: left !important;
  float: left;
}
#find-and-replace #replace-preview .match,
#find-and-replace #replace-preview .insert {
  background-color: #BBDEFB;
  border-color: #90CAF9;
  border-style: solid;
  border-width: 1px;
  border-radius: 0px;
}
[dir="ltr"] #find-and-replace .input-group-btn + .form-control {
  border-left: none;
}
[dir="rtl"] #find-and-replace .input-group-btn + .form-control {
  border-right: none;
}
#find-and-replace #replace-preview .replace .match {
  background-color: #FFCDD2;
  border-color: #EF9A9A;
  border-radius: 0px;
}
#find-and-replace #replace-preview .replace .insert {
  background-color: #C8E6C9;
  border-color: #A5D6A7;
  border-radius: 0px;
}
#find-and-replace #replace-preview {
  max-height: 60vh;
  overflow: auto;
}
#find-and-replace #replace-preview pre {
  padding: 5px 10px;
}
.terminal-app {
  background: #EEE;
}
.terminal-app #header {
  background: #fff;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2);
}
.terminal-app .terminal {
  width: 100%;
  float: left;
  font-family: monospace;
  color: white;
  background: black;
  padding: 0.4em;
  border-radius: 2px;
  -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
  box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.4);
}
.terminal-app .terminal,
.terminal-app .terminal dummy-screen {
  line-height: 1em;
  font-size: 14px;
}
.terminal-app .terminal .xterm-rows {
  padding: 10px;
}
.terminal-app .terminal-cursor {
  color: black;
  background: white;
}
.terminal-app #terminado-container {
  margin-top: 20px;
}
/*# sourceMappingURL=style.min.css.map */
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<style type="text/css">
    .highlight .hll { background-color: #ffffcc }
.highlight  { background: #f8f8f8; }
.highlight .c { color: #408080; font-style: italic } /* Comment */
.highlight .err { border: 1px solid #FF0000 } /* Error */
.highlight .k { color: #008000; font-weight: bold } /* Keyword */
.highlight .o { color: #666666 } /* Operator */
.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
.highlight .gd { color: #A00000 } /* Generic.Deleted */
.highlight .ge { font-style: italic } /* Generic.Emph */
.highlight .gr { color: #FF0000 } /* Generic.Error */
.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
.highlight .gi { color: #00A000 } /* Generic.Inserted */
.highlight .go { color: #888888 } /* Generic.Output */
.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
.highlight .gs { font-weight: bold } /* Generic.Strong */
.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
.highlight .gt { color: #0044DD } /* Generic.Traceback */
.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
.highlight .kp { color: #008000 } /* Keyword.Pseudo */
.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
.highlight .kt { color: #B00040 } /* Keyword.Type */
.highlight .m { color: #666666 } /* Literal.Number */
.highlight .s { color: #BA2121 } /* Literal.String */
.highlight .na { color: #7D9029 } /* Name.Attribute */
.highlight .nb { color: #008000 } /* Name.Builtin */
.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
.highlight .no { color: #880000 } /* Name.Constant */
.highlight .nd { color: #AA22FF } /* Name.Decorator */
.highlight .ni { color: #999999; font-weight: bold } /* Name.Entity */
.highlight .ne { color: #D2413A; font-weight: bold } /* Name.Exception */
.highlight .nf { color: #0000FF } /* Name.Function */
.highlight .nl { color: #A0A000 } /* Name.Label */
.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
.highlight .nv { color: #19177C } /* Name.Variable */
.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
.highlight .w { color: #bbbbbb } /* Text.Whitespace */
.highlight .mb { color: #666666 } /* Literal.Number.Bin */
.highlight .mf { color: #666666 } /* Literal.Number.Float */
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.highlight .mi { color: #666666 } /* Literal.Number.Integer */
.highlight .mo { color: #666666 } /* Literal.Number.Oct */
.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
.highlight .sc { color: #BA2121 } /* Literal.String.Char */
.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
.highlight .sx { color: #008000 } /* Literal.String.Other */
.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
.highlight .ss { color: #19177C } /* Literal.String.Symbol */
.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
.highlight .fm { color: #0000FF } /* Name.Function.Magic */
.highlight .vc { color: #19177C } /* Name.Variable.Class */
.highlight .vg { color: #19177C } /* Name.Variable.Global */
.highlight .vi { color: #19177C } /* Name.Variable.Instance */
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.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
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<style type="text/css">
    
/* Temporary definitions which will become obsolete with Notebook release 5.0 */
.ansi-black-fg { color: #3E424D; }
.ansi-black-bg { background-color: #3E424D; }
.ansi-black-intense-fg { color: #282C36; }
.ansi-black-intense-bg { background-color: #282C36; }
.ansi-red-fg { color: #E75C58; }
.ansi-red-bg { background-color: #E75C58; }
.ansi-red-intense-fg { color: #B22B31; }
.ansi-red-intense-bg { background-color: #B22B31; }
.ansi-green-fg { color: #00A250; }
.ansi-green-bg { background-color: #00A250; }
.ansi-green-intense-fg { color: #007427; }
.ansi-green-intense-bg { background-color: #007427; }
.ansi-yellow-fg { color: #DDB62B; }
.ansi-yellow-bg { background-color: #DDB62B; }
.ansi-yellow-intense-fg { color: #B27D12; }
.ansi-yellow-intense-bg { background-color: #B27D12; }
.ansi-blue-fg { color: #208FFB; }
.ansi-blue-bg { background-color: #208FFB; }
.ansi-blue-intense-fg { color: #0065CA; }
.ansi-blue-intense-bg { background-color: #0065CA; }
.ansi-magenta-fg { color: #D160C4; }
.ansi-magenta-bg { background-color: #D160C4; }
.ansi-magenta-intense-fg { color: #A03196; }
.ansi-magenta-intense-bg { background-color: #A03196; }
.ansi-cyan-fg { color: #60C6C8; }
.ansi-cyan-bg { background-color: #60C6C8; }
.ansi-cyan-intense-fg { color: #258F8F; }
.ansi-cyan-intense-bg { background-color: #258F8F; }
.ansi-white-fg { color: #C5C1B4; }
.ansi-white-bg { background-color: #C5C1B4; }
.ansi-white-intense-fg { color: #A1A6B2; }
.ansi-white-intense-bg { background-color: #A1A6B2; }

.ansi-bold { font-weight: bold; }

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<style type="text/css">
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<h1 id="Project:-Identify-Customer-Segments">Project: Identify Customer Segments<a class="anchor-link" href="#Project:-Identify-Customer-Segments">&#182;</a></h1><p>In this project, you will apply unsupervised learning techniques to identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data that you will use has been provided by our partners at Bertelsmann Arvato Analytics, and represents a real-life data science task.</p>
<p>This notebook will help you complete this task by providing a framework within which you will perform your analysis steps. In each step of the project, you will see some text describing the subtask that you will perform, followed by one or more code cells for you to complete your work. <strong>Feel free to add additional code and markdown cells as you go along so that you can explore everything in precise chunks.</strong> The code cells provided in the base template will outline only the major tasks, and will usually not be enough to cover all of the minor tasks that comprise it.</p>
<p>It should be noted that while there will be precise guidelines on how you should handle certain tasks in the project, there will also be places where an exact specification is not provided. <strong>There will be times in the project where you will need to make and justify your own decisions on how to treat the data.</strong> These are places where there may not be only one way to handle the data. In real-life tasks, there may be many valid ways to approach an analysis task. One of the most important things you can do is clearly document your approach so that other scientists can understand the decisions you've made.</p>
<p>At the end of most sections, there will be a Markdown cell labeled <strong>Discussion</strong>. In these cells, you will report your findings for the completed section, as well as document the decisions that you made in your approach to each subtask. <strong>Your project will be evaluated not just on the code used to complete the tasks outlined, but also your communication about your observations and conclusions at each stage.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># import libraries here; add more as necessary</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">iqr</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">Imputer</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="k">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="k">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="k">import</span> <span class="n">RandomForestClassifier</span>
<span class="kn">from</span> <span class="nn">helper_functions</span> <span class="k">import</span> <span class="n">show_images</span><span class="p">,</span> <span class="n">show_images_by_digit</span><span class="p">,</span> <span class="n">fit_random_forest_classifier2</span> 
<span class="kn">from</span> <span class="nn">helper_functions</span> <span class="k">import</span> <span class="n">fit_random_forest_classifier</span><span class="p">,</span> <span class="n">do_pca</span><span class="p">,</span> <span class="n">plot_components</span><span class="p">,</span> <span class="n">pca_results</span>
<span class="kn">from</span> <span class="nn">sklearn.cluster</span> <span class="k">import</span> <span class="n">KMeans</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="k">import</span> <span class="n">preprocessing</span>

<span class="c1"># magic word for producing visualizations in notebook</span>
<span class="o">%</span><span class="k">matplotlib</span> inline
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<h3 id="Step-0:-Load-the-Data">Step 0: Load the Data<a class="anchor-link" href="#Step-0:-Load-the-Data">&#182;</a></h3><p>There are four files associated with this project (not including this one):</p>
<ul>
<li><code>Udacity_AZDIAS_Subset.csv</code>: Demographics data for the general population of Germany; 891211 persons (rows) x 85 features (columns).</li>
<li><code>Udacity_CUSTOMERS_Subset.csv</code>: Demographics data for customers of a mail-order company; 191652 persons (rows) x 85 features (columns).</li>
<li><code>Data_Dictionary.md</code>: Detailed information file about the features in the provided datasets.</li>
<li><code>AZDIAS_Feature_Summary.csv</code>: Summary of feature attributes for demographics data; 85 features (rows) x 4 columns</li>
</ul>
<p>Each row of the demographics files represents a single person, but also includes information outside of individuals, including information about their household, building, and neighborhood. You will use this information to cluster the general population into groups with similar demographic properties. Then, you will see how the people in the customers dataset fit into those created clusters. The hope here is that certain clusters are over-represented in the customers data, as compared to the general population; those over-represented clusters will be assumed to be part of the core userbase. This information can then be used for further applications, such as targeting for a marketing campaign.</p>
<p>To start off with, load in the demographics data for the general population into a pandas DataFrame, and do the same for the feature attributes summary. Note for all of the <code>.csv</code> data files in this project: they're semicolon (<code>;</code>) delimited, so you'll need an additional argument in your <a href="https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html"><code>read_csv()</code></a> call to read in the data properly. Also, considering the size of the main dataset, it may take some time for it to load completely.</p>
<p>Once the dataset is loaded, it's recommended that you take a little bit of time just browsing the general structure of the dataset and feature summary file. You'll be getting deep into the innards of the cleaning in the first major step of the project, so gaining some general familiarity can help you get your bearings.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the general demographics data.</span>
<span class="n">azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_AZDIAS_Subset.csv&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span> 
<span class="c1"># Load in the feature summary file.</span>
<span class="n">feat_info</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;AZDIAS_Feature_Summary.csv&#39;</span><span class="p">,</span> <span class="n">sep</span><span class="o">=</span><span class="s1">&#39;, &#39;</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span> 
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Check the structure of the data after it&#39;s loaded (e.g. print the number of</span>
<span class="c1"># rows and columns, print the first few rows).</span>
<span class="nb">print</span><span class="p">(</span><span class="n">azdias</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">azdias</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<pre>(891221, 85)
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<style scoped>
    .dataframe tbody tr th:only-of-type {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>-1</td>
      <td>2</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>-1</td>
      <td>1</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>-1</td>
      <td>3</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>-1</td>
      <td>3</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 85 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">feat_info</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre>(85, 4)
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<table border="1" class="dataframe">
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      <th>type</th>
      <th>missing_or_unknown</th>
    </tr>
  </thead>
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    <tr>
      <th>0</th>
      <td>AGER_TYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>1</th>
      <td>ALTERSKATEGORIE_GROB</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1,0,9]</td>
    </tr>
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      <th>2</th>
      <td>ANREDE_KZ</td>
      <td>person</td>
      <td>categorical</td>
      <td>[-1,0]</td>
    </tr>
    <tr>
      <th>3</th>
      <td>CJT_GESAMTTYP</td>
      <td>person</td>
      <td>categorical</td>
      <td>[0]</td>
    </tr>
    <tr>
      <th>4</th>
      <td>FINANZ_MINIMALIST</td>
      <td>person</td>
      <td>ordinal</td>
      <td>[-1]</td>
    </tr>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">set</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="p">)</span>
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<pre>{&#39;categorical&#39;, &#39;interval&#39;, &#39;mixed&#39;, &#39;numeric&#39;, &#39;ordinal&#39;}</pre>
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<blockquote><p><strong>Tip</strong>: Add additional cells to keep everything in reasonably-sized chunks! Keyboard shortcut <code>esc --&gt; a</code> (press escape to enter command mode, then press the 'A' key) adds a new cell before the active cell, and <code>esc --&gt; b</code> adds a new cell after the active cell. If you need to convert an active cell to a markdown cell, use <code>esc --&gt; m</code> and to convert to a code cell, use <code>esc --&gt; y</code>.</p>
</blockquote>
<h2 id="Step-1:-Preprocessing">Step 1: Preprocessing<a class="anchor-link" href="#Step-1:-Preprocessing">&#182;</a></h2><h3 id="Step-1.1:-Assess-Missing-Data">Step 1.1: Assess Missing Data<a class="anchor-link" href="#Step-1.1:-Assess-Missing-Data">&#182;</a></h3><p>The feature summary file contains a summary of properties for each demographics data column. You will use this file to help you make cleaning decisions during this stage of the project. First of all, you should assess the demographics data in terms of missing data. Pay attention to the following points as you perform your analysis, and take notes on what you observe. Make sure that you fill in the <strong>Discussion</strong> cell with your findings and decisions at the end of each step that has one!</p>
<h4 id="Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">Step 1.1.1: Convert Missing Value Codes to NaNs<a class="anchor-link" href="#Step-1.1.1:-Convert-Missing-Value-Codes-to-NaNs">&#182;</a></h4><p>The fourth column of the feature attributes summary (loaded in above as <code>feat_info</code>) documents the codes from the data dictionary that indicate missing or unknown data. While the file encodes this as a list (e.g. <code>[-1,0]</code>), this will get read in as a string object. You'll need to do a little bit of parsing to make use of it to identify and clean the data. Convert data that matches a 'missing' or 'unknown' value code into a numpy NaN value. You might want to see how much data takes on a 'missing' or 'unknown' code, and how much data is naturally missing, as a point of interest.</p>
<p><strong>As one more reminder, you are encouraged to add additional cells to break up your analysis into manageable chunks.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Identify missing or unknown data values and convert them to NaNs.</span>
<span class="c1"># for each attribute in feat_info, change all value in the missing or unkowns to NaNs</span>
<span class="c1">#feat_info.missing_or_unknown</span>

<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span><span class="o">&lt;</span><span class="nb">len</span><span class="p">(</span><span class="n">feat_info</span><span class="p">):</span>
    
    <span class="c1"># define a null replace list</span>
    <span class="n">nulldict</span> <span class="o">=</span> <span class="p">{}</span>
    
    <span class="c1"># attribute name</span>
    <span class="n">attribute_name</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="c1">#print(attribute_name)</span>
    
    <span class="c1"># null character</span>
    <span class="n">bads</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">missing_or_unknown</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
    <span class="c1"># parsing null characters</span>
    <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
    <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
    
    <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>

    <span class="c1">#print(bads)</span>
    
    <span class="c1"># change the elements in this list. </span>
    <span class="c1"># to make all the number strings into numbers</span>
    <span class="c1"># kepp all the strings keep the same.</span>
    <span class="c1"># keep a null list if it&#39;s &#39;&#39;</span>
    <span class="n">real_bads</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bads</span><span class="p">:</span>       
        <span class="k">try</span><span class="p">:</span>
            <span class="n">x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
            <span class="c1">#print(&quot;lala:&quot;, str(x)== &quot;&quot;)</span>
            <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
                <span class="c1">#print(&quot;haha&quot;,x)</span>
                <span class="k">continue</span>
            <span class="k">else</span><span class="p">:</span>
                <span class="k">pass</span>      
        <span class="n">real_bads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        
    <span class="c1">#print(real_bads)</span>
    
    <span class="c1"># create toreplace dictionary</span>
    <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">real_bads</span><span class="p">:</span>
        <span class="n">nulldict</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">)</span>
    <span class="c1">#print(nulldict)</span>
    
    <span class="c1"># replace values for curresponding attribute</span>
    <span class="n">azdias</span><span class="p">[</span><span class="n">attribute_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">attribute_name</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span><span class="o">=</span><span class="n">nulldict</span><span class="p">)</span>
    
    <span class="n">i</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span>

<span class="n">azdias</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<style scoped>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>AGER_TYP</th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>NaN</td>
      <td>2.0</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>NaN</td>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>NaN</td>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>2.0</td>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>NaN</td>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 85 columns</p>
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<h4 id="Step-1.1.2:-Assess-Missing-Data-in-Each-Column">Step 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Step-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>How much missing data is present in each column? There are a few columns that are outliers in terms of the proportion of values that are missing. You will want to use matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.hist.html"><code>hist()</code></a> function to visualize the distribution of missing value counts to find these columns. Identify and document these columns. While some of these columns might have justifications for keeping or re-encoding the data, for this project you should just remove them from the dataframe. (Feel free to make remarks about these outlier columns in the discussion, however!)</p>
<p>For the remaining features, are there any patterns in which columns have, or share, missing data?</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Perform an assessment of how much missing data there is in each column of the</span>
<span class="c1"># dataset.</span>
<span class="n">column_name</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span>
<span class="c1">#print(column_name)</span>
<span class="n">missdf</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">column_name</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="n">missdf</span>
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<pre>AGER_TYP                 685843
ALTERSKATEGORIE_GROB       2881
ANREDE_KZ                     0
CJT_GESAMTTYP              4854
FINANZ_MINIMALIST             0
FINANZ_SPARER                 0
FINANZ_VORSORGER              0
FINANZ_ANLEGER                0
FINANZ_UNAUFFAELLIGER         0
FINANZ_HAUSBAUER              0
FINANZTYP                     0
GEBURTSJAHR              392318
GFK_URLAUBERTYP            4854
GREEN_AVANTGARDE              0
HEALTH_TYP               111196
LP_LEBENSPHASE_FEIN       97632
LP_LEBENSPHASE_GROB       94572
LP_FAMILIE_FEIN           77792
LP_FAMILIE_GROB           77792
LP_STATUS_FEIN             4854
LP_STATUS_GROB             4854
NATIONALITAET_KZ         108315
PRAEGENDE_JUGENDJAHRE    108164
RETOURTYP_BK_S             4854
SEMIO_SOZ                     0
SEMIO_FAM                     0
SEMIO_REL                     0
SEMIO_MAT                     0
SEMIO_VERT                    0
SEMIO_LUST                    0
                          ...  
OST_WEST_KZ               93148
WOHNLAGE                  93148
CAMEO_DEUG_2015           99352
CAMEO_DEU_2015            99352
CAMEO_INTL_2015           99352
KBA05_ANTG1              133324
KBA05_ANTG2              133324
KBA05_ANTG3              133324
KBA05_ANTG4              133324
KBA05_BAUMAX             476524
KBA05_GBZ                133324
BALLRAUM                  93740
EWDICHTE                  93740
INNENSTADT                93740
GEBAEUDETYP_RASTER        93155
KKK                      158064
MOBI_REGIO               133324
ONLINE_AFFINITAET          4854
REGIOTYP                 158064
KBA13_ANZAHL_PKW         105800
PLZ8_ANTG1               116515
PLZ8_ANTG2               116515
PLZ8_ANTG3               116515
PLZ8_ANTG4               116515
PLZ8_BAUMAX              116515
PLZ8_HHZ                 116515
PLZ8_GBZ                 116515
ARBEIT                    97375
ORTSGR_KLS9               97274
RELAT_AB                  97375
Length: 85, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate patterns in the amount of missing data in each column.</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">missdf</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
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<pre>(array([ 32.,   0.,   7.,  31.,   7.,   2.,   0.,   0.,   0.,   0.,   1.,
          0.,   0.,   1.,   0.,   0.,   1.,   0.,   0.,   1.,   0.,   0.,
          0.,   1.,   0.,   0.,   0.,   0.,   0.,   1.]),
 array([      0.        ,   29635.36666667,   59270.73333333,
          88906.1       ,  118541.46666667,  148176.83333333,
         177812.2       ,  207447.56666667,  237082.93333333,
         266718.3       ,  296353.66666667,  325989.03333333,
         355624.4       ,  385259.76666667,  414895.13333333,
         444530.5       ,  474165.86666667,  503801.23333333,
         533436.6       ,  563071.96666667,  592707.33333333,
         622342.7       ,  651978.06666667,  681613.43333333,
         711248.8       ,  740884.16666667,  770519.53333333,
         800154.9       ,  829790.26666667,  859425.63333333,  889061.        ]),
 &lt;a list of 30 Patch objects&gt;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Remove the outlier columns from the dataset. (You&#39;ll perform other data</span>
<span class="c1"># engineering tasks such as re-encoding and imputation later.)</span>
<span class="kn">from</span> <span class="nn">scipy.stats</span> <span class="k">import</span> <span class="n">iqr</span>
<span class="n">col_iqr</span> <span class="o">=</span> <span class="n">iqr</span><span class="p">(</span><span class="n">missdf</span><span class="p">)</span>
<span class="n">col_thrdq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">missdf</span><span class="p">,</span> <span class="mi">75</span><span class="p">)</span>
<span class="n">col_threshold</span> <span class="o">=</span> <span class="n">col_thrdq</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">col_iqr</span>
<span class="nb">print</span><span class="p">(</span><span class="n">col_threshold</span><span class="p">)</span>

