free-code-camp-task-4-fcc-forum-pageviewers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "import scipy as sc\n",
    "import numpy as np\n",
    "import matplotlib.ticker as mticker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Import data (Make sure to parse dates.Consider setting index column to 'date'.) </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-09</th>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-10</th>\n",
       "      <td>2329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-11</th>\n",
       "      <td>1716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-12</th>\n",
       "      <td>10539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-13</th>\n",
       "      <td>6933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>171584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>141161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>142918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-02</th>\n",
       "      <td>220144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>158549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1304 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             value\n",
       "date              \n",
       "2016-05-09    1201\n",
       "2016-05-10    2329\n",
       "2016-05-11    1716\n",
       "2016-05-12   10539\n",
       "2016-05-13    6933\n",
       "...            ...\n",
       "2019-11-29  171584\n",
       "2019-11-30  141161\n",
       "2019-12-01  142918\n",
       "2019-12-02  220144\n",
       "2019-12-03  158549\n",
       "\n",
       "[1304 rows x 1 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('fcc-forum-pageviews.csv', parse_dates= ['date'])\n",
    "df = df.set_index(['date'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Clean the data by filtering out days when the page views were in the top 2.5% of the dataset or bottom 2.5% of the dataset. </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(177613, 17876)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bottom = int(df.quantile(q=.025))\n",
    "top = int(df.quantile(q=.975))\n",
    "top, bottom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 33)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_data = df[df['value'] >= top]\n",
    "bottom_data = df[df['value'] <= bottom]\n",
    "len(top_data), len(bottom_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>19736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-26</th>\n",
       "      <td>18060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-27</th>\n",
       "      <td>19997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-28</th>\n",
       "      <td>19044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-29</th>\n",
       "      <td>20325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-24</th>\n",
       "      <td>138875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-29</th>\n",
       "      <td>171584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-30</th>\n",
       "      <td>141161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-01</th>\n",
       "      <td>142918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-03</th>\n",
       "      <td>158549</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1238 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             value\n",
       "date              \n",
       "2016-05-19   19736\n",
       "2016-05-26   18060\n",
       "2016-05-27   19997\n",
       "2016-05-28   19044\n",
       "2016-05-29   20325\n",
       "...            ...\n",
       "2019-11-24  138875\n",
       "2019-11-29  171584\n",
       "2019-11-30  141161\n",
       "2019-12-01  142918\n",
       "2019-12-03  158549\n",
       "\n",
       "[1238 rows x 1 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_data = df[(df['value'] < top) & (df['value'] > bottom)]\n",
    "cleaned_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<AxesSubplot:xlabel='date'>,\n",
       " <AxesSubplot:xlabel='date'>,\n",
       " <AxesSubplot:xlabel='date'>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NF7bib3dcSISFeZni46LJsPVcjIc8+S1qDewudTvNva+054A7RSQN+Bx4uKwHEpEkEVkiIksyMjKqUK4JBa/+bzMvzd7ILb1a8bKFeblKWuiqtlm0qZi3fpOGAVNUtQ1wPfCOiJzx2Ko6XlUTVDUhPj7eS09tgsk/v9nMX/+7iR/3bs3Ld/SyMK9As7gYCoqUrBMF/i7FBAFPfpv2AG1L3W7j3lfaaOA9AFVdCMQATb1RoAkdf/96Ey9/tYmf9G7NX2+/kPAw8XdJAc82izaV4UmgLwY6iUhHEYnCuej5yWnn7AIGAYjI+TiBbn0qBgBV5eWvNvH3rzfz0z5teMnC3GO2noupjAoDXVULgYeA2cB6nNEsa0XkeRG52T3tF8BYEVkJJAMj1Tr9DE6Y/+2rTfzzm83c3rcNL97W08K8Emy2qKkMjyYWqernOBc7S9/3TKn/rwMu8W5pJtipKv/33028OmcLQxLa8qefXECYhXml2HoupjJspqipEarKS7M38vrcrQy9qC1//LGFeVXUj4kgOiLMWujGIxboxutUlb98uZE35m1lWL92/OHWHhbmVSQiztBF60M3HrBAN16lqvz5iw28OX8bI/q34/e3WJhXlwW68ZQNAjZeo6r88fP1vDl/G3cltucFa5l7Rbyt52I8ZIFuvEJVeeGz9UxYsJ17BrTn+Vu6I2Jh7g22novxlAW6qTZV5flP1/HWt9sZeXEHnrvZwtyb4uOiOXQsn/zCYn+XYgKcBbqpFlXld/9Zx+TvdjDqkg48e1M3C3MvaxYXA0DmMWulm/JZoJsqU1We+2QtU77fwehLO/LMjRbmNcGm/xtP2SgXUyWqyjOz1vJOyk7GXtaRX19/voV5DbFAN56yQDeVVlysPPPJGt5N2cV9l5/DU9d1tTCvQRboxlMW6KZSiouV385aw/TUXdx3xTk8NdjCvKY1jY0CLNBNxSzQjceKi5XffLya5EW7uX/guTx5bRcLcx+IjginQZ1I0i3QTQUs0I1HiouVX324mplLdvPglefy+I8szH2pmc0WNR6wQDcVKi5WfvnvVby/NI2HrzqPx67pbGHuYza5yHjChi2achUVK0+6YT5uUCcLcz+x9VyMJ6yFbs6qqFh54oOVfLhsD49e3YlHr+7s75JqrZL1XFTV/qCas7IWuilTUbHy+PtOmP/86s4W5n4WHxfNiYIijuUX+bsUE8As0M0ZCouKeey9FXy0fA+/uKYzj1zdyd8l1Xqndi7K9XMlJpBZoJsfcMJ8JbNW7OWJa7vw8CAL80BQsp6L9aOb8lgfujmpsKiYR2eu4NNV+3hycBceGHiev0syLtss2njCAt0ATpg/MnMFn63ax1PXdeVnV5zr75JMKTb933jCAt1QUFTMozNW8Nnqffz6+q4kXW5hHmga1okkIkws0E25LNBruYKiYsYlL+eLNfv57Q3nM+ayc/xdkilDWJjQ1LaiMxWwQK/F8guLeTh5GbPXHuDpG7sx+tKO/i7JlCM+LtrWczHlslEutVR+YTEPTXfC/NmbLMyDga3nYipigV4L5RcW88C0Zfx33QF+d3N3Rl1iYR4MbD0XUxEL9Fomr7CIB6Yt5ev1B3j+lu7cc3EHf5dkPBQfF01mTh5FxervUkyA8ijQRWSwiGwUkS0i8tRZzrlDRNaJyFoRme7dMo035BUWcf+7y/h6fTq/v6U7dw/o4O+STCXEx0VTrHDoWL6/SzEBqsKLoiISDrwGXAOkAYtF5BNVXVfqnE7Ar4BLVPWwiDSrqYJN1eQWFHH/u0uZszGDF27twZ2J7f1dkqmk+NhTY9FLxqUbU5onLfR+wBZV3aaq+cAM4JbTzhkLvKaqhwFUNd27ZZrqyC0o4r53nDD/448vsDAPUifXc8m29VxM2TwJ9NbA7lK309z7SusMdBaR70QkRUQGl/VAIpIkIktEZElGRkbVKjaVkltQxNipS5i3KYM//+QChvdv5++STBXZei6mIt66KBoBdAIGAsOACSLS8PSTVHW8qiaoakJ8fLyXntqcTUmYf7vlIC/+tCdD+1mYB7Omce5m0TbSxZyFJ4G+B2hb6nYb977S0oBPVLVAVbcDm3AC3vjJifwixrzthPlfftqTOy5qW/EnmYBWNyqC2OgIa6Gbs/Ik0BcDnUSko4hEAUOBT04752Oc1jki0hSnC2ab98o0lXEiv4jRby/mu60Heem2C7kjwcI8VNhWdKY8FQa6qhYCDwGzgfXAe6q6VkSeF5Gb3dNmA5kisg6YAzyhqpk1VbQ5u+P5hdw7ZTELt2Xyf7dfyG192/i7JONF8bE2/d+cnUdruajq58Dnp933TKn/K/CY+2H8pCTMF20/xMt3XMiPe1uYh5r4+tGs33vU32WYAGUzRUPEsbxCRk52wvxvQ3pZmIeoeFtx0ZTDAj0EHMsrZNTkxSzZcYi/D+3NLb1OH1VqQkV8XDTZeYWcsM2iTRks0INcTl4hIycvYumuw/xjaG9uvrCVv0syNahkctFBG7poymCBHsSycwu4Z9Iilu06wj+H9uYmC/OQd2q2qAW6OZNtcBGkSsJ8VVoWrw7rzXUXtPR3ScYHTq3nYtP/zZks0IPQUTfMV6dl8erw3gzuYWFeWzSrb5tFm7OzQA8yWScKuHvSItbuyeLV4X0Y3KOFv0syPtSkXjRhYoFuymaBHkSyThRw91uprNt3lNdH9OFH3S3Ma5vwMKFxPdu5yJTNAj1IZB0v4K5Jqazfd5TXR/Tlmm7N/V2S8ROb/m/OxgI9CBw5ns+db6WyaX8Ob9zZl0HnW5jXZhbo5mxs2GKAO3I8nxET3TC/q4+FubH1XMxZWaAHsMPH8hk+IZXN6Tm8eXdfrupqYW6ckS4Hc/Iots2izWks0APUoWP5DJ+YypaMHMbf1Zcru9g2rcYRHxtNQZGSdaLA36WYAGOBHoAOHctn+IQUtmXkMPHuBAZamJtSSmaL2kgXczoL9ACTmZPH8AkpbD94jIn3JHB5Z9uqz/zQyUC3fnRzGhvlEkAO5uQxYkIqOw8dY9LIi7jkvKb+LskEIAt0czYW6AEiI9tpme8+fJxJ91zExRbm5ixOLdBl67mYH7JADwDp2bkMn5DKnsMnmDyyHwPObeLvkkwAi4uOICYyzFro5gwW6H6WfjSXYRNS2Hskl8mjLiLxHAtzUz4RsclFpkwW6H6UfjSXoRNS2J+Vy5RRF9Hfwtx4KD7W1nMxZ7JRLn5y4GguQ8encCArl7fv7WdhbirFWuimLBbofrA/yw3zo06YX9Shsb9LMkHGAt2UxQLdx/ZlnWDo+IVkZOcxdXQ/EizMTRXEx8Zw+HgB+YXF/i7FBBALdB/ae+QEQ8encDAnn7fv7Uff9hbmpmpKdi6yzaJNaRboPrLHDfNDOflMHd2Pvu0b+bskE8RO7S1qgW5OsVEuPpB2+DjDJqRw5HgB74zpT6+2Df1dkglyNlvUlMWjFrqIDBaRjSKyRUSeKue8n4qIikiC90oMbrsPHWfoeCfM3x1tYW68wxboMmWpMNBFJBx4DbgO6AYME5FuZZwXBzwCpHq7yGBVEuZHTxQwbUx/LrQwN17SJDYKsBa6+SFPWuj9gC2quk1V84EZwC1lnPd74C+ALTAB7Mp0wjwnr5DpYxPp2aahv0syISQ6IpyGdSNtPRfzA54Eemtgd6nbae59J4lIH6Ctqn7mxdqClhPmCzmWX8i0Mf3p0bqBv0syIaiZjUU3p6n2RVERCQNeBkZ6cG4SkATQrl276j51QNqZeYyh41M4UVDEtDH96d7KwtzUDJtcZE7nSQt9D9C21O027n0l4oAewFwR2QEkAp+UdWFUVceraoKqJsTHh97GDTsOHmPImynkFhQxfUyihbmpUbaeizmdJ4G+GOgkIh1FJAoYCnxSclBVs1S1qap2UNUOQApws6ouqZGKA9T2g8cYMn4h+UXFTB+bSLdW9f1dkglxJS10Vdss2jgqDHRVLQQeAmYD64H3VHWtiDwvIjfXdIHBYGtGDkPeXEhhkZI8NpHzW1qYm5oXHxdNbkExOXmF/i7FBAiP+tBV9XPg89Pue+Ys5w6sflnBY0t6DsMnpFCsSnJSIp2bx/m7JFNLNIuLASA9O4+4mEg/V2MCgU39r4Yt6dkMm5BCsULyWAtz41s2W9Sczqb+V9HmA9kMm+DMoZqR1J/zmlmYG9+yQDensxZ6FWw64LTMRWBGUqKFufELW6DLnM4CvZI27s9m2PgUwkTcMI/1d0mmlmpQJ5LIcLGhi+YkC/RK2LD/KMMmpBAR7oT5ufEW5sZ/wsKEprE1M7kor7CIFz5dR/KiXRQW2SYawcL60D20bu9RRkxMIToinOSkRDo2refvkoyhWVw06V4O9OJi5dEZK/hizX4AJizYxpPXduXa7s0REa8+l/Eua6F7YO3eLEZMTCEmMpwZFuYmgNTE9P8/f7mBL9bs57c3nM/4u/oiwM/eXcpP//U9i3cc8upzGe+yFnoF1uzJ4s63Uqkb6bTM2zexMDeBIz4umhW7s7z2eNNSdzJ+/jbuHtCe0Zd2RES4qmszPliaxt++3sTtbyzk6vOb88vBXehkw3QDjrXQy7FmTxYjJqZSLyqCGUkDLMxNwImPjebQsTyKiqs//X/epgyembWWK7vE88yN3U52r0SEhzG0XzvmPn4lT1zbhdRtmVz79/n88oNV7Ms6Ue3nNd5jgX4Wq9OcMI+NjmBGUiLtmtT1d0nGnCE+Lppihcxj1et22bg/mwenLaNz8zheGd6HiPAzo6FOVDgPXnke8568klGXdOSj5XsY+NJc/vLlBrJOFFTr+YPVqrQj/Oaj1RR74Q+qN1igl2FV2hFGTEw5GeZtG1uYm8DkjclF6dm53DtlMfWiw5k0MoHY6PJ7YhvXi+LpG7vxzS+u4PoLWvLGvK1c8dIcJi7YRl5hUZXrCEZzN2YwLXUXBwJkoxEL9NOs3H2EERNTqV8nkpn3WZibwBZfaj2XqjiRX8SYt5dw+Hg+b91zES0b1PH4c9s2rsvfhvTi04cvpWebhrzw2Xqu+us8PlyWFjAt1pqWW+D8AduVedzPlTgs0EtZvuswd05MpWHdSGbeN4A2jSzMTWBrVo0WelGx8ujM5azZk8U/h/au8s5a3Vs1YOq9/Zg2pj+N6kXy2HsrueGVb5m7MT3kl/bNLXDG6O86ZIEeUJbtOszdby2iUb0oZiYNoHVDz1sqxvhL02pM///zF+uZvfYAT9/Yjau7Na92LZec15RPHryUfw7rzbG8QkZOXsyIiamsSjtS7ccOVLluF9NuC/TAsXSnE+aNY6OYeV8irSzMTZCoExVOXHREpQP93ZSdTFiwnXsGtGfUJR29Vk9YmHDzha34+rEreO6mbmzYn83Nr37Hg9OXsePgMa89T6A42eVigR4Yluw4xN1vpRIfF83MpAGV6kM0JhDEx1VuK7p5mzJ49pO1XNW1GU/f2K1GaoqKCGPkJR2Z98RAxl11Hv9bn87VL8/j2VlrOBhCa8/kWZdL4Fi84xD3TFpE8/oxJI9NpEWDGH+XZEylNa3EbNEN+4+eHJ74z2G9yxye6E1xMZE89qMuzHtyIEMuasu7qbu44sU5/OPrzRzz805LR47nM3Xhjmr1859qoQfGePxaG+iLtrth3iCG5CQLcxO8mnkY6OlHc7l3sufDE72pWVwMf/jxBXz188u5vHM8f/t6E1e8NJd3UnZS4KfFv/7y5UaembWWBZsPVvkxSvrQD+bkcTzf/1sB1spAT9mWycjJi2jZIIYZYxNpXt/C3AQvT9ZzOZ5fyJipSzhyoqDSwxO96Zz4WP51Z18+fOBizomvx9Mfr+FHf5vP56v3+XxETFGx84dk4/7sKj9GSZcLwO4AaKXXukBfuDWTUZMX06phHZKTEmlmYW6CXHxcNDl5hWdtIRYVK4/MWMGaPVm8MqzqwxO9qU+7RsxMSuStexKIDBcemLaMW1//npRtmT6rITzMib+V1RiFk1tYRNPYKCAw+tFrVaB/v+Ugo6Ysok2jOiSPTTy5ya4xwaxk56KD2fllHv/T5+v5at0BnrmxG4POr/7wRG8REQad35wvHrmcF2/rSfrRXIaOT+HeKYvZsP9ojT9/ybua1O2HqrwWTm5BMZ3cHcss0H3ouy0HufftxbRrXJfkpMSTU6aNCXYnp//nnDn9/J2UnUz8djsjL+7ASC8OT/Sm8DDhjoS2zHl8IE9d15UlOw5x3T8W8Pj7K9lzpOa6MUpGBmVk57Fwa9XeGeQWFNGyYQyx0REBMRa9VgT6t5sPcu+UxbRvXI/pYxNPTsYwJhSUBHr60R/2o8/dmM5zn6xlUA0OT/SmmMhwfnbFucx/8krGXnYOn6zcy5V/ncufPl9P1nHvL/51MDuPG3q2JC4mgg+Xp1XpMXILiomJDKdt47rWQveFBZszGP32Yjo2rcf0sf0tzE3IKek6LD0Wff2+ozw0fTld3OGJ4WHBs9NQw7pR/Pr685nz+EBu6tmK8Qu2cdmL/+PNeVtPDhOsLlUlIzuPNo3qcMMFLflyzf4qjVLJKygiJiKcdo3rWKDXtHmbMhj99hI3zBNpYmFuQlDjelGEyak+4fSjuYyespjY6AgmjbyIej4cnuhNrRvW4f/uuJDPx11G3/aN+NMXG7jqr3N5f8nuaq//nnWigPyiYuJjo7m1d2uO5xfx1boDlX6c3MIiYiLDaNe4LrsPHff7omQhG+hzN6YzduoSzouPJXlsIo3rRfm7JGNqRHiY0MTdLPp4fiGj33aHJ45MCIn5Fee3rM/kUf1IHutc+3rig1Vc/48F/G/DgSoPddzuLkPQtnFd+nVoTOuGdfhw2Z5KPUZRsVJQpMREhtOucV3yCotZu/fMi7mn//E5mJNXY0M0QzLQ52xIJ2nqUjo1i3VXgLMwN6EtPjaa/UdzeWTGCtbudYYndm/l/+GJ3jTg3CZ8/OAlvDa8D3mFRdw7ZQlDx6ewfNfhSj/Wun1O8HZvVZ+wMOGWXq1YsDmjUmvilHT/xESGcW58LAA3vfotmw4449rTDh9n0P/Npedzszlw1LlgvffICW565Vtemr2x0jV7wqNAF5HBIrJRRLaIyFNlHH9MRNaJyCoR+UZE2nu/VM/8b8MB7ntnKZ1bWJib2iM+Lpp5mzICcniiN4kIN/RsyVePXcHvb+nO1owcfvz69zwwbSnbMnI8fpy1e49SPybi5KqqP+7dmmKFYRNSmLViT7ldOlvSc5i6cAfH851Aj44IJ/GcJvxjaC/iYiJ4ZMYKjuYW8LN3l7I14xjH8ov4fPU+jhzP555Ji8jJLeTGnq2q90KcRYWdayISDrwGXAOkAYtF5BNVXVfqtOVAgqoeF5H7gReBITVRcHm+XneA+6ctpWuL+rw7uj8N6kb6ugRj/CI+LhpVAnp4ojdFhodx14AO/KRPGyYs2Mb4+duYvfYAw/q1ZdygTuXOMSkoKmbJjkN0a1X/5L6pnZrHcX7L+qzfd5RHZqwgLiaCq7o2R1X57cdr6Ni0HqMv7ci7KTv5w+fryS0oZv0+pyUeExnmtvJbUy8qgjFTl3Dt3+azLyuXiXcn8Nf/buTDZXv4z8q97Dx0nKn39qNbq/o18rp4crWkH7BFVbcBiMgM4BbgZKCr6pxS56cAd3qzSE98te4AD0xbSreW9Zk6uj8N6liYm9rjJ31a06ReFE8O7urvUnyqXnQEj17dmRH92/PK/zYzPXUXHy7bw5jLziHp8nPOWK+muFh54v2VbDqQw8t3XPiDY2/e2Ze0w8cZPjGVVWlZXNmlGZO/28G01F2AM5dlzsYMLu8cT7hA8iLn/pjI8JOPcXW35gzv347pqbsYN6gTV3drTuaxPH7579WECbw+og+J5zSpsddDKuqcF5HbgMGqOsa9fRfQX1UfOsv5rwL7VfWFMo4lAUkA7dq167tz585qlu+YvXY/D01fRjd35xQLc2Nqp+0Hj/HX/27ks1X7aFIvinGDOjGsXzuiIsJQVX73n3VM+X4HT1zbhQevPK/Mx7j65XnEx0bTODaKz1bt+8GxZ2/qxj0DOpCyLZPhE1MBeOPOvgzu0eLkOXmFRaRuO8Sl5zUlLExQVf78xQa6tarPLb1aV/trFJGlqppQ1jGvjmcSkTuBBOCKso6r6nhgPEBCQoJXLvN+ucYJ8x6tGzB1dD/qx1iYG1NbdWxaj9eG9yHpsiP8+YsNPPvJWiZ9t53Hf9SFrRk5TPl+B2Mu7cgDA88962P0aFWfj1fsJSJMeHJwF+5IaMvAl+Zy/8BzT24G0v+cJvRo7XSbXNj2hxefoyPCubxz/MnbIsKvrj+/Br7aM3nSQh8APKeq17q3fwWgqn867byrgVeAK1Q1vaInTkhI0CVLllS64MycPP7xzWaeHNyVBZsyeDh5OT3bNODte/sRZ2FujHGpKvM2ZfDnLzawwV1R8ba+bXjptp4n+87L8s36A/xr7lZ+e2M3erVtCEBhUXGNrx3vqfJa6J4EegSwCRgE7AEWA8NVdW2pc3oDH+B0zWz2pKiqBvqnq/YyLnk5rRvVYe+RXHq1bciUURdZmBtjylRUrMxasYftB4/xyKBOARPMVVWtLhdVLRSRh4DZQDgwSVXXisjzwBJV/QR4CYgF3nf/8u1S1Zu99hWUcmPPVjSuF8W45OX0bdeISaMu8ulC/caY4BIeJvykTxt/l+ETFbbQa0pVW+gl8gqLiAxzhgsZY0xt4bOLor4UHRFe8UnGGFOLBHdnkjHGmJMs0I0xJkRYoBtjTIiwQDfGmBBhgW6MMSHCAt0YY0KE38ahi0gG4J3VuTzXFDjo4+esDqu35lnNvmE1e097VY0v64DfAt0fRGTJ2QbkByKrt+ZZzb5hNfuGdbkYY0yIsEA3xpgQUdsCfby/C6gkq7fmWc2+YTX7QK3qQzfGmFBW21roxhgTsizQjTEmRFigm0qR8vbuMrWa/Wz4nwW6qayTa+gHwy+wiHQRkaD6OReR4SJyofv/gH+NSwmq1zkUhcQ3QERuFZHf+7uOygi2mkVksIjMBv4qIj8G0AC+oi4i14hIKjCGIPk5F5GrRWQB8HegNwT2a1xCRG4QkU+B34vIJf6uxxPu798rItLY37V4U9DuWATgtrzuBZ4C2ovIf1V1gZ/LOiu3tRUGjCIIanbrjQT+CAwA/gK0AW4XkTWebgjuK269EcDTwDDgl6r6YenjgRaQbs0xwNtAM+AF4Bagrns8XFWL/Fdh+USkL/As8BxQH7hHRDqp6hQRCVPVYr8WeBr39f4x8AcgDpgrIh8FWp1VFRQtl7NxvwmbcVozDwAB3eJVRxGwhSCo2a03H/gSuMLdEPx7oADY7tfiyuDWWwAUAx+UhLmIXCYikf6trmxuzSeAaao6UFVn47zGd7nHAzbMXVcDC1T1c2AWsB8YJyINVLU40LqM3D/o24BLgUeAO3EaKSEh6AJdRG4Tkf6l7vpeVbNVdQJQT0RGu+cFzNcmIuNEZIKIjHHvmhfINZ9er6p+raqFInI98CHQBfijiAxxz/frL22pepPcu94AWorIZBFZDTwJvIXzbs7v9bo1lNQ8FkBVZ7n3h+P8sVwrIm39WWNZTq8bmAPcJCKN3D9MBUAW8EsIjC4jEblHRK4pddcaVc1U1X/j1PsTEYnyU3nepapB8YHzdnQesBf4GAhz75dS/78OWAs08ne9peoeCaQAg936fwWcW+p4QNVcRr2/Bs5zj/UDOpeqezbQIcDq/S3QCLgVmAZ0dX9GbgE+A9oF6Gt8TqnjFwCLgTh/11pB3b9xfy9fAT4FFgCTgWuB14F6fq63EfABsA9YBYS794dxalLlJcA3QJ/TPlf8/XpX5SMgWoSeUNV0nLd0g3G+Qfe5h0Tdt3aq+gWwHkgSkTgRud1P5ZY2CPiLqn4J/AKnv3REycEArPn0eqNw61XVRaq6yT1vPZABFPqlylNOrzcauE9VPwaSVHWDOr+hq4AjOC0yfyvrNb6z5KCqrgZygaH+Ke+syvpZvltVH8bpPnxeVUfh1F5HVY/5r1RQ1cPAf4HzgaXAM6WOqfvvd8AK4DoR6VryLq/keLAJyEA//S1xqa6IV4B1ON+kG0SkpRvmYZz6Wn4J/Amnb72Fj0o+Q6malwM3AqjqEmAh0Pq00QB+r7mcelOAVmWMXhiJc+Eu01c1llZOvd8BHUXkktMC5R6gDnDYp4WWUsFr3FpELnXPE5x3PzEB0j1U3mvdWUQuU9VdqvqVe94NwFbfV3pKqddtqqoewXnH8BMRae9mRnipr+vvOO+c5+G84wiIbrmqCMhAx/nFO0ndK9CqWqCqhTgXjTYA40qOq2qRiJwL/AunS6aPqr7iq4Ldvs+TPwh66qr5d0CYiFzu3l6D8w6jlXv+eTg/bD6tuZL17i1V790isgboCNyvTr9poNVb+vX9qYisBM5x6831Rb1VqHkv0NI9T3GC5Zg/WopVqLuFe/7lIjIP6IRzHcOfNZe0wHPdfxcDX+CMbkFVi9xgbw68CvwP6KWqL5T+/GATUMMWRSQReAI4KiLJwDduUP/gm4Szi8gnwKMi0gbnLV6+e/9DpboFfFHzAJyLbRtF5E1VzXbvj3D/+GzG6SMfIiLfqWqa+0NU0nrMcmv2yRDAKtbbAigJ7lU4XRnfB3C9zYHj7kNsAn6mqgt9UW81am5RqmaAx9UZYeQzXnitdwAPqOraAKg5HCcySg9HfBV4T0S6A+lALLALeFhVd/mq5poUMC10ERmI01L9ENiI06fYSJyxrKqqKiLRIhLt/nWdj/PDtQbnYkxzVc3ycZhfwam/7q2AX4vIjwDcXwCAbLe+aJxJOZE4F2sy3fMyfBjm1an3oHveCh+GuTfqXe3jMK/2z4R7rq/D3Buv9S4fh3l5NZe0wOuISGxJfcBHwGr362jknhcSYQ4BFOhAT2Cxqk4D3sWZ0JJT8hdWRJ4HJuK+LRWRn+FcGH0T6OmrUDxNX+A7VU3GmRDSHBjmtloQkReA6Tit8KdxfvgXuLfftnpDrl4IzpohOOuuqObncUY6nePeHoZz8favwAWquswvVdcgv3W5uN0rh0q1qOcDz4nIXpwXfT3wujjTzXcD5wHPqOoO9/wtwMWqusWPNW8EeolIK1XdKyI5QBPgVhGZg/OD9JSqbnU//16coVzZVm/w1xusNQdr3VWo+TzgiZKaccb3D1TVgJsU5y0+b6GLSEMR+Qz4Crij1NuhFThDEtvj9MMNxLkIczXOxaHhqrql5OKHOpNdfBLmZ6sZp3/2KDBFRP4NtMUZAhWnqpvcmreKezXdvXhb478AVm/NC8aag7VuL9RckhkpoRzm4Icdi0SkNfBTnG9EF2C+OmOxS47/B/idqi4RkY7Ai8AvVHWX+GltiDJqLpnqjDgzzC7B6cOfIc5sygdU9Ub3uM9rtnqt5lCqOxhr9hefBLqI3A3sBJar6lERicF5d/AEziy+8e5bpmjgMaCTqt4rztTzYcAwdSYW+YynNZfxeb8Fjqjqq1Zv6NTrPnfQ1ew+f9DVHYw1B4Ia63IRR0u3L+senNmG/xKRpqqaq6rHga9xLq4MAlDVPJzhiLEiMh8nzB/yVZhXsuarTvvcS0VkKXAZzjRoqzfI6w3WmoO17mCsOdDUSKCLs+Sn4ixPuUdVBwH3A4cotZO2OtNudwBd3H6yOuoMe7oHGKmqg1R1fU3U6IWau4pIAxGp5x7aBjytqtfqqQu3Vm+Q1husNQdr3cFYcyDyapeLOBcffg+EA5/jrI98m6re4x4Pw5lZNkRV57n3xeIMOboEaIczW3KP14qq2ZovxrmI21dV06ze4K83WGsO1rqDseZA5rUWujiD/JfivB3agvNNKgCuFJF+cHIK8XPuR4kbcIYprsAZG+rLMK9uzSvdmn0Vjlav1VymYKw7GGsOeOq9pSovA+4qdft1nLdMI4GlemrZyhbAe7jLruIsa3q5t+oI5ZqtXqs5lOoOxpoD/cOb35y6OFOCS9YcHgH8yf3/Cpz1EgASgGR/f+HBWLPVazWHUt3BWHOgf3ity0VVj6tqnp7aMusanPWywdlD83xxNpJNBpaBc1XbW89fFcFWs9Vb84KxZgjOuoOx5kDn9an/7kUOxVlX4RP37mycXVl6ANvV7SdX98+vvwVbzVZvzQvGmiE46w7GmgNVTQxbLMZZWOsg0NP9C/s0UKyq36oPL3pWQrDVbPXWvGCsGYKz7mCsOTDVRD8OkIjzTfoWGO3vfqVQrNnqtZpDqe5grDkQP2pk6r84m07cBbyszuzPgBdsNVu9NS8Ya4bgrDsYaw5EPl+cyxhjTM0IpA0ujDHGVIMFujHGhAgLdGOMCREW6MYYEyIs0I0xJkRYoJtaS0SeE5HHyzl+q4h082VNxlSHBboxZ3crYIFugoaNQze1ioj8BmdHrHRgN8563FlAEhCFsy73XUAvnK3MstyPn7oP8RoQDxwHxqrqBh+Wb0y5LNBNrSEifYEpQH+chemWAW8Ak1U10z3nBeCAqr4iIlOAT1X1A/fYN8DPVHWziPTHWer1qjOfyRj/8Ppqi8YEsMuAj9TZbBgRKVnZr4cb5A2BWGD26Z/obnt2MfB+qRVco2u6YGMqwwLdGKfVfquqrhSRkcDAMs4JA46oai/flWVM5dhFUVObzAduFZE6IhIH3OTeHwfsE5FInF1zSmS7x1DVo8B2EbkdnI0WRORC35VuTMUs0E2toarLgJk4mwt/ASx2Dz0NpALfAaUvcs4AnhCR5SJyLk7YjxaRlcBanL0tjQkYdlHUGGNChLXQjTEmRFigG2NMiLBAN8aYEGGBbowxIcIC3RhjQoQFujHGhAgLdGOMCRH/D0sHwj+rgHZkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IG+Bm4DeZF8cdPcuIOUQqpSYrpUYppUZ17949V2LlHPtG8pmZhKEACAab70fn2NalbXmeJCpgIqJOInVBs7nJ++mcM4mOlaXpSBeXVGYS+wH9gc9EZAVQBXwsInsBq4G+jr5Vui1ee5VLO8A6bY5C/1+fgqwtCpMq3FBI2EVvbJ+ETYl5iomJrRyi/DdJO64V9U0BDt+3Ky9ecyR7d22TOSEjSFpJKKW+UEr1UEr1U0r1wzIRHayUWgtMBy7TUU5jgG3aZDQDOElEOmuH9UnADL1vu4iM0VFNlwEv6beaDthRUBMd7a0WY23KPq98voaH3lqabzGKgoAe2Hw+CRvc3CqmtXbsKxLr0jRPNLyvk6hrDNK23M+Ivp3SFS8uXkJgnwXmAAeISK2IXBGn+6vAMqAGeAy4GkAptRm4A/hI//1Ot6H7/F0f8w3wmm6/GzhRRJYCJ+jXBozjOptc88+P+X8zl+RbjKLAuQDMObi1dh0RLxturFDX5mvp8T2wQmDLS/wpSJgcCVdcK6UuSrC/n2NbAdfE6DcFmOLSXg0MdWnfBIxLJF9rwiT4MxQSgaC7jywXM4mXPl3NsD4d2bd7u7j9Zi3ZQJe2ZQzt0zHrMtnE+/i27yEquinJ0sTWOolgVsqVRmLSchQRyqy5NhQQIZ9EhLkpFxOJnz33KX6f8M3vT43b77Ip8wFYcfdpOZDKwu3z2zOIKAUa4aPwGt1kr5MoL83+TMKk5SgiWvs03lBYBHVwjV1PwiZX96lbjedCwM3cFNMnoV8Hk7QSWD6JQE5mEkZJFBH2/WWCRwyFQHN0ExE+iewO3sVYAU+FzEwJ1kkkYSWwZhJGSRicJBkmZzBkE2d0Ezk0N6U7gVi2YScLVm5JW45ZSzbwocvCwXjiRcluX7ekZxKKhqZgThzXRkkUEcX3/GTIFb/412fc8PynOX1Peybhi1gnEcyyGagpmF7OorH/7z3OefTDtOW4bMp8Lv77vKj2eBOdj1ZEZDgKmZvsmYQ37LxNFWYmYXBiFtMZYjFtQS3/+WR14o4ZJCwE1vEInO2HmaYYqS3yyWVT5vPMvJWAewVJu+26Zz9xPT7ZyMU9uiqdmUkYwkg2nbDBkE2aZxLhT8DZDoFtKkCH9awlG7jlP1Y1BbeP72xrCjhmQnZ0U+iltx93XUhJmJmEwQWjIwyFQPM6ifDopmxPJQo1qikeTokbAtHmsmRnEnbBIaMkDGEU30/D0JIJmZt8ET6JLN+oTS6DbCGRaCLV4KwDEemTEPHk07FnEhVmnYTBialxbSgkwsxNYT6J1mducpLo88crFiRAowfHvDE3GVwxRYcMhUTQ6bh2tGd7GUOhm5tcP7+jzZniu3nFdbPCbfTgmN9eZxUvalee/aQZRkkUEcW4iMjQcrEHa7FqDoXItuO6sdDNTa5tza1hlfuiVlwLjR7Kki7fuAuA/t3bpiqmZ4ySKEJMCKyhELAfWvy+iCywWX7fQptJRD68uablcM4kXJSAU4kkUoK2JaFdeQl7dahIQtLU8JIqfIqIrBeRhY62P4nIVyLyuYj8R0Q6OfbdJCI1IvK1iJzsaB+v22pEZJKjvb+IzNPtz4tImW4v169r9P5+mfrQxYrJAmsoJMLMTWG5m1qXTyJSnETS/eSZBc3Oa5ciRG7RT05K/dawvV/3tjnxT3qZSTwJjI9omwkMVUodBCwBbgIQkcHAhcAQfcwjIuIXET/wMHAKMBi4SPcF+CNwv1Jqf2ALYNeruALYotvv1/0MmBDYXGBMe4mJtU4iE5du257GmE/U8Ry/+cBpXus36RWWb9gVt/+qzXt4b0l4oU37DEGlWJbg+FKdvG2/HvHTpGeKhEpCKTUL2BzR9oZSqkm/nEtzCdIJwHNKqXql1HKsQkKj9V+NUmqZUqoBeA6YoKvRjQWm6eOnAmc5zjVVb08DxkkrD+tJNue8IXUK7GG1IGn2SURWpkv/3MN/+wY/+cfHUe2j7nyTSx6bG3p94n3vxTxHttOD2ESav2Z7yOcUOZTZDyVKwbtfb4j7fraS3L9QlIQHfkhzNbk+wCrHvlrdFqu9K7DVoXDs9rBz6f3bdP9Wi9O5ZcgupgRnYuxL5I+qTJeZa/fml+toDATpN+kVTnvwfQA27qxnV0Oz43fp+p0xjw/k6DuMKhER8fPcuLM+SpFE/oJDWWKB3p3i+xlsc9v+CQouZYq0lISI3AI0Ac9kRpyU5bhKRKpFpHrDhvhauCVgVET2MUoiMSFzky98YEz3yjmVzEfLLSPGou+2x+wfa3FdrhzckfdKZGBJdWRSPyw/jhNnKnF7DUQiCsbcFAsRuRw4HbhENX+rq4G+jm5Vui1W+yagk4iURLSHnUvv76j7R6GUmqyUGqWUGtW9e/dUP1LBY8at3GGudWKcq4Qz6bh2rhMo9bBYLJajN1eKPnLGEjmTcK0eF1njmmZzUyyfS5k//Frs06VNcoKmSEpKQkTGA78EzlRK7Xbsmg5cqCOT+gMDgPnAR8AAHclUhuXcnq6VyzvAufr4icBLjnNN1NvnAm+rVu5NDIXJmalE1im0MMtCxJkqnDBzU3rndTqsIwdGN+oa8zuTeGL2irDXkUXB3FZFxzI3BZVyVRIr7j6Nz28/iXk3jwu1lXi4Npkg4XI9EXkWOA7oJiK1wG1Y0UzlwExtH5+rlPqxUmqRiLwALMYyQ12jlAro81wLzAD8wBSl1CL9Fr8CnhORO4FPgMd1++PA0yJSg+U4vzADn9dg8IQxNyXGHoP9EY/O6Y7NzlTgfg9lGMMWp2VQDq/c/+aSsNeR5ia3dN72JVu+YRcXPzaXTm1KActUVx9hbvr5iQMBK09TLnI1RZJQSSilLnJpftylze5/F3CXS/urwKsu7cuwop8i2+uA8xLJZzBkAzORSEwwlAU20ieR3sVzmo+cyvrOlxe79o81k8hVdFMkkeamUn+0orMVyRuL1wHN6TXsmUSHihLOHNGbG08aREetQJx0a1eWYaljk/3EHwZDEZKvAaaYcEbbZTILrLPynFMB/H32ctf+sWYSuYpuiiQ6vDW6jy/CUmSb2GyfRMc2pdx51jDX88+/eRyVZbmbUZi0HEWIScuRfYy5KTHhWWCb29N2XDc1H3/+3+Yk7N/QFOTbTbt5es6KcPnyNZOIeO12L0X+hp2ryINKRZnwnPToUEH7iujZRbYwSsJgcMFMJBJjD35RuZvSvHaJ0lJE0hhQnPe3D7n1pUVh4aPOmcSGHfXpCZUEkeO7270U2cd2sgeDiqAqrLVQRkkUEebhNndkuyZCSyDM3JRBn0STh3oKYf0DQTbtbLDe2/HWzuimQ+96k3Xb69KSyyvRw7v366GwZmKFoyKMkjAY3DE6IiHZyt3UkGRupqagCsniVDCRuuartTvSE8wjzpmDUsp1JhG5mK75WEvFFtBEwjiuDQY3jI5ITHN0k4SZR9Ix1U2ZvZyuSUbuNAaCofd0zh4iHdebdubG5OT0QSjl7huJpQSU0jOJAtISRkkYDC4Y015iQuskItYyzFu2icP6d6FvkiuCV23eze9ihLnGw7muItIB7CRX465TUSmSe+BQSqFU9IK8fGLMTQaDC8YnkZjmtBzhA/C/FtRy9D3vJH2+VAfxHz1V3SyTU0lEJdVLfeRdvnGX56itQNhMQrlGN8UyNyms61pIEYxGSRQhBTQTbbGYmURimusyS0YGtdIMpJn4vHYbpz34PjvrmxLmVIpF7ZbdYQrmk2+3cPy97/L03JXUrN8RlpXWjUDAOZtxv5dmL41OJw7w0Ns1VK/YUlC/cWNuMhhc8KIj6hoDnPvXD7ljwlBG7t056zIVGnakamRlOpsVG3exV8cKz6kkMjEw/v61L1m2YRcfLd/Mh9+4D8ROatbvpG25n14dK/n7+8vo360tV0y1ZiZnj+zDwtXb2K1Tk3/y7daQUz1uVtowc5NyXewXL8x3064GeuSgLKlXzEzCYHDBi2lh0XfbWbh6O7/9b/J29JZArOgmm+PufZefPfeJ9xNmYPZmm3EaAkEeez98hbZbwr8T7nuPw//wNoGg4s5XvgwpCID/fLKapet3snrrHsCqlnfnK18mlMFpXnqhupYfPlkds29JDOdDAU0kjJIwGNzwYm6yn3xbq2WquZ6E+0wCYptV3MjEdbTHXLfSp/GirrxkjH37q/UJ+wBMnrUstP2POSvj9o21sj8ybUc+KSBRDIbiIjQutlIHhv2xI0NgnSQTypmJy2jPJNyVROw3yGQaFmeq7283747TM7biMo5rQ1oUzu3TcvE2k7C+idapIhKbmyC5ezUTEWUhc5PLgrx4uZyylatrj8cqc5EUkuM6oZIQkSkisl5EFjrauojITBFZqv931u0iIg+KSI2IfC4iBzuOmaj7LxWRiY72Q0TkC33Mg6J/ebHew2DIBV4GrAL6HeeFgGMxXSa0RCbH6X9/vDqqLZ5FqdBydRXSYjovM4kngfERbZOAt5RSA4C39GuAU7Cq0Q0ArgIeBWvAxypWdBhW7YjbHIP+o8CVjuPGJ3iPVksrL8yXU5K51K31awmZm3yZCYHNxGW0nczzlkfXlY43Wyi0SoSFoyI8KAml1CysynBOJgBT9fZU4CxH+1PKYi5W/epewMnATKXUZqXUFmAmMF7v66CUmqtLkz4VcS639zAYso6XIaPZcV1YA0yuiJUq3ElS5qYMaNttexpj7gsqxaertrrvKzAl0RJWXPdUSq3R22uBnnq7D7DK0a9Wt8Vrr3Vpj/ceUYjIVSJSLSLVGzZsSOHjGAzheBmw7Kfn1jqTsMdVn2TGzZrt6xgMKm6bvsh9X4F9icVmboqLngFk9Qoneg+l1GSl1Cil1Kju3btnU5SCoIDunxZLUjOJwhpfcoa9ojkyLYeTRIPd2Hvf5Sf/WJBp0VwJqvAkfzvrm0Lb+apiF4tC+omnqiTWaVMR+r8dQLwa6OvoV6Xb4rVXubTHew+DIesk5ZPInhgFjT3bildFzW3XJ99uYc02y3ewbOMuXlu4Vp8vuffv1TG5Vcm/e3kxtVv2hF6f/9fmqndJlrDIOrFyO+WDVJXEdMCOUJoIvORov0xHOY0BtmmT0QzgJBHprB3WJwEz9L7tIjJGRzVdFnEut/cwGHKAB3NTgfyO//beN0kf8+7X63lvSXqm2bBU4TGefSNb65sCnP3Ih5x0/6yovk7fzl4dKujbpTLu+8+5aVxyAkeweE1zao1CMzcV0lTCSwjss8Ac4AARqRWRK4C7gRNFZClwgn4N8CqwDKgBHgOuBlBKbQbuAD7Sf7/Tbeg+f9fHfAO8pttjvYfBkHWSi27K7wDzh9e+SvqYy5/4iIlT5qf1vmE+CY+D2pqtVnW4HXVNUfucl/GI/bvywIUj05IvGQotuqmQHOkJE/wppS6KsStKjWvfwTUxzjMFmOLSXg0MdWnf5PYerZlCe9hpyXjySRTS414eCKUK98VZJhGhPeLZ/p17Sn2+nJpcCu235bZiPF+YFdcGgwtJ5W4qsAEmVwSdPgkPA/rTc1ZwsouZCaw61c4Z2f492uU0DLTQHNf1SZZwzSZGSRgMLiSz9qHY1klkyjzmJQTW2X7rS4vC0mg72dUQCLuK3duX53QmkQ1zU8fK0pSPNTMJQ1q0djNHLvAyjhbYw6dnMjUeOivTxRrQm4KK1xeu5eA7ZsY91876prDr6Y+TWTYbnHDfexk9362nD+bSMXunfHxjoHBuLqMkDAYXvCgAe5AsBmXx3dY9vPK5tTY1U5E8zuimEr/7iL5tTyM//scCNu9qiNrnfHpfu60Op1fC75OCCgNNlqrOlXFDgxPRZGYSBkNhk4wJqeDCJ10459EPueafH6OUyphpxT6N3ycxi+fEw5mptXbL7jBl65PiVhLx0qd7oZB8JEZJGAwuJGNuKpyfc2zWbLNCT5XK3MzHmbupJIX61BMenh3a3lUf7pMo8UlB5S9KFl8cE5wXCmgiYZREMVEMg1FrIjTbSOKLWblpF/0mvRIy/bixavNuHnNUN8skAaWSfkpdvXUPNet3RLXbMwmR1GYSS9btDG3vaQyEzcgsn0TxagnLT5P68flee+PEKIkipIh/O0VDthzXi7+zVvn+97PvYva5bMp87nr1SzY68gxlikBQJW0eO/LutznhvujQ1WBQhQbCWD4Jr9Q1BsLNTUU+kxARfGl8gEIyNyVcTGcwtEa8+CRCjuskzms/HccbBHbUWemuvSmq5AaToFIZWc3bGAjyl3dqQq9TmUk42dMQoSTSNNfkm3RlL6QV4GYmYTC44GmADvX1/oP268E0U2kXkj1NUGUmBHbTzvBopRJfekPJnsZAmGIW8uO4/vmJAzMyU09XyRXQRMIoCYPBDS+/0WQc14+++w1frtmO7d/1Yk5IZjaTCHu8CgQzE90UWdwnXXPT7oiZhMdF3BlnVL/O9OqQXHZZN6zorNSPNzMJgyEB327azbmPfhi30lg28TY78LZOYndDE398/Ssu/fu80NNl5sJQPSoJu39QZcQpur0uQkmkOZMIROTqVoq0bPqp4hOhc9uytM8jpDeTKKSwaqMkiogCum+ywpTZy3l9oRX1c/+bS6heuYWZi9flRZbkZhLxe6/V4adlJb5mc5OXmUSKzvNvN+2m36RXWLCyueqw0xeSCadoXWMg7LU/zQG9KaiiPks+HNd+n6SVTsNGksiM60aLURIicoOILBKRhSLyrIhUiEh/EZknIjUi8ryIlOm+5fp1jd7fz3Gem3T71yJysqN9vG6rEZFJ6cjakihed158fvfyYn78j48Bq+4AQEVpfp5jkvFJJMJebdyxsjS0CtdLkRsvA4Vbn4+/3QLAg2/VsKfBuo7OmUQmJjGRM6F0lUQwqMJ9EhK/mFG28Imk/Vms86S7TqIFKAkR6QP8FBillBoK+IELgT8C9yul9ge2AFfoQ64Atuj2+3U/RGSwPm4IMB54RET8IuIHHgZOAQYDF+m+hlZAXaM1ipaX+PMkgYcBOujN3NSgV0aV+MVTdJONl4HCrct3uurbe0s2MP6BWVH9M+E0j1RO6Y6rexoDrN/eHPKrlLc6z0fs1zW9N44gU7OXdEN4C0hHpG1uKgEqRaQEaAOsAcYC0/T+qcBZenuCfo3eP05Xo5sAPKeUqldKLccqPjRa/9UopZYppRqA53RfQyugmGYSifo2BZpzHNnjnpeBOpn8UU7uef3r0PbKTbsBh+NaJb9Owo3IFcHtytOLpp+xaB0/eqo6rM3LIPvUD0fz+MRRab23k0zljBLy41PJBin/ApVSq4F7gW+xlMM2YAGwVSlll52qBfro7T7AKn1sk+7f1dkecUys9ihE5CoRqRaR6g0b0ivJaMgPkamR6/VMYndDIMr+nQuS8UkkoknblnwiIeXgZaD2MpNQHtM32JmDg0lGN93x8mJPsh3arwt3nhVVOyxlRKC0JPHwVOL3UVmWudlmulFJNuKSu2nmDcfw9BWj0z95jknH3NQZ68m+P9AbaItlLso5SqnJSqlRSqlR3bt3z4cIOaHY6hYkw+76ZkWws76J6pWWXf1/nl7A9x75MOfyeJtJ2Oam+J3ttM8izWYEL5mgvZikUgmBTcaU8fjs5a7tkZ/Z5xMuHbOP9xN7oEOFNwdyJn0XmUos6HNJy9G3Sxt6d4pft7sQSWcufwKwXCm1QSnVCPwbOBLopM1PAFXAar29GugLoPd3BDY52yOOidVuaIE4B8SfPvtJ2L7Fa7bz/tLczhA9hYm6dGkKBFm1eTd1jc0zIHuW5BMJDerezE2ZVxJB5S0EdsHKLfSb9EpMebKdNqJTG0tB/PNHh3HzqYPYp2ubsP1HD+gW2s6Eo9l5rkzkjHJTNiLFGXSSjpL4FhgjIm20b2EcsBh4BzhX95kIvKS3p+vX6P1v65rY04ELdfRTf2AAMB/4CBigo6XKsJzb09OQ11DAOAe7Od9sitofL9dRNvAyBNrjvLPvPTO+5uh73mHQra8z6NbX+dHU6tBaD580D65eTD5eMoEmv+LaWwjstAW1UW3/93HzM1qmom/uPW84B/RsH9b2k+P2Y0jvjgAcsX83rjpmP9678fjQ/pq7TmHqD5rNNpm0/bvNAFI7T7TZyidSlLaAdHwS87Ac0B8DX+hzTQZ+BfxcRGqwfA6P60MeB7rq9p8Dk/R5FgEvYCmY14FrlFIB7be4FpgBfAm8oPsaijinTSycSsLt6TiTT4teSM7c1Nz2/tKNYX3e/HId0z+1FNxHK7YkVfjH21qK6D7Op2wb2ycRCHoLv926O7pI0Be1W5OSzQvjBvWgQ2W40/vwfeNHLJX4fWGKoTGD9aB9mXJcS3QFSZ+I54WMB/bqkLYMmSKtkASl1G3AbRHNy7AikyL71gHnxTjPXcBdLu2vAq+mI2NLooDW12Qex2c77aBe/PvjcMtizpWEh2c+r4vpVm3eHdq2n9Az5bh26xJ57t0NTezRpi+vWWC37o5e6e6sGeFF0XihvNQX9d0mO0bvyWBggz/NRXA2bmlFBG+zw09uPZGK0nyFfkdjVlwbCgLnYOcWzZTzhVUZDIHd4jLgeopcSjEENrLpqD++E9bfi5JoctECzvxMmfJJlJf4o1J6JFvD/ZgB3bn9jMF0bpPY0b3i7tPi7s+c49rdJ+Hl2nduW5bRiK10MUqiiAjdXi1wSuH88Wzf0xS1P9cx5/Gu8IKVW1i/vc7VdODW5vak6+UrTDQQL9uwk9cWro1qjxyInPWlg0qFFiraLP5uO3OXhfuB3JSYM11FprLY+n0SNRNLdoz2+YTLj+xPZQaevn2+zFhzfSJEprMSR+BCMWHqSRQTRXiDeSVMSdQ1UlbiC6uBnOuZRLxLfc6jH9KpTSn3nz/C6pvC+WMpAGsRobckgGP/33tRbbdPX8Q3G3bFPOazVVujTFSnPvg+EP6U7fbW7fWCuRc/Wc3fMlg5r7EpQkmkeB6vUUmXHLY3HStLeeTdb6L2ZWwxXYy0HMX4EzZKoggpwvssIc4fz866JtqU+cOVRJqpqJOWJ8FV3rq70dVx7RVbAeysb2L20o2MH7oXyzfu4vh73w31Wb+9LunzPvnhirj7b30pduzH+h11vPbFWvp2qXR94r31pUV8XruNf7lEPqVDfaShPstf9V1nD2P5xl2uSiLRYrrrTxhAj/YV3PyfL+K+h0/clVYh5WTyilESBcCClVs459EPef36oxm0V+yohuK7vbzjHJN21DdRWepnK822/EKaSUT2SSX1du2WPfz+1S+p3bKbV79Yy9hBPegZUcfgJ898zMvXHcXQPh2TPn8qfL5qG7dNt5TIkN7u92GmFQQQ9jAAsRPjvf/L45MOYCj1S2gxo5NY95PTlzDplEEcVNWRix+bF9o/rE9HT2YtiaFsEpmbhuXou04G45MoAOz02O993XpTijh/PLu1knCS++gmD31c1kkkoy8mz1rGl2t2APD2V+t5dv63UX1qt+wJZXLNNGX+8J+/0wSW7SfeI/brylH7W6G6e3cJX4Uc65tOtGLZbdw/cv9urs7qWOUvnIvpurYto1u78rD9keG3TubcNDa03a1tuauy6xGnoNFdZw/lv9cdFXN/vjBKogCwb6ZEv8vmJ9csC5QHnB9pV0MgKroj16UsvcwOmqObUv9CtrisR3Dy438s4MDfvJ6V4kuRA6XTGZ1tB+uUyw/lHz86DLDMP+ccXBXal4kVz4mI9dDhc4SuKhWtsMr8vpj3Yq+OzQqsQ2WJ60yiT6dK5t40LhWR84ZREgWA/aNIZAdvybmbIgelyJlESUHOJNL/PtzWI7ixcWd94k5JEmly+ckzH4e2d2dp9mJT6pjFdGtXzs2nDgq9TlVHuEVcxTpVLHOTJEjCWFHqI2ICFnLoA7zy06O47/zhcRXdXh3dZxOF+vBnfBIFgM/x5OKFlqgsIgfcqJlErtMue1qj4Llr2jgTIGaKeLOz2i17Mv5+TiKf5MsdDwWpftNus61Y302s+0kEqjpbM4LhfTtFRaFVlPqjQprn3DyOet02pHfHUEqRZCnUX7WZSRQAIXNTAntToT5pZILIj57vFaexzC3h31F4dFN9UyCq9nOm2FlvrR1Zu60uo6anTMyGMkG5Iy14qjOJXXr248WxHHMmAVx9/P689rOjObBXhyjfTHmJL3SsCDx00UjalZfQNcJ30ZIwSqIAsB9qvPoKC+R3nVEiP1ObiJlEphZveSXWNXYqj8jopssen8+abcmHrXqhZv0OXl+4ljF/eIuxjjDZWJw4uCdnDu8dt4+CqIV1+aI0zIaTmpa4+LC9AZh/yzjuOfeguGeKNYsq8fmoKPWHcidF3gflpf7QsX07t+GMONfYi2/l/FFVBRnR5MSYmwoACTmuE/kkWi6JfBJNuVYSMdqd5gd7q64xyF2vLGbe8s1Zk8e5vmHTrvjObrAePCJDSyMJKsWO+uzMfNIh1ZnEXWcN5Y4JQ/H7hM5tyoDYyiDSaf/nC0bQp3NllJkz8r4s8/uo01UTO7ctiyuPl3oY95w7nFv+8wVfrN5WsE9/RkkUAPaNnGjqX6D3UEaI/DGWRCyey3U6g1jfhbPZTnfREAjy2PvLcyGWZwTh4H068fqi6LQdNrsbAoy+660cSuWNdFZc27eNXf62XJe/vfb4/cPMdJE+kQ6VJRzar0vUOSPNTWUlPip03fWxB/SIK08nD7mkLLmt/4X6807L3CQinURkmoh8JSJfisjhItJFRGaKyFL9v7PuKyLyoIjUiMjnInKw4zwTdf+lIjLR0X6IiHyhj3lQchEblwckWXNT9kTJG5FjcmR8+kNv1+R0tWrMmYRDhl+/uDA3wqRAQCkmHtEv32KkRCZ+5nb523I9oP/i5AO4w1FeNSr5XgzVZD+cdGtXzu1nDKZjZSnDqjry32uP4rqx+8eVoU9nb1XotuyylFe6dcKzRbo+iQeA15VSg4DhWHUfJgFvKaUGAG/p1wCnYBUUGgBcBTwKICJdsNKNH4aVYvw2W7HoPlc6jstLedRs0+yTaL0hsJEfvVNlKUP7hK/6dZpPatbvdC2MkyqNgSBn/mV2THlsiiVBWyCoohbL5ZL7zh/OwJ7twtoeveRg/nzBCH5z+mDXYyr0U38mngTrm2wl4X4NnDOJw/ftypgENSwG9GjH5Uf2D70eVtUxYcRdh4pSvroj8ZB1+H7WeyeSIV+krLpEpCNwDHA5gFKqAWgQkQnAcbrbVOBdrEJEE4CndDW6uXoW0kv3namU2qzPOxMYLyLvAh2UUnN1+1PAWcBrqcpcqDSvk/BGkYxTSRE5+O5qCERFoDiV5KkPvE9DIMi5h1SRCVZv2cPntdtCr7/ZsJPfvLSQlz9fw/ybx4VqKWSqjkK2aQqqnCxKi+Thiw9m0Xfb+N7BVbyxaB1L1u0E4KwRvTllWK+4x3aqLGNtY11GsrCGzE2xlITjTZ69akzM84zs25n/OWZffuBQEMkQK0rvpWuOZFeDFbF2yWF7c+4hVXmP6ItFOvOb/sAG4AkRGQ4sAH4G9FRKrdF91gI99XYfYJXj+FrdFq+91qW9xeHz6LhuwROJqM++fntd1JOa09TToJPCBYMq7TUU67fX8a8Fq8La/jTj69D2ik272L9H+yg5K11i5nPFL/71Wdz9gTxps9MO6sVpB1nKwE7K+NBFI+NGAdl0aVvG2u11GTEr2rPO8hgDr9d7xucTbjr1wLTliWR4306hbREpWAUB6ZmbSoCDgUeVUiOBXTSblgDQs4asD20icpWIVItI9YYNxZf/yOtiulAaiBaoLSI/UUNARc0k3Ma9BpdSX99/fB4vVK+K7hyDix6by8PvRGcEtdnpWMjmjG7Kl4IA9xrUTnLlv7k8jt/D/v68ytKzg+WH2rQzcfRWIg7Yy1Lqh+zTOUFPQyLSURK1QK2udQ1WveuDgXXajIT+v17vXw30dRxfpdvitVe5tEehlJqslBqllBrVvXv3ND5Sfgg5roswjXCmiIwmOmXoXlFPe27V0updwjzfX7qRX0773PN7x6u/AFbCQZti8klkm46Vpdx+5pCY+88eaU38nU/N8fjhUZZJxx7g0+G4A3rwwaSxnDxkr7TP1dpJWUkopdYCq0TkAN00DlgMTAfsCKWJwEt6ezpwmY5yGgNs02apGcBJItJZO6xPAmbofdtFZIyOarrMca4WhT3uJE7wV5gD1LINO1m6bkda54j87McM7B5VjtKtUI9te7Zxu0ZnPDSbqbrOQiCoaPRSaNjBb/+7mJv+bdUPKBafhFt67EyTKDPv8YN6sOLu0+jfra2n8x09oDsr7j6Nvl3aZEI8+sTJGAtw7iFV/ODIfhl5r5ZMuuEP1wHPiMjnwAjg98DdwIkishQ4Qb8GeBVYBtQAjwFXA2iH9R3AR/rvd7YTW/f5uz7mG1qg0xqaBz/PT6kZ+P1/u2k3b325Lv0TYVVIO/H+WZ76XvlUNaPufDOq3e2j/+F7B/GLkwaGXjtj+u3xqd6xYvi6Zz+h/02vRp3ni9XNdRK+9+