Analyzing Marketing Campaigns with pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d18cae29",
   "metadata": {},
   "source": [
    "# Objectives\n",
    "- Clean data and initial exploratory \n",
    "- Key metrics analysis: converstion rate and retention rate\n",
    "  - def function to access both metrics \n",
    "- Inconsistence checking and resolving \n",
    "  - if the difference has impact on conversion rate by using assumption\n",
    "- A/B test, lift and statistical significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1336c481",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "663d553a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>user_id</th>\n",
       "      <th>date_served</th>\n",
       "      <th>marketing_channel</th>\n",
       "      <th>variant</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>age_group</th>\n",
       "      <th>date_subscribed</th>\n",
       "      <th>date_canceled</th>\n",
       "      <th>subscribing_channel</th>\n",
       "      <th>is_retained</th>\n",
       "      <th>is_house_ad</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>day_served</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a100000029</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0-18 years</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a100000030</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>19-24 years</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a100000031</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>24-30 years</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a100000032</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>30-36 years</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a100000033</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>36-45 years</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>NaT</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id date_served marketing_channel          variant  converted  \\\n",
       "0  a100000029  2018-01-01         House Ads  personalization       True   \n",
       "1  a100000030  2018-01-01         House Ads  personalization       True   \n",
       "2  a100000031  2018-01-01         House Ads  personalization       True   \n",
       "3  a100000032  2018-01-01         House Ads  personalization       True   \n",
       "4  a100000033  2018-01-01         House Ads  personalization       True   \n",
       "\n",
       "  language_displayed language_preferred    age_group date_subscribed  \\\n",
       "0            English            English   0-18 years      2018-01-01   \n",
       "1            English            English  19-24 years      2018-01-01   \n",
       "2            English            English  24-30 years      2018-01-01   \n",
       "3            English            English  30-36 years      2018-01-01   \n",
       "4            English            English  36-45 years      2018-01-01   \n",
       "\n",
       "  date_canceled subscribing_channel  is_retained  is_house_ad  channel_code  \\\n",
       "0           NaT           House Ads         True         True           1.0   \n",
       "1           NaT           House Ads         True         True           1.0   \n",
       "2           NaT           House Ads         True         True           1.0   \n",
       "3           NaT           House Ads         True         True           1.0   \n",
       "4           NaT           House Ads         True         True           1.0   \n",
       "\n",
       "   day_served  \n",
       "0         0.0  \n",
       "1         0.0  \n",
       "2         0.0  \n",
       "3         0.0  \n",
       "4         0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt = pd.read_csv('/QXJ/Python/Datacamp/Marketing/marketing_1.csv', index_col = 0, parse_dates = ['date_served','date_subscribed', 'date_canceled'])\n",
    "mkt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e650a643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10037, 12)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b1727f31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user_id', 'date_served', 'marketing_channel', 'variant', 'converted',\n",
       "       'language_displayed', 'language_preferred', 'age_group',\n",
       "       'date_subscribed', 'date_canceled', 'subscribing_channel',\n",
       "       'is_retained'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e143359",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2728"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.duplicated(subset = 'user_id').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3eb08622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['House Ads', 'Push', 'Facebook', 'Instagram', 'Email', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['marketing_channel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c85dc7be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>date_served</th>\n",
       "      <th>marketing_channel</th>\n",
       "      <th>variant</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>age_group</th>\n",
       "      <th>date_subscribed</th>\n",
       "      <th>date_canceled</th>\n",
       "      <th>subscribing_channel</th>\n",
       "      <th>is_retained</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10037</td>\n",
       "      <td>10021</td>\n",
       "      <td>10022</td>\n",
       "      <td>10037</td>\n",
       "      <td>10022</td>\n",
       "      <td>10037</td>\n",
       "      <td>10037</td>\n",
       "      <td>10037</td>\n",
       "      <td>1856</td>\n",
       "      <td>577</td>\n",
       "      <td>1856</td>\n",
       "      <td>1856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>7309</td>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>31</td>\n",
       "      <td>115</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>a100000882</td>\n",
       "      <td>1/15/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>control</td>\n",
       "      <td>False</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>19-24 years</td>\n",
       "      <td>1/16/18</td>\n",
       "      <td>4/2/18</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>12</td>\n",
       "      <td>789</td>\n",
       "      <td>4733</td>\n",
       "      <td>5091</td>\n",
       "      <td>8946</td>\n",
       "      <td>9793</td>\n",
       "      <td>9275</td>\n",
       "      <td>1682</td>\n",
       "      <td>163</td>\n",
       "      <td>15</td>\n",
       "      <td>600</td>\n",
       "      <td>1279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           user_id date_served marketing_channel  variant converted  \\\n",
       "count        10037       10021             10022    10037     10022   \n",
       "unique        7309          31                 5        2         2   \n",
       "top     a100000882     1/15/18         House Ads  control     False   \n",
       "freq            12         789              4733     5091      8946   \n",
       "\n",
       "       language_displayed language_preferred    age_group date_subscribed  \\\n",
       "count               10037              10037        10037            1856   \n",
       "unique                  4                  4            7              31   \n",
       "top               English            English  19-24 years         1/16/18   \n",
       "freq                 9793               9275         1682             163   \n",
       "\n",
       "       date_canceled subscribing_channel is_retained  \n",
       "count            577                1856        1856  \n",
       "unique           115                   5           2  \n",
       "top           4/2/18           Instagram        True  \n",
       "freq              15                 600        1279  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7923825b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                object\n",
       "date_served            object\n",
       "marketing_channel      object\n",
       "variant                object\n",
       "converted              object\n",
       "language_displayed     object\n",
       "language_preferred     object\n",
       "age_group              object\n",
       "date_subscribed        object\n",
       "date_canceled          object\n",
       "subscribing_channel    object\n",
       "is_retained            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8555ffa7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id                   0\n",
       "date_served              16\n",
       "marketing_channel        15\n",
       "variant                   0\n",
       "converted                15\n",
       "language_displayed        0\n",
       "language_preferred        0\n",
       "age_group                 0\n",
       "date_subscribed        8181\n",
       "date_canceled          9460\n",
       "subscribing_channel    8181\n",
       "is_retained            8181\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "55744ff0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10037 entries, 0 to 10036\n",
      "Data columns (total 12 columns):\n",
      " #   Column               Non-Null Count  Dtype \n",
      "---  ------               --------------  ----- \n",
      " 0   user_id              10037 non-null  object\n",
      " 1   date_served          10021 non-null  object\n",
      " 2   marketing_channel    10022 non-null  object\n",
      " 3   variant              10037 non-null  object\n",
      " 4   converted            10022 non-null  object\n",
      " 5   language_displayed   10037 non-null  object\n",
      " 6   language_preferred   10037 non-null  object\n",
      " 7   age_group            10037 non-null  object\n",
      " 8   date_subscribed      1856 non-null   object\n",
      " 9   date_canceled        577 non-null    object\n",
      " 10  subscribing_channel  1856 non-null   object\n",
      " 11  is_retained          1856 non-null   object\n",
      "dtypes: object(12)\n",
      "memory usage: 941.1+ KB\n"
     ]
    }
   ],
   "source": [
    "mkt.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c351957b",
   "metadata": {},
   "source": [
    "## Clean datat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f759cf0f",
   "metadata": {},
   "source": [
    "### 1. Data types and data merging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "79efaeb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "mkt['converted'] = mkt['converted'].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d9d40dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['personalization', 'control'], dtype=object)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['variant'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "963c018e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['English', 'German', 'Arabic', 'Spanish'], dtype=object)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['language_displayed'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e56d3fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['English', 'German', 'Arabic', 'Spanish'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['language_preferred'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2977d4b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['0-18 years', '19-24 years', '24-30 years', '30-36 years',\n",
       "       '36-45 years', '45-55 years', '55+ years'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['age_group'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e8c5b764",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['House Ads', 'Email', 'Push', 'Facebook', 'Instagram', nan],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['subscribing_channel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "184c7de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('bool')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mkt['is_retained'] = mkt['is_retained'].astype('bool')\n",
    "mkt['is_retained'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d8b0bcaf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>date_served</th>\n",
       "      <th>marketing_channel</th>\n",
       "      <th>variant</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>age_group</th>\n",
       "      <th>date_subscribed</th>\n",
       "      <th>date_canceled</th>\n",
       "      <th>subscribing_channel</th>\n",
       "      <th>is_retained</th>\n",
       "      <th>is_house_ad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a100000029</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0-18 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a100000030</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>19-24 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a100000031</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>24-30 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a100000032</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>30-36 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a100000033</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>36-45 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id date_served marketing_channel          variant  converted  \\\n",
       "0  a100000029      1/1/18         House Ads  personalization       True   \n",
       "1  a100000030      1/1/18         House Ads  personalization       True   \n",
       "2  a100000031      1/1/18         House Ads  personalization       True   \n",
       "3  a100000032      1/1/18         House Ads  personalization       True   \n",
       "4  a100000033      1/1/18         House Ads  personalization       True   \n",
       "\n",
       "  language_displayed language_preferred    age_group date_subscribed  \\\n",
       "0            English            English   0-18 years          1/1/18   \n",
       "1            English            English  19-24 years          1/1/18   \n",
       "2            English            English  24-30 years          1/1/18   \n",
       "3            English            English  30-36 years          1/1/18   \n",
       "4            English            English  36-45 years          1/1/18   \n",
       "\n",
       "  date_canceled subscribing_channel  is_retained  is_house_ad  \n",
       "0           NaN           House Ads         True         True  \n",
       "1           NaN           House Ads         True         True  \n",
       "2           NaN           House Ads         True         True  \n",
       "3           NaN           House Ads         True         True  \n",
       "4           NaN           House Ads         True         True  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a boolean column \n",
    "mkt['is_house_ad'] = np.where(mkt['marketing_channel'] == 'House Ads', \n",
    "                              True, False)\n",
    "mkt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "865a1093",
   "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>user_id</th>\n",
       "      <th>date_served</th>\n",
       "      <th>marketing_channel</th>\n",
       "      <th>variant</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>age_group</th>\n",
       "      <th>date_subscribed</th>\n",
       "      <th>date_canceled</th>\n",
       "      <th>subscribing_channel</th>\n",
       "      <th>is_retained</th>\n",
       "      <th>is_house_ad</th>\n",
       "      <th>channel_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a100000029</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0-18 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a100000030</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>19-24 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a100000031</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>24-30 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a100000032</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>30-36 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a100000033</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>personalization</td>\n",
       "      <td>True</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>36-45 years</td>\n",
       "      <td>1/1/18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>House Ads</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      user_id date_served marketing_channel          variant  converted  \\\n",
       "0  a100000029      1/1/18         House Ads  personalization       True   \n",
       "1  a100000030      1/1/18         House Ads  personalization       True   \n",
       "2  a100000031      1/1/18         House Ads  personalization       True   \n",
       "3  a100000032      1/1/18         House Ads  personalization       True   \n",
       "4  a100000033      1/1/18         House Ads  personalization       True   \n",
       "\n",
       "  language_displayed language_preferred    age_group date_subscribed  \\\n",
       "0            English            English   0-18 years          1/1/18   \n",
       "1            English            English  19-24 years          1/1/18   \n",
       "2            English            English  24-30 years          1/1/18   \n",
       "3            English            English  30-36 years          1/1/18   \n",
       "4            English            English  36-45 years          1/1/18   \n",
       "\n",
       "  date_canceled subscribing_channel  is_retained  is_house_ad  channel_code  \n",
       "0           NaN           House Ads         True         True           1.0  \n",
       "1           NaN           House Ads         True         True           1.0  \n",
       "2           NaN           House Ads         True         True           1.0  \n",
       "3           NaN           House Ads         True         True           1.0  \n",
       "4           NaN           House Ads         True         True           1.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new column\n",
    "channel_dict = {'House Ads': 1, 'Push': 5, 'Facebook':3, \n",
    "                'Instagram': 2, 'Email':4}\n",
    "mkt['channel_code'] = mkt['marketing_channel'].map(channel_dict)\n",
    "mkt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f9882933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse dates \n",
    "mkt['date_served'] = pd.to_datetime(mkt['date_served'])\n",
    "mkt['date_subscribed'] = pd.to_datetime(mkt['date_subscribed'])\n",
    "mkt['date_canceled'] = pd.to_datetime(mkt['date_canceled'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "984c7a23",
   "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>user_id</th>\n",
       "      <th>date_served</th>\n",
       "      <th>marketing_channel</th>\n",
       "      <th>variant</th>\n",
       "      <th>converted</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>age_group</th>\n",
       "      <th>date_subscribed</th>\n",
       "      <th>date_canceled</th>\n",
       "      <th>subscribing_channel</th>\n",
       "      <th>is_retained</th>\n",
       "      <th>is_house_ad</th>\n",
       "      <th>channel_code</th>\n",
       "      <th>day_served</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10032</th>\n",
       "      <td>a100007290</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>Email</td>\n",
       "      <td>control</td>\n",
       "      <td>True</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>45-55 years</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>2018-01-24</td>\n",
       "      <td>Email</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10033</th>\n",
       "      <td>a100007291</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>Email</td>\n",
       "      <td>control</td>\n",
       "      <td>True</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>55+ years</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Email</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10034</th>\n",
       "      <td>a100007292</td>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>Instagram</td>\n",
       "      <td>control</td>\n",
       "      <td>False</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>55+ years</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Email</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10035</th>\n",
       "      <td>a100007293</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>Email</td>\n",
       "      <td>control</td>\n",
       "      <td>True</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0-18 years</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Email</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10036</th>\n",
       "      <td>a100007294</td>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>Facebook</td>\n",
       "      <td>control</td>\n",
       "      <td>False</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0-18 years</td>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>NaT</td>\n",
       "      <td>Email</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          user_id date_served marketing_channel  variant  converted  \\\n",
       "10032  a100007290  2018-01-17             Email  control       True   \n",
       "10033  a100007291  2018-01-17             Email  control       True   \n",
       "10034  a100007292  2018-01-05         Instagram  control      False   \n",
       "10035  a100007293  2018-01-17             Email  control       True   \n",
       "10036  a100007294  2018-01-06          Facebook  control      False   \n",
       "\n",
       "      language_displayed language_preferred    age_group date_subscribed  \\\n",