<span class="c1"># for any column that have NaNs value number more than the threshold, the column is an outlier.</span>
<span class="n">afterdrop</span> <span class="o">=</span> <span class="n">missdf</span><span class="p">[</span><span class="n">missdf</span> <span class="o">&lt;=</span> <span class="n">col_threshold</span><span class="p">]</span>
<span class="n">drop</span> <span class="o">=</span> <span class="n">missdf</span><span class="p">[</span><span class="n">missdf</span> <span class="o">&gt;</span> <span class="n">col_threshold</span><span class="p">]</span>
<span class="c1">#print(afterdrop)</span>
<span class="n">keeplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">afterdrop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">droplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">drop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">keeplist</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">droplist</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">droplist</span><span class="p">)</span>
<span class="n">azdias_keep</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">keeplist</span><span class="p">]</span>
<span class="n">azdias_drop</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">droplist</span><span class="p">]</span>
<span class="n">azdias_keep</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<pre>291287.5
79 6
[&#39;AGER_TYP&#39;, &#39;GEBURTSJAHR&#39;, &#39;TITEL_KZ&#39;, &#39;ALTER_HH&#39;, &#39;KK_KUNDENTYP&#39;, &#39;KBA05_BAUMAX&#39;]
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2.0</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 79 columns</p>
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<h4 id="Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">Discussion 1.1.2: Assess Missing Data in Each Column<a class="anchor-link" href="#Discussion-1.1.2:-Assess-Missing-Data-in-Each-Column">&#182;</a></h4><p>(Double click this cell and replace this text with your own text, reporting your observations regarding the amount of missing data in each column. Are there any patterns in missing values? Which columns were removed from the dataset?)</p>
<p>Calculating upper inner fence as Q3 + 1.5<em>IQR</em>, we find the threshold for mild outlier is 291287.5. There are 79 colums have less missing data than the threshold, and 6 colums have more missing data. These 6 columns are droped out. The 6 colums are 'AGER_TYP', 'GEBURTSJAHR', 'TITEL_KZ', 'ALTER_HH', 'KK_KUNDENTYP', 'KBA05_BAUMAX'.</p>

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<h4 id="Step-1.1.3:-Assess-Missing-Data-in-Each-Row">Step 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Step-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4><p>Now, you'll perform a similar assessment for the rows of the dataset. How much data is missing in each row? As with the columns, you should see some groups of points that have a very different numbers of missing values. Divide the data into two subsets: one for data points that are above some threshold for missing values, and a second subset for points below that threshold.</p>
<p>In order to know what to do with the outlier rows, we should see if the distribution of data values on columns that are not missing data (or are missing very little data) are similar or different between the two groups. Select at least five of these columns and compare the distribution of values.</p>
<ul>
<li>You can use seaborn's <a href="https://seaborn.pydata.org/generated/seaborn.countplot.html"><code>countplot()</code></a> function to create a bar chart of code frequencies and matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.subplot.html"><code>subplot()</code></a> function to put bar charts for the two subplots side by side.</li>
<li>To reduce repeated code, you might want to write a function that can perform this comparison, taking as one of its arguments a column to be compared.</li>
</ul>
<p>Depending on what you observe in your comparison, this will have implications on how you approach your conclusions later in the analysis. If the distributions of non-missing features look similar between the data with many missing values and the data with few or no missing values, then we could argue that simply dropping those points from the analysis won't present a major issue. On the other hand, if the data with many missing values looks very different from the data with few or no missing values, then we should make a note on those data as special. We'll revisit these data later on. <strong>Either way, you should continue your analysis for now using just the subset of the data with few or no missing values.</strong></p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How much data is missing in each row of the dataset?</span>
<span class="n">missrow</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">missrow</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span>
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<pre>(array([  6.38947000e+05,   4.55550000e+04,   1.26070000e+04,
          3.62860000e+04,   3.83060000e+04,   3.04200000e+03,
          6.53700000e+03,   4.02100000e+03,   2.24300000e+03,
          7.24800000e+03,   6.77000000e+02,   1.60800000e+03,
          4.99000000e+02,   1.29000000e+02,   2.01000000e+02,
          1.14000000e+02,   2.40000000e+01,   1.70000000e+01,
          9.00000000e+00,   2.06000000e+02,   1.38010000e+04,
          3.91100000e+03,   6.22000000e+02,   4.98000000e+02,
          1.37000000e+02,   3.77000000e+02,   2.75240000e+04,
          4.94000000e+02,   4.55780000e+04,   3.00000000e+00]),
 array([  0.        ,   1.63333333,   3.26666667,   4.9       ,
          6.53333333,   8.16666667,   9.8       ,  11.43333333,
         13.06666667,  14.7       ,  16.33333333,  17.96666667,
         19.6       ,  21.23333333,  22.86666667,  24.5       ,
         26.13333333,  27.76666667,  29.4       ,  31.03333333,
         32.66666667,  34.3       ,  35.93333333,  37.56666667,
         39.2       ,  40.83333333,  42.46666667,  44.1       ,
         45.73333333,  47.36666667,  49.        ]),
 &lt;a list of 30 Patch objects&gt;)</pre>
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src="data:image/png;base64,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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Write code to divide the data into two subsets based on the number of missing</span>
<span class="c1"># values in each row.</span>
<span class="n">row_iqr</span> <span class="o">=</span> <span class="n">iqr</span><span class="p">(</span><span class="n">missrow</span><span class="p">)</span>
<span class="n">row_thrdq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">missrow</span><span class="p">,</span> <span class="mi">75</span><span class="p">)</span>
<span class="n">row_threshold</span> <span class="o">=</span> <span class="n">row_thrdq</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">row_iqr</span>
<span class="nb">print</span><span class="p">(</span><span class="n">row_threshold</span><span class="p">)</span>

<span class="c1"># for any column that have NaNs value number more than the threshold, the column is an outlier.</span>
<span class="n">row_keep</span> <span class="o">=</span> <span class="n">missrow</span><span class="p">[</span><span class="n">missrow</span> <span class="o">&lt;=</span> <span class="n">row_threshold</span><span class="p">]</span>
<span class="n">row_drop</span> <span class="o">=</span> <span class="n">missrow</span><span class="p">[</span><span class="n">missrow</span> <span class="o">&gt;</span> <span class="n">row_threshold</span><span class="p">]</span>
<span class="n">row_keeplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row_keep</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">row_droplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row_drop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">row_keeplist</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">row_droplist</span><span class="p">))</span>
<span class="c1">#print(row_droplist)</span>
<span class="n">azdias_row_keep</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row_keeplist</span><span class="p">]</span> <span class="c1"># select rows by integer number</span>
<span class="n">azdias_row_drop</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row_droplist</span><span class="p">]</span>
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<pre>7.5
747109 144112
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># most valued rows</span>
<span class="n">azdias_row_keep</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>2.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>...</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>...</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>6.0</td>
      <td>5.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>...</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>3.0</td>
    </tr>
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</table>
<p>5 rows × 79 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># most missing rows</span>
<span class="n">azdias_row_drop</span><span class="p">[:</span><span class="mi">5</span><span class="p">]</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>...</th>
      <th>PLZ8_ANTG1</th>
      <th>PLZ8_ANTG2</th>
      <th>PLZ8_ANTG3</th>
      <th>PLZ8_ANTG4</th>
      <th>PLZ8_BAUMAX</th>
      <th>PLZ8_HHZ</th>
      <th>PLZ8_GBZ</th>
      <th>ARBEIT</th>
      <th>ORTSGR_KLS9</th>
      <th>RELAT_AB</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>2.0</td>
      <td>1</td>
      <td>2.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>11</th>
      <td>2.0</td>
      <td>1</td>
      <td>6.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>13</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>...</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>6.0</td>
      <td>4.0</td>
    </tr>
    <tr>
      <th>14</th>
      <td>3.0</td>
      <td>1</td>
      <td>6.0</td>
      <td>3</td>
      <td>4</td>
      <td>3</td>
      <td>5</td>
      <td>5</td>
      <td>3</td>
      <td>4</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>15</th>
      <td>4.0</td>
      <td>2</td>
      <td>4.0</td>
      <td>4</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>1</td>
      <td>4</td>
      <td>2</td>
      <td>...</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
      <td>4.0</td>
      <td>8.0</td>
      <td>5.0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 79 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the distribution of values for at least five columns where there are</span>
<span class="c1"># no or few missing values, between the two subsets.</span>

<span class="c1"># random selecting columns from those kept column tags</span>
<span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">123</span><span class="p">)</span>
<span class="n">selecttag</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">azdias_keep</span><span class="o">.</span><span class="n">columns</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">selecttag</span>
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<pre>[&#39;FINANZ_ANLEGER&#39;,
 &#39;SEMIO_PFLICHT&#39;,
 &#39;GREEN_AVANTGARDE&#39;,
 &#39;CAMEO_DEUG_2015&#39;,
 &#39;ORTSGR_KLS9&#39;,
 &#39;LP_LEBENSPHASE_FEIN&#39;,
 &#39;FINANZ_SPARER&#39;,
 &#39;KONSUMNAEHE&#39;,
 &#39;KBA13_ANZAHL_PKW&#39;,
 &#39;HH_EINKOMMEN_SCORE&#39;]</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
<span class="n">figure</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">selecttag</span><span class="p">),</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">22</span><span class="p">))</span>
<span class="n">figure</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">3</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">selecttag</span><span class="p">)):</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">azdias_row_keep</span><span class="p">[</span><span class="n">selecttag</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Few or no missing rows&#39;</span><span class="p">)</span>
    <span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">azdias_row_drop</span><span class="p">[</span><span class="n">selecttag</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Many missing rows&#39;</span><span class="p">)</span>
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<h4 id="Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">Discussion 1.1.3: Assess Missing Data in Each Row<a class="anchor-link" href="#Discussion-1.1.3:-Assess-Missing-Data-in-Each-Row">&#182;</a></h4><p>(Double-click this cell and replace this text with your own text, reporting your observations regarding missing data in rows. Are the data with lots of missing values are qualitatively different from data with few or no missing values?)</p>
<p>From the shape of the bar plots, we can tell that for some of the columns, the data values with few or nomissing rows is qualitatively different from the many missing rows data, while some of the columns share consistant patterns.</p>

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<h3 id="Step-1.2:-Select-and-Re-Encode-Features">Step 1.2: Select and Re-Encode Features<a class="anchor-link" href="#Step-1.2:-Select-and-Re-Encode-Features">&#182;</a></h3><p>Checking for missing data isn't the only way in which you can prepare a dataset for analysis. Since the unsupervised learning techniques to be used will only work on data that is encoded numerically, you need to make a few encoding changes or additional assumptions to be able to make progress. In addition, while almost all of the values in the dataset are encoded using numbers, not all of them represent numeric values. Check the third column of the feature summary (<code>feat_info</code>) for a summary of types of measurement.</p>
<ul>
<li>For numeric and interval data, these features can be kept without changes.</li>
<li>Most of the variables in the dataset are ordinal in nature. While ordinal values may technically be non-linear in spacing, make the simplifying assumption that the ordinal variables can be treated as being interval in nature (that is, kept without any changes).</li>
<li>Special handling may be necessary for the remaining two variable types: categorical, and 'mixed'.</li>
</ul>
<p>In the first two parts of this sub-step, you will perform an investigation of the categorical and mixed-type features and make a decision on each of them, whether you will keep, drop, or re-encode each. Then, in the last part, you will create a new data frame with only the selected and engineered columns.</p>
<p>Data wrangling is often the trickiest part of the data analysis process, and there's a lot of it to be done here. But stick with it: once you're done with this step, you'll be ready to get to the machine learning parts of the project!</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># How many features are there of each data type?</span>
<span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">.</span><span class="n">value_counts</span><span class="p">()</span>
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<pre>ordinal        49
categorical    21
mixed           7
numeric         7
interval        1
Name: type, dtype: int64</pre>
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<h4 id="Step-1.2.1:-Re-Encode-Categorical-Features">Step 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Step-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>For categorical data, you would ordinarily need to encode the levels as dummy variables. Depending on the number of categories, perform one of the following:</p>
<ul>
<li>For binary (two-level) categoricals that take numeric values, you can keep them without needing to do anything.</li>
<li>There is one binary variable that takes on non-numeric values. For this one, you need to re-encode the values as numbers or create a dummy variable.</li>
<li>For multi-level categoricals (three or more values), you can choose to encode the values using multiple dummy variables (e.g. via <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html">OneHotEncoder</a>), or (to keep things straightforward) just drop them from the analysis. As always, document your choices in the Discussion section.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#droplist </span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Assess categorical variables: which are binary, which are multi-level, and</span>
<span class="c1"># which one needs to be re-encoded?</span>
<span class="n">catecolumns</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">==</span><span class="s2">&quot;categorical&quot;</span><span class="p">])</span>
<span class="n">catecolumns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">catecolumns</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">droplist</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">catecolumns</span><span class="p">))</span>

<span class="n">azdias_row_keep</span><span class="p">[</span><span class="n">catecolumns</span><span class="p">]</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
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<pre>18
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>LP_FAMILIE_GROB</th>
      <th>GFK_URLAUBERTYP</th>
      <th>NATIONALITAET_KZ</th>
      <th>GEBAEUDETYP</th>
      <th>VERS_TYP</th>
      <th>CAMEO_DEU_2015</th>
      <th>FINANZTYP</th>
      <th>ANREDE_KZ</th>
      <th>GREEN_AVANTGARDE</th>
      <th>LP_FAMILIE_FEIN</th>
      <th>CJT_GESAMTTYP</th>
      <th>SHOPPER_TYP</th>
      <th>LP_STATUS_GROB</th>
      <th>ZABEOTYP</th>
      <th>LP_STATUS_FEIN</th>
      <th>CAMEO_DEUG_2015</th>
      <th>OST_WEST_KZ</th>
      <th>SOHO_KZ</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>3.0</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>8.0</td>
      <td>2.0</td>
      <td>8A</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>5.0</td>
      <td>5.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>5</td>
      <td>2.0</td>
      <td>8</td>
      <td>W</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>1.0</td>
      <td>10.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>4C</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>5</td>
      <td>3.0</td>
      <td>4</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>NaN</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2A</td>
      <td>6</td>
      <td>2</td>
      <td>0</td>
      <td>NaN</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>3</td>
      <td>9.0</td>
      <td>2</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>5.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>6B</td>
      <td>5</td>
      <td>1</td>
      <td>0</td>
      <td>10.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>3.0</td>
      <td>6</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>8C</td>
      <td>2</td>
      <td>2</td>
      <td>0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>4.0</td>
      <td>8</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>6</th>
      <td>1.0</td>
      <td>12.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>4A</td>
      <td>4</td>
      <td>2</td>
      <td>0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>4</td>
      <td>2.0</td>
      <td>4</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>7</th>
      <td>1.0</td>
      <td>9.0</td>
      <td>1.0</td>
      <td>8.0</td>
      <td>1.0</td>
      <td>2D</td>
      <td>5</td>
      <td>1</td>
      <td>0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>1</td>
      <td>1.0</td>
      <td>2</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>8</th>
      <td>1.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1A</td>
      <td>6</td>
      <td>1</td>
      <td>1</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>6</td>
      <td>10.0</td>
      <td>1</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>9</th>
      <td>1.0</td>
      <td>12.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>1E</td>
      <td>1</td>
      <td>2</td>
      <td>1</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>4</td>
      <td>3.0</td>
      <td>1</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>10</th>
      <td>1.0</td>
      <td>8.0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>9D</td>
      <td>6</td>
      <td>2</td>
      <td>0</td>
      <td>1.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>3</td>
      <td>1.0</td>
      <td>9</td>
      <td>W</td>
      <td>0.0</td>
    </tr>
  </tbody>
</table>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#printing the number of different number of values in each row</span>
<span class="n">coding_col</span> <span class="o">=</span> <span class="p">[]</span>

<span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">catecolumns</span><span class="p">:</span>
    <span class="n">coldata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">azdias_row_keep</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;str&#39;</span><span class="p">))</span>
    <span class="n">x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coldata</span><span class="p">)</span>
    
    <span class="k">if</span> <span class="n">x</span><span class="o">&gt;</span><span class="mi">2</span><span class="p">:</span>
        <span class="n">coding_col</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">column</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">coldata</span><span class="p">)</span>
        <span class="k">continue</span>
        
    <span class="k">try</span><span class="p">:</span>
        <span class="n">coldata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span><span class="p">(</span><span class="n">coldata</span><span class="p">)</span>
    <span class="k">except</span><span class="p">:</span>
        <span class="n">coding_col</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">column</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">column</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">coldata</span><span class="p">)</span>
        <span class="k">continue</span>
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<pre>LP_FAMILIE_GROB 6 [&#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;nan&#39;]
GFK_URLAUBERTYP 12 [&#39;1.0&#39; &#39;10.0&#39; &#39;11.0&#39; &#39;12.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;6.0&#39; &#39;7.0&#39; &#39;8.0&#39; &#39;9.0&#39;]
NATIONALITAET_KZ 4 [&#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;nan&#39;]
GEBAEUDETYP 7 [&#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;6.0&#39; &#39;8.0&#39;]
VERS_TYP 3 [&#39;1.0&#39; &#39;2.0&#39; &#39;nan&#39;]
CAMEO_DEU_2015 45 [&#39;1A&#39; &#39;1B&#39; &#39;1C&#39; &#39;1D&#39; &#39;1E&#39; &#39;2A&#39; &#39;2B&#39; &#39;2C&#39; &#39;2D&#39; &#39;3A&#39; &#39;3B&#39; &#39;3C&#39; &#39;3D&#39; &#39;4A&#39; &#39;4B&#39;
 &#39;4C&#39; &#39;4D&#39; &#39;4E&#39; &#39;5A&#39; &#39;5B&#39; &#39;5C&#39; &#39;5D&#39; &#39;5E&#39; &#39;5F&#39; &#39;6A&#39; &#39;6B&#39; &#39;6C&#39; &#39;6D&#39; &#39;6E&#39; &#39;6F&#39;
 &#39;7A&#39; &#39;7B&#39; &#39;7C&#39; &#39;7D&#39; &#39;7E&#39; &#39;8A&#39; &#39;8B&#39; &#39;8C&#39; &#39;8D&#39; &#39;9A&#39; &#39;9B&#39; &#39;9C&#39; &#39;9D&#39; &#39;9E&#39;
 &#39;nan&#39;]
FINANZTYP 6 [&#39;1&#39; &#39;2&#39; &#39;3&#39; &#39;4&#39; &#39;5&#39; &#39;6&#39;]
LP_FAMILIE_FEIN 12 [&#39;1.0&#39; &#39;10.0&#39; &#39;11.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;6.0&#39; &#39;7.0&#39; &#39;8.0&#39; &#39;9.0&#39; &#39;nan&#39;]
CJT_GESAMTTYP 6 [&#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;6.0&#39;]
SHOPPER_TYP 5 [&#39;0.0&#39; &#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;nan&#39;]
LP_STATUS_GROB 5 [&#39;1.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39;]
ZABEOTYP 6 [&#39;1&#39; &#39;2&#39; &#39;3&#39; &#39;4&#39; &#39;5&#39; &#39;6&#39;]
LP_STATUS_FEIN 10 [&#39;1.0&#39; &#39;10.0&#39; &#39;2.0&#39; &#39;3.0&#39; &#39;4.0&#39; &#39;5.0&#39; &#39;6.0&#39; &#39;7.0&#39; &#39;8.0&#39; &#39;9.0&#39;]
CAMEO_DEUG_2015 10 [&#39;1&#39; &#39;2&#39; &#39;3&#39; &#39;4&#39; &#39;5&#39; &#39;6&#39; &#39;7&#39; &#39;8&#39; &#39;9&#39; &#39;nan&#39;]
OST_WEST_KZ 2 [&#39;O&#39; &#39;W&#39;]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_row_keep_encode</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">coding_col</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_row_keep_encode</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>CAMEO_DEUG_2015_2</th>
      <th>CAMEO_DEUG_2015_3</th>
      <th>CAMEO_DEUG_2015_4</th>
      <th>CAMEO_DEUG_2015_5</th>
      <th>CAMEO_DEUG_2015_6</th>
      <th>CAMEO_DEUG_2015_7</th>
      <th>CAMEO_DEUG_2015_8</th>
      <th>CAMEO_DEUG_2015_9</th>
      <th>OST_WEST_KZ_O</th>
      <th>OST_WEST_KZ_W</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>2.0</td>
      <td>...</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 196 columns</p>
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<h4 id="Discussion-1.2.1:-Re-Encode-Categorical-Features">Discussion 1.2.1: Re-Encode Categorical Features<a class="anchor-link" href="#Discussion-1.2.1:-Re-Encode-Categorical-Features">&#182;</a></h4><p>(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding categorical features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)</p>
<p>There are total 21 categorical columns. After get rid of the columns that have too much NaNs, I have 18 categorial columns left. There are 5 binary columns, and for the rest 13 columns, I created dummy variables for them.</p>