iHDLgludvo63U7eHb+t/yrepXrbKYQyeRM4tIxe7u2F3s8+r3nDee2M2LPhAwWaQXmKqU+BUa57IrKhav9E9fEOM8UYIpLezUwNPqIloVtZsrlYroT7nuPhkAwYWH4TDNzsbticlOQXdqWce3YAdz7xpKw9sdnLw/NPJ6d/y0TRvShQ2UJ//3su6hzRF7Tz1ZtBWDyrG/44ZH9+cs7NZ5lv3Ha59x48gGJO+aB5X84lRc/Xc1HK7bwz3nfZnSF+p1nDeMfc6NrXRTmvNaQaQpz9UYrw7Z+5HIxnZvDNx+8uXgda7btYb8e7RJ31tz3RnPk0SPvfsMj735Dhwr3WznW5/z9q19RUernz28uTUreTDhVs4GIcPbIKrq2Leef877NW3STIZpbTj2Qhd9tS9yxQDFKogCwf9BuNncnLfHJ7UdPVQPwjC5Ak4hgULnawrfXNbn0jp+/aNHq7Z7e00mhD752orx45qbLj+jH7oYmXqjOfClSQzRXHrNvvkVIC5MFtgCwlUNTgqd7Z9bR65/7hF+/GL8Ye67Z0xCIciR75c9vLkncCWgMBiMyhsbHLfrJZme9u2KJRypmnNm/Op5+XVNzxp40uGfiTg7KShKHnZ4xvDeDe8Wupe6VYvdJGLxhlEQBYA88TUlEpLz46XeuduJ8cuBvXufUB95P6diPVmzx1C+QxOK5QFCFKYmb/xOuVFOpyxBrxhKPqs5taJtiXp7Jl7m5/MJxzqxKdHrTeIuzSv2CP0LR3nPuQbx4zZH8+jTvC8eKJRzYkB5GSRQAtnJoTFR0SBucnIu4Ci0sNt6ag4+/9aYI4tEYUJ4/88LV21i3vbm+wz/nhSvVrXuS9y+4Ocfj0aO9tUCsb+fMhHW64Vx0aN8b7SN8NHc6ktuV+HyURijaAT3aMaJvJ350tHfTSK7Ttxvyg1ESeUApxZ/fXMI3G6y8NoGgN3OTjXMGsSvNWsS5VDJ/er3Z4ZxqiGZTIOj5aX7Cwx/wvUc+jLl/+57kZwXJcMeEIcz65fHW9llDueecg+L2f/36o9nX45oCJ87aCPYMYmhEIZtLx+xDqU6T0b6iJMqv43ztVtnt4L07AVat8XGDrBTZuczKa8gfRknkgS27G/nzm0u57PH5AKHFXYkWebmN5xt21KclSy4nIs7BLNkFbTYNgWDCYjpe+Xbz7oycJxaVZSWhQbt7+3LOP7Q5scBFo/tG9R+0VwdPOY4icaa5HtG3E0/84FBuOiXabGSnX+/dqTKqXoczdfaFLrI98YPRvHTNkdT8/lTOHGHJaGYSrQOjJPKA/QRmZ4G0X8dzssYiXSWRKKIqk3xQsym0nWqd5sP/8HamxMk6sTwn+/doxx++d1BozcXlR/TjicsPBeBn4wYw/5aoZUZxiczldfwBPShzyX76wv8czpM/OBS/T/BHlGZzKolfnzaYDyaNDdvfsbI0lF6jzEMElaHlYEJg84Azgyk0p1DYnYLpKF0lkS3n41NzVrBvt3YcNaCb6/4FK7dwaoLU0cXInWcNZcaitby/dKNryuvPbjsplL766uP246pj9g2L1vL5hO4RBZcS4XWs7tulTSjlRaRPwmlu8vuErnFKc9oKyCiJ1oFREnnANpfYvzE79n5PAiXh5j/Ysju9xV3Z0BH1TQF+o2syx1rRvTOGX+G8Q6qYMKJ4M8JfOmYfqlfErnXdsbI5H5WIhPwETuw6EF7DZlPxK0VGiEUGjJWX+BjQox3HDIxOmJlMCLKh+DFKIg9s2mk9/QeVYs22PSxdbzmwU0k9ne7TXDZmEm6hvMs3hkc9bdndQI3+3E5+ctx+7Nvd++rrQuGGEwaGKunZA3A6X82zV45hQE9v1yGVeyAyDXuk0hAR3rjhGNfCRW6mLEPLxSiJHKOU4ty/zgGsH7fTxp5wJuHSlq6SyIbJIFJJvL5wLT/+x4Kwtj+89hUj947O9Z/vp1S/T1K6Jj07lDPuQGvhm+ciUnGwS1p6IZWvMHLsj6z5bPVx96oYJdG6MN92FlFKRa3qXbFpt2N/eP9EMwm3MSfdmUA2zMqNEakrFq52z1vjZmpxUxJeVx2P3LsTw6s6Ju4YgyuP7s83vz81pWOdg6z9lJ7N+iCXH9GPOyYkzmA6/doj+cvFI6PaI0Nkk4kYK/WZYaM1Yb7tLDJ51jKG3jaD9TuaF3RdNHluaDtygPe6TiLsmIiBaNaSDUn94LOxTsI5k1BKRUXfnDS4J5WlftcQSjfFMfmyUbz606MTvu/d3zuIwb1TVxLp1ITu1akitG2PoZnSEd3aWU7ku85uXhB3/KAeHDvQWq/gtq7B5qCqTpx+UHRYbc8OFSz67cmM0BFLdUmYOo2OaF2k/XWLiF9EPhGRl/Xr/iIyT0RqROR5ESnT7eX6dY3e389xjpt0+9cicrKjfbxuqxGRSVFvXuC88oVV6nvN1mYlsdaxAjhyir+rIRDzqRvcy5Y6TSMLVm7hsinz+dOMrzzLmA1zk3MNRCCowgbLnxy3H4P2ak9dU8A1mqskhrlp3+5tExaR8QmM8lCu8qfjBtBdr4R+6KKR/PxEq2ZFqjriwkP7MmbfZvOQrWwyFV489Yejuf+C4Vxy2D4c1r8LYCnTvTpWcPSAbjx1xeiUztu2vISHLhrJ98fsEzWziEeiYkOGlkUmngl+BnzpeP1H4H6l1P7AFuAK3X4FsEW336/7ISKDgQuBIcB44BGtePzAw8ApwGDgIt236LCHile10rBxe2q+/vlPPZ2zbxdrwHSaNGyHeKSTOB5ZMTc5lYRSYWayUp9QUeZHKZg4ZX7UsWUxlERFqZ8PJo3lwDiJ6USE7x3ch2evHBNXvp+fOJBOOsqoa7sy9upozQL26mD9/3DSWH5/9rCYx19z/H786Kj+/OKkgXw4aSx3n3NQmJks0+amIb07cvbIqrC28hI/ZSU+nr7iMA7t1yXlc/ft0oY7zhqa1MAf6fQ2tGzSclyLSBVwGnAX8HNdZnQscLHuMhW4HXgUmKC3waqH/RfdfwLwnFKqHlguIjWA/WhUo5Rapt/rOd13cToy5xLnT6kxEOTqZz4O219a4oOIp+mtcUJanYNtaEGTo7F5y/uPOCvmJsfgGAyGv0ep3xdTEVj748s++fuH8K8FtVxxZH+G/+6NsH1+nyAinpy+9qBY6vdx7sFVtC0rYfxQqx5y706VtC2PbcJpU1bCjSfvn/Dc2Ygc+3/nD+fJD1Yw0mPd6GyQjlnOUHykO5P4M/BLwH507ApsVUrZ3tpawA567wOsAtD7t+n+ofaIY2K1RyEiV4lItYhUb9iwIc2PlEH0j0kpxV/f/SZqd4nL09vmXXGUhGO71O9DJNxcZI9JyVgDcjGTcA6WJX6faySNTaIn2r5d2vDzEwfSMaL+NcT/3HbuIZtjD7Di/9uVl+DzCacd1CvsvW0ZTxm6F/NuTm4FtP3xsnFtqzq34denD/acCTcbGHNT6yLlmYSInA6sV0otEJHjMiZRCiilJgOTAUaNGlVwy0D3NAb4au2O0Os+nSrx+yQsd1DPDuWs217veWDx+wS/SISSsLYTPei9vnBtaHtXQxOf127loKpO3t7YA07HdSCowhzppf74IabpPKXGUj59OlXy76uPZNPOejbqynKTxg/ijIN6xzVfWfJYTl4niWZfuYhuyifG3NS6SGcmcSRwpoisAJ7DMjM9AHQSEVv5VAGr9fZqoC+A3t8R2ORsjzgmVnvRYP+ULn5sHvt2b87uuX+Pdpw/KtzGPLBn+4Tnc45NJT7B5xNXc5NEmJv+5+lq3li0ljteXsxxf3onbM3CL/71GWf+5QM27axnV30Tr3we7jdJhSZHCOyPn17A1Dkrw+SOdOh+9puT+Nm4AXHNUF6I9XRtx/V3bVfOAXtZ11lEPDlrI69lMnK01HoLJrqpdZHy162UukkpVaWU6ofleH5bKXUJ8A5wru42EXhJb0/Xr9H731bWI9l04EId/dQfGADMBz4CBuhoqTL9HtNTlTffPPR2TWi7rMRHl7bN+Xlm/+p4OreJnSvHDXsmEQwqZi5eRyDY7CB2PujVNQaYsWgdVz29gMdnLw9bpwHwybdbAbj1pYXcPn0R1/zzY3457TPX91z03TbO/Mtstu5uYEUc53ijYyYxZ9mmsH2VZf6omUR5qY8bThzIkrtOSfSxw/jrpYfwP47SkE4d8cYNx3DJYXsDsZ3h8QhJqM85/5ZxUYo9Fvb1z2XyxFxizE2ti2w8E/wKy4ldg+VzeFy3Pw501e0/ByYBKKUWAS9gOaRfB65RSgW03+JaYAZW9NQLum/RM3PxOjo5bOpVndt4rF/d3MvvE0p8wiufr+HKp6qZMnt5KETWqSQ27vSWAPDVL9aGwnNj1T5+Zt63fF67jaueWsBx977LO1+vd+0Xr8JeRamLkkhxBe/4oXtx06nNKbGdZpCBPdtz7iHWoJ6JFcI92ldQXmI5sxNVmTPmJkNLIiNpOZRS7wLv6u1lNEcnOfvUAefFOP4urAipyPZXgVczIWM+cHNMA5x+UK+ogcZLlJGzS8fKMnw+4btt1sC+euseeupQTqddP54jPJJ4KTGCQcUq7UOZrxPYfbN+J8cf0COqb7xaEZUuSiJT0TKR57F9FD07JJdVFZoVlzMj6/Y6K725M0mfG/4M5G4qZPLpNDfkHmNdzCKRhV1sLhq9N+0iQiyTtUx0a1cWNu0XcTiuHf0S5YNy8vZXzTODyEV9T3y4gveXbgxr+/ObSznqj9H1HeIqiTI/J3pMs5EskWPXQVUd+c3pg7n3vOFJn+vYgd357ZlD+Om4AaG2kwZbIbKHJFiwZyurluqTMDOJ1oVRElkkVnqMUr+PNmURMwkPBifnmNO1XVlYNI8gvLF4nbXtaK9LkKLDXnkcyekPzeatL9eFXi/fGJ2xdWd9E7Vb9kS13/LiwpjvV1nqZ2ifjqy4+7SET+TJEmkrFxF+eFR/OiXp7wHLLDbxiH50cdRVOO2gXiz/w6ns0zV+idGWbm4yM4nWhVESWeLDbzZGlce08+T4fdCmzJpJ2L+368ZaT6yJQjJturQtD3tyrtmwMxSZ5HyCTVSiM17RoiumVrPou21c/sR8Zi3ZGLNfMuescOQZeut/j+WNG47xfN5E5GKRl5f3sL+XFqojjOO6lWFShafIhZPnsE+Xtvzx3Oji9rsbmrj4sXkAnDWiNy9++h3gCFEVoVfHSo4/oDvXjrVW7h7YqwNj9u1CMM6Dv3O20a1dGesdg/GsJc2LCJ05jm6N81TvhdMenJ3W8ZE4lUS3duWhusvp4BNrQC6Uwct+0m6x0U3G3NSqMDOJFJm7bDPPV69y3ef0AzhNE3ZJyPISH2UlPp74wWgO2ac5706Jzxe2xsAmGFRc9VR1WI3oeKaap+esZMm6HTH355PKstjpLlLFVg4FoiOa60m00KmEWSfRujBfdwYIBBW7G5rrRtQ7/ADOUNd7zxvOHROGMDiGSclaHGdtL/5uO//SSmjrnkbeWLyO1Vub7f8VcdJD72kMcNL9s8IipmZcnzmzTjrES2udKrYJKF66j1xiO+bPGB6dorslYGYSrQujJDzQ0BRkwC2v8sJHq5i7bBPXRCTq++W0zxn8mxmhQXnRd9tD+zpWljL1h6OZ+sPRdGlbxvcP7xfTrl3ik1C961MffJ8bp30OwA4deunEy9qCUx54P7S9j6Ne8ovXHJnw2GyRDSWxdxdvtaBzxf492rHi7tOSSr9dTBSKWc+QG4xPwgO76ptoDChuefGLsNXENv/3sbX4rL4pyJxvNnHlU9WhfeUlfo51KSbvht8nLFy9nTP/0uwHuOTvcxniUkgn3kzCxs4X9bNxA8L6j+jbif17tHOtMZ1tUl04F49//ugwPlqxxdM1MaSPyQLbujAzCQ/Y5TjdFER9U7P/YXtdI5/Xhq8vSOb3ZE/jnef4oGYTk2cti+qbzGDrDOPs383ykbz582N54MIRrv1n3Xh8wpTdqZKN8MkeHSo47aBeGT+vwWAwMwlPuCkHm9WOdQKj73qL0f3DC8AMS8Lk8PqitYk7aZJ5arb9Ip/fflJYHqPInEZH7t+VxoBi765taF9R6mm19gWj+nLN8ftzzJ/ecd0/om8nPl21lRMO7Mn3DnbN9G4oUpwPH4aWi1ESHohXezqyBOf85ZtD29N+fDh9s2QvLy/xMbpfl1CKDICrj9uPR1zqVtgKpUNFeERU5I/8r5ceQnvdx+kEtkNMO1aWcsVR/blv5pLQvvqmQKiym80fzxlGv65tGdCzPZ0qS/m0disjqjqZRVgtiCcuPzSUUdfQsjHmJg/ESzMRL4HeII8L47xw48kHsOTO5iypbctLeObKw/jyd+NDbb8cP8j12FizjtH9u/DQRSM5dZiVbsK5CrxLW0tZvHzdUdxymlU19qLRe4elqQBr1XVZiY/fnjkk1NajfQWH7duVLm2t/FIH793ZKIgWxvGDetA7Qc1xQ8vAzCQ80NAUbm4a0KMdbctL+HTVVi5/4qOYx7VLkC00kicuP5QfPOl+vu7ty8OymdoDf6kfhvftxE4dATVuUA/e+io8O2tFDP+FiHDG8N6MH7oXd57VFBa1cv0JA3mhehUDe7Zn765tqFm/g6uP3w+Ayw7fh2MHdqd65RYuPNQq+THxiH4s37iLJz9cETPVh8FgKD4k1RrHItIXeAroibWYeLJS6gER6QI8D/QDVgDnK6W26HrWDwCnAruBy5VSH+tzTQR+rU99p1Jqqm4/BHgSqMTKBvszlUDgUaNGqerq6nhdkubFT1Zz/fOfhl6fe0gVEw/vxxl/ib8aecXdpyX9Xkopjrj7bdbo7K4Al47ZmxtOGEjXduX8acZXLFm3k8cuG+V6fH1TgE+/3coFk+c2y3/NkaGUINmkrjHAvOWbPUdzGQyGwkFEFiilogaWdGYSTcD/KqU+FpH2wAIRmQlcDryllLpbRCZh1Y34FXAKVkGhAcBhwKPAYVqp3AaMwlI2C0RkulJqi+5zJTAPS0mMB15LQ+aUcCoIgN6dKqksy46lTkTYUdcU1nbnWcNC2zee7G5Ssikv8UcloMtVNtKKUu/hvgaDoThIpzLdGnsmoJTagVUYqA8wAZiqu00FztLbE4CnlMVcrDKnvYCTgZlKqc1aMcwExut9HZRSc/Xs4SnHuTLOjrpGPlu1lVlLNjDglldZsHKLa799u7XlurH740+Qm+DGkw9IWZZR/eKnok5Em6g05C0zPYTBYMg+GfFJiEg/YCTWE39PpZRdKHktljkKLAXiTHZUq9vitde6tLu9/1XAVQB77713Sp/h1y8u5O0v17Oj3nqKP+fRD3ntZ0fz1drtYf1G7N2JUr+Pfl3bcNLgnqH03DZ3nT2U4VWd0lpt+5eLD2bN1j2ceP+slI5v53BAnzm8N8P6dEpZFoPB0LpJW0mISDvg/4DrlVLbnasxlVJKRLL+GKuUmgxMBssnkco5fjV+EPOWbQ4piS5ty8LSWgAM7NmOHxzRH7DMQpMvG0VdY4Al63awqz5AiV84tF+XqHMnS7vyEgb0bM9zV41JKUuqzyd0a1fGwJ7tefCikWnLYzAYWi9pKQkRKcVSEM8opf6tm9eJSC+l1BptMrJDbVYDfR2HV+m21cBxEe3v6vYql/5ZoXenSp74waGc/9c5XH5kPyaM6MMFf5tDqd/HqcN6cePJB7hmMK0o9XNQVaesyDRm364pH/vRLSeY9AkGgyFtUlYSOlrpceBLpdR9jl3TgYnA3fr/S472a0XkOSzH9TatSGYAvxcR2xB/EnCTUmqziGwXkTFYZqzLgIdSldcLB/bqwOxJY2lb5qfE7+OdG4+j1OfLSnrrbGMUhMFgyATpzCSOBL4PfCEin+q2m7GUwwsicgWwEjhf73sVK/y1BisE9gcAWhncAdgLBH6nlLKXEV9Ncwjsa+QgsslZpyFyhbLBYDC0NlJeJ1GoZGOdhMFgMLR0Yq2TMGk5DAaDwRAToyQMBoPBEBOjJAwGg8EQE6MkDAaDwRAToyQMBoPBEJMWF90kIhuwQm+zSTdgY5bfI5MUi7zFIqdNschbLHKCkTVbeJF1H6VUVIbOFqckcoGIVLuFihUqxSJvschpUyzyFoucYGTNFunIasxNBoPBYIiJURIGg8FgiIlREqkxOd8CJEmxyFssctoUi7zFIicYWbNFyrIan4TBYDAYYmJmEgaDwWCIiVESBoPBYIiJURIGg8FgiIlREjEQkYtFZLjeNhV8MkSxXVcRKYrfiIicKSL75VuOloa5X42SiEJEThCR94E/AyPBqtWdV6FiICJn6YJNBU+RXdczReTn+ZbDC/q6zsGqEtkr3/LEw9yv2SHb92taNa5bCvoJoQKYCvQA7gQmAG30fr9SKpA/CZvRsvqwKvtNAvYRkTeUUu/nV7Joium6AohICfC/wE+AvUXkbaXUpwUopwBtgWeB9sCvgeuBfYDZIuJTSgXzJ2Ez5n7NHrm6X81MAusJQSm1B3hGKXWcUmoG8CFWeVYK6cbQsgawysCOxCrxWpBPZ8V0XQGUUk3A18Ag4OfA33R7ocmplFI7gX/o6/oWMANrQKNQFASY+zWb5Op+bdVKQkR+KiKPiciVAEqpl3S7H1gOLBKRvvmU0cYh649003tKqR1KqceAtrqmeEHY0Ivwut4tInYt9leUUnVKqT8DPUTkYt0v7wXPHbKeB6CUel63+4AtwCoRKc+njDbmfs0OeblflVKt8g+4HJgLjAfeA24G9nXsHwZ8BLQvQFlvAvZz7D8FWAR0LkBZC/K6AgLcAHwAnAt8qWXv4ehzNrC6AK5pLFm7O/ocAXyVb1lj3APmfs3ePZD1+zXvWjyPjAP+qJR6HcuuVwZcau9USn0B1AEX5ke8MCJlrQAusXcqpV7DummuEpH29pNmniiK66qsX9XxwK+VUtOwfoAHASc7+vwHWCIivwDLmVlAsg7HGtjsPh8CtSJyZj5kjMDcrxkmn/drq1MSjuntJ8DpAEqpaqyniT4icpTuJ1h23op8hb7FkXWOlvVIR/dfAX8AlgJ75VJOKOzrGvk+DlmrgaO1rK9jXbshInKAo/tPgHtEZC3Qp4BkXaJlHaT7dQC+AhqzLWMszP2adVnzcr+2eCUhIkNEpMJ+rZqdeh8APhE5Rr9eCHyHDiPUmrsHsEtv50LWI8UR655A1jVAb33c/sAjwIvAwUqphwpM1rxeV6DS+cIhaw3QXkSG6dfvAR2xIoYQkRHAY8D/YV3XqQUoazvdbztQBfTMgYxAyGYfUmwFfr8mI2u+x4FYsublfm2xSkJEDhKR2VhhbF0d7fZnXoplF71Ah4zVYj3R9HOc5hdKqSk5kPVgEXkDeBvrS/cia0+HrNuAa5VS31NKfVeAsubruo4Rkf8DHhaRkxw/Pjv0ez7QBJwkIiVKqcVYT192cZZNwNVKqfNycF3TlRXgQqXUk9mUU8t0uIg8BtwgIu3twdMhayHdr6nImq/79UgRmQr8WkS6OGS1ndB5uV9brJLAih2fppQ6Wym1GkJxzrZW3gG8D5QD9+ovojPWhQZAKdWQTQFFpFRE/oaVxvdBrGntccnKqpTaoJRaWgyyanmzel21TMdhPa3+GytM8FKgs1hrCJq0HDVYU/j9sGL4AerR5W+VUqu0TbqQZV1hn0cpVZcDWY8F/oL1kNAbuFlETtLv36S75f1+zZSsum8u7td9se6Bd7DWu9whIqfq92/U//Nyv7Y4JSEiPn3BdyorLAwROVFEOmFFCCAidwL/xHqiuRXrpnhfv86FScGmHJgFHK2UehlrkDhQPyUEtKy/NbKmxEHAR0qpZ4B/AKVY90RQy3qniDwOLMBSeqNFZAGwGUsBFousb+RY1kOAD5RSz2LN0nsCF4lIT1tWCuceKCZZRwNf6pngL4BPgTNEpJcta97uV69hUIX8B4wBBjped8CaRp6OZfecATyFFYrXD+vG2N/R30eOQtycsqLreTj2XQH81d6HNXj8k/DwQSOrt3tgBNYP6DZgHfAuMAW4ACtcNPIeaAd0MrImlPU0/VvqrV8/CDwD/A8wsMDugUKW9QzgWmCMfr0vln9kb/16MHA3VhTTUXm9B3LxJlm80J2AV7CmjL8G2jr23Qx8DJypXx8DvAQc7rwp8i2rHmB9ent/PUh0tvcZWZOWtZ1j32g92J6jX1+B5dgbbmRNTVZgAJYJ5w0sB+l/gBux7PYYWRPK2gv4L9aM5VasUOCT9b57gf/V236sld63AR3zIav9V+zmprZYs4Tr9PYxjn0vY80auujX1cBarJhnJPf5bVxlVRZB7fhdofsca+8zsiYt69H2DqXUfKA72maLZZvuhLU62cianKz2PbAUKwXEH4B/KaXOxnL8HmcfaGSNyyjgfaXU0UqpO4AHgKv0vveBYSJymLJMuKuBY5RS2/IkK1CEPgkRuUxEjhWRDspySE8GXsAa/A8TkT4ASqnPsZ4arhGRbljOwGE0O8+yfrE9yGqHBIqWx06pYCuyyBA4I2tyspZj5d65Wh86Duuhoc7ImrSso21ZlVINSql3lFLP6UMPBl63z2NkdZX1OP0dvwU87di9CWvNC8A8rHUb94lIO2AIsFJE2uRKVjeKQkmIRS8ReQeYiLV681ER6aasvCW7gTexHE9j7eOUUo9jZcq8HTgH+JFS6ttCk1UppXSE0C6s72SM3W5kTUnWcVqmemA60E5EZgEXYYVerjeypiTr2Ihjj9LO06OxZu5ZpchlvRjLnNhGKbVGmsNae2l5UUqtVUo9gKUspmA92P5Rf678kWv7VrJ/gF//H4iV9RIse91DwL8j+t6AFcXQEYcDCigtcFnbONrLjKwZkbUTUKnbKnHk4zGypn0P2D6q3sCpRtbkZXX0+S9wgt7uof+XUAA54+y/gp1JiIhfRH4P/F6seOcDgACEUuH+DDhC77N5DMvrPxOocUw5s5qqIAOyLndOj42sGZN1hYj0UUrtUUotM7JmTNZlIlKllPpOKfWqkTV5WZVSAREpAzZg5Vu6C5gpIp2VUk1KqR3ZlDUZClJJ6Au8AGsaVoOVf74ROF5ERkPIPne7/rM5Dcum+xkwTGV5NaeRtaBl/VTLutrImlFZ7Xug1siasqy/1YdVYGVyfQsrtcYJSqkt2ZY1afI9lYkxXTsa+L7j9SNYiasuBxboNh/W8vkXgH66bQJWNICR1chqZDWyFrKsVVhhz08BI3Ipa9KfLd8CxLjgbbCiZ2y73SXAH/T2p8B1ensU8KyR1chqZDWyFpGsz+VT1mT/CtLcpJTarZSqV81l+E7Est2BVSv3QBF5GSty6WOITrGcK4ys2cHImh2MrNkhSVkXQP5kTZaSxF3yh1iZMBVWzpXpunkH1mrqocBype24SqvpfGFkzQ5G1uxgZM0OxSSrVwpyJuEgiJXsbCNwkNbEtwJBpdRslQNHXxIYWbODkTU7GFmzQzHJ6o1827sS/WEt1goCs4Er8i2PkdXIamQ1srYUWb38if5QBYuIVGElurpPWatSCxYja3YwsmYHI2t2KCZZvVDwSsJgMBgM+aPQfRIGg8FgyCNGSRgMBoMhJkZJGAwGgyEmRkkYDAaDISZGSRgMBoMhJkZJGAwZRkRuF5FfxNl/logMzqVMBkOqGCVhMOSeswCjJAxFgVknYTBkABG5BatM5XpgFVYSt21YRe7LsGoMfB8YgVU+c5v+O0ef4mGgO7AbuFIp9VUOxTcYYmKUhMGQJiJyCPAkcBhW0syPgb8CTyilNuk+dwLrlFIPiciTwMtKqWl631vAj5VSS0XkMKwU02Oj38lgyD0FnQXWYCgSjgb+o3TBehGxs38O1cqhE1Y5zRmRB4pIO+AI4F+OzNHl2RbYYPCKURIGQ/Z4EjhLKfWZiFwOHOfSxwdsVUqNyJ1YBoN3jOPaYEifWcBZIlIpIu2BM3R7e2CNiJRiVSqz2aH3oZTaDiwXkfPAKkQjIsNzJ7rBEB+jJAyGNFFKfQw8D3wGvAZ8pHfdCswDPgCcjujngBtF5BMR2Q9LgVwhIp8Bi7BqNBsMBYFxXBsMBoMhJmYmYTAYDIaYGCVhMBgMhpgYJWEwGAyGmBglYTAYDIaYGCVhMBgMhpgYJWEwGAyGmBglYTAYDIaY/H9V9o+GvMcQrgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_data.plot(), bottom_data.plot(), cleaned_data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Create a `draw_line_plot` function that uses Matplotlib to draw a line chart </h2>\n",
    "<h2> The title should be \"Daily freeCodeCamp Forum Page\n",
    " Views 5/2016-12/2019\". The label on the x axis should be \"Date\" and the label on the y axis should be \"Page Views\". </h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = cleaned_data.index\n",
    "y = cleaned_data['value']\n",
    "def draw_line_plot():\n",
    "    fig, ax = plt.subplots(figsize = (15,5))\n",
    "    ax.plot(x,y,color='r',linewidth = 1.5)\n",
    "    ax.set(title = \"Daily freeCodeCamp Forum Page Views 5/2016-12/2019\", xlabel = \"Date\", ylabel = \"Page Views\")\n",
    "    fig.savefig('line_plot.png')\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "fig, ax = plt.subplots(figsize = (15,5))\n",
    "ax.plot(x,y, color = 'red', linewidth = 1.5)\n",
    "ax.set(title = \"Daily freeCodeCamp Forum Page Views 5/2016-12/2019\", xlabel = \"Date\", ylabel = \"Page Views\")\n",
    "fig.savefig('line_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Create a `draw_bar_plot` function that draws a bar chart </h2>\n",
    "<h2> It should show average daily page views for each month grouped by year. </h2>\n",
    "<h2> The legend should show month labels and have a title of \"Months\". </h2>\n",
    "<h2>  On the chart, the label on the x axis should be \"Years\" and the label on the y axis should be \"Average Page Views\".</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  '2016-05',\n",
       "  '2016-06',\n",
       "  '2016-07',\n",
       "  '2016-08',\n",
       "  '2016-09',\n",
       "  '2016-10',\n",
       "  '2016-11',\n",
       "  '2016-12'],\n",
       " ['2017-01',\n",
       "  '2017-02',\n",
       "  '2017-03',\n",
       "  '2017-04',\n",
       "  '2017-05',\n",
       "  '2017-06',\n",
       "  '2017-07',\n",
       "  '2017-08',\n",
       "  '2017-09',\n",
       "  '2017-10',\n",
       "  '2017-11',\n",
       "  '2017-12'],\n",
       " ['2018-01',\n",
       "  '2018-02',\n",
       "  '2018-03',\n",
       "  '2018-04',\n",
       "  '2018-05',\n",
       "  '2018-06',\n",
       "  '2018-07',\n",
       "  '2018-08',\n",
       "  '2018-09',\n",
       "  '2018-10',\n",
       "  '2018-11',\n",
       "  '2018-12'],\n",
       " ['2019-01',\n",
       "  '2019-02',\n",
       "  '2019-03',\n",
       "  '2019-04',\n",
       "  '2019-05',\n",
       "  '2019-06',\n",
       "  '2019-07',\n",
       "  '2019-08',\n",
