data-wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Wrangling\n",
    "- Processo de preparação de dados, também conhecido como 'Data Preparation'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento dos dados obtidos\n",
    "- Coletar, limpar, normalizar, combinar, estruturar e organizar os dados que serão analisados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lendo dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3</th>\n",
       "      <th>?</th>\n",
       "      <th>alfa-romero</th>\n",
       "      <th>gas</th>\n",
       "      <th>std</th>\n",
       "      <th>two</th>\n",
       "      <th>convertible</th>\n",
       "      <th>rwd</th>\n",
       "      <th>front</th>\n",
       "      <th>88.60</th>\n",
       "      <th>...</th>\n",
       "      <th>130</th>\n",
       "      <th>mpfi</th>\n",
       "      <th>3.47</th>\n",
       "      <th>2.68</th>\n",
       "      <th>9.00</th>\n",
       "      <th>111</th>\n",
       "      <th>5000</th>\n",
       "      <th>21</th>\n",
       "      <th>27</th>\n",
       "      <th>13495</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>?</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.5</td>\n",
       "      <td>110</td>\n",
       "      <td>5500</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>15250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   3    ?  alfa-romero  gas  std   two  convertible  rwd  front  88.60  ...  \\\n",
       "0  3    ?  alfa-romero  gas  std   two  convertible  rwd  front   88.6  ...   \n",
       "1  1    ?  alfa-romero  gas  std   two    hatchback  rwd  front   94.5  ...   \n",
       "2  2  164         audi  gas  std  four        sedan  fwd  front   99.8  ...   \n",
       "3  2  164         audi  gas  std  four        sedan  4wd  front   99.4  ...   \n",
       "4  2    ?         audi  gas  std   two        sedan  fwd  front   99.8  ...   \n",
       "\n",
       "   130  mpfi  3.47  2.68  9.00  111  5000  21  27  13495  \n",
       "0  130  mpfi  3.47  2.68   9.0  111  5000  21  27  16500  \n",
       "1  152  mpfi  2.68  3.47   9.0  154  5000  19  26  16500  \n",
       "2  109  mpfi  3.19  3.40  10.0  102  5500  24  30  13950  \n",
       "3  136  mpfi  3.19  3.40   8.0  115  5500  18  22  17450  \n",
       "4  136  mpfi  3.19  3.40   8.5  110  5500  19  25  15250  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# via arquivo local\n",
    "data = './imports-85.data'\n",
    "df = pd.read_csv(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Lendo colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attribute Information:     \n",
      "     Attribute:                Attribute Range:\n",
      "     ------------------        -----------------------------------------------\n",
      "  1. symboling:                -3, -2, -1, 0, 1, 2, 3.\n",
      "  2. normalized-losses:        continuous from 65 to 256.\n",
      "  3. make:                     alfa-romero, audi, bmw, chevrolet, dodge, honda,\n",
      "                               isuzu, jaguar, mazda, mercedes-benz, mercury,\n",
      "                               mitsubishi, nissan, peugot, plymouth, porsche,\n",
      "                               renault, saab, subaru, toyota, volkswagen, volvo\n",
      "  4. fuel-type:                diesel, gas.\n",
      "  5. aspiration:               std, turbo.\n",
      "  6. num-of-doors:             four, two.\n",
      "  7. body-style:               hardtop, wagon, sedan, hatchback, convertible.\n",
      "  8. drive-wheels:             4wd, fwd, rwd.\n",
      "  9. engine-location:          front, rear.\n",
      " 10. wheel-base:               continuous from 86.6 120.9.\n",
      " 11. length:                   continuous from 141.1 to 208.1.\n",
      " 12. width:                    continuous from 60.3 to 72.3.\n",
      " 13. height:                   continuous from 47.8 to 59.8.\n",
      " 14. curb-weight:              continuous from 1488 to 4066.\n",
      " 15. engine-type:              dohc, dohcv, l, ohc, ohcf, ohcv, rotor.\n",
      " 16. num-of-cylinders:         eight, five, four, six, three, twelve, two.\n",
      " 17. engine-size:              continuous from 61 to 326.\n",
      " 18. fuel-system:              1bbl, 2bbl, 4bbl, idi, mfi, mpfi, spdi, spfi.\n",
      " 19. bore:                     continuous from 2.54 to 3.94.\n",
      " 20. stroke:                   continuous from 2.07 to 4.17.\n",
      " 21. compression-ratio:        continuous from 7 to 23.\n",
      " 22. horsepower:               continuous from 48 to 288.\n",
      " 23. peak-rpm:                 continuous from 4150 to 6600.\n",
      " 24. city-mpg:                 continuous from 13 to 49.\n",
      " 25. highway-mpg:              continuous from 16 to 54.\n",
      " 26. price:                    continuous from 5118 to 45400.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# via arquivo local\n",
    "with open('./imports-85.names', 'r') as colunas:\n",
    "    print(colunas.read())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subistituindo cabeçalho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "colunas = [\"symboling\",\"normalized-losses\",\"make\",\"fuel-type\",\"aspiration\", \"num-of-doors\",\"body-style\",\n",
    "         \"drive-wheels\",\"engine-location\",\"wheel-base\", \"length\",\"width\",\"height\",\"curb-weight\",\"engine-type\",\n",
    "         \"num-of-cylinders\", \"engine-size\",\"fuel-system\",\"bore\",\"stroke\",\"compression-ratio\",\"horsepower\",\n",
    "         \"peak-rpm\",\"city-mpg\",\"highway-mpg\",\"price\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111</td>\n",
       "      <td>5000</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>?</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154</td>\n",
       "      <td>5000</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102</td>\n",
       "      <td>5500</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115</td>\n",
       "      <td>5500</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3                 ?  alfa-romero       gas        std          two   \n",
       "1          3                 ?  alfa-romero       gas        std          two   \n",
       "2          1                 ?  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "1         mpfi  3.47    2.68               9.0        111      5000       21   \n",
       "2         mpfi  2.68    3.47               9.0        154      5000       19   \n",
       "3         mpfi  3.19    3.40              10.0        102      5500       24   \n",
       "4         mpfi  3.19    3.40               8.0        115      5500       18   \n",
       "\n",
       "  highway-mpg  price  \n",
       "0          27  13495  \n",
       "1          27  16500  \n",
       "2          26  16500  \n",
       "3          30  13950  \n",
       "4          22  17450  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data, names = colunas)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(205, 26)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analisando tipos dos valores por coluna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                  object\n",
       "stroke                object\n",
       "compression-ratio    float64\n",
       "horsepower            object\n",
       "peak-rpm              object\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Alterando o tipo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "colunas = ['bore','stroke','horsepower','peak-rpm','price']\n",
    "df[colunas] = df[colunas].apply(pd.to_numeric, errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling              int64\n",
       "normalized-losses     object\n",
       "make                  object\n",
       "fuel-type             object\n",
       "aspiration            object\n",
       "num-of-doors          object\n",
       "body-style            object\n",
       "drive-wheels          object\n",
       "engine-location       object\n",
       "wheel-base           float64\n",
       "length               float64\n",
       "width                float64\n",
       "height               float64\n",
       "curb-weight            int64\n",
       "engine-type           object\n",
       "num-of-cylinders      object\n",
       "engine-size            int64\n",
       "fuel-system           object\n",
       "bore                 float64\n",
       "stroke               float64\n",
       "compression-ratio    float64\n",
       "horsepower           float64\n",
       "peak-rpm             float64\n",
       "city-mpg               int64\n",
       "highway-mpg            int64\n",
       "price                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tratamento de Dados Nulos\n",
    "- Valores numéricos inexistentes representados pelo valor de ponto flutuante **NaN**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Substituindo '?' por NaN (Not a Number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>fwd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.8</td>\n",
       "      <td>...</td>\n",
       "      <td>109</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>10.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>24</td>\n",
       "      <td>30</td>\n",
       "      <td>13950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>164</td>\n",
       "      <td>audi</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>four</td>\n",
       "      <td>sedan</td>\n",
       "      <td>4wd</td>\n",
       "      <td>front</td>\n",