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<h4 id="Step-1.2.2:-Engineer-Mixed-Type-Features">Step 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Step-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>There are a handful of features that are marked as "mixed" in the feature summary that require special treatment in order to be included in the analysis. There are two in particular that deserve attention; the handling of the rest are up to your own choices:</p>
<ul>
<li>"PRAEGENDE_JUGENDJAHRE" combines information on three dimensions: generation by decade, movement (mainstream vs. avantgarde), and nation (east vs. west). While there aren't enough levels to disentangle east from west, you should create two new variables to capture the other two dimensions: an interval-type variable for decade, and a binary variable for movement.</li>
<li>"CAMEO_INTL_2015" combines information on two axes: wealth and life stage. Break up the two-digit codes by their 'tens'-place and 'ones'-place digits into two new ordinal variables (which, for the purposes of this project, is equivalent to just treating them as their raw numeric values).</li>
<li>If you decide to keep or engineer new features around the other mixed-type features, make sure you note your steps in the Discussion section.</li>
</ul>
<p>Be sure to check <code>Data_Dictionary.md</code> for the details needed to finish these tasks.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">==</span><span class="s2">&quot;mixed&quot;</span><span class="p">]</span>
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<pre>15      LP_LEBENSPHASE_FEIN
16      LP_LEBENSPHASE_GROB
22    PRAEGENDE_JUGENDJAHRE
56                 WOHNLAGE
59          CAMEO_INTL_2015
64             KBA05_BAUMAX
79              PLZ8_BAUMAX
Name: attribute, dtype: object</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;PRAEGENDE_JUGENDJAHRE&quot; and engineer two new variables.</span>
<span class="n">pj_col_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;generation&#39;</span><span class="p">,</span><span class="s1">&#39;movement&#39;</span><span class="p">,</span><span class="s1">&#39;nation&#39;</span><span class="p">]</span>
<span class="c1">#replace the column PJ</span>
<span class="c1"># define dict</span>
<span class="n">pjdict</span> <span class="o">=</span> <span class="p">{</span>
<span class="mi">1</span><span class="p">:</span> <span class="s1">&#39;40s, Mainstream, E+W&#39;</span><span class="p">,</span>
<span class="mi">2</span><span class="p">:</span> <span class="s1">&#39;40s, Avantgarde, E+W&#39;</span><span class="p">,</span>
<span class="mi">3</span><span class="p">:</span> <span class="s1">&#39;50s, Mainstream, E+W&#39;</span><span class="p">,</span>
<span class="mi">4</span><span class="p">:</span> <span class="s1">&#39;50s, Avantgarde, E+W&#39;</span><span class="p">,</span>
<span class="mi">5</span><span class="p">:</span> <span class="s1">&#39;60s, Mainstream, E+W&#39;</span><span class="p">,</span>
<span class="mi">6</span><span class="p">:</span> <span class="s1">&#39;60s, Avantgarde, W&#39;</span><span class="p">,</span>
<span class="mi">7</span><span class="p">:</span> <span class="s1">&#39;60s, Avantgarde, E&#39;</span><span class="p">,</span>
<span class="mi">8</span><span class="p">:</span> <span class="s1">&#39;70s, Mainstream, E+W&#39;</span><span class="p">,</span>
<span class="mi">9</span><span class="p">:</span> <span class="s1">&#39;70s, Avantgarde, E+W&#39;</span><span class="p">,</span>
<span class="mi">10</span><span class="p">:</span> <span class="s1">&#39;80s, Mainstream, W&#39;</span><span class="p">,</span>
<span class="mi">11</span><span class="p">:</span> <span class="s1">&#39;80s, Avantgarde, W&#39;</span><span class="p">,</span>
<span class="mi">12</span><span class="p">:</span> <span class="s1">&#39;80s, Mainstream, E&#39;</span><span class="p">,</span>
<span class="mi">13</span><span class="p">:</span> <span class="s1">&#39;80s, Avantgarde, E&#39;</span><span class="p">,</span>
<span class="mi">14</span><span class="p">:</span> <span class="s1">&#39;90s, Mainstream, E+W&#39;</span><span class="p">,</span>
<span class="mi">15</span><span class="p">:</span> <span class="s1">&#39;90s, Avantgarde, E+W&#39;</span>
<span class="p">}</span>
<span class="c1"># replace number for mixed dict </span>
<span class="n">pj</span> <span class="o">=</span> <span class="n">azdias_row_keep_encode</span><span class="o">.</span><span class="n">PRAEGENDE_JUGENDJAHRE</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span> <span class="o">=</span> <span class="n">pjdict</span><span class="p">)</span>
<span class="c1"># splite and expand by ,</span>
<span class="n">pj</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pj</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">expand</span> <span class="o">=</span> <span class="kc">True</span><span class="p">))</span>
<span class="c1"># change column names</span>
<span class="n">pj</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pj_col_name</span>
<span class="n">pj</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
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      <th></th>
      <th>generation</th>
      <th>movement</th>
      <th>nation</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>90s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>2</th>
      <td>90s</td>
      <td>Avantgarde</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>3</th>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>4</th>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>5</th>
      <td>50s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
  </tbody>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#merge the newly created columns</span>
<span class="n">azdias_row_keep_encoded_mix1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">azdias_row_keep_encode</span><span class="p">,</span> <span class="n">pj</span><span class="p">],</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">azdias_row_keep_encoded_mix1</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>CAMEO_DEUG_2015_5</th>
      <th>CAMEO_DEUG_2015_6</th>
      <th>CAMEO_DEUG_2015_7</th>
      <th>CAMEO_DEUG_2015_8</th>
      <th>CAMEO_DEUG_2015_9</th>
      <th>OST_WEST_KZ_O</th>
      <th>OST_WEST_KZ_W</th>
      <th>generation</th>
      <th>movement</th>
      <th>nation</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>90s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>90s</td>
      <td>Avantgarde</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>2.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>50s</td>
      <td>Mainstream</td>
      <td>E+W</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 199 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_row_keep_encoded_mix1</span><span class="o">.</span><span class="n">CAMEO_INTL_2015</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<pre>1    51
2    24
3    12
4    43
5    54
Name: CAMEO_INTL_2015, dtype: object</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate &quot;CAMEO_INTL_2015&quot; and engineer two new variables.</span>
<span class="n">ci_col_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;wealth&#39;</span><span class="p">,</span><span class="s1">&#39;life_stage&#39;</span><span class="p">]</span>
<span class="n">cidict</span> <span class="o">=</span> <span class="p">{</span>
<span class="s1">&#39;11&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
<span class="s1">&#39;12&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Young Couples With Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;13&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Families With School Age Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;14&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Older Families &amp;  Mature Couples&#39;</span><span class="p">,</span>
<span class="s1">&#39;15&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Elders In Retirement&#39;</span><span class="p">,</span>
<span class="s1">&#39;21&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
<span class="s1">&#39;22&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Young Couples With Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;23&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Families With School Age Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;24&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
<span class="s1">&#39;25&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Elders In Retirement&#39;</span><span class="p">,</span>
<span class="s1">&#39;31&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
<span class="s1">&#39;32&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Young Couples With Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;33&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Families With School Age Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;34&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
<span class="s1">&#39;35&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Elders In Retirement&#39;</span><span class="p">,</span>
<span class="s1">&#39;41&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
<span class="s1">&#39;42&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Young Couples With Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;43&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Families With School Age Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;44&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
<span class="s1">&#39;45&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Elders In Retirement&#39;</span><span class="p">,</span>
<span class="s1">&#39;51&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
<span class="s1">&#39;52&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Young Couples With Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;53&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Families With School Age Children&#39;</span><span class="p">,</span>
<span class="s1">&#39;54&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
<span class="s1">&#39;55&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Elders In Retirement&#39;</span>
<span class="p">}</span>

<span class="c1"># replace number for mixed dict </span>
<span class="n">ci</span> <span class="o">=</span> <span class="n">azdias_row_keep_encoded_mix1</span><span class="o">.</span><span class="n">CAMEO_INTL_2015</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span> <span class="o">=</span> <span class="n">cidict</span><span class="p">)</span>
<span class="c1"># splite and expand by ,</span>
<span class="n">ci</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ci</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39; - &#39;</span><span class="p">,</span> <span class="n">expand</span> <span class="o">=</span> <span class="kc">True</span><span class="p">))</span>
<span class="c1"># change column names</span>
<span class="n">ci</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">ci_col_name</span>
<span class="n">ci</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>wealth</th>
      <th>life_stage</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>Poorer Households</td>
      <td>Pre-Family Couples &amp; Singles</td>
    </tr>
    <tr>
      <th>2</th>
      <td>Prosperous Households</td>
      <td>Older Families &amp; Mature Couples</td>
    </tr>
    <tr>
      <th>3</th>
      <td>Wealthy Households</td>
      <td>Young Couples With Children</td>
    </tr>
    <tr>
      <th>4</th>
      <td>Less Affluent Households</td>
      <td>Families With School Age Children</td>
    </tr>
    <tr>
      <th>5</th>
      <td>Poorer Households</td>
      <td>Older Families &amp; Mature Couples</td>
    </tr>
  </tbody>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#merge the newly created columns</span>
<span class="n">azdias_row_keep_encoded_mix2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">azdias_row_keep_encoded_mix1</span><span class="p">,</span> <span class="n">ci</span><span class="p">],</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">azdias_row_keep_encoded_mix2</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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        vertical-align: middle;
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        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>CAMEO_DEUG_2015_7</th>
      <th>CAMEO_DEUG_2015_8</th>
      <th>CAMEO_DEUG_2015_9</th>
      <th>OST_WEST_KZ_O</th>
      <th>OST_WEST_KZ_W</th>
      <th>generation</th>
      <th>movement</th>
      <th>nation</th>
      <th>wealth</th>
      <th>life_stage</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>90s</td>
      <td>Mainstream</td>
      <td>E+W</td>
      <td>Poorer Households</td>
      <td>Pre-Family Couples &amp; Singles</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>90s</td>
      <td>Avantgarde</td>
      <td>E+W</td>
      <td>Prosperous Households</td>
      <td>Older Families &amp; Mature Couples</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>2.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
      <td>Wealthy Households</td>
      <td>Young Couples With Children</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>70s</td>
      <td>Mainstream</td>
      <td>E+W</td>
      <td>Less Affluent Households</td>
      <td>Families With School Age Children</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>50s</td>
      <td>Mainstream</td>
      <td>E+W</td>
      <td>Poorer Households</td>
      <td>Older Families &amp; Mature Couples</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 201 columns</p>
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<h4 id="Discussion-1.2.2:-Engineer-Mixed-Type-Features">Discussion 1.2.2: Engineer Mixed-Type Features<a class="anchor-link" href="#Discussion-1.2.2:-Engineer-Mixed-Type-Features">&#182;</a></h4><p>(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding mixed-value features. Which ones did you keep, which did you drop, and what engineering steps did you perform?)</p>

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<h4 id="Step-1.2.3:-Complete-Feature-Selection">Step 1.2.3: Complete Feature Selection<a class="anchor-link" href="#Step-1.2.3:-Complete-Feature-Selection">&#182;</a></h4><p>In order to finish this step up, you need to make sure that your data frame now only has the columns that you want to keep. To summarize, the dataframe should consist of the following:</p>
<ul>
<li>All numeric, interval, and ordinal type columns from the original dataset.</li>
<li>Binary categorical features (all numerically-encoded).</li>
<li>Engineered features from other multi-level categorical features and mixed features.</li>
</ul>
<p>Make sure that for any new columns that you have engineered, that you've excluded the original columns from the final dataset. Otherwise, their values will interfere with the analysis later on the project. For example, you should not keep "PRAEGENDE_JUGENDJAHRE", since its values won't be useful for the algorithm: only the values derived from it in the engineered features you created should be retained. As a reminder, your data should only be from <strong>the subset with few or no missing values</strong>.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If there are other re-engineering tasks you need to perform, make sure you</span>
<span class="c1"># take care of them here. (Dealing with missing data will come in step 2.1.)</span>

<span class="c1"># create dummy variables for pj and ci columns</span>
<span class="n">azdias_row_keep_encoded_mix3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep_encoded_mix2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">pj_col_name</span><span class="p">)</span>
<span class="n">azdias_row_keep_encoded_mix4</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep_encoded_mix3</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ci_col_name</span><span class="p">)</span>
<span class="n">azdias_row_keep_encoded_mix4</span><span class="o">.</span><span class="n">shape</span>
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<pre>(747109, 218)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">azdias_row_keep_encoded_mix4</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>3</th>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>5</td>
      <td>...</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>1.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 239 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Do whatever you need to in order to ensure that the dataframe only contains</span>
<span class="c1"># the columns that should be passed to the algorithm functions.</span>

<span class="c1"># I am droping all the other mixed columns</span>
<span class="c1"># since the columns are mixed, thus they don&#39;t mean anything.</span>

<span class="n">mixcolumns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">==</span><span class="s2">&quot;mixed&quot;</span><span class="p">])</span><span class="o">-</span><span class="nb">set</span><span class="p">(</span><span class="n">droplist</span><span class="p">))</span>
<span class="n">final_azdias</span> <span class="o">=</span> <span class="n">azdias_row_keep_encoded_mix4</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">mixcolumns</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">final_azdias</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">coding_duo_col</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">final_azdias</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">coding_mul_col</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">final_azdias</span><span class="o">.</span><span class="n">shape</span>
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<pre>(747109, 233)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">final_azdias</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>CJT_GESAMTTYP</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>FINANZTYP</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1.0</td>
      <td>2</td>
      <td>5.0</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>3.0</td>
      <td>2</td>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
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    <tr>
      <th>2</th>
      <td>4.0</td>
      <td>2</td>
      <td>2.0</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>6</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
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    <tr>
      <th>3</th>
      <td>3.0</td>
      <td>1</td>
      <td>5.0</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
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      <td>...</td>
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      <td>2</td>
      <td>2.0</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
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      <td>2</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
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      <td>0</td>
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      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
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<p>5 rows × 233 columns</p>
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<h3 id="Step-1.3:-Create-a-Cleaning-Function">Step 1.3: Create a Cleaning Function<a class="anchor-link" href="#Step-1.3:-Create-a-Cleaning-Function">&#182;</a></h3><p>Even though you've finished cleaning up the general population demographics data, it's important to look ahead to the future and realize that you'll need to perform the same cleaning steps on the customer demographics data. In this substep, complete the function below to execute the main feature selection, encoding, and re-engineering steps you performed above. Then, when it comes to looking at the customer data in Step 3, you can just run this function on that DataFrame to get the trimmed dataset in a single step.</p>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">clean_data</span><span class="p">(</span><span class="n">azdias</span><span class="p">,</span> <span class="n">feat_info</span><span class="p">):</span>
    <span class="sd">&quot;&quot;&quot;</span>
<span class="sd">    Perform feature trimming, re-encoding, and engineering for demographics</span>
<span class="sd">    data</span>
<span class="sd">    </span>
<span class="sd">    INPUT: Demographics DataFrame</span>
<span class="sd">    OUTPUT: Trimmed and cleaned demographics DataFrame</span>
<span class="sd">    &quot;&quot;&quot;</span>
    
    <span class="c1"># Put in code here to execute all main cleaning steps:</span>
    <span class="c1"># convert missing value codes into NaNs, ...</span>
    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="k">while</span> <span class="n">i</span><span class="o">&lt;</span><span class="nb">len</span><span class="p">(</span><span class="n">feat_info</span><span class="p">):</span>

        <span class="c1"># define a null replace list</span>
        <span class="n">nulldict</span> <span class="o">=</span> <span class="p">{}</span>

        <span class="c1"># attribute name</span>
        <span class="n">attribute_name</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="c1">#print(attribute_name)</span>

        <span class="c1"># null character</span>
        <span class="n">bads</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">missing_or_unknown</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="c1"># parsing null characters</span>
        <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;[&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
        <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;]&quot;</span><span class="p">,</span> <span class="s2">&quot;&quot;</span><span class="p">)</span>
        <span class="n">bads</span> <span class="o">=</span> <span class="n">bads</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;,&quot;</span><span class="p">)</span>

        <span class="c1">#print(bads)</span>

        <span class="c1"># change the elements in this list. </span>
        <span class="c1"># to make all the number strings into numbers</span>
        <span class="c1"># kepp all the strings keep the same.</span>
        <span class="c1"># keep a null list if it&#39;s &#39;&#39;</span>
        <span class="n">real_bads</span> <span class="o">=</span> <span class="p">[]</span>
        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bads</span><span class="p">:</span>       
            <span class="k">try</span><span class="p">:</span>
                <span class="n">x</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
                <span class="c1">#print(&quot;lala:&quot;, str(x)== &quot;&quot;)</span>
                <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">==</span> <span class="s2">&quot;&quot;</span><span class="p">:</span>
                    <span class="c1">#print(&quot;haha&quot;,x)</span>
                    <span class="k">continue</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="k">pass</span>      
            <span class="n">real_bads</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

        <span class="c1"># create toreplace dictionary</span>
        <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">real_bads</span><span class="p">:</span>
            <span class="n">nulldict</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s1">&#39;nan&#39;</span><span class="p">)</span>

        <span class="c1"># replace values for curresponding attribute</span>
        <span class="n">azdias</span><span class="p">[</span><span class="n">attribute_name</span><span class="p">]</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">attribute_name</span><span class="p">]</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span><span class="o">=</span><span class="n">nulldict</span><span class="p">)</span>

        <span class="n">i</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span>
        
    <span class="c1"># remove selected columns and rows, ...</span>
    <span class="c1"># Perform an assessment of how much missing data there is in each column of the</span>
    <span class="c1"># dataset.</span>
    <span class="n">column_name</span> <span class="o">=</span> <span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span>
    <span class="c1">#print(column_name)</span>
    <span class="n">missdf</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">column_name</span><span class="p">]</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
    
    <span class="c1"># Remove the outlier columns from the dataset. (You&#39;ll perform other data</span>
    <span class="c1"># engineering tasks such as re-encoding and imputation later.)</span>
    <span class="n">col_iqr</span> <span class="o">=</span> <span class="n">iqr</span><span class="p">(</span><span class="n">missdf</span><span class="p">)</span>
    <span class="n">col_thrdq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">missdf</span><span class="p">,</span> <span class="mi">75</span><span class="p">)</span>
    <span class="n">col_threshold</span> <span class="o">=</span> <span class="n">col_thrdq</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">col_iqr</span>

    <span class="c1"># for any column that have NaNs value number more than the threshold, the column is an outlier.</span>
    <span class="n">afterdrop</span> <span class="o">=</span> <span class="n">missdf</span><span class="p">[</span><span class="n">missdf</span> <span class="o">&lt;=</span> <span class="n">col_threshold</span><span class="p">]</span>
    <span class="n">drop</span> <span class="o">=</span> <span class="n">missdf</span><span class="p">[</span><span class="n">missdf</span> <span class="o">&gt;</span> <span class="n">col_threshold</span><span class="p">]</span>

    <span class="n">keeplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">afterdrop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">droplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">drop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">azdias_keep</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">keeplist</span><span class="p">]</span>
    <span class="n">azdias_drop</span> <span class="o">=</span> <span class="n">azdias</span><span class="p">[</span><span class="n">droplist</span><span class="p">]</span>
    
    <span class="c1"># How much data is missing in each row of the dataset?</span>
    <span class="n">missrow</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">isna</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="c1"># Write code to divide the data into two subsets based on the number of missing</span>
    <span class="c1"># values in each row.</span>
    <span class="n">row_iqr</span> <span class="o">=</span> <span class="n">iqr</span><span class="p">(</span><span class="n">missrow</span><span class="p">)</span>
    <span class="n">row_thrdq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">missrow</span><span class="p">,</span> <span class="mi">75</span><span class="p">)</span>
    <span class="n">row_threshold</span> <span class="o">=</span> <span class="n">row_thrdq</span> <span class="o">+</span> <span class="mf">1.5</span><span class="o">*</span><span class="n">row_iqr</span>


    <span class="c1"># for any column that have NaNs value number more than the threshold, the column is an outlier.</span>
    <span class="n">row_keep</span> <span class="o">=</span> <span class="n">missrow</span><span class="p">[</span><span class="n">missrow</span> <span class="o">&lt;=</span> <span class="n">row_threshold</span><span class="p">]</span>
    <span class="n">row_drop</span> <span class="o">=</span> <span class="n">missrow</span><span class="p">[</span><span class="n">missrow</span> <span class="o">&gt;</span> <span class="n">row_threshold</span><span class="p">]</span>
    <span class="n">row_keeplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row_keep</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
    <span class="n">row_droplist</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">row_drop</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

    <span class="n">azdias_row_keep</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row_keeplist</span><span class="p">]</span> <span class="c1"># select rows by integer number</span>
    <span class="n">azdias_row_drop</span> <span class="o">=</span> <span class="n">azdias_keep</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">row_droplist</span><span class="p">]</span>
    
    <span class="c1"># select, re-encode, and engineer column values.</span>
    <span class="c1"># Assess categorical variables: which are binary, which are multi-level, and</span>
    <span class="c1"># which one needs to be re-encoded?</span>
    <span class="n">catecolumns</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">==</span><span class="s2">&quot;categorical&quot;</span><span class="p">])</span>
    <span class="n">catecolumns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">catecolumns</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span><span class="n">droplist</span><span class="p">))</span>
    <span class="c1">#printing the number of different number of values in each row</span>

    <span class="c1">#printing the number of different number of values in each row</span>
    <span class="n">coding_col</span> <span class="o">=</span> <span class="p">[]</span>

    <span class="k">for</span> <span class="n">column</span> <span class="ow">in</span> <span class="n">catecolumns</span><span class="p">:</span>
        <span class="n">coldata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">azdias_row_keep</span><span class="p">[</span><span class="n">column</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">&#39;str&#39;</span><span class="p">))</span>
        <span class="n">x</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">coldata</span><span class="p">)</span>

        <span class="k">if</span> <span class="n">x</span><span class="o">&gt;</span><span class="mi">2</span><span class="p">:</span>
            <span class="n">coding_col</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">column</span><span class="p">)</span>
            <span class="c1">#print(column, x, coldata)</span>
            <span class="k">continue</span>

        <span class="k">try</span><span class="p">:</span>
            <span class="n">coldata</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">float_</span><span class="p">(</span><span class="n">coldata</span><span class="p">)</span>
        <span class="k">except</span><span class="p">:</span>
            <span class="n">coding_col</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">column</span><span class="p">)</span>
            <span class="c1">#print(column, x, coldata)</span>
            <span class="k">continue</span>
            
    <span class="n">azdias_row_keep_encode</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep</span><span class="p">,</span> <span class="n">columns</span> <span class="o">=</span> <span class="n">coding_col</span><span class="p">)</span>

    <span class="c1"># Investigate &quot;PRAEGENDE_JUGENDJAHRE&quot; and engineer two new variables.</span>
    <span class="n">pj_col_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;generation&#39;</span><span class="p">,</span><span class="s1">&#39;movement&#39;</span><span class="p">,</span><span class="s1">&#39;nation&#39;</span><span class="p">]</span>
    <span class="c1">#replace the column PJ</span>
    <span class="c1"># define dict</span>
    <span class="n">pjdict</span> <span class="o">=</span> <span class="p">{</span>
    <span class="mi">1</span><span class="p">:</span> <span class="s1">&#39;40s, Mainstream, E+W&#39;</span><span class="p">,</span>
    <span class="mi">2</span><span class="p">:</span> <span class="s1">&#39;40s, Avantgarde, E+W&#39;</span><span class="p">,</span>
    <span class="mi">3</span><span class="p">:</span> <span class="s1">&#39;50s, Mainstream, E+W&#39;</span><span class="p">,</span>
    <span class="mi">4</span><span class="p">:</span> <span class="s1">&#39;50s, Avantgarde, E+W&#39;</span><span class="p">,</span>
    <span class="mi">5</span><span class="p">:</span> <span class="s1">&#39;60s, Mainstream, E+W&#39;</span><span class="p">,</span>
    <span class="mi">6</span><span class="p">:</span> <span class="s1">&#39;60s, Avantgarde, W&#39;</span><span class="p">,</span>
    <span class="mi">7</span><span class="p">:</span> <span class="s1">&#39;60s, Avantgarde, E&#39;</span><span class="p">,</span>
    <span class="mi">8</span><span class="p">:</span> <span class="s1">&#39;70s, Mainstream, E+W&#39;</span><span class="p">,</span>
    <span class="mi">9</span><span class="p">:</span> <span class="s1">&#39;70s, Avantgarde, E+W&#39;</span><span class="p">,</span>
    <span class="mi">10</span><span class="p">:</span> <span class="s1">&#39;80s, Mainstream, W&#39;</span><span class="p">,</span>
    <span class="mi">11</span><span class="p">:</span> <span class="s1">&#39;80s, Avantgarde, W&#39;</span><span class="p">,</span>
    <span class="mi">12</span><span class="p">:</span> <span class="s1">&#39;80s, Mainstream, E&#39;</span><span class="p">,</span>
    <span class="mi">13</span><span class="p">:</span> <span class="s1">&#39;80s, Avantgarde, E&#39;</span><span class="p">,</span>
    <span class="mi">14</span><span class="p">:</span> <span class="s1">&#39;90s, Mainstream, E+W&#39;</span><span class="p">,</span>
    <span class="mi">15</span><span class="p">:</span> <span class="s1">&#39;90s, Avantgarde, E+W&#39;</span>
    <span class="p">}</span>
    <span class="c1"># replace number for mixed dict </span>
    <span class="n">pj</span> <span class="o">=</span> <span class="n">azdias_row_keep_encode</span><span class="o">.</span><span class="n">PRAEGENDE_JUGENDJAHRE</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span> <span class="o">=</span> <span class="n">pjdict</span><span class="p">)</span>
    <span class="c1"># splite and expand by ,</span>
    <span class="n">pj</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">pj</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">,</span> <span class="n">expand</span> <span class="o">=</span> <span class="kc">True</span><span class="p">))</span>
    <span class="c1"># change column names</span>
    <span class="n">pj</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">pj_col_name</span>