       "10032             German             German  45-55 years      2018-01-17   \n",
       "10033             German             German    55+ years      2018-01-17   \n",
       "10034             German             German    55+ years      2018-01-17   \n",
       "10035             German             German   0-18 years      2018-01-17   \n",
       "10036             German             German   0-18 years      2018-01-17   \n",
       "\n",
       "      date_canceled subscribing_channel  is_retained  is_house_ad  \\\n",
       "10032    2018-01-24               Email        False        False   \n",
       "10033           NaT               Email         True        False   \n",
       "10034           NaT               Email         True        False   \n",
       "10035           NaT               Email         True        False   \n",
       "10036           NaT               Email         True        False   \n",
       "\n",
       "       channel_code  day_served  \n",
       "10032           4.0         2.0  \n",
       "10033           4.0         2.0  \n",
       "10034           2.0         4.0  \n",
       "10035           4.0         2.0  \n",
       "10036           3.0         5.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add dayofweek column based on date_served\n",
    "mkt['day_served'] = mkt['date_served'].dt.dayofweek\n",
    "mkt.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ae6a4292",
   "metadata": {},
   "outputs": [],
   "source": [
    "mkt.to_csv('/QXJ/Python/Datacamp/Marketing/marketing_1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db28bf66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10037 entries, 0 to 10036\n",
      "Data columns (total 15 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   user_id              10037 non-null  object        \n",
      " 1   date_served          10021 non-null  datetime64[ns]\n",
      " 2   marketing_channel    10022 non-null  object        \n",
      " 3   variant              10037 non-null  object        \n",
      " 4   converted            10037 non-null  bool          \n",
      " 5   language_displayed   10037 non-null  object        \n",
      " 6   language_preferred   10037 non-null  object        \n",
      " 7   age_group            10037 non-null  object        \n",
      " 8   date_subscribed      1856 non-null   datetime64[ns]\n",
      " 9   date_canceled        577 non-null    datetime64[ns]\n",
      " 10  subscribing_channel  1856 non-null   object        \n",
      " 11  is_retained          10037 non-null  bool          \n",
      " 12  is_house_ad          10037 non-null  bool          \n",
      " 13  channel_code         10022 non-null  float64       \n",
      " 14  day_served           10021 non-null  float64       \n",
      "dtypes: bool(3), datetime64[ns](3), float64(2), object(7)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "mkt.info()\n",
    "# confirm the dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "788598ba",
   "metadata": {},
   "source": [
    "### 2. Initial exploratory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4ba5d48c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_served\n",
       "2018-01-01    362\n",
       "2018-01-02    374\n",
       "2018-01-03    348\n",
       "2018-01-04    323\n",
       "2018-01-05    319\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate unique users by served_date \n",
    "daily_users = mkt.groupby('date_served')['user_id'].nunique()\n",
    "daily_users.head()\n",
    "# each day has around 300 new cusotmers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "a5f7a75c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/2tv/WuDaZNYkIpKKquoi9O6eT3ZWe6eTRNeirqqLEGlpJT9HK4qKiKQSdUoUEUkBlXXtr0EdE1vpY3WN5lGLiKQaBWoRkRSwtbbjMRU9CwFYUa151CIiqUaBWkQkBWyt7XhMRWkQqHVioohIylGgFhEJWWubs25jZJtTPvqXxJq7aMqHiEiqUaAWEQnZuo0R2px2247H5Odk06c4X81dRERSkAK1iEjINndJ3MaUD4hO+9CUDxGR1KNALSISsqrtNHWJiXZLVKAWEUk1CtQiIiGr3Ebb8XjlpQWs2NCAu3dFWSIikiAFahGRkFXWxqZ8bC9QFxJpaWP9pqauKEtERBKkQC0iErLKugglhbkU5G67A6KWzhMRSU0K1CIiIauqi2xzhY+Y8iBQax61iEhqUaAWEQnZ9tqOx3wyQq21qEVEUokCtYhIyLbXdjymtCiXwtxstR8XEUkxCtQiIiFy92jb8R7bXoMawMyo6Kml80REUo0CtYhIiGobWmhqaUtoDjVE51GvrFGgFhFJJQrUIiIh2rwGdYKBuqK0QCPUIiIpRoFaRCREm7skbqfteEx5SSFrNzbR2NyazLJERGQHKFCLiISoMsG24zEVPbV0nohIqlGgFhEJ0Y5O+fhkLWotnScikioUqEVEQlRZG6EgN4vi/JyE9q9QcxcRkZSjQC0iEqLKugh9iwsws4T279ejADNYrkAtIpIyFKhFREJUWdeY8JJ5AHk5WfQr1kofIiKpRIFaRCREVXWRhE9IjCnX0nkiIilFgVpEJESJth2PV16qbokiIqlEgVpEJCSNza3UNbYk1HY8XkVpISs3NNLW5kmqTEREdoQCtYhISCpro2tQJ7pkXkxFz0KaWttYuymSjLJERGQHKVCLiIQktgb1jpyUCNFuiaC1qEVEUoUCtYhISHa07XhMudaiFhFJKQrUIiIh2dG24zGx5i4rqhWoRURSgQK1iEhIKusayc4yyoryduhxPQpz6J6fwwqNUIuIpAQFahGRkFTWRujdPY+srMS6JMaYmdaiFhFJIQrUIiIhibUd3xnlpYWsrFGgFhFJBQrUIiIhiQbqHZs/HVNeWqg51CIiKUKBWkQkJDvTdjymorSQ6vpm6ptaOrkqERHZUQrUIiIhaGltY92mCH12cspHRanWohYRSRUK1CIiIVi3qQn3He+SGKO1qEVEUocCtYhICGJtx3d+DnV0ZFtL54mIhE+BWkQkBDvbdjymf48Cskwj1CIiqUCBWkQkBJvbjvfYuTnUOdlZ9O9RoBFqEZEUoEAtIhKCWNvx3t13rEtivPLSQo1Qi4ikAAVqEZEQVNY1UlqUS35O9k4fo7y0UCPUIiIpIKmB2sxKzewhM5trZnPM7CAzKzOz58xsfnDZM27/q81sgZnNM7PjklmbiEiYKmt3vqlLTEXPQlbXNNLa5p1UlYiI7Ixkj1DfBDzt7qOAMcAc4CrgBXcfDrwQ3MbMRgNnAnsBxwO3mNnOD92IiKSwjrQdjykvLaS51Vm7MdJJVYmIyM5IWqA2sx7AYcAdAO7e5O4bgFOAycFuk4FTg+unAPe7e8TdFwELgPHJqk9EJExVHWg7HlOhpfNERFJCMkeodwOqgDvN7F0zu93MugH93H0VQHDZN9i/AlgW9/jlwTYRkYzi7lTVReizk23HY2LNXVZUK1CLiIQpmYE6B9gf+Iu77wdsIpjesRXWzrbPTAw0s4vNbLqZTa+qquqcSkVEulBNQzNNrW0dnvJRoW6JIiIpIZmBejmw3N2nBrcfIhqw15jZAIDgsjJu/0Fxjx8IrNzyoO5+m7uPc/dxffr0SVrxIiLJElsyb2fbjscUF+RSXJCjQC0iErKkBWp3Xw0sM7ORwaajgdnAY8CkYNsk4NHg+mPAmWaWb2bDgOHAtGTVJyISlo62HY9XUVrIig2NHT6OiIjsvJwkH/97wD1mlgcsBM4jGuIfNLMLgKXA6QDu/qGZPUg0dLcAl7h7a5LrExHpch1tOx5Pa1GLiIQvqYHa3WcC49q56+it7H8tcG0yaxIRCVtH247HqygtZMaS6g4fR0REdp46JYqIdLHKughFedl0z+/4mEZ5aSE1Dc1sjLR0QmUiIrIzFKhFRLpYZV2kwyckxpQHa1Gv0rQPEZHQbDdQm9lvzKyHmeWa2QtmttbMzu6K4kREMlFlbWOnzJ+GT5bOW65ALSISmkRGqI9191rgJKJL240AfpTUqkREMlhVJ7Qdj6noqbWoRUTClkigzg0uTwTuc/f1SaxHRCTjdeaUj77FBWRnmQK1iEiIEjkj5jEzmws0AN8xsz6AFj0VEdkJ9U0tbIy00LeDbcdjsrOM/j0KWKm1qEVEQrPNEWozywL+CxwEjHP3ZqAeOKULahMRyTibl8zrpCkfEDR3qdYItYhIWLYZqN29DbjR3atjTVbcfVPQBVFERHZQZ7Udj1fRU81dRETClMgc6mfN7MtmZkmvRkQkw3Vm2/GY8tICVtc20trmnXZMERFJXCJzqC8HugGtZtYAGODu3iOplYmIZKDObDseU15aSGubU1nXyICSwk47roiIJGa7gdrdi7uiEBGRXUFVXYScLKNnUV6nHbM8WIt6RXWDArWISAgSaexiZna2mf0suD3IzMYnvzQRkcwTWzIvK6vzZtENjAVqzaMWEQlFInOobyG6ysfXgtsbgT8nrSIRkQzWmWtQxwwojTV30dJ5IiJhSCRQT3D3SwjWnnb3aqDzvqsUEdmFdGbb8Zju+TmUFOaquYuISEgSCdTNZpYNOEDQ2KUtqVWJiGSoqroIfTpxDeqY8lItnSciEpZEAvUfgUeAvmZ2LfAa8KukViUikoGaW9tYt6mp00eoIdrcRSPUIiLhSGSVj3vMbAZwNNEl80519zlJr0xEJMOs29gE0Gltx+NVlBYwddG6Tj+uiIhsXyKrfOwOLHL3PwMfAMeYWWmyCxMRyTSfrEGdnCkfdY0t1DY2d/qxRURk2xKZ8vEw0aYuewC3A8OAe5NalYhIBop1SezsVT7gk7WoNe1DRKTrJRKo29y9BTgNuMndfwAMSG5ZIiKZp7Ku89uOx1T0VKAWEQlLoqt8nAWcAzwebMtNXkkiIpkpNuWjd/fknJQIsEJrUYuIdLlEAvV5RBu7XOvui8xsGPDP5JYlIpJ5quoilHXLIy8nkX96d0yf7vnkZptGqEVEQpDIKh+zgUvjbi8Crk9mUSIimaiyLpKU6R4AWVlG/5ICVlQrUIuIdLXtBmozW0TQ1CWeu++WlIpERDJUMtqOx9Na1CIi4dhuoAbGxV0vAE4HypJTjohI5qqqbWT3Pr2Sdvzy0kLe+lhrUYuIdLXtTuRz93VxPyvc/Q/AUckvTUQkc7g7VRsjSVmDOqaitJDVtY00t7Yl7TlEROSzEpnysX/czSyiI9bFSatIRCQDVdc309zqSZtDDdER6jaHNbWNDOxZlLTnERGRT0tkyseNcddbgEXAGckpR0QkM1XF1qBOQtvxmIrNzV0UqEVEulIiq3wc2RWFiIhksmS2HY9Rt0QRkXB0/mKoIiLyGclsOx5TXhoN6ysUqEVEupQCtYhIF0hm2/GYorwcehblKlCLiHQxBWoRkS5QWddIt7xsuuUncurKzqvoqbWoRUS62nYDtZkVmdnPzOxvwe3hZnZS8ksTEckcVXUR+vZI3vzpmPISBWoRka6WyAj1nUAEOCi4vRz4ZdIqEhHJQMnukhhTXlrIiuoG3D/T4FZERJIkkUC9u7v/BmgGcPcGwJJalYhIhqmqiyR1/nRMRWkhm5paqW1oSfpziYhIVCKBusnMCgEHMLPdiY5Yi4hIgiprG7tkhLqiZ3TpPJ2YKCLSdRIJ1P8LPA0MMrN7gBeAHye1KhGRDLIp0sKmptakrkEdo7WoRUS6XiKNXZ4zs3eAzxGd6nGZu69NemUiIhmiK5bMi9Fa1CIiXW+7gdrMDguu1gWXo80Md38leWWJiGSOrmg7HtO7Wz552VkaoRYR6UKJLIj6o7jrBcB4YAZwVFIqEhHJMF3RdjwmK8soLy3QCLWISBdKZMrHyfG3zWwQ8JukVSQikmG6ou14vPJSrUUtItKVdqZT4nJg70R2NLPFZjbLzGaa2fRgW5mZPWdm84PLnnH7X21mC8xsnpkdtxO1iYiknMq6CLnZRs+i3C55vvLSQo1Qi4h0oUTmUN9MsGQe0QA+FnhvB57jyC1OYrwKeMHdrzezq4LbV5rZaOBMYC+gHHjezEa4e+sOPJeISMqprGukT/d8zLpmCf/y0kIq6yI0tbSRl7Mz4yYiIrIjEplDPT3uegtwn7u/3oHnPAU4Irg+GXgJuDLYfr+7R4BFZraA6HztNzvwXCIioauqi9CnC9qOxwwsLcQd1tQ2MqisqMueV0RkV5XIHOrJHTi+A8+amQO3uvttQD93XxUce5WZ9Q32rQDeinvs8mCbiEhaq6qLdGmwja1FvWJDgwK1iEgXSGTKxyw+mfLxqbsAd/d9t/Hwg919ZRCanzOzudt6qna2feZ5zexi4GKAwYMHb+NwIiKpobIuwgFDem5/x06yeS3qas2jFhHpColM+XgquPxHcPl1oJ7odI1tcveVwWWlmT1CdArHGjMbEIxODwAqg92XA4PiHj4QWNnOMW8DbgMYN25ce0FfRCRlNLW0sX5TU5et8AHqligi0tUSOVvlYHf/sbvPCn6uAo5z9yXuvmRrDzKzbmZWHLsOHAt8ADwGTAp2mwQ8Glx/DDjTzPLNbBgwHJi2cy9LRCQ1rN0Y65LYdXOoC3Kz6d09j5U1CtQiIl0hkRHqbmZ2iLu/BmBmE4FuCTyuH/BIcFZ7DnCvuz9tZm8DD5rZBcBS4HQAd//QzB4EZhM9+fESrfAhIumuK9uOx4sundfYpc8pIrKrSiRQXwD83cxKgtsbgPO39yB3XwiMaWf7OuDorTzmWuDaBGoSEUkLXdl2PF55SSHzK+u69DlFRHZViazyMQMYY2Y9AHP3muSXJSKSGbqy7Xi88tJCXv6oCnfvsvWvRUR2VVsN1GZ2trv/08wu32I7AO7+uyTXJiKS9iprI5hBr+55Xfq8FT0LaWhuZUN9Mz27de1zi4jsarY1Qh2bJ13cFYWIiGSiyroIZUV55GZ3bcfCitjSeRsaFKhFRJJsq4Ha3W8NLv+v68oREcksVXWNXbpkXkx8c5e9K0q2s7eIiHREIo1d+gAXAUPj93f37Z6YKCKyq6usi9C3C9uOx2gtahGRrpPIKh+PAq8CzwNaxk5EZAdU1UUY0a/rZ8716pZHfk6WArWISBdIJFAXufuVSa9ERCTDtLU5VXWRLl+DGqInkFeUFrJSa1GLiCRdImfJPG5mJya9EhGRDFNd30RLm4cyhxqi0z6Wa4RaRCTpEgnUlxEN1Q1mVmtmdWZWm+zCRETS3SddErt+DjVAeWmBpnyIiHSBRBq7aNk8EZGdUBlSl8SYitIiquoiRFpayc/JDqUGEZFdQSKrfBzW3nZ3f6XzyxERyRyb246HNuUjOjK+uqaRIb26bWdvERHZWYmclPijuOsFwHhgBnBUUioSEckQsbbjYc2hroitRV3doEAtIpJEiUz5ODn+tpkNAn6TtIpERDJEZW2E7vk5FOUlMnbR+eKbu4iISPLsTC/c5cDenV2IiEimCWvJvJgBwZQPLZ0nIpJcicyhvhnw4GYWMBZ4L4k1iYhkhMqQ2o7H5Odk06c4Xyt9iIgkWSLfQ06Pu94C3OfuryepHhGRjFFZF2HfgaWh1lBeWqgpHyIiSZbIHOrJXVGIiEimCXvKB0BFaQFzV9WFWoOISKbbmTnUIiKyHRsjLdQ3taZAoI6OULv79ncWEZGdokAtIpIElbXhLpkXU15aSKSljfWbmkKtQ0Qkk201UJvZP4LLy7quHBGRzBB22/EYLZ0nIpJ82xqhPsDMhgDnm1lPMyuL/+mqAkVE0lHYbcdjYs1dtNKHiEjybOukxL8CTwO7Ee2MaHH3ebBdRETaEXbb8ZjN3RK1FrWISNJsdYTa3f/o7nsCf3f33dx9WNyPwrSIyDZU1jWSl51FSWFuqHWUFuVSmJutEWoRkSRKZNm8b5vZGODQYNMr7v5+cssSEUlvVbUR+hTnY2bb3zmJzIzy0gJWVCtQi4gky3ZX+TCzS4F7gL7Bzz1m9r1kFyYiks4q6yKhr/ARU15ayMoaBWoRkWRJpFPihcAEd98EYGa/Bt4Ebk5mYSIi6ayyrpGhvbqFXQYAA3sWMmdVbdhliIhkrETWoTagNe52K58+QVFERLZQWRcJfYWPmPKSQtZubKKxuXX7O4uIyA5LZIT6TmCqmT0S3D4VuCNpFYmIpLlISysb6ptDX4M6pjxu6bzd+nQPuRoRkcyz3RFqd/8dcB6wHqgGznP3PyS5LhGRtLV2Y7QrYdhL5sV8Eqi1dJ6ISDIkMkKNu78DvJPkWkREMkKqtB2PGdhTzV1ERJIpkTnUIiKyA1Kl7XhMvx4FmKn9uIhIsihQi4h0slRpOx6Tl5NF3+J8BWoRkSTZZqA2s2wze76rihERyQRVdRHMoFe3vLBL2ay8tFBTPkREkmSbgdrdW4F6MyvponpERNJeVV0jvbrlk5OdOl8CVihQi4gkTSInJTYCs8zsOWBTbKO7X5q0qkRE0lhlbep0SYypKC3k2dlraGtzsrLUSkBEpDMlEqifCH5ERCQBlXWRlFkyL6a8tJCmljbWboqkzMmSIiKZYruB2t0nm1khMNjd53VBTSIiaa2yrpFR/YvDLuNT4teiVqAWEelc253gZ2YnAzOBp4PbY83ssSTXJSKSllrbnLUbm1JmhY+YilKtRS0ikiyJnDFzDTAe2ADg7jOBYUmrSEQkjVXXN9Ha5ik3ChwL1CuqFahFRDpbIoG6xd1rttjmyShGRCTdVdbGmrqk1gh1j8IcuuVlay1qEZEkSOSkxA/M7GtAtpkNBy4F3khuWSIi6amyLrXajseYmdaiFhFJkkRGqL8H7AVEgPuAWuD7iT5B0BzmXTN7PLhdZmbPmdn84LJn3L5Xm9kCM5tnZsft0CsREUkBqdZ2PF5Fz0JW1ihQi4h0tu0Ganevd/efAkcDR7r7T929cQee4zJgTtztq4AX3H048EJwGzMbDZxJNLwfD9xiZtk78DwiIqGrSrG24/HKSws1h1pEJAkSWeXjQDObBbxPtMHLe2Z2QCIHN7OBwBeA2+M2nwJMDq5PBk6N236/u0fcfRGwgOjJkCIiaaOqLkJxQQ4Fuak3HlBRWkh1fTP1TS1hlyIiklESmfJxB/Addx/q7kOBS4A7Ezz+H4AfA21x2/q5+yqA4LJvsL0CWBa33/Jgm4hI2qisa0y5ExJjykuj01BWbtiRLxlFRGR7EgnUde7+auyGu78G1G3vQWZ2ElDp7jMSrKW9XrifWU3EzC42s+lmNr2qqirBQ4uIdI3K2tTtRFhRWgRoLWoRkc621VU+zGz/4Oo0M7uV6AmJDnwVeCmBYx8MfNHMTgQKgB5m9k9gjZkNcPdVZjYAqAz2Xw4Minv8QGDllgd199uA2wDGjRun5ftEJKVU1kUYO6g07DLaFRuh1tJ5IiKda1vL5t24xe3/jbu+3SDr7lcDVwOY2RHAFe5+tpndAEwCrg8uHw0e8hhwr5n9DigHhgPTtv8SRERSg7un9JSPfj0KyDKNUIuIdLatBmp3PzJJz3k98KCZXQAsBU4Pnu9DM3sQmA20AJe4e2uSahAR6XR1kRYam9tScoUPgNzsLPr1KNAItYhIJ9tuYxczKwXOAYbG7+/ulyb6JO7+EsE0EXdfR3QJvvb2uxa4NtHjioikkqoUXoM6pkLNXUREOl0iJyU+STRMzwJmxP2IiEicVG07Hm9o727MWFLNlQ+9z+yVtWGXIyKSERJpPV7g7pcnvRIRkTSXqm3H4/34uJHkZhuPvLuCB6YvY/zQMs6ZOITj9upPbnYiYywiIrKlRP71/IeZXWRmA4K24WVmVpb0ykRE0kw6TPno26OA607bl6lXf56fnrgnq2ob+O6973LIr1/kjy/M3/waREQkcYmMUDcBNwA/5ZPVPRzYLVlFiYiko8q6CHk5WfQoTOSf1nCVFOVy0WG7cf4hw3hpXiWT31zC7577iJtfnM8X9hnApIlD2W9wz7DLFBFJC4n8q385sIe7r012MSIi6ayqLkLf4nzM2utTlZqys4yj9+zH0Xv24+OqjfzjzSU8NGM5/5m5kjEDSzjnoKF8Yd8BKdlKXUQkVSQy5eNDoD7ZhYiIpLtUXoM6Ebv36c41X9yLt35yND8/ZS82Rlr44b/e4+DrX+SGZ+ayqkarg4iItCeREepWYKaZTQE2T67bkWXz0om7s35TE2Xd8tJqlElEwldZG2H3Pt3DLqPDuufncM5BQ/nG54bw+oJ1TH5zMX956WP++vJCjh3dj0kThzJhWJn+jRQRCSQSqP8T/GSs9ZuaeH3BWl5fsJZX569lxYYGDhjSk2tO3ot9BpaEXZ6IpInKugif261X2GV0GjPjkOG9OWR4b5atr+efU5fwwNvLeOqD1YzqX8w5Bw3l1P3KKcpL/TnjIiLJZO7b7SKessaNG+fTp0/f4cc1NrcyfXE1ry6o4vUFa/lwZS3uUJyfw0G792JU/2LunbaUdZuaOOOAQVxx3MiUXgZLRMLX2NzKqJ89zQ+PGcH3jh4edjlJ09jcymMzV3LXG4uZvaqWHgU5nDFuEOccNJTBvYrCLk9EJGnMbIa7j2vvvkQ6JS7ik9U9NnP3tFnlo63Nmb2qltcWrOW1+Wt5e/F6Ii1t5GQZ+w/pyQ8+P4JDhvdm34oScoJ1WC88bDdufmE+d76+mCdnreLSo4czaeJQ8nK0TquIfNbmJfNStO14ZynIzeaMAwdx+riBTF9SzeQ3FnPXG4u54/VFHD2qHzd8ZV96dssLu0wRkS6VyPd08Um8ADgdSPl1qJdX1/Pa/LW8umAtbyxYS3V9MwAj+nXn6xOGcOjw3owfVka3/Pbfgh4Fufz0C6M5c/xgfvn4bK59cg73TVvKz04azZGj+nblSxGRNFC1MfXXoO5MZsaBQ8s4cGgZa2obuWfqUv768sdcePd07rlwglYFEZFdynYDtbuv22LTH8zsNeD/JaeknVPT0MybH6/dPAq9eF10YZK+xfkcOaovh+zRm0P26E3fHjv2P7vd+3TnzvPGM2VuJb94fDbn3fU2R47sw/+cNDojTj4Skc4Razu+K04P69ejgMuPGcGe/Yv5zr3v8IMHZvKnr+1PdpZOWhSRXUMiUz72j7uZRXTEujhpFe2ATZEWfvvMPF5dsJZZyzfQ5lCUl83nduvFOQcN5ZDhvRnet3unnIl+5Ki+HLxHbya/sZg/vjCf437/CucdPJTvHT2cHgW5nfBqRCSdVQVtx9N52byOOmGfAfz0xD355RNz+NWTc/jZSaPDLklEpEskMuXjxrjrLcBi4IykVLODFq7dxF9e/pgxA0v47pF7cMjwPowdVJq0ec55OVlcdNhunLpfBb99Zh63v7aIR95dwY+OG8npBwwiS6MxIrusyroIWQa9uu+6gRrgwkN3Y8WGBu54bREVpYWcf8iwsEsSEUm6tF7lY8ReY3z6jOmhjRDPWl7DNf/9kBlLqtmnooT/PXk044am/PRyEUmCKx96nxfnVfL2Tz8fdimha21zLrnnHZ6ZvZpbvrY/J+wzIOySREQ6bFurfGx3KNfM8s3sa2b2EzP7f7Gfzi9zx/UozA11usU+A0t46FsHcdOZY6mqi/CVv77JZfe/q25iIrugqo2RXXq6R7zsLOMPZ45l/8E9+f4DM5mxZH3YJYmIJFUicyMeBU4hOt1jU9yPED3T/ZSxFbx4xeF876g9eOqD1Rz125e5+YX5NDa3hl2eiHSRdG873tkKcrP52znjKC8t5MLJ01lYtTHskkREkiaRQD3Q3b/q7r9x9xtjP0mvLM0U5eXww2NH8sLlh3PEyD7c+NxHfP53L/PUrFWk87QaEUlMZW1kl1kyL1Fl3fK467wDyTLj3DvfZm2wtKCISKZJJFC/YWb7JL2SDDGorIi/nH0A9144gW55OXz7nnf42t+mMnd1bdiliUiStLY5azdGdskl87ZnSK9u3HHugVTWNXLBXW9T39QSdkkiIp0ukUB9CDDDzOaZ2ftmNsvM3k92Yelu4h69eeLSQ/jFKXsxZ3UtJ970Kj/7zwdUb2oKuzQR6WTrNkVo88zvkrizxg4q5eaz9mfWihouvW8mrW361k5EMksigfoEYDhwLHAycFJwKduRk53FNw4ayktXHMHZnxvCPVOXcPgNU7j91YVEWjS/WiRTxJq6aA711h0zuh//98W9eH7OGq557ENNhRORjJJIp8QlXVFIJistyuPnp+zN1ycM4don5/DLJ+Zw95tLuPL4UZy4T/9OaTwjIuGJtR3voznU2/SNg4ayfEMDt768kIqehXzr8N3DLklEpFMkpwOKtGtk/2LuPn88k88fT2FuNpfc+w5f+eubvLO0OuzSRKQDqjRCnbArjxvFyWPKuf6puTw6c0XY5YiIdAoF6hAcPqIPT152KNeftg9L19dz2i1v8N1732HZ+vqwSxORnVAZtB3XSYnbl5Vl/Pb0fRk/rIwf/et93lq4LuySRES2a+WGbfcYUaAOSXaWceb4wbx0xRFcetQePD9nDUff+DLXPTmHmobmsMsTkR1QWRehR0EOBbnZYZeSFvJzsvnbN8YxuFcRF989nflr6sIuSUSkXZW1jVzz2IccccNL29xPgTpk3fJzuPzYkUy54ghOHlPOba8u5IgbpjD5jcU0t7aFXZ6kqebWNia/sZif/3e2Ond2gcraCH17aP70jigpyuWu8w4kPzebc+98m8raxrBLEhHZbN3GCL96cg6H3TCFf7y1hNP2r9jm/pbOZ1qPGzfOp0+fHnYZneqDFTVc+8Qc3ly4jt36dOPqE/bk83v21YmLkhB359nZa7j+qbksWruJLIt2rPv+54dz3sHDyM3WZ+hkOO2W1ynIzebeiz4Xdilp54MVNZxx65sM692NB755EN3zt3uuvIhI0tTUN/O3Vxdy5+uLaGhu5dT9Krjs6OEM6dUNM5vh7uPae5z+75pi9q4o4d6LJnD7OdHf10V3T+esv73FBytqQq5MUt37yzdw5m1v8c1/zCA7y7jz3AN56Yojmbh7L3715FxOvOlV3vxY81WToWpjRCck7qS9K0r489f3Z+7qOi655x19MycioahrbOaPL8znkN+8yJ+mLODIUX159geH87szxjKkV7ftPl4j1CmsubWN+6ct5ffPz2f9piZO26+CK44bSXlpYdilSQpZuaGBG56ZxyPvrqBXtzx+cMwIzjxwEDlxo9HPz17D/z3+IcvWN3DK2HJ+euKemqLQSdydUT97mkkTh/KTE/cMu5y0df+0pVz171l8ddwgrv/yPvpWTkS6RH1TC3e/uYS/vvwxG+qbOXZ0P35wzAj2HNDjM/tua4Ra362lsNygMcwp+1Vwy5SP+fvri3hi1iouOnQ3vnXE7vpqdBe3MdLCX15awO2vLgLgO0fszreP2J3igtzP7Pv50f04ZHhvbnnpY/768se8MKeS739+OOdOHPqp4C07rraxhUhLG326a4S6I84cP5gVGxq4+cUFVPQs5NKjh4ddkohksMbmVu6dupRbXvqYtRsjHDGyD5cfM4J9B5bu1PGUyNJAj4JcrjphFF+fMJgbnpnHn6Ys4P63l3H5MSM4Y9xABaJdTEtrG/e/vYw/PP8Razc2cerYcn50/CgqtvPNRUFuNpcfM4LT9qvgmv9+yC+fmMNDM5bz81P2Zvywsi6qPvNUBUvmqe14x11+zAhWbGjgd899RHlpIV85YGDYJYlIhmlqaePB6cv404sLWF3byMTde3HrN/bngCEd+/+gpnykoXeXVnPtE3OYvqSaEf2685MT9+SIkX3DLkuSzN15aV4Vv3pyDvMrNzJ+aBk//cKejBlUulPHenb2Gn7+39ms2NDAaftXcPUJe2od5Z3wxoK1fO32qdx70QQm7t477HLSXlNLG+fdNY2pC9dz13njOWS43lMR6biW1jb+/e4K/vjCfJZXNzBuSE8uP3bEDv27va0pHwrUacrdefqD1Vz/9FyWrKtnv8GlDOvdjd7d8+ndPS+4DH6K8ygrytNIdhqbvbKWXz05h9cWrGVY725cdcIojh3dr8PzTOubWvjzlAXc9spCCnKy+eGxIzj7c0P0t7IDHp25gsvun8nzlx/OHn27h11ORqhtbOaMv77J8uoG/vWtg9qdyygikojWNufx91fyh+fns2jtJvYdWMIPjx3JYcN77/D/QxWoM1hTSxt3v7mYx99fRVVdhLUbI0RaPnuWvBn0LMprN2z37hZcBtt6dc8jP0cNKlLBmtpGbnx2Hv+asZySwlwuO3o4X58whLyczg28C6s28r+Pfcir89cyekAPfnHqXh3++mtX8bdXFnLtk3N4/5pj6dHO/HXZOatqGvjSn98A4JFLJjKgRCdji2zNR2vq2FCfnKZwWRY9pys3O4u8HCMnK4vcnCxys4287CxysqPXc7OyyMpKnZOJ29qcZz5cze+e+4j5lRsZ1b+YHx47skNLEStQ70LcnY2RFtZubGLtxghr6yKs3dQUvdwY+/nkvk1Nre0ep0dBDr2759OnOJ99B5YwYVgvDhxWRkmhAkNXqG9q4bZXFnLrywtpbXMmTRzCd48cTklR8t5/d+epD1bzi8dns6qmkdMPGMiVJ4yit06226Zrn5jN3W8uYe4vjtfKFJ1szqpazvjrm5SXFvLPCydoSpJIO+asquXEP75KKsS57CyLhuvsrCBsf3I9d4vbeTlZFOZlU5SXTVFeDkV52XTLy6YwuL55e342RbnZdMvP+cz+7fVWcHdenFvJjc9+xOxVtezepxuXHzOSE/bu3+HAr0AtW9XQ1Npu0I7dXlXTwAcramlqbcMM9uzfgwm7lTFhWBnjh/WirFte2C8ho7S2OQ+/s5wbn53HmtoIX9h3AFceN4rBvYq6rIZNkRZufnEBt7+6kKK8bH50/Ci+Nn4w2Sk08pBKLrv/Xd5ZWs2rPz4q7FIy0usL1jLp79Noc2fsoFKOGtWXI0b2Za/yHvoAIwL84IGZPPPham75+v5Jad7V2ua0tLXR1OI0t7bR3NpGS6vTtJXrza1tNG1xvbnVaQn2aQquR1raqG9qpaGphU1NrTQ0tbKpqWWHPhjkZUdDeTSIR4N2Q3MrCyo3MqRXEd///HC+OKai0/7/pUAtHdLY3MrMZRuYunA9Uxet452l1TQ2R6eVjOjXnQnDejFhtzLGDyujb7HWNt5Zr81fy7VPzmHOqlr2G1zK/3xhz1CnXSyorOP/Pfohb3y8jr0revCLU/Zmv8E9Q6snVZ1121s0tbbx8Lcnhl1Kxpq3uo4nZ61iyrxK3l8ebXLVtzifI0f25chRfTh4j97tLhcpkulWbmjgsN9M4ZyDhvL/Th4ddjkd5u5EWtrYFGmJhu3mVjZFWoKw3Up9U3T7Z4J4pIX65lbqIy20tDkn7TuA0/Yf2OkfMBSopVM1tbTx/vINTF20nqmL1jNj8frNU0d269ONCcPKNofsTJn36O58uLKWusaWTj92U2sbd72+iCnzqhhUVsiVx4/iC/sMSInRN3fn8fdX8csnZlNZF+HMAwfxo+NG6ZuJOEff+BIj+hXzl7MPCLuUXUJVXYSXP6piyrxKXvmoirrGFnKzjQOHlm0O2Lv36Z4S//2IJNsvH5/NnW8s5uUfHcHAnl33TeauSoFakqqltY0PVtYydeE6pi5az9uL1lMXiQbPwWVF0YC9Wy8mDCtjUFn6/Qff1NLG/z72AfdNW5a05yguyOF7R+3BpIlDU/KE0I2RFm56/iP+/vpiigtyuOLYkZy4zwAFa2Dfa57hS/tV8H+n7B12Kbuc5tY23llSzZR5VUyZW8m8NXUADCorjIbrkX05aPdeFOQm77+ptjansi7C0vX10Z91m1i6vp7G5jZ+/ZV9dd6JJE1NQzMTr3uBz4/ux01n7hd2ObsEBWrpUq1tzpxVtdER7IXrmLZ4/eazjytKCxk/LDoH+5jR/eiV4ie8rdsY4dv3vMO0Rev55mG7JW297z0HFFNalPrhdN7qOn726AdMW7QeiP4+96koYZ+BJdHLihJ67kIhu7G5lVE/e5ofHTeSS47cI+xydnkrNjTw0rxKpsyt5PUF62hobiU/J4uJu/fiyFHRgL0zH+obmlpZVl3P0nX1nwTn4GfZ+vpPrayUZTCgpJCVNQ1887DdueqEUZ35EkU2+8tLH/Prp+fyxKWHsFd5Sdjl7BIUqCVUbW3OR5V1m+dgT124nnWbmijrlseNZ4zhyBRtSjN3dS0XTp5OZV2E33x5X07dryLsklKCuzNt0XreW76B95fX8MGKGhavq998/8Cenw3ZYX1YcHfWbWpidU0jtQ3NwVJPWeRkGXnB9dgZ6VteT+QklmXr6zn0N1P4zZf35YwDB3XBK5JENTa3Mm3Rel6cW8mUeZUsCf5G9+jbnSNH9uHIUX0ZN6SMvJws3J2quFHmJeuiQTl2u7Iu8qljd8vLZnCvbgwpK2JwryIGlRVFr5cVUV5aSF5OFpc/MJPHZ61iyhVHbLeLqciOirS0cuivpzCiXzH/vHBC2OXsMkIJ1GZWALwC5BNtcf6Qu/+vmZUBDwBDgcXAGe5eHTzmauACoBW41N2f2dZzKFCnJ3fngxW1/Oih95i7uo6LDh3Gj44b1elrK3fEc7PX8P3736Vbfg63nTOOsTvRjXBXUtPQzIcranh/RQ2zVtQwa3kNS9d/ErIHlRWyb0Upe1eUsO/AEvYuL+nwEoDuTnV9Mys3NLC6ppFVNQ2srGlkdU1jdFttI6tqGmlqZ132RMSvvbq10N3S1sZHazZy53kHpuwHQ4latHYTL86t5KV5lUxduJ6m1ja65+cwoKSAZdX1m0+0hui6/QN6FESDcq9oUI5e78bgsiJ6FuVud4728up6jrrxZU7et5wbzxiT7Jcnu5gHpy/jxw+9z93nj+ewEX3CLmeXEVagNqCbu280s1zgNeAy4DRgvbtfb2ZXAT3d/UozGw3cB4wHyoHngRHu3v5CyShQp7vG5laufWIO/3hrCWMGlnDzWft36fJw7XF3bnnpY3777Dz2qSjhtm+Mo3+JVi7ZGTX1zXywsmbzKPb7KzawbH3D5vuH9Cpi72AEe9+KEvaqKNk839Td2VDfzKpPBeUGVm1o3LxtVU3jZ5oY5WQZ/XoUUF5aQP+SQspLChhQEr1eUphLa9snyz41t7O8U/z27d0Xu56XncUNXxmT1DXCpXNtirTw+oK1TJlXxbqNEQbHjTQPLitiYM/CTjmX4bon53Dbqwt54nuHMrpc3R6lc7S1Ocf94RVysrN48tJDdAJuFwp9yoeZFREN1N8G7gaOcPdVZjYAeMndRwaj07j7dcFjngGucfc3t3ZcBerM8NSsVfz44ffB4bov78NJ+5aHUkdjcytXPvw+j85cycljyrnhK/sm9WSmXVH1piY+WPnJKPasFTUsr/50yM4yY1VNw6dGDCHaMKB/jwL6ByG5vLSQ/luE597d81OqU5fs2mrqmznshimMGVTK3eePD7scyRAvzl3D+XdN5/dfHcOX9hsYdjm7lG0F6pwkP3E2MAPYA/izu081s37uvgogCNWx70krgLfiHr482LblMS8GLgYYPHhwMsuXLnLCPgPYu6KES+9/l+/e+y6vL1jL/ztpLwrzui7Mrqlt5OK7p/Pe8hquOHYElxy5hz71J0HPbnkcOrwPhw7/5CvK6k1N0YC9ooYPV9ZgZhw9qi8DSgsZEBeee3fPV3MZSSslRbl898g9uPbJObw2fy2HDO8ddkmSAW59eSHlJQWhDT5J+5IaqIPpGmPNrBR4xMy2ta5Ue/+n/MzwubvfBtwG0RHqzqhTwjeorIgHv3kQNz77EX99+WNmLKnmT1/bnxH9ipP+3O8v38BFd0+nrrGFv559AMfv3T/pzymf6Nktj8NG9NE8QMlI3zhoCHe9sZjrnprDf3c/RN+gSIe8tyzaA+J/vrBnUroiys7rkt+Gu28AXgKOB9YEUz0ILiuD3ZYD8afJDwRWdkV9khpys7O46oRR3H3+eNZvauKLf3qN+6ctJZnTkh57byWn//VNcrKyeOhbExWmRaRTFeRm86PjRvLhyloee0//S5OOue2VhRQX5HDmeH1Dn2qSFqjNrE8wMo2ZFQKfB+YCjwGTgt0mAY8G1x8DzjSzfDMbBgwHpiWrPkldh43ow5OXHcoBQ3py1b9ncen9M6lrbO7U52hrc258dh6X3vcu+w4s4dHvHqyThkQkKb44ppy9yntwwzPzaGze6nn2Itu0dF09T32wiq9PGEL3/KROMJCdkMwR6gHAFDN7H3gbeM7dHweuB44xs/nAMcFt3P1D4EFgNvA0cMm2VviQzNa3uIC7z5/Aj44byZOzVnHSza/x/vINnXLsTZEWvn3PDG5+cQFnjBvIPy+cQO8UbzAjIukrK8v4yYl7smJDA/94c0nY5Uiauv21hWRnGecdPDTsUqQdauwiKe/txeu57L53qdoY4crjR3HBIcN2+oTB5dX1XDh5Oh+tqeOnXxjN+QcP1cmHItIlJv19Gu8ureaVHx+ZFp1RJXWs39TExOtf4OR9y7nhdK1rHpZtrfKhGe2S8g4cWsaTlx3KESP78ssn5nDh5Oms39S0w8d5e/F6TvnT66zY0MCd543vUDAXEdlRV50wirpIC7e89HHYpUia+cebS2hsbuPiw3YLuxTZCgVqSQulRXnc9o0DuObk0bw6fy0n3vQqUxeuS/jxD7y9lK/97S16FObyn0sO5nCtKCEiXWzPAT04bb+B3PXGYpZX12//ASJEeyTc/eZijhrVl+FdsPKV7BwFakkbZsa5Bw/j39+ZSGFeNmf97S1uen4+rW1bn7bU0trGz/87mysfnsWEYb34z3cOZvc+3buwahGRT/zw2BEA/O7Zj0KuRNLFQzOWs25Tk0anU5wCtaSdvStK+O/3DuGUsRX8/vmP+Prtb7GmtvEz+9U0NHP+5On8/fVFnDtxKHedd6DaQ4tIqMpLCzn/4GE8MnMFH6yoCbscSXGtbc7try5kzMASJgwrC7sc2QYFaklL3fNz+P1Xx/Lb08fw3rIaTrjpVabMq9x8/8KqjXzpltd5Y8FarjttH6754l7kaBF8EUkB3z5id0oKc/n103PDLkVS3HOzV7N4XT0XH7a7zvlJcUoYkta+csBA/vu9Q+hbnM95d77NtU/MZsrcSk798+tsqG/mngsncJYWwBeRFFJSmMv3jhrOq/PX8spHVWGXIynK3bn1lYUMLitS07E0oEAtaW+Pvt35zyUH843PDeFvry7ivLveZkBJIY9ecjATdusVdnkiIp9x9ucGM6iskOuemkvbNs4DkV3X9CXVvLt0AxceOoxstaxPeQrUkhEKcrP5xal7c+s3DuD8g4fx8HcmMqisKOyyRETalZ+TzRXHjmTOqlr+M3NF2OVICrr15YX0LMrl9AMGhV2KJECBWjLKcXv15/+dPFptWUUk5Z28bzn7VJTwW7Ukly0sqNzI83PW8I2DhlKYlx12OZIABWoREZEQZGUZV584ipU1jUx+Y3HY5UgKuf3VheTnZDHpoCFhlyIJUqAWEREJycTde3PkyD78acoCqneiA6xknsq6Rv79zgq+csBAenXPD7scSZACtYiISIiuOmFPNkVa+POUBWGXIilg8huLaW5r48JD1cglnShQi4iIhGhk/2K+csBA7n5zCcvWqyX5rmxTpIV/vrWU40b3Z1jvbmGXIztAgVpERCRkPzhmBGZw47Pzwi5FQvTA28uoaWjm4sM1Op