       "  '2019-09',\n",
       "  '2019-10',\n",
       "  '2019-11',\n",
       "  '2019-12'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2016_to_2019 = df.groupby([df.index.strftime('%Y-%m')])['value'].mean().reset_index(name = 'Average Page Views')\n",
    "data_2016_to_2019_month = []\n",
    "data_2016_to_2019_views =  []\n",
    "for i in data_2016_to_2019['date']:\n",
    "    data_2016_to_2019_month.append(i)\n",
    "for j in data_2016_to_2019['Average Page Views']:\n",
    "    data_2016_to_2019_views.append(j)\n",
    "\n",
    "data_2016_to_2019_dict = dict(zip(data_2016_to_2019_month,data_2016_to_2019_views))\n",
    "\n",
    "data_2016_list = []\n",
    "for i in range (0,4):\n",
    "    data_2016_list.append(0)\n",
    "data_2017_list = []\n",
    "data_2018_list = []\n",
    "data_2019_list = []\n",
    "for i in data_2016_to_2019['date']:\n",
    "    if '2016' in i:\n",
    "        data_2016_list.append(i)\n",
    "for i in data_2016_to_2019['date']:\n",
    "    if '2017' in i:\n",
    "        data_2017_list.append(i)\n",
    "for i in data_2016_to_2019['date']:\n",
    "    if '2018' in i:\n",
    "        data_2018_list.append(i)\n",
    "for i in data_2016_to_2019['date']:\n",
    "    if '2019' in i:\n",
    "        data_2019_list.append(i)\n",
    "data_2016_list, data_2017_list, data_2018_list, data_2019_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  0,\n",
       "  0,\n",
       "  0,\n",
       "  11242.695652173914,\n",
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       "  23314.225806451614,\n",
       "  31049.1935483871,\n",
       "  41476.86666666667,\n",
       "  27398.322580645163,\n",
       "  40448.63333333333,\n",
       "  27832.41935483871],\n",
       " [32785.16129032258,\n",
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       "  29369.09677419355,\n",
       "  30878.733333333334,\n",
       "  34244.290322580644,\n",
       "  43577.5,\n",
       "  65806.83870967742,\n",
       "  47712.45161290323,\n",
       "  47376.8,\n",
       "  47438.709677419356,\n",
       "  57701.566666666666,\n",
       "  48420.58064516129],\n",
       " [58580.096774193546,\n",
       "  65679.0,\n",
       "  62693.77419354839,\n",
       "  62350.833333333336,\n",
       "  56562.87096774193,\n",
       "  70117.0,\n",
       "  63591.06451612903,\n",
       "  62831.6129032258,\n",
       "  65941.73333333334,\n",
       "  112592.0,\n",
       "  78688.33333333333,\n",
       "  80047.48387096774],\n",
       " [102056.51612903226,\n",
       "  105968.35714285714,\n",
       "  91214.48387096774,\n",
       "  89368.43333333333,\n",
       "  91439.90322580645,\n",
       "  162402.6,\n",
       "  100804.45161290323,\n",
       "  117991.03225806452,\n",
       "  97268.83333333333,\n",
       "  150862.70967741936,\n",
       "  181026.7,\n",
       "  173870.33333333334])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2016_views_list = []\n",
    "for i in range(0,4):\n",
    "    data_2016_views_list.append(0)\n",
    "data_2017_views_list = []\n",
    "data_2018_views_list = []\n",
    "data_2019_views_list = []\n",
    "for i in data_2016_to_2019_dict.keys():\n",
    "    if '2016' in i:\n",
    "        data_2016_views_list.append(data_2016_to_2019_dict.get(i))\n",
    "    elif '2017' in i:\n",
    "        data_2017_views_list.append(data_2016_to_2019_dict.get(i))\n",
    "    elif '2018' in i:\n",
    "        data_2018_views_list.append(data_2016_to_2019_dict.get(i))\n",
    "    elif '2019' in i:\n",
    "        data_2019_views_list.append(data_2016_to_2019_dict.get(i))\n",
    "data_2016_views_list, data_2017_views_list, data_2018_views_list, data_2019_views_list"
   ]
  },
  {
   "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>months</th>\n",
       "      <th>January</th>\n",
       "      <th>February</th>\n",
       "      <th>March</th>\n",
       "      <th>April</th>\n",
       "      <th>May</th>\n",
       "      <th>June</th>\n",
       "      <th>July</th>\n",
       "      <th>August</th>\n",
       "      <th>September</th>\n",
       "      <th>October</th>\n",
       "      <th>November</th>\n",
       "      <th>December</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11242.695652</td>\n",
       "      <td>19124.366667</td>\n",
       "      <td>23314.225806</td>\n",
       "      <td>31049.193548</td>\n",
       "      <td>41476.866667</td>\n",
       "      <td>27398.322581</td>\n",
       "      <td>40448.633333</td>\n",
       "      <td>27832.419355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>32785.161290</td>\n",
       "      <td>31113.071429</td>\n",
       "      <td>29369.096774</td>\n",
       "      <td>30878.733333</td>\n",
       "      <td>34244.290323</td>\n",
       "      <td>43577.500000</td>\n",
       "      <td>65806.838710</td>\n",
       "      <td>47712.451613</td>\n",
       "      <td>47376.800000</td>\n",
       "      <td>47438.709677</td>\n",
       "      <td>57701.566667</td>\n",
       "      <td>48420.580645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>58580.096774</td>\n",
       "      <td>65679.000000</td>\n",
       "      <td>62693.774194</td>\n",
       "      <td>62350.833333</td>\n",
       "      <td>56562.870968</td>\n",
       "      <td>70117.000000</td>\n",
       "      <td>63591.064516</td>\n",
       "      <td>62831.612903</td>\n",
       "      <td>65941.733333</td>\n",
       "      <td>112592.000000</td>\n",
       "      <td>78688.333333</td>\n",
       "      <td>80047.483871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>102056.516129</td>\n",
       "      <td>105968.357143</td>\n",
       "      <td>91214.483871</td>\n",
       "      <td>89368.433333</td>\n",
       "      <td>91439.903226</td>\n",
       "      <td>162402.600000</td>\n",
       "      <td>100804.451613</td>\n",
       "      <td>117991.032258</td>\n",
       "      <td>97268.833333</td>\n",
       "      <td>150862.709677</td>\n",
       "      <td>181026.700000</td>\n",
       "      <td>173870.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "months        January       February         March         April  \\\n",
       "2016         0.000000       0.000000      0.000000      0.000000   \n",
       "2017     32785.161290   31113.071429  29369.096774  30878.733333   \n",
       "2018     58580.096774   65679.000000  62693.774194  62350.833333   \n",
       "2019    102056.516129  105968.357143  91214.483871  89368.433333   \n",
       "\n",
       "months           May           June           July         August  \\\n",
       "2016    11242.695652   19124.366667   23314.225806   31049.193548   \n",
       "2017    34244.290323   43577.500000   65806.838710   47712.451613   \n",
       "2018    56562.870968   70117.000000   63591.064516   62831.612903   \n",
       "2019    91439.903226  162402.600000  100804.451613  117991.032258   \n",
       "\n",
       "months     September        October       November       December  \n",
       "2016    41476.866667   27398.322581   40448.633333   27832.419355  \n",
       "2017    47376.800000   47438.709677   57701.566667   48420.580645  \n",
       "2018    65941.733333  112592.000000   78688.333333   80047.483871  \n",
       "2019    97268.833333  150862.709677  181026.700000  173870.333333  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year = ['2017','2018','2019']\n",