    <span class="c1">#merge the newly created columns</span>
    <span class="n">azdias_row_keep_encoded_mix1</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">azdias_row_keep_encode</span><span class="p">,</span> <span class="n">pj</span><span class="p">],</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
  
    <span class="c1"># Investigate &quot;CAMEO_INTL_2015&quot; and engineer two new variables.</span>
    <span class="n">ci_col_name</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;wealth&#39;</span><span class="p">,</span><span class="s1">&#39;life_stage&#39;</span><span class="p">]</span>
    <span class="n">cidict</span> <span class="o">=</span> <span class="p">{</span>
    <span class="s1">&#39;11&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
    <span class="s1">&#39;12&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Young Couples With Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;13&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Families With School Age Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;14&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Older Families &amp;  Mature Couples&#39;</span><span class="p">,</span>
    <span class="s1">&#39;15&#39;</span><span class="p">:</span> <span class="s1">&#39;Wealthy Households - Elders In Retirement&#39;</span><span class="p">,</span>
    <span class="s1">&#39;21&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
    <span class="s1">&#39;22&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Young Couples With Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;23&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Families With School Age Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;24&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
    <span class="s1">&#39;25&#39;</span><span class="p">:</span> <span class="s1">&#39;Prosperous Households - Elders In Retirement&#39;</span><span class="p">,</span>
    <span class="s1">&#39;31&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
    <span class="s1">&#39;32&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Young Couples With Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;33&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Families With School Age Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;34&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
    <span class="s1">&#39;35&#39;</span><span class="p">:</span> <span class="s1">&#39;Comfortable Households - Elders In Retirement&#39;</span><span class="p">,</span>
    <span class="s1">&#39;41&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
    <span class="s1">&#39;42&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Young Couples With Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;43&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Families With School Age Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;44&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
    <span class="s1">&#39;45&#39;</span><span class="p">:</span> <span class="s1">&#39;Less Affluent Households - Elders In Retirement&#39;</span><span class="p">,</span>
    <span class="s1">&#39;51&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Pre-Family Couples &amp; Singles&#39;</span><span class="p">,</span>
    <span class="s1">&#39;52&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Young Couples With Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;53&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Families With School Age Children&#39;</span><span class="p">,</span>
    <span class="s1">&#39;54&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Older Families &amp; Mature Couples&#39;</span><span class="p">,</span>
    <span class="s1">&#39;55&#39;</span><span class="p">:</span> <span class="s1">&#39;Poorer Households - Elders In Retirement&#39;</span>
    <span class="p">}</span>

    <span class="c1"># replace number for mixed dict </span>
    <span class="n">ci</span> <span class="o">=</span> <span class="n">azdias_row_keep_encoded_mix1</span><span class="o">.</span><span class="n">CAMEO_INTL_2015</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">to_replace</span> <span class="o">=</span> <span class="n">cidict</span><span class="p">)</span>
    <span class="c1"># splite and expand by ,</span>
    <span class="n">ci</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">ci</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39; - &#39;</span><span class="p">,</span> <span class="n">expand</span> <span class="o">=</span> <span class="kc">True</span><span class="p">))</span>
    <span class="c1"># change column names</span>
    <span class="n">ci</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">ci_col_name</span>
    
    <span class="c1">#merge the newly created columns</span>
    <span class="n">azdias_row_keep_encoded_mix2</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">azdias_row_keep_encoded_mix1</span><span class="p">,</span> <span class="n">ci</span><span class="p">],</span><span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>
    
    <span class="c1"># If there are other re-engineering tasks you need to perform, make sure you</span>
    <span class="c1"># take care of them here. (Dealing with missing data will come in step 2.1.)</span>

    <span class="c1"># create dummy variables for pj and ci columns</span>
    <span class="n">azdias_row_keep_encoded_mix3</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep_encoded_mix2</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">pj_col_name</span><span class="p">)</span>
    <span class="n">azdias_row_keep_encoded_mix4</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">get_dummies</span><span class="p">(</span><span class="n">azdias_row_keep_encoded_mix3</span><span class="p">,</span> <span class="n">columns</span><span class="o">=</span><span class="n">ci_col_name</span><span class="p">)</span>
    
    <span class="c1"># Do whatever you need to in order to ensure that the dataframe only contains</span>
    <span class="c1"># the columns that should be passed to the algorithm functions.</span>

    <span class="c1"># I am droping all the other mixed columns</span>
    <span class="c1"># since the columns are mixed, thus they don&#39;t mean anything.</span>

    <span class="n">mixcolumns</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span><span class="n">feat_info</span><span class="o">.</span><span class="n">attribute</span><span class="p">[</span><span class="n">feat_info</span><span class="o">.</span><span class="n">type</span><span class="o">==</span><span class="s2">&quot;mixed&quot;</span><span class="p">])</span><span class="o">-</span><span class="nb">set</span><span class="p">(</span><span class="n">droplist</span><span class="p">))</span>
    <span class="n">final_azdias</span> <span class="o">=</span> <span class="n">azdias_row_keep_encoded_mix4</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">mixcolumns</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span>

    <span class="c1"># Return the cleaned dataframe.</span>
    <span class="k">return</span> <span class="n">final_azdias</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">x</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">azdias</span><span class="p">,</span> <span class="n">feat_info</span><span class="p">)</span>
<span class="n">x</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
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        vertical-align: top;
    }

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</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>5</td>
      <td>4</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2</td>
      <td>1</td>
      <td>4</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>5</td>
      <td>1</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2</td>
      <td>4</td>
      <td>2</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>2.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>4</td>
      <td>3</td>
      <td>4</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2</td>
      <td>3</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>2</td>
      <td>5</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 212 columns</p>
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<h2 id="Step-2:-Feature-Transformation">Step 2: Feature Transformation<a class="anchor-link" href="#Step-2:-Feature-Transformation">&#182;</a></h2><h3 id="Step-2.1:-Apply-Feature-Scaling">Step 2.1: Apply Feature Scaling<a class="anchor-link" href="#Step-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>Before we apply dimensionality reduction techniques to the data, we need to perform feature scaling so that the principal component vectors are not influenced by the natural differences in scale for features. Starting from this part of the project, you'll want to keep an eye on the <a href="http://scikit-learn.org/stable/modules/classes.html">API reference page for sklearn</a> to help you navigate to all of the classes and functions that you'll need. In this substep, you'll need to check the following:</p>
<ul>
<li>sklearn requires that data not have missing values in order for its estimators to work properly. So, before applying the scaler to your data, make sure that you've cleaned the DataFrame of the remaining missing values. This can be as simple as just removing all data points with missing data, or applying an <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html">Imputer</a> to replace all missing values. You might also try a more complicated procedure where you temporarily remove missing values in order to compute the scaling parameters before re-introducing those missing values and applying imputation. Think about how much missing data you have and what possible effects each approach might have on your analysis, and justify your decision in the discussion section below.</li>
<li>For the actual scaling function, a <a href="http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html">StandardScaler</a> instance is suggested, scaling each feature to mean 0 and standard deviation 1.</li>
<li>For these classes, you can make use of the <code>.fit_transform()</code> method to both fit a procedure to the data as well as apply the transformation to the data at the same time. Don't forget to keep the fit sklearn objects handy, since you'll be applying them to the customer demographics data towards the end of the project.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># If you&#39;ve not yet cleaned the dataset of all NaN values, then investigate and</span>
<span class="c1"># do that now.</span>

<span class="n">final_azdias</span> <span class="o">=</span> <span class="n">x</span>

<span class="n">fill_NaN</span> <span class="o">=</span> <span class="n">Imputer</span><span class="p">(</span><span class="n">missing_values</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">strategy</span><span class="o">=</span><span class="s1">&#39;median&#39;</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">imputed_azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fill_NaN</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">final_azdias</span><span class="p">))</span>
<span class="n">imputed_azdias</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">columns</span>
<span class="n">imputed_azdias</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">index</span>

<span class="n">imputed_azdias</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>4.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>3.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>5.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>2.0</td>
      <td>4.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>1.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>2.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1.0</td>
      <td>4.0</td>
      <td>3.0</td>
      <td>4.0</td>
      <td>1.0</td>
      <td>3.0</td>
      <td>2.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>1.0</td>
      <td>2.0</td>
      <td>3.0</td>
      <td>1.0</td>
      <td>5.0</td>
      <td>2.0</td>
      <td>2.0</td>
      <td>5.0</td>
      <td>0.0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>0.0</td>
      <td>1.0</td>
      <td>0.0</td>
      <td>0.0</td>
    </tr>
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<p>5 rows × 212 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply feature scaling to the general population demographics data.</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">StandardScaler</span><span class="p">()</span>
<span class="n">scaled_imputed_azdias</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">imputed_azdias</span><span class="p">))</span>
<span class="n">scaled_imputed_azdias</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">columns</span>
<span class="n">scaled_imputed_azdias</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">index</span>
<span class="n">scaled_imputed_azdias</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>-1.724298</td>
      <td>0.962859</td>
      <td>-1.503661</td>
      <td>1.559941</td>
      <td>-1.049535</td>
      <td>1.490708</td>
      <td>1.013168</td>
      <td>1.331159</td>
      <td>-0.536372</td>
      <td>1.028254</td>
      <td>...</td>
      <td>-0.565829</td>
      <td>1.578456</td>
      <td>-0.551501</td>
      <td>-0.419565</td>
      <td>-0.418938</td>
      <td>-0.411214</td>
      <td>-0.29289</td>
      <td>-0.524215</td>
      <td>1.489042</td>
      <td>-0.326504</td>
    </tr>
    <tr>
      <th>2</th>
      <td>0.205425</td>
      <td>0.962859</td>
      <td>-1.503661</td>
      <td>0.884836</td>
      <td>-1.773085</td>
      <td>-0.555630</td>
      <td>0.291141</td>
      <td>1.331159</td>
      <td>1.864378</td>
      <td>1.028254</td>
      <td>...</td>
      <td>-0.565829</td>
      <td>-0.633531</td>
      <td>1.813233</td>
      <td>-0.419565</td>
      <td>-0.418938</td>
      <td>-0.411214</td>
      <td>-0.29289</td>
      <td>1.907613</td>
      <td>-0.671573</td>
      <td>-0.326504</td>
    </tr>
    <tr>
      <th>3</th>
      <td>1.170287</td>
      <td>0.962859</td>
      <td>0.685685</td>
      <td>-0.465374</td>
      <td>1.121117</td>
      <td>-0.555630</td>
      <td>-1.152912</td>
      <td>-0.831846</td>
      <td>-0.536372</td>
      <td>-0.162095</td>
      <td>...</td>
      <td>-0.565829</td>
      <td>-0.633531</td>
      <td>-0.551501</td>
      <td>2.383421</td>
      <td>-0.418938</td>
      <td>-0.411214</td>
      <td>-0.29289</td>
      <td>-0.524215</td>
      <td>-0.671573</td>
      <td>3.062745</td>
    </tr>
    <tr>
      <th>4</th>
      <td>0.205425</td>
      <td>-1.038573</td>
      <td>0.685685</td>
      <td>0.209731</td>
      <td>0.397566</td>
      <td>-1.237743</td>
      <td>0.291141</td>
      <td>-0.831846</td>
      <td>-0.536372</td>
      <td>1.028254</td>
      <td>...</td>
      <td>1.767318</td>
      <td>-0.633531</td>
      <td>-0.551501</td>
      <td>-0.419565</td>
      <td>-0.418938</td>
      <td>2.431825</td>
      <td>-0.29289</td>
      <td>-0.524215</td>
      <td>-0.671573</td>
      <td>-0.326504</td>
    </tr>
    <tr>
      <th>5</th>
      <td>-1.724298</td>
      <td>0.962859</td>
      <td>-0.044097</td>
      <td>-1.140479</td>
      <td>1.121117</td>
      <td>-0.555630</td>
      <td>-0.430886</td>
      <td>1.331159</td>
      <td>-0.536372</td>
      <td>1.028254</td>
      <td>...</td>
      <td>-0.565829</td>
      <td>1.578456</td>
      <td>-0.551501</td>
      <td>-0.419565</td>
      <td>-0.418938</td>
      <td>-0.411214</td>
      <td>-0.29289</td>
      <td>1.907613</td>
      <td>-0.671573</td>
      <td>-0.326504</td>
    </tr>
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<p>5 rows × 212 columns</p>
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<h3 id="Discussion-2.1:-Apply-Feature-Scaling">Discussion 2.1: Apply Feature Scaling<a class="anchor-link" href="#Discussion-2.1:-Apply-Feature-Scaling">&#182;</a></h3><p>(Double-click this cell and replace this text with your own text, reporting your decisions regarding feature scaling.)</p>
<p>I used mean for the Imputer median. Data median can reflect the average level of current column, and keep the data mostly integers.
Then it is scaled from 0 to 1 using sample mean and sample data.</p>

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<h3 id="Step-2.2:-Perform-Dimensionality-Reduction">Step 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Step-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>On your scaled data, you are now ready to apply dimensionality reduction techniques.</p>
<ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html">PCA</a> class to apply principal component analysis on the data, thus finding the vectors of maximal variance in the data. To start, you should not set any parameters (so all components are computed) or set a number of components that is at least half the number of features (so there's enough features to see the general trend in variability).</li>
<li>Check out the ratio of variance explained by each principal component as well as the cumulative variance explained. Try plotting the cumulative or sequential values using matplotlib's <a href="https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html"><code>plot()</code></a> function. Based on what you find, select a value for the number of transformed features you'll retain for the clustering part of the project.</li>
<li>Once you've made a choice for the number of components to keep, make sure you re-fit a PCA instance to perform the decided-on transformation.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply PCA to the data.</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">()</span>
<span class="n">pca</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">scaled_imputed_azdias</span><span class="p">)</span>
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<pre>PCA(copy=True, iterated_power=&#39;auto&#39;, n_components=None, random_state=None,
  svd_solver=&#39;auto&#39;, tol=0.0, whiten=False)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="o">.</span><span class="n">shape</span>
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<pre>(212,)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the variance accounted for by each principal component.</span>
<span class="n">num_components</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span><span class="p">)</span>
<span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="n">num_components</span><span class="p">,</span><span class="mi">20</span><span class="p">)</span>
<span class="n">vals</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">explained_variance_ratio_</span>
 
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">))</span>
<span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
<span class="n">cumvals</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">cumsum</span><span class="p">(</span><span class="n">vals</span><span class="p">)[</span><span class="n">ind</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
<span class="n">ax</span><span class="o">.</span><span class="n">bar</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">vals</span><span class="p">[</span><span class="n">ind</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">width</span> <span class="o">=</span> <span class="mi">7</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">cumvals</span><span class="p">,</span><span class="s1">&#39;.-&#39;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ind</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">gradient</span><span class="p">(</span><span class="n">vals</span><span class="p">[</span><span class="n">ind</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span><span class="s1">&#39;r-.&#39;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ind</span><span class="p">:</span>
    <span class="c1">#print(vals[i-1], ind[i])</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="sa">r</span><span class="s2">&quot;</span><span class="si">%s%%</span><span class="s2">&quot;</span> <span class="o">%</span> <span class="p">((</span><span class="nb">str</span><span class="p">(</span><span class="n">vals</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="mi">100</span><span class="p">)[:</span><span class="mi">4</span><span class="p">])),</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">vals</span><span class="p">[</span><span class="n">i</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">va</span><span class="o">=</span><span class="s2">&quot;bottom&quot;</span><span class="p">,</span> <span class="n">ha</span><span class="o">=</span><span class="s2">&quot;center&quot;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>

<span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">yaxis</span><span class="o">.</span><span class="n">set_tick_params</span><span class="p">(</span><span class="n">width</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">length</span><span class="o">=</span><span class="mi">12</span><span class="p">)</span>
 
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Principal Component&quot;</span><span class="p">)</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Variance Explained (%)&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Explained Variance Per Principal Component&#39;</span><span class="p">)</span>
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<pre>Text(0.5,1,&#39;Explained Variance Per Principal Component&#39;)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">while</span> <span class="n">i</span><span class="o">&lt;</span><span class="nb">len</span><span class="p">(</span><span class="n">cumvals</span><span class="p">):</span>
    <span class="k">if</span> <span class="n">cumvals</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">&gt;=</span><span class="mf">0.9</span><span class="p">:</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;The component number is </span><span class="si">%s</span><span class="s2"> when variance explained first exceed 09.&quot;</span><span class="o">%</span><span class="k">ind</span>[i])
        <span class="k">break</span>
    <span class="n">i</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span>
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<pre>The component number is 121 when variance explained first exceed 09.
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-apply PCA to the data while selecting for number of components to retain.</span>

<span class="c1"># after 50, the varience doesn&#39;t decline much. Thus I choose number of components as 50.</span>
<span class="n">pca2</span> <span class="o">=</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span> <span class="o">=</span> <span class="mi">121</span><span class="p">)</span>
<span class="n">pcaazdias</span> <span class="o">=</span> <span class="n">pca2</span><span class="o">.</span><span class="n">fit_transform</span><span class="p">(</span><span class="n">scaled_imputed_azdias</span><span class="p">)</span>
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<h3 id="Discussion-2.2:-Perform-Dimensionality-Reduction">Discussion 2.2: Perform Dimensionality Reduction<a class="anchor-link" href="#Discussion-2.2:-Perform-Dimensionality-Reduction">&#182;</a></h3><p>(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding dimensionality reduction. How many principal components / transformed features are you retaining for the next step of the analysis?)</p>
<p>When number of column selected more than 121, introducing more columns doesn't significantly decline the varience much. Thus I choose number of components as 121, where the percentage of variance explained is above 0.9.</p>

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<h3 id="Step-2.3:-Interpret-Principal-Components">Step 2.3: Interpret Principal Components<a class="anchor-link" href="#Step-2.3:-Interpret-Principal-Components">&#182;</a></h3><p>Now that we have our transformed principal components, it's a nice idea to check out the weight of each variable on the first few components to see if they can be interpreted in some fashion.</p>
<p>As a reminder, each principal component is a unit vector that points in the direction of highest variance (after accounting for the variance captured by earlier principal components). The further a weight is from zero, the more the principal component is in the direction of the corresponding feature. If two features have large weights of the same sign (both positive or both negative), then increases in one tend expect to be associated with increases in the other. To contrast, features with different signs can be expected to show a negative correlation: increases in one variable should result in a decrease in the other.</p>
<ul>
<li>To investigate the features, you should map each weight to their corresponding feature name, then sort the features according to weight. The most interesting features for each principal component, then, will be those at the beginning and end of the sorted list. Use the data dictionary document to help you understand these most prominent features, their relationships, and what a positive or negative value on the principal component might indicate.</li>
<li>You should investigate and interpret feature associations from the first three principal components in this substep. To help facilitate this, you should write a function that you can call at any time to print the sorted list of feature weights, for the <em>i</em>-th principal component. This might come in handy in the next step of the project, when you interpret the tendencies of the discovered clusters.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the first principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>
<span class="c1"># HINT: Try defining a function here or in a new cell that you can reuse in the</span>
<span class="c1"># other cells.</span>

<span class="k">def</span> <span class="nf">weight_map</span><span class="p">(</span><span class="n">pca</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
    <span class="n">weight</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="n">n</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
    <span class="n">feature</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">columns</span>
    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">weight</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">feature</span><span class="p">):</span>
        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;there is something wrong&quot;</span><span class="p">)</span>
        <span class="k">return</span>
    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">adict</span> <span class="o">=</span> <span class="p">{}</span>
    <span class="k">while</span> <span class="n">i</span><span class="o">&lt;</span><span class="nb">len</span><span class="p">(</span><span class="n">weight</span><span class="p">):</span>
        <span class="n">adict</span><span class="p">[</span><span class="n">feature</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">weight</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
        <span class="n">i</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span>
    <span class="k">return</span> <span class="n">adict</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># the weightmap</span>
<span class="n">weightmap1</span> <span class="o">=</span> <span class="n">weight_map</span><span class="p">(</span><span class="n">pca2</span><span class="p">,</span> <span class="n">scaled_imputed_azdias</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># the sorter</span>
<span class="kn">import</span> <span class="nn">operator</span>
<span class="n">sorted_weightmap1</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">weightmap1</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">sorted_weightmap1</span>
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 (&#39;HH_EINKOMMEN_SCORE&#39;, 0.18535080928856359),
 (&#39;LP_STATUS_GROB_1.0&#39;, 0.19197009638754972)]</pre>
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<div class="prompt input_prompt">In&nbsp;[47]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the second principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>

<span class="c1"># the weightmap</span>
<span class="n">weightmap2</span> <span class="o">=</span> <span class="n">weight_map</span><span class="p">(</span><span class="n">pca2</span><span class="p">,</span> <span class="n">scaled_imputed_azdias</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="c1"># the sorter</span>
<span class="n">sorted_weightmap2</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">weightmap2</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">sorted_weightmap2</span>
</pre></div>