1uFKhFRERCNqCkkAsOGcZ/Zq5US/JdVEtrG3e8togDh/Zk/8E9wy5HdpACtYiISAr41hG707Mol189OQd3NXvZ1TwxaxUrNjRwkeZOpyUFahERkRTQoyCXS48ezhsfr+NltSTfpbg7t72ykN36dOPze/YLuxzZCQrUIiIiKeLrE4YwuKyI65+aS6taku8y3vh4HR+urOWiQ3cjS23G05ICtYiISIrIy8nix8ePZO7qOv79zvKwy5EucusrC+ndPZ8v7VcRdimykxSoRUREUsgX9hnAmIEl/O65j9SSfBcwZ1Utr3xUxbkTh1CQqzbj6UqBWkREJIWYGVedsCerahq58/XFYZcjSfa3VxZSlJfN2Z9Tm/F0pkAtIiKSYg7avRdHj+rLLVMWsF4tyTPWyg0NPPbeSs4YN4jSorywy5EOUKAWERFJQVeeMIpNTS386UW1JM9Ud76+CAcuOGRY2KVIBylQi4iIpKAR/Yo5Y9wg/vHWYpauU0vyTFPb2Mx905Zx4j4DGFRWFHY50kEK1CIiIinqB8eMIDvLuEEtyTPOvVOXsjHSwjcPUyOXTKBALSIikqL69SjgokN347/vreS9ZRvCLkc6SVNLG3e+voiJu/di74qSsMuRTqBALSIiksIuPmw3yrrlcd1TakmeKR6duYI1tREu1uh0xlCgFhERSWHFBblcdvRw3lq4npfmqSV5unN3/vbqQkb1L+bwEX3CLkc6iQK1iIhIijtr/GCG9iriuqfmqCV5mntpXhUfrdnIRYfuhpnajGcKBWoREZEUF21JPoqP1mzkztcXhV2OdMCtr3xM/x4FnDymPOxSpBMpUIuIiKSBE/buz9Gj+vKrJ+fw/Ow1YZcjO+H95Rt4a+F6zj9kKHk5imCZRL9NERGRNGBm/PGs/dirvITv3feuVv1IQ7e+spDi/BzOGj847FKkkylQi4iIpIlu+Tncce44enXP44LJb6vhSxpZsm4TT81axdcmDKa4IDfscqSTKVCLiIikkb7FBdx13niaW51z75xG9aamsEuS7VhQWcc37phGXk4W5x2sNuOZSIFaREQkzezRtzu3TxrH8g0NXHj3dBqbW8MuSbbitflr+dItb1Df1MK9F32O/iUFYZckSaBALSIikoYOHFrG788YyztLq7n8wZm0aTm9lHPv1KVMunMa5SWFPPKdg9l/cM+wS5IkUaAWERFJU1/YdwA/PXFPnpy1mmufnBN2ORJobXN++fhsfvLILA7ZozcPffsgBpUVhV2WJFFO2AWIiIjIzrvgkGEsr27gjtcWUVFayPmHaI5umDZFWrjs/pk8P2cNkw4aws9OGk1OtsYvM50CtYiISBozM3520mhW1TTwiydmM6CkgBP2GRB2WbukVTUNXHDXdOauruWak0dzrk5A3GXoI5OIiEiay84ybjpzP8YOKuX7D8xkxpL1YZe0y5m1vIZT//w6S9fXc8ekAxWmdzEK1CIiIhmgIDeb288Zx4CSAi6cPJ2FVRvDLmmX8fQHqznj1jfJycrioW8fxJGj+oZdknSxpAVqMxtkZlPMbI6ZfWhmlwXby8zsOTObH1z2jHvM1Wa2wMzmmdlxyapNREQkE/Xqns9d543HzDj3zrdZuzESdkkZzd259eWP+fY9MxjRv5hHLpnIqP49wi5LQpDMEeoW4IfuvifwOeASMxsNXAW84O7DgReC2wT3nQnsBRwP3GJm2UmsT0REJOMM7d2NOyaNo7KukQsmT6e+qSXskjJSU0sbVz08i+uemsuJ+wzggYs/R99irTG9q0paoHb3Ve7+TnC9DpgDVACnAJOD3SYDpwbXTwHud/eIuy8CFgDjk1WfiIhIptpvcE9uOnM/3l++gUvvm0mr1qjuVDX1zUz6+zQemL6M7x21BzefuR8FuRoD3JV1yRxqMxsK7AdMBfq5+yqIhm4gNtGoAlgW97DlwbYtj3WxmU03s+lVVVVJrVtERCRdHbdXf645eS+en7OGax77EHeF6s6weO0mvnTL68xYUs3vzhjDD48dSVaWhV2WhCzpy+aZWXfgYeD77l5rttU/uvbu+Mx//e5+G3AbwLhx4/Svg4iIyFZMmjiUFRsauO2VhQzsWcg3D9897JLS2tSF6/jmP2dgwD8vnMD4YWVhlyQpIqmB2sxyiYbpe9z938HmNWY2wN1XmdkAoDLYvhwYFPfwgcDKZNYnIiKS6a46fhQrNzRw3VNzGVBayBfHlIddUlp6eMZyrvr3+wwqK+LOcw9kSK9uYZckKSSZq3wYcAcwx91/F3fXY8Ck4Pok4NG47WeaWb6ZDQOGA9OSVZ+IiMiuICvL+O3pYxg/rIwrHnyPtxauC7uktNLW5tzwzFx++K/3OHBoGY98+2CFafmMZM6hPhj4BnCUmc0Mfk4ErgeOMbP5wDHBbdz9Q+BBYDbwNHCJu7cmsT4REZFdQkFuNrd94wAGlRVy8d3TWVBZF3ZJHVK9qYkH3l7Kf95dwYwl1VTVRZIyR7yxuZXv3vcOf57yMWeNH8Tk88dTUpTb6c8j6c/S+SSFcePG+fTp08MuQ0REJC0sW1/Pl255g/ycLB75zkT69kifZd7cnbcXV3Pv1CU8+cFqmlraPnV/UV42g8uKGFRWxJCyIgb3+uR6Rc9C8nN2bBWOyrpGLrp7Bu8v38BPTtiTCw8dxjbOA5NdgJnNcPdx7d6nQC0iIrLrmLW8hq/e9ia79enGAxcfRLf8pK9P0CE19c08/M5y7p22lAWVGynOz+HU/Sr46oGDKMjNYsm6epauj/4sW1+/+XYkLnCbwYAeBdGA3avok+DdqxuDy4roWZT7qbA8Z1UtF06ezvpNTdx05liO3at/GC9dUowCtYiIiGw2ZW4lF949nUOH9+b2c8aRk90lq+gmzN15Z2k190xdyhPvryLS0saYQaV8ffxgThozgKK8bX8IcHeq6iIsjQvYy4LQvWR9PVV1n+4g2T0/Z/Nodv+SAv41fRndC3K4Y9KB7F1RksyXKmlEgVpEREQ+5b5pS7n637M488BBXHfaPikxnaGmoZlH3lnOfdOWMW9NHd3zczhlbDlfmzCYvco7L9jWN7WwvLrhs2F73SaWVTcwekAP/nr2AfQvSZ8pMZJ82wrUqf09j4iIiCTFWeMHs6K6gT9NWcDAnoV896jhodTh7ry7bAP3Tl3K4++vpLG5jX0HlnD9aftw8pjypExJKcrLYUS/Ykb0K/7MfW1trkYtssMUqEVERHZRPzx2BCs3NPDbZz8i0tLG/oN7MrhXEQN34iS+HVXb2Myj767gnqlLmbu6jm552Xxpv4F8fcLgUKdZKEzLzlCgFhER2UWZGdd/eV/W1zdx84sL4rZD/9hJfGXRk/gGByfzDS4roqxb3k5NEXF33ltew31Tl/LYeytpaG5lr/IeXPulvTllbAXdU/wESZGt0V+uiIjILiwvJ4s7zz2Qqo2RuLnEn8wtfmV+FWtqP30SX7e87E+tmBEN3NEVMypKC8nL+fRJjhsjLfzn3RXcO3Ups1fVUpibzSljyzlr/GD2HViSEvO3RTpCgVpERGQXZ2b0LS6gb3EBBwwp+8z9DU2tLK/+bNheWLWJl+ZVfWqJuiyDASWFm4N2qztPzlpFfVMrew7owS9O3ZtTx5ZTXKAGKZI5FKhFRERkmwrzshner5jhWzmJr2pjdIm6peuiy9LFRrpfnFfJpkgLX9hnAF+bMJixg0o1Gi0ZSYFaREREdlpWltGvRwH9ehRw4NDPjm67u0K0ZLzUWsldREREMorCtOwKFKhFRERERDpAgVpEREREpAMUqEVEREREOkCBWkRERESkAxSoRUREREQ6QIFaRERERKQDFKhFRERERDpAgVpEREREpAMUqEVEREREOkCBWkRERESkAxSoRUREREQ6QIFaRERERKQDzN3DrmGnmVkdMC9Jhy8BanTcpB473Y6bzGOn23GTeWwdN/nHTrfjJvPY6XbcZB473Y6bzGOn23GTeex0O24yjz3S3Yvbvcfd0/YHmJ7EY9+m46ZnzXov9F5kwnHTsWa9F3ov9F7ovQj7uEmueau5U1M+tu6/Om7Sj51ux03msdPtuMk8to6b/GOn23GTeex0O24yj51ux03msdPtuMk8drodN9nHble6T/mY7u7jwq5DRERERDLbtnJnuo9Q3xZ2ASIiIiKyS9hq7kzrEWoRERERkbCl+wi1SNKY2fFmNs/MFpjZVcG2sWb2lpnNNLPpZjY+7DolM5jZ382s0sw+iNt2jZmtCP7eZprZiWHWKJnBzAaZ2RQzm2NmH5rZZcH2G8xsrpm9b2aPmFlpyKVKhtjG39wYM3vTzGaZ2X/NrEfYte4sjVCLtMPMsoGPgGOA5cDbwFnAH4Dfu/tTQbj5sbsfEVadkjnM7DBgI3C3u+8dbLsG2Ojuvw2zNsksZjYAGODu75hZMTADOBUYCLzo7i1m9msAd78yvEolU2zjb24ycIW7v2xm5wPD3P1nIZa60zRCLdK+8cACd1/o7k3A/cApgAOxT9AlwMqQ6pMM4+6vAOvDrkMyn7uvcvd3gut1wBygwt2fdfeWYLe3iAZskQ7b2t8cMBJ4JdjtOeDL4VTYcQrUIu2rAJbF3V4ebPs+cIOZLQN+C1zd9aXJLua7wVfwfzeznmEXI5nFzIYC+wFTt7jrfOCpLi9IMt4Wf3MfAF8M7jodGBRSWR2mQC3SPmtnmwPfBn7g7oOAHwB3dGlVsqv5C7A7MBZYBdwYajWSUcysO/Aw8H13r43b/lOgBbgnrNokM7XzN3c+cImZzQCKgaYw6+sIBWqR9i3n05+UBxKd3jEJ+Hew7V9Ep4aIJIW7r3H3VndvA/6G/t6kk5hZLtFgc4+7/ztu+yTgJODrrpOspBO19zfn7nPd/Vh3PwC4D/g4zBo7QoFapH1vA8PNbJiZ5QFnAo8RDdWHB/scBcwPqT7ZBQQn8sR8iejXoyIdYmZG9Nu1Oe7+u7jtxwNXAl909/qw6pPMs42/ub7BZRbwP8Bfw6mw47TKh8hWBKt4/AHIBv7u7tea2SHATUAO0Ah8x91nhFelZAozuw84AugNrAH+N7g9luh0o8XAN919VSgFSsYI/h17FZgFtAWbfwL8EcgH1gXb3nL3b3V9hZJptvE3Nxy4JLj9b+DqdP1mRIFaRERERKQDNOVDRERERKQDFKhFRERERDpAgVpEREREpAMUqEVEREREOkCBWkRERESkAxSoRUREREQ6QIFaRERERKQDFKhFRERERDpAgVpEREREpAMUqEVEREREOkCBWkRERESkAxSoRUREREQ6QIFaRERERKQDMi5Qm5mFXYOIiIiI7DoyJlDHBeluW9kuIiIiItLpMiZQu7ub2YnAU2b2MzP7fNx2hWoRERERSYqMCdRmtj9wIXAbUAScaGZfAYVqEREREUmejAjUZjYIeBB4x93/AdwMLAQmmtmZEA3VIZYoIiIiIhkqIwK1uy8DHgUuMbPh7r4S+BewAjjYzPqHWqCIiIiIZCxLt4Hb2NSNYBrHSKDA3d8L7vsZ8EXgbHefFwTpfHdf0s5xxgDNQIu7f9R1r0BEREREMklO2AXsqNjUDTP7AvBb4FUz2w841d1/YWZNwH/M7FR3n9feMczsJOBXwLtAo5n9HFjl7m1d8ypEREREJFOkzZQPM6sws/8G1/cAbgCOA54ERgMPmNlQd/81cA/QeyvHGUI0TE8CfgzkB3cVJvcViIiIiEgmSqspH2b2OrAJOIVoYN4L+DlwOHAXcABwjLsv2sYxRgM3u/vRZlYCzADeC477lLvfl9QXISIiIiIZJS1GqM0sC8DdDwbagKeDExGHA8+4ewPRVT42AiXbOpa7zwayzOw1YB7RZfa+DzwCnB6MYIuIiIiIJCQtArW7t8WF6uOBBjN7HlgG9DKz/wO+C1zg7jO3fLyZDTezfnHHOxK4CPg3cEMQzl8BWpL+YkREREQko6RFoIZ2Q3Ud8EeiQbgUuMndZ2z5ODM7BXgB+H9mNjjueHOIjnb/Odh0CDCY6MofIiIiIiIJSas51BCd/hFbjcPMHgYK3f3ELe8LbpcQPUFxLlAJ9CMavJcG9w8HbgK6E50qcra7z+rK1yMiIiIi6S3tAjV8JlQ/Akxx9z9uZd9hwDpgFNGTGQuBP7r74rh9RgFr3X1tsmsXERERkcySNlM+4sVP/wCmAcXx95vZYDPLN7Nu7r7I3WvdfRrwGNAIXGpm2WY23sx6uvtchWkRERER2RlpGahhc6jOJxqmH4ltDxq+PAncDNwZdFOMPWYq0Rblq4BXgWfZIoyLiIiIiOyItJzyEc/Mcty9JWhJPpBomP4eMAc4G/gh0bWpP4x7zB+ITv842d0/6PqqRURERCRTpF3r8S25e0tw6Wa2EngTmA9UuvuNZtYMPGtmR7r7R2bWEzgSOE1hWkREREQ6Ku1HqGFzK/KewELgFmCGu/8m7v4fE21P/h13rzezAndvDKdaEREREckkaT9CbWYnAb8CqoFZRJfJ+6OZZbv7dcFuDwJXAw3B7UiXFyoiIiIiGSmtA7WZTQR+C5zl7u+a2W3AeGAi8JaZZQP3E23acgDRBjDVngnD8iIiIiKSEtJ6ykcQqEe4+13B7T7AXe7+BTPbDfgfosvkjQfOU9MWEREREels6R6os4Fu7l4bXB8A/Bc40d1XmdkQYEWwT02YtYqIiIhIZkrbdagB3L3V3WuDmwZsANYHYfps4CdArsK0iIiIiCRLWo9Qt8fM7iLauOVY4FxN8xARERGRZMqYQB00dskl2tAlFzja3eeHW5WIiIiIZLqMCdQxZnYu8HZ8Z0QRERERkWTJxEBtWhZPRERERLpKxgVqEREREZGulNarfIiIiIiIhE2BWkRERESkAxSoRUREREQ6QIFaRCQDmdk1ZnbFNu4/1cxGd2VNIiKZSoFaRGTXdCqgQC0i0gm0yoeISIYws58C5wDLgCpgBlADXAzkAQuAbwBjgceD+2qALweH+DPQB6gHLnL3uV1YvohI2lKgFhHJAGZ2AHAXMAHIAd4B/grc6e7rgn1+Caxx95vN7C7gcXd/KLjvBeBb7j7fzCYA17n7UV3/SkRE0k9O2AWIiEinOBR4xN3rAczssWD73kGQLgW6A89s+UAz6w5MBP5lZrHN+ckuWEQkUyhQi4hkjva+crwLONXd3zOzc4Ej2tknC9jg7mOTVpmISAbTSYkiIpnhFeBLZlZoZsXAycH2YmCVmeUCX4/bvy64D3evBRaZ2ekAFjWm60oXEUlvmkMtIpIh4k5KXAIsB2YDm4AfB9tmAcXufq6ZHQz8DYgAXwHagL8AA4Bc4H53/3mXvwgRkTSkQC0iIiIi0gGa8iEiIiIi0gEK1CIiIiIiHaBALSIiIiLSAQrUIiIiIiIdoEAtIiIiItIBCtQiIiIiIh2gQC0iIiIi0gEK1CIiIiIiHfD/AROn92NZ/E3aAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12,6))\n",
    "daily_users.plot()\n",
    "\n",
    "plt.title('daily number of users who see ads')\n",
    "plt.xlabel('date')\n",
    "plt.ylabel('number of unique users')\n",
    "plt.xticks(rotation = 45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "470d9e64",
   "metadata": {},
   "source": [
    "## Maketing metrics\n",
    "- conversion rate\n",
    "- retention rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "7759cbf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1030"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the number of converted users\n",
    "subscribers = mkt[mkt['converted'] == True]['user_id'].nunique()\n",
    "subscribers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "bdb5bdb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7309"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total users \n",
    "users = mkt['user_id'].nunique()\n",
    "users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "ff1869c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.09 %\n"
     ]
    }
   ],
   "source": [
    "conv_rate = subscribers / users\n",
    "print(round(conv_rate * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "03e7c2b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "67.57 %\n"
     ]
    }
   ],
   "source": [
    "retained = mkt[(mkt['is_retained'] == True)&(mkt['converted'] == True)]\\\n",
    "['user_id'].nunique()\n",
    "retention_rate = retained / subscribers\n",
    "print(round(retention_rate * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9eaed5",
   "metadata": {},
   "source": [
    "### 3. Cusomer segmentation analysis by conversion rate and retention rate\n",
    "- age/gender/location\n",
    "- past interaction with the business\n",
    "- marketing channels users interacted with"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31e3cdea",
   "metadata": {},
   "source": [
    "**Compare retention rate by subscribing channel**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "4c4df680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58.05 %\n"
     ]
    }
   ],
   "source": [
    "# segment only the ones click house ad\n",
    "house_ad = mkt[mkt['subscribing_channel'] == 'House Ads']\n",
    "\n",
    "# calculate retention rate\n",
    "retained = house_ad[(house_ad['is_retained'] == True)]['user_id'].nunique()\n",
    "subscribers = house_ad[house_ad['converted'] == True]['user_id'].nunique()\n",
    "r_rate_house = retained / subscribers\n",
    "\n",
    "print(round(r_rate_house * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "ba4bd88b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87.58 %\n"
     ]
    }
   ],
   "source": [
    "# compare across the all channels\n",
    "email = mkt[mkt['subscribing_channel'] == 'Email']\n",
    "\n",
    "# calculate retention rate\n",
    "retained = email[(email['is_retained'] == True)]['user_id'].nunique()\n",
    "subscribers = email[email['converted'] == True]['user_id'].nunique()\n",
    "r_rate_email = retained / subscribers\n",
    "\n",
    "print(round(r_rate_email * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "89d8daf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "70.13 %\n"
     ]
    }
   ],
   "source": [
    "# compare across the all channels\n",
    "push = mkt[mkt['subscribing_channel'] == 'Push']\n",
    "\n",
    "# calculate retention rate\n",
    "retained = push[(push['is_retained'] == True)]['user_id'].nunique()\n",
    "subscribers = push[push['converted'] == True]['user_id'].nunique()\n",
    "r_rate_push = retained / subscribers\n",
    "\n",
    "print(round(r_rate_push * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3256bd0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.78 %\n"
     ]
    }
   ],
   "source": [
    "# compare across the all channels\n",
    "facebook = mkt[mkt['subscribing_channel'] == 'Facebook']\n",
    "\n",
    "# calculate retention rate\n",
    "retained = facebook[(facebook['is_retained'] == True)]['user_id'].nunique()\n",
    "subscribers = facebook[facebook['converted'] == True]['user_id'].nunique()\n",
    "r_rate_facebook = retained / subscribers\n",
    "\n",
    "print(round(r_rate_facebook * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "bf9e17fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.1 %\n"
     ]
    }
   ],
   "source": [
    "# compare across the all channels\n",
    "instagram = mkt[mkt['subscribing_channel'] == 'Instagram']\n",
    "\n",
    "# calculate retention rate\n",
    "retained = instagram[(instagram['is_retained'] == True)]['user_id'].nunique()\n",
    "subscribers = instagram[instagram['converted'] == True]['user_id'].nunique()\n",
    "r_rate_instagram = retained / subscribers\n",
    "\n",
    "print(round(r_rate_instagram * 100, 2), '%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3913bb",
   "metadata": {},
   "source": [
    "retention rate of different **subscribing channel**\n",
    "- House_ad: 58.05%\n",
    "- Push: 70.13%\n",
    "- Instagram: 68.1%\n",
    "- Email: 87.58%\n",
    "- Facebook: 68.78%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "6919fd97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "subscribing_channel\n",
      "Email        87.58\n",
      "Facebook     68.78\n",
      "House Ads    58.05\n",
      "Instagram    68.10\n",
      "Push         70.13\n",
      "Name: user_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# alternative to calculate the all the segmentations \n",
    "retained = mkt[mkt['is_retained'] == True].groupby('subscribing_channel')\\\n",
    "['user_id'].nunique()\n",
    "subscribers = mkt[mkt['converted'] == True].groupby('subscribing_channel')\\\n",
    "['user_id'].nunique()\n",
    "print(round(retained/subscribers * 100, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2651425e",
   "metadata": {},
   "source": [
    "**Compare conversion rate by displayed language**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3b9f7a81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "language_displayed\n",
      "Arabic     0.500000\n",
      "English    0.131260\n",
      "German     0.716216\n",
      "Spanish    0.200000\n",
      "Name: user_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Group by language_displayed and count unique users\n",
    "total = mkt.groupby('language_displayed')['user_id'].nunique()\n",
    "\n",
    "# Group by language_displayed and count unique conversions\n",
    "subscribers = mkt[mkt['converted'] == True].groupby('language_displayed')['user_id'].nunique()\n",
    "\n",
    "# Calculate the conversion rate for all languages\n",
    "language_conversion_rate = subscribers/total\n",
    "print(language_conversion_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20a52fa",
   "metadata": {},
   "source": [
    "*whether there is any difference in the conversion rate based on when in the month*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d8753812",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_served  language_displayed\n",
      "2018-01-01   Arabic                100.00\n",
      "             English                 9.28\n",
      "             German                 20.00\n",
      "             Spanish                  NaN\n",
      "2018-01-02   Arabic                   NaN\n",
      "                                    ...  \n",
      "2018-01-30   Spanish                  NaN\n",
      "2018-01-31   Arabic                   NaN\n",
      "             English                 5.36\n",
      "             German                   NaN\n",
      "             Spanish                  NaN\n",
      "Name: user_id, Length: 81, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Group by date and language_displayed and count unique users\n",
    "total = mkt.groupby(['date_served','language_displayed'])['user_id'].nunique()\n",
    "\n",
    "# Group by date and language_displayed and count unique conversions\n",
    "subscribers = mkt[mkt['converted'] == True].groupby(['date_served','language_displayed'])['user_id'].nunique()\n",
    "\n",
    "# Calculate the conversion rate for all languages by dates\n",
    "date_language_conversion_rate = subscribers/total\n",
    "print(round(date_language_conversion_rate * 100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cc579f81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_subscribed\n",
      "2018-01-01    77.78\n",
      "2018-01-02    70.27\n",
      "2018-01-03    72.97\n",
      "2018-01-04    50.00\n",
      "2018-01-05    57.50\n",
      "2018-01-06    75.68\n",
      "2018-01-07    52.63\n",
      "2018-01-08    63.89\n",
      "2018-01-09    67.50\n",
      "2018-01-10    68.29\n",
      "2018-01-11    61.54\n",
      "2018-01-12    39.13\n",
      "2018-01-13    50.00\n",
      "2018-01-14    61.54\n",
      "2018-01-15    71.43\n",
      "2018-01-16    70.19\n",
      "2018-01-17    61.63\n",
      "2018-01-18    73.33\n",
      "2018-01-19    68.42\n",
      "2018-01-20    80.95\n",
      "2018-01-21    76.19\n",
      "2018-01-22    77.27\n",
      "2018-01-23    71.43\n",
      "2018-01-24    65.22\n",
      "2018-01-25    56.52\n",
      "2018-01-26    80.00\n",
      "2018-01-27    57.14\n",
      "2018-01-28    66.67\n",
      "2018-01-29    75.00\n",
      "2018-01-30    71.43\n",
      "2018-01-31    66.67\n",
      "Name: user_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Daily retention rate\n",
    "total = mkt.groupby('date_subscribed')['user_id'].nunique()\n",
    "subscribers = mkt[mkt['is_retained'] == True].groupby('date_subscribed')['user_id'].nunique()\n",
    "\n",
    "date_r_rate = subscribers/total\n",
    "print(round(date_r_rate * 100,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f71a07",
   "metadata": {},
   "source": [
    "### 4. Plottig the result "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3df4de5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "language_conversion_rate.sort_values(ascending = False).plot(kind='bar')\n",
    "plt.title('Conversion rate by languages')\n",
    "plt.ylabel('Conversion rate')\n",
    "plt.xlabel('languages')\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97aaf0e3",
   "metadata": {},
   "source": [
    "**Cohort analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0c0b445a",
   "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_subscribed</th>\n",
       "      <th>retention_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0.702703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0.729730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0.575000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  date_subscribed  retention_rate\n",
       "0      2018-01-01        0.777778\n",
       "1      2018-01-02        0.702703\n",
       "2      2018-01-03        0.729730\n",
       "3      2018-01-04        0.500000\n",
       "4      2018-01-05        0.575000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a new df and reset the index \n",
    "daily_r_rate = pd.DataFrame(date_r_rate.reset_index())\n",
    "\n",
    "# rename the columns \n",
    "daily_r_rate.columns = ['date_subscribed', 'retention_rate']\n",