       "      <td>99.4</td>\n",
       "      <td>...</td>\n",
       "      <td>136</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.19</td>\n",
       "      <td>3.40</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>5500.0</td>\n",
       "      <td>18</td>\n",
       "      <td>22</td>\n",
       "      <td>17450.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "3          2               164         audi       gas        std         four   \n",
       "4          2               164         audi       gas        std         four   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "3        sedan          fwd           front        99.8  ...          109   \n",
       "4        sedan          4wd           front        99.4  ...          136   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "1         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "2         mpfi  2.68    3.47               9.0      154.0    5000.0       19   \n",
       "3         mpfi  3.19    3.40              10.0      102.0    5500.0       24   \n",
       "4         mpfi  3.19    3.40               8.0      115.0    5500.0       18   \n",
       "\n",
       "   highway-mpg    price  \n",
       "0           27  13495.0  \n",
       "1           27  16500.0  \n",
       "2           26  16500.0  \n",
       "3           30  13950.0  \n",
       "4           22  17450.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace('?', np.nan, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Analisando Dados Nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling  normalized-losses   make  fuel-type  aspiration  num-of-doors  \\\n",
       "0      False               True  False      False       False         False   \n",
       "1      False               True  False      False       False         False   \n",
       "2      False               True  False      False       False         False   \n",
       "3      False              False  False      False       False         False   \n",
       "4      False              False  False      False       False         False   \n",
       "\n",
       "   body-style  drive-wheels  engine-location  wheel-base  ...  engine-size  \\\n",
       "0       False         False            False       False  ...        False   \n",
       "1       False         False            False       False  ...        False   \n",
       "2       False         False            False       False  ...        False   \n",
       "3       False         False            False       False  ...        False   \n",
       "4       False         False            False       False  ...        False   \n",
       "\n",
       "   fuel-system   bore  stroke  compression-ratio  horsepower  peak-rpm  \\\n",
       "0        False  False   False              False       False     False   \n",
       "1        False  False   False              False       False     False   \n",
       "2        False  False   False              False       False     False   \n",
       "3        False  False   False              False       False     False   \n",
       "4        False  False   False              False       False     False   \n",
       "\n",
       "   city-mpg  highway-mpg  price  \n",
       "0     False        False  False  \n",
       "1     False        False  False  \n",
       "2     False        False  False  \n",
       "3     False        False  False  \n",
       "4     False        False  False  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_nulos = df.isnull()\n",
    "dados_nulos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantidade de dados nulos por colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "normalized-losses    41\n",
       "price                 4\n",
       "stroke                4\n",
       "bore                  4\n",
       "peak-rpm              2\n",
       "horsepower            2\n",
       "num-of-doors          2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qtd_nulos = df.isna().sum()\n",
    "qtd_nulos[qtd_nulos > 0].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling             0\n",
       "normalized-losses    41\n",
       "make                  0\n",
       "fuel-type             0\n",
       "aspiration            0\n",
       "num-of-doors          2\n",
       "body-style            0\n",
       "drive-wheels          0\n",
       "engine-location       0\n",
       "wheel-base            0\n",
       "length                0\n",
       "width                 0\n",
       "height                0\n",
       "curb-weight           0\n",
       "engine-type           0\n",
       "num-of-cylinders      0\n",
       "engine-size           0\n",
       "fuel-system           0\n",
       "bore                  4\n",
       "stroke                4\n",
       "compression-ratio     0\n",
       "horsepower            2\n",
       "peak-rpm              2\n",
       "city-mpg              0\n",
       "highway-mpg           0\n",
       "price                 4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quantidade / porcentagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_nulos</th>\n",
       "      <th>Porcentagem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>normalized-losses</th>\n",
       "      <td>41</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>4</td>\n",
       "      <td>0.019512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke</th>\n",
       "      <td>4</td>\n",
       "      <td>0.019512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bore</th>\n",
       "      <td>4</td>\n",
       "      <td>0.019512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>peak-rpm</th>\n",
       "      <td>2</td>\n",
       "      <td>0.009756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>horsepower</th>\n",
       "      <td>2</td>\n",
       "      <td>0.009756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num-of-doors</th>\n",
       "      <td>2</td>\n",
       "      <td>0.009756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Total_nulos  Porcentagem\n",
       "normalized-losses           41     0.200000\n",
       "price                        4     0.019512\n",
       "stroke                       4     0.019512\n",
       "bore                         4     0.019512\n",
       "peak-rpm                     2     0.009756\n",
       "horsepower                   2     0.009756\n",
       "num-of-doors                 2     0.009756"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_dados_nulos = df.isnull().sum().sort_values(ascending=False)\n",
    "porcentagem = (df.isnull().sum()/df.isnull().count()).sort_values(ascending=False)\n",
    "df_nulos = pd.concat([total_dados_nulos, porcentagem], axis=1, keys=['Total_nulos', 'Porcentagem'])\n",
    "df_nulos.query('Total_nulos != 0', inplace = True)\n",
    "df_nulos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotando porcentagem de dados nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Porcentagem de valores nulos')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "sns.barplot(x = df_nulos.index, y = df_nulos['Porcentagem'] * 100)\n",
    "plt.xlabel('Colunas', fontsize=12)\n",
    "plt.ylabel('Porcentagem', fontsize=12)\n",
    "plt.title('Porcentagem de valores nulos', fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Métodos de limpeza de dados Nulos\n",
    "Alguns métodos de limpeza de dados nulos\n",
    "\n",
    "- fillna(method='bfill')\n",
    "- fillna(method='ffill')\n",
    "- dropna(how='any')\n",
    "- fillna(mean)\n",
    "- fillna(median)\n",
    "- fillna(freq)\n",
    "- fillna('Valor fixo')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Método 'bfill'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         0\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sem_nulos = df.fillna(method='bfill')\n",
    "df_sem_nulos.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Método 'ffill'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    3\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         0\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sem_nulos2 = df.fillna(method='ffill')\n",
    "df_sem_nulos2.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retirada de todos os dados nulos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         0\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sem_nulos3 = df.dropna(axis = 0, how='any')\n",
    "df_sem_nulos3.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pela média dos valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         2\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = ['normalized-losses','bore','stroke','horsepower','peak-rpm','price']\n",
    "df_mean = df[colunas].astype('float64').mean()\n",
    "df_sem_nulos4 = df.fillna(df_mean)\n",
    "df_sem_nulos4.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pela mediana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         2\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colunas = ['normalized-losses','bore','stroke','horsepower','peak-rpm','price']\n",
    "df_median = df[colunas].astype('float64').median()\n",
    "df_sem_nulos5 = df.fillna(df_median)\n",
    "df_sem_nulos5.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### pela frequencia de valores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "four    114\n",
       "two      89\n",
       "Name: num-of-doors, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'four'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].value_counts().idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "symboling            0\n",