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    <div class="prompt output_prompt">Out[47]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[(&#39;FINANZ_SPARER&#39;, -0.22370716608503635),
 (&#39;FINANZ_UNAUFFAELLIGER&#39;, -0.21311433861966525),
 (&#39;SEMIO_REL&#39;, -0.2122634494038835),
 (&#39;SEMIO_TRADV&#39;, -0.20614606276611699),
 (&#39;SEMIO_PFLICHT&#39;, -0.20282062449107946),
 (&#39;FINANZ_ANLEGER&#39;, -0.20051638937517935),
 (&#39;generation_90s&#39;, -0.19919506009386628),
 (&#39;SEMIO_RAT&#39;, -0.16558041208463303),
 (&#39;SEMIO_KULT&#39;, -0.16273692838640005),
 (&#39;ONLINE_AFFINITAET&#39;, -0.16066566668104793),
 (&#39;FINANZTYP_1&#39;, -0.13221427907851641),
 (&#39;SEMIO_FAM&#39;, -0.12987398719858839),
 (&#39;SEMIO_MAT&#39;, -0.1265445783526486),
 (&#39;ZABEOTYP_4&#39;, -0.10563661911238373),
 (&#39;LP_STATUS_FEIN_2.0&#39;, -0.0947711623531452),
 (&#39;ZABEOTYP_5&#39;, -0.091327974556469071),
 (&#39;FINANZTYP_4&#39;, -0.090008766423687034),
 (&#39;LP_STATUS_FEIN_5.0&#39;, -0.080916195550972833),
 (&#39;GFK_URLAUBERTYP_9.0&#39;, -0.071692748267490919),
 (&#39;FINANZTYP_3&#39;, -0.071582268513941436),
 (&#39;PLZ8_ANTG1&#39;, -0.067295431802190234),
 (&#39;LP_FAMILIE_GROB_4.0&#39;, -0.066230034551302636),
 (&#39;ANZ_PERSONEN&#39;, -0.063250855171988224),
 (&#39;MOBI_REGIO&#39;, -0.063047851380460831),
 (&#39;SEMIO_SOZ&#39;, -0.06211090239232081),
 (&#39;KBA05_GBZ&#39;, -0.061008733528605849),
 (&#39;ZABEOTYP_1&#39;, -0.060697511366785466),
 (&#39;KBA05_ANTG1&#39;, -0.058943833708755028),
 (&#39;generation_80s&#39;, -0.055287336189531716),
 (&#39;CJT_GESAMTTYP_4.0&#39;, -0.053948308294859519),
 (&#39;PLZ8_GBZ&#39;, -0.052482231353308387),
 (&#39;SHOPPER_TYP_0.0&#39;, -0.052317617056513922),
 (&#39;INNENSTADT&#39;, -0.051662169733219881),
 (&#39;KONSUMNAEHE&#39;, -0.050401636267563256),
 (&#39;NATIONALITAET_KZ_3.0&#39;, -0.047772811510102937),
 (&#39;LP_FAMILIE_GROB_3.0&#39;, -0.046281936521259733),
 (&#39;wealth_Prosperous Households&#39;, -0.045356756256722114),
 (&#39;LP_FAMILIE_GROB_5.0&#39;, -0.043643455377605014),
 (&#39;NATIONALITAET_KZ_2.0&#39;, -0.042592005843245954),
 (&#39;CJT_GESAMTTYP_6.0&#39;, -0.041259876062982245),
 (&#39;BALLRAUM&#39;, -0.04110995614597078),
 (&#39;LP_FAMILIE_FEIN_7.0&#39;, -0.041030637720168073),
 (&#39;GFK_URLAUBERTYP_2.0&#39;, -0.040499742986613768),
 (&#39;CAMEO_DEUG_2015_4&#39;, -0.039014108675945021),
 (&#39;life_stage_Young Couples With Children&#39;, -0.038922228141944085),
 (&#39;HEALTH_TYP&#39;, -0.037977161803984437),
 (&#39;LP_FAMILIE_FEIN_8.0&#39;, -0.03791801426313135),
 (&#39;GFK_URLAUBERTYP_12.0&#39;, -0.037490691513275318),
 (&#39;LP_FAMILIE_FEIN_11.0&#39;, -0.034989821088330493),
 (&#39;CJT_GESAMTTYP_3.0&#39;, -0.034979257386465297),
 (&#39;LP_FAMILIE_FEIN_6.0&#39;, -0.034620094260775391),
 (&#39;CJT_GESAMTTYP_5.0&#39;, -0.034223561503223907),
 (&#39;MIN_GEBAEUDEJAHR&#39;, -0.034047820420056316),
 (&#39;GEBAEUDETYP_RASTER&#39;, -0.0323833234057306),
 (&#39;CAMEO_DEU_2015_4A&#39;, -0.031054248908550488),
 (&#39;ZABEOTYP_2&#39;, -0.030853255505207865),
 (&#39;CAMEO_DEUG_2015_2&#39;, -0.030119569383039169),
 (&#39;nation_ W&#39;, -0.029985009228335094),
 (&#39;LP_FAMILIE_FEIN_4.0&#39;, -0.029611945701988825),
 (&#39;KBA13_ANZAHL_PKW&#39;, -0.027691920103753938),
 (&#39;wealth_Wealthy Households&#39;, -0.027520480918402698),
 (&#39;LP_FAMILIE_FEIN_5.0&#39;, -0.026258198024378281),
 (&#39;life_stage_Families With School Age Children&#39;, -0.026256443454330269),
 (&#39;OST_WEST_KZ_W&#39;, -0.024778721959482854),
 (&#39;LP_FAMILIE_FEIN_10.0&#39;, -0.024113183803300672),
 (&#39;LP_FAMILIE_FEIN_3.0&#39;, -0.023759227911165082),
 (&#39;SHOPPER_TYP_1.0&#39;, -0.022850513957336366),
 (&#39;VERS_TYP_1.0&#39;, -0.02282021936795732),
 (&#39;CAMEO_DEU_2015_2A&#39;, -0.022071087419077217),
 (&#39;SEMIO_VERT&#39;, -0.02191853081463091),
 (&#39;CAMEO_DEU_2015_4C&#39;, -0.021878791334820565),
 (&#39;GEBAEUDETYP_1.0&#39;, -0.021810157509895199),
 (&#39;GFK_URLAUBERTYP_1.0&#39;, -0.0211782270601952),
 (&#39;CAMEO_DEUG_2015_3&#39;, -0.019949200980195179),
 (&#39;LP_STATUS_FEIN_4.0&#39;, -0.019839375415147593),
 (&#39;LP_STATUS_FEIN_8.0&#39;, -0.019667228972715611),
 (&#39;life_stage_Older Families &amp;  Mature Couples&#39;, -0.01759255422353526),
 (&#39;LP_STATUS_GROB_2.0&#39;, -0.016497852323519625),
 (&#39;CAMEO_DEU_2015_3B&#39;, -0.016238238152825255),
 (&#39;CAMEO_DEU_2015_3C&#39;, -0.015001954246369409),
 (&#39;CAMEO_DEU_2015_3A&#39;, -0.014780658234737133),
 (&#39;CAMEO_DEU_2015_2C&#39;, -0.014668997299734258),
 (&#39;movement_ Avantgarde&#39;, -0.013448988254123448),
 (&#39;GREEN_AVANTGARDE&#39;, -0.013448988254123447),
 (&#39;GFK_URLAUBERTYP_11.0&#39;, -0.013106005749326584),
 (&#39;CAMEO_DEU_2015_4B&#39;, -0.013105884948057108),
 (&#39;CAMEO_DEU_2015_2B&#39;, -0.012992224878189386),
 (&#39;CAMEO_DEU_2015_5C&#39;, -0.012906881769812698),
 (&#39;LP_STATUS_GROB_4.0&#39;, -0.012816931556198268),
 (&#39;CAMEO_DEU_2015_5B&#39;, -0.012589046408497588),
 (&#39;CAMEO_DEU_2015_2D&#39;, -0.011558329843568568),
 (&#39;LP_FAMILIE_FEIN_9.0&#39;, -0.010695585856584902),
 (&#39;nation_ E&#39;, -0.010332711019124231),
 (&#39;LP_STATUS_FEIN_6.0&#39;, -0.0080711379104162154),
 (&#39;CAMEO_DEU_2015_9A&#39;, -0.0070484182738837359),
 (&#39;CAMEO_DEUG_2015_5&#39;, -0.0068095615607931112),
 (&#39;LP_STATUS_FEIN_9.0&#39;, -0.0060224456402232299),
 (&#39;CAMEO_DEU_2015_6B&#39;, -0.0057611105817264013),
 (&#39;KBA05_ANTG2&#39;, -0.005025530882664486),
 (&#39;wealth_Comfortable Households&#39;, -0.0041949854644928132),
 (&#39;CAMEO_DEU_2015_4D&#39;, -0.0038784866728031413),
 (&#39;CAMEO_DEU_2015_7A&#39;, -0.0037333959067602676),
 (&#39;CAMEO_DEU_2015_5A&#39;, -0.0036778148499132719),
 (&#39;CAMEO_DEU_2015_1A&#39;, -0.0034624953245854374),
 (&#39;CAMEO_DEUG_2015_1&#39;, -0.0030635396848648906),
 (&#39;life_stage_Older Families &amp; Mature Couples&#39;, -0.0028449546743457938),
 (&#39;LP_STATUS_GROB_3.0&#39;, -0.0024190133044616306),
 (&#39;SOHO_KZ&#39;, -0.0020198357863194733),
 (&#39;CAMEO_DEU_2015_1B&#39;, -0.0016661070241367748),
 (&#39;CAMEO_DEU_2015_1D&#39;, -0.0015874463976950992),
 (&#39;CAMEO_DEU_2015_3D&#39;, -0.001154486154534918),
 (&#39;CAMEO_DEU_2015_6A&#39;, -0.00054778192986081964),
 (&#39;CAMEO_DEU_2015_1C&#39;, -2.4254220403229339e-06),
 (&#39;CAMEO_DEU_2015_4E&#39;, 0.00024170113708984434),
 (&#39;GEBAEUDETYP_5.0&#39;, 0.00029153546518861478),
 (&#39;CAMEO_DEU_2015_1E&#39;, 0.0009402825805645968),
 (&#39;GEBAEUDETYP_4.0&#39;, 0.00095871101051467464),
 (&#39;GEBAEUDETYP_6.0&#39;, 0.00097590808938321843),
 (&#39;SHOPPER_TYP_2.0&#39;, 0.001488286336983551),
 (&#39;CAMEO_DEU_2015_5E&#39;, 0.0024941650841802531),
 (&#39;LP_STATUS_GROB_5.0&#39;, 0.0025521259625561465),
 (&#39;LP_STATUS_FEIN_10.0&#39;, 0.0025521259625561465),
 (&#39;CAMEO_DEU_2015_5F&#39;, 0.0029184040610448253),
 (&#39;GEBAEUDETYP_8.0&#39;, 0.0041153649355474024),
 (&#39;GEBAEUDETYP_2.0&#39;, 0.0047045959665539972),
 (&#39;CAMEO_DEU_2015_9B&#39;, 0.0051243764448466829),
 (&#39;CAMEO_DEU_2015_6C&#39;, 0.0053289603043453378),
 (&#39;ANZ_TITEL&#39;, 0.0063252686755442301),
 (&#39;movement_ Mainstream&#39;, 0.0064040992958357534),
 (&#39;GFK_URLAUBERTYP_10.0&#39;, 0.0066506403794210738),
 (&#39;CAMEO_DEU_2015_6D&#39;, 0.0067906055807248128),
 (&#39;CAMEO_DEU_2015_7B&#39;, 0.0078601651822649797),
 (&#39;PLZ8_HHZ&#39;, 0.0084634698939124384),
 (&#39;CAMEO_DEU_2015_5D&#39;, 0.0086612246774658737),
 (&#39;CAMEO_DEU_2015_9C&#39;, 0.0086886023124966519),
 (&#39;LP_STATUS_FEIN_7.0&#39;, 0.0092630004769086432),
 (&#39;CAMEO_DEU_2015_7D&#39;, 0.010148228130816329),
 (&#39;CAMEO_DEU_2015_8B&#39;, 0.010644738627611734),
 (&#39;CAMEO_DEU_2015_6F&#39;, 0.011543210081780416),
 (&#39;CAMEO_DEU_2015_7C&#39;, 0.011997049212421951),
 (&#39;CAMEO_DEUG_2015_7&#39;, 0.012230733883355957),
 (&#39;CAMEO_DEU_2015_7E&#39;, 0.012275232642920412),
 (&#39;CAMEO_DEUG_2015_6&#39;, 0.012655181728356443),
 (&#39;CAMEO_DEU_2015_9D&#39;, 0.014084335110680602),
 (&#39;CAMEO_DEU_2015_8C&#39;, 0.016884190310522712),
 (&#39;wealth_Less Affluent Households&#39;, 0.01744184850228616),
 (&#39;GFK_URLAUBERTYP_6.0&#39;, 0.018085423373128066),
 (&#39;CAMEO_DEU_2015_8A&#39;, 0.018453320126782323),
 (&#39;KKK&#39;, 0.018737359371807233),
 (&#39;GFK_URLAUBERTYP_8.0&#39;, 0.020293290954670324),
 (&#39;CAMEO_DEUG_2015_9&#39;, 0.020851557172819175),
 (&#39;GEBAEUDETYP_3.0&#39;, 0.020976096819724052),
 (&#39;ZABEOTYP_6&#39;, 0.022537316310481),
 (&#39;GFK_URLAUBERTYP_5.0&#39;, 0.022854674208723662),
 (&#39;LP_STATUS_GROB_1.0&#39;, 0.023247568199386559),
 (&#39;ANZ_HH_TITEL&#39;, 0.024507702653322708),
 (&#39;OST_WEST_KZ_O&#39;, 0.024778721959482854),
 (&#39;SEMIO_DOM&#39;, 0.025147504518726421),
 (&#39;CAMEO_DEU_2015_6E&#39;, 0.025156348551243379),
 (&#39;nation_ E+W&#39;, 0.025195918251702122),
 (&#39;life_stage_Pre-Family Couples &amp; Singles&#39;, 0.026000225364226033),
 (&#39;VERS_TYP_2.0&#39;, 0.026359095578230914),
 (&#39;GFK_URLAUBERTYP_3.0&#39;, 0.028818326255515666),
 (&#39;REGIOTYP&#39;, 0.029742633235722911),
 (&#39;KBA05_ANTG3&#39;, 0.033452581961779519),
 (&#39;LP_FAMILIE_GROB_2.0&#39;, 0.035707385057971837),
 (&#39;LP_FAMILIE_FEIN_2.0&#39;, 0.035707385057971837),
 (&#39;CAMEO_DEU_2015_8D&#39;, 0.036585457914168999),
 (&#39;CAMEO_DEU_2015_9E&#39;, 0.03691582982264071),
 (&#39;ANREDE_KZ&#39;, 0.037592810979951374),
 (&#39;GFK_URLAUBERTYP_7.0&#39;, 0.038882049104445648),
 (&#39;HH_EINKOMMEN_SCORE&#39;, 0.040447156007389795),
 (&#39;life_stage_Elders In Retirement&#39;, 0.041020520256646729),
 (&#39;CAMEO_DEUG_2015_8&#39;, 0.041131423209824026),
 (&#39;PLZ8_ANTG2&#39;, 0.046702588320239609),
 (&#39;LP_STATUS_FEIN_3.0&#39;, 0.046705544319328156),
 (&#39;ANZ_HAUSHALTE_AKTIV&#39;, 0.047303241874143927),
 (&#39;RELAT_AB&#39;, 0.047589948331749392),
 (&#39;wealth_Poorer Households&#39;, 0.050220654374041523),
 (&#39;ARBEIT&#39;, 0.051900571682071552),
 (&#39;KBA05_ANTG4&#39;, 0.05300069788196636),
 (&#39;WOHNDAUER_2008&#39;, 0.053443045919672597),
 (&#39;SEMIO_KAEM&#39;, 0.056389893659761853),
 (&#39;GFK_URLAUBERTYP_4.0&#39;, 0.057709790625320605),
 (&#39;LP_FAMILIE_GROB_1.0&#39;, 0.058868512488252804),
 (&#39;LP_FAMILIE_FEIN_1.0&#39;, 0.058868512488252804),
 (&#39;NATIONALITAET_KZ_1.0&#39;, 0.063016947120313527),
 (&#39;ORTSGR_KLS9&#39;, 0.063885507494807175),
 (&#39;generation_40s&#39;, 0.064092364053431758),
 (&#39;generation_70s&#39;, 0.064184359488427517),
 (&#39;EWDICHTE&#39;, 0.064595889402553702),
 (&#39;PLZ8_ANTG4&#39;, 0.0651176326999299),
 (&#39;FINANZTYP_6&#39;, 0.067965072919279806),
 (&#39;CJT_GESAMTTYP_1.0&#39;, 0.068206723279371384),
 (&#39;PLZ8_ANTG3&#39;, 0.068566450207515783),
 (&#39;SHOPPER_TYP_3.0&#39;, 0.075085028766687656),
 (&#39;SEMIO_KRIT&#39;, 0.075093683562064903),
 (&#39;FINANZ_HAUSBAUER&#39;, 0.085234381135536594),
 (&#39;FINANZ_MINIMALIST&#39;, 0.085975248726305131),
 (&#39;FINANZTYP_2&#39;, 0.086452907575558666),
 (&#39;FINANZTYP_5&#39;, 0.094169439228056206),
 (&#39;LP_STATUS_FEIN_1.0&#39;, 0.0988094223672146),
 (&#39;generation_50s&#39;, 0.10236712487624443),
 (&#39;CJT_GESAMTTYP_2.0&#39;, 0.10412223496757493),
 (&#39;W_KEIT_KIND_HH&#39;, 0.10695458303125145),
 (&#39;generation_60s&#39;, 0.11224395010656649),
 (&#39;RETOURTYP_BK_S&#39;, 0.15774344925973569),
 (&#39;SEMIO_LUST&#39;, 0.15958109791926631),
 (&#39;SEMIO_ERL&#39;, 0.17794563718676543),
 (&#39;ZABEOTYP_3&#39;, 0.19957517581074286),
 (&#39;FINANZ_VORSORGER&#39;, 0.2147550737019065),
 (&#39;ALTERSKATEGORIE_GROB&#39;, 0.22690999344871654)]</pre>
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<div class="prompt input_prompt">In&nbsp;[48]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Map weights for the third principal component to corresponding feature names</span>
<span class="c1"># and then print the linked values, sorted by weight.</span>

<span class="c1"># the weightmap</span>
<span class="n">weightmap3</span> <span class="o">=</span> <span class="n">weight_map</span><span class="p">(</span><span class="n">pca2</span><span class="p">,</span> <span class="n">scaled_imputed_azdias</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span>
<span class="c1"># the sorter</span>
<span class="n">sorted_weightmap3</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">weightmap3</span><span class="o">.</span><span class="n">items</span><span class="p">(),</span> <span class="n">key</span><span class="o">=</span><span class="n">operator</span><span class="o">.</span><span class="n">itemgetter</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
<span class="n">sorted_weightmap3</span>
</pre></div>

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    <div class="prompt output_prompt">Out[48]:</div>


<div class="output_text output_subarea output_execute_result">
<pre>[(&#39;ANREDE_KZ&#39;, -0.32000947912023336),
 (&#39;SEMIO_KAEM&#39;, -0.28449678460712668),
 (&#39;SEMIO_DOM&#39;, -0.25129034079749274),
 (&#39;SEMIO_KRIT&#39;, -0.24513627732339191),
 (&#39;SEMIO_ERL&#39;, -0.1950125118959562),
 (&#39;FINANZ_ANLEGER&#39;, -0.15576050008223458),
 (&#39;SEMIO_RAT&#39;, -0.13857639572148253),
 (&#39;movement_ Mainstream&#39;, -0.11914876998707179),
 (&#39;LP_STATUS_FEIN_4.0&#39;, -0.092448508698649956),
 (&#39;FINANZTYP_1&#39;, -0.091348090288750225),
 (&#39;SHOPPER_TYP_2.0&#39;, -0.091287015619700901),
 (&#39;INNENSTADT&#39;, -0.08400489894317828),
 (&#39;BALLRAUM&#39;, -0.077378496632644636),
 (&#39;LP_STATUS_FEIN_2.0&#39;, -0.075766032404893491),
 (&#39;FINANZ_HAUSBAUER&#39;, -0.067137163176881759),
 (&#39;KONSUMNAEHE&#39;, -0.066936069192284342),
 (&#39;HH_EINKOMMEN_SCORE&#39;, -0.063164608918550094),
 (&#39;wealth_Prosperous Households&#39;, -0.060676895289334606),
 (&#39;ZABEOTYP_4&#39;, -0.059831066973003444),
 (&#39;PLZ8_ANTG1&#39;, -0.056610703533695905),
 (&#39;LP_FAMILIE_GROB_3.0&#39;, -0.05154157621941962),
 (&#39;GEBAEUDETYP_RASTER&#39;, -0.051038337839326237),
 (&#39;CAMEO_DEUG_2015_4&#39;, -0.049789553365217322),
 (&#39;ZABEOTYP_3&#39;, -0.048470391252763527),
 (&#39;FINANZ_SPARER&#39;, -0.047963596740209058),
 (&#39;LP_STATUS_GROB_4.0&#39;, -0.046444499857599422),
 (&#39;LP_STATUS_FEIN_9.0&#39;, -0.044751134748834409),
 (&#39;generation_90s&#39;, -0.044463876437944035),
 (&#39;KKK&#39;, -0.043437070104535853),
 (&#39;nation_ E+W&#39;, -0.042665054744205802),
 (&#39;SHOPPER_TYP_3.0&#39;, -0.0392328034532511),
 (&#39;LP_STATUS_GROB_2.0&#39;, -0.037644232186722054),
 (&#39;CJT_GESAMTTYP_2.0&#39;, -0.037500936009006038),
 (&#39;LP_FAMILIE_FEIN_4.0&#39;, -0.037476198230044742),
 (&#39;PLZ8_GBZ&#39;, -0.036457542584141286),
 (&#39;GEBAEUDETYP_1.0&#39;, -0.035400800899101123),
 (&#39;CAMEO_DEU_2015_4A&#39;, -0.034974096450441007),
 (&#39;CAMEO_DEU_2015_4C&#39;, -0.032089485182028206),
 (&#39;FINANZ_UNAUFFAELLIGER&#39;, -0.031445386922549959),
 (&#39;FINANZTYP_2&#39;, -0.030140945575326591),
 (&#39;ZABEOTYP_5&#39;, -0.02970800855563312),
 (&#39;CAMEO_DEUG_2015_3&#39;, -0.029331712849089385),
 (&#39;NATIONALITAET_KZ_3.0&#39;, -0.028532667713139233),
 (&#39;life_stage_Young Couples With Children&#39;, -0.028408566666773181),
 (&#39;GFK_URLAUBERTYP_9.0&#39;, -0.026907089212465023),
 (&#39;MOBI_REGIO&#39;, -0.025865303669538733),
 (&#39;life_stage_Families With School Age Children&#39;, -0.025675888948401587),
 (&#39;life_stage_Older Families &amp; Mature Couples&#39;, -0.025521095283694652),
 (&#39;LP_FAMILIE_FEIN_3.0&#39;, -0.02542605900391165),
 (&#39;LP_FAMILIE_FEIN_5.0&#39;, -0.025354347504488766),
 (&#39;REGIOTYP&#39;, -0.024935212952072461),
 (&#39;KBA05_GBZ&#39;, -0.022944616816040625),
 (&#39;GFK_URLAUBERTYP_12.0&#39;, -0.022089224593453794),
 (&#39;CAMEO_DEU_2015_6B&#39;, -0.020086228608956723),
 (&#39;KBA13_ANZAHL_PKW&#39;, -0.019611926688683029),
 (&#39;LP_STATUS_FEIN_5.0&#39;, -0.018594017854309551),
 (&#39;MIN_GEBAEUDEJAHR&#39;, -0.017895141137310323),
 (&#39;CAMEO_DEU_2015_3C&#39;, -0.017857507691209161),
 (&#39;CAMEO_DEUG_2015_6&#39;, -0.017488310380305006),
 (&#39;CAMEO_DEU_2015_3D&#39;, -0.017222407725815566),
 (&#39;SEMIO_PFLICHT&#39;, -0.016354494186116016),
 (&#39;CJT_GESAMTTYP_6.0&#39;, -0.016265176847271762),
 (&#39;LP_STATUS_GROB_1.0&#39;, -0.015951758960344977),
 (&#39;FINANZTYP_6&#39;, -0.015935288636328204),
 (&#39;CAMEO_DEU_2015_4B&#39;, -0.015378458764515591),
 (&#39;CAMEO_DEU_2015_5C&#39;, -0.015048849506354656),
 (&#39;KBA05_ANTG1&#39;, -0.013708135387622864),
 (&#39;VERS_TYP_1.0&#39;, -0.013580646486900357),
 (&#39;GFK_URLAUBERTYP_2.0&#39;, -0.012751761893804342),
 (&#39;CAMEO_DEU_2015_3B&#39;, -0.012543839730077003),
 (&#39;KBA05_ANTG2&#39;, -0.011600320761901695),
 (&#39;CAMEO_DEU_2015_5B&#39;, -0.011400584194520927),
 (&#39;SEMIO_TRADV&#39;, -0.010485263528466147),
 (&#39;LP_STATUS_FEIN_8.0&#39;, -0.010153663232765829),
 (&#39;CAMEO_DEU_2015_8C&#39;, -0.0094231762054577031),
 (&#39;generation_70s&#39;, -0.0091885974008659721),
 (&#39;LP_FAMILIE_FEIN_6.0&#39;, -0.0090714553545711181),
 (&#39;CAMEO_DEU_2015_7A&#39;, -0.0088800869838978856),
 (&#39;OST_WEST_KZ_O&#39;, -0.0084874769416381658),
 (&#39;LP_FAMILIE_FEIN_7.0&#39;, -0.0083006865073003196),
 (&#39;CAMEO_DEU_2015_3A&#39;, -0.0072511983904176795),
 (&#39;LP_FAMILIE_GROB_4.0&#39;, -0.0066778099284501178),
 (&#39;LP_FAMILIE_GROB_1.0&#39;, -0.0059963801866331186),
 (&#39;LP_FAMILIE_FEIN_1.0&#39;, -0.0059963801866331186),
 (&#39;CAMEO_DEU_2015_9A&#39;, -0.0057852511860917791),
 (&#39;GFK_URLAUBERTYP_11.0&#39;, -0.0048037126158335898),
 (&#39;wealth_Less Affluent Households&#39;, -0.004535747331288871),
 (&#39;ONLINE_AFFINITAET&#39;, -0.0040580466962751591),
 (&#39;CAMEO_DEU_2015_6C&#39;, -0.0036935351927001288),
 (&#39;CAMEO_DEU_2015_4D&#39;, -0.0031793398554302954),
 (&#39;CAMEO_DEU_2015_4E&#39;, -0.002880903347351802),
 (&#39;CAMEO_DEU_2015_2B&#39;, -0.002010521244781857),
 (&#39;CAMEO_DEU_2015_8D&#39;, -0.0015879872979820602),
 (&#39;CAMEO_DEU_2015_6A&#39;, -0.0013294920339877851),
 (&#39;CAMEO_DEUG_2015_7&#39;, -0.0011354213378416812),
 (&#39;CAMEO_DEU_2015_6F&#39;, -0.00092116809726065861),
 (&#39;GFK_URLAUBERTYP_1.0&#39;, -0.00066742978294894936),
 (&#39;CAMEO_DEU_2015_6E&#39;, -0.0003459022840415091),
 (&#39;CAMEO_DEU_2015_6D&#39;, -0.00031876216432760105),
 (&#39;CAMEO_DEU_2015_9E&#39;, -2.5766530469116621e-05),
 (&#39;KBA05_ANTG3&#39;, 0.00020751974810251624),
 (&#39;GEBAEUDETYP_2.0&#39;, 0.00049017090779395281),
 (&#39;CAMEO_DEU_2015_7E&#39;, 0.00053791263448417872),
 (&#39;GEBAEUDETYP_5.0&#39;, 0.00056596245219305648),
 (&#39;GFK_URLAUBERTYP_7.0&#39;, 0.00063230548220339201),
 (&#39;SOHO_KZ&#39;, 0.0006374731871482787),
 (&#39;wealth_Comfortable Households&#39;, 0.00094874988758805875),
 (&#39;LP_FAMILIE_FEIN_9.0&#39;, 0.001056386792230564),
 (&#39;CAMEO_DEU_2015_5E&#39;, 0.0014325289991706013),
 (&#39;NATIONALITAET_KZ_1.0&#39;, 0.0016244503852599352),
 (&#39;CAMEO_DEUG_2015_5&#39;, 0.0016483815285462126),
 (&#39;CAMEO_DEU_2015_5A&#39;, 0.0016654388254378205),
 (&#39;CAMEO_DEU_2015_7D&#39;, 0.0019756418837778688),
 (&#39;ZABEOTYP_2&#39;, 0.0019822733387289087),
 (&#39;CAMEO_DEU_2015_5F&#39;, 0.0021605714714343018),
 (&#39;GEBAEUDETYP_6.0&#39;, 0.0023915996172302837),
 (&#39;GEBAEUDETYP_4.0&#39;, 0.0026030375591750521),
 (&#39;CAMEO_DEU_2015_7C&#39;, 0.0026282554895237348),
 (&#39;life_stage_Elders In Retirement&#39;, 0.0029655134290847681),
 (&#39;GFK_URLAUBERTYP_6.0&#39;, 0.0031822015119969682),
 (&#39;LP_FAMILIE_FEIN_8.0&#39;, 0.0033939247215500614),
 (&#39;GEBAEUDETYP_8.0&#39;, 0.004580581160673349),
 (&#39;CAMEO_DEU_2015_7B&#39;, 0.0056964567598436506),
 (&#39;GFK_URLAUBERTYP_3.0&#39;, 0.0057257667862756151),
 (&#39;CAMEO_DEU_2015_2A&#39;, 0.006124952009411197),
 (&#39;GFK_URLAUBERTYP_5.0&#39;, 0.0077385419269766567),
 (&#39;HEALTH_TYP&#39;, 0.0083155717708823032),
 (&#39;OST_WEST_KZ_W&#39;, 0.0084874769416381727),
 (&#39;CAMEO_DEU_2015_1B&#39;, 0.0091292794331141736),
 (&#39;generation_40s&#39;, 0.0091302415172192959),
 (&#39;LP_FAMILIE_FEIN_11.0&#39;, 0.0092633942508591905),
 (&#39;CJT_GESAMTTYP_1.0&#39;, 0.0093249554434487424),
 (&#39;GFK_URLAUBERTYP_10.0&#39;, 0.0096428126478495316),
 (&#39;LP_FAMILIE_FEIN_10.0&#39;, 0.0097611534976043829),
 (&#39;CAMEO_DEU_2015_2C&#39;, 0.010095666903473086),
 (&#39;CAMEO_DEU_2015_8A&#39;, 0.011596266880773613),
 (&#39;ALTERSKATEGORIE_GROB&#39;, 0.011895981911644406),
 (&#39;FINANZTYP_4&#39;, 0.012284043522282427),
 (&#39;CAMEO_DEU_2015_1A&#39;, 0.012710078603226619),
 (&#39;CJT_GESAMTTYP_4.0&#39;, 0.012859519310217756),
 (&#39;CJT_GESAMTTYP_5.0&#39;, 0.013136934252968024),
 (&#39;LP_FAMILIE_GROB_5.0&#39;, 0.014010190108415109),
 (&#39;CAMEO_DEUG_2015_8&#39;, 0.014523895749211684),
 (&#39;CAMEO_DEU_2015_1C&#39;, 0.015053258658600606),
 (&#39;CAMEO_DEU_2015_9B&#39;, 0.015345171607048246),
 (&#39;CAMEO_DEU_2015_9D&#39;, 0.015366188856414451),
 (&#39;GFK_URLAUBERTYP_8.0&#39;, 0.015412871786407507),
 (&#39;CJT_GESAMTTYP_3.0&#39;, 0.017509594644131446),
 (&#39;ANZ_PERSONEN&#39;, 0.018114736418321814),
 (&#39;nation_ E&#39;, 0.018518060758691099),
 (&#39;ANZ_TITEL&#39;, 0.018840746738425083),
 (&#39;ANZ_HH_TITEL&#39;, 0.01918159340929566),
 (&#39;CAMEO_DEU_2015_1E&#39;, 0.020310191153377444),
 (&#39;WOHNDAUER_2008&#39;, 0.020574394643395801),
 (&#39;VERS_TYP_2.0&#39;, 0.020732046319644728),
 (&#39;CAMEO_DEU_2015_5D&#39;, 0.021435362886032712),
 (&#39;generation_50s&#39;, 0.021691566793592385),
 (&#39;generation_80s&#39;, 0.021977080639589894),
 (&#39;CAMEO_DEU_2015_8B&#39;, 0.023156427527799686),
 (&#39;LP_FAMILIE_GROB_2.0&#39;, 0.024176006042269892),
 (&#39;LP_FAMILIE_FEIN_2.0&#39;, 0.024176006042269892),
 (&#39;generation_60s&#39;, 0.024431312863508518),
 (&#39;FINANZTYP_3&#39;, 0.024584141198234401),
 (&#39;wealth_Poorer Households&#39;, 0.024929834596255335),
 (&#39;SEMIO_LUST&#39;, 0.025038272911790796),
 (&#39;GFK_URLAUBERTYP_4.0&#39;, 0.025532632689183866),
 (&#39;ANZ_HAUSHALTE_AKTIV&#39;, 0.02590171189960928),
 (&#39;CAMEO_DEUG_2015_2&#39;, 0.026257217673501327),
 (&#39;LP_STATUS_FEIN_6.0&#39;, 0.02658407339557042),
 (&#39;CAMEO_DEU_2015_1D&#39;, 0.027427680549999538),
 (&#39;PLZ8_HHZ&#39;, 0.027564637498889107),
 (&#39;CAMEO_DEU_2015_2D&#39;, 0.028927395066916769),
 (&#39;CAMEO_DEUG_2015_9&#39;, 0.029032176501763634),
 (&#39;KBA05_ANTG4&#39;, 0.029427024358765375),
 (&#39;CAMEO_DEU_2015_9C&#39;, 0.029717032423303834),
 (&#39;NATIONALITAET_KZ_2.0&#39;, 0.030681162937684817),
 (&#39;LP_STATUS_FEIN_7.0&#39;, 0.030983316863917285),
 (&#39;life_stage_Older Families &amp;  Mature Couples&#39;, 0.034347630903768987),
 (&#39;GEBAEUDETYP_3.0&#39;, 0.037163853354281851),
 (&#39;life_stage_Pre-Family Couples &amp; Singles&#39;, 0.038361986313331313),
 (&#39;LP_STATUS_GROB_3.0&#39;, 0.038827915107441095),
 (&#39;CAMEO_DEUG_2015_1&#39;, 0.039268941472902913),
 (&#39;FINANZ_VORSORGER&#39;, 0.03974494754158775),
 (&#39;LP_STATUS_FEIN_1.0&#39;, 0.040792416264379468),
 (&#39;nation_ W&#39;, 0.041758864416447543),
 (&#39;SHOPPER_TYP_1.0&#39;, 0.043008189708854297),
 (&#39;ARBEIT&#39;, 0.044912237196835229),
 (&#39;wealth_Wealthy Households&#39;, 0.045518771786741144),
 (&#39;PLZ8_ANTG2&#39;, 0.049270093475083689),
 (&#39;W_KEIT_KIND_HH&#39;, 0.049533761708890074),
 (&#39;RELAT_AB&#39;, 0.049618932379362206),
 (&#39;ZABEOTYP_6&#39;, 0.050709542693182078),
 (&#39;LP_STATUS_FEIN_3.0&#39;, 0.05219062036490929),
 (&#39;PLZ8_ANTG4&#39;, 0.065780310689268165),
 (&#39;PLZ8_ANTG3&#39;, 0.066649031715374035),
 (&#39;RETOURTYP_BK_S&#39;, 0.077773627608868801),
 (&#39;SEMIO_MAT&#39;, 0.085207042271106023),
 (&#39;LP_STATUS_GROB_5.0&#39;, 0.094476639002108759),
 (&#39;LP_STATUS_FEIN_10.0&#39;, 0.094476639002108759),
 (&#39;ORTSGR_KLS9&#39;, 0.097421790089567542),
 (&#39;EWDICHTE&#39;, 0.098390818402049454),
 (&#39;SHOPPER_TYP_0.0&#39;, 0.10859953975565259),
 (&#39;SEMIO_REL&#39;, 0.11171864951096391),
 (&#39;ZABEOTYP_1&#39;, 0.12096187212039476),
 (&#39;FINANZ_MINIMALIST&#39;, 0.12321075874418666),
 (&#39;GREEN_AVANTGARDE&#39;, 0.12668255109107385),
 (&#39;movement_ Avantgarde&#39;, 0.12668255109107385),
 (&#39;FINANZTYP_5&#39;, 0.14171060449370981),
 (&#39;SEMIO_KULT&#39;, 0.2321325990777085),
 (&#39;SEMIO_FAM&#39;, 0.24463098458252625),
 (&#39;SEMIO_SOZ&#39;, 0.24478657040937776),
 (&#39;SEMIO_VERT&#39;, 0.29942524785657637)]</pre>
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<h3 id="Discussion-2.3:-Interpret-Principal-Components">Discussion 2.3: Interpret Principal Components<a class="anchor-link" href="#Discussion-2.3:-Interpret-Principal-Components">&#182;</a></h3><p>(Double-click this cell and replace this text with your own text, reporting your observations from detailed investigation of the first few principal components generated. Can we interpret positive and negative values from them in a meaningful way?)</p>
<p>First pricipal components:</p>

<pre><code>Most positive: ('LP_STATUS_GROB_1.0', 0.19197009560886788)

Most negative: ('MOBI_REGIO', -0.19238053821542189)

</code></pre>
<p>Second pricipal components:</p>

<pre><code>Most positive: ('ALTERSKATEGORIE_GROB', 0.22690999438296039)

Most negative: ('FINANZ_SPARER', -0.22370716243969535)

</code></pre>
<p>Third pricipal components:</p>

<pre><code>Most positive: ('SEMIO_VERT', 0.29942533189662018)

Most negative: ('ANREDE_KZ', -0.32000951191378058)


</code></pre>
<p>We are doing PCA for population data. For each component, it represents a group of people.</p>
<p>Here the first principal component is mainly a group of people that are singles. These people usually have higher movements. i.e. if they have lower movements, these people are less likely to group within this component. This principal component mainly describ the group of people that are young and active.</p>
<p>For the second principal component, people are most characterized by their age. The elder people tend to be grouped together. The finicial topology is the factor that most likely to impact this grouping, meaning that lower income will negatively impact this grouping. This principal component mainly describ the group of people that are elder and wealthy.</p>
<p>For the third principal component, people are mostly characterized by their personality topology and their gender, meaning that persons with high affinity personality and been a female tend to be grouped togeter. This principal component mainly describ the group of people that are warm hearted women.</p>
<p>The PCA is showing that these three type of people are important component in the population structure.</p>

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<h2 id="Step-3:-Clustering">Step 3: Clustering<a class="anchor-link" href="#Step-3:-Clustering">&#182;</a></h2><h3 id="Step-3.1:-Apply-Clustering-to-General-Population">Step 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Step-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>You've assessed and cleaned the demographics data, then scaled and transformed them. Now, it's time to see how the data clusters in the principal components space. In this substep, you will apply k-means clustering to the dataset and use the average within-cluster distances from each point to their assigned cluster's centroid to decide on a number of clusters to keep.</p>
<ul>
<li>Use sklearn's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans">KMeans</a> class to perform k-means clustering on the PCA-transformed data.</li>
<li>Then, compute the average difference from each point to its assigned cluster's center. <strong>Hint</strong>: The KMeans object's <code>.score()</code> method might be useful here, but note that in sklearn, scores tend to be defined so that larger is better. Try applying it to a small, toy dataset, or use an internet search to help your understanding.</li>
<li>Perform the above two steps for a number of different cluster counts. You can then see how the average distance decreases with an increasing number of clusters. However, each additional cluster provides a smaller net benefit. Use this fact to select a final number of clusters in which to group the data. <strong>Warning</strong>: because of the large size of the dataset, it can take a long time for the algorithm to resolve. The more clusters to fit, the longer the algorithm will take. You should test for cluster counts through at least 10 clusters to get the full picture, but you shouldn't need to test for a number of clusters above about 30.</li>
<li>Once you've selected a final number of clusters to use, re-fit a KMeans instance to perform the clustering operation. Make sure that you also obtain the cluster assignments for the general demographics data, since you'll be using them in the final Step 3.3.</li>
</ul>

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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Over a number of different cluster counts...</span>
<span class="c1"># run k-means clustering on the data and...    </span>
<span class="c1"># compute the average within-cluster distances.</span>

<span class="k">def</span> <span class="nf">kmean_n</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">n_list</span><span class="p">):</span>
    <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
    <span class="n">score_list</span> <span class="o">=</span> <span class="p">[]</span>
    <span class="k">while</span> <span class="n">i</span><span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">n_list</span><span class="p">):</span>
        <span class="n">data_kmean</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span> <span class="o">=</span> <span class="n">n_list</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
        <span class="n">data_model</span> <span class="o">=</span> <span class="n">data_kmean</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
        <span class="n">data_score</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">data_model</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">data</span><span class="p">))</span>
        <span class="n">score_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_score</span><span class="p">)</span>
        <span class="nb">print</span><span class="p">(</span><span class="n">data_score</span><span class="p">)</span>
        <span class="n">i</span> <span class="o">=</span> <span class="n">i</span><span class="o">+</span><span class="mi">1</span>
    <span class="k">return</span> <span class="n">score_list</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Investigate the change in within-cluster distance across number of clusters.</span>
<span class="c1"># HINT: Use matplotlib&#39;s plot function to visualize this relationship.</span>

<span class="n">kn</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">25</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
<span class="n">azdias_score</span> <span class="o">=</span> <span class="n">kmean_n</span><span class="p">(</span><span class="n">pcaazdias</span><span class="p">,</span> <span class="n">kn</span><span class="p">)</span>
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<pre>149196652.753
128631845.575
123199699.403
119061180.772
114984676.886
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">kn</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kn</span><span class="p">,</span> <span class="n">azdias_score</span><span class="p">,</span><span class="s2">&quot;o-&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;K&quot;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;SSE&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;SSE vs. K&#39;</span><span class="p">)</span>
</pre></div>

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<pre>[ 1  6 11 16 21]
</pre>
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<pre>Text(0.5,1,&#39;SSE vs. K&#39;)</pre>
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<img src="data:image/png;base64,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<div class="prompt input_prompt">In&nbsp;[104]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Re-fit the k-means model with the selected number of clusters and obtain</span>
<span class="c1"># cluster predictions for the general population demographics data.</span>

<span class="n">kmean_opt</span> <span class="o">=</span> <span class="n">KMeans</span><span class="p">(</span><span class="n">n_clusters</span> <span class="o">=</span> <span class="n">kn</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">kmean_model</span> <span class="o">=</span> <span class="n">kmean_opt</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">pcaazdias</span><span class="p">)</span>
<span class="n">kmean_pcaazdias</span> <span class="o">=</span> <span class="n">kmean_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">pcaazdias</span><span class="p">)</span>
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<div class="prompt input_prompt">In&nbsp;[105]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">kmean_pcaazdias</span>
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<pre>array([5, 7, 0, ..., 7, 5, 2], dtype=int32)</pre>
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<h3 id="Discussion-3.1:-Apply-Clustering-to-General-Population">Discussion 3.1: Apply Clustering to General Population<a class="anchor-link" href="#Discussion-3.1:-Apply-Clustering-to-General-Population">&#182;</a></h3><p>(Double-click this cell and replace this text with your own text, reporting your findings and decisions regarding clustering. Into how many clusters have you decided to segment the population?)</p>
<p>I decided to use 16 clusters. There is a kink at 6, but I prefer a lower SSE.</p>

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<h3 id="Step-3.2:-Apply-All-Steps-to-the-Customer-Data">Step 3.2: Apply All Steps to the Customer Data<a class="anchor-link" href="#Step-3.2:-Apply-All-Steps-to-the-Customer-Data">&#182;</a></h3><p>Now that you have clusters and cluster centers for the general population, it's time to see how the customer data maps on to those clusters. Take care to not confuse this for re-fitting all of the models to the customer data. Instead, you're going to use the fits from the general population to clean, transform, and cluster the customer data. In the last step of the project, you will interpret how the general population fits apply to the customer data.</p>
<ul>
<li>Don't forget when loading in the customers data, that it is semicolon (<code>;</code>) delimited.</li>
<li>Apply the same feature wrangling, selection, and engineering steps to the customer demographics using the <code>clean_data()</code> function you created earlier. (You can assume that the customer demographics data has similar meaning behind missing data patterns as the general demographics data.)</li>
<li>Use the sklearn objects from the general demographics data, and apply their transformations to the customers data. That is, you should not be using a <code>.fit()</code> or <code>.fit_transform()</code> method to re-fit the old objects, nor should you be creating new sklearn objects! Carry the data through the feature scaling, PCA, and clustering steps, obtaining cluster assignments for all of the data in the customer demographics data.</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Load in the customer demographics data.</span>
<span class="n">customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">&#39;Udacity_CUSTOMERS_Subset.csv&#39;</span><span class="p">,</span><span class="n">sep</span><span class="o">=</span><span class="s1">&#39;;&#39;</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Apply preprocessing, feature transformation, and clustering from the general</span>
<span class="c1"># demographics onto the customer data, obtaining cluster predictions for the</span>
<span class="c1"># customer demographics data.</span>