    "daily_r_rate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5af04888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V4/ry7P/Wc/TALlFZ9bu8qJSk+BgyOjvVXVnpqXy5ehv7K6tdv8/WIDrLc40kItx3Rha791Xy909WRzqcoKipUf4zdwOvzc+PdCimFXp3iVONdHojqpF8xcYIw3q2b/SX5d2nZTKkRwq/fOV7ikv2N+naobRicwmDu6cQG+O0nWSltaeqRllTXBaReCLVm6tVJAZwMvvF2b15/us81m2NzH9iMK3bVsbOvZUU7t7Prr2HXnc5E1pvf1/IEX070rNDm7BeNyk+licuHcWeiqqom95bVT09klIPbMtKd36ORHXSph17yf7dR9z83wXsqwhvgmg1iQHgtpMHkRgXw+9nr4h0KM02L2/ngZ8PxTpOEzpristYsbmUM4bXPXYhlAZ0S+GBM4fyvzXbmPrF2ojEEEhxaTk791YyxGcEeN9ObWmbEBuRgW5//3g15VXVzF5axMVPf8OWMJawWlVi6JaSxM3HD+Cj5Vv4ak3LnkljXt4O2iU4xfTlRTaAzwTPO4sLEYHTDo9MYgC4eExvTh+exqNzVrFg486GXxAG3hsw3xJDTIwwpEf4G6DXb9vDrIUF/PioDJ75cTZri8s464n/sSQ/PHNWtarEAHD10f3o1bEND76TS3UUFVMbKydvJxMGdqFLcuIh2V3OhIaq8s7iIsZmdKJ7alLE4hARfn/u4aS1T+JnMxZSsr8yYrF4rfDcgA3pkVpre1Z6KsuLSsK6cNHfP15NfKzwk4n9OTGrO6/9ZDxxMTFc+M+vec/TPhRKrS4xJMXHcuepQ1ixuZRXcjY1/IIotKVkPxt37GVMRienu9whOl+LCWzq52v577cbmvTalVtKWVNcxhkjmtboHEzt28Tzt0tGUbR7P3fPWhLxFeNWbC6hZ4c2B/VqzEprT+n+KvJ37gtLHGuKS3ljUQFXjsuga0oi4JRi3rj5aLLSUvnJfxfwxCerQ/p5tbrEAHD64Wlk9+3Io3NWUtqEO5EPlm1mynPfhbVOz1eOp30hO6MTWemprCkuo7KBKQjMoWFraTl//mAl97y+lJlN6LH2zvdFxAicOqxHCKJrvCP6duS2kwbxzuKiiN/ILS8qqdW+4HVgBHSYSu5/+Wg1beNjueG4/rW2d01J5MXrjuKckek8MmcV//fyopD1WmqVicHbfXVbWQVPfuq+cWvz7v3c8EION7wwn89WbuWzlcUhjLJu8/J2kBQf41lNK5WK6hrWtoKeVqb53v6+kOoaJSstlTtnLebrte7b0lSVtxcXMr6/U0UZLX5yXH+OHtCZB97KZU1xZNrTyquqWbt1z4GBbb6G9EglRsKzNsPyohLeXVzEVUf3o1O7hIP2J8XH8peLR/KrUwbzxqJCLn1mLltLgz/jbatMDAAjenfgvFE9mfa/9WzaUf888jU1ygvf5HHiY5/z2cqt3DF5CO0i1BMBIGfDDkb17kh8bAxZnoYwq04yAK8vLGBYz1RmXH8UGZ3bccML813PLry0oIQN2/dGrDdSXWJihMcuGkmbhFhueXFhRPrur97yw1QY/tokxNKvS7uwtPX95cNVpCTFcd0xh9V5jIhw86QB/OOy0eQWlXDOk18FPbZWmxgAfjV5MLExwh/eq7v76qotpVww9Wvue3MZI3t3YM7/HctPJvZnSFpqRHoDlZVXkVtYwpiMjgD069KOhLgYa4A2rN5SypKC3Zw7qhft28QzbcoYEuNimfLcPIpLG672fGdxIXExwuQoqUby1T01iUcvHMGKzaU8PDv8syWv2By44dkrMy015FVJS/J3Myd3C9dOOMzV7A2nHp7GazeOp7pGueAfX/NR7pagxdKqE0Na+zbccNxhvLukiO/W76i1b39lNY/OWcnpf/+S9dv28NhFI3jhmrH09Q6FTwt/TwSAhRt3UqNO+wJAXGwMg7unWJfVKLBh+x6ueu47/vLhKhZt2hX2wVmzFhYQGyOc5Wk47t2pLdOmZLNjTwXXNrBOgrc30oSBXejQ9uAqimgwaUg3rpnQj+e/2cCHQfySc2N5UQmJcTF1zvyalZ5K/s597N4Xut5Tj324kg5t47l6Qobr1wzr2Z43bzma/t2Sue6FHP4zt2mdEvy5TgwicqqIvCMiuSLS27PtWhE5oRHnmCwiK0VkjYjcGWD/r0RkkeexVESqRaST2/MHcv2xh9EjNYkH38k98Ic8d912Tvvblzz+yRrOHJHOx7+YyHmjex2YQhicO4TS8vD1RPCal7eTGIFRfToc2JbluVuJdK+NQ91Tn67li9XbePyT1Zzz5FeMeegjbnt5EW99X8juvaHtbllTo7y5sIBjB3Y50FMFYHivDjz+o1EsLdjNz2YsqrOL9sJNuyjYta/Oldqixe2TB5OVlspdsxaHdbW4FZtLGNzjh6kw/GWFeG2G+Rt28unKrVx/7GGkJDVurrfuqUm8fP04jh/cjfveXMrsIHRndZUYROQy4BVgNdAP8EYeC9zu8hyxwJPAqUAW8CMRqTXFoqr+WVVHqupI4C7gc1XdcdDJGqFtQhx3nDqYJQW7mf51Hne8tphLnp5LVY3yn2uO5LGLRgZs5PEOhV8W5rr9nLwdZKal1vrlyExLYceeCopD0Mhk3Nmxp4I3FhVwUXZvcu49ib9dMpJjBnbh05XF/GzGQkY9OIcLp37Nk5+uIbcw+El87vrtFO7ez7mjex2078Ss7tx/5lA+Wr6FB9/JDfj6d74vIiE2hpOHdg9qXMGWGOc0rpbsq+Lu18PThdWZCqOUzDqqkeCH74NQJYa/fLiKzu0SuLKJs0O3SYjlyctGM7pPR37+0iLmrtverHjclhhuB65T1f8DfMurc4GRLs8xFlijqutUtQJ4CTi7nuN/BMxwee56nT2iJyN6tee37+Ty2oJ8bjyuPx/8/FgmDKx78Y3B3VOIkfCu3lRZXcPCjbsYk1G7kJRpDdAR99K8jZRX1TBlfAad2iVw9sie/PWSUeTcexIzfzKemycNYF9lNX/+YCWn/f1Lxj38CXe/viRo81zNWlBAcmIcJ2cF/mK/cnwG10zox/Sv85j2v/W19tXUKLOXFHHsoK6kNvJuNBIG90jhFycP4oNlW3g9DKu+bS0tZ8eeioA9kry6pSTRJTkhJH+D367bzv/WbOMnE/vTLrHpE14nxcfyryuz6dO5Ldf9O4cVm5seq9vEMBD4JsD2MqDuNFtbT8C3o3K+Z9tBRKQtMBmY6fLc9YqJcaYJPnNEOm/dcjR3njqENgn1zwrp7YkQzqHwuYUl7KusJtvT8OyVGcGJvIyTsF/4ZgNHD+jMYL9+7rExwhF9O/KLkwfzzk+P4du7T+BP5w9nVJ8OvDxvE499uKrZ199XUc17S4o4dViPemczvfu0TE4Z2p0H383lg2WbD2zP2bCTzSX7ObOedZ2jzbXHHMaYjI7c/+YyCneFtjo30FQYgYSiAVpVefTDVXRLSeTyo/o2+3wd2ibw/NVjaZsQy5Rp85r82blNDIXAoADbjwXcDhQIVHlXVznxTOCruqqRROR6EckRkZytW7e6unhWeiqP/2gUQ9Pbu4sW5xchnCWGeXnO2/UvMaQmxdOrYxvrmRQhc5ZtoWj3fqaM79fgsd1Tk7hoTG/+cfkRXJTdi5e+20TR7uZ9sc3J3cyeimrOC1CN5Cs2RvjrxaMY3qsDt760kEWbdgFOb6TEuBhOyIzuaiRfsTHCoxeOpFqVX70W2llYvT2S6qtKAuc7ZPWW4A42/WrNdr5bv4ObJw0I2noPPTu04fmrx7KnvIorp33XpFKr28TwNPB3ETna87y3iFwJ/An4h8tz5AO9fZ73wkk4gVxCPdVIqvq0qmaranbXrl1dXr7xwtETwVdO3k76dGobcA6bcHSXM4FN/3o9vTu14fghjVtY5qaJA6hRZepnzZtB9PWFBfTs0IYj+zXcD6NNQizPXpFN15RErn1+Hnnb9jB7SREnZHYjuRnVFJHQp3Nb7j09i6/WbOeFIPW2CWR5UQnp7ZMa7CIa7MGmTmlhJentk7hkbO+GX9AIQ3qk8vQV2WzYvpfr/p3T6LEhrhKDqv4JmAV8CLQDPgWmAlNV9UmX15oHDBSRfiKSgPPl/5b/QSLSHjgOeNPleUPGW7RcEYYvZFUlZ8OOg6qRvLLSUsnbtifs87If6pYW7GZe3k6uHJdRZ4+VuvTu1JYLjujFjO82sXl306ZXKS7dz5ert3H2yHRiXF6/a0oiz00ZS0VVDec+9RXbyiqivjdSXX40tjcTB3fl4feWh2ydlRVFpQxpoBoJCPpg089WbmXhxl3ccvzAgMuhNte4/p157OIR5GzYya0vLWzUpKKuu6uq6j1AF5xG5KOArqp6XyNeXwXcAnwALAdeUdVlInKjiNzoc+i5wBxV3eP23KFy4BchDIkhb/tetpVVHFSN5JWZlkqNOpOgRZuKqhpyC0t4bX4+j3ywstlVJ9Hkua/yaJsQy4XZTbuju3mSp9TwedNKDW8tcqbAOG90wOa4Og3olszTV2RTVl5F24TYqFxG0w0R4Y/nDycxLpbbXvmeqiDPGeZMhVF2YD6k+vTr0o7EuJigJAZV5bEPV9G7UxsuzK6/irA5zhiezn2nZ/HBsi088NYy1728XJUtRWQacKuqlgI5PtvbAY+r6tVuzqOqs4HZftum+j2fDkx3c75Q65aSSOd2CWGp2/+hfaHuEgM4dysje3cIeTx12bW3gtyiEnILS1heVEpuUQlrikuprP7hF+77/F38++qxtcaFtETbysp5+/tCLh7Tm/Ztmtabp3entpw/uhcvfreRn0zs3+iprl9fWMDhPdszoFvDX1z+jjqsM89fNZbS8qoGO1tEs+6pSfzunGH8dMZCpn6+lluOHxi0c68pLqOqRusc8ewrLjYmaGszfJi7hSUFu/nzBcOJjw3tOOOrJ/RjS8l+/vnFOrqnJrr6/NxWOl4J3An43662Aa4AXCWGlkZEwla3n5O3g45t4+nfNTng/l4d25CSGBf2Bujd+yqZ/lUeSwp2kVtYQqFPlUi3lEQy01KZOLgrWWmpZKal8uXqrfzm7VzeXFTIOaMad5cbbV78diMV1TVcOT6jWee5edIAZi7I5x+freWBs4a6ft2qLaUsKyzh/jOzGj64DuMH1N0luyU5c0Q6c3K38NePVjNxcDeG9XTfiaQ+3jUYGuqR5JWZlsoHyzajqk2+8ampcUoL/bq049ww/Y3cMXkIxaXlPDJnFd1Sk7iogRJwvYnBM+pYPI+OIuI7hiEWOB0I79j1MMtKT2X613lUVteENLPPy9vJEX071fnLFhMjDElLCWti2Lh9L1c/P4+1W8sY0DWZMf06HUgAmWmptUbgevXr0o63vi/kt+/kctygrnQMMHiwJaioquE/czdw7KCuDOgWOFm71adzW84b3ZMZ323kpon96eay1DBrgTMFxplRsHZCNHjw7KF8u247v3jle9685eig9OLxToWR0bmtq+Oz0lN5ad4mNpfsJ61909bLfm/pZlZsLuWvF48kLsSlBa+YGKdKbltZOXfNWkLXBmbXbSiqbUAxTrfSXGCrz2Mz8CzwVLOjjmKZaSlUVNWwbmvomjy2lpazftueOquRfojF6T4bjjl6cvJ2cM5TX7GtrJwXrz2KD287jr9dMoobjuvPsYO6BkwK4HQzfPi8wynZV8nvIzAZWrC8t7SI4tJyrmpmacHrlkkDqapR/uGyraG6RnlzUQHHDeoaVVNkR1KHtgn88YLhrNxSyl+CMD4EnK6qg3ukuP6Cbm4DdHWN8pePVjGwW3LYE35CXAz/uPwIMtNSuOm/C+o9tqFPYxJwAk6J4QLgeJ/HBKCPqj7U/JCjV1aaU2QN5Z36/A1O+0J2HQ3PP8SSyp6KajbtrH8a8eZ6fWE+lz7zLe3bxPP6TUczrn/nRr1+SI9Urj/2MF6dn9+o9QKiyXNf5dGvSzuOGxSc7tB9OrflvFE9efHbjRS7WABq7rrtFO3eH7aqhpZi0uBuXHpkH57+ct1BE2M2ljMVRuDFeeoypJlzJr2/dDNrisu49cSBje7lFgzJiXFMmzKGLin1l+TrTQyq+rmqfoYzP9KbnufexzeqWtc4hFbjsK7tSIiNCWk7w7y8nSTGxTCsZ8MjLyF0SaqmRnl0zkr+7+XvGd23A6/fNL7O2SYb8rMTBtK3c1vueX1pRObXb46FG3eyaNMurhzX13UXUTduOX4AVTXK1M/XNXjsrAUFpCTGcVIdU2Acyu45LZPeHdvyi1cXUVZe94yyDdlaVs72PRWu2xfA+WLt27ltk78Pnv3fOvp2bsupwyI3Cr1bShIvXH1kvce4HcewQVVrRCRdRI4SkWN9H0GJNkrFx8YwqEdySEsMOXk7GNG7Q4N9mQf3SAnZSlL7K6v56UsLefyTNVyU3Yt/X31ks6ZnToqP5aFzDmf9tj08+emaIEYaes9/nUdyYhznHxHcboR9OzuNjf/9dkO96yfsq6jm/aVFnHZ4WtBGw7Ym7RLjePSiEeTv3MdD7za9utI7lb2bHkm+stJSm/Q3OH/DDhZu3MU1E/pFpLTgK6OBGz63s6umi8hnOKOXvwI+wxnk5n20apk9UkMyYybA3ooqlvoszFOfpPhYDuuaTG6Q12bYWlrOJU/PZfaSIu48dQh/PH84CXHNbxSbMLAL543qyT8+W8uqKBx/EUhxyX7eXVLEBUf0avT0x27cMskpNfyznlKDdwqMcxs5duFQMiajE9cfexgzvtvIpyuatgTvigNzJDWuK3BWWip52/c2urTy7Jfrad8mnguCfMMRCm7/+v8KVONMl70XOAa4EGeg2uSQRBZFstJT2b6nIiRrqy7auIvqGm2wfcEr2PM3rdjsLA24cnMp/7jsCG48rn9Qxx/cc3omKUlx3DVrSdgXtmmK/3y7kaoaZUqQGp39ZXRpxzkjnVJDXb9PMxc4U2CMdfk7cai67aRBDOiWzEOzlzdqVK/X8qIS0tonNbpk7K16WtmI2Us3bt/LB8s2c9mRfWibEP1Tk7hNDMcBd6jqCpweSltVdRZwB/BgqIKLFt5fhGUhqE6al7cTERjdp+ESgxNLCgW79gVlYZhPVxRz/lNfU1VTw6s3jgvJko+dkxO55/Qs5m/YyYvfbQz6+YOpvKqaF7/dwKTB3RosajfHLccPoKKqhqe/OLiHUnHJfv63eivnjHI/BcahKjEulltPGMia4jLeW9r4xWlWbC5tVPuCl3dthsZUJ037aj2xMdLsMTHh4jYxtMHpugqwA/COr88Fhgc7qGgTykbfnA07GNw9xfXI2gMrSTVjrnWA6V+t55rn55HRpR1v3jwhaAOGAjl/dE/G9+/MH99bwRYXPXIi5d3FRWwrqwhZacGrn6fU8MLcg0sNb31fSI3CuaOiv7ohGpx2eBr9u7bjiU/WNKpEWl5VzZriskb1SPJyShnxrhugd++t5JWcTZw5Ir3RI98jxW1iWAEM8fy8CLhRRPoCNwOhX0kjwtq3iadnhzZBb/Stqq5hwYaddc6PFEgwlhicOT+fB97O5YTM7rxywzh6tA/tL6uI8NC5h1NeXcNv3l4W0ms1lary3Fd5DOiWzDH1LOAULN5SwzNf1m5rmLWggOG92jd7UN2hIjZGuOX4AazYXMqcRqwTvbZ4D1U12qQSg4g0qgH6xe82sreimmsnHNboa0WK28TwN8Bbz/Bb4GRgHXATcHcI4oo6WenBX5thxeZS9lQcvDBPfbp65m9qTpL699wNDOqezNTLj2jWilGN0a9LO249YSCzl2zmozAv9O7Ggo07WVKwmyvHZ4RljqfDuiZz9sie/PubPLZ51jZesbmE3KISzrOxC41y5vB0Mjq35fFPVrvuILK8iQ3PXplpqazYXNrgpH4VVTVM/3o9Rw/ofKAKqiVw2131v57J7VDVBUAGMAZngNurIYsuimSmpbI+yNNe17UwT31ExElSTaxKWr2llO837eKi7N5h7zJ33TGHMah7Mr9+cyl7mtH/PBSe+yqPlKS4sH4pHyg1fOGUGl5fUECcTYHRaHGxMdw0aQDLCkv4xGUPpRWbS0iIiyGjc9PakrLSUimvqiFve/0zIry7pJAtJeVce0zLKS2Ai8QgIvEisllEDsz+pap7VXWBqrbMYa1NkBWCaa9z8nbSs0Mb0js0bs6VzLRUVm1u2kpSr87PJy5GIjLBXUJcDA+fN5yikv08Oic4UxoEQ9Hufby3dDOXjOkdthIUQP+uyZw1Ip1/f+OMa3jDMwVGZ5sCo9HOHdWTXh3b8PdP1rgqNazYXMrg7u6nwvDnvftfVk/JXVV59sv1DOyWzMQgjaAPlwY/FVWtBCqpexnOQ0KwF+lQVebl1b0wT30y01KoqG78/E1V1TXMWlDApCHdIjb/zhF9O3LZkX2Y/vV6FufvikgM/v4zdwM1qlwxLiPs177l+IGUV1Xzk/8sYEtJuY1daKL42BhumjiA7zft4ovVDd+vNnYqDH/9uyY3OCPCN+u2s6ywhGsm9GtxU9C7TZePA3eJSPR3wA2RYE97vWnHPopLy12PX/DV1PmbPl+1lW1l5VwY4QE2t08eQpfkRO6cuSToC6801v7Kal78diMnZnandyd3M2wG0wDPZGrzN+wkJTGOE1vQuszR5vwjepLePom/f1x/W8PW0nK2lTVuKgx/CXExDOiWfGD0dCDPfrmezu0SWuT0824TwzHA2UCBiHwsIm/5PkIYX9TwTnsdrDmTGlqYpz5Nnb/p1Zx8OrdLYFIj1y4OttSkeH579lByi0qYEeGxDW9/X8jOvZVcdXRGxGL46fEDEYHTh9sUGM2RGBfLjRP7M3/DTr5Zu73O47w3VEOa2PDslZVed8+kNcVlfLKimB+P69si/0/dJoZtwEyc1dc2Atv9Hq6IyGQRWSkia0TkzjqOmSgii0RkmYh87vbc4ZCVlsqKIE17nbNhBylJcQxqwspc8bExDOzeuPmbduyp4OMVWzhnVM+QrxjlxilDe9CvSzu+dFHsD6VXc/I5rEs7xh3WuBlkg2lAt2RevWEcd546pOGDTb0uyu5Nt5RE/v7J6jqPWeHpuJHZyDmS/GWlpbKtrDzgvFf/+t96EuJi+PFRfZt1jUhxVTWkqlc190IiEgs8CZyEM+fSPBF5S1VzfY7pgLO+w2RV3SgiUbVQbaZn2uuNO/Y2e2TsvLydZPft2OTRrVlpqXyyotj1SlJvLCygslpDur5sY4gIQ9NTWbRpV8Ri2Lh9L9/l7eBXpwyOeB1wU6oUzcGS4mO54bj+PPhOLt+t38HYfgd/rsuLSumRmtTsRaR+GPhaSreUH8YCbS8rZ9aCfM4f3bPFdiQI563jWGCNqq5T1QrgJZzqKV+XArNUdSOAqjZtdqwQ8fZEaG47w449FawpLmvWl0FmWuPmb3p1fj6H92zf6JkkQ2loenvydwZneo+mmLUwHxFaZB2wqdulY/vQJTmBx+soNSwvKmny+AVfdXVI+c/cjZRX1XBNCxrQ5i+ciaEnsMnneb5nm69BOEuIfiYi80XkirBF58Kg7p5pr5uZGOZv2Ak0bvyCP+/diptYlhbsZnlRSdSUFrwOdPkr2h32a6sqsxYUMO6wzvRsZHdhE93aJMRy3TGH8eXqbSzYuLPWvoqqGtZuLTuw4E5ztG/rmRHB529wf2U1L8zN4/gh3Vr06PVwJoZAZXX/yvo44AictaRPAe4TkUEHnUjkehHJEZGcrVu3Bj/SOiTFx9K/a/PXZsjJ20FCbAzDezV9fqIsn2JsQ16bn09CbAxnRdnAqaFNmIwsWHI27GTjjr2cPzq6kqUJjsuP6kvHtvE8/nHtUsParWVUVjdtKoxAnAboH25s3lxUwLayCq6d0C8o54+UcCaGfKC3z/NegP8KcPnA+6q6xzN47gtghP+JVPVpVc1W1eyuXcM7cCSziYt0+JqXt4PDe7VvVm+FQHcrgZRXVfPGogJOGtq9WQvvhEKX5ES6pyZGJDHMnJ9P24TYkMwoayKvXWIc1x5zGJ+u3FprvMyBqTCaMYbBl++MCN4BbVlpqY1eDjfahDMxzAMGikg/EUkALgH8u7q+CRwjInEi0hY4EmfNh6iRlZ5K4e797Npb0aTX76+sZknB7mZVI3llpqU0WHr5eHkxu/ZWRnzsQl2y0lLrHT0aCvsrq3l3cRGTh/UI60hnE15XjOtLalIcj3/ywwqCKzaXkhAX0+Qla/35zojw+aqtrC4u49pjWt6ANn+u/ypEpDfOeIZu+CUUVX2soderapWI3AJ8AMQC01R1mYjc6Nk/VVWXi8j7wGKgBnhWVZe6fjdh4Fu3P75/42fh/H7TLiqrtUnjF/x5eybtr6yus/Txas4meqQmcczA6BySPzS9PV+s3lbvewi2OblbKC2vsmqkVi4lKZ6rJ/Tjrx+tJrew5MBEmIO6Jzd5Kgx/vtWhs5cU0T01kTOGR1eVbVO4XdrzMmAN8Azwc+CnPo9b3F5MVWer6iBV7a+qD3m2TVXVqT7H/FlVs1R1mKr+1fU7CZPG1O0HkuNpeD6ib/MTQ6b3bmVz4FiKS/bz+aqtnDe6Z8TXmK3L0PRUqms0rEt/zlqQT3r7pIiOXTDhcdX4fiQnxvHEp05bw/Ki0maPX/DlnRHh9YX5/G/NNq4cnxGUZXEjze07+C3wKJCqqhmq2s/n0XL7ZDVB15REuiQ3vV58Xt4OBnVPDkp9f0PdZ2ctLKBGieo1Zt1MRhZMxSX7+WLVVs4d3dNWSDsEtG8bz5TxGcxespmv125jW1l5UHokeYkImWmpzMvbSduEWC4b2zIHtPlzmxi641TrBG/O6RasqWszbN69n7nrtnNkv+Dcqfbu2JZ2CbEBY1FVXs3ZxBF9O3JY1+jtNte7Y1tSEuPC1gD95iJnhbTzrBrpkHH1hH60TYjl9tcWA01fg6Eu3pubi7J7076tu5UYo53bxDAbpyHY4PxirS4upaKqcRPA/emDFdTUOOsSBIMzf1NqwJ5JCzftYu3WPVHb6OwVEyNkpqeyrDD0YxlUlZkL8hnZuwP9ozhZmuDq1C6BH4/rS/7OfUDzp8Lwd2S/TrSJj43ofFvB5rbx+UPgj541GZbgTMN9gKrOCnZg0SwrLZXKamXt1jLX/aEX5+9i1oICbjyuP306B28Wz6y0VN5YWHDQ1Biv5uSTFB/D6cPTgnatUBmanspL322iukZD2hayrLCEFZtLefDsoQ0fbFqV6445jOe/zqNDm4RmT4Xhb/KwHkwc3I02CS1vsry6uE0M//T8G2gZT8XpZXTI8B0K7yYxqCoPvpNLl+QEbp7UP6ixZKal8sLcDeTv3Hdg2uh9FdW8830hpw1LIyUp+ou2WWmp7KusZv22PSEdLTprQQHxsbZC2qGoS3Iivz5jKOVVwa8NF5FWlRTA/SR6Lb+ZPYj6dWlHYlyM63aG2Us2My9vJw+fd3jQv6i99aXLCksOJIYPlm2mtLyKC6JsCoy6DE13RoDnFpWELDFUVtfw5qICThgSfQP9THhcemSfSIfQYtgXfhPExcYwuIe7tRn2V1bz8HvLGdIjhYuyezd4fGMN6ZFKjNTumfTq/E306tiGo4LUyB1qA7o5q2GFsp3hi1Vb2b6ngvOjvM3FmGjgOjGIyOki8oWIbBORrSLyuYicFsrgollWmtMzqaH1Zad9tZ78nfv49RlZIak/b5MQS0aXdgcSQ/7OvXy9djsXHNGrxXTHTIhz1pcIZc+kmQvy6dQugYmDo3OgnzHRxO0At2uB14G1wB3AncB64HURuTp04UWvzLRUdu6tZHPJwYt0eBWX7uepT9dyYmZ3xg9o/CjpxsTiLb3MnF+AKi1uVO9Qz2pYbhZyb6zdeyv5KLeYs0akR8UiRcZEO7d/JXcAt6nqVar6L89jCvBLnCRxyHGzNsNjc1axv7Kau08L7cpcWWmpB9Y1eG3BJsb37xyR9YubY2h6e7bvqWBLibv1JRrj7cWFVFTXRPVAP2OiidvE0Ad4P8D294DWMdSvkYZ4Zmesq/pjWeFuXs7ZxJXjM0I+wMzbS+rf3+Sxace+qFt3wY0fRkAHv51h5oJ8BnVPPjCvjTGmfm4Tw0acJTn9nQxsCF44LUdKUjx9OrUNOGeSqvK7d5bToU08Pzt+YMhj8XaZ/cfna0lOjGPy0Ogfu+AvMy0VkeCvzbBuaxkLN+7i/NG9WvyMl8aEi9txDI8Aj4vIaOBrnLELE4Af40ykd0jKTAvcM+nD3C18s247vz17aFiGyHdPTaRTuwR27KngkjG9W2Sf6uTEODI6twv6nEmzFhQQY8t3GtMorkoMqvpP4GIgEydJPAoMAS5S1adDF150y0prT972PewprzqwraKqht/PXs6AbslcOjY8/aadibycqq2WWI3klZWWGtRlPmtqlNcXFjBhYFe6pyY1/AJjDNCI7qqq+rqqTlDVzp7HBFV9M5TBRbvMtBRUncU/vP79TR552/dy7+mZQZvz3Y1Th6UxaXBXRvdp/nTekZKVnsqmHfvYva+y4YNdmLt+OwW79nH+aCstGNMY1nevGfx7Ju3YU8HfPl7NcYO6MnFwt7DGcvlRfXnuqrEtuh59qIueXo0xa0EByYlxnJxly3ca0xh1JgYRKRGRLp6fSz3PAz7CF2506dmhDalJcQfaGf7y4Sr2VlRz7+mZEY6sZfJOjRGMdoa9FVW8t6SI0w9Pa5FtLsZEUn2Nzz8FSn1+bvbIIxGZDPwNZ9K9Z1X1D377J+Ks+7zes2mWqv62udcNFe8iHcuLSli1pZQXv9vIZUf2YWD34M73fqjompJI15TEoHRZfX/pZvZUVHOeVSMZ02h1JgZVfd7n5+nNvZCIxAJP4nR7zQfmichbqprrd+iXqnpGc68XLplpqbw8bxMPvpNLu4RYfn7ioEiH1KJ5R0A316wFBfTu1IYxGZ2CEJUxhxa3U2KsE5GDZmQTkQ4iss7ltcYCa1R1napWAC8BZ7sPNTplpTtTRn+5ehs/O2EgnYI81/uhZmh6KmuKy5o1PXLhrn18tXYb545qOfNFGRNN3DY+ZxB4zYVEwG3/yJ7AJp/n+Z5t/saJyPci8p5nYaCo5h113K9LO64YlxHZYFqBrLT2VNUoqzaXNfkcbyzyzhdl1UjGNEW9A9xE5Dyfp6eLiG/lbyxwAj+0BzQk0K2bf7vFAqCvqpZ5Zm59Azho6LCIXA9cD9CnT2TnWB/UPYUTM7tzzYR+JMRZJ6/m8vZMyi3azeG92jf69c5a1/mMyehI387tgh2eMYeEhkY+v+b5V4F/+e2rBPKAX7i8Vj7guyBBL6DQ9wBVLfH5ebaIPCUiXVR1m99xTwNPA2RnZwd/Os5GSIiL4dkrsyMZQqvSp1NbkhPjmtwz6dv1O1i/bQ+3TBoQ5MiMOXTUmxi8K7eJyHpgjP8XdCPNAwaKSD+gALgEuNT3ABHpAWxRVRWRsThVXdubcU3TwsTEOKO4m5oYXp63iZSkOE47vOXNF2VMtHC7tGe/5l5IVatE5BbgA5xqqGmqukxEbvTsnwpcAPxERKqAfcAlGooJ+k1UG5renldyNlFdo41a3Gj33kpmLyniouyWOV+UMdHC7SR6iMiROG0K3fBrtFbVn7k5h6rOBmb7bZvq8/MTwBNuYzKtU1Z6KnsrqtmwfU+jpix/Y1EB5VU1XDwm+EuoGnMocZUYROSXwJ+ANTjtAr538XZHb4LK29NrWWGJ68Sgqsz4biPDeqYyrGfjG62NMT9wW2K4FfiZ547emJAa1D2F+FhhWWEJZ45Id/Waxfm7WbG5lN+dMyzE0RnT+rntX5mKXxWQMaGSEBfDwG6B17qoy0vzNpEUH8NZI90lEmNM3dwmhhnA5FAGYowvZ2qM3bjpe7CnvIq3FhVw+uHppCaFfmEkY1o7t1VJm4DfiMjRwGKcMQwHqOpjwQ7MHNqy0lN5dX4+xaXlDS6y8+7iIvZUVPOjsdbobEwwuE0M1wJlwHjPw5cClhhMUHmn4M4tLGkwMbw0byMDuiVzRN+Wu0iRMdEkbOMYjGkM71Klywp3M2lI3YserdpSyoKNu7j39MwWvUiRMdGk0ZP7iEh3EbFJgUxIpSTF07dz2wZHQL/03SbiY4VzR9mEecYEi9tpt+NF5E8iUooznUWGZ/sfReSmEMZnDmFD01PrTQzlVdXMWpjPyVk96JycGMbIjGnd3N753w+cCVwOlPts/w6YEuSYjAGcdoaNO/ZSsr8y4P4Plm1h195KLrFGZ2OCym1i+BFwo6q+CdT4bF8K2JJlJiS8I6CX11FqeHneRnp2aMPR/buEMyxjWj23iSEd2BBgexyNmG/JmMbwrs0QqDppw/Y9fLVmOxeP6W2rtBkTZG4TwzLg2ADbLwLmBy8cY37QLTWJLsmJAUdAv5KziRiBC7PdLiBojHHL7d3+b4D/iEhvnCmzLxSRITjrKZwequCMyQrQAF1VXcOrOflMHNyNtPZtIhSZMa2XqxKDqr6NUzo4GaeN4X6cJTfPVNWPQheeOdQNTU9l9ZZSyquqD2z7dOVWikvLbXptY0LEdfuAqn6As8iOMWEzND2Vqhpl9ZayA9NpvzxvI11TEjm+noFvxpimczuOYZ2IdA6wvYOIrAt+WMY4fKfGANi8ez+frCjmgiN6ER9r4yyNCQW3f1kZOG0L/hIB10NORWSyiKwUkTUicmc9x40RkWoRucDtuU3r1LdTW9olxLKscDcAr83fRI3CxdlWjWRMqNRblSQi5/k8PV1Edvs8j8VZ6jPPzYVEJBZ4EjgJyAfmichbqpob4Lg/YtVWBoiJETLTnAbomhrl5ZxNjDusMxld2kU6NGNarYbaGF7z/KvAv/z2VeIkhV+4vNZYYI2qrgMQkZeAs4Fcv+N+CswExrg8r2nlhqan8tr8fL5au41NO/bxy5MHRzokY1q1equSVDVGVWOAjUA373PPI1FVB6vqOy6v1RNnXQevfPyqoUSkJ3AuMNX9WzCtXVZ6KnsqqvnzBytp3yaeU4b2iHRIxrRqbrur9lPVbc28VqDhqf7Lc/0VuENVqwMc+8OJRK4XkRwRydm6dWszwzLRztsAvTh/N+eO6klSfKDmLmNMsLju1iEiN4nIMhHZKyKHebbdKSIXuTxFPuDbYtgLKPQ7Jht4SUTygAuAp0TkHP8TqerTqpqtqtldu3Z1+xZMCzWwezJxnmkvbMI8Y0LPbXfVnwP3Ak9T+86/ALjF5bXmAQNFpJ+IJACXAG/5HuApmWSoagZO+8ZNqvqGy/ObVioxLpah6amM7tOBIT1SIx2OMa2e2wFuNwLXqeq7IvI7n+0LgKFuTqCqVSJyC05vo1hgmqouE5EbPfutXcHU6ekrsg+UGowxoeU2MfTFmWLbXyXgerIaVZ0NzPbbFjAhqOoUt+c1rV9D6z4bY4LHbRvDOmB0gO2ncXB3U2OMMS2Y2xLDI8ATItIWp41hnIj8GLgduDpUwRljjAk/V4lBVZ8TkTjg90Bb4AWchuefqerLIYzPGGNMmDWYGDwJ4XrgDVV9RkS6ADGqWhzy6IwxxoRdg20MqloF/BmI9zzfZknBGGNaL7eNz3OBI0IZiDHGmOjgtvH5GeAREemDs8bzHt+dqrog2IEZY4yJDLeJ4UXPv48F2KcEXqvBGGNMC+Q2MfQLaRTGGGOihtvuqhtCHYgxxpjoYIvmGmOMqcUSgzHGmFosMRhjjKnFEoMxxphaLDEYY4ypxVWvJBHpBDwEnAB0wy+hqKotq2WMMa2E23EM/wJG4SztWYgzqM0YY0wr5DYxnACcpKrfNudiIjIZ+BvOSOlnVfUPfvvPBh4EaoAq4Oeq+r/mXNMYY0zjuE0MxUBZcy4kIrHAk8BJQD4wT0TeUlXfFeA+Bt5SVRWR4cArwJDmXNcYY0zjuG18vgf4rYgkN+NaY4E1qrpOVSuAl4CzfQ9Q1TJV9VZTtcOqrIwxJuzqLDGIyBJqfzH3A4pFZANQ6Xusqg53ca2ewCaf5/nAkQGuey7wME4j9+kuzmuMMSaI6qtKei3I15IA2w4qEajq68DrInIsTnvDiQedSOR6nFXl6NOnT5DDNMaYQ1udiUFVfxPka+UDvX2e98Lp4VTX9b8Qkf4i0kVVt/ntexqnhxTZ2dlW3WSMMUHkqo1BRNaJSOcA2zuIyDqX15oHDBSRfiKSAFwCvOV3vgEiIp6fRwMJwHaX5zfGGBMEbnslZRB4MZ5EnDv/BqlqlYjcAnzgOdc0VV0mIjd69k8FzgeuEJFKYB9wsU9jtDHGmDCoNzGIyHk+T08Xkd0+z2Nxxjesd3sxVZ0NzPbbNtXn5z8Cf3R7PmOMMcHXUInB2wCtOKOffVUCecAvghyTMcaYCKo3MahqDICIrAfG+DcCG2OMaX3cLu1paz4bY8whwm3jMyJyJHXPrvqzIMdljDEmQtxOu/1L4E/AGg6eXdV6DRljTCvitsRwK/AzVX0ilMEYY4yJPLeT6KXi183UGGNM6+Q2McwAJocyEGOMMdHBbVXSJuA3InI0sJiDZ1d9LNiBGWOMiQy3ieFanIV6xnsevhSwxGCMMa2EjWMwxhhTi9s2hgNEJFlE2oUiGGOMMZHnOjGIyM0ishHYDZSIyAYRuSl0oRljjIkEtwPc7gbuAh4B/ufZfAzwBxFJVdU/hCg+Y4wxYea28flG4HpVneGz7WMRWQ38HrDEYIwxrYTbqqRuOCuw+fsO6B68cIwxxkSa28SwCrg0wPZLgZXBC8cYY0ykua1KegB4RUSOBb7CGbswATgOuDA0oRljjIkEVyUGVZ0FHAlsBs4AzvL8PFZV33B7MRGZLCIrRWSNiNwZYP9lIrLY8/haREa4PbcxxpjgcL0eg6rOBy5v6oVEJBZ4EjgJyAfmichbqprrc9h64DhV3SkipwJP4yQkY4wxYeI6MQCISCcCL9STG/gVtYwF1qjqOs+5XgLOBg68VlW/9jl+LtCrMfEZY4xpPrfjGEYBzwGHezfhtDN4/411cZqeOJPxeeVTf2ngGuA9N/EZY4wJHrclhmlAAc6CPVto2qptEmBbwPOIyCScxDChjv3XA9cD9OnTpwmhGGOMqYvbxDAQuFBV1zTjWvlAb5/nvXCWCa1FRIYDzwKnqur2QCdS1adx2h/Izs62pUWNMSaI3I5j+B+Q2cxrzQMGikg/EUkALgHe8j1ARPoAs4Afq+qqZl7PGGNME7gtMVwDPCsihwFLOXihni8aOoGqVonILcAHOG0S01R1mYjc6Nk/Ffg10Bl4SkQAqlQ12+2bMcYY03yi2nBNjIhMxFneM9D0F6qqbhqfQyI7O1tzcnIidXljjGmRRGR+XTfebksM/wQ+Bh6m6Y3PxhhjWgC3iaEXcJqqrg1lMMYYYyLPbePzh8ARoQzEGGNMdHBbYngfeNTTlXQJBzc+zwp2YMYYYyLDbWJ4yvPv3QH2uR35bIwxpgVwlRhU1fXa0MYYY1q2Rn/hi0gvEbFEYYwxrVRTvuBzgYwgx2GMMSZKNCUxBJoMzxhjTCthVULGGGNqaUpi+D2wI9iBGGOMiQ6NWsENQFUfDkUgxhhjokOzqpJEpLeITAtWMMYYYyKvuW0MnYArgxGIMcaY6FBvVZKIXNHA621dTWOMaWUaamOYDuyl7mm2rVeTMca0Mg19sRcCV6hqSqAHcHQYYjTGGBNGDSWG+cDoevYrjRjwJiKTRWSliKwRkTsD7B8iIt+ISLmI/NLteY0xxgRPQ1VJjwDJ9exfA0xycyERiQWeBE4C8oF5IvKWqub6HLYD+BlwjptzGmOMCb56E4OqftnA/j3A5y6vNRZYo6rrAETkJeBsnLmXvOcrBopF5HSX5zTGGBNk4Ww87gls8nme79lmjDEmioQzMQRqi6irt1P9JxK5XkRyRCRn69atzQzLGGOMr3Amhnygt8/zXji9nhpNVZ9W1WxVze7atWtQgjPGGONo9FxJzTAPGCgi/YAC4BLg0lBcqLKykvz8fPbv3x+K05sgSUpKolevXsTHx0c6FGOMj7AlBlWtEpFbgA9w1oiepqrLRORGz/6pItIDyAFSgRoR+TmQpaoljblWfn4+KSkpZGRkIGLLR0QjVWX79u3k5+fTr1+/SIdjjPERzhIDqjobmO23barPz5txqpiaZf/+/ZYUopyI0LlzZ6yNyJjo02qntLCkEP3s/8iY6NRqE4MxxpimscQQQbt27eKpp55q1jmmT59OYeEPnbuuvfZacnNz63lFaCxatIjZs2c3fKAxJupZYggDVaWmpuag7aFIDM8++yxZWVnNOmddqqqq6txnicGY1sMSQ4jk5eWRmZnJTTfdxOjRo3nwwQcZM2YMw4cP5/777wfgzjvvZO3atYwcOZJf/epXAPz5z38+6Djvua677jqGDh3KySefzL59+3jttdfIycnhsssuY+TIkezbt4+JEyeSk5MDwIwZMzj88MMZNmwYd9xxx4HYkpOTueeeexgxYgRHHXUUW7ZsqfN9TJkyhdtuu41JkyZxxx138N133zF+/HhGjRrF+PHjWblyJRUVFfz617/m5ZdfZuTIkbz88svs2bOHq6++mjFjxjBq1CjefPPNUH3UxpggC2uvpEj4zdvLyC1sVG/XBmWlp3L/mUMbPG7lypU899xznHPOObz22mt89913qCpnnXUWX3zxBX/4wx9YunQpixYtAmDOnDmsXr36oOP69OnD6tWrmTFjBs888wwXXXQRM2fO5PLLL+eJJ57gkUceITs7u9a1CwsLueOOO5g/fz4dO3bk5JNP5o033uCcc85hz549HHXUUTz00EPcfvvtPPPMM9x77711vo9Vq1bx0UcfERsbS0lJCV988QVxcXF89NFH3H333cycOZPf/va35OTk8MQTTwBw9913c/zxxzNt2jR27drF2LFjOfHEE2nXrl3TP3hjTFi0+sQQSX379uWoo47il7/8JXPmzGHUqFEAlJWVsXr1avr0qb0A3pw5c+o8rl+/fowcORKAI444gry8vHqvPW/ePCZOnIh3ZPhll13GF198wTnnnENCQgJnnHHGgXN9+OGH9Z7rwgsvJDY2FoDdu3dz5ZVXsnr1akSEysrKgK+ZM2cOb731Fo888gjgdCHeuHEjmZmZ9V7LGBN5rT4xuLmzDxXv3bGqctddd3HDDTfU2u//5V7fcYmJiQeex8bGsm/fvnqvrVr3NFTx8fEHuorGxsbW23bg+z4A7rvvPiZNmsTrr79OXl4eEydOrPP6M2fOZPDgwfWe2xgTfayNIQxOOeUUpk2bRllZGQAFBQUUFxeTkpJCaWlpg8fVx/8cXkceeSSff/4527Zto7q6mhkzZnDcccc1+73s3r2bnj2dSXGnT59eZxynnHIKjz/++IEEtXDhwmZf2xgTHpYYwuDkk0/m0ksvZdy4cRx++OFccMEFlJaW0rlzZ44++miGDRvGr371qzqPq8+UKVO48cYbDzQ+e6WlpfHwww8zadIkRowYwejRozn77LOb/V5uv/127rrrLo4++miqq6sPbJ80aRK5ubkHGp/vu+8+KisrGT58OMOGDeO+++5r9rWNMeEh9VU5tATZ2dnq7YXjtXz5cqvLbiHs/8qYyBCR+aqaHWiflRiMMcbU0uobn407Dz30EK+++mqtbRdeeCH33HNPhCIyxkSKJQYDwD333GNJwBgDtOKqpJbednIosP8jY6JTq0wMSUlJbN++3b54oph3oZ6kpKRIh2KM8dMqq5J69epFfn6+LQIT5bxLexpjoktYE4OITAb+hrO057Oq+ge//eLZfxqwF5iiqgsae534+HhbLtIYY5oobFVJIhILPAmcCmQBPxIR//mhTwUGeh7XA/8IV3zGGGMc4WxjGAusUdV1qloBvAT4D8U9G/i3OuYCHUQkLYwxGmPMIS+ciaEnsMnneb5nW2OPMcYYE0LhbGMItPK7f7chN8cgItfjVDUBlIvIUpcxtAd2uzy2scfbsdEVRzQcGy1xRMOx0RJHNBwbLXHUPfWxqoblAYwDPvB5fhdwl98x/wR+5PN8JZDWwHlzGhHD042M2fXxdmx0xRENx0ZLHNFwbLTEEQ3HRksc9X13hrMqaR4wUET6iUgCcAnwlt8xbwFXiOMoYLeqFgUxhrdDeLwdG11xRMOx0RJHNBwbLXFEw7HRFEdAYZ1dVUROA/6K0111mqo+JCI3AqjqVE931SeAyTjdVa9S1Zy6zuc5Z47WMUOgMcaYwOr77mzx026LyPWq+nSk4zDGmJakvu/OFj8lhiUFd0RksoisFJE1InKnZ9tIEZkrIotEJEdExkY6zmgiItNEpNi3c4OIPCAiBZ7PbJGnFGx8iEhvEflURJaLyDIRudWz/c8iskJEFovI6yLSIcKhRpV6PrcRIvKNiCwRkbdFJDUY16vvu7PFlxhMwzyDC1cBJ+F0AZ4H/AinWu8vqvqe5wvudlWdGKk4o42IHAuU4YytGebZ9gBQpqqPRDK2aOYZe5SmqgtEJAWYD5wD9AI+UdUqEfkjgKreEblIo0s9n9vzwC9V9XMRuRrop6ohXRKxxZcYjCt1DS5UwHv30R4ojFB8UUlVvwB2RDqOlkZVi9QzlY2qlgLLgZ6qOkdVqzyHzcVJFMajrs8Np1vpF57DPgTOD3UslhgODXUNHPw58GcR2QQ8gtOF2DTsFk91yDQR6RjpYKKZiGQAo4Bv/XZdDbwX9oBaCL/PbSlwlmfXhUDvUF/fEsOhoa6Bgz8B/k9VewP/B/wrrFG1TP8A+gMjgSLg0YhGE8VEJBmYCfxcVUt8tt8DVAH/jVRs0SzA53Y1cLOIzAdSgIpQx2CJ4dCQT+27jF441UZXArM8217FqXIy9VDVLaparao1wDPYZxaQiMTjfLn9V1Vn+Wy/EjgDuEytgfMggT43VV2hqier6hHADGBtqOOwxHBoqGtwYSFwnOeY44HVEYqvxfCb1PFcnGK+8eEZj/QvYLmqPuazfTJwB3CWqu6NVHzRqp7PrZvn3xjgXmBqyGOxpH1oqGNw4QSc9S/igP3ATao6P3JRRhcRmQFMBLoAW4D7Pc9H4lTF5QE3BHl0fovn+b36ElgC1Hg23w38HUgEtnu2zVXVG8MfYXSq53MbCNzseT4LZyqhkH5xW2IwxhhTi1UlGWOMqcUSgzHGmFosMRhjjKnFEoMxxphaLDEYY4ypxRKDMcaYWiwxGGOMqcUSgzHGmFosMRhjjKnFEoMxxphaLDEYY4ypxRKDMcaYWiwxGGOMqcUSgzHGmFosMRhjjKnFEoMxxphaWkxiEJGySMdgjDGHghaTGIwxxoRHi0oMIpIsIh+LyAIRWSIiZ3u2Z4jIchF5RkSWicgcEWkT6XiNMaYlajFrPnuqkjoAbVW1RES6AHNxFsruC6wBslV1kYi8Arylqv+JWMDGGNNCxUU6gEYS4PcicixQA/QEunv2rVfVRZ6f5wMZYY/OGGNagZaWGC4DugJHqGqliOQBSZ595T7HVQNWlWSMMU3QotoYgPZAsScpTMKpQjLGGBNELaLEICJxOCWC/wJvi0gOsAhYEcm4jDGmNWoRjc8iMgJ4RlXHRjoWY4xp7aK+KklEbgRmAPdGOhZjjDkUtIgSgzHGmPCJ+hKDMcaY8Iq6xCAivUXkU89I5mUicqtneycR+VBEVnv+7ejZ3tlzfJmIPOF3rh95RkgvFpH3PYPijDHG1CPqqpJEJA1IU9UFIpKCM1jtHGAKsENV/yAidwIdVfUOEWkHjAKGAcNU9RbPeeKAQiBLVbeJyJ+Avar6QNjflDHGtCBRV2JQ1SJVXeD5uRRYjjPC+Wzgec9hz+MkC1R1j6r+D9jvdyrxPNqJiACpOInCGGNMPaJ6HIOIZOCUBr4FuqtqETjJQ0S61fdazyC4nwBLgD3AauDm0EZsjDEtX9SVGLxEJBmYCfxcVUua8Pp44Cc4iSUdWAzcFdQgjTGmFYrKxOD5Up8J/FdVZ3k2b/G0P3jbIYobOM1IAFVdq05DyivA+NBEbIwxrUfUJQZPe8C/gOWq+pjPrreAKz0/Xwm82cCpCoAsEenqeX4STnuFMcaYekRjr6QJwJc4bQM1ns1347QzvAL0ATYCF6rqDs9r8nAalxOAXcDJqprrGTV9K1AJbACmqOr2sL0ZY4xpgaIuMRhjjImsqKtKMsYYE1mWGIwxxtRiicEYY0wtlhiMMcbUYonBGGNMLZYYjDHG1GKJwZh6iMh0EVHPo1JEij3TvN/sGaHv9jwTPeewqd9N1LPEYEzDPgLSgAzgZOBt4DfAl55p341pVSwxGNOwclXdrKoFqrrIM1XLRGA0cDuAiFwuIvNEpNRTqnhVRHp69mUAn3rOtdVTcpju2ScicruIrBWRfZ6FpS4P9xs0xpclBmOaQFWXAu8D53s2JQD3AyOAM4AuwAzPvk0+xw3FKX3c6nn+O+AanCnhs4CHgX+KyOkhfgvG1Cmq12MwJsrlAicCqOo0n+3rPGuBLBeRXqqaLyI7PPuKVXUbgKca6jacub2+9OxfLyJjcRLFu2F5F8b4scRgTNMJoAAiMhqnxDAS6OTZB86kj/l1vD4LSALeFxHfScvigbzgh2uMO5YYjGm6LJzSQTvgA5xG6h/jrBXSBWeW4IR6Xu+tyj0TZ8ZgX5XBDdUY9ywxGNMEIjIMmIzTRjAEJxHcrarrPfvP83tJheffWJ9tuUA50FdVPwltxMa4Z4nBmIYlikgPnDv8rsAJOGuEzAceAdrifMHfIiJPApnAg37n2IBT7XS6iLwN7FPVUhF5BHjEs0DVF0AycBRQo6pPh/6tGXMw65VkTMNOBIpwqns+Bs7CGcdwrKruUdWtOKsKnoNTCrgfp1H5AFUt8Gx/CNgCPOHZdR/wAPBLYBnwIU4PpvWhfEPG1McW6jHGGFOLlRiMMcbUYonBGGNMLZYYjDHG1GKJwRhjTC2WGIwxxtRiicEYY0wtlhiMMcbUYonBGGNMLZYYjDHG1PL/5LaVdj0yna8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cohort analysis plot\n",
    "plt.figure(figsize = (12,6))\n",
    "daily_r_rate.plot('date_subscribed','retention_rate')\n",
    "plt.title('Daily subscriber quality\\n', size = 16)\n",
    "plt.ylabel('1-month retention rate', size = 14)\n",
    "plt.xlabel('Date',size = 14)\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "58bbee43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_served  language_preferred\n",
       "2018-01-01   Arabic                  4\n",
       "             English               355\n",
       "             German                  5\n",
       "             Spanish                11\n",
       "2018-01-02   Arabic                  4\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customer languages preferance \n",
    "language = mkt.groupby(['date_served', 'language_preferred'])\\\n",
    "['user_id'].count()\n",
    "language.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6c44ab26",
   "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>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>4.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>3.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "language_preferred  Arabic  English  German  Spanish\n",
       "date_served                                         \n",
       "2018-01-01             4.0    342.0     5.0     11.0\n",
       "2018-01-02             4.0    355.0     5.0     10.0\n",