    "views = [data_2017_views_list,data_2018_views_list,data_2019_views_list]\n",
    "dict_views_2017_to_2019 = dict(zip(year,views))\n",
    "df_views_2017_to_2019 = pd.DataFrame(dict_views_2017_to_2019)\n",
    "df_views_2016 = pd.DataFrame({'2016' : data_2016_views_list})\n",
    "df_views_new = pd.concat([df_views_2016,df_views_2017_to_2019], axis = 1)\n",
    "df_views_new['2016'] = df_views_new['2016'].fillna(0) \n",
    "df_views_new['months'] = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "df_views_final = df_views_new.set_index('months')\n",
    "df_views_final.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_views_final.T.plot.bar(figsize = (15,10), xlabel = \"Years\", ylabel = \"Average Page Views\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_bar_plot():\n",
    "    df_bar = df_views_final.T\n",
    "    df_bar.plot.bar(figsize = (15,10), xlabel = \"Years\", ylabel = \"Average Page Views\")\n",
    "    fig.savefig('bar_plot.png')\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Create a `draw_box_plot` function that uses Searborn to draw two adjacent box plots </h2>\n",
    "<h2> These box plots should show how the values are distributed within a given year or month and how it compares over time.</h2>\n",
    "<h2> The title of the first chart should be \"Year-wise Box Plot (Trend)\" and the title of the second chart should be \"Month-wise Box Plot (Seasonality)\".</h2>\n",
    "<h2> Make sure the month labels on bottom start at \"Jan\" and the x and x axis are labeled correctly.</h2>\n",
    "<h2>For each chart, make sure to use a copy of the data frame.</h2>"
   ]
  },
  {
   "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>date</th>\n",
       "      <th>value</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-09</td>\n",
       "      <td>1201</td>\n",
       "      <td>2016</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-10</td>\n",
       "      <td>2329</td>\n",
       "      <td>2016</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>1716</td>\n",
       "      <td>2016</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-12</td>\n",
       "      <td>10539</td>\n",
       "      <td>2016</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-13</td>\n",
       "      <td>6933</td>\n",
       "      <td>2016</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>171584</td>\n",
       "      <td>2019</td>\n",
       "      <td>Nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>2019-11-30</td>\n",
       "      <td>141161</td>\n",
       "      <td>2019</td>\n",
       "      <td>Nov</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>142918</td>\n",
       "      <td>2019</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>220144</td>\n",
       "      <td>2019</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>158549</td>\n",
       "      <td>2019</td>\n",
       "      <td>Dec</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1304 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date   value  year month\n",
       "0    2016-05-09    1201  2016   May\n",
       "1    2016-05-10    2329  2016   May\n",
       "2    2016-05-11    1716  2016   May\n",
       "3    2016-05-12   10539  2016   May\n",
       "4    2016-05-13    6933  2016   May\n",
       "...         ...     ...   ...   ...\n",
       "1299 2019-11-29  171584  2019   Nov\n",
       "1300 2019-11-30  141161  2019   Nov\n",
       "1301 2019-12-01  142918  2019   Dec\n",
       "1302 2019-12-02  220144  2019   Dec\n",
       "1303 2019-12-03  158549  2019   Dec\n",
       "\n",
       "[1304 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_box = df.copy()\n",
    "df_box.reset_index(inplace=True)\n",
    "df_box['year'] = [d.year for d in df_box.date]\n",
    "df_box['month'] = [d.strftime('%b') for d in df_box.date]\n",
    "df_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 0, 'Year'),\n",
       " Text(0, 0.5, 'Page Views'),\n",
       " Text(0.5, 1.0, 'Year-wise Box Plot (Trend)')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={\"figure.figsize\":(15,20)})\n",
    "sns.boxplot(x=df_box['year'], y = df_box['value']).set(xlabel = \"Year\", ylabel = \"Page Views\", title = \"Year-wise Box Plot (Trend)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x972 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [13, 13.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "fig, axes = plt.subplots(1, 2)\n",
    "\n",
    "sns.boxplot(data = df_box, x = 'year', y = 'value', ax=axes[0])\n",
    "axes[0].set(title = \"Year-wise Box Plot (Trend)\", ylabel = \"Page Views\", xlabel=\"Year\")\n",
    "sns.boxplot(data = df_box, x = df_box['month'], y = 'value', ax=axes[1])\n",
    "axes[1].set(title = \"Month-wise Box Plot (Seasonality)\", ylabel = \"Page Views\", xlabel=\"Month\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1008 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 14))\n",
    "sns.boxplot(ax=ax1, data=df_box, x=df_box[\"year\"], y=df_box[\"value\"])\n",
    "ax1.set(xlabel=\"Year\", ylabel=\"Page Views\", title=\"Year-wise Box Plot (Trend)\")\n",
    "sns.boxplot(ax=ax2,data=df_box,x=df_box[\"month\"],y=df_box[\"value\"],order=[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"])\n",
    "ax2.set_title(\"Month-wise Box Plot (Seasonality)\")\n",
    "ax2.set_xlabel(\"Month\")\n",
    "ax2.set_ylabel(\"Page Views\")\n",
    "y_ticks = [\"0\",\"20000\",\"40000\",\"60000\",\"80000\",\"100000\",\"120000\",\"140000\",\"160000\",\"180000\",\"200000\"]\n",
    "ax1.yaxis.set_major_locator(mticker.FixedLocator([int(s) for s in y_ticks]))\n",
    "ax1.set_yticklabels(y_ticks)\n",
    "ax2.yaxis.set_major_locator(mticker.FixedLocator([int(s) for s in y_ticks]))\n",
    "ax2.set_yticklabels(y_ticks)\n",
    "fig.savefig('box_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_box_plot():\n",
    "    # Prepare data for box plots (this part is done!)\n",
    "    df_box = df.copy()\n",
    "    df_box.reset_index(inplace=True)\n",
    "    df_box['year'] = [d.year for d in df_box.date]\n",
    "    df_box['month'] = [d.strftime('%b') for d in df_box.date]\n",
    "\n",
    "    # Draw box plots (using Seaborn)\n",
    "    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 14))\n",
    "    sns.boxplot(ax=ax1, data=df_box, x=df_box[\"year\"], y=df_box[\"value\"])\n",
    "    ax1.set(xlabel=\"Year\", ylabel=\"Page Views\", title=\"Year-wise Box Plot (Trend)\")\n",
    "    sns.boxplot(ax=ax2,data=df_box,x=df_box[\"month\"],y=df_box[\"value\"],order=[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"])\n",
    "    ax2.set_title(\"Month-wise Box Plot (Seasonality)\")\n",
    "    ax2.set_xlabel(\"Month\")\n",
    "    ax2.set_ylabel(\"Page Views\")\n",
    "    y_ticks = [\"0\",\"20000\",\"40000\",\"60000\",\"80000\",\"100000\",\"120000\",\"140000\",\"160000\",\"180000\",\"200000\"]\n",
    "    ax1.yaxis.set_major_locator(mticker.FixedLocator([int(s) for s in y_ticks]))\n",
    "    ax1.set_yticklabels(y_ticks)\n",
    "    ax2.yaxis.set_major_locator(mticker.FixedLocator([int(s) for s in y_ticks]))\n",
    "    ax2.set_yticklabels(y_ticks)\n",
    "    # Save image and return fig (don't change this part)\n",
    "    fig.savefig('box_plot.png')\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}