<span class="n">customers_clean</span> <span class="o">=</span> <span class="n">clean_data</span><span class="p">(</span><span class="n">customers</span><span class="p">,</span> <span class="n">feat_info</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">customers_clean</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
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<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GEBURTSJAHR</th>
      <th>GREEN_AVANTGARDE</th>
      <th>...</th>
      <th>wealth_Less Affluent Households</th>
      <th>wealth_Poorer Households</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>2</td>
      <td>2</td>
      <td>NaN</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>1</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>NaN</td>
      <td>0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.0</td>
      <td>2</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>NaN</td>
      <td>1</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>NaN</td>
      <td>0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>3</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>5</td>
      <td>2</td>
      <td>1960.0</td>
      <td>0</td>
      <td>...</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
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<p>5 rows × 223 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># aline the test data with the training data</span>

<span class="n">missing_cols</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">columns</span> <span class="p">)</span> <span class="o">-</span> <span class="nb">set</span><span class="p">(</span> <span class="n">customers_clean</span><span class="o">.</span><span class="n">columns</span> <span class="p">)</span>
<span class="n">redundent_cols</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span> <span class="n">customers_clean</span><span class="o">.</span><span class="n">columns</span> <span class="p">)</span> <span class="o">-</span>  <span class="nb">set</span><span class="p">(</span> <span class="n">customers_clean</span><span class="o">.</span><span class="n">columns</span> <span class="p">)</span><span class="o">.</span><span class="n">intersection</span><span class="p">(</span><span class="nb">set</span><span class="p">(</span> <span class="n">final_azdias</span><span class="o">.</span><span class="n">columns</span> <span class="p">)))</span>

<span class="nb">print</span><span class="p">(</span><span class="n">missing_cols</span><span class="p">,</span> <span class="n">redundent_cols</span><span class="p">)</span>

<span class="c1"># get rid of redundent columns</span>
<span class="n">no_redundent_customer</span> <span class="o">=</span> <span class="n">customers_clean</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="n">redundent_cols</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>

<span class="c1"># add missing columns</span>
<span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">missing_cols</span><span class="p">:</span>
    <span class="n">no_redundent_customer</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">customers_clean</span><span class="p">)</span>

<span class="n">aligned_customer</span> <span class="o">=</span> <span class="n">no_redundent_customer</span>
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<pre>{&#39;GEBAEUDETYP_5.0&#39;, &#39;SOHO_KZ&#39;} [&#39;KK_KUNDENTYP_5.0&#39;, &#39;SOHO_KZ_1.0&#39;, &#39;AGER_TYP_2.0&#39;, &#39;AGER_TYP_1.0&#39;, &#39;KK_KUNDENTYP_4.0&#39;, &#39;KK_KUNDENTYP_3.0&#39;, &#39;AGER_TYP_3.0&#39;, &#39;GEBURTSJAHR&#39;, &#39;KK_KUNDENTYP_6.0&#39;, &#39;KK_KUNDENTYP_1.0&#39;, &#39;SOHO_KZ_0.0&#39;, &#39;ALTER_HH&#39;, &#39;KK_KUNDENTYP_2.0&#39;]
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<pre>(191652, 212)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">aligned_customer</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<table border="1" class="dataframe">
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    <tr style="text-align: right;">
      <th></th>
      <th>ALTERSKATEGORIE_GROB</th>
      <th>ANREDE_KZ</th>
      <th>FINANZ_MINIMALIST</th>
      <th>FINANZ_SPARER</th>
      <th>FINANZ_VORSORGER</th>
      <th>FINANZ_ANLEGER</th>
      <th>FINANZ_UNAUFFAELLIGER</th>
      <th>FINANZ_HAUSBAUER</th>
      <th>GREEN_AVANTGARDE</th>
      <th>HEALTH_TYP</th>
      <th>...</th>
      <th>wealth_Prosperous Households</th>
      <th>wealth_Wealthy Households</th>
      <th>life_stage_Elders In Retirement</th>
      <th>life_stage_Families With School Age Children</th>
      <th>life_stage_Older Families &amp;  Mature Couples</th>
      <th>life_stage_Older Families &amp; Mature Couples</th>
      <th>life_stage_Pre-Family Couples &amp; Singles</th>
      <th>life_stage_Young Couples With Children</th>
      <th>GEBAEUDETYP_5.0</th>
      <th>SOHO_KZ</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>2</td>
      <td>2</td>
      <td>1</td>
      <td>1.0</td>
      <td>...</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>1</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>3</td>
      <td>2</td>
      <td>0</td>
      <td>1.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4.0</td>
      <td>2</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>1</td>
      <td>2.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>4.0</td>
      <td>1</td>
      <td>5</td>
      <td>1</td>
      <td>5</td>
      <td>2</td>
      <td>1</td>
      <td>2</td>
      <td>0</td>
      <td>2.0</td>
      <td>...</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>0</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>4</th>
      <td>3.0</td>
      <td>1</td>
      <td>3</td>
      <td>1</td>
      <td>4</td>
      <td>4</td>
      <td>5</td>
      <td>2</td>
      <td>0</td>
      <td>3.0</td>
      <td>...</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>0</td>
      <td>1</td>
      <td>0</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
<p>5 rows × 212 columns</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">#feature transformation</span>
<span class="n">imputed_customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">fill_NaN</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">aligned_customer</span><span class="p">))</span>

<span class="c1"># Apply feature scaling to the general population demographics data.</span>
<span class="n">scaled_imputed_customers</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">imputed_customers</span><span class="p">))</span>
<span class="n">scaled_imputed_customers</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="n">aligned_customer</span><span class="o">.</span><span class="n">columns</span>
<span class="n">scaled_imputed_customers</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">aligned_customer</span><span class="o">.</span><span class="n">index</span>
<span class="n">scaled_imputed_customers</span><span class="o">.</span><span class="n">shape</span>
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<pre>(191652, 212)</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># PCA</span>
<span class="n">customers_pca</span> <span class="o">=</span> <span class="n">pca2</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaled_imputed_customers</span><span class="p">)</span>
    
<span class="c1"># clustering</span>
<span class="n">customers_kmean</span> <span class="o">=</span> <span class="n">kmean_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">customers_pca</span><span class="p">)</span>
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<h3 id="Step-3.3:-Compare-Customer-Data-to-Demographics-Data">Step 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Step-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>At this point, you have clustered data based on demographics of the general population of Germany, and seen how the customer data for a mail-order sales company maps onto those demographic clusters. In this final substep, you will compare the two cluster distributions to see where the strongest customer base for the company is.</p>
<p>Consider the proportion of persons in each cluster for the general population, and the proportions for the customers. If we think the company's customer base to be universal, then the cluster assignment proportions should be fairly similar between the two. If there are only particular segments of the population that are interested in the company's products, then we should see a mismatch from one to the other. If there is a higher proportion of persons in a cluster for the customer data compared to the general population (e.g. 5% of persons are assigned to a cluster for the general population, but 15% of the customer data is closest to that cluster's centroid) then that suggests the people in that cluster to be a target audience for the company. On the other hand, the proportion of the data in a cluster being larger in the general population than the customer data (e.g. only 2% of customers closest to a population centroid that captures 6% of the data) suggests that group of persons to be outside of the target demographics.</p>
<p>Take a look at the following points in this step:</p>
<ul>
<li>Compute the proportion of data points in each cluster for the general population and the customer data. Visualizations will be useful here: both for the individual dataset proportions, but also to visualize the ratios in cluster representation between groups. Seaborn's <a href="https://seaborn.pydata.org/generated/seaborn.countplot.html"><code>countplot()</code></a> or <a href="https://seaborn.pydata.org/generated/seaborn.barplot.html"><code>barplot()</code></a> function could be handy.<ul>
<li>Recall the analysis you performed in step 1.1.3 of the project, where you separated out certain data points from the dataset if they had more than a specified threshold of missing values. If you found that this group was qualitatively different from the main bulk of the data, you should treat this as an additional data cluster in this analysis. Make sure that you account for the number of data points in this subset, for both the general population and customer datasets, when making your computations!</li>
</ul>
</li>
<li>Which cluster or clusters are overrepresented in the customer dataset compared to the general population? Select at least one such cluster and infer what kind of people might be represented by that cluster. Use the principal component interpretations from step 2.3 or look at additional components to help you make this inference. Alternatively, you can use the <code>.inverse_transform()</code> method of the PCA and StandardScaler objects to transform centroids back to the original data space and interpret the retrieved values directly.</li>
<li>Perform a similar investigation for the underrepresented clusters. Which cluster or clusters are underrepresented in the customer dataset compared to the general population, and what kinds of people are typified by these clusters?</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Compare the proportion of data in each cluster for the customer data to the</span>
<span class="c1"># proportion of data in each cluster for the general population.</span>

<span class="n">figure</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">7</span><span class="p">))</span>
<span class="n">figure</span><span class="o">.</span><span class="n">subplots_adjust</span><span class="p">(</span><span class="n">hspace</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="n">wspace</span><span class="o">=.</span><span class="mi">3</span><span class="p">)</span>