       "2018-01-03             3.0    334.0     3.0      8.0\n",
       "2018-01-04             2.0    305.0     2.0     14.0\n",
       "2018-01-05             1.0    303.0     2.0     14.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transfer to wide list, set level to 1 to unstack the 2rd index \n",
    "language = pd.DataFrame(language.unstack(level = 1))\n",
    "language.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0029292b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mmzn++OO5/vrr+de//rXb3ApN3XXXXVxzzTV8+ctfJhKJEI/HKS8vZ/Xq1cyZM4d77rmHc889lyeeeIILLrhgr99yOgsKxrRG1XVeSxUfDXfLXZsa+i2YXqe14qMvfOELAEydOpUnn3wSgNdff52nnnoKcGMi9e/fv9XjH3nkkcyePZuysjK+8IUvMG7cOADGjh2bCkZTp05l7dq1e/9mmrCgYExrItUQrU0rPhrmltYsNTe0cUWfDXl5eYCrjE6ObNrRMebOP/98Dj/8cJ599llOPvlk7r33Xvbdd9/UsZPHz0TxkdUpGNOa5BAXyaBQPBQQa5ZqOuToo49m7ty5ADz//PPs2LGj1e0/+ugj9t13X775zW9yxhlnsHTp0q5IJmBBwZjWNQ0K/oC7b0Nd9GrJOoXk7brrrmt1+xtvvJHnn3+eKVOm8Nxzz1FaWkpJSUmL2z/66KNMmDCByZMns3LlSi688MLOfgst6pKhszNl2rRpumDBgmwnw/Rky/4Gj10EV7zuKiAB/nis68h2QfabD/ZG3XHo7HA4jN/vJxAI8NZbb3HllVd2epPWlnR06GyrUzCmNcnB8JIVzeCapVZuyE56TLe0fv16zj33XBKJBKFQiHvuuSfbSWqRBQVjWlNdDuKDwrQhvPqUQpkN4Gvab9y4cfz3v//NdjLaxeoUjGlNdTkUDgJf2nThJcOhdhvEwtlLlzEZYkHBmNZUVzQuOoK0Zqk2BpLpeSwoGNOa6nIobjLta6pXszVLNT2PBQVjWpPemzmpxAsK1izV9EAWFIxpiSrUbNl9jKMSyykYKC8v5/zzz2ffffdl6tSpHHnkkamhLLozCwrGtKR+J8QjUNQkKBT0B3+eDXXRi6kqZ511FscccwwfffQRCxcu5JFHHqGsrKxd+8fjuTvTcEaDgoj0E5HHRWSliKwQkSNFZICIvCAiq71l/7TtrxeRNSKySkROzmTajGlTqjdzk+IjEZuBrZd7+eWXCYVCXHHFFal1o0eP5uqrryYej/O9732P6dOnc+ihh/LHP/4RgHnz5nHcccdx/vnnM3HiRObNm8exxx7LueeeywEHHMB1113HQw89xIwZM5g4cSIffvghAP/4xz84/PDDOeywwzjhhBMoL3fTw95000187WtfY+bMmey7777cfvvtnfLeMt1P4TbgX6p6toiEgELgB8BLqnqriFwHXAdcKyKHAOcB44HhwIsicoCq5m5INT1bam7mZobILhluM7DlgJ/P/zkrt6/s1GMeNOAgrp1xbavbLFu2jClTpjT73H333Uffvn159913CYfDfOpTn+Kkk04CYP78+bz//vuMHTuWefPmsWTJElasWMGAAQPYd999ueSSS5g/fz633XYbv/vd7/jtb3/L0Ucfzdtvv42IcO+99/J///d//OpXvwJg5cqVvPLKK1RVVXHggQdy5ZVXEgwG9+r9ZywoiEgf4BjgqwCqGgEiInImMNPb7H5gHnAtcCbwiKqGgY9FZA0wA3grU2k0plUt5RTANUvdtLhLk2Ny11VXXcXrr79OKBRi9OjRLF26NDWLWmVlJatXryYUCjFjxgzGjh2b2m/69OmUlro6qv322y8VPCZOnMgrr7wCQFlZGbNmzWLTpk1EIpFG+5966qnk5eWRl5fHkCFDKC8vZ+TIkXv1XjKZU9gXqAD+LCKTgIXANcBQVd0EoKqbRCR5GTYCeDtt/zJvXSMichlwGcCoUaMyl3pjmg6Gl67PcPjgX64yWqRr02VS2rqiz5Tx48fzxBNPpB7//ve/Z+vWrUybNo1Ro0bxu9/9jpNPblwCPm/ePIqKihqtSx8K2+fzpR77fL7UsNtXX301//M//8MZZ5zBvHnzuOmmm5rdP32o7r2RyTqFADAFuFNVDwNqcEVFLWnun7XbaH2qereqTlPVaYMHD25mF2M6SXU5+IKQ32/350qGuXkW6iu7PFkm+44//njq6+u58847U+tqa2sBOPnkk7nzzjuJRqMAfPDBB9TU1Ozxa1VWVjJihLs+vv/++/ci1e2TyaBQBpSp6jve48dxQaJcREoBvOWWtO33Sdt/JGDNO0z2JOdm9jXzN7Fmqb2aiPC3v/2N//znP4wdO5YZM2Zw0UUX8fOf/5xLLrmEQw45hClTpjBhwgQuv/zyvbqCv+mmmzjnnHP49Kc/zaBBgzrxXTQvo0Nni8hrwCWqukpEbgKSeadtaRXNA1T1+yIyHngYV48wHHgJGNdaRbMNnW0y6sEvQs1WuPw/uz+39g34y+fgK0/Bfsd3fdp6se44dHY25drQ2VcDD3ktjz4C/h8udzJXRC4G1gPnAKjqMhGZCywHYsBV1vLIZFV1ecOczE3ZUBemh8poUFDVxUBz0egzLWw/G5idyTQZ027VW6B0cvPP2VAXpoeyHs3GNCcRd0VHzbU8AggWuApoyymYHsaCgjHNqd0OGm++j0JSn+E2fLbpcSwoGNOc1nozJ5UMs+Ij0+NYUDCmOTWt9GZOKhluxUemx7GgYExzkr2Zm46Qmq5kmMtRJKyRXG80e/Zsxo8fz6GHHsrkyZN555132t6pA4466qhWny8uLu7U10vKdJNUY7qn9hQf9Sl19Q41FQ1TdJpe4a233uKZZ55h0aJF5OXlsXXrViKRSKe+xptvvtmpx2svyykY05zqLRAogLySlrdJ9mGweoVeZ9OmTQwaNCg19tCgQYMYPnw4Y8aM4dprr2XGjBnMmDGDNWvWAHs2/HUyJ7Bp0yaOOeYYJk+ezIQJE3jttddS29xwww1MmjSJI444InXMvWU5BWOaU73Fzc3c2mB3ydyB1Stkzeaf/pTwis4dOjvv4IMY9oMftLrNSSedxC233MIBBxzACSecwKxZszj22GMB6NOnD/Pnz+eBBx7gW9/6Fs8888xeDX/98MMPc/LJJ3PDDTcQj8dTYyzV1NRwxBFHMHv2bL7//e9zzz338MMf/nCv378FBWOaU13eeiUzuCapYDOw9ULFxcUsXLiQ1157jVdeeYVZs2Zx6623AvClL30ptfz2t78N7N3w19OnT+drX/sa0WiUs846i8mTJwMQCoU47bTTAJg6dSovvPBCp7w3CwrGNKemAgbs2/o2RYNB/DbZTha1dUWfSX6/n5kzZzJz5kwmTpyYGsFU0nKXyft7M/z1Mcccw6uvvsqzzz7LV77yFb73ve9x4YUXEgwGU8fvrGGzweoUjGledbk76bfG53e5CSs+6nVWrVrF6tWrU48XL17M6NGjAXj00UdTyyOPPBLYu+Gv161bx5AhQ7j00ku5+OKLWbRoUWe8hRZZTsGYpuJRqN3WdvERuHoFKz7qdaqrq7n66qvZuXMngUCA/fffn7vvvptnnnmGcDjM4YcfTiKRYM6cOUDD8NcjRozgiCOO4OOPP273a82bN49f/OIXBINBiouLeeCBBzL1toAMD52daTZ0tsmIXRvh1wfDqb+G6Re3vu0jX4btH8HXbdbYrpLLQ2ePGTOGBQsWdMm8B+3V0aGzrfjImKZam5u5KRvqwvQwVnxkTFOtzc3cVEkp1O+EaJ0bOdX0amvXrs12Evaa5RSMaao9vZmTUs1SrQWS6RksKBjTVE07xj1Ksg5sWdGd60K70p58ThYUjGmqeguESiBU2Pa2NtRFl8vPz2fbtm0WGNqgqmzbto38/PwO7ZfROgURWQtUAXEgpqrTRGQA8CgwBlgLnKuqO7ztrwcu9rb/pqr+O5PpM6ZZ1eXtKzqCtJyCFR91lZEjR1JWVkZFRUW2k5Lz8vPzG/WObo+uqGg+TlW3pj2+DnhJVW8Vkeu8x9eKyCHAecB4YDjwoogcoKo2LrHpWtUV7Wt5BJDfF4KFVnzUhYLBYKNhIkznykbx0ZlAskvf/cBZaesfUdWwqn4MrAFmdH3yTK9XXe4Gw2sPEWuWanqUTAcFBZ4XkYUicpm3bqiqbgLwlsl8+ghgQ9q+Zd46Y7pW9Zb25xTAZmAzPUqmi48+paobRWQI8IKItDbGbXNjFO9Wk+QFl8sARo0a1TmpNCYpWg/hyvbXKYCbbKfs3cylyZgulNGcgqpu9JZbgKdwxUHlIlIK4C299n+UAfuk7T4S2C1Prqp3q+o0VZ02eHA7s/jGtFd75mZuqmSYyylYaxjTA2QsKIhIkYiUJO8DJwHvA08DF3mbXQT83bv/NHCeiOSJyFhgHDA/U+kzplntmZu5qZLhEKuHuh2ZSZMxXSiTxUdDgae88b4DwMOq+i8ReReYKyIXA+uBcwBUdZmIzAWWAzHgKmt5ZLpcR3ozJ6U3Sy0c0PlpMqYLZSwoqOpHwKRm1m8DPtPCPrOB2ZlKkzFt6shgeEnpQ10MHd/5aTKmC1mPZmPSpYqPOlBflcwp2AxspgewoGBMuupyKOgPgVD79ykpdUtrlmp6AAsKxqSr6WAfBYBAHhQOtBnYTI9gQcGYdNVbOlZ0lFRSajkF0yNYUDAmXXV5x3MK4IKCDXVhegALCsak6+gQF0klw2ykVNMjWFAwJilcDdHajvVRSOoz3AWUeKzz02VMF7KgYEzSnnRcSyoZBmjDMYzppiwoGJOU6ri2J0Eh2YHNKptN92ZBwZikPRkML6lPsq+CVTab7s2CgjFJezIYXpJ1YDM9hAUFY5Kqy0F8UDSo4/sWDgJfwJqlmm7PgoIxSdVbvJO7v+P7+nxQPMxyCqbbs6BgTFL1lj2rZE7qU2p1Cqbbs6BgTFJ1+d4FhZJhNlKq6fYsKBiTtKe9mZNKhlvxken2LCgYA25+5ZpOKD4KV0KkpvPSZUwXs6BgDED9TohH9qw5apI1SzU9QMaDgoj4ReS/IvKM93iAiLwgIqu9Zf+0ba8XkTUiskpETs502oxJ2ZNpOJtKBgVrlmq6sa7IKVwDrEh7fB3wkqqOA17yHiMihwDnAeOBU4A/iMgetA00Zg/szRAXSZZTMD1Au4KCiHxKRIq8+xeIyK9FZHQ79hsJnArcm7b6TOB+7/79wFlp6x9R1bCqfgysAWa0610Ys7f2ZjC8JBvqwvQA7c0p3AnUisgk4PvAOuCBduz3W2/7RNq6oaq6CcBbJv+FI4ANaduVeeuMybzOKD7KK4FQsTVLNd1ae4NCTFUVdzV/m6reBpS0toOInAZsUdWF7XwNaWadNnPcy0RkgYgsqKioaOehjWlDzRbwBSG/394dp6TUJtsx3Vp7g0KViFwPXAA865X1B9vY51PAGSKyFngEOF5EHgTKRaQUwFt6l2iUAfuk7T8S2C0frqp3q+o0VZ02ePAezKVrTHOSvZl9e1nN1seCgune2vsPmAWEgYtVdTOuWOcXre2gqter6khVHYOrQH5ZVS8AngYu8ja7CPi7d/9p4DwRyRORscA4YH5H3owxe6y6HIo64SLDcgqmmwu0tYGXK3hQVU9IrlPV9bSvTqE5twJzReRiYD1wjnfMZSIyF1gOxICrVDW+h69hTMdUb2loPbQ3Skpd6yNVkOZKRI3JbW0GBVWNi0itiPRV1co9eRFVnQfM8+5vAz7Twnazgdl78hrG7JXqLVA6ae+PU1LqOsHVboeigXt/PGO6WJtBwVMPvCciLwCpPvyq+s2MpMqYrpSIQ03F3jVHTUpvlmpBwXRD7Q0Kz3o3Y3qe2u2g8b1rjpqU6tW8CYZN3PvjGdPF2hUUVPV+ESkARqnqqgynyZiuVdMJvZmTUr2arbLZdE/t7dF8OrAY+Jf3eLKIPJ3BdBnTdZK9mfdmMLykkmFuaUHBdFPtbZJ6E27IiZ0AqroYGJuRFBnT1TqjN3OSP+iatlpQMN1UR3o0N215tFtvY2O6pc4YDC9dSakNdWG6rfZWNL8vIucDfhEZB3wTeDNzyeokqlCxElY8A5sWw8Sz4eAz977XqulZqsshUODGLuoM1oHNdGPtDQpXAzfgejXPAf4N/G+mErVXEgn4ZCGs/IcLBts/dOuLBsPKZ2DoRDjuejjwc9a5yDjVW6B4cOf9HvqUwsZFnXMsY7pYe1sf1eKCwg1eD+ciVa3PaMo6Ih6Fta+5ILDqn+4qzReAMZ+GI78OB57qigbeexz+cys8cj4MPwyOuwH2P8GCQ29Xs5dzMzdVUur6PcSjro7BmG6kXUFBRB4GrgDiwEKgr4j8WlVbHf8o4+p3wpOXwQf/gvpKCBbC/p+Bg06HA06Cgv6Nt580CyZ8EZY+Av/5OTx0NoycAcf9APadacGht6reAgP27bzjpU+202+f1rc1Jse0t3D9EFXdhZsQ55/AKOArmUpUu23/GFY/73IC5z0M3/8IZj3oTv5NA0KSPwCHXQDfWAin/QZ2fQJ/PQv+ciqsfaNLk29yRGcNhpfUZ7hbWr2C6YbaW6cQFJEgLijcoapREcl+66OB4+C777sTfUcFQjDtazD5y7DwfnjtV/CXz7kcw3E/hH2md3pyTQ6KR6F2WycXH1lfBdN9tTencBfwMVAEvOpNxbkrY6lqr7ziPQsI6QJ5cPhlcM1iOPmnUL4M7jsB/nYVhKs6JZkmh9VsdcvOao4KUOLlFN75I8y/BzYudsHHmG6g1TOqiPxP2sPf4PomXAC8DhyXwXR1vWABHHkVTP2qyzW8/htY9zp84R7Yx6aK7rE6Y27mpgoHwGFfcXVd//yuWxcogBFTYOQ0GDnd3ZI5CmNySFuX2c013B6Na4l0E25GtZ4lVASf+TGMOwmevBT+dAoc8z1329tcick9ndmbOUkEzrzD9ZPZuR7K3oWyBW751h8g4eUa+u7TECTGf76hLsKYLBI39XIHdxIZALyoqlM6P0ntN23aNF2wYEHmXqB+Fzz3fVgyB0ZMgy/cDQP3y9zrma733wfh71fBNUug/5jMv160Hja/5wUKL1hUrnctli58GgYfkPk0mF5PRBaq6rTmntujrr2quh3o+e038/vA5++Cs/8M21bDXZ+GRQ+4K0DTM3TmYHjtEcx3jRiO/Dqc82f49ntwxetuToe/nArly7smHca0YI+CgogcD+zo5LTkrglfgCvfgpFT4emr4dELoGZbtlNlOkP1FgiVQKgwe2kYNhH+3z/B54f7T3M5CWOypNWgICLvicjSJrcy3DzLX++aJOaIviPgK3+HE/8XPvg33HkUrHkp26nKvsoyWPYU/PsGuP90eOM2d9XbXVRv6dxK5j01aBx89VlXIf2X0+ATGybDZIAqrGx9vrRW6xS8pqeNDglsU9Wa5rZvsm8+8CqQh6vQflxVb/TqIx4FxgBrgXNVdYe3z/XAxbie099U1X+39hoZr1NoyaalrhK6YiUcfgVMvwRCxa6JbLCo5w64F6mFTUsal4dXbXTP+fOg/2jY+oHrJf75u7pH/cufTwVNwNeey3ZKnB3rXHCt2wEXPGEt30znUIXVL8Ars2HTYuTmXS3WKexRRXN7iIjgxkiq9jq+vQ5cA3wB2K6qt4rIdUB/Vb1WRA7BDbY3AxgOvAgcoKotXnZmLSgAROvghRth/h93fy5Y5Fox5RW7Zaik4fHA/V3LphFTXXFBrqrbAds+hK2r3QCDZe9C+fuQiLnn+49paFo5cpobaNAfdONL/fM7EIvAibe4gJnLQfJ302DoIXDuA9lOSYPKMrj/DFffcf5cGPOpbKfItCYWdv2dcpEqfPQKvPJT9x/uNxqOvRaZckHXB4VGLyJSiAsKVwIPADNVdZOIlALzVPVAL5eAqv7M2+ffwE2q+lZLx81qUEja+F934oxUQ7gaIjXufrOPq2D7R+7KtHCgG4xv3Emw3/GubXtXC1e7UWS3ebftH8K2Ne5+3faG7ULFXht7LwiMmOZGFW3Jro2u7mXNizD2WDjz97k7BtDPRrlhUT6X3WG8dlO12eUYKsvgS3NcT3uTe9a/7ZqtDz4Ixp0IB5wM+xyeGwMhrn0dXp4N69+EPiPhmO+6IX78wVZbH2W04b03oupCYH/g96r6jogMVdVNAF5gSBbojgDeTtu9zFvX9JiXAZcBjBo1KpPJb5/hh7lbe9XtcHURq5932bmlj4L43A9p3EnuNnR85gbn+2QhvHgzVKyC6s2NnysZ7op8DjnD5WgG7OceD9y/Y7maPsPhy4/DovtdXcOdR8EpP3NDiuTSoIPReghX5kadQlMlw1wdwwNnwsOz4LyH3EWEyS0L/+IumoqHwNt3wpu3Q15f2O84FyD2P7H1C6hMWP8OvPIT+PhVKB4Gn/slTLmw3bmZrsop9AOews3L8Lqq9kt7boeq9heR3wNvqeqD3vr7gH+q6hMtHTcncgp7IxF3FYqr/+0qrzcvdev7jHRXHRPP6dyig+V/hycvd4MF7nc8DNzXO/HvDwPGuiKuzrZjrRsyZN3rcMBn4fTboKQTO4rtjZ3r4bcT4fTbYepF2U5N82q2wV/PdEH83AfgwM9mO0UmKVwNvzzATd51xu2uX9NH89z/efULXnNncbnscSe7/3Tp5JaLUxMJ17ExHnHDosQj7oLRHwR/yN18gZYvrD5Z6IqJ1rzoBng8+ttufLdgwW6bZi2nkKSqO0VkHnAKUC4ipWnFR16XUsqA9DKGkcDGrkhf1vj8rs36PtPh+B+6KRzXvOACxHuPwcI/wxFXwQk37l2ZpaobtuOlm10l8HkPd93VS/8xcNE/4J273Ov/4XA49deumW+2ZaI3c2crGug+v79+wTWFPvvPLidnsm/F0xCtgcnnu8f5fdx3c8gZ7gS/eakrEfjg3zDvZzDvp1A4yG0Xi3gn/2QACDfU17VKGgJEo2Dhhx0fQ8EAOOFmmHHpHl/kZbKieTAQ9QJCAfA88HPgWFwLpmRF8wBV/b6IjAcepqGi+SVgXM5WNGdatA5e+DHMvxtKJ7mTwZ605olF4Nlvu567E74IZ/7BdaDKhooP4G9XuCuaCV+EI7/hrmKa/sDTf/SdXdyk6nIvZQvc7HzL/w6XvuKu5nJZfSU8dI5L96e/44Jt4QCX6ysY4O7n9+vYUCzxqKvnitS4WyDUufNK9HR/Oc3V+Xzzv23/Tmu2uiv4j1/1Jl9K/80H3UXfbv+DoKt/TOYa4pG0YJK2Lh5xld3DJsD0S13QaUNrOYVMBoVDgfsBP64/xFxVvUVEBgJzcXMyrAfO8XpIIyI3AF8DYsC3VLXVdoI9OigkrXzWDcMQj8Kpv4JJ57V/37od8OhX3Kx0x14LM6/Pfpl+PAZv/Abm/bxhDKDW+Lw/TH6/xsVdybqOfqPdyawl4SpXRJc+/lCtNzJqsBBGHwXn/jW7ndfaK1zlcgsfzWt5m7y+UNi/IVgEC7yGDjW7N3yIRxrvKz645KXcD5C5IFn0OPMHMPPabKemw7ISFLpCrwgKAJWfuH4R696AQ2e54NDWJPPbPoSHz3U/3jPucC1scsm2D10/j0ZXP2lZ6UZXQ1E3veU2r3VU/c6G44gP+o1qXDEeyHdzJJctgC3L3dUWuPk3RnrFdSOnw+CDu98gh6oQ3gW1210LsbodULvDLeu2e+vT7sfCXrPoZBPp5C39cZELkM99H4Yc4oqrsn3xkOte/QW8/BO4Zqnrn9PNZL1OweylviPcH/XVX7hpRMvehbP/1HKrp3VvwiNfdvcv/Lu7Gs41A/fb885ttdt3b0K7bY1rHhipdtvk93VNZw86zWtGOyU7zX47m4h7b/l9gbGde+za7fDc91w5+AEnd+6xexJVWDwHRh/dLQNCWyyn0N2sexOeuMRVkp5wExzx9catGZY8Ck9/w109nz+3e/Qq7iyq7nOJVEP/sbndaS4XxaPwe6+N/RVvdL9cVFfZMB/uO9H1vznsgmynZo90+iipJotGH+VG1TzgZHj+BldEVF3hTogvz4anLnN9Hi55sXcFBHBX0SVD3fu2gNBx/qBr6VaxEpY8nO3U5K7FD7sxqg7uma3A7J/THRUOgFkPuk4pH78Kd30K5pwHr/6fu3K54ElX0WhMRx18hmu2/MpPXaW0aSxaD8uehINPb1crn+7IgkJ3JeLaIl/6smuZ88G/XHHSGXe03hrHmNaIwEn/C1Wb3CxxprEPnnPNgyd/KdspyRgrNOzuhk2Ay/8DOzfYrF2mc4w6wlXQv/FbN2d5Vw/TkMsWz3HDwYw9NtspyRjLKfQEwQILCKZznXCT60D5n59nOyW5o3qL64A2aVZuj3C8lywoGGN2N2icyyUs/DNsXZPt1OSGpXNB4zCp5xYdgQUFY0xLZl7nOgK+dFO2U5IblsyB4VNg8IHZTklGWVAwxjSveAh86hpY8Q83HHNvtvk9N8lUcvC7HsyCgjGmZUde5UaRff6Hri9Mb7V4jhuHa8IXs52SjLOgYIxpWagIjvsBlM13OYbeKB6F9+bCgaf0jKFS2mBBwRjTuskXuOkmX7zJnSB7mzUvuQEZe3gFc5IFBWNM6/wBN3HL9g/d9JO9zZKHvTnVT8x2SrqEBQVjTNsOONmNCjrvVjftZG9RtwNWPeemxu0lIwVYUDDGtE0ETrrFTVD05u3ZTk3Xef9JN6dHLyk6AgsKxpj2GjHVtb558w43n3hvsGSOm3iodFK2U9JlLCgYY9rv+B+5CeZfmZ3tlGTe1tVuQqtJ5/WqmegyFhREZB8ReUVEVojIMhG5xls/QEReEJHV3rJ/2j7Xi8gaEVklIjb1kzG5ZsBYNzrv4oegbGG2U5NZS+a46V4PzbGpbDMskzmFGPAdVT0YOAK4SkQOAa4DXlLVccBL3mO8584DxgOnAH8QkZ476pQx3dUx34M+I+HRL8OujdlOTWYkEm4Ww/2Oh5Jh2U5Nl8pYUFDVTaq6yLtfBawARgBnAvd7m90PnOXdPxN4RFXDqvoxsAaYkan0GWP2UOEAOP8RCFe5yZ164mQ8a1+DXWW9qoI5qUvqFERkDHAY8A4wVFU3gQscwBBvsxHAhrTdyrx1TY91mYgsEJEFFRUVGU23MaYFQ8fDF++DTUvhqSvclXVPsmQO5PWBg07Ndkq6XMaDgogUA08A31LV1ho4N1eTs9tgK6p6t6pOU9Vpgwfb5B/GZM2Bp8BJP4EVT/esiudwNSx/Gsaf5eYq6WUyGhREJIgLCA+p6pPe6nIRKfWeLwW2eOvLgH3Sdh8J9NACS2N6iCOvgikXwmu/dGXw3V20Dp6/AaI1MKnnj4janEy2PhLgPmCFqv467amngYu8+xcBf09bf56I5InIWGAcMD9T6TPGdAIR+NyvXG/np78BG7rxX/aThfDHY9xQHodf4aYl7YUymVP4FPAV4HgRWezdPgfcCpwoIquBE73HqOoyYC6wHPgXcJWqxjOYPmNMZwiEYNZfoe9IeOR82Lk+2ynqmFgEXv4J3HuiqzS/4En47M97Vd+EdKLdeIz0adOm6YIFC7KdDGMMQMUHcO8J0HcEXPw85JVkO0VtK1/mKso3L3XFRaf8DAr6ZTtVGSciC1V1WnPPWY9mY0znGHwAnPsXqFgFT1wCiQxm9MsWwpwvwZ9PdcNu7Fjbsf0TcXj9N3D3TKjaBLMegs/f2SsCQlssp2CM6Vzz74F/fheO/Aac3MmtkjYthVd+Ch88BwUDoKQUtixzzw2dCAefBged5prMtlT8s+1Dlzsomw8HnwGn/QaKBnVuOnNcazmFQFcnxhjTw824FLZ+AG/d4Sa5n3Lh3h+zfDnM+6mb/S2/Lxz/Q1cZnFcC2z+Glc/Aimfc0N7zfgb9x7jgcNBpsM8M8PldX4p374UXfuzqQb5wL0w8u9fWHbTEcgrGmM4Xj8HD58DHr8JX/gZjP71nx9m62p3k338SQsVw5NfhiK+3XMxTVQ6r/umCxEf/gUQUiobAgZ+FHR+79Ox/ApzxO+gzfE/fXbfXWk7BgoIxJjPqdsJ9J7qpLD/9Xeg3CvrtA31HuaEyWrtC3/4R/Of/YOmjEMiHwy+Ho77ZsTmS6yth9Qsud7HmRbfu5Nkw5aJenzuwoGCMyY7tH8EDZ+7eTDVYCH338YKEt+w3GoqHwNK5sPhh8Adh+iXwqW9B8V6OXhCtB01AqHDvjtNDWJ2CMSY7BuwL1yx101pWboCdG7zlener3ACfLIK67Q37+EMuGHz6fzpvhNJgfuccpxewoGCMySwRV+xTOKDlGczC1VBZ5kYmHXyw6+tgssKCgjEm+/KKYchB7mayyjqvGWOMSbGgYIwxJsWCgjHGmBQLCsYYY1IsKBhjjEmxoGCMMSbFgoIxxpgUCwrGGGNSLCgYY4xJyVhQEJE/icgWEXk/bd0AEXlBRFZ7y/5pz10vImtEZJWInJypdBljjGlZJnMKfwFOabLuOuAlVR0HvOQ9RkQOAc4Dxnv7/EFE/BlMmzHGmGZkLCio6qvA9iarzwTu9+7fD5yVtv4RVQ2r6sfAGmBGptJmjDGmeV1dpzBUVTcBeMsh3voRwIa07cq8dbsRkctEZIGILKioqMhoYo0xprfJlYrm5qZBanb2H1W9W1Wnqeq0wYP3cuINY4wxjXR1UCgXkVIAb7nFW18G7JO23UhgYxenzRhjer2uDgpPAxd59y8C/p62/jwRyRORscA4YH4Xp80YY3q9jE2yIyJzgJnAIBEpA24EbgXmisjFwHrgHABVXSYic4HlQAy4SlXjmUqbMcaY5mUsKKjql1p46jMtbD8bmJ2p9BhjjGlbrlQ0G2OMyQEWFIwxxqRYUDDGGJNiQcEYY0yKBQVjjDEpFhSMMcakWFAwxhiTYkHBGGNMigUFY4wxKRYUjDHGpFhQMMYYk2JBwRhjTIoFBWOMMSkZGyXVmN5oY/VGHvvgMZ796Fn65fVj6tCpTBs6jSlDp9A/v3+2k2dMmywoGNNOibo6xO9HQqHG6zXBmxvf5NGVj/LqJ68CcPSIo6mP1fPYB4/x4IoHAdi/3/6pIDF16FQGF9p0srlIVdFoFF+T73lvRcvLqV++HF9BAYFBgwgMHoyvTx9EmpuNOHssKJi9kqitpW7xYmoXLKD2v/9FxEdgsPvBN3fzFRVlO8ntotEo9R98QN2SJdQvWULd4iVE1q0DQIJBfIWFUFhAVSBGBbuo9Ec5Kj/E5wePYfTQgyguHwQK8fBotlVtYVvVZnZUVbCr5lG2xB7mhTgUSz79fEWUSAFFoWJC+YXuRBQMutcIhZBgcLfHvr59CY0ZQ2j0aEKjRuHLz8/yp9W9JWprqXv/feqWLEnd4hVbCY0eTf74Q8g/xN3yDj6YQP/25fYS9fXUL19O3eKGY8Y2b95tO8nLSwWI1G1Iw338fjQadbdIpPn73mPxB/AVFuIrKsJX5C0Lm1kWFraadlHVPfogc8G0adN0wYIFWXt9jceJbtqM1te1ex/Jz8ffty++4uKcu0Joj/iuXdQuWkTdggXUvruAumXLIBYDn4/8gw6CYIBYRQXxiq1oNLrb/r7CQgKDB+MfPIjg0GEER4wgOHw4wRHD3XL4cHwFBW2mQ6NRImVlRNauJbJunbt59xOVuwgMG5Z2zBGpYwdHjCAweBDia1ydFt2ypVEAqHv/fbS+HgD/oEEUTJpE/vhDAKF82zrWbHyPLVvXEYokGEIJpb4B9IkF0No6EjU1JGprwedDvBN5+i3si1NDhCqtZWeihjBuksE+UsCAQB/6+ArJ1wBEoo3/+MmTQTjckHARAqXDXIAYPbohWIweQ2jkiFSuRuNxEnVe2mpqSdTWeulMPq5B68NIMNBsmiUYbLw+Lw//gAH4+/XrVr9jVSXy8VrvRL2YuiVLCX/wAcTddxAaPZqCyZMIDB9OZM0a6pctJ7qxYbr4wPDSVJBI3gKDBxMtK2sIAIsXU79ypftfAMGRIymYNMn9hiaMR6MxYhUVLd4Su3Z1/I2JQAfO5YesWrlQVac1e6juHBQOGzdO//P733s/1FDzP95QwzpfYaF7rgM/YlUlVl5OZG3jE09k7VqiGzY0e+Jrl0AAf9+++Pv1a1g2ue8rLmr+D9poXfJ+wP0wOpmGw9QtXepyAu8uILxypfvxBYMUTJxI4fTpFE6bRsFhh+EvbsgFqCrxnTub/dHHt24lumULsc3lRDdvTv15kvz9+zcEC++Gz+ed/NcSWbuO6CefpP7IgHf17E6M/r79iG3eRPSTjUQ3biS+c2ej40swSKC01AWg4iLqly8ntnGTezIYJP+Qg1N/4oJJk/GVDmV99XoWli/ksVWPsWL7CgoDhZy+3+nMOnAW4/qP2+PPN6EJVu9YzRsb3+DVsldZvGUxcY3TN68vnxr+KY4ZeQxHjziavnl9U/vEq6u93+PaJkFxHYnKyrQP0o+/b18SdXVoXfsvXDokGGxypdvkqneQu/L1FRQ0/Hb9/k5PhiYSJKqqiFdWEt+5090qK4nvaLgf2bCeuiVLU5+Rr7iYgkMPpWCyd8I+9NBmcwKxHTsIr1xJ/fLl1C9bTv3y5S7X6J07JT8/dQEhhYUUTJzofjuTJ1Fw6KEEBg3q0HtJhMPEKrYSq9gCqm38/71bIJAW+GtTFyYu6NekXQS45ZBvfKP7BAUROQW4DfAD96rqrS1tOyG/QB8bM6ZjL+D3t5ytSi7z84huLnd/uPXrG/2hJBTyrsjcCSg4ejT+4uL2vbYqifpww482+cNt8jj5A8slkp9PweTJFE6bRuH06RRMOrRDxRbxhPLx1hqWbazk/U8qWbZxFwlVRvbJZ19fLaOiuxhWu4N+Vdso2FFBfNMmohvdST39D5f83N1nP4bw0OHsGFBKhS+fLVVhtlTVUxuO068wSN+CIP0LQ/T3RelTuY2iHRWEtm0hkQwYn3xCfNcu8g8+KBUEfAfuz9q6T1i+bTkrtq9gxbYVrNqxirqY+w3s13d/Tht7NkcNPRFN5FEbiVMdjlEbjlMTjlETiVETjlEbiSMCIb+fYEAI+X0E/T5CAbcM+t265ONoPEFNJM7W2h2s3LmA1dXvsqFuEWGtAoQS9qcoPp5AeAJ5OoL+BSH6F4boVxiiX2GQ/oVB+hWGGBCro++2TRRVbCS0+RN8uyrRggK0oBDNLyCRX0A8L59YfgHxvAJieQVEQ3lEQ/loKI8BecKAPD/FvgTEYi0WVSTqw8S3b3OBfkuToN8kCO/G52vm4q3hPv52NopU0Lo69x+qrIREouWX7NuX4NChFEw61AWASZOI7zOayvo4O2oj7KyNsrM2yo7aCJV1UcLROMP6FjCifwEj+hUwsn8B+cGGYBavriG8aiX1y5YTWb+evP33p+CwyeTttx8SaLlUXlWJJ5RoXInEE0RiCaLxhlskpm4ZTxCNJfD5xP1u/D6CgYb76b+j5Lq4qvcb9H6L3u+wOhyjNhKjOhyn1nv+Oycd2D2Cgoj4gQ+AE4Ey4F3gS6q6vLntB40aoGd993j8ccUfTxCIK/6YumU8gT/ecD8YUwqiPgqjPgqjUBCFgoiSF0mQF4kTisQIhqP46yP4w1ESA/ujI0fgG7UPwVGjyBu7L8Vj96Nkn33I66QKKFUlpjGi8SjRRJRILMqucD2VlTuprthKorqGfIQ8FfJVyAdCCSGkEEokIBZv+MPGYsQSMaqjVVRHaqiOVlMTraY6Uk11tMatj9ZQHammLlbLxRMuZnDhkLa/E7+PvAMPomDC+N0qWJu68sUrKQgUUBLsQzxWQE1dHjuqgmzaIWzYKtTX56PxQoJSxEHD+hH0+yjbUcuW6mqQMPjCiC+C+MP0L1L6FyfoW6gM1Rp8GmFTSKiKhKkJh6mNhqmLRUhoDJE4SAwk7t1XNJGHJkLQaOnuF/gLKQ4V0idUTGGenxrdQA3rqPetJ+Lb6I4FkMiDyAg0PJxY3XCitcOJh4cCbefIAj63TSyx5/+vvAAUlmzCX7ySRP4KooH17gn1IQRQ9aMJP6p+0IC3dDfVgHdf0j4X9znt/nnFvOcUEkE0kYdoHgHJJ+QroMBfSGGwkOJQEf3yiulXUEz/gmISCUmd2CKxROp+LBwltLOG/J01FOyqpWBXDcF4hKBGCSYi+ONR/IkYvngUfyyGxGP44jEkFsMXi+NXwSd+/OLH7/MT8JZ+8RPw+fFLIPVYQ0Hqi0LUFgSoyvdTlR+gMgQ784QdIdgRhB3BBGGNEY0niMVCRGMBwpEg8XgI4nmohhr/RuJ5CEFUG3/PfQsCDO2Tz9A+eQwpyWdISR5D+uQzsChEXSTOrnCMXXVRqupjVNVH2ZVc1iUfR6kKx0ho8rNPfu7xZr4T77EKeN9lw3fa8J033PeDJBBfpOF/5Ev+p8KN1uMLs+rqx1oMCrlW0TwDWKOqHwGIyCPAmUCzQaFG4rwZqm3HYX2Agi/S+MOSlq8soMK7LXYPP/Fu4H4s6kcIeEvvykbc6UK8O8mfVHqpjpIgoTGUGIr3R9xDyR+GeD8IfPXuG037Vv3iZ2D+QAYVDmJQwSgGFwxmVMEg+h5wNgOKhu3xazdVE46wfHMFVZFKIloN/tqGzzcP/CMgvYq5IliMIISLainW+G7HCwObgc0JWJVcGXELCQXw5wUpkgABX5CQP0jIH6IgECI/GMInPqoju6iJ1lAbraU+XofS+Luu9m5Jfi2iiNEMkhPp6x9Df/9Y+gVLCQUCqSv8kF8ozAtQFPJTlBegMBSgKM/dL0reDwUoygsQCrjfhLsqTF4FJojGG64Ek1eJkViCoN+XOlZhyL1GoMkVc0VtBa9/8jobqjYQTbgLiWg8Sn0sTE00TG00Ql00TF00QjgWIRyPEE8kCPjyCfgCBCRIwBck6A8S8gUJ+oIE/SFC/iB5vhCIUFlfQ2V9NVWRGmpjtdTFatkV28r2aD2JurQTTFvygKHeLUmD7sQbD5FIhNBEn9SJmEQoFbRFEt5/NdzMCS6C+Gp2+/8m/5ONT54BSPjxhwMEfAH8PoG8CIH8MD7qiWo90P7/XwzvNBAHdnq39ijwbg2LLpfnz6cgUEhxsIiiYGHDf6oZuRYURgAb0h6XAYenbyAilwGXAewzajTz/9/T7TqwqhLz/pBh789YG41QFa5mV8RdQddEa6mO1FATrSEcjxKORYjEo4QTESKxCNFEjGgi0ugPGU1ESKiSUNwyod4S4qqN1sUVfPjcH9EXJC/gTmb5gYZbQTBEYTCPwmA+fh9E4zHCcfcHj8QbXjd1PxEhloiSUOHUQ8YxtGgIgwoGMbhwMIMKBtE/rz9+X+eX4TZVGAoim67i4P4FjB/el0NKS9hvaJC+RRGqYruorK9kZ3gnlRFvGXbluoUBdxVaGCikKFhEYbCQooBbJtfn+wsJ+UPuMxF/hys2VZVwPOyCRKyW2mgttbFaaqI1JDTB/v32p7SoNCMVpn6f4Pf5GxU97KnBhYP5/LjPd0Kq9kx9NE5FVZjNu2rZvKuKUNBHYdBPUZ4/FcgK8wLkBXy7fZZ5/jwCvobTTTSeoNYr5kgv2qgOx1L/Txc8taGIJdawLhpPUBcNE/DDgMICBhblpxWlNSwLQy3/XlSV+ng9NdEa6qJ11MTcRURNtIb6eOtFuKpKVTjG9uoIu+qi5AX9FOf5KQoFKcrzEwy0XAQWTAZkn/v/B31BAr6AF6xDXrB261Q17VzTcIvEG5+HIokIAQk0+t8UBYsoDBRSECjY7RwgreR2c6346BzgZFW9xHv8FWCGql7d3PbZbn1kjDHdkYi0WHyUa8NclAH7pD0eCWxsYVtjjDGdLNeCwrvAOBEZKyIh4DygfeVDxhhj9lpO1SmoakxEvgH8G9ck9U+quizLyTLGmF4jp4ICgKr+E/hnttNhjDG9Ua4VHxljjMkiCwrGGGNSLCgYY4xJsaBgjDEmJac6r3WUiFRBqz220/UFKtvcas+278nb5ko6cmHbXElHLmybK+nobtvmSjoOVNWSZp9R1W57AxZ0YNu7O3jsdm/fk7fNlXTkwra5ko5c2DZX0tHdts2VdLR27uxNxUf/yOD2PXnbXElHLmybK+nIhW1zJR3dbdtcSkezunvx0QJtYfwOY4wxzWvt3Nndcwp3ZzsBxhjTDbV47uzWOQVjjDGdq7vnFEw7iMgpIrJKRNaIyHXeuski8raILBaRBSIyI9vpzCUi8icR2SIi76etu0lEPvE+s8Ui8rlspjEXicg+IvKKiKwQkWUico23/hcislJElorIUyLSL8tJzSmtfG6TROQtEXlPRP4hIn0ynhbLKfRsLU1xCvwW+I2qPued3L6vqjOzlc5cIyLH4CZne0BVJ3jrbgKqVfWX2UxbLhORUqBUVReJSAmwEDgLNwz+y+oGvfw5gKpem72U5pZWPrf7ge+q6n9E5GvAWFX9USbTYjmFni81xamqRoDkFKcKJK86+mLzVjSiqq8C27Odju5GVTep6iLvfhWwAhihqs+rqjf5NW/jgoTxtPS5AQcCr3qbvQB8MdNpsaDQ8zU3xekI4FvAL0RkA/BL4PquT1q39A2vCORPItI/24nJZSIyBjgMeKfJU18DnuvyBHUTTT6394EzvKfOofEkZBlhQaHna24yVgWuBL6tqvsA3wbu69JUdU93AvsBk4FNwK+ympocJiLFwBPAt1R1V9r6G4AY8FC20pbLmvncvgZcJSILgRIgkuk0WFDo+Vqa4vQi4Elv3WO4YibTClUtV9W4qiaAe7DPrFkiEsSd2B5S1SfT1l8EnAZ8Wa0yczfNfW6qulJVT1LVqcAc4MNMp8OCQs/X0hSnG4FjvW2OB1ZnKX3dhlcZmPR5XNbepBERweU6V6jqr9PWnwJcC5yhqrXZSl+uauVzG+ItfcAPgbsynhYL2D2f17rotzRMcTpbRI4GbsPNvlcPfF1VF2YvlblFROYAM4FBQDlwo/d4Mq74bS1wuapuykoCc5T3u3oNeA9IeKt/ANwO5AHbvHVvq+oVXZ/C3NTK5zYOuMp7/CRwfaZzWRYUjDHGpFjxkTHGmBQLCsYYY1IsKBhjjEmxoGCMMSbFgoIxxpgUCwrGGGNSLCgYY4xJsaBgjDEmxYKCMcaYFAsKxhhjUiwoGGOMSbGgYIwxJsWCgjHGmBQLCsYYY1IsKBhjjEmxoGCMMSalWwQFEanOdhqMMaY36BZBwRhjTNfoNkFBRIpF5CURWSQi74nImd76MSKyQkTuEZFlIvK8iBRkO73GGNMddYs5mr3io35AoaruEpFBwNu4Sa1HA2uAaaq6WETmAk+r6oNZS7AxxnRTgWwnoAME+KmIHAMkgBHAUO+5j1V1sXd/ITCmy1NnjDE9QHcKCl8GBgNTVTUqImuBfO+5cNp2ccCKj4wxZg90mzoFoC+wxQsIx+GKjYwxxnSinM8piEgAlxN4CPiHiCwAFgMrs5kuY4zpiXK+ollEJgH3qOqMbKfFGGN6upwuPhKRK4A5wA+znRZjjOkNcj6nYIwxpuvkVE5BRPYRkVe8zmjLROQab/0AEXlBRFZ7y/7e+oHe9tUickeTY33J6+S2VET+5fVtMMYY04qcyimISClQqqqLRKQE1+fgLOCrwHZVvVVErgP6q+q1IlIEHAZMACao6je84wSAjcAhqrpVRP4PqFXVm7r8TRljTDeSUzkFVd2kqou8+1XAClwntTOB+73N7scFClS1RlVfB+qbHEq8W5GICNAHFySMMca0ImebpIrIGFwu4B1gqKpuAhc4RGRIa/t6fRmuBN4DaoDVwFWZTbExxnR/OZVTSBKRYuAJ4FuqumsP9g8CV+KCynBgKXB9pybSGGN6oJwLCt4J/QngIVV90ltd7tU3JOsdtrRxmMkAqvqhukqTucBRmUmxMcb0HDkVFLzy//uAFar667SnngYu8u5fBPy9jUN9AhwiIoO9xyfi6ieMMca0ItdaHx0NvIarC0h4q3+Aq1eYC4wC1gPnqOp2b5+1uIrkELATOElVl3sd364BosA64Kuquq3L3owxxnRDORUUjDHGZFdOFR8ZY4zJLgsKxhhjUiwoGGOMSbGgYIwxJsWCgjHGmBQLCsZ0gIjERWSxN4rvEhH5HxFp9X8kImNE5PyuSqMxe8OCgjEdU6eqk1V1PK5T5OeAG9vYZwxgQcF0C9ZPwZgOEJFqVS1Oe7wv8C4wCBgN/BUo8p7+hqq+KSJvAwcDH+NG+b0duBWYCeQBv1fVP3bZmzCmFRYUjOmApkHBW7cDOAioAhKqWi8i44A5qjpNRGYC31XV07ztLwOGqOpPRCQPeAPXS//jrnwvxjQnZ4fONqYbEW8ZBO4QkclAHDighe1PAg4VkbO9x32BcbichDFZZUHBmL3gFR/FcSP33giUA5Nw9XVNJ39K7QZcrar/7pJEGtMBVtFszB7yRuG9C7jDG6K9L7BJVRPAVwC/t2kVUJK267+BK71h4hGRA7ypZY3JOsspGNMxBSKyGFdUFMNVLCeHef8D8ISInAO8gpv1D9wkTzERWQL8BbgN1yJpkTdcfAXeFLPGZJtVNBtjjEmx4iNjjDEpFhSMMcakWFAwxhiTYkHBGGNMigUFY4wxKRYUjDHGpFhQMMYYk2JBwRhjTMr/B6WIhmbkdB6uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "language.plot()\n",
    "plt.title('Daily language preferences')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Users')\n",
    "plt.legend(loc = 'upper right',\n",
    "          labels = language.columns.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "acac23b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "language_preferred  age_group  \n",
       "Arabic              0-18 years     19\n",
       "                    19-24 years    26\n",
       "                    24-30 years    19\n",
       "                    30-36 years    19\n",
       "                    36-45 years    19\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customer languages preferance \n",