       "normalized-losses    0\n",
       "make                 0\n",
       "fuel-type            0\n",
       "aspiration           0\n",
       "num-of-doors         0\n",
       "body-style           0\n",
       "drive-wheels         0\n",
       "engine-location      0\n",
       "wheel-base           0\n",
       "length               0\n",
       "width                0\n",
       "height               0\n",
       "curb-weight          0\n",
       "engine-type          0\n",
       "num-of-cylinders     0\n",
       "engine-size          0\n",
       "fuel-system          0\n",
       "bore                 0\n",
       "stroke               0\n",
       "compression-ratio    0\n",
       "horsepower           0\n",
       "peak-rpm             0\n",
       "city-mpg             0\n",
       "highway-mpg          0\n",
       "price                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sem_nulos5 = df.fillna(df['num-of-doors'].value_counts().idxmax())\n",
    "df_sem_nulos5.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### por um valor fixo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['num-of-doors'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sem_nulos6 = df.fillna('Não informado')\n",
    "df_sem_nulos6['num-of-doors'].isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformando Dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>symboling</th>\n",
       "      <th>normalized-losses</th>\n",
       "      <th>make</th>\n",
       "      <th>fuel-type</th>\n",
       "      <th>aspiration</th>\n",
       "      <th>num-of-doors</th>\n",
       "      <th>body-style</th>\n",
       "      <th>drive-wheels</th>\n",
       "      <th>engine-location</th>\n",
       "      <th>wheel-base</th>\n",
       "      <th>...</th>\n",
       "      <th>engine-size</th>\n",
       "      <th>fuel-system</th>\n",
       "      <th>bore</th>\n",
       "      <th>stroke</th>\n",
       "      <th>compression-ratio</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>peak-rpm</th>\n",
       "      <th>city-mpg</th>\n",
       "      <th>highway-mpg</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>13495.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>convertible</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>88.6</td>\n",
       "      <td>...</td>\n",
       "      <td>130</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>3.47</td>\n",
       "      <td>2.68</td>\n",
       "      <td>9.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>21</td>\n",
       "      <td>27</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>alfa-romero</td>\n",
       "      <td>gas</td>\n",
       "      <td>std</td>\n",
       "      <td>two</td>\n",
       "      <td>hatchback</td>\n",
       "      <td>rwd</td>\n",
       "      <td>front</td>\n",
       "      <td>94.5</td>\n",
       "      <td>...</td>\n",
       "      <td>152</td>\n",
       "      <td>mpfi</td>\n",
       "      <td>2.68</td>\n",
       "      <td>3.47</td>\n",
       "      <td>9.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>16500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   symboling normalized-losses         make fuel-type aspiration num-of-doors  \\\n",
       "0          3               NaN  alfa-romero       gas        std          two   \n",
       "1          3               NaN  alfa-romero       gas        std          two   \n",
       "2          1               NaN  alfa-romero       gas        std          two   \n",
       "\n",
       "    body-style drive-wheels engine-location  wheel-base  ...  engine-size  \\\n",
       "0  convertible          rwd           front        88.6  ...          130   \n",
       "1  convertible          rwd           front        88.6  ...          130   \n",
       "2    hatchback          rwd           front        94.5  ...          152   \n",
       "\n",
       "   fuel-system  bore  stroke compression-ratio horsepower  peak-rpm city-mpg  \\\n",
       "0         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "1         mpfi  3.47    2.68               9.0      111.0    5000.0       21   \n",
       "2         mpfi  2.68    3.47               9.0      154.0    5000.0       19   \n",
       "\n",
       "   highway-mpg    price  \n",
       "0           27  13495.0  \n",
       "1           27  16500.0  \n",
       "2           26  16500.0  \n",
       "\n",
       "[3 rows x 26 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city-mpg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city-mpg\n",
       "0        21\n",
       "1        21\n",
       "2        19\n",
       "3        24\n",
       "4        18"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['city-mpg']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformando 'Miles per Gallon (MPG) ' para 'Litros por Quilometros (L/km)' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['city-L/100km'] = 235/df['city-mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.190476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12.368421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9.791667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city-L/100km\n",
       "0     11.190476\n",
       "1     11.190476\n",
       "2     12.368421\n",
       "3      9.791667\n",
       "4     13.055556"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['city-L/100km']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['highway-L/100km'] = 235/df['highway-mpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highway-L/100km</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.038462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.681818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   highway-L/100km\n",
       "0         8.703704\n",
       "1         8.703704\n",
       "2         9.038462\n",
       "3         7.833333\n",
       "4        10.681818"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['highway-L/100km']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Normalização/Padronização de Dados\n",
    "- Simple Scaling\n",
    "- Min-max \n",
    "- Z-score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizando dados originais das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>168.8</td>\n",
       "      <td>64.1</td>\n",
       "      <td>48.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>171.2</td>\n",
       "      <td>65.5</td>\n",
       "      <td>52.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.2</td>\n",
       "      <td>54.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176.6</td>\n",
       "      <td>66.4</td>\n",
       "      <td>54.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   length  width  height\n",
       "0   168.8   64.1    48.8\n",
       "1   168.8   64.1    48.8\n",
       "2   171.2   65.5    52.4\n",
       "3   176.6   66.2    54.3\n",
       "4   176.6   66.4    54.3"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### boxplot das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d8982e6a0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='variable', y='value', data=pd.melt(df[['length','width','height']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### distplot das colunas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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09RCJRNeVLk0ZISIiEh+Frh5ClS4REZHCptDVQyh0iYiIFDaFrh6iq4H0mjJCREQkXgpdPURXla6yMj+tRCIRXZtERERkO4WuHiKT0AWwdWs07REREZG2FLp6iK5CV58+fqvQJSIiEg+Frh5CoUtERKSwKXT1EF0NpFfoEhERiZdCVw/gnMZ0iYiIFLqMQpeZnWpm75jZu2Z2TQfPTzSzV82sxczObfdcwszmB7cZYTVctmtt9dtMuhe3bMl/e0RERGRHnXRIeWZWDPwaOBmoBeaa2Qzn3Nspu30AfAm4qoNT1DvnxobQVkkjOQ2ExnSJiIgUrkwqXUcA7zrn3nPONQEPAWem7uCcW+acewNozUMbd25bnoN394aNf8jbSyh0iYiIFL5MQteewIcpX9cGj2Wq3MxqzGy2mZ3V0Q5mNiXYp2b16tVZnHonsOq70PIhrL7GD77Kg2To0kB6ERGRwpVJ6LIOHssmPeztnJsAXAj8wsz23eFkzk1zzk1wzk0YMmRIFqcucE3vQeObUHYYtCyHltq8vExLi9+q0iUiIlK4MgldtcBeKV8PA5Zn+gLOueXB9j3gb8BhWbRv51Y/22+rf+S3ze/n5WWy6V7UQHoREZF4ZBK65gKjzGykmfUBzgcyugrRzAaZWVlwvxr4JPB250f1II2vgZVBxelg/VXpEhER6cW6DF3OuRbgSuAZYCHwiHNugZndYGZnAJjZ4WZWC5wH/NbMFgSHHwDUmNnrwAvAre2ueuzZGl6DsoPB+kDZ/tCyKi8vk0mlq7jYj/lS6BIREYlHl1NGADjnZgIz2z12Xcr9ufhux/bHvQwckmMbd07O+dBVeY7/unQf2PpiXl4qk4H04KtdCl0iIiLx0Iz0+ZJYDa3rfKULfOhKrAEX/qwamVS6wIcujekSERGJh0JXvjQv8ds+wcWapcOABLTWhf5S2YQuVbpERETikVH3omRpw7TtVy6WBqGreDe/bd0MxQNCfTl1L4qIiBQ+VbryJbEGMCgd6b8u2d1vWzeG/lKZXL0ICl0iIiJxUujKl5bVUFQFReX+622ha1PoL6XuRRERkcKn0JUvidVQnDK7/rbuxfArXZmGrrIyDaQXERGJi0JXviRWQ0n19q+LKoFSSGwO/6VU6RIRESl4Cl350NrouxFTK11mUDwwr5UuDaQXEREpXApd+ZBY47fF7RbvLhqQlzFdmQ6kLy1V6BIREYmLQlc+JFb7bUn70FXpp4wI++U0pktERKTgKXTlQzJ07VDp6get4ZeashnT1di4fX8RERGJjkJXPiRWg/UF69f2cesHLvzQlc08XQD19aE3QURERLqg0JUPLcF0EWZtHy/qB64RXLilpmwqXaBxXSIiInFQ6MqHxJodx3PB9sqXC7fUlOnVi2VlfqtxXSIiItFT6Aqba/Ghq/14LvCVLoDWcFNPtpUudS+KiIhET6ErbM3vA61QvOuOzyUrXSEPpk8koKhox97M9pKhS5UuERGR6Cl0ha1psd921L1YlJ/uxZaWrqtcoDFdIiIicVLoClsydCXXWkyVx+5FhS4REZHCptAVtubFYGV+9vn2tg2kD797satB9KDQJSIiEieFrrA1LfbjuToaYLWt0hX+1YuqdImIiBQ2ha6wNS2Gkg4G0QNYH6AEnLoXRUREehuFrjC5Zmhe1vGVi0lF/UKvdLW0qHtRRESk0GXwX7W0sWHa9vtVU9o+17wUSLQNXan7A1g5uIZQm6RKl4iISOFTpStMjQv8tmSP9PtYmV8KKESZhq7iYl8RU+gSERGJnkJXmDIOXfFUugD69VPoEhERiYNCV5gaF0DpCCgqT79PUTm0xlPpAh+6NCO9iIhI9BS6wtT0FvQ5uPN98lTpymQgPajSJSIiEheFrrC4Zmh8B8oO6nw/Kw99TFemywAB9O+v0CUiIhIHha6wNC0GmqEsnkqXxnSJiIgUNoWusDS+5beZVrqcC+2lFbpEREQKn0JXWBpeA0qgz4Gd72flgAPXFNpLK3SJiIgUPoWusDS+5qtcRWWd75d8PsRxXRpILyIiUvgUusLgHDS8CuXjut7XgukkQhzXlc1AeoUuERGReCh0haFlOSRWQ9lhXe9ryUpXeKFL3YsiIiKFT6ErDA2v+m1Wla5wuxcVukRERAqbQlcYGl8DDMoP7XrfAuleDPHiSREREcmAQlcYGl6DPqOgqKLrfZPdiyEuBZRtpcs5aAh3qjARERHpQobXvEmnGuZByW6wYVrX+xaFX+nK9upF8NWuvn1Da4KIiIh0QZWuXDV/BC0fQunIzPYPeSC9c9Daml2lCzSuS0REJGoKXbmqn+W3pftmtn/IA+mbm/1WoUtERKSwKXTlqn6WD1Kle2W2vxUDJaFVupqCie0VukRERAqbQleu6l+G8sPBshgeZ2WhDaTvbqVry5ZQXl5EREQypNCVi9YGP4i+7zHZHVdUHnr3YqYD6SuCCyzr6kJ5eREREcmQQlcuGuYBzdmHLiuLrXuxstJvFbpERESipdCVi/qX/bbvUdkdZ+FXujINXclK1+bNMG2av4mIiEj+KXTlov6fUDoKSnbN7jgrD73SlWn3oipdIiIi8VDo6i7nfKUr265FAOtTEJUuERERiY5CV3clVkFiNfT7ZPbHxti9WFbmq2KqdImIiEQro9BlZqea2Ttm9q6ZXdPB8xPN7FUzazGzc9s9d4mZLQ5ul4TV8Ng1veu3fbsTuvqAawqnGVkOpDfzXYyqdImIiESry9BlZsXAr4HPAAcCF5jZge12+wD4EvBgu2MHAz8GjgSOAH5sZoNyb3YBaH4PigZBn/2zP9bK/HQTYTQjy0oX+C5GhS4REZFoZVLpOgJ41zn3nnOuCXgIODN1B+fcMufcG0Bru2NPAZ5zzq1zzq0HngNODaHd8WuphfJxYN3oobUyoBlcIudmZDuQHnylS92LIiIi0cokMewJfJjydW3wWCYyOtbMpphZjZnVrF69OsNTx8i1QstyKDu4e8cXBYtet+a+Fo8qXSIiIjuHTEKXdfCYy/D8GR3rnJvmnJvgnJswZMiQDE8do8RaPyar7JDuHW9B6HK5l5u6E7pU6RIREYleJqGrFkhdzXkYsDzD8+dybOFq+chvu1vpSoau1twXQMx2ID2o0iUiIhKHTELXXGCUmY00sz7A+cCMDM//DDDJzAYFA+gnBY/t3FpW+G2f9tcTZGhb6FKlS0REpLfoMnQ551qAK/FhaSHwiHNugZndYGZnAJjZ4WZWC5wH/NbMFgTHrgP+Lz64zQVuCB7buSXWglVAcWX3jg+x0pXtgtegSpeIiEgcMvqv2jk3E5jZ7rHrUu7PxXcddnTsvcC9ObSx8CTWQvEu3T8+xEpXd7oXNU+XiIhI9DQjfXfkHLr6+G1M3YsDB0Jj4/ZjRUREJP+y6JQSwK+5mFgHZWNgw7TuncPKg3OFN5A+m+7Fqiq/3brVBzARERHJP1W6stW6CWgumO7F7lS6BgVrAmzNfZowERERyZBCV7YSa/02lO7F8AbSK3SJiIgUNoWubLVu8tviqu6fI8QxXd0ZSK/QJSIiEj2Frmy1bvTbogHdP4cV+eAVwpiuZKWrKIt/SYUuERGR6Cl0ZSuxCTAo6uYcXUlWFtqYrpISsI4WXEpDoUtERCR6Cl3Zat0ERRVgWfTndcTKQlsGKJuuRVDoEhERiYNCV7ZaN+Ve5YJQK13Zhq7SUujfH7bknvlEREQkQwpd2WrdBEUhTG4VUujqTqULfLVLlS4REZHoKHRlq3VTboPok6wstIH0Cl0iIiKFT6ErG85BYmN4oSum7kWAwYPVvSgiIhIlha5suK1Asx9InyvrE9pA+myWAErabTfYtCnnlxcREZEMKXRlI7HBb4v6534uKw9tTFd3Qtfuuyt0iYiIREmhKxut6/22qF/u54pxygjwla6Ghu0z2ouIiEh+KXRlIxGELgsjdAUz0rvWnE7T3dC1++5+q2qXiIhINBS6spEIsdJVVOa3LrdLCHMZ0wUKXSIiIlFR6MpGa5iVriB05djFmMuYLlDoEhERiYpCVzbCrHRtC125Dabv7pQRqnSJiIhES6ErG4n1gIH1zf1cMVe6dt3VbxW6REREoqHQlY3W9T5wWQhvW0iVru4OpO/Tx6+/qNAlIiISDYWubCTWh9O1CNtDV45LAXW30gUwYIBCl4iISFQUurKR2BBO1yKEWulS6BIRESl8Cl3ZaM1DpSum7kXwoWvjxpxeXkRERDKk0JWNxHqwEJYAglAH0ucSujZvzunlRUREJEMKXdko0EpXLt2LjY1Ql/sSkCIiItIFha5shDqQvo/f5jiQvrvzdIEPXQArV+bUBBEREcmAQlemWhvANYQzGz34aSesb06VLud86Mql0gUKXSIiIlFQ6MpUa4iz0ScVVeQ0pqu52W+7W+kaONBvP/64200QERGRDCl0ZSoR4rqLSUX9c6p0NTX5rSpdIiIihU+hK1NhrruYlGOlK9fQVVEBZqp0iYiIREGhK1Oteah0WX9wuVe6utu9WFzsg5cqXSIiIvmn0JWpxAa/Db3SFV/oAt/FqEqXiIhI/il0ZWpb92JIk6NC7N2L4EOXKl0iIiL5p9CVqW3diyGtvQg5D6TP9epFUKVLREQkKgpdmUqsh6JKsBwSTnsFUun6+GM/55eIiIjkj0JXphLroWhQuOcMaSB9LqGrshIaGmDr1u6fQ0RERLqm0JWp1vVQHHLoSla6ullmCmMgfUWF365e3f1ziIiISNcUujKVWA9FVeGes6g/4MDVd+vwMCpdCl0iIiLRUOjKVL4qXdDtwfRhVLoqK/12zZrun0NERES6ptCVqUQ+Q1f3BtOre1FERGTnodCVqcSG/Aykh5wrXbkOpAdVukRERPIth/+uexHXDG5L/ipdrnuVruQ8XdmErmnT2n5dXg6lpap0iYiI5JsqXZlIzkYfeujKrdLV2Oi3uXQvmkF1tUKXiIhIvil0ZWLbEkCFNaYrGbpKS3NrxpAh6l4UERHJN4WuTLTmq9KV29WLDQ1+m8uYLlClS0REJAoKXZnY1r0Y8jxdOQ6kT4YuVbpEREQKn0JXJvLdvdjNgfSqdImIiOw8MgpdZnaqmb1jZu+a2TUdPF9mZg8Hz79iZiOCx0eYWb2ZzQ9uU8NtfkTy1r2Y+0D6kpLcBtKDr3StX7/9akgREREJX5c1EjMrBn4NnAzUAnPNbIZz7u2U3b4CrHfO7Wdm5wO3AZOD55Y458aG3O5o5evqRSsG6wetm7p1eEODn/IhV0OG+O26dbDbbrmfT0RERHaUSaXrCOBd59x7zrkm4CHgzHb7nAn8V3D/MeBEM7Pwmhmz1g0+HFmf8M9dXOUnXu2GsEJXdbXfqotRREQkfzIJXXsCH6Z8XRs81uE+zrkWYCOwS/DcSDN7zcz+bmbHdfQCZjbFzGrMrGZ1If7Pn48lgJKKBm2vpGWpoQHKynJvQrLSpcH0IiIi+ZNJ6OqoYuUy3GcFsLdz7jDgO8CDZjZghx2dm+acm+CcmzAkmQAKSb5C14Zp4BqgaUG3DlelS0REZOeRSeiqBfZK+XoYsDzdPmZWAgwE1jnnGp1zawGcc/OAJcAncm105FrXh3/lYlJRP2jd2q1Dwx7TpdAlIiKSP5mErrnAKDMbaWZ9gPOBGe32mQFcEtw/F/irc86Z2ZBgID5mtg8wCngvnKZHKLE+/Dm6kqwfuPpuHdrYGE7o2iXoCFb3ooiISP50efWic67FzK4EngGKgXudcwvM7Aagxjk3A7gH+IOZvQuswwczgInADWbWAiSAy51z6/LxjeRVYj2UHZqfcxf1jb3SVVoKVVWqdImIiORTRtNqOudmAjPbPXZdyv0G4LwOjnsceDzHNsavNY8D6a2/r3S5VrDs5qoNK3SBZqUXERHJN81I3xXXAq2b83j1Yl/AdWuurrCuXgTNSi8iIpJvCl1dSc6hla+B9Nav7etkQZUuERGRnYdCV1fytQRQUlG/tq+ThTBDlypdIiIi+ZXjUsm9QCIY9188OD/nz6HSFdbVi+ArXatXg3PQfi2BadO2358yJZzXExER6W1U6erKttC1S+f7dVdy0evE2qwPDbvS1dwMmzeHcz4RERFpS5XnYnbWAAAcK0lEQVSuriTD0JbnoPGN8M9fFEzQn1iZ9aFhj+kCX+0asMOaASIiIpIrVbq6kgxdyYpU2IoqAIOW7oWusK5e1PqLIiIi+aXQ1ZXEWsC2j70KmxX54NXycVaHtbT4W5jdi6DB9CIiIvmi0NWVxDofuLKcuDQrRQOy7l5sbPTbfHQvioiISPg0pqsribX561pMKhqQdfdi2KErWelS96KIFIymJli0CN55B1pboaQE9t/f34qL426dSNYUuroSWejKrnuxocFvwwpdFRV+fJgqXSISq6YmmDED7rsP/vxnf1l1e/37w2mnwVe/CiedBEXqtJGdg0JXV6IKXYnXO54kK42twRrZ5eXbA1guzGDXXeHj7LKfiEjupk3zlaw5c3zgWrsWqqpg4kQYORJ2391XuZqbYflyWLIEZs6ERx+FPfeE6dN9+BIpcApdXWldB8W75/c1igeAa/DrLxYPzOiQujq/rawMJ3SB/2xbujScc4mIZGzFCh+cli2DvfaCCy6Agw7quIK1995w1FHw+c/DvHnw5JNw8slwzjnw299uHyshUoAUurqSWAul++b3NYqC2e6bl0HxoRkdkgxdFRXhdQnusw88+2w45xIR6ZJz8Mtfwo03+vENX/4yHH54Zt2FpaU+fI0fD5s2wfXXw5gxvltSVS8pUOoI74xrgtbN+e9eLNnVb5sWZ3zIli1+2z/Epu2zj6/ch1U5ExFJa8sWX9H61rfgwAN9aDryyOzHZ5WWwrXXwiuv+C7JU06B//f/fKATKTCqdHUmESxCne/QVRyEruZ3Mz4ktdIVln328dtly/zFQSIiebF8uR8I/+abcOutPixlOJ61Q8kFYq+4An7/e/jOd+CPf4QLL8zsKkctKisRUejqTHI2esv3QPpyKN4tq0pXPkLXyJF+u2SJQpeIZCEZejKxciXceaf/ELvyShg0KLx2lJX5ADVjBjz9tF9M9rLLfDVMpACoe7Ez25YACjHZpNNnFDTFW+lKBq0FC8I7p4jINsuWwe23+2khvvtdP1g+bEVFcNZZcP758PrrcNddGjMhBUOhqzP5XncxVZ/R0PgWuNaMds9H6Bo82Fe75s4N75wiIgC8/Tb8/Oe+GnX11TB8eH5f71OfgksvhcWL/Riv5IemSIwUujqTWOW3RZX5f62+n/TTUzQtzGj3ujo/BKJv33Cbcdxx8Ne/+nUdm5o0b5eIhGDOHPjVr/x6Y9//Puy2WzSve9RRcPnlUFsLd9wB69dH87oiaWhMV2eSS/NEEbr6He+3W/8OZV2X3Lds8Vcu5jL2tCPnnuuvuL7oInjpJT99zqRJ8LnPhfs6ItJLPP88PPIIfOIT8LWvhf+XYlcOPRS+8Q34zW/gpz+Fb34z/NCXzZi2jmggf6+hSldnWj72c2hZBNm0dCSUDIMtmU2UVVcXXtfitGnbbx995IdZPPKIv5rxnHP83F0ffhjOa4lIL9HaCo895j9MDjvMB5+oA1fS6NH+isbGRh+8PvggnnZIr6fQ1ZnESijJ82z0SWZQ+XmoewpaVnW5e5ihK1VRkb+g6Kc/hX/8A+6+21/48/LL4b+WiPRQzc3+w+O55+D4430lJ+4rCIcP92PJSkvhZz+Df/0r3vZIr6TQ1ZmWj6EkorEHAFVfAVpg431d7pqv0AU+eA0Y4O8PGuR7BRZmNtRMRHq7ujo/cH3ePD9e4YILCmdB6t1398Fr0CA/E/6rr8bdIullCuQ3oUC1rPTzZ0Wl7EDoeyxs+G2XVzHmM3S1N3q0H9u1cWM0ryciO6mVK+G22+D993116+STwx94mqvBg+Gqq2DYML9W45NP+q5QkQgodHUm8XF03Ysbpvlbn9F+ZvpV3/Ffp5EcSB+F0aP9VtV4EUlr3jy4+Wb/4fTtb/s1EQtVRYWfJ+zoo+FPf4LTT9el2hIJha50WrdAa1203YsA5YeBVUB954Oooqx0DRvmV9KorY3m9URkJ7JpEzzwgL8SZ4894Ic/hP32i7tVXSsthUsu8d2fL7wABx8MDz2kNRslrxS60mlZ4bfFEVW6kqwUyg8NJkptSbvbpk3Rha6SEv9ZqisYRWQb5/xyOwcf7OeXOflk3223yy5xtyxzZnDCCfDaa35m6AsugGOP9VcRKXxJHih0pdMcJIzSvaN/7bJDwdV3uhbj+vV+aEJUhg1T6BKRwD//6cPKmWdCZaWf8PTcc/1faDuj/feH2bPhd7+D997zs0Qffrj/Wt2OEiKFrnRagnlc4ghdffYHitLOTt/U5IdNhLlObFf22stX11aujO41RaSAbN0KDz7oZ3k/9lh45x0/4ej8+b5KtLMrLoavftUvG/Sb30B9vb8YYI89/ASrl17q59L5wx/8OLCXX4ZFi3woa2pSZUwyspP+WRKB5iB0lQyL/rWLyqB0eNoFsJMrWURZ6Ro61G8XLIhuBQ8RidmKFfCXv8DTT/uuxC1bYNQov4j0l74U3RiHKFVUwBVX+OWDXn/df+8vvAB//jNMn57+uKIiP/lrZSVUV/sPyhEj/CzT1dVRtV4KnEJXOs0f+Okiisrjef3S/WDrC9DasEMbkqErykpXauj69Keje10RyUBYy9Bs2QJ//7sPWs89B2+95R+vroYLL/Rjno4/vnDm3conMxg71t+uvdY/tmEDrFkD69b5D+K1a/1cOs895ytj9fW+S2DNGl8JfP55f1yyWnbUUf6+9FoKXem0fBBP12JSn1Gw9TlomAP9JrZ5Ko7QNXAg9OvnQ5eI9CCbNvn5qv74R1/RaW6GsjLfhXjxxX6A/NixvSNodaWqyt/aKy7e8bFEApYv93PtvPGGX0/tz3+Gfff1C9oeemjhzWEmeafQlU7zBxktPJ03ffb1260v7RC6VgWrBA0Zkt8mpP7xbOb/QFPoEukB6uvhlVdg7lxYssSPR9p3X78+4qRJfiB5XOsk9hTFxX4w7F57wYkn+nA7axa8+CL853/6q5M++1kfaKXXUOjqiEtA81KoOD2+NhRVQMkeUP/PHZ5KDmbfPeLZLIYO9b0NzukPNJGd0po1vits1iy/+PPQof4//ptugkMO0S92Pg0YAKecAiedBHPmwMyZvsI4bJj/MD/zTL3/vYBCV0ea3wfXGFxFGKPSfaF+ll8SyLaX9pNXMO+6a7TNGTrUT8fz8ccaliCyU1m/3g+Enz3b/8d+xBF+yocRI/zzY8bE2brepbjYz4R/xBG+0jhzJpx9NhxzDNx+O3zyk3G3UPJIoasjTYuC7TudLsWTd6X7QP0/fHvKDtz28MqV/srFPn2ibU4yaC1YoNAlslNoaPBjiZ591peoTzjBdx9GOSBUOlZc7AfWH364v3/99X4c3Zlnwi23wAEHxN1CyQONjOxIMnRFte5iOslxXe2WBKqt3X41YZT23NNvNa5LpMC1tvoJTK+7Dp56yo8b+slPYPJkBa5CU1zsrx5dvBhuvBH++lc/y/+UKX4gvvQoCl0daVzk1z8sinkOmuLdoHiXHULXsmXbewWiVFnpV/hQ6BIpYEuXwm23wX33+ZL4977nJ/3UXFGFrX9/v27lkiVw5ZV+TrD99vOPbdwYd+skJApdHWmcDyUxlJLaM4O+x7QJXc7B++/HE7rM4KCDFLpEClJdnZ8t/bbb/DxSl17ql+fZd9+4WybZGDIE7rzTz3Z/1llw881+gtVrr9VabD2AxnS119oADfOh/6fibonX9xioexJa1kBJNatW+T969tsvnuYcdJBfCURXMIoUiEQC/vY3+N//9WO4TjrJX5GYzZQPuU6uKuHbZx//Yfvd7/pux9tv98sQnXMOfPObfuC9PoR3Ogpd7TXOB5qhtEDWEut7jN82zIaK07dNEH3wwfE056CDfOhbvnz7GC8RicmLL/r/gOfPh9Gj4fzz4xnwubMr5NA5fjw88YQfV/LrX8Pdd8Ojj/r1Lj/3OR/CjjxSk9fuJPSv1F79bL8tlNBVPgEo8ZOk4q8whviu8D4omC82Gf5EJAYvv+xnij/+eFi9Gi67DL79bQWunmzECF/pqq2Fe+6B/ff33ZDHHAN77+1/Bu6/X12QBU6VrvbqZvr5uYoL5Aqfon7Q95Ow5c/Abfztb/53Ld+z0aczdqyvaM+Z4+f5E5GIOOfn2frJT+CZZ/xEfT/7mV+Y+f77426dRKV/f/jyl/1twwZ/derPfua7Iu++2+9TXe0XJh8xwt+GDYOSTv67T669KXmn0JUqsQm2/g0GfyvulrRV8VlY/T3Wfvwhf/3rXnzzm/E1parKV9lefDG+Noj0Khs3+u6kqVNh3jz/H+rtt8PXvub/A5beq6oKLrrIL1Te2goffeTXevzXv3x3xKxZfr+SEr8cUTKEjRjhQ7u6JCOn0JVq82NAM1ScCU2Fc4newzP/jcmHf4/7pz5OS8u3qKiIdwjCxIm+ut3cDKWl8bVDpMf6+GO/OPKMGX7G8sZG37f/m9/AF74AFTFPZyOFp6io7VqPzvmVCJYu9ePBli3z3dIvvOD379t3ewDbbTc/Q75mvc47ha4k52D9XVB2iB+8XiChq6EBnnpuf0YPOJxPj7uHU075JnvsEe8VKyedBHfdBX/5C3zmM7E2RWTnt2GDn4flrbfg1Vf9WlsLF/rnhg713YcXXOD/U9TVaj1TPv6KNvPztA0e7Afjg6+GrVixPYQtXeq7qp9+2j8/bJifIf+II/xt/HgYODD8tvViGYUuMzsVuBMoBu52zt3a7vky4D5gPLAWmOycWxY8dy3wFSABfMM590xorQ/Tpgf8lYu7/75gPtjmvjqEyV/1vxe7l1/G7d+Zwn9c+gy1G06NtV2nnOKr2g88oNAl0qnWVj9n1kcf+QHOtbX+lrz/r3/5bdLAgX7tvS98wf9yHXpowXweSQ9QVOQvO99zz+1rPDY1+XA1Z8722xNPbD9mxAg/piT1tt9+fiZ9yZo55zrfwawY+BdwMlALzAUucM69nbLP14AxzrnLzex84Gzn3GQzOxD4b+AIYCjwF+ATzrlEutebMGGCq6mpyfHbylLDm/DBROgzGoa/7BeXjnPNReDu+0bztauOZejQYs46Cw46sInPH34gzpXwxGtzaE4MiKVdyfGWV14Jv/2t/+N89OhYmiISvZYWWLPGXzGYevvzn/3kpJs3t93W1fkqeqqiIh+uBg/2Szwk/xPcc0+/RI9ClsStrs5Xwj780P/BUFvrF/1tbfXPl5b6n93qan87/XQ/hcXIkf7neJddelUoM7N5zrkJGe2bQeg6GrjeOXdK8PW1AM65W1L2eSbYZ5aZlQAfA0OAa1L3Td0v3etFErpcAlo3QnMt1P0J1t0CRZWw98vQZ4TfJ+LQ1doKGzf14Y0Fu3Dn1IN54k8jmfTpD/nvR/fiscf8PkOrnue0g09l7ZZDmbP0FtbUjaOxZTAQ3Yd0MnStWuUvjtlzT/jd7+Cww6Bfv8iaIeI55395OrslEv6v+YYGqK/32+Qt+fWWLX7A+saNvruvo+2aNX6MTEfM/C9AZaUfb1VZ2fZ+VZUPVIMGwYABGsAsO5/mZt81+dFH/rZmzfZbfX3bfc188BoyxA/YHzLEf538nejo1r8/