<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">customers_kmean</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Customer Clusters&#39;</span><span class="p">)</span>
<span class="n">sns</span><span class="o">.</span><span class="n">countplot</span><span class="p">(</span><span class="n">kmean_pcaazdias</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;General Clusters&#39;</span><span class="p">)</span>
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<pre>Text(0.5,1,&#39;General Clusters&#39;)</pre>
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4G1wCHtsbaqbquq7wEXAcuSBHg+cEk7/gLgmIG2LmjPLwEOb/UlSZKkncYk56j/JnB5e74AuGNg37pWtqXyxwPfGAj9U+UPaKvtv6fVf5AkK5KsSbJm48aNO3xCkiRJ0qhMJKgneS1wP/CeqaJpqtUQ5Vtr68GFVedW1ZKqWjJ//vytd1qSJEkao3njfsEky4EXAYdX1VSAXgfsP1BtIXBnez5d+deAPZLMa1fNB+tPtbUuyTzgcWw2BUeSJEnqu7FeUU+yFDgVeHFV3TewaxVwfFux5QBgMfAp4FpgcVvhZVe6G05XtYD/ceDYdvxy4NKBtpa358cCHxv4B4EkSZK0U5jN5RnfC1wFPDXJuiQnAX8DPBZYneT6JH8LUFU3ARcDNwMfAU6uqh+0q+WnAFcAtwAXt7rQBf5XJ1lLNwf9vFZ+HvD4Vv5q4L+WdJQkjU+SP0hyU5Ibk7w3ySOHWVp3e5fvlaS5YtamvlTVCdMUnzdN2VT9M4Ezpym/DLhsmvLb6FaF2bz8O8Bx29VZSdJIJVkA/D5wYFV9O8nFdJ+KHk23tO5F7WLNScA5DCytm+R4uqV1/9tmy/c+EfjnJD/RXuZtwAvppjxem2RVVd08xtOUpFnlN5NKkmbLPGC3dr/Qo4D1bP/Sutu1fO8YzkmSxsagLkkauar6CvAXwO10Af0e4Dq2f2nd7V2+9wFchlfSzsygLkkauSR70l3hPoBuysqj6b5lenPbWlp3mGV6f1TgMrySdmIGdUnSbHgB8MWq2lhV3wc+CDybtrRuqzPd0rpstrTulpbv3dqyvpI0JxjUJUmz4XbgsCSPanPND6db2Wt7l9bdruV7x3BekjQ2Y//CI0nS3FdV1yS5BPg03TdRfwY4F/gn4KIkb2hlg0vrvqstrbuJLnhTVTe1FWNubu2cXFU/AEgytXzvLsDKgeV7JWlOMKhLkmZFVZ0BnLFZ8XYvrbu9y/dK0lzh1BdJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRD8ybdAUmSNBqXrjxqqOOW/eblI+6JpFHwirokSZLUQwZ1SZIkqYdmLagnWZlkQ5IbB8r2SrI6ya3t556tPEnOTrI2yeeSHDxwzPJW/9YkywfKn5nkhnbM2UmytdeQJEmSdiazeUX9fGDpZmWnAVdW1WLgyrYNcBSwuD1WAOdAF7qBM4BDgUOAMwaC9zmt7tRxS7fxGpIkSdJOY9aCelV9Eti0WfEy4IL2/ALgmIHyC6tzNbBHkv2AI4HVVbWpqu4GVgNL277dq+qqqirgws3amu41JEmSpJ3GuFd92beq1gNU1fok+7TyBcAdA/XWtbKtla+bpnxrr/EgSVbQXZXnSU960rDnJEmSdhK/+MG3DnXcP/3KK0bcE2nb+nIzaaYpqyHKt0tVnVtVS6pqyfz587f3cEmSJGnWjDuo39WmrdB+bmjl64D9B+otBO7cRvnCacq39hqSJEnSTmPcQX0VMLVyy3Lg0oHyE9vqL4cB97TpK1cARyTZs91EegRwRdt3b5LD2movJ27W1nSvIUmSJO00Zm2OepL3As8D9k6yjm71ljcCFyc5CbgdOK5Vvww4GlgL3Ae8DKCqNiV5PXBtq/e6qpq6QfXldCvL7AZc3h5s5TUkSZKkncasBfWqOmELuw6fpm4BJ2+hnZXAymnK1wAHTVP+9eleQ5IkSdqZjHvVF0mS5oT1bz916GP3+703jbAnkuaqvqz6IkmSJGmAQV2SJEnqIYO6JEmS1EMGdUmSJKmHDOqSJElSDxnUJUmSpB4yqEuSJEk9ZFCXJEmSesigLkmSJPWQQV2SJEnqIYO6JEmS1EMGdUmSJKmHDOqSJElSDxnUJUmSpB4yqEuSJEk9ZFCXJEmSesigLkmSJPWQQV2SJEnqIYO6JEmS1EMGdUmSJKmHDOqSJElSDxnUJUmzIskeSS5J8vkktyR5VpK9kqxOcmv7uWermyRnJ1mb5HNJDh5oZ3mrf2uS5QPlz0xyQzvm7CSZxHlK0mwxqEuSZstbgI9U1U8CTwduAU4DrqyqxcCVbRvgKGBxe6wAzgFIshdwBnAocAhwxlS4b3VWDBy3dAznJEljY1CXJI1ckt2B5wLnAVTV96rqG8Ay4IJW7QLgmPZ8GXBhda4G9kiyH3AksLqqNlXV3cBqYGnbt3tVXVVVBVw40JYkzQkGdUnSbPhxYCPwziSfSfKOJI8G9q2q9QDt5z6t/gLgjoHj17WyrZWvm6b8AZKsSLImyZqNGzeO5swkaUwM6pKk2TAPOBg4p6qeAXyLH01zmc5088triPIHFlSdW1VLqmrJ/Pnzt91rSeoRg7okaTasA9ZV1TVt+xK64H5Xm7ZC+7lhoP7+A8cvBO7cRvnCacolac4wqEuSRq6qvgrckeSprehw4GZgFTC1csty4NL2fBVwYlv95TDgnjY15grgiCR7tptIjwCuaPvuTXJYW+3lxIG2JGlOmDfpDkiS5qxXAO9JsitwG/AyugtEFyc5CbgdOK7VvQw4GlgL3NfqUlWbkrweuLbVe11VbWrPXw6cD+wGXN4ekjRnGNQlSbOiqq4Hlkyz6/Bp6hZw8hbaWQmsnKZ8DXDQDnZTknrLqS+SJElSDxnUJUmSpB4yqEuSJEk9ZFCXJEmSesibSSVJ0gP83buOHPrY33npFSPsifTQZlCXJEmSZmjDWz8+9LH7vOIXtqu+U18kSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeqhiQT1JH+Q5KYkNyZ5b5JHJjkgyTVJbk3yviS7trqPaNtr2/5FA+2c3sq/kOTIgfKlrWxtktPGf4aSJEnSjhl7UE+yAPh9YElVHQTsAhwPvAk4q6oWA3cDJ7VDTgLurqqnAGe1eiQ5sB33NGAp8PYkuyTZBXgbcBRwIHBCqytJkiTtNCY19WUesFuSecCjgPXA84FL2v4LgGPa82Vtm7b/8CRp5RdV1Xer6ovAWuCQ9lhbVbdV1feAi1pdSZIkaacx9qBeVV8B/gK4nS6g3wNcB3yjqu5v1dYBC9rzBcAd7dj7W/3HD5ZvdsyWyh8kyYoka5Ks2bhx446fnCRJkjQik5j6sifdFe4DgCcCj6abprK5mjpkC/u2t/zBhVXnVtWSqloyf/78bXVdkiRJGptJTH15AfDFqtpYVd8HPgg8G9ijTYUBWAjc2Z6vA/YHaPsfB2waLN/smC2VS5IkSTuNSQT124HDkjyqzTU/HLgZ+DhwbKuzHLi0PV/Vtmn7P1ZV1cqPb6vCHAAsBj4FXAssbqvI7Ep3w+mqMZyXJEmSNDIzCupJrpxJ2UxU1TV0N4V+Grih9eFc4FTg1UnW0s1BP68dch7w+Fb+auC01s5NwMV0If8jwMlV9YM2j/0U4ArgFuDiVleStAWjHOclSaMxb2s7kzySblWWvdvc8qn537vTzS8fSlWdAZyxWfFtdCu2bF73O8BxW2jnTODMacovAy4btn+S9FAxW+O8JGnHbTWoA78DvIpusL6OHw3g36Rbq1yStHNznJekntpqUK+qtwBvSfKKqnrrmPokSRoTx3lJ6q9tXVEHoKremuTZwKLBY6rqwlnqlyRpjBznJal/ZhTUk7wLeDJwPfCDVlyAA7gkzQGO85LUPzMK6sAS4MC2LKIkae5xnJeknpnpOuo3Ak+YzY5IkibKcV6SemamV9T3Bm5O8ingu1OFVfXiWemVJGncHOclqWdmGtT/dDY7IUmauD+ddAckSQ8001Vf/mW2OyJJmhzHeUnqn5mu+nIv3d3/ALsCDwe+VVW7z1bHJEnj4zgvSf0z0yvqjx3cTnIMcMis9EiSNHaO85JG7fL3fW2o4476b3uPuCc7r5mu+vIAVfWPwPNH3BdJUk84zkvS5M106suvDGw+jG69XdfalaQ5wnFekvpnpqu+/NLA8/uBLwHLRt4bSdKkOM5LUs/MdI76y2a7I5KkyXGcl6T+mdEc9SQLk3woyYYkdyX5QJKFs905SdJ4OM5LUv/M9GbSdwKrgCcCC4D/1cokSXOD47wk9cxMg/r8qnpnVd3fHucD82exX5Kk8XKcl6SemWlQ/1qS30iyS3v8BvD12eyYJGmsHOclqWdmGtR/E/g14KvAeuBYwBuPJGnucJyXpJ6Z6fKMrweWV9XdAEn2Av6CbmCXJO38HOcn6Nq/+6VtV5rGz/3O/xpxTyT1yUyvqP/M1OANUFWbgGfMTpckSRPgOC9JPTPToP6wJHtObbQrLTO9Gi9J6j/HeUnqmZkOwn8J/HuSS+i+UvrXgDNnrVeSpHFznJeknpnpN5NemGQN8HwgwK9U1c2z2jNJ0tg4zktS/8z4Y802YDtoS9Ic5TgvSf0y0znqkiRJksbIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPTSRoJ5kjySXJPl8kluSPCvJXklWJ7m1/dyz1U2Ss5OsTfK5JAcPtLO81b81yfKB8mcmuaEdc3aSTOI8JUmSpGFN6or6W4CPVNVPAk8HbgFOA66sqsXAlW0b4ChgcXusAM4BSLIXcAZwKHAIcMZUuG91Vgwct3QM5yRJkiSNzNiDepLdgecC5wFU1feq6hvAMuCCVu0C4Jj2fBlwYXWuBvZIsh9wJLC6qjZV1d3AamBp27d7VV1VVQVcONCWJEmStFOYxBX1Hwc2Au9M8pkk70jyaGDfqloP0H7u0+ovAO4YOH5dK9ta+bppyh8kyYoka5Ks2bhx446fmSRJkjQikwjq84CDgXOq6hnAt/jRNJfpTDe/vIYof3Bh1blVtaSqlsyfP3/rvZYkbbcku7SLMh9u2wckuabdW/S+JLu28ke07bVt/6KBNk5v5V9IcuRA+dJWtjbJ1t5HJGmnNImgvg5YV1XXtO1L6IL7XW3aCu3nhoH6+w8cvxC4cxvlC6cplySN3yvp7kOa8ibgrHY/0t3ASa38JODuqnoKcFarR5IDgeOBp9Hdb/T2Fv53Ad5Gdx/TgcAJra4kzRljD+pV9VXgjiRPbUWHAzcDq4CplVuWA5e256uAE9vqL4cB97SpMVcARyTZs91EegRwRdt3b5LD2movJw60JUkakyQLgV8E3tG2Azyf7gINPPh+pKn7lC4BDm/1lwEXVdV3q+qLwFq6BQQOAdZW1W1V9T3golZXkuaMeRN63VcA72kfed4GvIzuHw0XJzkJuB04rtW9DDiabnC+r9WlqjYleT1wbav3uqra1J6/HDgf2A24vD0kSeP118AfAY9t248HvlFV97ftwXuI/uu+o6q6P8k9rf4C4OqBNgeP2fw+pUM370CSFXSrgPGkJz1pB09HksZrIkG9qq4Hlkyz6/Bp6hZw8hbaWQmsnKZ8DXDQDnZTkjSkJC8CNlTVdUmeN1U8TdXaxr4tlU/3ifCD7keqqnOBcwGWLFky7f1KktRXk7qiLkma254DvDjJ0cAjgd3prrDvkWReu6o+eA/R1H1H65LMAx4HbGLL9yOxlXJJmhMM6pKkkauq04HTAdoV9T+sqpckeT9wLN2c8s3vR1oOXNX2f6yqKskq4B+S/BXwRLovsfsU3ZX2xUkOAL5Cd8Ppr4/p9CSN0U1/e9dQxz3td/cdcU/Gz6AuSRqnU4GLkrwB+Azty+/az3clWUt3Jf14gKq6KcnFdIsO3A+cXFU/AEhyCt3CArsAK6vqprGeiSTNMoO6JGlWVdUngE+057fRrdiyeZ3v8KNFBDbfdyZw5jTll9EtOCBJc9Ik1lGXJEmStA0GdUmSJKmHDOqSJElSDxnUJUmSpB6+Ech7AAARf0lEQVQyqEuSJEk9ZFCXJEmSesigLkmSJPWQQV2SJEnqIYO6JEmS1EMGdUmSJKmHDOqSJElSDxnUJUmSpB4yqEuSJEk9ZFCXJEmSesigLkmSJPWQQV2SJEnqIYO6JEmS1EMGdUmSJKmH5k26AzuDZ/6/F066C2Nx3ZtPnHQXJEmSZsVX//LzQx/7hNf85Ah7MnNeUZckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQwZ1SZIkqYcM6pIkSVIPGdQlSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQwZ1SZIkqYfmTboDkiRJ6oc3fWj90Mee+sv7jbAnAq+oS5IkSb1kUJckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQxML6kl2SfKZJB9u2wckuSbJrUnel2TXVv6Itr227V800MbprfwLSY4cKF/aytYmOW3c5yZJkiTtqEleUX8lcMvA9puAs6pqMXA3cFIrPwm4u6qeApzV6pHkQOB44GnAUuDtLfzvArwNOAo4EDih1ZUkSZJ2GhMJ6kkWAr8IvKNtB3g+cEmrcgFwTHu+rG3T9h/e6i8DLqqq71bVF4G1wCHtsbaqbquq7wEXtbqSJEnSTmNSV9T/Gvgj4Idt+/HAN6rq/ra9DljQni8A7gBo++9p9f+rfLNjtlQuSZIk7TTGHtSTvAjYUFXXDRZPU7W2sW97y6fry4oka5Ks2bhx41Z6LUmSJI3XJK6oPwd4cZIv0U1LeT7dFfY9ksxrdRYCd7bn64D9Adr+xwGbBss3O2ZL5Q9SVedW1ZKqWjJ//vwdPzNJkiRpRMYe1Kvq9KpaWFWL6G4G/VhVvQT4OHBsq7YcuLQ9X9W2afs/VlXVyo9vq8IcACwGPgVcCyxuq8js2l5j1RhOTZIkSRqZeduuMjanAhcleQPwGeC8Vn4e8K4ka+mupB8PUFU3JbkYuBm4Hzi5qn4AkOQU4ApgF2BlVd001jORJEmSdtBEg3pVfQL4RHt+G92KLZvX+Q5w3BaOPxM4c5ryy4DLRthVSZIkaaz8ZlJJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkkYuyf5JPp7kliQ3JXllK98ryeokt7afe7byJDk7ydokn0ty8EBby1v9W5MsHyh/ZpIb2jFnJ8n4z1SSZo9BXZI0G+4HXlNVPwUcBpyc5EDgNODKqloMXNm2AY4CFrfHCuAc6II9cAZwKHAIcMZUuG91Vgwct3QM5yVJY2NQlySNXFWtr6pPt+f3ArcAC4BlwAWt2gXAMe35MuDC6lwN7JFkP+BIYHVVbaqqu4HVwNK2b/equqqqCrhwoC1JmhMM6pKkWZVkEfAM4Bpg36paD12YB/Zp1RYAdwwctq6Vba183TTlm7/2iiRrkqzZuHHjKE5HksbGoC5JmjVJHgN8AHhVVX1za1WnKashyh9YUHVuVS2pqiXz58+fSZclqTcM6pKkWZHk4XQh/T1V9cFWfFebtkL7uaGVrwP2Hzh8IXDnNsoXTlMuSXOGQV2SNHJtBZbzgFuq6q8Gdq0CplZuWQ5cOlB+Ylv95TDgnjY15grgiCR7tptIjwCuaPvuTXJYe60TB9qSpDlh3qQ7IEmak54DvBS4Icn1reyPgTcCFyc5CbgdOK7tuww4GlgL3Ae8DKCqNiV5PXBtq/e6qtrUnr8cOB/YDbi8PSRpzjCoS5JGrqr+lennkQMcPk39Ak7eQlsrgZXTlK8BDtqBbkpSrzn1RZIkSeohg7okSZLUQwZ1SZIkqYcM6pIkSVIPGdQlSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQwZ1SZIkqYcM6pIkSVIPGdQlSZKkHjKoS5IkST1kUJckSZJ6aN64XzDJ/sCFwBOAHwLnVtVbkuwFvA9YBHwJ+LWqujtJgLcARwP3Af+9qj7d2loO/I/W9Buq6oJW/kzgfGA34DLglVVVYzlBSZI0573okvcMddyHj33JiHuiuWwSV9TvB15TVT8FHAacnORA4DTgyqpaDFzZtgGOAha3xwrgHIAW7M8ADgUOAc5Ismc75pxWd+q4pWM4L0mSJGlkxh7Uq2r91BXxqroXuAVYACwDLmjVLgCOac+XARdW52pgjyT7AUcCq6tqU1XdDawGlrZ9u1fVVe0q+oUDbUmSJEk7hYnOUU+yCHgGcA2wb1Wthy7MA/u0aguAOwYOW9fKtla+bpry6V5/RZI1SdZs3LhxR09HkiRJGpmJBfUkjwE+ALyqqr65tarTlNUQ5Q8urDq3qpZU1ZL58+dvq8uSJEnS2EwkqCd5OF1If09VfbAV39WmrdB+bmjl64D9Bw5fCNy5jfKF05RLkiRJO42xB/W2ist5wC1V9VcDu1YBy9vz5cClA+UnpnMYcE+bGnMFcESSPdtNpEcAV7R99yY5rL3WiQNtSZIkSTuFsS/PCDwHeClwQ5LrW9kfA28ELk5yEnA7cFzbdxnd0oxr6ZZnfBlAVW1K8nrg2lbvdVW1qT1/OT9anvHy9pAkSZJ2GmMP6lX1r0w/jxzg8GnqF3DyFtpaCaycpnwNcNAOdFOSJEmaqElcUZckaWI2nvPuoY+d//LfGGFPJGnrJro8oyRJkqTpGdQlSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQ66jLkmStJM79gOfHvrYS3714BH2RKPkFXVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT1kEFdkiRJ6iGDuiRJktRDBnVJkiSphwzqkiRJUg8Z1CVJkqQeMqhLkiRJPWRQlyRJknrIoC5JkiT10LxJd0CSJOmhatkllw997KXHHjXCnqiPvKIuSZIk9ZBBXZIkSeohg7okSZLUQwZ1SZIkqYcM6pIkSVIPGdQlSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeohg7okSZLUQwZ1SZIkqYcM6pIkSVIPGdQlSZKkHjKoS5IkST1kUJckSZJ6yKAuSZIk9ZBBXZIkSeqhORvUkyxN8oUka5OcNun+SJJGz7Fe0lw2b9IdmA1JdgHeBrwQWAdcm2RVVd082Z7poew5b33OpLswFv/2in+bdBf0EOFYL2mum6tX1A8B1lbVbVX1PeAiYNmE+yRJGi3HeklzWqpq0n0YuSTHAkur6rfa9kuBQ6vqlM3qrQBWtM2nAl8Ya0e3bm/ga5PuRM/5O9o2f0fb1rff0Y9V1fxJd2JnMJOxfshxflR/E7YznnZG2ZbtjK+th3o7Mxrr5+TUFyDTlD3oXyRVdS5w7ux3Z/slWVNVSybdjz7zd7Rt/o62zd/RTm2bY/0w4/yo/iZsZzzt9LFPc7WdPvZprrYzZa5OfVkH7D+wvRC4c0J9kSTNDsd6SXPaXA3q1wKLkxyQZFfgeGDVhPskSRotx3pJc9qcnPpSVfcnOQW4AtgFWFlVN024W9url1Nyesbf0bb5O9o2f0c7qVkc60f1N2E742lnlG3Zzvjasp0ZmJM3k0qSJEk7u7k69UWSJEnaqRnUJUmSpB4yqPdMkkcm+VSSzya5KcmfTbpPfZRkjySXJPl8kluSPGvSfeqTJK9McmP7G3rVpPvTB0lWJtmQ5MaBsj9N8pUk17fH0ZPsoyYrydIkX0iyNslpO9DOg/7Whmhj/yQfb+PbTUleuQNtjfR9JckuST6T5MM70MaXktzQ/r9bswPtjOS9IMlTB8aB65N8c9ixM8kftN/zjUnem+SRQ7Yz1Di+hbFurySrk9zafu45ZDvHtf78MMmMlyDcQltvbv/dPpfkQ0n2GLKd17c2rk/y0SRPHKadgX1/mKSS7D1kf0b6vmJQ75/vAs+vqqcDPwssTXLYhPvUR28BPlJVPwk8Hbhlwv3pjSQHAb9N962NTwdelGTxZHvVC+cDS6cpP6uqfrY9Lhtzn9QTSXYB3gYcBRwInJDkwCGbO5/p/9a2x/3Aa6rqp4DDgJN3oD+jfl95JaMZc3+h/X+3I2tOj+S9oKq+MDUOAM8E7gM+tL3tJFkA/D6wpKoOorvJ+fgh2tmRcfx8Hvz3dxpwZVUtBq5s28O0cyPwK8AnZ9iXrbW1Gjioqn4G+D/A6UO28+aq+pn23+7DwJ8M2Q5J9gdeCNw+gza22A4jfF8xqPdMdf6zbT68Pbzjd0CS3YHnAucBVNX3quobk+1Vr/wUcHVV3VdV9wP/AvzyhPs0cVX1SWDTpPuh3joEWFtVt1XV94CLgGXDNDSKv7WqWl9Vn27P76ULoAuGbGtk7ytJFgK/CLxjmONHaRbfCw4H/qOqvjzk8fOA3ZLMAx7FcGv7Dz2Ob+HvbxlwQXt+AXDMMO1U1S1Vtd3f4r6Ftj7azg3garrvQRimnW8ObD6aGfxtb+X/0bOAP5pJG9toZ2QM6j3UPla8HtgArK6qaybdp575cWAj8M728es7kjx60p3qkRuB5yZ5fJJHAUfzwC+F0QOd0j42XTmTj4M1Zy0A7hjYXseQwXjUkiwCngEM/V4wwveVv6YLMj8cti9NAR9Ncl2SFUO2MVvvBccD7x3mwKr6CvAXdFdk1wP3VNVHh2hq1OP4vlW1vvVxPbDPDrQ1G34TuHzYg5OcmeQO4CXM7Ir6dG28GPhKVX122H4MGNn7ikG9h6rqB+0jnIXAIe0jMP3IPOBg4JyqegbwLWb2Md5DQlXdAryJ7mPFjwCfpfsYXQ92DvBkuukA64G/nGx3NEGZpmzin2YmeQzwAeBVm1053C6jeF9J8iJgQ1VdN2w/Bjynqg6mm2p0cpLnDtHGyN8L0n1x1ouB9w95/J50V68PAJ4IPDrJb2xvOw+lcTzJa+nO7T3DtlFVr62q/VsbpwzRh0cBr2XIkL+Zkb6vGNR7rH2E9wl2fK7jXLMOWDdwRegSusFaTVWdV1UHV9Vz6T6Wu3XSfeqjqrqrBZgfAn9PN/1BD03reOAVy4UMN2VhZJI8nC6kv6eqPjiKNnfwfeU5wIuTfIluatDzk7x7yH7c2X5uoJsLPsz/e7PxXnAU8OmqumvI418AfLGqNlbV94EPAs8epqERj+N3JdkPoP3csANtjUyS5cCLgJfUaL7Y5x+AXx3iuCfT/ePqs+3veyHw6SRP2N6GRv2+YlDvmSTzp+58TrIb3f/0n59sr/qlqr4K3JHkqa3ocODmCXapd5Ls034+ie7Gn6E+xp3rpt64ml+m+7hZD03XAouTHNCuqh4PrJpUZ5KEbu71LVX1VzvY1kjeV6rq9KpaWFWL6H4/H6uq7b5anOTRSR479Rw4giH+35ul94IT2LHx8nbgsCSPav8ND2fIG1xHPI6vApa358uBS3egrZFIshQ4FXhxVd23A+0M3mT7Yob7276hqvapqkXt73sdcHD7G9ve/oz0fWXejhysWbEfcEFbgeBhwMVVNfQSWHPYK4D3tDfU24CXTbg/ffOBJI8Hvg+cXFV3T7pDk5bkvcDzgL2TrAPOAJ6X5Gfppjh8CfidiXVQE1VV9yc5BbiCbqWOlVV10zBtTfe3VlXnbWczzwFeCtzQ5pYD/PGQK0j07X1lX+BDXY5lHvAPVfWRIdsa2XtBm/7wQnZgHKiqa5JcAnyabjrHZxj+K+WHGse3MNa9Ebg4yUl0/5g4bsh2NgFvBeYD/5Tk+qo6csi2TgceAaxufwtXV9XvDtHO0e0faz8EvgxstY0ttTPE/6NjeV/JaD5pkCRJkjRKTn2RJEmSesigLkmSJPWQQV2SJEnqIYO6JEmS1EMGdUmSJKmHDOqSJElSDxnUJUmSpB76v1Sp+DOhXoF0AAAAAElFTkSuQmCC
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is overrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>
<span class="n">centroid_6</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">pca2</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">kmean_model</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="mi">5</span><span class="p">]))</span>
<span class="n">overrepresent</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">centroid_6</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">aligned_customer</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span>
<span class="n">overrepresent</span>
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<pre>CJT_GESAMTTYP_6.0                 -7.834709e-03
LP_FAMILIE_FEIN_3.0               -5.709012e-03
wealth_Less Affluent Households   -2.112364e-03
LP_STATUS_GROB_3.0                -1.098698e-03
CJT_GESAMTTYP_4.0                 -1.392440e-04
CAMEO_DEU_2015_2A                  5.740000e-07
CAMEO_DEU_2015_4C                  7.297677e-05
CAMEO_DEU_2015_1E                  1.063559e-04
CAMEO_DEU_2015_5B                  1.244167e-04
CAMEO_DEU_2015_6E                  1.491066e-04
CAMEO_DEU_2015_2B                  1.505723e-04
CAMEO_DEU_2015_4D                  2.570500e-04
CAMEO_DEU_2015_1C                  2.756572e-04
CAMEO_DEU_2015_5C                  2.985050e-04
CAMEO_DEU_2015_4A                  4.640696e-04
nation_ W                          5.421518e-04
CAMEO_DEU_2015_4B                  5.557555e-04
CAMEO_DEU_2015_3C                  6.132411e-04
CAMEO_DEU_2015_4E                  6.762072e-04
LP_FAMILIE_FEIN_1.0                1.003190e-03
CAMEO_DEU_2015_7C                  1.064466e-03
HH_EINKOMMEN_SCORE                 1.067983e-03
CAMEO_DEU_2015_7D                  1.132588e-03
CAMEO_DEU_2015_3D                  1.169080e-03
LP_STATUS_GROB_5.0                 1.459690e-03
CAMEO_DEU_2015_7A                  1.470173e-03
CAMEO_DEU_2015_5F                  1.536175e-03
CAMEO_DEU_2015_6F                  1.606044e-03
LP_STATUS_FEIN_1.0                 2.016920e-03
CAMEO_DEU_2015_6A                  2.589330e-03
                                       ...     
KKK                                3.402516e+00
GEBAEUDETYP_RASTER                 3.417313e+00
ORTSGR_KLS9                        3.641440e+00
GFK_URLAUBERTYP_1.0                3.659889e+00
PLZ8_GBZ                           3.754130e+00
SEMIO_VERT                         3.847988e+00
FINANZ_UNAUFFAELLIGER              3.984363e+00
WOHNDAUER_2008                     4.023365e+00
FINANZ_ANLEGER                     4.201158e+00
FINANZ_HAUSBAUER                   4.231218e+00
SEMIO_KRIT                         4.236063e+00
SEMIO_KAEM                         4.443960e+00
SEMIO_DOM                          4.584231e+00
SEMIO_SOZ                          4.648305e+00
FINANZ_SPARER                      4.672115e+00
SEMIO_MAT                          4.683061e+00
KBA13_ANZAHL_PKW                   4.938526e+00
INNENSTADT                         5.053472e+00
SEMIO_FAM                          5.082404e+00
SEMIO_TRADV                        5.135223e+00
SEMIO_RAT                          5.140816e+00
SEMIO_KULT                         5.249232e+00
W_KEIT_KIND_HH                     5.659302e+00
SEMIO_REL                          5.681722e+00
SEMIO_PFLICHT                      5.822876e+00
RELAT_AB                           6.786540e+00
ANZ_HAUSHALTE_AKTIV                6.972103e+00
ANZ_HH_TITEL                       1.536060e+01
PLZ8_ANTG1                         5.244605e+02
KBA05_ANTG1                        1.992497e+03
Length: 212, dtype: float64</pre>
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<div class="prompt input_prompt">In&nbsp;[116]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># What kinds of people are part of a cluster that is underrepresented in the</span>
<span class="c1"># customer data compared to the general population?</span>
<span class="n">centroid_15</span> <span class="o">=</span> <span class="n">scaler</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">pca2</span><span class="o">.</span><span class="n">inverse_transform</span><span class="p">(</span><span class="n">kmean_model</span><span class="o">.</span><span class="n">cluster_centers_</span><span class="p">[</span><span class="mi">14</span><span class="p">]))</span>
<span class="n">underpresent</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">data</span> <span class="o">=</span> <span class="n">centroid_15</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="n">aligned_customer</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span><span class="o">.</span><span class="n">sort_values</span><span class="p">()</span>
<span class="n">underpresent</span>
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<pre>life_stage_Elders In Retirement             -0.001520
wealth_Prosperous Households                -0.001076
life_stage_Young Couples With Children      -0.001036
GEBAEUDETYP_5.0                             -0.001024
LP_STATUS_FEIN_5.0                          -0.000924
LP_STATUS_FEIN_7.0                          -0.000810
LP_STATUS_FEIN_6.0                          -0.000710
SOHO_KZ                                     -0.000588
wealth_Poorer Households                    -0.000582
LP_STATUS_FEIN_8.0                          -0.000543
CAMEO_DEU_2015_5A                           -0.000448
LP_STATUS_FEIN_9.0                          -0.000443
CAMEO_DEU_2015_7B                           -0.000406
LP_STATUS_FEIN_10.0                         -0.000354
CAMEO_DEU_2015_5F                           -0.000348
CAMEO_DEU_2015_8A                           -0.000334
CAMEO_DEU_2015_4C                           -0.000330
OST_WEST_KZ_O                               -0.000294
FINANZTYP_3                                 -0.000286
CAMEO_DEU_2015_5D                           -0.000277
FINANZTYP_2                                 -0.000251
CAMEO_DEU_2015_6B                           -0.000228
CAMEO_DEU_2015_5E                           -0.000201
CAMEO_DEU_2015_9B                           -0.000200
CAMEO_DEU_2015_9C                           -0.000199
CAMEO_DEU_2015_6C                           -0.000132
CAMEO_DEU_2015_5C                           -0.000131
CAMEO_DEU_2015_4E                           -0.000128
CJT_GESAMTTYP_3.0                           -0.000122
CAMEO_DEU_2015_5B                           -0.000101
                                             ...     
RETOURTYP_BK_S                               3.522324
SEMIO_REL                                    3.567914
SEMIO_MAT                                    3.620197
SEMIO_TRADV                                  3.635560
PLZ8_GBZ                                     3.656122
SEMIO_RAT                                    3.685985
FINANZ_VORSORGER                             3.709642
SEMIO_PFLICHT                                3.717153
SEMIO_FAM                                    3.765213
SEMIO_KULT                                   3.821916
WOHNDAUER_2008                               3.830783
ARBEIT                                       3.870762
BALLRAUM                                     3.894385
EWDICHTE                                     3.918571
FINANZ_MINIMALIST                            3.923112
KKK                                          3.954999
INNENSTADT                                   3.964006
ONLINE_AFFINITAET                            3.976548
SEMIO_SOZ                                    4.219052
SEMIO_KAEM                                   4.224727
SEMIO_KRIT                                   4.521266
SEMIO_VERT                                   4.528304
GEBAEUDETYP_RASTER                           4.594399
SEMIO_ERL                                    4.665135
SEMIO_DOM                                    4.693445
SEMIO_LUST                                   4.787234
RELAT_AB                                     5.156523
ANZ_HAUSHALTE_AKTIV                          8.227628
PLZ8_ANTG1                                 683.557711
KBA05_ANTG1                               1992.894545
Length: 212, dtype: float64</pre>
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<h3 id="Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">Discussion 3.3: Compare Customer Data to Demographics Data<a class="anchor-link" href="#Discussion-3.3:-Compare-Customer-Data-to-Demographics-Data">&#182;</a></h3><p>(Double-click this cell and replace this text with your own text, reporting findings and conclusions from the clustering analysis. Can we describe segments of the population that are relatively popular with the mail-order company, or relatively unpopular with the company?)</p>
<p>This segment of population that is overrepresented don't actively shopping with the mail-order company. The factor that impact their grouping is mostly driven by people's daily needs that live in the area. Thus the more share of the house or more house holders have positive impact on this group of people. This group of people don't earn much, and not very likely to have kids, and may not very social.</p>
<p>The segment of population that is underrepresented share some similar characteristics with the overrepresented one, such as driven by basic daily need and residential area, or the person tend to be a household. This group of people may have low affinity as to personality, but have higher income and living condition.</p>

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<blockquote><p>Congratulations on making it this far in the project! Before you finish, make sure to check through the entire notebook from top to bottom to make sure that your analysis follows a logical flow and all of your findings are documented in <strong>Discussion</strong> cells. Once you've checked over all of your work, you should export the notebook as an HTML document to submit for evaluation. You can do this from the menu, navigating to <strong>File -&gt; Download as -&gt; HTML (.html)</strong>. You will submit both that document and this notebook for your project submission.</p>
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