lJX5QJd6Kynp+LEC+uMkm9CVSffinkDqxB+1wJHp9nHOtZjZRmCX4PHZ7Y6Nf0rNxVXQWrf96/6nwu7ToHSv2Jr0iQmTWbLU951XVDRx4w/ncs2351M8+LJt+yzfcCLPLXyMiaO+ymfHTALglfdu4/Xa70Xe3l139WN8zz4bjj3WP/bSS9vvi4Rq6FAfeNoHqnzo18+HpIED/ba62i+lU13t//Po6Pb4473qL3vphUpL/Xxge++943PnnefHwSxd6oPZqlXbq8CrVvkukbVr/R83W7eG2y6zzm8HHODHShaITCpd5wGnOOe+Gnz9BeAI59x/pOyzINinNvh6Cb5L8QZglnPu/uDxe4CZzrnH273GFCA5Ucho4J0Qvrd8qQbWxN2IAqb3p3N6f9LTe9M5vT+d0/vTOb0/ncvl/RnunMto9sxMKl21QGoJaBiwPM0+tUH34kBgXYbH4pybBhTwOgzbmVlNpmXE3kjvT+f0/qSn96Zzen86p/enc3p/OhfV+5PJwIK5wCgzG2lmfYDzgRnt9pkBXBLcPxf4q/MltBnA+WZWZmYjgVHAnHCaLiIiIrLz6LLSFYzRuhJ4Bj9lxL3OuQVmdgNQ45ybAdwD/MHM3sVXuM4Pjl1gZo8AbwMtwNc7u3JRREREpKfKaJ4u59xMYGa7x65Lud8AnJfm2JuAm3JoY6HZKbpBY6T3p3N6f9LTe9M5vT+d0/vTOb0/nYvk/elyIL2IiIiI5E6TxYiIiIhEQKGrE2a2zMzeNLP5ZlYTPDbYzJ4zs8XBdlDc7YyDmY0O3pfkbZOZfcvMrjezj1IePy3utkbFzO41s1Vm9lbKYx3+vJj3SzN718zeMLNx8bU8Gmnen5+a2aLgPXjCzKqCx0eYWX3Kz9HU+FoejTTvT9rfJzO7Nvj5ecfMTomn1dFJ8/48nPLeLDOz+cHjvernx8z2MrMXzGyhmS0ws28Gj+vzh07fn+g/f5xzuqW5AcuA6naP3Q5cE9y/Brgt7nbGfcNfYPExMBy4Hrgq7jbF9D5MBMYBb3X18wKcBjyNX07gKOCVuNsf0/szCSgJ7t+W8v6MSN2vN9zSvD8d/j4BBwKvA2XASGAJUBz39xD1+9Pu+Z8B1/XGnx9gD2BccL8Sv3Tfgfr86fL9ifzzR5Wu7J0J/Fdw/7+As2JsS6E4EVjinHs/7obEyTn3Iv7q3VTpfl7OBO5z3mygysz2iKal8ejo/XHOPeucawm+nI2fy69XSvPzk86ZwEPOuUbn3FLgXfyE1D1WZ++PmRnwefxav72Oc26Fc+7V4P5mYCF+9Rd9/pD+/Ynj80ehq3MOeNbM5pmfNR9gN+fcCvD/kMCusbWucJxP2w+7K4Ny7b29tfs1Rbqfl46W14p/iax4fRn/13fSSDN7zcz+bmbHxdWoAtDR75N+fto6DljpnFuc8liv/PkxsxHAYcAr6PNnB+3en1SRfP4odHXuk865ccBngK+b2cS4G1RozE+YewbwaPDQfwL7AmOBFfiSv+yoo9Vae+2lxGb2Q/xcfg8ED60A9nbOHQZ8B3jQzAbE1b4Ypft90s9PWxfQ9g+/XvnzY2YVwOPAt5xzmzrbtYPHevzPT7r3J8rPH4WuTjjnlgfbVcAT+PL9ymQZNtiuiq+FBeEzwKvOuZUAzrmVzrmEc64V+B09vMsjA+l+XjJaIqs3MLNLgNOBi1wwoCLoNlsb3J+HH7P0ifhaGY9Ofp/08xMwv/TcOcDDycd648+PmZXiA8UDzrk/Bg/r8yeQ5v2J/PNHoSsNM+tvZpXJ+/gBd2/RdsmjS4D/jaeFBaPNX5jtxgWcjX/PerN0Py8zgC8GVxEdBWxMdgP0JmZ2KvB94Azn3NaUx4eYWXFwfx/8EmLvxdPK+HTy+6Ql1rY7CVjknKtNPtDbfn6CMW33AAudcz9PeUqfP6R/f2L5/In7qoJCvQH74K8Oeh1YAPwweHwX4HlgcbAdHHdbY3yP+gFrgYEpj/0BeBN4A/+LvUfc7Yzw/fhvfFm6Gf+X5FfS/bzgy/u/xv8F9SYwIe72x/T+vIsfWzI/uE0N9v1c8Hv3OvAq8G9xtz+m9yft7xPww+Dn5x3gM3G3P473J3h8OnB5u3171c8PcCy+e/CNlN+l0/T50+X7E/nnj2akFxEREYmAuhdFREREIqDQJSIiIhIBhS4RERGRCCh0iYiIiERAoUtEREQkAgpdItIpM5tqZv8n7nakMrMJZrbazP7DzH5gZp+O4DWdme2X79cRkZ5LU0aI9GJmtgzYDb8ERgJ4G7gPmOb8LOgFycy+C7yGn0/nE/jJDevz/JoOGOWcezefryMiPZdCl0gvFoSurzrn/mJmA4HjgTuBvznnLo21cQUm7tBlZiXOuZauHhORwqXuRREBwDm30Tk3A5gMXGJmBwOY2XQzuzG4f4KZ1ZrZ98xslZmtMLOzzOw0M/uXma0zsx8kz2lmRWZ2jZktMbO1ZvaImQ0OnhsRdNldYmYfmNmaYOHZ5LFHmFmNmW0ys5Vmlrp8xxlmtsDMNpjZ38zsgJTnhprZ40H341Iz+0Ym52zPzK4Ovr/lZvblds8NNLP7gtd438x+ZGYdfp6aWXHQBbrEzDab2Twz2yt47k4z+zBozzwzOy7luOvN7DEzu9/MNgFfSvNYmZn9Imjn8uB+WVf/3iISPYUuEWnDOTcHv8zKcWl22R0oB/YErsMvxHwxMD445rpgvTKAbwBn4StoQ4H1+OVHUh0LjAZODI5NBqg7gTudcwOAfYFHAMzsE/glYb4FDAFmAk+aWZ8g+DyJX75jz+Cc3zKzUzo7Z3vBmmxXASfj1107qd0udwED8cuFHQ98EUhXGfwOfo3S04ABwJeB5Dpvc4GxwGDgQeBRMytPOfZM4DGgCnggzWM/BI4KznMoflHsH6Vpi4jESKFLRDqyHB8EOtIM3OScawYeAqrxQWazc24Bfs2yMcG+/45ft7TWOdcIXA+ca2YlKef7iXOu3jmXXOv00JTX2c/Mqp1zdc652cHjk4GnnHPPBW24A+gLHAMcDgxxzt3gnGtyzr2HD4Xnd3HO9j4P/N4595ZzbkvQbsBXroI2XBt8z8uAnwFfSHOurwI/cs6947zXnXNrAZxz9zvn1jrnWpxzPwPK8AE0aZZz7n+cc60pY9baP3YRcINzbpVzbjXwk07aIiIxUugSkY7sCaxL89xa51wiuJ8MAitTnq8HKoL7w4Engm7ADcBC/ID93VL2/zjl/taUY7+CHyS/yMzmmtnpweNDgfeTBwQD/j8M2jwcGJp8veA1f5DyeunO2d7Q4JxJ76fcrwb6tHvs/eD1O7IXfmHhHZjZd81soZltDNo6MDh/0ocdHNb+sTbvR3B/aJq2iEiMSrreRUR6EzM7HB8g/hHC6T4Evuyc+2cHrzOiswOdc4uBC4Iuw3OAx8xsF3wV7pCU8xg+2HwENAJLnXOjsjlnUM1KtSI4Z9LeKffX4Ctmw/FXeyaf/yjNt/IhvivzrdQHg/Fb38d3gS5wzrWa2XrAUpvc0bfR7uvlQVsWpLRleZq2iEiMVOkSEQDMbEBQ+XkIuN8592YIp50K3GRmw4PXGGJmZ2bYnovNbEhQydoQPJzAj8P6rJmdaGalwHfxYetlYA6wycy+b2Z9g0HsBwdBsrNztvcIfpD6gWbWD/hx8omgyvdI8H1VBt/bd4D703wrdwP/18xGmTcmCI+V+Kk6VgMlZnYdfsxXtv4b+FHw3lbjx9mla4uIxEihS0SeNLPN+IrMD4Gfk35QeLbuBGYAzwavMRs4MsNjTwUWmFldcJ7znXMNzrl38AP378JXnf4N+LdgDFci+HossDR4/m58t13ac7Z/Yefc08AvgL8C7wbbVP8BbAHew1cEHwTuTfN9/Bwf0p4FNgH34MegPQM8DfwL3yXYQMfdiV25EagB3gDeBF4NHhORAqN5ukREREQioEqXiIiISAQUukREREQioNAlIiIiEgGFLhEREZEIKHSJiIiIREChS0RERCQCCl0iIiIiEVDoEhEREYmAQpeIiIhIBP4/7IYFyIlBoqQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df['length'] , color='red', label='length')\n",
    "sns.distplot(df['width'] , color='blue', label='width')\n",
    "sns.distplot(df['height'] , color='gold', label='height')\n",
    "plt.xlabel('Dimensões do carro', fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Método Simple Scaling - Normalizando"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811148</td>\n",
       "      <td>0.886584</td>\n",
       "      <td>0.816054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.822681</td>\n",
       "      <td>0.905947</td>\n",
       "      <td>0.876254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.915629</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.848630</td>\n",
       "      <td>0.918396</td>\n",
       "      <td>0.908027</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.811148  0.886584  0.816054\n",
       "1  0.811148  0.886584  0.816054\n",
       "2  0.822681  0.905947  0.876254\n",
       "3  0.848630  0.915629  0.908027\n",
       "4  0.848630  0.918396  0.908027"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_1 = df\n",
    "\n",
    "df_1['length'] = df_1['length']/df_1['length'].max()\n",
    "df_1['width'] = df_1['width']/df_1['width'].max()\n",
    "df_1['height'] = df_1['height']/df_1['height'].max()\n",
    "\n",
    "df_1[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotagem dos dados normalizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d87431080>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x='variable', y='value', data=pd.melt(df_1[['length','width','height']]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "sns.distplot(df_1['length'] , color=\"red\", label='length')\n",
    "sns.distplot(df_1['width'] , color=\"blue\", label='width')\n",
    "sns.distplot(df_1['height'] , color=\"gold\", label='height')\n",
    "plt.xlabel('Dimensões do carro', fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Método Min-Max - Normalizando"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizando dados - Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449254</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.540299</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.441667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.770149</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.658333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.770149</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.658333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.770149</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.553731</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.413433  0.316667  0.083333\n",
       "1  0.413433  0.316667  0.083333\n",
       "2  0.449254  0.433333  0.383333\n",
       "3  0.529851  0.491667  0.541667\n",
       "4  0.529851  0.508333  0.541667\n",
       "5  0.540299  0.500000  0.441667\n",
       "6  0.770149  0.925000  0.658333\n",
       "7  0.770149  0.925000  0.658333\n",
       "8  0.770149  0.925000  0.675000\n",
       "9  0.553731  0.633333  0.350000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2 = df\n",
    "\n",
    "df_2['length'] = (df_2['length']-df_2['length'].min())/(df_2['length'].max()-df_2['length'].min())\n",
    "df_2['width'] = (df_2['width']-df_2['width'].min())/(df_2['width'].max()-df_2['width'].min())   \n",
    "df_2['height'] = (df_2['height']-df_2['height'].min())/(df_2['height'].max()-df_2['height'].min())   \n",
    "\n",
    "df_2[['length','width','height']].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Normalizando dados - Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.413433</td>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.449254</td>\n",
       "      <td>0.433333</td>\n",
       "      <td>0.383333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.491667</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.529851</td>\n",
       "      <td>0.508333</td>\n",
       "      <td>0.541667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0  0.413433  0.316667  0.083333\n",
       "1  0.413433  0.316667  0.083333\n",
       "2  0.449254  0.433333  0.383333\n",
       "3  0.529851  0.491667  0.541667\n",
       "4  0.529851  0.508333  0.541667"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_3 = df\n",
    "scaler = MinMaxScaler(feature_range=(0,1))\n",
    "\n",
    "df_3['length'] = scaler.fit_transform(df_3[['length']])\n",
    "df_3['width'] = scaler.fit_transform(df_3[['width']])\n",
    "df_3['height'] = scaler.fit_transform(df_3[['height']])\n",
    "\n",
    "df_3[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotagem dos dados normalizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df_3['length'] , color='red', label='length')\n",
    "sns.distplot(df_3['width'] , color='blue', label='width')\n",
    "sns.distplot(df_3['height'] , color='gold', label='height')\n",
    "plt.xlabel('Dimensões do carro', fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Método Z-Score - Padronização"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Padronizando dados - Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.425480</td>\n",
       "      <td>-0.842719</td>\n",
       "      <td>-2.015483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.425480</td>\n",
       "      <td>-0.842719</td>\n",
       "      <td>-2.015483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.230948</td>\n",
       "      <td>-0.190101</td>\n",
       "      <td>-0.542200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.206750</td>\n",
       "      <td>0.136209</td>\n",
       "      <td>0.235366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.206750</td>\n",
       "      <td>0.229440</td>\n",
       "      <td>0.235366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0 -0.425480 -0.842719 -2.015483\n",
       "1 -0.425480 -0.842719 -2.015483\n",
       "2 -0.230948 -0.190101 -0.542200\n",
       "3  0.206750  0.136209  0.235366\n",
       "4  0.206750  0.229440  0.235366"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_4 = df\n",
    "\n",
    "df_4['length'] = (df_4['length']-df_4['length'].mean())/(df_4['length'].std())\n",
    "df_4['width'] = (df_4['width']-df_4['width'].mean())/(df_4['width'].std())\n",
    "df_4['height'] = (df_4['height']-df_4['height'].mean())/(df_4['height'].std())\n",
    "\n",
    "df_4[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Padronizando dados - Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>length</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.426521</td>\n",
       "      <td>-0.844782</td>\n",
       "      <td>-2.020417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.426521</td>\n",
       "      <td>-0.844782</td>\n",
       "      <td>-2.020417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.231513</td>\n",
       "      <td>-0.190566</td>\n",
       "      <td>-0.543527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.207256</td>\n",
       "      <td>0.136542</td>\n",
       "      <td>0.235942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.207256</td>\n",
       "      <td>0.230001</td>\n",
       "      <td>0.235942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     length     width    height\n",
       "0 -0.426521 -0.844782 -2.020417\n",
       "1 -0.426521 -0.844782 -2.020417\n",
       "2 -0.231513 -0.190566 -0.543527\n",
       "3  0.207256  0.136542  0.235942\n",
       "4  0.207256  0.230001  0.235942"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_5 = df\n",
    "scaler = StandardScaler()\n",
    "\n",
    "df_5['length'] = scaler.fit_transform(df_5[['length']])\n",
    "df_5['width'] = scaler.fit_transform(df_5[['width']])\n",
    "df_5['height'] = scaler.fit_transform(df_5[['height']])\n",
    "\n",
    "df_5[['length','width','height']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotagem dos dados padronizados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.distplot(df_5['length'] , color='red', label='length')\n",
    "sns.distplot(df_5['width'] , color='blue', label='width')\n",
    "sns.distplot(df_5['height'] , color='gold', label='height')\n",
    "plt.xlabel('Dimensões do carro', fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformando dados - Binning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando 4 bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "binwidth = (max(df['horsepower'])-min(df['horsepower']))/4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 48., 108., 168., 228.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(min(df['horsepower']), max(df['horsepower']), binwidth)\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grupo de nomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_names = ['Low', 'Medium', 'High']   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>horsepower</th>\n",
       "      <th>horsepower-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>111.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>111.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>154.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>102.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>115.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>110.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>110.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>110.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>140.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>160.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>101.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>101.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>121.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>121.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>121.0</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>182.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>182.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>182.0</td>\n",
       "      <td>High</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>48.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>70.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    horsepower horsepower-binned\n",
       "0        111.0            Medium\n",
       "1        111.0            Medium\n",
       "2        154.0            Medium\n",
       "3        102.0               Low\n",
       "4        115.0            Medium\n",
       "5        110.0            Medium\n",
       "6        110.0            Medium\n",
       "7        110.0            Medium\n",
       "8        140.0            Medium\n",
       "9        160.0            Medium\n",
       "10       101.0               Low\n",
       "11       101.0               Low\n",
       "12       121.0            Medium\n",
       "13       121.0            Medium\n",
       "14       121.0            Medium\n",
       "15       182.0              High\n",
       "16       182.0              High\n",
       "17       182.0              High\n",
       "18        48.0               Low\n",
       "19        70.0               Low"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['horsepower-binned'] = pd.cut(df['horsepower'], bins, labels=group_names,include_lowest=True )\n",
    "df[['horsepower','horsepower-binned']].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(df['price'].min(), df['price'].max(), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['price-binned'] = pd.cut(df['price'], bins, labels=group_names, include_lowest=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>price-binned</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>6849.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>7463.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>11248.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13495.0</td>\n",
       "      <td>Low</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price price-binned\n",
       "92    6849.0          Low\n",
       "146   7463.0          Low\n",
       "177  11248.0          Low\n",
       "0    13495.0          Low\n",
       "9        NaN          NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['price','price-binned']].sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotando bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9d86f8da20>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BHhl17KYDHg8DHzzEexsPcXw5cEKyhUpmiltL52CYOVOLnC4lbRhjOGdmJfcu38GGPf0cP7XY6ZJERMRB6sAvE6p7MEwsbqnSyNhBTqgrocjnYdmWwy2tFBGRXKAwJhOqYyAEoGnKUdwuw/y6EjbsGWAoHHO6HBERcZDCmEyo9oEwAFVFCmOjndRQSsxaVrf1Ol2KiIg4SGFMJlR7fwi/101BflLLE3NKXamf8oI8VrUqjImI5DKFMZlQHQMhjYodgjGGk+pL2NQ+QP9wxOlyRETEIQpjMqE6+kNavH8Y8+tLscDrOzU6JiKSqxTGZMIMR2L0h6JUamTskKqLfUwt9mmqUkQkhymMyYRp7x+5k7KqMM/hStLb/PoStncF6R4MO12KiIg4QGFMJozaWiRnfn0pAKs0VSkikpMUxmTCtPeHcBko18jYYZUX5NFQ5mdVa4/TpYiIiAMUxmTCtA+EKAvk4XHpx+xI5teXsqt3mL19w06XIiIik0y/JWXCdA6ENUWZpBPrSzBoqlJEJBcpjMmEsNbSNRimQlOUSSn2eZlWHmD97n6nSxERkUmmMCYTYjAcIxyLU16gMJas2dVF7OwZYiAUdboUERGZRApjMiG6Em0aygMKY8maU10EwMY9Gh0TEcklCmMyIfaFsTKNjCWtptRHQZ6bjXsHnC5FREQmkcKYTIj9I2MKY0lzGcPs6iI27Oknbq3T5YiIyCRRGJMJ0TUYptjnwevWj9jROK66kGA4RlvPkNOliIjIJNFvSpkQXYNhTVGOw6wpRRhgg9aNiYjkDIUxmRDdwbAW749DYb6HujI/G/Zo3ZiISK5QGJOUi8Ti9A1FtF5snGZPKWJHV5ChcMzpUkREZBIojEnK9QQjWLR4f7yOqy7EAhv3aqpSRCQXKIxJyulOymNTXxbA53WxUVOVIiI5QWFMUq4rqB5jx8LtMsyaUsSGvf1YtbgQEcl6CmOSct2DYTwuQ1G+x+lSMtac6kL6h6Ps7ht2uhQREZlgCmOScl2DYcoL8jDGOF1Kxpo9ZWRrJN1VKSKS/RTGJOX2hTEZv2K/lylF+WzpUBgTEcl2CmOSUtZauoJq+JoKjRUFbOsMamskEZEspzAmKTUYjhGOxtXwNQUaKwOEonF292rdmIhINlMYk5TqVluLlGmsKABga+egw5WIiMhEUhiTlFKPsdQpDeRR6veytUNhTEQkmymMSUrt7zGmacqUaKwsYGtnUP3GRESymMKYpFTXYJgin4c8j360UmF6RYCBUJTOxIijiIhkH/3GlJTqGgxr8X4K7V83pqlKEZGspTAmKdWtHmMpNaUon0Cem62dQadLERGRCaIwJikTjcfpHYqox1gKGWOYXlGgOypFRLKYwpikTE8wgkV3UqZaY0WArsEwfcMRp0sREZEJoDAmKbO/rYXWjKXUvnVj2zRVKSKSlRTGJGXUY2xi1Jb68bqNFvGLiGQphTFJme7BMB6XodDncbqUrOJ2GRrKA1o3JiKSpRTGJGX2bRDuMsbpUrJOY0UBu3uHGY7EnC5FRERSTGFMUkY9xiZOY0UBFq0bExHJRgpjkjI9wQilAa/TZWSlaeUBXEabhouIZCOFMUmJ4UiMoUhMe1JOkDyPi6klPlq7NTImIpJtFMYkJXqCIz2wNDI2cerLArR2DxHXpuEiIllFYUxSoic40tZCI2MTp6HMTygap3NAm4aLiGQThTFJie4hjYxNtLqyAICmKkVEsozCmKRET3Ckx1hBvnqMTZQpRfnkuV3s6B5yuhQREUkhhTFJiX13UqrH2MRxGUNdmZ+dGhkTEckqCmOSEt3BMKVaLzbh6kv9tPUOE43HnS5FRERSRGFMUqInGKHUr/ViE62+PEAsbtndO+x0KSIikiIKY3LMIrE4A6GoRsYmQX2ZH4BWrRsTEckaSYUxY8wiY8x6Y0yLMebGMV7PN8bcm3h9qTGm8YDXvpA4vt4Y844Djn/WGLPaGPOGMeZuY4wvFV9IJl9vosdYme6knHClfi8FeW6FMRGRLHLEMGaMcQO3ApcAzcDlxpjmUaddB3Rba2cBtwDfTLy3GVgCzAMWAT80xriNMXXAp4EF1toTAHfiPMlA3UMjfa80MjbxjDGJ5q9axC8iki2SGRlbCLRYazdba8PAPcDiUecsBn6ZePwAcJExxiSO32OtDVlrtwAtiesBeAC/McYDBIC2Y/sq4pSeQY2MTab6Mj/t/SFCkZjTpYiISAokE8bqgB0HPG9NHBvzHGttFOgFKg71XmvtTuDbwHZgF9BrrX18PF9AnNc9FMZloMinMDYZ6ssCWGBnj6YqRUSyQTJhbKzGUaM3xzvUOWMeN8aUMTJq1gTUAgXGmCvH/HBjrjfGLDfGLG9vb0+iXJlsPcEIxX4vbpd6jE0GLeIXEckuyYSxVqDhgOf1vHlKcf85iWnHEqDrMO+9GNhirW231kaA3wFnj/Xh1trbrLULrLULqqqqkihXJltPMEypX+vFJktBvoeygFfrxkREskQyYWwZMNsY02SMyWNkof2Do855ELg68fgDwJPWWps4viRxt2UTMBt4mZHpyTONMYHE2rKLgLXH/nXECT3BiNaLTbKRRfwaGRMRyQZH3EjQWhs1xnwSeIyRux5/Zq1dbYz5KrDcWvsgcAdwpzGmhZERsSWJ9642xtwHrAGiwA3W2hiw1BjzALAycfwV4LbUfz2ZaLG4pXcoojspJ1lDmZ/Xd/bSPxzRWj0RkQyX1K7O1tpHgEdGHbvpgMfDwAcP8d6bgZvHOP5l4MtHU6ykn76hCBbdSTnZ6soCAOzsHuL4Gv1vLyKSydSBX46Jeow5o67UjwF2aKpSRCTjKYzJMelJdN8v1cjYpMrzuJhSnE+b2luIiGQ8hTE5Jj3BkZGxEm0SPulqS/zs6lUYExHJdApjcky6gxGKfB68bv0oTbbaUj99w1H6hyNOlyIiIsdAv0HlmIz0GNOomBNqSn0A7OoddrgSERE5Fgpjckx6gmpr4ZTakpFO/Fo3JiKS2RTGZNzi1tIzpIavTvF53ZQX5CmMiYhkOIUxGbeBUJRY3GpkzEG1pX7aNE0pIpLRFMZk3HoGR+6k1MiYc2pLfHQNhhkKx5wuRURExklhTMate2hfjzGNjDmltnRk3ZhaXIiIZC6FMRk3NXx13r4wpnVjIiKZS2FMxq0nGMbvdZPvcTtdSs4qzPdQ7PNo3ZiISAZTGJNx6wnqTsp0UFvq18iYiEgGUxiTcesOhrVeLA3Ulvpp7w8RjsadLkVERMZBYUzGxVqrkbE0UVviwwK7+zRVKSKSiRTGZFyGwjHCsbhGxtKAFvGLiGQ2hTEZl7+1tdDImNNK/F78XrfCmIhIhlIYk3HpCY40fNXImPOMMdSV+rVhuIhIhlIYk3HpTvQYK/NrZCwd1JT62N03TCxunS5FRESOksKYjEtPMEyex4U/Tz3G0kFtqZ9Y3LK3X6NjIiKZRmFMxqUnGKHU78UY43QpAtSWaBG/iEimUhiTcekJhinTerG0UVGYR57bxc4ejYyJiGQahTEZl+5gRHdSphGXMUwt8WnDcBGRDKQwJkctFIkxFInpTso0U1vqY1fvMHGrRfwiIplEYUyO2r4eY+q+n15qS/yEo3G6B8NOlyIiIkdBYUyOmnqMpaeafYv41W9MRCSjKIzJUesJqvt+OqouzsdlYJfuqBQRySgKY3LUeoJh3C5DYb7H6VLkAB63iylFPtq0iF9EJKMojMlR6070GHOpx1jaqSnxsUvtLUREMorCmBw19RhLX7WlfvpDUfqHI06XIiIiSVIYk6PWox5jaaum1AegTcNFRDKIwpgclUgsTn8oqjCWpmqKR+6o1CJ+EZHMoTAmR6V3aN+dlJqmTEf+PDdlAa/aW4iIZBCFMTkq3ft7jGlkLF3VlPi1YbiISAZRGJOjsq/HmBbwp6/aUh9dg2FCkZjTpYiISBIUxuSo9ATDuAwU+zQylq5qS/xYYHefpipFRDKBwpgclZ5ghGKfF7dLPcbSVU1pYlskTVWKiGQEhTE5Kt3BsNaLpblin4dAnlvtLUREMoTCmByVkR5jWi+Wzowx1Jb6tS2SiEiGUBiTpMXilr7hCGUaGUt7NSU+9vSFiMWt06WIiMgRKIxJ0vqGI8SteoxlgtoSP7G4ZW+/pipFRNKdwpgkbV9bC60ZS3/7t0XSpuEiImlPYUyStq/ha5lfI2PprrIwH6/bsEvrxkRE0p7CmCStJxHGSjQylvZcxjC12KdtkUREMoDCmCStJxihKN+D160fm0xQU+pnV+8Q1moRv4hIOtNvVUnaSFsLjYplitoSP8ORON2JtX4iIpKeFMYkaSMNX7VeLFPUJhbxqxO/iEh6UxiTpMStpWdII2OZpLrYh8ugTvwiImlOYUySMhCKEotbjYxlEK/bRWVhvu6oFBFJcwpjkpR9PcbK/BoZyyS1pX5NU4qIpDmFMUnKvrYWpQUaGcskNSU++oajDISiTpciIiKHoDAmSdHIWGaqLfUDaKpSRCSNJRXGjDGLjDHrjTEtxpgbx3g93xhzb+L1pcaYxgNe+0Li+HpjzDsOOF5qjHnAGLPOGLPWGHNWKr6QTIzuYBi/102+1+10KXIUakq0LZKISLo7YhgzxriBW4FLgGbgcmNM86jTrgO6rbWzgFuAbybe2wwsAeYBi4AfJq4H8D3gz9ba44GTgLXH/nVkoqjHWGYK5Hko9Xtp08iYiEjaSmZkbCHQYq3dbK0NA/cAi0edsxj4ZeLxA8BFxhiTOH6PtTZkrd0CtAALjTHFwFuAOwCstWFrbc+xfx2ZKOoxlrlqSv0aGRMRSWPJhLE6YMcBz1sTx8Y8x1obBXqBisO8dwbQDvzcGPOKMeZ2Y0zBuL6BTDib6DFWppGxjFRT4qNjIEQ4Gne6FBERGUMyYcyMcWz0ZneHOudQxz3AqcCPrLWnAIPAm9aiARhjrjfGLDfGLG9vb0+iXEm1oUiMcDSukbEMVVvixwK7+zQ6JiKSjpIJY61AwwHP64G2Q51jjPEAJUDXYd7bCrRaa5cmjj/ASDh7E2vtbdbaBdbaBVVVVUmUK6m2b2/DUt1JmZG0LZKISHpLJowtA2YbY5qMMXmMLMh/cNQ5DwJXJx5/AHjSWmsTx5ck7rZsAmYDL1trdwM7jDFzEu+5CFhzjN9FJsi+HmNlGhnLSCV+L36vW9siiYikKc+RTrDWRo0xnwQeA9zAz6y1q40xXwWWW2sfZGQh/p3GmBZGRsSWJN672hhzHyNBKwrcYK2NJS79KeCuRMDbDFyb4u8mKbKvx5jupsxMxhhqSn3qNSYikqaOGMYArLWPAI+MOnbTAY+HgQ8e4r03AzePcfxVYMHRFCvO6AmGyXO7COSpx1imqi3x89LmTmJxi9s11lJOERFxijrwyxF1J3qMjXQrkUxUU+IjGrd0DIScLkVEREZRGJMj6gmGNUWZ4fZti6RF/CIi6UdhTI5oZGRMi/czWWVhPh6XURgTEUlDCmNyWKFojKFITBuEZzi3y1Bb6menwpiISNpRGJPD2n8nZYFGxjJdXdlIGIvFR/dsFhERJymMyWHt7zGmkbGM11DmJxKztPdrEb+ISDpRGJPD2t99X2vGMl59aQCA1u6gw5WIiMiBFMbksHqCYdwuQ6EvqZZ0ksbKC/PweV20dmvdmIhIOlEYk8PqDkYo8XtxqcdYxnMZQ31pgNYejYyJiKQThTE5rJ5gmDL1GMsadWV+dvcOE4nFnS5FREQSFMbksHqG1GMsm9SX+YlbtGm4iEgaURiTQ4rE4vQPR9V9P4vUl2kRv4hIulEYk0PqHRq5k7LMr5GxbFHi91Lk82gRv4hIGlEYk0Pa3/BVI2NZpb7UrzAmIpJGFMbkkPY1fNWasexSXx6gYyDEcCTmdCkiIoLCmBxGdzCCYWRqS7JHfakfQKNjIiJpQmFMDqknGKbY78XtUo+xbFJXNhLGdmoRv4hIWlAYk0PqDka0XiwLBfI8VBTksUMjYyIiaUFhTA6pOximXOvFslJdmZ+dPQpjIiLpQGFMxhSNx+kbilBWoDCWjerLAvQORegbjjhdiohIzlMYkzH1BCNYoFxhLCs17F83ptExERGnKYzJmLoGR9palGlYlq1tAAAgAElEQVSaMivVlPhxGdihRfwiIo5TGJMx7QtjGhnLTnkeFzUlfrZ1KoyJiDhNYUzG1D0YxuMyFPk8TpciE6SpsoAdXUFCUTV/FRFxksKYjKkrGKYskIfLqMdYtmqsKCAat6xq7XW6FBGRnKYwJmPqHgxrijLLNVYEAHh5S5fDlYiI5DaFMXkTay2dg2HKCtTwNZsF8j1MKcpXGBMRcZjCmLzJUCRGKBpXw9cc0FRZwIpt3URjcadLERHJWQpj8ibdgyONQDVNmf0aKwsYCEVZu6vf6VJERHKWwpi8SVcw0WNMYSzrNVYUALB0S6fDlYiI5C6FMXmT/T3GNE2Z9Ur8XqaVB1i2VevGREScojAmb9I1GCaQ5ybf63a6FJkEC5vKeXlLF9Zap0sREclJCmPyJt1BtbXIJQubyukORmjZO+B0KSIiOUlhTN6kSz3GcsoZTeUALFWLCxERRyiMyUFicUtPMKz1YjlkWnmA6mL1GxMRcYrCmBykbyhC3OpOylxijGFhU4XWjYmIOERhTA6yr62Fpilzy8LGMnb3DdPaPeR0KSIiOUdhTA7SrbYWOWlhUwWgdWMiIk5QGJODdA2GcRko9mtfylwye0oh5QV5PLex3elSRERyjsKYHKQrGKY0kIfbZZwuRSaRy2W4YM4Unlrfrn0qRUQmmcKYHKRrUHdS5qqL506hdyjCim3dTpciIpJTFMbkIN2DYd1JmaPOO66KPLeLJ9btdboUEZGcojAm+4UiMQbDMd1JmaMK8z2cMaOcv6zZ43QpIiI5RWFM9lNbC3lbczWbOwbZ3K6tkUREJovCmOy3r61FWUB3UuaqC4+fAsATazVVKSIyWRTGZL+uYATQyFguqy8LcPzUIv6yVlOVIiKTRWFM9usaDOPzuvB73U6XIg56W3M1y7d105OYthYRkYmlMCb7dSfaWhijHmO57KK51cTilqfXqwGsiMhkUBiT/ToHQ2prIcyvK6GqKF9TlSIik0RhTACIxS1dg2EqC/OdLkUc5nIZLpwzhb+ubyccVTd+EZGJpjAmAPQEw8QtCmMCwMXN1fSHoizbqo3DRUQmmsKYANAxEAKgslDTlALnzqrE53Xx0KpdTpciIpL1kgpjxphFxpj1xpgWY8yNY7yeb4y5N/H6UmNM4wGvfSFxfL0x5h2j3uc2xrxijHnoWL+IHJuOgZE75zQyJgD+PDfvnl/LH1/dSd9wxOlyRESy2hHDmDHGDdwKXAI0A5cbY5pHnXYd0G2tnQXcAnwz8d5mYAkwD1gE/DBxvX0+A6w91i8hx65jIITP6yKQp7YWMuIjZzUSDMf47YpWp0sREclqyYyMLQRarLWbrbVh4B5g8ahzFgO/TDx+ALjIjPRHWAzcY60NWWu3AC2J62GMqQcuBW4/9q8hx6pzYGTxvtpayD4n1pdwckMpd760DWut0+WIiGStZMJYHbDjgOetiWNjnmOtjQK9QMUR3vtd4F+Bw96uZYy53hiz3BizvL1dfY8mSsdASFOU8iZXnTmdze2DPN/S6XQpIiJZK5kwNtZQyeh/Jh/qnDGPG2PeBey11q440odba2+z1i6w1i6oqqo6crVy1CKxOD1DESq0eF9GuXR+DeUFefzqxa1OlyIikrWSCWOtQMMBz+uBtkOdY4zxACVA12Heew5wmTFmKyPTnhcaY349jvolBToHtXhfxubzuvnQggb+snYPO3uGnC5HRCQrJRPGlgGzjTFNxpg8RhbkPzjqnAeBqxOPPwA8aUcWmTwILEncbdkEzAZettZ+wVpbb61tTFzvSWvtlSn4PjIOHf372loojMmb/d0Z07DA3Uu3O12KiEhWOmIYS6wB+yTwGCN3Pt5nrV1tjPmqMeayxGl3ABXGmBbgn4EbE+9dDdwHrAH+DNxgrY2l/mvIsejc12NMWyHJGBrKA1x0/BTuWbadUFT/+YqIpJonmZOstY8Aj4w6dtMBj4eBDx7ivTcDNx/m2k8DTydTh0yMjsEwRfke8r1qayFju+qsRv6y9mUeXrWL951aP2mfa61lMByjbyjCYChKQ3kAn35ORSTLJBXGJLt1DISo0BSlHMZ5sypprinm64+u44I5UyZ0Q/mdPUPct2wHv3ullbaeYWLxv90vVOTzcMkJU3nPyXWcMaMCt0utWEQk8ymMCR0DYeZOLXK6DEljLpfhWx+cz+IfPM9X/rSa7y05JaXXt9by9Pp27nxpG0+v34sFzptdxeKT6ij2eyj2ecn3unh2YwcPr9rFfctbmVKUz7+8Yw4fXNBwxOuLiKQzhbEcNxyJMRiKavG+HNG82hI+deFsbvnLBi45YSqLTqhJyXU3tw/w5QdX8+zGDqYU5XPDBbP40IIGGsoDbzr3vafUM/zeGE+s3cvPn9/CvzywipXbe/jKZc3kezR9KSKZSWEsx2mDcDkan7hgJo+v2c0Xf/8GpzeWH9P09lA4xq1PtXDbM5vJ97j48rubufLM6Xjdh7+vyOd1c+n8Gt4xr5r/+b8N/OjpTaxp6+WHV55GXal/3PWIiDglqY3CJXvt2yBca8YkGV63i//50En0DUe46cHV47qGtZbHVu/m4u/8lR881cK75tfwxOffyrXnNB0xiB3I43bx/xYdz4+vPI1N7YO8+/vPsXJ797hqEhFxksJYjusYCGGACrW1kCQdP7WYf7r4OB5etYufPrP5qPat3NY5yN//Yhn/cOcKCvM93Hv9mXznwyczpcg37noWnTCVBz95DkU+Dx/95XK2dwbHfS0REScojOW4joEQpQEvnqMYkRD5h7fM4MLjp3DzI2u56o6Xj9idf0/fMN96bB1vu+UZXt7SxZcunctDnz6XM2ZUpKSeGVWF/OLahcSt5dpfvExvMJKS64qITAb9Bs5xnQNhLd6Xo+Zxu7jj6gV87T0nsHJ7N4tueYb7lu0gFI3tHymLxy1Pr9/L9b9aztnfeJJbn9rEO+ZN5cnPn89Hz5txVFOSyWiqLOAnV57G9q4gH79rBZFYPKXXFxGZKFrAn8OstXQMhDhlWpnTpUgGMsZw5ZnTecvsKv7lgdf419+u4l9/uwqAfI8LlzEMRWJUFOTx0fOauPz0aTRWFkxoTWfMqOAb75vP5+5/jS/9/g2+8f4TMUa9yEQkvSmM5bCOgTChaFx3UsoxmVYR4O6PncmfVrXR2j1EKBIjFI0TisY5bXoZb59XPaltJ95/Wj1bOwf5/pMtNNcWc/XZjZP22SIi46EwlsO2dAwC2iBcjp3LZVh8cp3TZez32YuP442dvXz90bWcN7uSGVWFTpckInJIWjOWw7YqjEmWcrkM33j/fPI9bj5//2sHbakkIpJuFMZy2OaOQdzGUBrwOl2KSMpVF/v4j8vmsXJ7D7c/u9npckREDklhLIdt6RigvDAPlxY4S5ZafHItb28e6dS/cU+/0+WIiIxJYSyHbWof1BSlZDVjDDe/90QK8tx87v7XiKrdhYikIYWxHBWKxtjSMUh1scKYZLeqony+9p4TWdXay0+f3eJ0OSIib6IwlqM2tw8Si1uqi8e/DY1Iprh0fg0Xz63m+09uZFfv4XcLEBGZbApjOWpDYv2Mwpjkii+/u5lo3PJfj6xzuhQRkYMojOWoDXv68biMGr5KzmgoD/Dxt87kT6+18eKmTqfLERHZT2EsR63fPcCMqgI8Lv0ISO74+PkzqS/z85UHV2vvShFJG/pNnKM27OlndnWR02WITCqf181N72pm/Z5+7nxxm9PliIgACmM5KRiOsr0ryByFMclBb2uu5q3HVXHL/21gb/+w0+WIiCiM5aKWvQMAHKcwJjnIGMOX393McDTGtx9b73Q5IiIKY7lo/e6ROynnTFUYk9w0o6qQa85u5P4Vraxu63W6HBHJcQpjOWjDnn7yPS6mlQecLkXEMZ+8cDalfi83P7wWa7WRuIg4R2EsB63fM8CsKYW4XdqTUnJXid/LZ992HC9s6uQva/c6XY6I5DCFsRy0YXe/Fu+LAFcsnMasKYX81yNrCUfV6kJEnKEwlmN6hyLs7hvmOK0XE8HjdvHFd85lS8cgv35JrS5ExBkKYzlmY2IbJI2MiYw4f04V582u5HtPbKQnGHa6HBHJQQpjOWZ9IoxpZExkhDGGL13aTP9whO89sdHpckQkBymM5ZgNu/spzPdQW6INwkX2mTO1iCULp3Hni9vY1D7gdDkikmMUxnLM+j39zK4uxBjdSSlyoM9efBw+r5uvP7LO6VJEJMcojOWYDXsGtF5MZAxVRfnccMEs/rJ2Dy+0dDhdjojkEIWxHNIxEKJrMKxtkEQO4dpzGqkv8/OfD68lFlcjWBGZHApjOWSDtkESOSyf182NlxzP2l19PLBih9PliEiOUBjLIfvvpNTImMghXXpiDadNL+Pbj29gIBR1uhwRyQEKYzlkw55+ygJeKgvznC5FJG0ZY/j3dzXT3h/ih0+1OF2OiOQAhbEcsm53P8dVF+lOSpEjOLmhlPedWsftz25ha8eg0+WISJZTGMsRkVicNW19nFhX4nQpIhnhxkuOJ8/j4qsPrXG6FBHJcgpjOWLDnn5C0TjzG0qdLkUkI0wp8vGZi2bz5Lq9PLluj9PliEgWUxjLEa+39gIwXyNjIkm7+uxGZlYV8NU/rSEUjTldjohkKYWxHPFaay/FPg/TKwJOlyKSMfI8Lr5y2Ty2dga5/dktTpcjIlnK43QBMjle39nD/PpSLd6XN/nN0u1Ol5D2mmuK+e5fNuAyhhK/1+lyUu6KM6Y5XYJITtPIWA4YjsRYt6uf+fWaohQZj0tPrMFaeGhVm9OliEgWUhjLAWt39RGNW4UxkXEqK8jjouOnsLqtjzVtfU6XIyJZRmEsB7y+M7F4v153UoqM17mzq5ha7ONPq9oIRbSYX0RSR2EsB7y2o5fKwnxqSnxOlyKSsdwuw3tPqaNvKMLja9XqQkRSR2EsB4ws3i/R4n2RY9RQHuCMGRW8tKmTHV1Bp8sRkSyhMJblBkNRWvYOaL2YSIq8vbmaIp+HP7y6k1jcOl2OiGQBhbEs98bOXuIWhTGRFPF53Vx2Uh27eod5dmO70+WISBZQGMtyWrwvknrNtcWcWFfCE2v30tYz5HQ5IpLhkgpjxphFxpj1xpgWY8yNY7yeb4y5N/H6UmNM4wGvfSFxfL0x5h2JYw3GmKeMMWuNMauNMZ9J1ReSg73W2ktdqZ/KwnynSxHJKotPqiWQ5+b+FTuIxuJOlyMiGeyIYcwY4wZuBS4BmoHLjTHNo067Dui21s4CbgG+mXhvM7AEmAcsAn6YuF4U+Jy1di5wJnDDGNeUFHi9tYcTtR+lSMoF8j2879Q69vSF+MvavU6XIyIZLJmRsYVAi7V2s7U2DNwDLB51zmLgl4nHDwAXmZFb9xYD91hrQ9baLUALsNBau8tauxLAWtsPrAXqjv3ryIF6gxG2dgaZ36AwJjIR5kwtZsH0Mp7d2M62zkGnyxGRDJVMGKsDdhzwvJU3B6f951hro0AvUJHMexNTmqcAS5MvW5KxamcPAPPrtF5MZKJcemINpQEv969oJRRVM1gROXrJhLGxmlONvp/7UOcc9r3GmELgt8A/WWvH3GPEGHO9MWa5MWZ5e7vuXDoaq1pHFu9rmlJk4uR73XzgtAa6B8M89Noup8sRkQyUTBhrBRoOeF4PjN4td/85xhgPUAJ0He69xhgvI0HsLmvt7w714dba26y1C6y1C6qqqpIoV/ZZ1dpDY0WAkoDX6VJEslpTZQHnz6lixfZuVm7vdrocEckwyYSxZcBsY0yTMSaPkQX5D44650Hg6sTjDwBPWmtt4viSxN2WTcBs4OXEerI7gLXW2u+k4ovIway1LN/azanTypwuRSQnXHh8NY0VBfzx1Z3s7Rt2uhwRySBHDGOJNWCfBB5jZKH9fdba1caYrxpjLkucdgdQYYxpAf4ZuDHx3tXAfcAa4M/ADdbaGHAOcBVwoTHm1cSfd6b4u+W0DXsG6BwMc9bMCqdLEckJbpdhyekN5Lld3L1sO+Go2l2ISHI8yZxkrX0EeGTUsZsOeDwMfPAQ770ZuHnUsecYez2ZpMiLmzoAFMZEJlGx38uHFjTwixe28tCqNt53ar3TJYlIBlAH/iz1wqZOGsr91JcFnC5FJKfMri7ircdVsXxbNyu3af2YiByZwlgWisctS7d0cdYMjYqJOOGiudXMqCrgD6/uZEdX0OlyRCTNKYxloTW7+ugdimiKUsQhbpfhitOnUeTz8Oul2+gdijhdkoikMYWxLPTipk4AzppR6XAlIrkrkO/hqrMaCUXj3LV0GxHtXykih6AwloVe3NzJjMoCppb4nC5FJKdNLfbxodMaaO0e4vev7GSk44+IyMEUxrJMNBbn5S1dnKkpSpG00FxbzMVzp/Dqjh6e3qBdRETkzZJqbSGZ4422PgZCUS3eF0kjF8yZQsdAmP9bs4din5fTpqsZs4j8jcJYlnkh0V/sTIUxkbRhjOF9p9YxMBzl96+0UpjvYc7UIqfLEpE0oWnKLPPipk6Oqy6kqijf6VJE5AAel4srzphGdbGP37y8jdZutbwQkREKY1kkHI2zfGu3pihF0pTP6+aasxspzPfwyxe20tEfcrokEUkDCmNZZFVrD0ORmPqLiaSxIp+Xa89uAuD25zbTOaBAJpLrFMayyAubOjEGzmhSGBNJZ5VF+Vx37gyiccvtz22hazDsdEki4iCFsSzyXEsHc6cWU1aQ53QpInIEU0t8XHduE+FonNuf3Uy3AplIzlIYyxIdAyGWb+3i4rlTnC5FRJJUU+LnunObCEXj/PS5zRohE8lRCmNZ4vHVe4hbWHRCjdOliMhRqC318/fnNBGKxPnJM5vY1TvkdEkiMskUxrLEo2/sorEiwNwa9S4SyTR1ZX6uf8sMDPDTZzezuWPA6ZJEZBIpjGWBnmCYFzd1suiEGowxTpcjIuNQXezjH986kyKfl188v5XVbb1OlyQik0RhLAv835o9ROOWd5441elSROQYlAby+IfzZlBT4uM3S7fzXEuHNhcXyQEKY1ng0Td2U1fq58S6EqdLEZFjFMj3cN25M5hbU8wjr+/i/hWthKNxp8sSkQmkMJbh+oYjPLexg0UnTNUUpUiWyPOMbJ30tuZqXtvRw23PbFLrC5EspjCW4Z5cu5dwLK4pSpEs4zKGC+ZM4SNnTacrGObWp1tYv7vP6bJEZAIojGW4R9/YRXVxPqc0lDldiohMgDlTi/nE+bMo9nn55Yvb+MOrOzVtKZJlFMYy2GAoytPr21k0byoul6YoRbJVZWE+Hz9/JufNqmTZli5+8NRGdnQFnS5LRFJEYSyDPb2+nVA0rkavIjnA63ZxyYk1XHduE5GY5SfPbOLPb+zWKJlIFlAYy2APv95GRUEeC5vKnS5FRCbJjKpCPn3hbE6ZVsYzG9v57hMbWLtLa8lEMpnCWIba2zfM46v3cNnJtbg1RSmSU/x5bt5/aj3XnzeDPLeLO1/axp0vbdPeliIZSmEsQ921dDsxa7n6rEanSxERhzRWFvCpC2ezaN5UWvb2c8v/beChVW0MhqJOlyYiR8HjdAFy9ELRGHct3c4Fc6bQWFngdDki4iC3y/CW46o4uaGUJ9bt4cVNnazY1s1bj6vi7JmV5Hn0b26RdKcwloEeeX0XHQMhrjm70elSRCRNFPu9vPeUes6ZWclja/bw+Jo9vLS5k4vnVnPKtDItZxBJY/onU4ax1vLz57cys6qA82ZXOl2OiKSZKcU+rjpzOtefN4MSv5ffvbKT7z+5kbW7+rTPpUiaUhjLMK/s6GFVay/XnN2o7Y9E5JAaKwv4x7fO5IqF04hby50vbeOnz25RfzKRNKRpygzzi+e3UpTv4X2n1jtdioikOWMMJ9SVMLemmGVbu3hi3V5+9NdNnFBXwjuaq6kozHe6RBFBYSyj7Okb5pHXd3H12Y0U5Ov/OhFJjttlOHNGBac0lPJsSwfPbmxnTVsvZzRVcMHxU5wuTyTn6Td6BrnrpW3ErOUjZ013uhQRyUD5XjcXz61mYVM5T67dy9Itnazc3k04GuPvz20ikKdfCSJO0JqxDLG3f5ifPb+Vt82tZnqF2lmIyPgV+7y855Q6Pn3RbGZWFfLtxzdw/ree5vevtGqRv4gDFMYyxLcfW08oGuPGS453uhQRyRJTinxceeZ0HvjHs6gp8fHZe1/jw7e9xPrd/U6XJpJTFMYywKrWHu5f0cq15zQxo6rQ6XJEJMssaCzn9584h6+/70Q27Onnnf/7LDc/vIYBdfIXmRQKY2nOWstXHlxNRUE+n7pwltPliEiWcrkMly+cxpOfO58PLajnp89u4aL/eZo/vdamqUuRCaYwlub+8OpOVm7v4V8XzaHI53W6HBHJcuUFeXz9ffP5/SfOpqoon0/d/Qp/d/tSWvZq6lJkoiiMpbHBUJRvPLqOk+pL+ID6ionIJDplWhl/vOFc/nPxPN7Y2csl33uW//7zOobCMadLE8k6CmNp7H+f3MievhBfvmweLu0rJyKTzO0yXHVWI09+/nwuO6mOHz69ibd/9688tX6v06WJZBWFsTT1+Ord/OSvm1lyegOnTitzuhwRyWGVhfn8z4dO4u6PnUme28W1P1/GJ+5awe7eYadLE8kKCmNpaOOefj5776ucVF/CVy6b53Q5IiIAnDWzgkc/8xY+//bjeGLtXi7+zl/5+fNbiMW1wF/kWCiMpZneYISP/Wo5/jwPP77qNHxet9MliYjsl+dx8ckLZ/P4Z9/CqdPL+I8/reE9tz7PqtYep0sTyVgKY2kkFrd88u6V7OwZ4idXnUpNid/pkkRExjS9ooBfXns6P7jiFHb3DbP41uf5wu9ep2Mg5HRpIhlHYSxNxOOW//jTap7d2MFXF5/AadPLnS5JROSwjDG8a34tT3zurVx7dhP3L9/B+d96mp/8dROhqO66FEmWwlgaGI7EuOE3K/nVi9v42HlNXL5wmtMliYgkrdjn5aZ3N/PYZ9/CGU3lfP3RdbztO8/wh1d2aj2ZSBIUxhzWMRBiyW0v8efVu/nSpXP5t3fOdbokEZFxmVlVyB3XnM6d1y0kkOfmn+59lbff8lf+9FobcYUykUNSGHPQxj39vPeHz7Nudx8/vvI0PnreDIxRPzERyWznza7ikU+fxw//7lTcLsOn7n6FRd97hvuW72A4oulLkdE8TheQiwZDUX7wVAt3PLuFYr+Xe68/i5MaSp0uS0QkZVwuwztPrGHRvKk8/PoufvBkC//6wCq+8eg6lpzewJVnTqe2NDtvUorG4nQFwwRDMYLhGEORGEOJv4PhKEPhGMORGPsGCy0ctP+ntWCx2ANe38cAxoDBsO/f7saYA44nnicec9Brfzvuchn8XjcF+W78Xg+F+R5K/F5KC7wU5Xs0MDDJFMYmkbWWB19r478eWcuevhDvP7We/3fJHKYU+ZwuTURkQrhchnefVMu75tfw4qZOfvHCVn7810385JnNnD2zgnfPr+Ud86ZSEsiMvXd7gxG2dQ2yo2uIPX3DtA+E2NsXSvw9TMdAiM7BMJm8t7rbZSgL5FFT4qOmxEdtqX//37WlPmpK/Ewpysfj1uRaqhibQT8xCxYssMuXL3e6jKPWG4zwh1d3cs+yHazd1cf8RDPXdOis/5ul250uQUQcdsUZk3vT0I6uIPcs286fXtvF9q4gXrfhvNlVvGV2JWfNrOS46kLHRmbiccvuvmG2dQbZ3jXIts4g27qCbO8Msq1zkL7h6EHne92GqsJ8qor2/fFRVZRPZWEeBXkeAnlu/HluAnke/N59j934vG5cidEqAMzfRrbg4NGukZfN/tGyfSNpI38DB4ykjX7Njrx40HNrR1opDUdiDIZHRusGQzF6gmF6ghG6g2G6BsO09Q6zq2eIXb3DDIQO/t5ul6Gu1M/0igDTygOJvwv2Py/I11gPgDFmhbV2wRHPSyaMGWMWAd8D3MDt1tpvjHo9H/gVcBrQCXzYWrs18doXgOuAGPBpa+1jyVxzLJkUxnqDEZZu6eShVbv48+rdhKNxTqgr5uqzGnn/qfVps9ekwpiIOMVay86eIVa19rK6rZfuYASAgjw3TVWF1Jb4qC4e+VMa8OJKQUCLxuP0DUXpGQrTG4zQOxShZyhCbzBC52CY7mD4oDtAXQbKAnmUF/ztT0VBHmUFeZT4vPjy3Cmpy2lHCuR9wxF29QzT1jvErp5hdvYEE4F15O/eochB51cW5jO9IsD08gDTKg4OaxUFeTkzDZpsGDtidDXGuIFbgbcBrcAyY8yD1to1B5x2HdBtrZ1ljFkCfBP4sDGmGVgCzANqgb8YY45LvOdI18wYg6EoWzoG2dQ+wGs7enlpcydrd/dhLRT7PFx+egMfOr2BebUlTpcqIpI2jDHUlwWoLwvwzhNr6BoMs7l9gM0dg2ztHOSNnb37z/W6DcU+L0U+D0U+L4U+D3luFx63wety4XYZrLXE4paotcRidmStVmRkfVYwHKN3KMLAcJTRQxCBPDclfi/VxfnMrSlKBK58ygvyKPF7cafJP56dVOzzUjzVy5ypRWO+vm/6dl9A294ZZFvXIC9t7uT3r+48aNq2IM/NtIoCpu8bUasI0FAWoLIwn4rCkcDrzbEp0GTGERcCLdbazQDGmHuAxcCBwWkx8JXE4weAH5iR2LsYuMdaGwK2GGNaEtcjiWtOuu7BMH3Dkb8ttty38DLxfDjy/9u79xg5qzKO49/fXrrtdrVLLyDh1lKKARUrKJQgUC6BgibgBbExSJQIGjCQAAaM8ULUQIhgMBECgVANIIhUGkNokUsLCIUCLW0pDcpFCoTtjV6A7na3j3+cM+10O9OdduvO7OzvkzTzzjtn3j19cmb3mfec933Sh3nlhrQ+YOWGTlasTadwC1qaGjjqoL247JRDmXLwaCYf2E5Lk0samZn1JZ15Gs0Xx8iG5nYAAAkPSURBVKebXm/a3EPHhk7eX7+JjvWbWL+pmw2bunn3g4/Z2NnN5p4tlLtjhoDhzY1bpwlHNDfyqU8OZ9SI5rRQvXXY1u1hTUPrD///w6jWZo5obeeI/Xe8GG3T5h5WrP1427RvTthe69jAY6920NWzZcfjjWhmzMhhjGlLifFeI5vTtG9LEyOHNdLa0kRrvgChdVgTI4Y10tQgmnOC3tTQQHOjaMz7Ghu03RQwQPuI5pqZpaokGdsPeLvo+QrgmHJtIqJb0jpgTN7/bK/37pe3+zrmgLvy/kX8c1lHn+0+ObwprwloYcrBY5g4biQTx7Vx8Lg2xo9tdfJlZrYHDG9u5MDRaQ1SOT1b8tmwLVtoUPrj29igupg6rBfDmxs5ZO82Dtm7bYfXCmv0Vqz9mNX54ofVG7tY82Enqz7sYvXGTl5ftZE1b23mo65uPurac7dGWfCzUxnb1rLHjtcflSRjpUZ07+8i5dqU21/qa0jJ7zeSLgQuzE83Slpepp8AY4FVO3nd+uYY9o/j13+OYf84fv035GL4nT17uEERv3HXDciPOaiSRpUkYyuAA4qe7w+8W6bNCklNwChgTR/v7euYAETErcCtFfQTSQsqWShn5TmG/eP49Z9j2D+OX/85hv3j+O26SibKnwcmSZogaRhpQf6sXm1mAefn7W8Cj0W6THMW8G1JLZImAJOA5yo8ppmZmVnd6/PMWF4Ddgkwm3QbijsiYqmka4AFETELuB34c16gv4aUXJHb3UdamN8NXBwRPQCljrnn/3tmZmZmtW1Q3fS1L5IuzNOatpscw/5x/PrPMewfx6//HMP+cfx2XV0lY2ZmZmaDjW+uYmZmZlZFdZGMSZomabmkf0u6qtr9GSwkvSlpsaSFkhbkfaMlPSLptfxY/QKaNUTSHZI6JC0p2lcyZkpuyuPyZUlHVq/ntaFM/H4p6Z08DhdKOrPotatz/JZLOr06va4tkg6Q9LikZZKWSro07/c4rMBO4udxWCFJwyU9J2lRjuGv8v4JkubnMXhvvkCPfBHfvTmG8yWNr2b/a9GgT8a0rVzTGcDhwHSlMkxWmZMiYnLRZchXAY9GxCTg0fzctrkTmNZrX7mYnUG6gngS6V55Nw9QH2vZnewYP4Ab8zicHBEPAWj7cmrTgD/mz/tQ1w1cHhGHAVOAi3OsPA4rUy5+4HFYqU7g5Ij4PDAZmCZpCqkU4o15DK4llUqEopKJwI25nRUZ9MkYReWaIqILKJRWst1zFjAjb88Azq5iX2pORMwjXTFcrFzMzgL+FMmzQLukfQemp7WpTPzK2VpOLSLeAIrLqQ1ZEfFeRLyYtzcAy0iVTTwOK7CT+JXjcdhLHksb89Pm/C+Ak0klEWHHMVgYm/cDp0gukVCsHpKxUuWadvbBsm0CmCPpBaVKBwD7RMR7kH5pAXtXrXeDR7mYeWxW7pI8hXZH0dS449eHPN3zBWA+Hoe7rFf8wOOwYpIaJS0EOoBHgP8AH0REd25SHKftSiYChZKJltVDMlZJuSYr7biIOJI0jXGxpBOq3aE647FZmZuBiaTpjveA3+X9jt9OSGoD/gZcFhHrd9a0xL4hH8cS8fM43AUR0RMRk0kVdI4GDivVLD86hn2oh2SsknJNVkJEvJsfO4CZpA/U+4UpjPzYd+V0Kxczj80KRMT7+Rf7FuA2tk0BOX5lSGomJRJ3RcQDebfHYYVKxc/jcPdExAfAE6T1d+1KJRFh+zhtjaG2L5loWT0kYy6ttBskjZT0icI2cBqwhO1LW50PPFidHg4q5WI2C/huvpptCrCuMI1k2/Rav/Q10jiE8uXUhrS81uZ2YFlE3FD0ksdhBcrFz+OwcpLGSWrP2yOAU0lr7x4nlUSEHcdgqZKJllVSKLymlSvXVOVuDQb7ADPzGsom4O6IeFjS88B9ki4A/gucU8U+1hxJ9wBTgbGSVgC/AK6ldMweAs4kLfj9CPjegHe4xpSJ31RJk0nTFm8CF8HOy6kNcccB5wGL85odgJ/icVipcvGb7nFYsX2BGfmq0gbgvoj4h6RXgL9I+jXwEinphTIlE20b34HfzMzMrIrqYZrSzMzMbNByMmZmZmZWRU7GzMzMzKrIyZiZmZlZFTkZMzMzM6siJ2NmVlMkjZe0pO+WZmb1wcmYmdWNort/17TB0k8zGxhOxsysFjVKuk3SUklzJI2QNFnSs7mQ88xCIWdJT0j6raS5wKWSzpG0RNIiSfNym0ZJ10t6Pr//orx/qqR5+XivSLpFUkN+bbqkxflY1+V935J0Q96+VNLreXuipKfy9lGS5kp6QdLsohJF2/VzYMNpZrXM387MrBZNAqZHxA/y3c+/AfwE+HFEzJV0Denu/Zfl9u0RcSKApMXA6RHxTqFkC3ABqQzQlyS1AE9LmpNfOxo4HHgLeBj4uqR/AdcBRwFrgTmSzgbmAVfm9x0PrJa0H/Bl4Mlc8/APwFkRsVLSucBvgO/37qeZWYGTMTOrRW9ERKFUzQvARFIiMzfvmwH8taj9vUXbTwN35iSuUET7NOAISYW6eaNICV8X8FxEFM5w3UNKrDYDT0TEyrz/LuCEiPi7pLZc1/UA4G7gBFJi9gDwaeCzwCO51FgjUFwHsrifZmaAkzEzq02dRds9QHu5htmHhY2I+KGkY4CvAAtzvUGRzqrNLn6TpKmkWoTFIrcv5xlSfcflwJOks17HApcDBwJLI+LYvvppZlbgNWNmNhisA9ZKOj4/Pw+YW6qhpIkRMT8ifg6sIp3Bmg38KE8jIulQSSPzW46WNCGvFTsXeAqYD5woaWwuhjy96OfNA67Ijy8BJwGdEbGOlKCNk3Rs/jnNkj6z58JgZvXIZ8bMbLA4H7hFUivwOunsVCnXS5pEOrv1KLAIeBkYD7yoNH+4Ejg7t38GuBb4HCnBmhkRWyRdDTyej/NQRDyY2z9JSvDmRUSPpLeBVwEioitPhd4kaRTpd+zvgaV7KAZmVocU0fsMvZnZ0JCnKa+IiK9Wuy9mNnR5mtLMzMysinxmzMzMzKyKfGbMzMzMrIqcjJmZmZlVkZMxMzMzsypyMmZmZmZWRU7GzMzMzKrIyZiZmZlZFf0PFyQCm3FunWsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "sns.distplot(df_sem_nulos['horsepower'], bins=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformando dados - Variáveis categóricas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dummy variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fuel-type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fuel-type\n",
       "0       gas\n",
       "1       gas\n",
       "2       gas\n",
       "3       gas\n",
       "4       gas"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['fuel-type']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diesel</th>\n",
       "      <th>gas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   diesel  gas\n",
       "0       0    1\n",
       "1       0    1\n",
       "2       0    1\n",
       "3       0    1\n",
       "4       0    1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_variable_1 = pd.get_dummies(df['fuel-type'])\n",
    "dummy_variable_1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df, dummy_variable_1], axis=1)\n",
    "df.drop('fuel-type', axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['symboling', 'normalized-losses', 'make', 'aspiration', 'num-of-doors',\n",
       "       'body-style', 'drive-wheels', 'engine-location', 'wheel-base', 'length',\n",
       "       'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders',\n",
       "       'engine-size', 'fuel-system', 'bore', 'stroke', 'compression-ratio',\n",
       "       'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price',\n",
       "       'city-L/100km', 'highway-L/100km', 'horsepower-binned', 'price-binned',\n",
       "       'diesel', 'gas'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  }
 ],
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