    "language_age = mkt.groupby(['language_preferred', 'age_group'])\\\n",
    "['user_id'].count()\n",
    "language_age.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "fc87aac8",
   "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>age_group</th>\n",
       "      <th>0-18 years</th>\n",
       "      <th>19-24 years</th>\n",
       "      <th>24-30 years</th>\n",
       "      <th>30-36 years</th>\n",
       "      <th>36-45 years</th>\n",
       "      <th>45-55 years</th>\n",
       "      <th>55+ years</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arabic</th>\n",
       "      <td>19</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>English</th>\n",
       "      <td>1421</td>\n",
       "      <td>1560</td>\n",
       "      <td>1442</td>\n",
       "      <td>1251</td>\n",
       "      <td>1260</td>\n",
       "      <td>1240</td>\n",
       "      <td>1101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>German</th>\n",
       "      <td>31</td>\n",
       "      <td>29</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spanish</th>\n",
       "      <td>68</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>69</td>\n",
       "      <td>55</td>\n",
       "      <td>67</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "age_group           0-18 years  19-24 years  24-30 years  30-36 years  \\\n",
       "language_preferred                                                      \n",
       "Arabic                      19           26           19           19   \n",
       "English                   1421         1560         1442         1251   \n",
       "German                      31           29           35           16   \n",
       "Spanish                     68           67           72           69   \n",
       "\n",
       "age_group           36-45 years  45-55 years  55+ years  \n",
       "language_preferred                                       \n",
       "Arabic                       19           21         22  \n",
       "English                    1260         1240       1101  \n",
       "German                       19           25         12  \n",
       "Spanish                      55           67         52  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "language_age = pd.DataFrame(language_age.unstack(level = 1))\n",
    "language_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e0fd3cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "language_age.plot(kind = 'bar')\n",
    "plt.title('Language preferences by age group')\n",
    "plt.xlabel('Language')\n",
    "plt.ylabel('Users')\n",
    "plt.legend(loc = 'upper right', labels = language_age.columns.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9918529a",
   "metadata": {},
   "source": [
    "### 5. Convertion attributes\n",
    "- define functions of conversion rate and retention rate\n",
    "- inconsistence checking: language_displayed <> language_perferred\n",
    "- Expected conversion rate - actual conversion rate \n",
    "  - calculate conversion_rate index based on English "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c50d19b7",
   "metadata": {},
   "source": [
    "**Build retention_rate function**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "752c948c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_subscribed\n",
      "2018-01-01    77.78\n",
      "2018-01-02    70.27\n",
      "2018-01-03    72.97\n",
      "2018-01-04    50.00\n",
      "2018-01-05    57.50\n",
      "2018-01-06    75.68\n",
      "2018-01-07    52.63\n",
      "2018-01-08    63.89\n",
      "2018-01-09    67.50\n",
      "2018-01-10    68.29\n",
      "2018-01-11    61.54\n",
      "2018-01-12    39.13\n",
      "2018-01-13    50.00\n",
      "2018-01-14    61.54\n",
      "2018-01-15    71.43\n",
      "2018-01-16    70.19\n",
      "2018-01-17    61.63\n",
      "2018-01-18    73.33\n",
      "2018-01-19    68.42\n",
      "2018-01-20    80.95\n",
      "2018-01-21    76.19\n",
      "2018-01-22    77.27\n",
      "2018-01-23    71.43\n",
      "2018-01-24    65.22\n",
      "2018-01-25    56.52\n",
      "2018-01-26    80.00\n",
      "2018-01-27    57.14\n",
      "2018-01-28    66.67\n",
      "2018-01-29    75.00\n",
      "2018-01-30    71.43\n",
      "2018-01-31    66.67\n",
      "Name: user_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Daily retention rate\n",
    "total = mkt.groupby('date_subscribed')['user_id'].nunique()\n",
    "subscribers = mkt[mkt['is_retained'] == True].groupby('date_subscribed')['user_id'].nunique()\n",
    "\n",
    "date_r_rate = subscribers/total\n",
    "print(round(date_r_rate * 100,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "42bff320",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retention_rate(df, columns):\n",
    "    retained = df[df['is_retained'] == True]\\\n",
    "    .groupby(columns)['user_id'].nunique()\n",
    "    \n",
    "    converted = df[df['converted'] == True]\\\n",
    "    .groupby(columns)['user_id'].nunique()\n",
    "    \n",
    "    retention_rate = retained / converted \n",
    "    \n",
    "    return retention_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9d61971f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date_subscribed  subscribing_channel\n",
       "2018-01-01       Email                  1.000000\n",
       "                 Facebook               0.875000\n",
       "                 House Ads              0.687500\n",
       "                 Instagram              0.750000\n",
       "                 Push                   1.000000\n",
       "                                          ...   \n",
       "2018-01-31       Email                  1.000000\n",
       "                 Facebook               0.833333\n",
       "                 House Ads              0.500000\n",
       "                 Instagram              0.666667\n",
       "                 Push                   0.500000\n",
       "Name: user_id, Length: 155, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_retention = retention_rate(mkt, ['date_subscribed',\n",
    "                                       'subscribing_channel'])\n",
    "daily_retention"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f85c4db6",
   "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>subscribing_channel</th>\n",
       "      <th>Email</th>\n",
       "      <th>Facebook</th>\n",
       "      <th>House Ads</th>\n",
       "      <th>Instagram</th>\n",
       "      <th>Push</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_subscribed</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subscribing_channel  Email  Facebook  House Ads  Instagram      Push\n",
       "date_subscribed                                                     \n",
       "2018-01-01            1.00  0.875000   0.687500   0.750000  1.000000\n",
       "2018-01-02            0.75  1.000000   0.588235   0.625000  1.000000\n",
       "2018-01-03             NaN  0.800000   0.647059   0.909091  0.666667\n",
       "2018-01-04            1.00  0.666667   0.466667   0.500000       NaN\n",
       "2018-01-05            1.00  0.571429   0.500000   0.636364  1.000000"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_retention = pd.DataFrame(daily_retention.unstack(level = 1))\n",
    "daily_retention.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "496ef844",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "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"
    }
   ],
   "source": [
    "plt.figure(figsize = (20,10))\n",
    "daily_retention.fillna(0).plot()\n",
    "plt.xticks(rotation = 30)\n",
    "plt.xlabel('Subscribed date')\n",
    "plt.ylabel('Retention rate')\n",
    "plt.title('Cohort chart')\n",
    "plt.legend(loc = 'upper left', labels = daily_retention. columns.values )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "31097fea",
   "metadata": {},
   "outputs": [
    {
     "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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icg4wD6gceSARuUlENojIhsbGxgmJenK4uVgEBmaG1SQzDGNsahvDNzBTKR/GSwMzWnjWyDHhN4ESEdkE3Aq8Agyc9CLVu1V1laquqqgYvaxLuPxtZwOLp+dTWTJ6KOFoFOVmMLs4xyLJDMMYldpmPxlpwuzinJD7TivIpnpa/pTIh/HSwNQBc4KeVwKHg3dQ1XZVfb+qno7jg6kAar0S1NHTz4u1LVy6NPJZuFTuDePvHeBn62unTGy+YcSa2kY/c0tzSU8L75a6ZmEZL+1rSfkKIV4amJeAahGZLyKZwLXAw8E7iEixuw3gg8DTqurZXXzda00MjNFcLBTLZhayt8lPT/+gB8riy+9eruNLj2xn2+HUNKCG4TX7mv1hTY8FWLuwjK6+QbbUtXonKgHwzMCo6gBwC/AYsAO4X1W3icjNInKzu9syYJuI7MSJNrvNKz3gTI8V5WRw5tziiF+7bGYhg0PKa/WpVzJm3WtOp7227v44KzGM5GNoSKltiszArJ5fhggpP02WHnqX6FHVR4FHR6y7K+jxc0C1lxoCDA0pT+5q5MLFFWEPY4NZOqMAcErGnFZZFGt5cWNgcGi4CVJHjxkYw4iUI+099A4MhRWiHKAkL5PlMwt5dk8zt75uUm6BcWHKZPK/eriNps5eLl0aXZDAvLI8cjLSUi5U+dXD7XT0OHEV7WZgDCNiAlWUIxnBAKxZUMbGA8dScto9wJQxMC3+PhaU53HR4sj9LwBpPmHJjNRz9K+vaRp+HDA0hmGEz94oDczaRWX0DQzx8oFjXshKCKaMgbl4yTT+9s8XU5o3dnOxUCybWciOI+0pVTJmfU0TS2cUIALt5oMxjIjZ1+QnJyON6QXZEb3u7KpS0nyS0n6YKWNgYsHymQW09wxwuK0n3lJiQnffIBv2HeP8ReUUZKXTbiMYw4iY2iY/88py8fnGr8w+koLsDE6bXZTSCZdmYCIgUDJmR4qE827Y30Lf4BDnLSqnMCfDfDCGEQX7mvxjNhkLxdqFZWw+2Iq/NzV/3JmBiYClroHZmSKl+9fXNJPuE86ZX0pBdob5YAwjQgYGhzjQ0hWyTP9YrF1YzsCQcv+Ggyk19R7ADEwE5GelM7c0N2Vqkq2vaeLMuSXkZaVTmJ1uPhjDiJC6Y90MDGnEDv4AZ88v4ax5JXz5ke186H83cjRFpt8DmIGJkFQpGdPa1cerh9tYu8gpvFeQnWE+GMOIkFo3gizaKbKs9DTu//AaPnvlMtbVNHLZ957i3hcPpMxoxgxMhCybWUhts5+uvuS+GT+3pxlVOH+R03StMCfdEi0NI0ICBibaKTJwUiA+dOEC/nzbhZwyu5BP/34r1//kBfa7LQCSGTMwEbJsZiGqsOtock+Trd/TRF5mGivnFANQmJ1hU2SGESG1TX4Ks9MnlP4QoKo8j1998Fy+fvVpvHqojTd+/2l++szepG5MZgYmQpbPTI3eMOtrmlm9oIwMt2xOYXY6nb0DVlHZMCIgUINMJLIQ5bHw+YTrV8/lL5+8kPMWlvNvf9zB//vRs0n7g9bTWmSpSGVJDgVZ6UnthznU2k1tk593nztveF1BdgZDCv6+AQqyM+KozkgmDjR30TMQutRJRpqPqrLcmN2Iw2VgcIj+QQ27PXqk1Db5ObuqJObHnVmUw0/fu4pHthzhSw9v483/+Qwfu2QRH714EZnpyTMuMAMTISLC0iR39AfKw5znOvjB8cGAUy7GDIwRDutrmnjXT18Ie/+73n0Wl586w0NFJ3KwpYv33vMi0wuzufemc2N+/J7+QQ63dVNVflIT3pggIrx15SzOX1TOlx/ZxvefeI3Gjl6+dvVpnpzPC8zARMHSGYU88MohhoY04uzdRGB9TRPl+ZksmV4wvK7QNSrtPf3MInRXPsP4w5Yj5GWm8a23r0BGbWDrMKTKx+97hR1H2ifNwOw40s5773mRho5e9rd00d03GPNRzIGWLlQjr0EWKaV5mfzg2jN468pZLA66ZpMBMzBRsGxmIf/3/H7qjnUztyz81suJgKqyvqaZtQvLT5iuCIxa2ruTOzrOmByGhpQndtRz0ZIK3rxiVsj9v/mnnZMWFfVibQs3/vwl8jLTuf2KpXzzTzvZUtfK6gVloV8cAXsboytyGS2vWxZ5J954kzyTeQnEspnOr4hkLN2/u76Tps7e4fDkAMenyCySzAjN5rpWGjt6uWx5eDe9qvJc9jV3eawK/rLtKO/+7xeoKMjidx9dyzWrnK7trxxsjfm59rkGM5I+MFMNMzBRsMStPpyMfph1Af9L9YkGpiBoiswwQvHEjnrSfMIlS8JrfzGvLM/zEcx9Lx7g5l9sZNnMQn5781pmF+dQmpfJ/PI8XvGgJH5to5/y/Mzh6WXjZMzAREFuZjrzy/KS0sA8W9NEVVkus4tP9LMUZh938htGKB7fXs85VaUU54aX/1FVlsuxrn5P2nKrKnc+WcPtv9/KBdUV3Puh1SfkpZwxp5iXD7TGPDu+tjmyNslTEU8NjIhcLiK7RKRGRG4fZXuRiDwiIptFZJuIvN9LPbFk2cxCdiRZ0cv+wSGe39vMeSOmxyDYB2MjGGN89jf72V3fGfb0GDgjGHDCmmPJ0JDy5Ue2853HdvG202fx0/euIjfzRNfyGXOLaezo5VBrd0zPXdvkn1AG/1TAMwMjImnAncAVwHLgOhFZPmK3jwHbVXUlcDFwh4hMPCV2Elg2s4CDLd1J5bPYUteKv29wVAOTme4jO8NnIxgjJI9vrweI0MA4wTD7YjhN1jcwxG2/3sTPnt3HjefP53vXnD6cOBzMGXOdPJVXDrTG7NydvQM0dvQyP8oaZFMFL0cw5wA1qrpXVfuA+4CrRuyjQIE44Uz5QAuQFHe4QG+YZMqwXfdaMyJOL/DRcApeJo/BNOLDX7bXs3RGAXNKw4+gnOvuGys/TGfvADf+/CUe2XyY269YyufetGzMlIGlMwrIzvDFtDXxvkCRS5siGxcvDcxs4GDQ8zp3XTA/BJYBh4GtwG2qOuShppgRMDDbkqj52Po9TZw6q4iSMeomFWZbV0tjfFr8fWzY1xLR6AUcv+X0wqyYRZJ94r5XeHZPM99++wpuvmjhuBUC0tN8rKgsjukIZm+TRZCFg5cGZrRvfKSX7Y3AJmAWcDrwQxEpPOlAIjeJyAYR2dDY2BhrnVExsyibWUXZSdNP2987wCsHjg2X5x+NwhwreGmMz992NjCkkU2PBYhVJNnQkLKupol3r547HIYcijPnlrD9cDu9YZS1CYd9MaiiPBXw0sDUAcHffiXOSCWY9wO/V4caoBZYOvJAqnq3qq5S1VUVFRWeCY4EEeHCxRWs39PEwGDiD7pe3NdC/6CelP8SjPWEMULx+PajzCjM5rTZRRG/tqosNrkwh1q76ekfYsmMk36LjskZc4vpGxzi1UOxmXGobfIzqyib7AxvapylCl4amJeAahGZ7zrurwUeHrHPAeB1ACIyHVgC7PVQU0y5oLqCjp4BNte1xltKSJ6taSIzzceqeaVj7lOYbT1hjLHp6R/k6d1NvH75tKiKVs4ry6Oxo3fCvZRqGjsBWDQtP+zXnOG2pYhVPkxtk98c/GHgmYFR1QHgFuAxYAdwv6puE5GbReRmd7evAmtFZCvwV+BTqtrklaZYc96iMnwCT+9OfMnrapo5a17JuPWYCrIzrFSMMSbP7mmiu3+Q10dZsiQQSbZ/gqOYmvrIDcy0wmxmF+fELKPfQpTDw9NaZKr6KPDoiHV3BT0+DLzBSw1eUpybyYrKYp5+rZF/vGxxvOWMSXNnLzuOtPMvb1wy7n7W1dIYj8e315Oflc6ahdHV9ArckPc3+4eDZKKhpqGT0rzMiJt8nTmvhJf3T3wEc8zfR1t3vyVZhoFl8k+QC6vL2XywlbauxL0xP+sGIqwNcWMozM6gd2AoZo5QI3Vwils2cNHiCrLSo/M7zB3OhZngCKaxk0UV4Y9eApwxp5hDrd3Ut/dM6PyBCDIzMKExAzNBLlxcwZA60weJyrN7mijITg/pmLVyMcZYbIqwuOVoFGZnUJaXOaFIMlWlpqGThRFMjwU4Y24xMHE/zD4zMGFjBmaCrJxTTEFWOk+/lhjh06OxrqaJcxeUkT5KlnMwVi7GGIvHt0dW3HIs5pXlsq8p+hFMU6czPRWJ/yXAKbOKyEz3TTgfprbJT5pPIko0naqYgZkgGWk+1i4q4+ndTTEvphcLDjR3cbCle9zw5ADBXS0NI5jHt9ezen4pRbkTqxxcVZbHgZboDUxNg+Pgr47CwGSm+zh1VuGEM/prm/3MKckZtSyNcSL2CcWAC6orONTaPTw3m0is3xNojxyGgbGS/cYo1Db5qWmIrLjlWMwty+VwWzc9/dH5+aIJUQ7mjLklbKlro38CuWu1jVZFOVzCMjAiMk9EXu8+zhGR5Orb6TEXVjvJn8/sTrxpsnU1TUwvzGJhGDH71tXSGI0n3OKW0YYnB1NVlocq1B2LbhSzp6GTvMw0ZhZlR/X6M+eW0DswxM4j0dUQVFX2NfutREyYhDQwIvIh4LfAj91VlcCDHmpKOuaW5VJVlsszryWWo39oSHluj1OeP5zEOOtqaYzG41EUtxyL4arKUfphAg7+aBI94bijP9ppsoaOXrr6Bq3IZZiEM4L5GHAe0A6gqq8BE/P0pSAXVFfw3N5m+gYSp2zMjqPttPj7OG9h6OkxsK6Wxsm0+PvYsL+FN8RgegyO58JEW7a/piG6EOUAM4uymV6YFXUk2d5GK3IZCeEYmF633D4AIpLOyUUrpzwXLq6gq2+QjTFI5IoVgUKc4fhfAPIy0/CJOfmN4/x1R71b3HJGTI5XnJtBYXZ6VNn8HT39HG3viSpEOYCIcObckqgz+rcdbgMsRDlcwjEwT4nIZ4AcEbkM+A3wiLeyko9zF5SS7hOeSaBw5U0HW5ldnMOMMOerRcQtF2MjGMPh8e31zCzK5tTZ0WfeByMiVJXnsT+KSLI97ughWgd/gDPmFrO/uYumzt6IXjc0pPzyhQOsnFN8UstxY3TCMTC3A404/Vo+DDyqqp/1VFUSUpCdwZlzSxIqH2broTZWVEZW9dYpF2MjGMMpbvnMa028ftn0qH0eozG3NDeqZMtAiPLEDYzT4XJThPkwT+5qoLbJzwfOq4rp55HKhGNgblXVn6jqO1T17ar6ExG5zXNlSciFi8t59VA7zRH+MvKC1q4+9jd3cVqEBqYgy7paGg7ra9ziljHyvwSoKsuj7lh3xKHCNQ2dZKQJ8yYYbHDa7CLSfcIrByObzr5nfS0zCrO58rSZEzr/VCIcA/PeUda9L8Y6UoIL3HDldTXxjybbesiZK15ZWRzR6wpzrKul4RAobnnugrFbPETDvLJcBoeUQ8e6I3pdTUMnVWV5IStShCI7I43lswp5eX9r2K/ZcaSd9TXN3LB2niVYRsCYn5SIXCcijwDzReThoOVJIDnaOE4yp84uoiQ3IyHK92+pcwzMqRE2hio0H4xBUHHLJdEXtxyLQARWpJFkexo7Jzw9FuCMOcVsrmtlcCi8eKX/WV9LTkYa158zNybnnyqMV67/WeAIUA7cEbS+A9jipahkJc0nnLeonGdea0RV4zpPu6WulfnleRTlRFbaoyA7w3wwBq8cbKWpszdm4cnBRNMXpqd/kP3Nft68IjbTU2fMLeHnz+1nd31HyNYBTZ29PLjpMNesqqQ4N7IWAVOdMQ2Mqu4H9gNrJk9O8nNhdQV/2HKEXfUdLI2gpWus2VLXxtlVkU9tOFNkNoKZ6jy+vZ50n3DxBItbjkZFfha5mWkRGZh9zX6GdOIO/gBnuo7+Vw60hjQwv3h+P30DQ7z/vPkxOfdUIpxM/nNF5CUR6RSRPhEZFJHYNLZOQS5Y7OScPBPHabKGjh6OtPVEHEEGzgims3eAoTCnDozU5PHtR1m9oDTiEXA4iEjEkWSBCLKFE0iyDGZOaQ5leZkhM/p7Bwb5xfP7uWRJRczOPZUIx1v1Q+A64DUgB/gg8J9eikpmZhblUD0tP67hyltd/8tKtw95JBRmp6MKnRPsm97Q3sPABAoKGvFjb2Mnexr9XBaD2mNjUVWWF5EPpqahE5HYGRgR4Yy5xSEz+h/edJimzj5uPH9BTM471QgrHEJVa4A0VR1U1f8BLvFWVnJz4eIKXqxtibpi7ETZXNeGT+CUWZFP0RXGoCdM38AQl97xFD9dVxv1MYz4cddTewBiHp4czLzyXA62dIftZK9p6KSyJIeczNgFHJwxt4Q9jf4xu9GqKves38eS6QWctyi6NtFTnXAMTJeIZAKbROTbIvKPQFh1EkTkchHZJSI1InL7KNv/RUQ2ucur7vRbbGMi48AF1eX0DgzxYm1LXM6/ta6V6mkF5GaOF8MxOrHoCdPs76Wzd4AndzZEfQwjPvxmw0Hu31DHRy9eSGWJdw21qsry6Bsc4khbeKHKE61BNhrDHS7HyId5bm8zO46084HzLbEyWsIxMO9x97sF8ANzgH8I9SIRSQPuBK4AlgPXicjy4H1U9Tuqerqqng58GnhKVeNzV44hq+eXkZnu4+k4lO9XVbbUtUWcYBkgFl0tmzud0nWvHGiN2yjOiJzth9v53IOvsmZBGZ+8bLGn54okkmxwSNnb5I+Zgz/AyspifMKYHS7vWVdLaV4mV50+O6bnnUqMa2BcI/E1Ve1R1XZV/bKqftKdMgvFOUCNqu51i2XeB1w1zv7XAfeGrTyByclM45yq0riU7z/c1kOzv4+VURqY403Hoh/BBGo89Q0OJVTxT2Ns2nv6+egvN1KUk8F/XHfGhJMZQxGoqhyOgak71kXfwFDMDUxeVjpLZhSOWviytsnPX3c28O7Vc8nOiG0e0FRi3P8iVR0EKtwpskiZDRwMel7nrjsJEckFLgd+N8b2m0Rkg4hsaGxMnFpf43FBdTm76juob++Z1PNucS+W0yLM4A8Qi54wgREMwLN74p90aoyPqvLP92/m4LFu7nzXmVQUZHl+zhmF2WSm+8KKJItVDbLRCDj6R0ZN/mx9Lek+4d1r5sX8nFOJcH6m7APWi8jnReSTgSWM1402aTmWR+8twPqxpsdU9W5VXaWqqyoqKsI4dfy5cLGjc7KnybYcaiMjTVg2M7qmozGZIvM7I5gl0wt4do8VfUh0fvLMXv6yvZ5PX7E0qtypaPD5nFDlcCLJhg1MRewb6Z4xp5iOngH2NnUOr2vr7uc3G+t4y8pZTCuIrnOm4RCOgTkM/MHdtyBoCUUdjr8mQKV7rNG4lhSZHguwdEYB5flZkz5NtqWulSUzCqIu71GQHQMnf2cfmek+Xr98Glvq2ujstcoAicoLe5v51p93ccWpM7jx/MlNJKwqyw1riqymoZPy/CyKcmOfk3PmPCfh8uUgP8yvXzpAV98gH7DEygkT0sC4fpeTljCO/RJQLSLz3Sm2a4GHR+4kIkXARcBDkYpPZESEC6vLWVfTNGlJiwEH/4oop8cAMtJ85GSkTSibv6mzj/K8TNYuLGdwSHkpTtF0xvg0dPRwy72vMLc0l2+/fcWkR0rNc3NhVMe/PmoaO1k0zZsGX/PLnHJKgXyYgcEhfv7sflbPL424jp9xMp558lR1ACfy7DFgB3C/qm4TkZtF5OagXa8G/qKq0fVQTWAuXFxBi7+PbYcnp/DBvuYuOnoGonbwB5hoT5hmfy9l+VmcNa+EzDSf+WESkIHBIW791St09PTzo3efOTw1OplUleXS0z9EQ8fY7S1U1QlR9sD/As5U3elziocjyR7bVs+h1u5JH82lKp6Giqjqo6q6WFUXqurX3HV3qepdQfv8TFWv9VJHvDi/2ikbM1lZ/VvqWgE4bXbxhI5TkD2xnjAt/j7K8jPJzkjjjLnFPLfX/DCJxnf/spsXalv42ttOi1vNvHlhRJI1dvTS0TMQ8xyYYM6YW8yu+g46ewf473V7mVeWy+s8rGIwlbDGBh5Snp/FKbMKJ62N8pa6NrLSfSyePrGLsTB7giOYzj7K8pxIpLULy9l2uJ3Wrr4QrzImi8e313PXU3u47py5/MNZlXHTEciFGc/RfzyCLPYO/gBnzi1BFX7+7D5ePtDK+9ZWkeazxMpYEE6xywoR+YyI3C0i9wSWyRCXClxQXcHG/cfwT4Kje0tdK6fMKpxwDkPBBHrCqCpNnb2U5zuR7WsXlaEKz+81P0wisL/Zzyfv38Spswv54luWh36Bh8wuziHdJ+OGKtc0eheiHCBQs+8HT7xGQVY671g1Z/wXGGETzp3oIaAIeAL4Y9BihMGF1eX0DyrPezxNNDikvHqofUIO/gCFORlRJ1r6+wbpHRiizDUwKyuLyclI8/z9B9PdN0hX30DIJdKWvZONqoZ0gEdCT/8gH/nFy/hE+NG7zop7AmF6mo/Kkhz2jTNF9lp9J/lZ6Uwv9C43pygng0XT8ukbHOKdZ88hPyvyEkvG6ITzSeaq6qc8V5KinFVVQk5GGn/b2eDpvG5NQyfd/YNRlegfiTNFFt0IptnN4g9MkWWm+1hVVTJpjv7vPraLHz4ZTqEJKM/PZN2nLo37jXY0ntzZwOcfepWLFlfwtatPi8kxv/PYLrYfaeee961izgT72seKeWV5449gGjpZOC3f8wi3s+aWsLexk/eurfL0PFONcAzMH0TkSlV91HM1KUhWehpvOGU6f9hyhM+/eblnN7OAgz8WIxhniiy6EUyTm8UfGMGA44f51p930tjR62mWeO/AIL98YT9nzSsJ2Ylx19EOfv/KIQ62dFE93bv5/Uhp8ffxlUe28eCmw+RlpvGrFw9w3TlzJxwyu6/Jz8+f3ce1Z8/h0qWJ48CuKsvl5f3HxuwAW9PYyYXV3idX3/b6at68cmbCGN5UIRwDcxvwGRHpAwI/a1VV49euMcm4ZtUcHtp0mMe2HfWscN6Wujbys9JZUD7xfIHCnHT6Bofo6R+M2CAGRjDl+ccNydqFTqnz5/c285aVsyasbyye2N7Asa5+Pv66ai5aPP5NaeP+Y/z+lUPUHetOCAOjqvxhyxG+9PA22nv6ue111bxnzTwu+95TfONPO/jFjasn9Cv+O4/tIjPd53kRy0iZV5ZHR+8Ax7r6Kc07sSJVW3c/jR29VE8waCUcZhXnMKs4x/PzTDXCSbQsUFWfqma7jwvMuETGmgVlVJbk8JsNdZ6dY8uhNk6dXYgvBtEvw+Viopgma/Y7I5jgm8UpswopyEr3vGzMrzccZFZRNucvKg+575wS52ZSdyz8tr1ecbSthw/970ZuvfcVKktyeOTW8/nHyxZTnp/FrZdWs76mmacmUHLo5QPH+OPWI3zoggVMK0ys0ifjRZIdLxFjnSSTlbDCjUTkrSLyXXd5s9eiUg2fT3jHWXNYV9PEwZbY39D6BobYcbidlTGYHgPHBwPRlYsJjGCCDUx6mo/VC0p5zkM/zOHWbp55rZG3n1UZVohpeX4Wmek+Dh4Lrx+JF6gq9754gMu+9xTrahr57JXL+P1HzzshL+Xd585jXlku33h0Z9jNuUae4+t/3EF5fhY3XZh4XRmP58KcbGD2eFjk0pgcwglT/ibONNl2d7nNXWdEwNtXVSICv90Y+1HM7voO+gaHou4BM5KJdLVs6uyjICv9pKm1NQvL2dfcxeFWb27ov91Yhyphh5j6fEJlSU7cRjD7m/1c/5MX+PTvt3LK7EL+fNuFfOjCBScZx8x0H//6xqXsqu/gd1H87zy2rZ4N+4/xj5dVk5eA0VFzSnMQgX1NJ38PNY2dZKb7zC+SxIQzgrkSuExV71HVe3DK6l/prazUY3ZxDucvKue3G+ui+iU6HptdB3/MRjAT6GoZyOIfyZoFjh/mOQ+myYaGlN9sPMjahWUR3YwqS3Kpi8MI5mfra3nj95/m1UNtfP3q0/jVB8+lahzf2ZWnzeD0OcXc8fguuvrC/076B4f41p93smhaPu9M0NyOrPQ0ZhXljDqCqWnoZEF5niU9JjHhZuQVBz22CnBR8s6z53CotTvmIbtb69ooyc2gsiQ2TsqJ+WCcOmQjWTqjgJLcDE/8MM/vbeZgSzfvPDuym6gzgplcA/P49nq+9Mh2zl1Qxl8+eSHXr54b0m8mInz2Tcuob+/lnnW1YZ/rvhcPUNvk5/bLl3reQGwiVJXnjpoLEwhRNpKXcP7rvgG8IiI/E5GfAxuBr3srKzW5bPl0inMz+PVLB0PvHAGb69o4rbI4ZrkCx6fIovHB9FGWd/IIxucT1iws47k9TTFNHgS4f8NBCrLTeeMpMyJ6XWVJDi3+vkmpsgBwzN/HZx7YytIZBdz9nlXMLAr/B8HZVaW8Yfl07npq73DH0PHo6Onn+0+8xjnzS3ndsmkTke0588ryODDCN9nTP8jBY13m4E9ywokiuxc4F/i9u6xR1fu8FpaKZKWn8bbTZ/OXbfUc88emNldP/yC76ztYEcPS4hPpatnU2TfqCAacabLDbT0n3UwmQlt3P3969ShvO312xCHVlSXOdNpkjWK+9Mg2jvn7uOOalWSmRz6i+NQVS+nuH+QHT7wWct8fP7WXZn8fn71y2aSX4Y+UeaW5tPj7aAvy+e1t9KNqDv5kZ8z/chFZ6v49E5iJ00DsIDDLXWdEwTWr5tA3OMRDmw7F5HjbDrczOKQxyeAPkJORRppPIp4iGxpSWvzH65CNZM1CJ3w4ltNkD28+TO/AENdE4WOonMRQ5T+/epSHNh3mlksXccqs6L6rhRX5XH/OXH714gH2NHaOud/Rth5+um4vb1k5a7jOViITiCQ7EDRNNhk1yAzvGe9nVKAt8h2jLN/1WFfKsnxWIafNLuLXG+piMlUUyOCP5Y1ERKKqqNza3c+QMuoUGcDCijymFWTF1MDc/9JBls0s5NTZkadmzZmkEUyLv4/PPbiV5TML+dgliyZ0rNteX012uo9v/3nnmPt87/FdDA3Bv75xyYTONVlUlZ+cC1PT0IlPYH4MEoeN+DGmgVHVm9yHV6jqJcELFkU2Ia45ew47jrTHpBHZ1ro2phVkMT3GCXTRVFQerkM2xhSZSMAP0xwT47r9cDtbD7XxzlWVUU0DlednkpXu83wE84WHXqWtu587rllJxgSd7eX5Wdx80UIe21bPS/tOrlC982g7v9lYxw1r5iVNeO9cV2dwJNmehk7mlOYmZJ04I3zC+W9/Nsx1Rpi8deUsstJ9MXH2b65rjUn9sZFE09VyuA7ZGCMYcMrGNHX2DmdpT4T7NxwkM80XdfkdEfE8kuyPW47why1HuO111SybGZsCGB+8YAHTC7P4+qM7TjLU33h0JwVZ6dxy6cRGSpNJbqZTLTk4kqymodMc/CnAeD6YGSJyFpAjImeIyJnucjGQHD+NEpSinAyuOHUGD206RE//YNTH6ejpZ2+TP6b+lwAFWZF3tWz2jz+CAafwJUzcD9M7MMiDmw7xhlOmUzKOQQuFl7kwTZ29fP6hVzltdhE3X7QwZsfNyUzjny5bwisHWnl069Hh9etea+Kp3Y3ccukiinOj/0ziwbyyvGEfzMDgEHubvGuTbEwe441g3ojja6kEvsdx/8sngc94Ly21uWbVHNp7Bnhs29HQO4/Bq4faUcUTA1OYkx5xmHLzKJWURzKnNJfKkpwJJ1w+vr2e1q7+qJz7wVSW5HDQgykyVeXzD75KZ88Ad1yzMuZ5KP9wViVLZxTw7cd20jcwxNCQ8vVHdzC7OIcb1lTF9FyTwbzS3GEfzIGWLvoH1XJgUoDxfDA/d/0t7xvhg3mrqv4+nIOLyOUisktEakTk9jH2uVhENonINhF5Ksr3kXScu6CMOaU5E5om23qoFYhNif6RFGRnRBym3NzZiwiUhPj1vGZBGc/tbWZoAhUNfv3SQWYX53BeGIUtx6OyJJfWrv6o+9+MxR+2HOFPrx7lE5dVs9iDas1pPuH2K5ayv7mLX76wnwc3HWL7kXb+9fIlSem3qCrPo6Gjl66+gaA2yWZgkp1w+8FcD1QF76+qXxnvRSKSBtwJXIYT4vySiDysqtuD9ikG/gu4XFUPiEhiZ4TFkEABzO89vpuDLV1ROWQ317VRWZJzUpnzWFCYHXlXy2Z/H6W5mSFLe6xdVMZvNtax/Uh7VH1ODrV2s66miVsvrZ5wGZE5pTnDx1w6I2NCxwrQ0NHD5x96lZVzirnpAu8KTF60uILzFpXxH399jZyMNE6dXchbVnjXDsFLAlWV9zd3WYhyChFuy+SrgAHAH7SE4hygRlX3qmofcJ97nGCuB36vqgcAVLUhXOGpwNvPcgpg/mZDdKOYrXVtnkyPgTNF1tk7EFHdtObO0euQjWTNAmfUEW0b5d9ucAtbnlUZ1euDGU62bImNH0ZV+dwDr9LVN8gd71jhaYkWEeHTVyyjtbufw209fObKZTFp1xAPqoKqKtc0dDK9MGu4ooSRvITz31+pqu9U1W+r6h2BJYzXzcZJzAxQ564LZjFQIiJ/F5GNInLDaAcSkZtEZIOIbGhsjL4vRqIxqziHC6oroiqAeczfx4GWLk+mx+B4PbLOCEYxzf7e4VbJ4zGjKJsF5XlROfoDhS3PWxRZYcuxiHWy5cObD/OX7fX802WLWTTN+0Zmp84u4tZLFvHeNfOGAyiSkbnDfWG62NNgDv5UIawwZRGJpin4aD+lRt5F04GzgDfhBBV8XkROarmnqner6ipVXVVR4X371MnknavmcLith3U1kRXA3HqoDfDGwQ/He8JEEkkW7ggGYM3CMl7Y20z/4FBEup7b20zdse4JO/cDlOVlkp3hi0kkWUN7D194aBtnzC3mgx5OjY3kk29YwpevOnXSzucFhdkZlOVlsr/Zz55Gv4UopwjhGJjzgY2us36LiGwVkS1hvK4OCL4LVAKHR9nnz6rqV9Um4GlgZTjCU4XXL59GSW4G90c4TRbI4J9or/axiKaiclNn7wmtksdj7cJy/H2Dw4YyXO7fcJDCKApbjoWTC5M74UgyVeUzD2ylp3+Q775jpZWYj4K5Zbm8sLeFzt4BG8GkCOEYmCuAauANwFuAN7t/Q/ESUC0i80UkE7gWeHjEPg8BF4hIuojkAquBHeGKTwWy0tN42xmzeTzCAphb6tpYUJHn2Tx1pD1h+gaGaO8ZGDfJMphzF5QCkfWHaetyC1ueEXlhy/GIRbLl7vpOntjRwG2vr2ah/fqOiqqyPPY2Oe5dC1FODcKpprwfZyRyqfu4K8zXDQC3AI/hGI37VXWbiNwsIje7++wA/gxsAV4Efqqqr0b7ZpKVd57tFMB84JXwC2BuqWuLaQXlkUTa1bLFNY6lYU6RleVnsXRGQUQG5qHNh+iLsrDleMyJQbLlzqNO2Z/XLZ0eC0lTkkAkGVgEWaoQMkxZRL4IrAKWAP8DZAC/AM4L9VpVfRR4dMS6u0Y8/w7wnfAlpx5LZxSyorKI+zcc5P3nVYWsq9XQ3sPR9h7PHPwQZGDCHMEEepSE4+QPsGZhGb964QC9A4NkpYcekdy/4SDLZxbGfFqwsiSHtu5+2nv6ox4R7jraQbpPrDjjBAhEkhVmp1MR5lSrkdiEM0V2NfBW3NBkVT0MeB8eM8W4ZtUcdh7tCMsnsaXOWwc/QEF2ZD1hmt0RzFil+kdjzYIyegeG2HSgNeS+2w638eqh9oi7VoZDIFT50ARGMbvrO1hQkRdVnxfDITCCWTQtP+F72BjhEU6iZZ+qqogogIjYTzQPeMvKWXz1D9v5p/s3j9ufHZy+GT4h6r4i4RAwMOGWiwlVSXk0Vi8owyfwxYe3hQw5PtDcRWa6j6tOj30i4fFQ5e6oC1Luqu9gpYcjyqlAYARj02OpQzgG5n4R+TFQLCIfAj4A/NRbWVOPopwMbr10EX/cejSkP8DnE961eh45md6VBElP85GbmRb2CCbggwk3TBmc9/yec+fx4r5jYb3nj13sTRHHgIE5GGWnTX/vAAdburnmrNiPrqYSxbkZXHv2HN6yMjmrERgnE9LAqOp3ReQyoB3HD/MFVX3cc2VTkFsureaWS6vjLWMYp1xMeAamqbOPzDQfBVnh/GY5TiLkb5TmZZKbmRa1o/81t3bW4hk2czwRRIRv/sOKeMswYkg4Tv5vqeqngMdHWWekMJH0hGnu7KUsPzMp586P94WJbgSz+2gHAEs8KGppGMlMOB7Jy0ZZd0WshRiJR0EEI5hmf/hZ/InIRPrC7KrvIDvDlzQdJA1jshiv4dhHRGQrsMTN4A8stTh5K0aKU5gd4QgmghDlRGNCI5j6DqqnFVj2vmGMYLwpsl8BfwK+AQT3culQ1ZObgRspR0F2BrVN4RTOdnwwyZx9XVmSQ3vPAG3d/RTlRJYLs+toBxdUp1aNPMOIBeM1HGtT1X2qeh0nZvL7RGT+pCk04kZhTnpYiZaq6lZSTu4pMoi8qvIxfx8NHb0smZG8xtUwvCKkD8bN5P8U8Gl3VSZOJr+R4gS6WqqO30qgq2+Qnv6hiHJgEo05wwYmMj/M7nrHwe9F10rDSHYsk98Yk8LsDPoHlZ7+8UvqN3e6OTBJPYI5nmwZCQEDs8RClA3jJMIxMH3q/IS1TP4pRrjlYpr8ThZ/uKX6E5Hi3AzyMtMiniLbVd9BQXY6MwqzPVJmGMlLOAZmZCb/E8BPvJVlJAKFOeH1hGnpjDyLP9EI9IWJeARztJMl0wuSMv/HMLxm3ERLca6aXwNLsUz+KcfxrpbjO/qb/ZHXIUtEKktyIioXo6rsqu/gTStmeqjKMJKXcQ2MW+TyQVU9i6BMfmNqUBBmT5imFPDBgGNgXqxtQVXDGpE0dPTS1t1vGfyGMQbhTJE9LyJne67ESDiKwuxq2dzZR35Weky7TMaDOaW5dPQOhF1BetdRiyAzjPEIpzLhJcCHRWQ/TiSZ4AxurCpdijM8ggnhg2n29ya1/yXAcFXlY10U5YZuhXA8RNlyYAxjNMIxMFZ3bIpyvG1y6BFMsk+PQXCyZXdYXTN3He2gPD8r6X1PhuEVIafIVHX/aEs4BxeRy0Vkl4jUiMjto2y/WETaRGSTu3whmjdheEN2ho90n4QOU+7spTSJ65AFOJ4LE56jf3d9h2XwG8Y4eNbfVUTSgDtxRkDLgetEZPkouz6jqqe7y1e80mNEjohQmBO6onKzvy+iVsmJSlFOBvlZ6WGFKg8NKbvrO83/Yhjj4GUD8XOAGlXdq6p9wH3AVR6ez/CAghAVlYeGlJYkL9UfIJK+MHXHuunuH7QIMsMYBy8NzGzgYNDzOnfdSNaIyGYR+ZOInOKhHiMKCrMzxg1TbuvuZ3BIk7pUfzDhJlvuCjj4rUSMYYyJlwZmtESCkVUTXwbmqepK4D+BB0c9kMhNIrJBRDY0NjbGVqUxLqG6Wh5Pskz+EQwE+sJ0hyzwGYggq07iFgWG4TVeGpg6nDL/ASqBw8E7qGq7qna6jx8FMkSkfOSBVPVuVV2lqqsqKqzvxmRSkDW+DyZQ6DKZ65AFU1mSQ2ev0xdmPHYd7WB2cc5wKLdhGCfjpYF5CagWkfkikglcCzwcvIOIzHDL0SAi57h6mj3UZERIYU76uGHKzf7kr0MWTCBU+WDL+NNkTgSZTY8Zxnh4ZmBUdQC4BXgM2AHcr6rbRORmEbnZ3e3twKsishn4D+BaDTU3YUwqgZ4wY9Hc6U6RpYwPJnSocv/gEHsaLYLMMEIRTqJl1LjTXo+OWHdX0OMfAj/0UoMxMQqzM/D3DTIwOER62sm/R5o6+xCBktzUmCqaUxq68di+Jj/9g2o5MIYRAi+nyIwUINATprN39GmyZn8vJbmZoxqfZKQoJ4OC7PRxRzC7rIulYYRFatwVDM8Y7gkzhh+mubOP0hQoExNMqFDl3Uc78AksrLARjGGMhxkYY1wKhnvCjO6HSZU6ZMEEQpXHYld9B1XleUlfPdowvMYMjDEuhSEqKjf5e1MmRDlAZUkOB491jZkLs7u+0zL4DSMMzMAY41IYoidMc2dqlIkJprIkl66+QY51nWxUe/oH2dfsN/+LYYSBGRhjXArH6WrZPzhEW3d/yoQoB5gzTqhyTUMnqlgOjGGEgRkYY1yOT5GdPII5lmJJlgGC+8KMxLpYGkb4mIExxiU/OzBFdvIIpmm4TExqGZjZ44xgdtd3kJnmo6osd7JlGUbSYQbGGJc0n5CfNXq5mOOFLlNriqwoJ4PC7NH7wuyq72DhtPyUyfsxDC+xq8QIidMT5uQRTKDQZaqFKYMzTXawZZQRzNEOlky3/BfDCAczMEZICrNHr6jc1JmaIxiAOaUn58K09/RzuK3HesAYRpiYgTFCMlZXy2Z/HxlpQmG2pyXt4kIgmz84F+Y1t0SM5cAYRniYgTFCUpgz+gimubOX0rxM3I4LKUVlSQ7d/YO0uJFyALuOdgIWQWYY4WIGxghJ4VgjmM6+lMuBCTBaqPLu+g7yMtOYXZwTL1mGkVSYgTFCUpCdMWqiZZM/9bL4AxzvC3PcwOw62kH19AJ8vtQbsRmGF5iBMUJSmJNOe8/ASbW5mjtTrw5ZgICBORiUC7O7vsP8L4YRAWZgjJAUZGcwOKR09w+esL7Fn3qVlAMUZGdQnJsxnGzZ1NlLs7/PIsgMIwLMwBghOV6P7LgfpqtvgK6+wZQMUQ4QXLZ/91GLIDOMSDEDY4SkYJRyMcNJlinqgwGoLD7eeGy4i6W1STaMsPHUwIjI5SKyS0RqROT2cfY7W0QGReTtXuoxomO4q2WwgfGnZh2yYJwRjNMXZnd9ByW5GVSk8IjNMGKNZwZGRNKAO4ErgOXAdSKyfIz9vgU85pUWY2Ic72p5fIqsOZDFn6JhyuAYmJ7+IZo6+9h1tIPF0wtSMufHMLzCyxHMOUCNqu5V1T7gPuCqUfa7Ffgd0OChFmMCjNYTJjBFVpqiTn44ngtz8FiX08XSHPyGERFeGpjZwMGg53XuumFEZDZwNXCXhzqMCRLoahk8gmkarqScugZmTqljYF6qbaGzd8Ay+A0jQrw0MKPNJYxscv594FOqOjjKvscPJHKTiGwQkQ2NjY2x0meESWAEM9LJn5uZRm5m6tUhCxDoC/PXnc7g2kYwhhEZXt4d6oA5Qc8rgcMj9lkF3OfOa5cDV4rIgKo+GLyTqt4N3A2watWqkUbK8JisdB+Zab4TwpSbO3tTevQCkJ+VTkluBhv3HwNg8TQzMIYRCV6OYF4CqkVkvohkAtcCDwfvoKrzVbVKVauA3wIfHWlcjPgjIif1hGn2p24dsmAqS3IZHFJmFGZTlJsRbzmGkVR4ZmBUdQC4BSc6bAdwv6puE5GbReRmr85reINTUTl4BNOX0iHKAQIlYyyD3zAix9MJdFV9FHh0xLpRHfqq+j4vtRgT4+QRTC+nzS6Ko6LJIeDoty6WhhE5lslvhEVhUEVlVXVK9U+lEYxFkBlGxJiBMcIiuKtle/cAA0Oa0nXIAiyfWUiaTzhjbnG8pRhG0pG6MaZGTCnMPt7VMpADMxV8MKuqStn4uddTnJv679UwYo2NYIywKMxJHw5TngpZ/MGYcTGM6DADY4RFQXYG3f2D9A8OTYk6ZIZhTBwzMEZYFA6X7B+gaQpUUjYMY+KYgTHCoiCoXExgBFMyRabIDMOIDjMwRlgM94TpHqC5s4/i3Awy0uzfxzCMsbE7hBEWwV0tm/29lNnoxTCMEJiBMcJiuCdMT7+bZGkOfsMwxscMjBEWwV0tm/1Tow6ZYRgTwwyMERbHfTCOk99ClA3DCIUZGCMsCrLSEYFjXX0c6+qfEnXIDMOYGGZgjLDw+YT8zHT2N3cBmJPfMIyQmIExwqYwJ4PaJj+AOfkNwwiJGRgjbAqy09kXMDA2gjEMIwRmYIywKczOwN83CNgIxjCM0JiBMcImEKoMVofMMIzQmIExwiYQqpzuk+HES8MwjLHw1MCIyOUisktEakTk9lG2XyUiW0Rkk4hsEJHzvdRjTIzACKY0LxOfT+KsxjCMRMezjpYikgbcCVwG1AEvicjDqro9aLe/Ag+rqorICuB+YKlXmoyJERi1mP/FMIxw8HIEcw5Qo6p7VbUPuA+4KngHVe1UVXWf5gGKkbAU5ji/R8z/YhhGOHhpYGYDB4Oe17nrTkBErhaRncAfgQ+MdiARucmdQtvQ2NjoiVgjNIGeMBaibBhGOHhpYEabpD9phKKqD6jqUuBtwFdHO5Cq3q2qq1R1VUVFRWxVGmETmCIrtTpkhmGEgZcGpg6YE/S8Ejg81s6q+jSwUETKPdRkTICAk9/qkBmGEQ5eGpiXgGoRmS8imcC1wMPBO4jIIhER9/GZQCbQ7KEmYwIEwpTNB2MYRjh4FkWmqgMicgvwGJAG3KOq20TkZnf7XcA/ADeISD/QDbwzyOlvJBgLK/JYPb+Us6tK4y3FMIwkQJLtfr5q1SrdsGFDvGUYhmEkFSKyUVVXTeY5LZPfMAzD8AQzMIZhGIYnmIExDMMwPMEMjGEYhuEJZmAMwzAMTzADYxiGYXiCGRjDMAzDE8zAGIZhGJ6QdImWItII7I/y5eVAUwzleIFpnDiJrg8SX2Oi64PE1xhPfaOde56qTmq14KQzMBNBRDZMdiZrpJjGiZPo+iDxNSa6Pkh8jfHUlyifjU2RGYZhGJ5gBsYwDMPwhKlmYO6Ot4AwMI0TJ9H1QeJrTHR9kPga46kvIT6bKeWDMQzDMCaPqTaCMQzDMCYJMzCGYRiGJ5iBmSIEWlMbqYuIZMRbQypg10rsMAMzAUQkzf2bkP+Q4vCPIlKZyK2oRaRaRLLjrWMsRGSFiOTHW8dYuN/zl4BPBJ7HVdAo2LUSG+J1rUT7/ZmBiQIReZ+IvALcFm8tYyEiNwBPAmcA7Yl4YYvIVSKyB/gK8FMRKY23pmBE5F0isgX4MvBrEcmMt6aRiMi7cb7nG4B3AyTSDdKuldgQr2tlot+fGZgIEZGlwEeBPwAXisgCVVURSZjPUkTOA34G/LOq3qCq7YGbTqJcPO4F8kHgelW9DmgAPisii+OrzEFErgA+DHxEVa8GFgJvcbfF/TMUkTQRuRH4EPCvqroAOCQip8RZ2jB2rcSGeF0rsfj+EuaLTmREpCDwWFV34vxa/HdgO3CLu34oPuocRmhcD7wELHO33S4ibxGR/Hj+ug3WGFgFBD63+4B/AK6M10ghMA3g8ndVvVBV14tIEbDX3Ufi/BmmAajqIPCQql6kqi+KyDKgA+czjRvBU4kJfK0Ea0zUa2XklOykXCuxvteZgQmBiNwOvCIi3xKR97mrd6lqC/AAsFBELnT3jcvnOULjje7qjwI/F5FNQDFwK/Ad91dJPDV+W0Sudz+/rcB7RaQEWAVsAGYAs+Og7yvAF0QkUAyw110/HXgUaMW5qOP5GQY0TgNQ1SZ3vajqDqAKON1dN+n/iyLyr8Df3e/4BlfjzgS7VoI1vt9dnWjXSkDjd0TkWuAYk3CteHKvU1VbxliAS4GngfnAJcARYEXQ9nwcx+ovg9alJYDGM91tHwNWuY8rgAeBNybI57gYmAd8D/gj8EvgFODvQNUkassCPo1TofsB4A2j7FPk/i0FHgaunOTPb1yNgf854OPAXXH4fstwppnuxzFwbwdeAGYH7RPXa2UcjfPc7R8DznIfx+VaGUXjO1yNZcACL68Vr+51NoIZnwzgFVWtVdUngR8A3wja7gd+C3SKyFdF5Ns4vyLjrfHrAKp6p6pucB83Ai04N8nJZqTG/wTuUNX9qvpJ4EZVfZeqbgPqJlljP84c83LgeeASEZkfvIOqtrl/W3Dmv0smUV9IjepMl4Ez6moTh8m8tv3AX1T1GlXdBPwV5xd35Yh94nmtjKVxNgxfKxvdx/G6VkZqfAJ4FVigqns9vlY8udeZgRmfXKBM3LBAVf0mMFNE3uE+V6AHOA34CNCoqnsSQOO0gEZwnIQicgewAme+ebIZqfHrwGwReaf7/KiIzBGRO3Eu+F2TJUyd+eTdquoHfo1zUzxHRLLguKPX/Qy/i/PLclI/wzA0BnxHO4H3q8Ok+TlUtQd4JGjVAM7ndMTVJ/G+VsbRWBe8XzyvlTE0rgTqg/bx6lrx5F5nBoaxo0VU9QGc6KE3B63+NvDJoOffALYBc1X1O4mm0f2ley/OL5SLVLUmgTR+Iuj5D4E04E3ujXQy9fW6f/cB64CLgKVBr1uBM20R+Ax3e6EvWo1BI5hnga+LSLpXEVDj6OsIeloGNKjqAXdbwFn+LeJ7rYyp0X3dAhwHejyvlXE1utxJlNdKsBM/eJTr2b0uFvN3yboAVwE/B04fsV6ALPfxtThzk1Xu87nuF1zgPs9OYI2ZQDZQmsAa893nuXHS53MfB/wYhThTeNcD7wHe7K6viONnOJ7GdwNXe6ktCn3n487VA2/E9RkBOQms8WL3cVkCawx8jnlRnPcKnFyf/wU+G7Q+LYxrNOp7XTpTjMBwXUQuAb6KM7+9RkT2q+qxoOF8r/uL5tc4c9+fEyfh6C3APnV/aagzrE1UjX3uIRNZYyeAqnbFSZ+6+lqAVlVtF5HXcC6sZhzHOerMy8eUWGtMFH3AhUCmiPwIZ0rlMwCq2p2gGlcAt7sam5NAY1ijFneU5MPJlboR+CLO/8vnReQDqnqPOqPfQc/udV5a60RbcNsTuI+rgJk40RM/wxkSB7b53C+zEbgAKALOw/nl8S+mMbE1RqHvCM4vPMGZdtoLfCbBPsNJ1Rilvje5634JHABuS8DPcEpoHHHeK4HqoOefAD7pPk7z8hqdMiMYEbkFeJ2IPA3cq85cNsAREXkjcJGI1KjqIZz48jZgsaoec/dbLyLP6/H5btOYgBqj1Lc8oE9E9gGnqUd+oGTQOFF9OD6/j6lqqxf6TGPY530G+F9VfVScyg/pqjqAk1S60919Gl5eo15a70RZgKtxIkIuAf4Hx6G8Mmj7SuAXjDKXjWPhxTQmvsYJ6ktPgv9FzzVOUF9GEnyGKa1xjPOeHvz/gzN6WjvKa2N+jU6VKLLVwI/Uie/+ElBLUPE2Vd2M86WcJiKXipPRGpg7HVT30zeNCa9xIvoGPNaWLBonoq9/EvSZxsjPG/AjDogT2j4H2CgilSLyoaDzxvwaTWkD4zq5wJmvvh5AVffjZMPmichbg3a/F6eg3K9xwgNNY5JoTHR9yaAx0fWZxgmf9yp3+1L3XLfhVKXwNJk0pQyMOEUJA4+DixL+FugK+pCP4JRZWC4O+TiZq1txyiP8C3hT9tw0pr6+ZNCY6PpMY8zPu8w1QgtwfDDzcYIJvhXJeSMlJQyMiKwWkYeAn4jIB0QkS1VVjmc4H8Op4fQR98tow6mtk+1+sD04kRpvUtUjpjExNSa6vmTQmOj6TKNn581xz1sDnK+qH/Hqswkm6Q2MOFnWd+JY7t/ihAAughMynHOAx3Cs+d0iMgunuVC/u9+AqjaYxsTVmOj6kkFjouszjZNy3q2qOmklcJLewABnATWq+n/A4ziZ6wfc4SAi8lUciz4d+Cecuj6/wklk+qZpTBqNia4vGTQmuj7T6N15vzHaAT1HJyFkL5YLTg2m1UHPp+FUkf0aTuG654F7gH/G+QJ+BSwacQzPypKYxqmhLxk0Jro+05iY543pZxfPk0f4YRcAv8cpo3APUBK0bSlOIb0bgr6YR3H7orjrfKYx8TUmur5k0Jjo+kxjYp7XiyWZpsj6gL/hFPc7jNOMBxhu7bmU46W3N7r7BIaOPp2c8uWmMfX1JYPGRNdnGhPzvDEnoQ2MiNwgIheJSLE65cp/itOEZzewSkQWB+3+F+CL7nzktcCpQBN42wPcNKa+vmTQmOj6TGNintdrxB1SJQzuhzYDZz5xCNgD5OGE9DW5+1QD7wV6VfWr7roc4G6ceco04OOqut00JqbGRNeXDBoTXZ9pTMzzTirxnqMLXjjeB2Ex8Av3cTpO74vfjdj3auC/gGpcR5a77wzTmNgaE11fMmhMdH2mMTHPO9lLQlRTFpF04CtAmog8itNQaRCG6+d8HDgsIhep6lPu+gdEZBnwJyBfRC5R1R3AUdOYmBoTXV8yaEx0faYxMc8bN+Jt4XCiIDYDP8JpjPM0cDlOH4Rzgvb7CPBk0PN3AH7gJ8A005jYGhNdXzJoTHR9pjExzxvPJf4CnCY37wl6/l/uB/w+YKO7zoczV3k/MD/odReYxuTQmOj6kkFjouszjYl53ngu8RcAuUAWx+ck3wV8w328CbjVfbwKp2mPaUxCjYmuLxk0Jro+05iY543nEvcwZVXtUtVePV5L5zKc9p0A78epAvoHnNLWL5vG5NSY6PqSQWOi6zONiXneeJIQTn4AcaqBKk4dnYfd1R3AZ3DivGvVaS0aN0zjxEl0fZD4GhNdH5jGRDxvPIj7CCaIISADJ2FohWvJPw8Mqeq6BPnATePESXR9kPgaE10fmMZEPO/kE+85uuAFOBfnw18H3BhvPaZxaupLBo2Jrs80JuZ5J3tJqEx+EakE3gN8T51yCQmHaZw4ia4PEl9jousD05iI551sEsrAGIZhGKlDIvlgDMMwjBTCDIxhGIbhCWZgDMMwDE8wA2MYhmF4ghkYwwiBiAyKyCYR2SYim0XkkyIy7rUjIlUicv1kaTSMRMQMjGGEpltVT1fVU3DKe1wJfDHEa6oAMzDGlMbClA0jBCLSqar5Qc8XAC8B5cA84P9wOhEC3KKqz4rI88AyoBb4OfAfwDeBi3EKHt6pqj+etDdhGHHADIxhhGCkgXHXHQOW4tSQGlLVHre97b2qukpELgb+WVXf7O5/E04vj38TkSxgPfAOVa2dzPdiGJNJwhS7NIwkQ9y/GcAPReR0nM6Ei8fY/w04dafe7j4vwmmBawbGSFnMwBhGhLhTZINAA44vph5YiePT7BnrZTj9Ph6bFJGGkQCYk98wIkBEKoC7gB+qM79cBBxR1SGc2lJp7q4dQEHQSx8DPiIiGe5xFotIHoaRwtgIxjBCkyMim3CmwwZwnPrfc7f9F/A7EXkH8CRO73SALcCAiGwGfgb8ACey7GUREZxGU2+bHPmGER/MyW8YhmF4gk2RGYZhGJ5gBsYwDMPwBDMwhmEYhieYgTEMwzA8wQyMYRiG4QlmYAzDMAxPMANjGIZheIIZGMMwDMMT/j+qkJPLE4w7LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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l/NbwrC7NZ/n8vLGxHhOdeCaYJ4BKEVnlDtxfCdw97pgjwCsARGQBsA44GMeYUtJka2BC4j1VuaVnkL6hUVaUvLTfe2tFKU8fOUnfkA2bednehg6qFhbiz3Sm9W5cWsyJrsGx6hCpQFXZWdvMtopSsjPj97ElIlxaVc4jB1oZGB6N23nSRdz+Uqo6AtwA3Ae8APxCVfeJyHUicp172BeAC0XkWeDPwCdV1b4SjzPZKv4QJ8HE78NibIry+ASzppThUeXxQzat06uCQeXZhk42Li0au23TMufnPW7XWSqoPdFNY+cAO6ri30W+fV0ZA8NBHj1o3b/Rius6GFW9V1XXquoaVf0397ZbVfVW9+fjqvpqVT1DVU9X1Z/EM55UFUkLprGzn2AwPivrQwlmZVgXGcA5K+eT7cvgkQP2RvSqQ229dA+OjM0eA9iwqAhfhqTUTLLQCvt4TE8e7/zVJeRkZdhsshiwlfwpoLFzgLxsH4U5E5eOW1Kcw/Co0toTn43HjrT1kiGnpkSH5Gb7OGtFMQ+/aI1OrwqNtWxcdqoFk5vtY+2CAHtSaBxmZ00LGxYVJqQQZU6WjwvXlFJd22LlkKJkCSYFNHX1s7AoB2e50MuNTVXujE832eG2PhYX507Y972topTnG7to703NhXtz3Z6jneRm+agoK3jJ7ZuWFvHssc6U+ADt7Btm95GTcVm9P5kd68o40t7HwdbehJ1zLrIEkwImW8UfcmpfmPgM9Ne3972seyzkQrdszCM2m8yT9jZ0cPqSQjJ9L32rb1xaTEffMEdSYL3HQ3UtjAY1IeMvIaGuOOsmi44lmBTQ1DnAwsKJB/jhVNdVvBLMkbbelw3wh2xcUkTAn8muOhuH8Zrh0SD7jnexMWz8JSQ06J8K62Gqa1oozsti87J5CTvnsvl5VJYXWNmYKFmC8biR0SDN3YNTtmAKczPJz/bFpVxMZ/8wJ/uGX7LIMlymL4PzVpewy+qSec7+E90MjgRfMoMsZN3CAP7MjLFFmF4VDCoP7G/m4soyfBkTdxHHy46qch471EZvnAvJzmWWYDyutWeI0aBOOoMMnLn78VoLc8SdQbZiki4ygG0VJRxp77PyGh4TmiW2aYIWTJYvgw2LCz0/k+zZY5209gwltHssZPu6MoZH1aqGR8ESjMc1uiv0Q1WTJxOvtTD17aeqKE8mVL7fWjHesrehg6LcrEn/dpuWFvPc8U5G4zS9PRaqa5sRgUvWJm6AP2TLivkU+DOtbEwULMF43NgamCnGYMBJQI1xKBdzah+YyRNMRXkB5QE/u2w9jKfsOeossJxs9uHGpUX0DY1S1+zdjeOqa1vYvKyY+fmJ38UjOzODiypLqa6x6cqzZQnG406t4p+mBVOUS2vPUMzLW9S39VIW8JPvn3gNDjhddFsrSnmkrjVuiz3NzAwMj1J7onvC8ZeQ0OC/V9fDtPYMsrehI67FLaezY105TV0D1DR1Jy2GVGYJxuOaugbwZ2ZQnJc15XGhtTCNMV4LM76K8mQuXFNCW+8QtSfsjegF+453MRrUCWeQhawuzSfgz/Rs4csHaltQJakJ5hJ3a4Bq6yabFUswHhdaAzNZN0fI4jhNVa5v65t0inI4G4fxllDSmGiAPyQjQzh9SZFnB/qra5spLfBz2uKX7eCRMAsKczhtcaGth5klSzAe19TZP+UMspB47Gw5MDxKU9fApIsswy0uzmV1ab4lGI/Y29BJecA/7f+djcuKeKGxi8ERb1UOHhkN8uD+FnasKxvb5C5ZdqwrZ3f9STr7hpMaRyqyBONxTgtm6gF+gAVFfuf4GM4kC007nmoGWbitFaU8dqjdtlH2gD3uFsnT2bS0mOFRpabRW12bTx/toGtgZGwDsGTaUVVGUOHBF23R5UxZgvGwYFA50TUQUQvGn+mjLOCPaRfZ4QhmkIXbWlFC39Aoz3h88d5c1zUwzMGWXjZNMcAfEpoE4LVxmOqaZnwZwrbK0mSHwuZl8yjOy7JxmFmwBONhbb1DDI/qtDPIQhYX58Z0Z8v6NmcNTCRdZAAXrC5FxMZhku05d0xl47LiaY9dUpxLSX6250rG3F/TzJYV8yjMmXpySyL4MoRL1pbxQG2LzZKcIUswHnZqDUxkCWZJcU5Mx2COtPcRyMmcdgZbSFFeFmcsKbIEk2ShZLFxyfQtGBHx3BbKjZ391DR1e6J7LGTHunLaeofYe8xbidjr0irBNHenzhaxcGoVfyRjMOCshTne0R+zRWH1bX2sKMmbdgZbOGcb5Q6r35REexs6WD4/j3kRLk7cuLSYuuYez/zNQgUmE1mefzoXry1DxKorz1TaJJjfPH2MC794PwdavLtqebymrql3shxvcXEuA8NBOmI026W+rXfKGmQT2bqmlJGgbaOcTHvHbZE8nU3LiggqPOeRb+fVNc0sKc6lsrxg+oMTZH5+NpuXFVvZmBlKmwSztaKUTJ9wS/WBZIcSscbOAbJ8QkmE30RD9cpi0U02Mhqk4WR/RIssw21ZOY/szAzrJkuS1p5BjnX0T7n+ZbzQbDMvrIcZHBllV10r29eVzajlnAiXritnT0MnLd3x2Tl2LkqbBFMW8HPVucv5zTPHUqbqb1PnAAsKcyJeBxDLxZaNnQOMBDXiKcohOVk+tqyYZ3XJkmRsi+QZtGBKC/wsKc71RMmYJw6dpHdoNKmr9ycTGhN6cL9NV45U2iQYgL+9eA0+EW7ZmRqtmMbO/ohnkEFsE8zhtlAV5Zl1kYHTWnyhsYvWHvuml2h7jnaSIXB6BAP84ZyB/uS3YKprm8nOzODCipJkh/IyGxYVUhbwc791k0UsrRLMwqIcrtiylLt2H41L5eFYa+ocYGGEA/wAJfnZZGdmcDwG9cjq22a2yDLc1rFtlK0Vk2h7GzqoKC+YsjjpRM5YWsSR9j5O9g5F/JiW7kG+/ofamK1wDwaV+2uaOX91CXnZM4s/ETIyhO1ry3hwfwsjtpg4ImmVYACuu2QNqvC9Bw4mO5QpqepYHbJIiQhLYrTx2JH2PrIzM1gQiPz8IacvLsSXIdQ2dUUdh4mcqroD/MUzfmxozCbSabjBoPLRnz/Dd+6v45O/3BuTmYv/9fAhDrX28ubNi6N+rnjZWlFK98AIdSk0WSiZ0i7BLJufx1vOXMIdjx/x9LTljr5hBkeCEa+BCVlUlBObLrLWXlbMz5tVHahMXwalBdk2GJpgxzr6aesdimgF/3ihLrVIt1C+7aGDPFzXyraKUn6/r4mfPX5kxucM92xDJ1+5r4ZXb1jAW85cEtVzxdMGt/Cm10rreFXaJRiAD+2oYHg0yPcfOpTsUCYV6T4w48VqZ8sj7X2z6h4LKQ/k0GwJJqFCYyizacEU5WaxujQ/ohX9e4528LX7arn8jIX86P3nclFlKZ+/53n2z3Krht7BET5y59OU5Pv58ls3em72WLhVpflk+zJ4odFa55FIywSzsjSfN25azE8erad9Bn3O4VSVOx8/MrbaPtaaupxWSKRrYEIWF+dyonsgqoKTquqU6Z8/8wH+kPKA31owCbanoYMsn1C1KDCrx0eyor/HTQYLCnP44ls2kpEhfP3tmwjkZPKRO56e1YZ3/3z3Pg639fLNKzdHvDg0WbJ8GVSUF/CCbUAWkbRMMOC0YvqHR/nBw7Nrxdx0fx2f+tWz3PZgfMZyTrVgIh/kB6dcjCpRJb6W7kH6h0dZWTr7FkxZwG8tmATbe7ST9YsK8Wf6ZvX4jUuLae4enPL/zmd/8xxH2/v41pWbKXJLCJUHcvjaFZuoaermi/e+MKNz/vaZY9y1u4EbdlRw/mrvzRybyPpFhdRYCyYiaZtgKhcEuOz0hdz+yGE6+2c2C+b3zzXx9T/uJyOOhR2bOgfwZQhlAf+MHheLqcr17TOrojyR8oCftp5BRq04YEIEg8pzx2a2gn+8Tcucx062HubXTzfwq6ePceMr1rJl5fyX3Ld9XTnXbFvF7X+p54/Pn4jofEfa+vjMr5/jrOXF3PiKylnHnWjrFwVo7h6kzabhTyttEwzADTsq6R4c4fZHDkf8mBcau/jYL55h07JibnzFWmpPdMdlskBj5wDlAT++GQ6yh1o80WydfGqK8uy7yMoKcwgq9iZMkIOtvXQPjsxq/CVkw6IifBkyYTdZfVsvn/n1c5y7cj43XFox4eM//tp1nLa4kE/ctWfaFvTwaJCP3Pk0CHzryjPJ9KXOR9H6Re5Av3WTTSt1/qpxsGFxIa9cX84Pdh2iJ4JCf209g1xz+5MU+DO57d1n84r1zsreeLRinDUwM58iHItyMfVtvfgyZGyXzNkoK3BaXtZNlhiRbJE8ndxsH2sXBF624HJoJMhH7niaTF8G/3Hl5km/9PgzfXznqjMZHAny0Z8/M2Xr9Zt/2s8zRzv497ecwbIoWsrJULXQGeOygf7pRZRgRGSFiLzS/TlXRGY3iuhBN1xaSUffMD95tH7K44ZGglz/06do6Rnktqu3sKAwhw2LCpmXl8VDL8Y+wcx0FX9IXnYm8/Kyousia+tjcXEO2Zmz//5RXugkGBvoT4y9DZ3kZfuoiLJA5CZ3RX/4upZv/HE/exo6+fJbz5j2S8fqsgI+98bT+MvBNm59YOKKGY/UtXLLzgO8fctS3rDJu2teJlNS4Kcs4OcFm6o8rWk/QUTkb4C7gO+5Ny0FfhPHmBJq87JiLqos5fsPHaR/aPIZMP9yzz4eP9TOV966kc3uRk4ZGcKFFaXsqmuNWYl8OLXIcmHh7FoQi6NcbFnf3seKKGaQgTMGA6m3RUKq2tPQwemLi2bcpTrexqXFdPYPj3WTPvxiK7c+cIB3nrec156+KKLnuOJsJ3F844/7eerIyZfc1947xEd/8QyrSvP53BtPiyrWZFq/qJAaW0g8rUi+on4I2Ap0Aajqi4D3KtFF4SOvqKS1Z4g7Jlks9uNH6/npY0e47pI1vHncIrCLKko50TVIXXPsVvZ2DYzQNzQ6qxYMRL8WxinTH123RWhyQnOXtWDibXg0yPPHu6Ia4A8JPceehg7aegb56C+eobK8gH963YaIn0NE+Le3nM6iohxuvPNpugacSTSqyifu2sPJ3mG+feWZniwHE6n1CwO8eKInquUA6SCSBDOoqmOLRUQkE4jo67qIvFZEakWkTkQ+Nckx20XkGRHZJyIPRBZ2bJ2zcj7nr57P9x488LJ5/I8caOVzd+/j0qpyPv6adS97bKju1sMxHIcZ28lylglmSRRbJ3f2D9PRNxx1gvFn+ijKzaLFBvnjrrapm8GRYERbJE9n3cIA/swM9hzt5ON37aWzf5hvX3Umudkzm/pcmJPFt648k+MdA3zm18+hqvz40Xr+9EIzn7ysasbFOL2malGAodEgh1p7kx2Kp0WSYB4QkU8DuSLyKuB/gHume5CI+ICbgcuADcBVIrJh3DHFwC3AG1X1NOCKmYUfOx++tJITXYP8z+6GsduOtPXxwZ8+xarSfL41yeDmsvl5rCzJ4+EYjsOc2slydglmUVEO3QMjY98cZ+JIW2iKcnRdZOB0k1kLJv5Cg/KzKREzXpYvgw2LC/nZ4/XcX9PMP16+fmzW1EydvWIeH31lJXfvOc5X7qvlX//3BbavK+P9W1dGHWeyhV4TG+ifWiQJ5lNAC/As8LfAvar6jxE87lygTlUPui2gO4E3jTvmncCvVPUIgKomrQ72hWtKOGt5MbfuPMDwaJCewRGu+dETqML3r95CIGfyfem3VZby6MG2mDWXo23BhNbCNM6imyxUpj+aRZYh5YV+G4NJgL0NHRTnZUW1bincpqXFDAwHeeX6cq6+YEVUz3X99grOXz2f7+48QGFOFl+7YpOnS8FEanVpAVk+sYH+aUSSYD6sqv+pqleo6ttU9T9F5MYIHrcEOBp2vcG9LdxaYJ6I7BSR3SJy9URPJCLXisiTIvJkS0t8NvsRET58aSXHOvr55e4G/u7OZzjQ0sst7zqLlaVTf5vfVlFK79Aoz0RYKHA6jZ0DiDgrpGcjmsWWR2KwyDLE6pHFX/fAMPc+28jWNaUx++B+3cZFXLimhK+8Lfpk4MsQvvmOM9lWUcp3rjqT0oKZLRz2quzMDCrKAzbQP41IEsx7JrjtvRE8bqL/mePHbjKBs4HXAa8B/klE1r7sQaq3qeoWVd1SVlYWwalnZ/u6Ms5YUsRnf7uPP71wgn963fqxMZapXLC6lAwhZtOVmzoHKC3wz3qacGgq6WzWwtS39VIW8MdkALbMrUcWyxl25qV+/Gg9XQMjXHfJmpg95zkr5/Ozvzmf+TGqC7awKIefXHMeF6xJjVIwkVq/MGBVlacx6SeYiFwlIvcAq0Tk7rBLNRDJTlINwLKw60uB4xMc83tV7VXVVuBBYNPMfoXYcVoxFQyNBrnynGW858KVET2uKC+LM5YWx2zBZWPXzPaBGa8s4CczQ2a1qdrhtj5WRjnAH1Ie8DM4EqRrYPpFrGbm+odG+a+HDnHJ2jLOiMH4i5mZqkUBmroGZrRJW7qZ6mvqI0AjUAp8Pez2bmBvBM/9BFApIquAY8CVOGMu4X4L3OTOTMsGzgP+I7LQ4+PVpy3knhu2sX5RYEbdAxdVlPLdBw7QNTBM4RTjNZFo6uxnZRRlWnwZwoLCnFlNVT7S1hdRqy0SoanKLd0DFOVG95qYl7vj8SO09Q7x4UlKt5j4Ghvob+riwjWxec/MNZO2YFS1XlV3quoFqvpA2OUpVZ32K6l7zA3AfcALwC9UdZ+IXCci17nHvAD8HidhPQ58X1Wfi8UvFo0zlhbNuDbS1opSRoPKYwfboz7/THeynMiS4twZd5ENDI/S1DUQ9RTlEFsLEz+DI6N878EDnLdq/ssKT5rEqFpom49NZ9qOdhE5H/gOsB6nleEDelV12rmLqnovcO+4224dd/2rwFdnELMnnbWimNwsHw+/2MKrNiyY9fP0DI7QPTDCwhmW6R9vcXEOT9afnP7AMKEB/lglmNAkBVsLE3t37W7gRNcgX79ic7JDSVtlAT+lBX6bqjyFSL6m3wRcBbwI5ALX4CQcE8af6ePcVfOjXnAZmqIcKlo5W4uLc2nqHJhRufxYVFEOF6pHZi2Y2BoeDfLdnQfYvKyYrRVza+A81axfFLCqylOIqB9IVesAn6qOqup/AzviG1ZquqiylAMtvbMaXA8ZWwNTGH2CGQnqjIpN1rtrYFbEaD1FwJ9JTlaGrYWJsbufOU7DyX5u2FExJ9aUpLKqhQFqT3QzYiVjJhRJgukTkWzgGRH5ioh8FIjNV9w5ZqxsTBTTlU+t4o++iwyYUcmY+rY+CnMyKc6LzYC8iIxNVTaxMRpUbt5Zx/pFhWPbRZjkWb+okKGR4NgCZfNSkSSYd7vH3QD04kw9fms8g0pVVQsDlBZkR9VNFmrBhLqXZms2iy3r2/tYUZIf02/Fttgytn7/XBMHW3r50I411nrxgNBAv63on9iUCcatJ/Zvqjqgql2q+i+q+jG3y8yMIyJsdcv3B2e5VXBj1wAl+dnkZM1uX/WQ2SSYI229LI/RAH9IecBvCSZGVJXv3P8iq8vyuSzC0vkmvtaU55OZITbQP4kpE4yqjgJlbheZicC2ilJae4aoPTG7bzSz3clyvMKcLAL+zIjXwoyMBmk42R+zRZYhTsFLG4OJhT+/0ExNUzcf2l4R9b4vJjb8mc4mbzbQP7FI6oEcBnaJyN04XWQAqOo34hVUKttWeWocZjZVaBs7B1gS5QyykMUzWAtzvGOAkaBGvdHYeGUBP10DIwwMj0bdKktnqspN1XUsnZfLGzen3i6Qc1nVwgCPH4p+/dtcFMkYzHHgd+6xgbCLmcCiolzWlOXPehymqbM/Ji0YcAb6I53RVt/ufHeIfReZuxbGusmisquujWeOdnD99jVkzXARsImv9YsKOd45QEeflYwZb9oWjKr+SyICmUsuqizjzieOMDgyij8z8m/tA8OjnOwbjnoGWcii4lz2uHuFTOewuwYmmhI1EykLrYXpHmRZjKY/p6Pv3P8iCwr9vO3spckOxYxT5fZU1DR1c/5qW5cUzr4KxcHWilIGhoM8Vd8xo8fFag1MyKqSfNp7h/jkXXvHnnsyR9p68WdmUB6IbTn1soJT9cjM7DxxuJ3HDrVz7cVrZvSFxSTG+oVOh44N9L+cJZg4OH/1fHwZwsN1M9u7ptFNAtHWIQt51/nLef/WVfzq6Qa2f62ar95XM+kul/VtfSyfn0dGjAePQ9OtrYts9m66v46S/GyuOnfZ9AebhCsL+CnJz7aaZBOwBBMHgZwsNi8r5uG6SHY1OKWpyxkvidUYTF52Jp99wwb+/LHtvHrDQm6uPsD2r+7kv3cdYmjkpSuP69v6YlYiJlxJvp8MwaYqz9KzDZ08sL+FD1y0KiZ79JjYExGqFtnmYxOZNsGISJmIfFpEbhORH4QuiQgulW2rKOXZhg46+yZuMUykMcqtkiezvCSPb191JvfcsI2qhQH+5Z7neeU3HuB3e4+jqqgqR9r7YlbkMpwvQygt8Fs9slm6qfpFCnMyeff50W1dbOJr/cJCak90z6j2XzqIpAXzW6AI+BPwv2EXM4VtlaUEFR45EPlssqZOZ9+UeH1TPWNpET+95jx++L5zyMv2ccPPnubNN+/id3sb6R8ejUuCAacLweqRzVxtUzf37TvBe7euIhDlHkMmvqoWFTIwbCVjxovkkyxPVT8Z90jmmM3LisnP9vFwXSuXnTH9quvRoPLiiZ6Yjb9MRkTYvq6ciyrL+NVTDXzjj/v58B1PA7GrojxeecBvJftn4ZaddeRn+3hfhDurmuSpcgf6axq7WVNWEPHjhkeDfOn/aiLeFXNHVTlv2JQ666AiSTC/E5HL3b1dTISyfBmcv7okovUwR9v7+Pv/2cPjh9q5fnvs9lafii9DuGLLMt6waTE/fOQwO2ub2by0OC7nKg/ksO+49U/PRP/QKL9/rokrz1nGvHwrpOF1lQsK8LklY163MfIyPr9+6hj/9fAhlhTnkhFBf9Laham1BDGSBHMj8GkRGQJCAwoayYZj6W5bZSl/rmnmaHvfhGtAVJX/2d3A5+95HoCvXbGJt561JKEx5mT5uO6SNVx3SfwSW3mhn9aeQUaDaiVOIvSXg60MjgR5ZRSb15nE8Wf6WFOWP6OB/pHRILfsrOOMJUXcfcPWOVm8dNqcqaoBVc1Q1Rz354All8hsC5Xvn6AV09ozyLU/3s0n7trL6UsK+f3fXcTbzl46J/+TlQX8BBXaeq2bLFLVNS3kZjmb2JnUULWwcEZVlf/32UYOt/XxoTm8r09E05RF5I0i8jX38vp4BzVXVJQXsKDQ/7IE84d9TbzmPx7kgf0tfOZ16/nZNeezdN7cXeUeWrxpa2Eio6pU1zaztaLUFlamkPWLCjnW0U9n//QzR4NB5ebqOtYuKODVc7iVGsk05S/hdJM9715udG8z0wiV73/ELd/fPTDMP/zPHq798W4WFuXwuw9v45qLVsd8caPXlLn1yGwtTGQOtPTQcLKfS6tsQ7FUUrXIGR+pjaCy8h+eP8H+Ez18aEfFnH7/RzIGczmwWVWDACJyO/A08Kl4BjZXXFRZyq+eOsYPHznMfz18iMbOfj58aQUfvrSS7Mz0WOc61oKxtTARqa5xKkBsX1eW5EjMTGxYFNp8rGvKrk2nMvaLrCzJ43URzDBNZZEuuCgGQvWoi+ITytwU2kb58797nlWl+dx1/YWctXxekqNKrLJQgrGpyhG5v6aZqoWBsU3jTGooD/iZl5c17UD/A/tbeO5YF19560Yy53hl7EgSzBeBp0WkGhDgYuD/xTWqOaQ8kMNV5y4jJ8vHx1+zLi3LfeRk+SjMybSNxyLQPTDME4fbueai1ckOxcyQiEw70O/sSlrH4qIc3nxmYmeMJkMk5frvEJGdwDk4CeaTqtoU78Dmki/+1cZkh5B05YU5NgYTgV11rYwElR3WPZaS1i8q5I7Hj0w6Jf/Rg+3srj/J5990Wlp0kU/6G4pIlfvvWcAioAE4Cix2bzMmYmUFfkswEaiuaSGQk8nZK9KrG3WuqFoUoH94lCPtfRPef3N1HWUBP2/fkh6VsadqwXwMuBb4+gT3KXBpXCIyc1J5oZ+nj3QkOwxPC01Pvnht2Zzvm5+rwgf6V5W+tPTSU0dO8nBdK5++vCpttg+fNMGo6rXuj5ep6ks6z0UkvgWzzJxT7ha8VNU5u6gsWvuOd9HcPciOdTY9OVVVlBeQIVDT2MXl42aI3Xx/HcV5WbzrvPSpjB3J16RHIrzNmEmVB3IYGA7SPTiS7FA8a2dtMwCXrLXxl1SVk+VjdVkBL4xbC7PveCd/rmnm/VtXke9Pn4k+k/6mIrIQWALkisiZOAP8AIXA3F12buIiNFW5uWuQQis9P6Hq2hY2Li0ae61Malq/qJCnj5x8yW23VB8g4M/kPWlWGXuqVPoa4L3AUuAbYbd3A5+OY0xmDgovF1NRHnk583RxsneIp4+c5IZLK5MdiolS1cIA9+w5TtfAMIU5WdQ1d3Pvc418cPsainLT68vVVGMwtwO3i8hbVfWXCYzJzEHlhW4LxjYem9CDL7YQVKw8zByw3i0Zs7+pmy0r53NL9QFyMn28f+uqJEeWeJHuB/NOYGX48ar6+XgFZeaesgJnXogVvJxYdU0zJfnZbFxihTJS3fqwmWTlgRx+u+c4771wJSUF6df1GUmC+S3QCewG7NPBzEphbibZmRm2FmYCo0Hlgf0t7FhXPqcLH6aLhYU5FOVm8UJTN883duMT4dqL07MyQyQJZqmqvnY2Ty4irwW+BfiA76vqhFWYReQc4FHgHap612zOZbxNRJytky3BvMyehg5O9g2z3brH5gQRYf2iALvqWjne0c/btyxjQWF6ruyIaJqyiJwx0ycWER9wM3AZsAG4SkQ2THLcl4H7ZnoOk1pCa2HMS+2saSZD4OLK0mSHYmKkamEh9W19BJW47hbrdZEkmG3AbhGpFZG9IvKsiOyN4HHnAnWqelBVh4A7gTdNcNyHgV8CzRFHbVJSWcBPs5Xsf5n7a5s5e8U8ivOykx2KiZHQQP+bNy+ZcLv0dBFJF9lls3zuJTi1y0IagPPCDxCRJcBbcMrOnDPZE4nItThla1i+fPkswzHJVh7I4dGD7dMfmEaauwZ47lgXH3/NumSHYmLoosoytqyYx0deUZHsUJJq2haMqtYDy4BL3Z/7InkcpxZmvuTpxl3/Jk515tFpYrhNVbeo6payMlvlnKrKA346+4cZHJnyz51Wdu53Nhez8jBzy+LiXO66/kJWlORPf/AcNm0LRkT+GdgCrAP+G8gCfgJsneahDTiJKWQpcHzcMVuAO93aVKXA5SIyoqq/iSR4k1rKwhZbLp2Xvt0G4XbWNrOg0D/WpWLMXBJJS+QtwBuBXgBVPQ5E8m54AqgUkVUikg1cCdwdfoCqrlLVlaq6ErgL+KAll7nr1GJLG4cBGB4N8tD+VnasK7cCoGZOimQMZkhVVUQUQEQiavOp6oiI3IAzO8wH/EBV94nIde79t842aJOaygO22DLck4dP0j04wg6bnmzmqEgSzC9E5HtAsYj8DfB+4PuRPLmq3gvcO+62CROLqr43kuc0qStUj8xaMI6dtc1k+YStFTY92cxNkWyZ/DUReRXQhTMO81lV/WPcIzNzzvz8bESgpcvWwgBU1zZz7qr5FKRR+XaTXiIZ5P+yqn4S+OMEtxkTsUxfBiX5tnUyQMPJPvaf6EmbrXNNeopkkP9VE9w227UxJs1ZuRhHda0zPXm7TU82c9hUG45dD3wQWD1u5X4A2BXvwMzcVF5oLRhwysMsm5/LmrL0Xidh5rapush+Bvwf8EXgU2G3d6uqLcc2s1JW4OeFxq5kh5FUA8Oj7DrQyju2LLPpyWZOm7SLTFU7VfWwql7FS1fyZ4hI+u2cY2KivNBPa88Qo8HxRR3Sx2OH2hkYDlr1ZDPnTTsG467k/yTw/9ybsnFW8hszY+WBHEaDysm+oWSHkjTVNc34MzO4YHVJskMxJq7iuZLfmJcJlYtJ16rKqkp1bTMXrikhJ8uX7HCMiatIEsyQqipuocpIV/IbM5FTiy3Tcy3ModZe6tv6bPW+SQuRJJjxK/n/BPxnfMMyc1WoXEy6ziQLTU+26skmHUy50FKcKS4/B6qwlfwmBsIrKqejnbXNVJQXpPUmVCZ9TJlg3CKXv1HVswlbyW/MbOVm+wj4M9MywfQOjvDYwXbec+GKZIdiTEJE0kX2qIhMutukMTNVVuhPyzGYXXWtDI0GrXvMpI1IquztAP5WROpxZpIJTuNmY1wjM3NWecCflrPIqmtbKPBnsmXl/GSHYkxCRJJgrO6YianyQA57GjqSHUZCqSo7a5vZVlFKdmYkHQfGpL5IyvXXJyIQkz7K3BaMqqZNqZTaE900dg7wd68sS3YoxiSMfZUyCVce8NM/PErP4EiyQ0mY6hqrnmzSjyUYk3Dlhek3Vbm6ppkNiwpZUJiT7FCMSRhLMCbhygrSa7FlZ98wu4+cZEeVdY+Z9GIJxiRcqAWTLgnmoboWRoPKpVYexqQZSzAm4cbqkXWlx1qY6poWivOy2LxsXrJDMSahLMGYhCvKzSLbl0FLz9xvwQSDygP7m7m4sgxfRnrMmDMmxBKMSTgRoSzgpyUNFls+e6yT1p4hG38xackSjEmKsoA/LcZgqmubEYGLKy3BmPRjCcYkRXkgPeqRVde2sHlZMSUF/mSHYkzCWYIxSVFe6J/z62BaewbZ29BhxS1N2rIEY5KirCCHk33DDI0Ekx1K3DxQ24KqbS5m0pclGJMUY6v55/BMsuraZkoL/Jy2uDDZoRiTFJZgTFLM9bUwI6NBHtzfwo51ZWTY9GSTpizBmKSY61snP320g66BEXbY6n2TxizBmKQoD8ztemTVNc34MoRtlaXJDsWYpLEEY5KitCAbkbmbYO6vaWbLinkU5mQlOxRjksYSjEmKTF8GJfnZtMzBtTCNnf3UNHVb95hJe3FNMCLyWhGpFZE6EfnUBPe/S0T2updHRGRTPOMx3lJaMDfXwuysdTYXs+rJJt3FLcGIiA+4GbgM2ABcJSIbxh12CLhEVTcCXwBui1c8xnvKC3PmZBdZdU0zS4pzqSwvSHYoxiRVPFsw5wJ1qnpQVYeAO4E3hR+gqo+o6kn36qPA0jjGYzymPOCneY4VvBwcGWVXXSvb15UhYtOTTXqLZ4JZAhwNu97g3jaZDwD/N9EdInKtiDwpIk+2tLTEMESTTGUBP609gwSDmuxQYuaJQyfpHRq11fvGEN8EM9HXtwk/SURkB06C+eRE96vqbaq6RVW3lJVZVdq5ojzgZySonOwbSnYoMVNd20x2ZgYXVpQkOxRjki6eCaYBWBZ2fSlwfPxBIrIR+D7wJlVti2M8xmPm4lqY6tpmzl9dQl52ZrJDMSbp4plgngAqRWSViGQDVwJ3hx8gIsuBXwHvVtX9cYzFeFCoHtlcSTD1bb0cbOllxzprZRsDELevWao6IiI3APcBPuAHqrpPRK5z778V+CxQAtziDoiOqOqWeMVkvKWsYG6ViwlNT7bxF2MccW3Hq+q9wL3jbrs17OdrgGviGYPxrlMtmLmx2PL+mmZWleazsjQ/2aEY4wnWUWySJi87kwJ/ZkynKu+ub+eOx4+iSZiY9peDbbzrvOWJP7ExHmUJxiTVqtJ89h3vjNnz/ccfX+SJw+2UJmGL4iXFufzVmbaUy5gQSzAmqbavK+Pm6jo6+4YpyouuMGTv4AiPHWrjfVtX8enL18coQmPMbFmxS5NU29eVE1R48MXoF9DuqmtleFTZbrO4jPEESzAmqTYvK6Y4L4vq2uaon6u6toUCfyZbVsyPQWTGmGhZgjFJ5csQLllbxgO1LVGVjFFVdtY2s62ilOxM+29tjBfYO9Ek3aVV5bT1DrH32OwH+2uaumnsHLAS+cZ4iCUYk3QXV5Yh4pS5n61QF9slNv5ijGdYgjFJNy8/mzOXFbMzinGYnTUtnLa4kAWFOTGMzBgTDUswxhN2rCtnT0PnrMrGdPYNs/vISSvRYozHWIIxnhDav/6B/TOfrvxQXQujQWVHlXWPGeMllmCMJ2xYVEhZwD+r6crVNS0U52Wxedm8OERmjJktSzDGEzIyhB3rynhwfwsjo8GIHxcMKg/sb+aStWX4MmyLYmO8xBKM8Ywd68rpHhjhqSMdET/m2WOdtPYM2fiLMR5kCcZ4xtbKUjIzZEbdZNW1zYjAxWtt/MUYr7EEYzyjMCeLLSvnzWg9THVtC5uXFTM/PzuOkRljZsMSjPGUHevKqWnq5nhH/7THtvYMsrehw7rHjPEoSzDGU0KlXkLbD0/lwf0tqGLlYYzxKEswxlMqygtYUpwb0TjM/TXNlAX8bFhUmIDIjDEzZQnGeIqIsKOqjF11rQyOjE563MhokAf3t7B9bRkZNj3ZGE+yBGM8Z8e6cvqGRnn8UPukxzx9tIOugZGxCgDGGO+xBGM854I1JWRnZlBdM/k4THVNM5kZwrbK0gRGZoyZCUswxnPysjO5YHXJlNWVq2tb2LJyHoU5WQmMzBgzE5ZgjCftWFfGwdZeDrf2vuy+xs5+XmjssunJxnicJRjjSdvXhaYrv7wVE5rCbOMvxnibJRjjSStL81ldms/9E6yHqa5pZklxLpXlBUmIzBgTKUswxrO2ryvn0YNt9A2NjN02ODLKrrpWtq8rQ8SmJxvjZZZgjGddWlXO0EiQvxxoG7vtycMn6R0atdX7xqQASzDGs85ZNY+8bN9LVvVX1zSTnZnBBWtKkhiZMSYSlmCMZ/kzfWytKKW6pgVVBeD+2mbOX11CXnZmkqMzxkzHEozxtB3ryjnW0c+LzT3Ut/VysKWXHets7xdjUoF9DTSett1NJtU1zeRk+QBs/YsxKcISjPG0xcW5VC0MUF3rJJjVpfmsLM1PdljGmAjEtYtMRF4rIrUiUicin5rgfhGRb7v37xWRs+IZj0lNO6rKefLwSf5yoG1sAaYxxvvilmBExAfcDFwGbACuEpEN4w67DKh0L9cC341XPCZ17VhXzkhQGRwJsqPKxl+MSRXxbMGcC9Sp6kFVHQLuBN407pg3AT9Sx6NAsYgsimNMJgWdtbyYQE4muVk+zl01P9nhGGMiFM8xmCXA0bDrDcB5ERyzBGgMP0hErsVp4bB8+fKYB2q8LdOXwTXbVjM4Moo/05fscIwxEYpngpmojofO4hhU9TbgNoAtW7a87H4z9934yspkh2CMmaF4dpE1AMvCri8Fjs/iGGOMMSkongnmCaBSRFaJSDZwJXD3uGPuBq52Z5OdD3SqauP4JzLGGJN64tZFpqojInIDcB/gA36gqvtE5Dr3/luBe4HLgTqgD3hfvOIxxhiTWHFdaKmq9+IkkfDbbg37WYEPxTMGY4wxyWG1yIwxxsSFJRhjjDFxYQnGGGNMXFiCMcYYExcS2sgpVYhIC1A/y4eXAq0xDCceLMboeT0+8H6MXo8PvB9jMuOb6NwrVDWhxfxSLsFEQ0SeVNUtyY5jKhZj9LweH3g/Rq/HB96PMZnxeeW1sS4yY4wxcWEJxhhjTFykW4K5LdkBRMBijJ7X4wPvx+j1+MD7MSYzPk+8Nmk1BmOMMSZx0q0FY4wxJkEswRhjjIkLSzBpQkQm2tzNzCEikpXsGOYCe6/EjiWYKIiIz/3Xk/8h3X12PioiS9XDg20iUikiOcmOYzIislFECpIdx2Tcv/PngL8LXU9qQBOw90psJOu9Mtu/nyWYWRCR94rI08CNyY5lMiJyNVANnAl0efGNLSJvEpEDwOeB74vI/GTHFE5E3iUie4F/AX7ubpznKSLy1zh/56uBv4axbTA8wd4rsZGs90q0fz9LMDMkIlXAB4HfAReLyGpVVRHxzGspIluBHwL/oKpXq2pX6EPHK28e9w1yDfBOVb0KaAb+UUTWJjcyh4hcBvwtcL2qvgVYA7zBvS/pr6GI+ETkA8DfAJ9Q1dXAMRE5LcmhjbH3Smwk670Si7+fZ/7QXiYigdDPqlqD823xP4DngRvc24PJic4xLsZdOFtWr3fv+5SIvEFECpL57TY8xtBNQOh1uxN4K3B5sloKoW4A105VvVhVd4lIEXDQPUaS/Br6AFR1FPitql6iqo+LyHqgG+c1TZrwrkQPv1fCY/Tqe2V8l2xC3iux/qyzBDMNEfkU8LSIfFlE3uveXKuq7cCvgTUicrF7bFJez3ExfsC9+YPA7SLyDFAMfBj4qvutJJkxfkVE3um+fs8C7xGRecAW4ElgIbAkCfF9HvisiISKAQ66ty/A2ZW1A+dNnczXMBRjOYCqtrq3i6q+AKwENru3Jfz/ooh8Atjp/o2vdmOs8dh7JTzG0BbtXnuvhGL8qohcCZwkAe+VuHzWqapdJrkAlwIPAquAHUAjsDHs/gKcgdWfht3m80CMZ7n3fQjY4v5cBvwGeI1HXse1wArgG8D/Aj8FTgN2AisTGJsf+H84Fbp/Dbx6gmOK3H/nA3cDlyf49ZsyxtD/OeAjwK1J+PuW4HQz/QInwb0NeAxYEnZMUt8rU8S4wr3/Q8DZ7s9Jea9MEOMVbowlwOp4vlfi9VlnLZipZQFPq+ohVa0GvgV8Mez+XuAuoEdEviAiX8H5FpnsGP8dQFVvVtUn3Z9bgHacD8lEGx/jd4Cvq2q9qn4M+ICqvktV9wENCY5xGKePeQPwKLBDRFaFH6Cqne6/7Tj93/MSGN+0MarTXQZOq6tTHIl8b/cCf1DVt6vqM8Cfcb5xLx13TDLfK5PFuATG3iu73Z+T9V4ZH+OfgOeA1ap6MM7vlbh81lmCmVoeUCLutEBV/RKwSESucK8rMACcAVwPtKjqAQ/EWB6KEZxBQhH5OrARp7850cbH+O/AEhF5h3u9SUSWicjNOG/42kQFpk5/8n5V7QV+jvOheK6I+OHUQK/7Gn4N55tlQl/DCGIMjR3VAO9TR8LGOVR1ALgn7KYRnNep0Y1Pkv1emSLGhvDjkvlemSTGTcCJsGPi9V6Jy2edJRgmny2iqr/GmT30+rCbvwJ8LOz6F4F9wHJV/arXYnS/6d6B8w3lElWt81CMfxd2/SbAB7zO/SBNZHyD7r+HgYeBS4CqsMdtxOm2CL2G++MR32xjDGvBPAL8u4hkxmsG1BTxdYddLQGaVfWIe19osPzLJPe9MmmM7uNW4wygJ/O9MmWMrpuZ5XslfBA/vJUbt8+6WPTfpeoFeBNwO7B53O0C+N2fr8Tpm1zpXl/u/oED7vUcD8eYDeQA8z0cY4F7PS9J8WW4P4fGMQpxuvDeCbwbeL17e1kSX8OpYvxr4C3xjG0W8W3D7asHXoM7ZgTkejjG7e7PJR6OMfQ65s/ivJfhrPX5EfCPYbf7IniPzvqzLpM0E2qui8gO4As4/dsXiEi9qp4Ma84Put9ofo7T9/0ZcRYcvQE4rO43DXWatV6Ncch9Si/H2AOgqn1Jik/d+NqBDlXtEpEXcd5YbTgD56jTLx9TsY7RK/EBFwPZIvJdnC6VTwOoar9HY9wIfMqNsS0FYoyo1eK2kjJw1kp9APhnnP8v/yQi71fVH6jT+h2N22ddPLO11y642xO4P68EFuHMnvghTpM4dF+G+8dsAS4CioCtON88Pm4xejvGWcTXiPMNT3C6nQ4Cn/bYa5jQGGcZ3+vc234KHAFu9OBrmBYxjjvv5UBl2PW/Az7m/uyL53s0bVowInID8AoReRC4Q52+bIBGEXkNcImI1KnqMZz55Z3AWlU96R63S0Qe1VP93RajB2OcZXwbQvGJyGHgDI3TOFAqxBhtfDhjfh9S1Y54xGcxRnzeh4Afqeq94lR+yFTVEZxFpTXu4eXE8z0az+ztlQvwFpwZITuA/8YZUN4Udv8m4CdM0JeNk+HFYvR+jFHGl5kC/xfjHmOU8WWlwGs4p2Oc5Lybw///4LSeLpzgsTF/j6bLLLLzgO+qM7/7c8Ahwoq3qeoenD/KGSJyqTgrWkN9p6PqvvoWo+djjCa+kTjHlioxRhPfcALisxhnft7QOOKIOFPblwG7RWSpiPxN2Hlj/h6d0wnGHeQCp7/6nQCqWo+zGjZfRN4YdvgdOAXlfo4zPdBiTJEYvR5fKsTo9fgsxqjP+yb3/ir3XDfiVKWI62LSOZVgxClKGPo5vCjhXUBf2IvciFNmYYM4CnBWrj6LUx7h4xCfsucW49yPLxVi9Hp8FmPMz7veTUKrccZgVuFMJvjyTM47U3MiwYjIeSLyW+A/ReT9IuJXVZVTK5xP4tRwut79Y3Ti1NbJcV/YAZyZGq9T1UaL0Zsxej2+VIjR6/FZjHE7b6573jpgm6peH6/XJlzKJxhxVlnfjJO578KZAlgBL1nhnAvch5PNbxORxTibCw27x42oarPF6N0YvR5fKsTo9fgsxoSc91lVTVgJnJRPMMDZQJ2q/hj4I87K9SNucxAR+QJORl8A/D1OXZ+f4Sxk+pLFmDIxej2+VIjR6/FZjPE77xcnesK40wRM2YvlBacG03lh18txqsj+G07hukeBHwD/gPMH+BlQMe454laWxGJMj/hSIUavx2cxevO8MX3tknnyGb7YAeBXOGUUfgDMC7uvCqeQ3tVhf5h7cfdFcW/LsBi9H6PX40uFGL0en8XozfPG45JKXWRDwP04xf2O42zGA4xt7VnFqdLbu91jQk3HDE1M+XKLce7Hlwoxej0+i9Gb5405TycYEblaRC4RkWJ1ypV/H2cTnv3AFhFZG3b4H4B/dvsjrwROB1ohvnuAW4xzP75UiNHr8VmM3jxvvInbpPIM90VbiNOfGAQOAPk4U/pa3WMqgfcAg6r6Bfe2XOA2nH5KH/ARVX3eYvRmjF6PLxVi9Hp8FqM3z5tQye6jC79wah+EtcBP3J8zcfa++OW4Y98C3AJU4g5kuccutBi9HaPX40uFGL0en8XozfMm+uKJasoikgl8HvCJyL04GyqNwlj9nI8Ax0XkElV9wL391yKyHvg/oEBEdqjqC0CTxejNGL0eXyrE6PX4LEZvnjdpkp3hcGZB7AG+i7MxzoPAa3H2QTg37Ljrgeqw61cAvcB/AuUWo7dj9Hp8qRCj1+OzGL153mRekh+As8nNu8Ou3+K+wO8Fdru3ZeD0Vf4CWBX2uIssxtSI0evxpUKMXo/PYvTmeZN5SX4AkAf4OdUn+S7gi+7PzwAfdn/egrNpj8WYgjF6Pb5UiNHr8VmM3jxvMi9Jn6asqn2qOqinaum8Cmf7ToD34VQB/R1OaeunLMbUjNHr8aVCjF6Pz2L05nmTyROD/ADiVANVnDo6d7s3dwOfxpnnfUidrUWTxmKMntfjA+/H6PX4wGL04nmTIektmDBBIAtnwdBGN5P/ExBU1Yc98oJbjNHzenzg/Ri9Hh9YjF48b+Ilu48u/AKcj/PiPwx8INnxWIzpGV8qxOj1+CxGb5430RdPreQXkaXAu4FvqFMuwXMsxuh5PT7wfoxejw8sRi+eN9E8lWCMMcbMHV4agzHGGDOHWIIxxhgTF5ZgjDHGxIUlGGOMMXFhCcaYaYjIqIg8IyL7RGSPiHxMRKZ874jIShF5Z6JiNMaLLMEYM71+Vd2sqqfhlPe4HPjnaR6zErAEY9KaTVM2Zhoi0qOqBWHXVwNPAKXACuDHODsRAtygqo+IyKPAeuAQcDvwbeBLwHacgoc3q+r3EvZLGJMElmCMmcb4BOPedhKowqkhFVTVAXd72ztUdYuIbAf+QVVf7x5/Lc5eHv8qIn5gF3CFqh5K5O9iTCJ5ptilMSlG3H+zgJtEZDPOzoRrJzn+1Th1p97mXi/C2QLXEoyZsyzBGDNDbhfZKNCMMxZzAtiEM6Y5MNnDcPb7uC8hQRrjATbIb8wMiEgZcCtwkzr9y0VAo6oGcWpL+dxDu4FA2EPvA64XkSz3edaKSD7GzGHWgjFmerki8gxOd9gIzqD+N9z7bgF+KSJXANU4e6cD7AVGRGQP8EPgWzgzy54SEcHZaOrNiQnfmOSwQX5jjDFxYV1kxhhj4sISjDHGmLiwBGOMMSYuLMEYY4yJC0swxhhj4sISjDHGmLiwBGOMMSYuLMEYY4yJi/8PZhT5IizS4JEAAAAASUVORK5CYII=\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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def trend_plot(df):\n",
    "    for col in df:\n",
    "        plt.plot(df.index, df[col].fillna(0))\n",
    "        plt.title('Daily ' + col + 'retention rate')\n",
    "        plt.xlabel('Date')\n",
    "        plt.xticks(rotation = 30)\n",
    "        plt.ylabel('retention rate')\n",
    "        plt.show()\n",
    "\n",
    "trend_plot(daily_retention)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0af3126f",
   "metadata": {},
   "outputs": [
    {
     "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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\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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WxchHTKkYs4L2roNKZX/AlMrEZpIejV5TyYDJaxjTYUrFmBXsjFMq+/qCdeffFzm07b2HZ6lYqxYjnzClYswK2rsHx58HzVLpHx49pJmkR5M1lTTyEFMqxqxgZ9cQLbVlQPBiFBOnPnrUV1hMxcg/TKkYs4L2riFaGyuoqygJoKUSPazwESBcHKKmrJgDVlVv5BGmVIxZQXv3EAvrK2isDAdPqUwRqAfHBRY0eQ1jOkypGAXPSDTGnt4IC+rKaaoqDZw7aSr3F1hTSSP/MKViFDx7eiKowsL6cpqqg3XnPxwdY2QsRs0k2V/gpBVboN7IJ0ypGAXPzi4n82tBfTnNVaXsC5BS6Z+iQ7GHWSpGvmFKxSh4drrV9AvrnJhKXyRKZDQYrVqmanvv0VgZpmtwhLGYZlMsw0gZUypGwdPeNURIYF5tGU3VTu1HUDKqpmp779FYVUpMoXswGPIaxkyYUjEKnp1dQ8ytKSNcHBovKAxKXGXcUpkmUA/QGRAlaBgzYUrFKHjauwdZWF8OQNN465OgKJXJ2957eP2/giKvYcyEKRWj4NnZNcSCOk+puBfpgPT/msn91WSdio08w5SKUdCMxZQ9PREW1E9QKgFJ0+2fYuqjR0Olp1SCIa9hzIQpFaOg6eiNEI0pC+srACgPF1EZLgqMpTJT9lddRZiQ+J9YcOtT7dzwwEu+HsOYHZhSMQoar+W95/4CJ6MqKDGKvkiUcFGIspKiSdcXhYSGyjD7fVYqv3hsOz95ZJuvxzBmB6ZUjILGa3nvBerBiVMEpUq9f3h0SteXR2Nlqe/ur13dEboHRn09hjE7MKViFDQ7DziWyvy6eKVSGij311SuLw+/q+pjMWV3zxB9w1FGx2K+HceYHZhSMQqa9u4hmqpKD3EvBan/V39k8rb38TRWlfpap7K3b5jRMadiv3vQrBUjPUypGAWN0/K+/JBlTZVhDgyOEA3AXXnfcAKWis/t+tu7D45atsp9I10SUioi0ioir3Kfl4tItb9iGUZm2Nk1NJ5O7NFUXYoqdAXgrrwvEUvF7Vc2EvVHCcYrlSCcEyO/mVGpiMh7gJuB77qLFgK/9VEmw8gIsZhObqkEqFVL//Ao1VO0vfdorPK3X1l7V7xSMUvFSI9ELJX3A6cDvQCquhmY46dQhpEJ9vcPMxKNsbAuwEolwUA9+CfvLnN/GRkkEaUyrKrj3zQRKQasD7cReLyW9xPdX35fpBNFVaed+ujR5HNTyfbuIZY0OsWh5v4y0iURpfJnEfkEUC4i5wH/C/zOX7EMI308t45XTe/hWSq57qc1HI0RjWkCMRVPXn+UYHvXEEfOqSJcFLLsLyNtElEq1wL7gPXAPwB3qOonfZXKMDLAZNX0ADVlxYSLQjmfAOm1aKmewf3V4FoqfsVUdnU7DTfrKkrM/WWkzfTfZod/UtWvAd/zFojINe4ywwgs7d2D1FeUUDnhoi0iNFaFc14A6bW9n8n9VV3qKMH9PlhWPUOj9A1HWVBfTn1F2AL1RtokYqlcOcmyqzIsh2FknMnSiT2aqkpz3qplvO39FLNUPDwl6If7q33cmqugrqLEYipG2kx5iyQibwMuA5aKyG1xq6qBzpl2LCJlwANAqXucm1X10yLSAPwKWAK0AW9W1a5UP4BhTEV71xBHNFdNuq6pKpxz91f/DFMf42msCvsSqPcyv+bXlVFfEWbL/v6MH8OYXUz3bX4Y2A00AV+JW94HPJPAvoeBc1S1X0RKgAdF5E7gDcC9qnqdiFyLE7P5eErSG8YUqCo7u4Y4Y0XzpOubqkp5fndflqU6lN4Z2t7H41dTyfa4DLn6yhK6tpulYqTHlN9mVd0GbANelsqOVVUB77anxH0ocAlwlrv8JuB+TKkYGaZrcJSh0bHDgvQeja77S1URkSxL5+C5v2pmKH4Ep6r+xb2ZtyLau4cIF4doqiylriJM9+BITs+Jkf8kUlF/moj8TUT6RWRERMZEpDeRnYtIkYg8BewF7lHVR4G5qrobwP07aSGliFwtIutEZN2+ffsS/kCGAfHpxFPFVMKMjim9Q9FsinUI/QkG6sFxf/mR/dXePcT82jJCIaG+ooTRMWVgZCzjxzFmD4kE6r8JvA3YDJQD7wa+kcjOVXVMVU/Aae1yqogck6hgqnqDqq5R1TXNzZO7MAxjKnZ2OXNUpgrUN1c7tR+5jKvMNPUxnsaqUoZGxxgcyawSbI9LZqircFKXu3weCGYUNgk1lFTVF4EiV0n8CDg7mYOoajeOm+sCoENEWgDcv3uT2ZdhJIIXK5hY+OgRhFYt/cNRSotDhItn/hk2js+qz+wF36tRAah3lYoVQBrpkIhSGRSRMPCUiHxZRP4FqJzpTSLSLCJ17vNy4FXAC8BtHExTvhK4NRXBDWM6dnYNUV1aTG355PEKr1VLLqvq+4Zn7lDs4YcSHI6OsbdveHyAWX2Fc66sVsVIh0SUyhXudh8ABoBFwBsTeF8LcJ+IPAP8DSemcjtwHXCeiGwGznNfG0ZGma5GBYJhqSQy9dHDDyW4uzsCHOw4UGdKxcgA036jRaQI+E9VvRyIAP+R6I5V9RngxEmWdwLnJimnYSTFZC3v46mvCBOSHLu/IjO3vfdo8NxfGSzY3DWh4Wadub+MDDCtpaKqY0Cz6/4yjLxhZ9fglOnEAEUhocHniYoz0Z/A1EeP8aaSGQyij3dx9iyVcrNUjPRJ5BvdBjzkVtUPeAtV9at+CWUY6dAzNEpfJDplkN6jqarUl35aidIXibKoYXoZPcrDRVSGizLq/mrvGkIEWmodpVJcFKK6rNgsFSMtElEqu9xHCKdFi2EEmvF+VtO4v8BTKrmNqSQaqAe3YDOD8u7qHmJOdekh2WfWVNJIlxm/0aqacBzFMIJAe/fkLe8n0lQVZtv2gWm38ZP+4eiMbe/jyXT/r/buofHML4/6ihKzVIy0SKhOxTDyiXa38HG6QD04d/65an+vqk5MJRlLpTKz7rr2uBoVD69Vi2GkiikVo+DY2TVEWUloPGNqKpp8qlJPhKHRMcZimnD2FzgFkJlyf8Viyu7uyGEuwnprf2+kiSkVo+Bw0okrZmyK6M1+z4W10p9EixYPr/+X06s1Pfb3DzMyFpvUUrGYipEOM36jRaQZeA/O/JPx7VX1nf6JZRips7PrcLfOZHgFkPv6h1ncmFgWVqbw2t4nG6iPxpwmmLUViVs4kzExndijviJMXyRKdCxGcZHdcxrJk8g3+lbgL8AfAWtfagSe9u4hjl1YO+N2nlLxY07JTIxPfUxCqYxbVgPDaSuViYWPHvWVzn67h0bHz49hJEMi3+gKVbV5J0ZeMDgS5cDAyIxBeoCmavcinYNalYPur2RiKp4SHOGINBt3e2nXE7O/DlbVj5hSMVIiEfv2dhG5yHdJDCMDHJy5PrNS8S7SuahV6fNmqSQRUxlv1ZIBedu7h6guKz5sQNjBppIWrDdSIxGlcg2OYomISJ/7SGhIl2Fkm50ztLyPJ1wcoqasODdKJQ33VyZqVXZNkk4MB9vf20wVI1USKX60Knojb9g5w8THiTRVl+ak/X1/CoH6+gzOVJkqmcHrVGwFkEaqJPSNFpHXAWe4L+93W9gbRuBo7xoiXBSiOcF4QFNVaU6mP3pTHyuTcH+VFIWoqyjJSKfi9u4hTl3acNjycUvF0oqNFElkRv11OC6w59zHNe4ywwgcTusRZ+Z6IjTnqP9X//Ao5SVFlCSZtusUQKZ3we+NOA03J7NUKsJFlBSJxVSMlEnkNuki4ARVjQGIyE3Ak8C1fgqWDVSV3T0R1rf3MDgS5aJjWygtLsq1WEYa7OwanLGRZDyNVelfpFMh2RYtHo0ZUIJeOvHEzC8AEbFWLUZaJPqtrgMOuM9nLgAIIKpKe/cQG9p72NDey/r2Hja09xwS9PzGvS/y6dcdzZkr0szXNHJGe9cQZ6+ck/D2TVWl9AyNMhKNJTQrPlP0RpJrJunRWBlm897+tI49VY2Kh9OqxZSKkRqJfKu/CDwpIvcBghNb+VdfpcoQj2zp5IFN+1jf3sOzu3o54CqQopCwfE4V5xw1h2MX1nLMglq6B0f43O3Pc+UPH+P8o+fyqdeuTiiDyAgOkVFn5noylsp4AeTA8PhckWzQn2Tbe4/GqjCPbk3vgu+lXS+cIu3aadVi7i8jNRLJ/vqFiNwPnIKjVD6uqnv8FiwT3P3sHn7y120sn1vNq1bN4dgFjgJZ1VJDWcnhbq7Tj2zi+3/Zyjf/9CKv+uqfef9ZR/KeM5ZNuq0RPHb3HDpzPREa4/p/ZVWppOr+qiyla3AkrTYqO7udZIapihvrK0po2z+Y0r4NY8pvtYgcpaoviMhJ7qKd7t/5IjJfVZ/wX7z0uObc5Xz8gqMSVgqlxUW8/+wjufTEBXzh98/zlXs2cfMTO/n0xas556i5PktrpMvOBFvex+NdWPdncPZ7IvRFRmmqqkz6fU1VYVSd4sTm6tQq3nd1R2iZJpmhviLMk4PdKe3bMKa7VfoQcDXwlUnWKXCOLxJlEK/lRLIsqCvnW39/Em/bvJ9P37aBd964jnOPmsOnLz46640HjcRJdOJjPF7q8f6+7CoVx/2VfP+uxjh3XapKpb1rcFprzgnUj6KqM3Z6NoyJTGk/q+rV7tMLVfXs+AdORljB84rlTdx5zRl84qKjeGRLJ6/6f3/mq/dsIjoWy7VoxiS0dw9RFBLm1ZQl/J5c9f/qG44m1aLFoyEDBZCTTXyMp76ihJGxGIMj1j/WSJ5EnLIPJ7isIAkXh7j6jCO498NnccHR8/j6vZu55pdPMWqKJXDs7BpiXk1ZUrGGinAx5SVFWa1VicWcqY+pBOrHOxWnKO9INOYkM0yrVKwA0kid6WIq84AFQLmInIgTpAeoAWadD2hebRlff9uJHLewls///nkU5WtvPTHp4jXDP9q7hpJyfXk0VWduomIiDI6OoZpcixYPrwnmgRR7c+3piaA6vYswvlXLwvqUDmPMYqb7Vp8PXAUsBL4at7wP+ISPMgWad79yGYCjWPRJvv42UyxBYWfXIKcd0Zj0+5qqMjv7fSZSaXvvUVteQlFIUnZ/7ex2khmmtVQqzVIxUmdKpaKqNwE3icgbVfU3WZQp8Lz7lcsQET53+3N84OdP8I23nZTVwjnjcEbHYuzpjUxZezEdTVWl7DiQvRTa8bb3KVgqoZDQUBlOuf/Xru6Z066t/b2RDol8q28Xkcs4fJzwZ/0SKh941yuWIsBnXcXyzctMseSSPT0RYppYy/uJNFWFeXJ7d+aFmoJU2t7H01gZTtmy8jLkWuqmTmaIH9RlGMmSyFXwVuASIAoMxD1mPe98xVI+c/Fq/vBcB+//+ROMRC14nyt2ppBO7NFUVcqBgWHGYpppsSZlvO19Ctlf4PUrS81Sae8epLm6dNoed3XlrqUyYJaKkTyJfKsXquoFvkuSp1x1+lJEhE/f9izv+9kTfPvvzWLJBe3dyc1RiaepqpSYOjGEbIzQ9drep+L+AidY/3RXd0rv3dUdmbHjQHFRiOqyYoupGCmRUEqxiBzruyR5zJUvX8JnLzmaPz7fwft+9jjDUcvvzzY7uwYRIaVWK41ppukmS/+wYwGkUvwIjrwHUnV/dSeWIVdXUWLuLyMlElEqrwAeF5GNIvKMiKwXkWf8FizfePvLlvC5S4/hj8/v5b0/fcIUS5Zp7xpiTnVpSlbieFPJLGWAjVsqKbq/mqpK6RuOEhlN7jsWizmduhPpjVZvTSWNFEnkW31hKjsWkUXAj4F5QAy4QVW/JiINwK9wAv9twJtVtSuVYwSNK05rRYB/++0G/vEnj/OdK062+SxZor17KOWu0uP9v7JkqaSrVBrdlN8DAyPTVsZPpHNghJFoLCGlYjNVjFSZ8bZOVbcBi4Bz3OeDibwPJ7D/YVVdBZwGvF9EVuMM97pXVZcD91IAw77iufy0Vj536THct3Eftz65K9fizBqmmrmeCF7/r31Z6v/VPxylMlxEUYLTKSfSmKJl5cWdErNUSsxSMVIikXHCnwY+zsEZKiXAT2d6n6ru9joZq2of8DxOhf4lwE3uZjcBlyYtdcC5fO1imqpK+euWzlyLMisYiym7e4ZSCtID1JQXU1Ikhwxs85P+SGpt7z28/l/Jdlb20okTsW7qzVIxUiQRi+P1wOtw04hVdRdQncxBRGQJcCLwKDBXVXe7+9oNTDqmT0SuFpF1IrJu3759yRwu54gIa5c28OiWTlSzk6Y6m9nbF2F0TFNKJwbn/9VYWZq1TsV9w6Mpu77gYP+vZIP1M018jKeuooTeSNSapxpJk4hSGVHnyqgAIpLUEAgRqQJ+A3xQVXsTfZ+q3qCqa1R1TXNz/o33XbusgV09kfH6CcM/xlvep+j+Aqf/VzZjKqlmfsGh7e+Tob17iOrSYmrLZz6211SyZ8hcYEZyJKJUfi0i3wXqROQ9wB+B7yeycxEpwVEoP1PV/3MXd4hIi7u+BdibvNjBZ+1SpwfVI+YC852DNSqp9znNZv+vVDsUe1SGi6gqLWZzR3Kz6nd2Td/yPp46a9VipEgigfr/Bm7GUQ4rgX9X1a/P9D5xpvv8AHheVeMbUt4GXOk+vxKnYr/gWD6nirqKEh7beiDXohQ8OzNgqTRWlmatU3FfJLVZKh4iwgXHzOOO9bsZcFu+JMKuBGtU4KClYnEVI1kSCdR/SVXvUdWPqupHVPUeEflSAvs+HbgCOEdEnnIfFwHXAeeJyGbgPPd1wREKCacuaeBRUyq+s7NriKaqMOXh1NO3HffXSFZiYM7Ux9SVCsBbTlnEwMgYv1+/O+H3JFqjAvEzVcxSMZIjEffXeZMsm7F2RVUfVFVR1eNU9QT3cYeqdqrquaq63P1bsFfdtcsa2X5gkN09Flfxk50zjMdNhOaqUkbGYvRGEr/zT5X+4WhKbe/jWdNaz7LmSn79tx0JH7NnaDQF95dZKkZyTKlUROS9IrIeWOlW0nuPrYBV1CfA2qUNADy6pWD1Zs4ZHYvxwp4+FjcmlT9yGNlq1TLmTn1MJ6UYHBfYm9csYt22Ll7cO3NsJZnMLzg4U8XcX0ayTGep/By4GCcGcnHc42RVvTwLsuU9q1pqqC4r5tGtFqz3i7uf3cO+vmEuPWF+WvvJVquWgRHHEqpJU6kAvOGkBRSFhP9dN7O1kmyGXGW4iJIiMfeXD6jqeHJJITKlUlHVHlVtU9W3cWhFfUhElmZNwjymKCScsqTBLBUfufGhNlobKzh75aTlTgmTrVYt/Wm2aIlnTnUZ5xw1h988sZPRGepJkqmmB8cSslYt/nD3s3s448v3jVuPhUYqFfVhEqioNxzWLm1gy/4B9vZFci1KwbF+Zw/rtnVx5cuWEEqx5YlHtpSK1/crnTqVeN6yZhH7+0f40wvTZ+a3dw9RUiTMqU68tX99RYnNVPGB53b1MhZTNifgtsxHslJRP5tZu8ypV7HU4szzo4e3Uhku4u/WLEx7X/UVJYjge62K1/Y+3ZiKx1krm5lTXTpjwL69a4iW2vKklG9dRdgC9T7Q1umMrt7WWZizDn2vqJ/tHDO/hspwkbnAMsy+vmFuf3o3f3fyQmoycNdfXBSiocL/qvp0OxRPpLgoxBtPXsh9G/fS0Tu1Nbyre4j504wQnoy68hK6LaaScTxl0rZ/MMeS+EOqFfXf81eswqG4KMTJSxosWJ9hfv7odkbGYlz58iUZ22dTlf/9vzylkolAvceb1ywipnDz4zun3MapUUmu40C9WSq+MKstFbcq/lccXlH/jSzIVjCsXdrApo5+DmSpC26hMxKN8dNHt3HWymaWNVdlbL/Z6P/VP5zeKOHJWNpUyalLG/jfdTsmLd4cHYvR0RtJuuFmXaVjqVhT1MzRPTgy3k+tbTYqFdft9duJFfVZkq1g8OpVHjNrJSPcsX43+/qGuSqDVgq4rVp8VvyZzP6K5y1rFtHWOThpB4c9PRFiCguSdH/VV4QZGYsxOGJTTDPFNtdKOaK5kh0HhhiLFZ7CTsT99YiInOK7JAXMcQvrKCsJWcuWDPGjh9tY1lzJGcsz2706O+6vUUSgMpxZpXLRsS1UlxZPGrA/mE6crPvLiVV1W6fijOFZJ2esaGZkLMaeaeJg+UoiSuVs4K8i8pLNqE+NcHGIkxbXW7A+AzyxvYund3Rz1cvTTyOeSFN1mIGRMYZ8vDPvG45SFS7OuOzl4SIuPmE+d2zYfVi7+vHCx2TdX17/L3PbZgzPUnnl8ibn9f7Cc4ElolQuBI4AzsGpqH+t+9dIgrVLG3l+Ty89lk2TFjc+1EZ1aTFvOCn9NOKJNFX6X6uS7tTH6XjLmkVERmPc9vShY6y9IruW2uTdX4BlgGWQts4BWmrLWDmvxn1deBlgCc2on+yRDeEKibXLGlCFv7WZtZIqHb0R7li/mzefsijjMQlwLBXwV6n0ZaBD8VQct7CWo+ZVH+YCa+8eoqmqlLKS5Lo411tTyYyzvXOQ1sYKWmrKCBeH2HZgdloqRgY4YVEd4aKQpRanwU8f2caYKle+bIkv+z9YVe/fRdTpUOyPUvGaTK5v7+G5XQeHrLYnMUclnjqbqZJx2joHaW2oJBQSFtWXs60Aa1VMqWSJspIiTlhUZ5X1KRIZHePnj27n3KPmsrgx9QmP05GNVi19w1GqMtSiZTJef+ICwkUhfh3XZNKpUUnO9QU2/THT9A9H2d8/TGuT8/1d0lhZkGnFplSyyNplDWzY1Tteq2Akzu+e3kXnwAjvOH2Jb8docNu9+zkBsi8y6pv7C5yW9a8+ei63PNlOZHQMVXUmPqYwb6akKER1aXHBur9ueOClhOfRZAKv2HGJO6ahtbGS7QcGC64OyJRKFlm7tJGxmLLO4ipJoarc+HAbK+ZW8fIjGn07TllJEdVlxf66vyJRqn1yf3m85ZRF9AyN8ofnOugcGCEyGkt5iJlXAFmI3PDAVn78SFvWjrfdDcq3upb2kqYKBkfG2JelMdbZwpRKFjmptY7ikFi9SpKs29bFs7t6uerlS3GaPPhHc1Wprz9yP2MqHqcf0cSCunJ+/bcd45lfiU58nEihtmo5MDDC/v5hNnf0Z60AsW1cqTiWyuIGR7lsK7AMMFMqWaQiXMxxC2t5dIsF65PhRw9tpba8hEtPTG8QVyL4WQAZdavTM9X2fipCIeFNaxby4Iv7x2ujUgnUg9epuPAslU0dfQAMR2PsOJCdi/q2zgGaqsLjNxWeG6ytwGpVTKlkmbXLGnlmZw+DI7mNq9y3cS+f/d1zRGcY7pQoW/cP8IGfP5HxTKH27iHufraDt56yiIoMV6FPRmNV2LdWLQPDTlGlX3Uq8bxpzSJE4LsPbAFgYZLV9B71FSUFmf3lKRWAjXHP/aStc2DcSgFH0ReFxCwVIz1OXdpANKY8ub07ZzL0Rkb5yK+f5ocPbeXLd29Me3/9w1He8+N13P7Mbh56MbNW2E/+ug1V5YqXtWZ0v1PRVFXqW/ZXnztLxe+YCjgTHl9xZBP7+4epDBdRU57aMevKSwqyon7jnj4qw07dzqY92VEqXo2KR0lRiIX15QWXAWZKJcusaa0nJOTUBfat+16kc2CEs1c2c8MDW/jdhArsZFBVPvq/T7NlXz8hged39878pgQZGhnjl3/bzqtXz2NhvT9pxBNpqiqle3B0xvG8qXBw6qP/SgWcgD04d8SpxqLqKsL0RqIZs2iDwqaOPo6eX8vihoqsWCqR0TF29URobTh0HJWXAVZImFLJMtVlJRyzoJZHchSs3945yI8ebOPvTl7Id69Yw5rWej528zO8sCc1ZXD9n1/izg17+NcLV3HknKqMKpVbn2qne3CUq3xMI55IY5WTVuzHmAI/2t5Px3mr51JfUTIeEE4Fr6p+Yj+xfEZV2binjxXzqlgxt/oQV5hfeHGbJU2H/i9aGyrYun+goNKKTankgLVLG3hqRzeR0ey3FP/inc9TXCR89PyVhItDfPvyk6guK+bqHz+edF+yP2/ax3/dvZGLj5/Pu1+5lFUtNRlVKjc+3Maqlprx0QHZwCuA3OdDsN6vtvdTUVpcxI/fuZZPXLQq5X3Uu7U7hRSs7+gdpjcSZcXcalbMrWLLvgFGov5aYhMzvzxaGyvoi0QLKm3blEoOWLu0kZFojKd2dGf1uI9s6eTODXt475lHMLfGqbCeU13G9ZefzO6eIa751ZMJp1du7xzkn3/xJCvnVvOlNx6LiLCqpYZdPZGMBHb39kV4YU8fbzhxge9pxPE0+9j/qzfixlR8zv6K59iFtWkNMvNatfQMFU5cxXN3rZhbzcp51URjylafM7AOFj4eaqmMZ4AVUFzFlEoOOGVJAyIk1bJFVdMykcdiyuduf475tWW854xlh6w7ubWez7zuaO7fuI//+eOmGfc1OBLl6p+sQ1X57hUnj2dlrWpxOq8+lwFrZUN7D+A0Scwmfvb/8txf2YqpZILxppIDhXMn7QXmHUulGvA/A2xb5yC15SXjStrDc4cVUlzFlEoOqK0o4ah5NQk3l/xb2wHO/u/7ed/Pnkg5YPqbx3fy7K5err1o1aTdai87dTFvWbOIb/zpRe5+ds+U+1FVrv3NejZ29PH1t514iDm/qsX5gT6/O/0f6PqdvYjA0Quyq1QaXaXiR6uWbLu/MoHX/r6QCiA3dvTRXF1KQ2WYZc2VFIXE9wwwJ5348NjWwvoKRKCtgBpLmlLJEWuXNvD4tq5pfbkj0RhfuusF3vzdv9I/PMadG/Zw7f+tT9pi6R+O8l9/2MhJi+u4+LiWSbcREf7jkqM5fmEtH/7107y4t3/S7X7w4FZue3oXH3n1Ss5aOeeQdXOqy2iqKs1IXGXDrh6WNlVm/QJcGS6irCTki/urLxIlJFARTq4FfS7xmkoWks9/U0cfK10LpbS4iKVNlVmxVCbGU8BpDdRSUzbuHisETKnkiNOWNRAZjbG+vXvS9Zs6+rj0Ww9x/f0v8ZY1i/jzR8/ig69azs2P7+S6u15I6ljX3/8i+/qG+dRrV08bnygrKeL6y0+mrCTE1T9ZR1/k0AvJwy/t54t3vsD5R8/lfWcdMek+VrVUZ0aptPdwbJatFHCUq1Or4o/7q6q0OKsxonSpKi2mOCQFY6nEYsrmjv5xtxfAyrnVbPZRqYyOxWjvHjosnuLRWmDdik2p5IhTlzqNER+ZMGI4FlO+/5ctvPYbD9LRG+F7b1/DdW88jsrSYq45dzlXnNbKd/+8hRseeCmh4+w4MMj3/rKV15+4gBMX18+4/fy6cr552Uls6xzkQ79+mpgbuG/vHuIDP3+SpU2VfOXNJ0x5YVzdUsPmjv606jz29w+zuyeSE6UC/hVAOgO6shekzwQiUlCtWnZ2DTE0OsbKeQeTF1bMrWbbgUHfxki3dw0xFtNJLRVw4iqFVFVvSiVHNFSGWTG36pDmkru6h7j8B4/y+d8/zxnLm7n7X87gvNVzx9eLCJ953dG89rgWvnDHC9z8+M4Zj3PdXS8QEvjYBSsTlu20ZY188qJV3PNcB9+670Uio2O896ePMxqN8d0rTp7WJbWqpYaRsRhb9qV+57XeDdIfPT9XSiXsi6Xid9t7vyikVi3xmV8eK+dVocqULt908ayQyWIqzvJKOgdGDvMM5Cu+KRUR+aGI7BWRDXHLGkTkHhHZ7P6d+da5gDl1aQOPtx0gOhbj1qfaOf9/HuCpHd186Y3H8r23nzyeiRRPUUj46ptP4JXLm/j4b57h3uc7ptz/urYD/P6Z3fzjmUfQUptcQ8F3nL6E15+4gK/+cRNv/8FjPLOzh6++5QSOmCE91csAS8cFtmGnq1QW1KS8j3RoqiplT89QxuuIstGh2A8KqVOxV+i4PE6p+J0Btm1Cy/uJtBZYt2I/LZUbgQsmLLsWuFdVlwP3uq9nLWuXNjIwMsZl33+Ua375FCvmVnPnNa/kLacsntbvHi4Ocf3lJ3PM/Bre97MnJp17H4spn739OebVlHH1hBTiRBARvvD6Y1k1r4bH2g7wz+cuP8RqmoplzZWEi0JpKZX17U6QviZHrqJXLm+ma3CUN3/3r7S7reMzQf9wNGvV9JmkrqJwZqps3NPHwvryQ5R7a2Ml4eKQb5X1bZ0DVISLaJ7kJtE7PphSmRFVfQCYeLW7BLjJfX4TcKlfx88H1i5zKsWf2NbFR89fya+uPm1Kv+tEqkqL+eFVp7Cgvpx33fi3w9qs3PJkO8/s7OHjF65MubtvebiIH73jFL70xmP54LnLE3pPSVGI5XOr0qpVeXZXL8fkKJ4C8JrjWvje29ewdd8AF3/jQR5+aX9G9puPMRUoPEsl3vUFjvV/ZHMVG31KK97uZn5NdaPoWTCFEqzPdkxlrqruBnD/zplh+4JmTnUZ33jbidz6gdN5/9lHUlyU3L+jsaqUH7/zVCrCxbz9B4+N9xcaHIny5btf4PhFdVxy/IK0ZJxbU8ZbTllMKJR4xlI67VoODIzQ3j3EsTlyfXmct3ouv/3A6TRUhrniB4/x/b9sSbs/U18kP91fdZUldA2O5n1/qtGxGC/t6z9MqQCsnOdfD7C2zoFxF9dkVJYW01xdOj4ZMt8JbKBeRK4WkXUism7fvn25Fsc3Lj5+floB6YX1Ffz4XacyHI1xxQ8eZX//MN/58xY6eof599euTkoZZIrVLTXs7x9hb18k6fd6QfpjchSkj+eI5ip++/7TOW/VXD7/++f54K+eSitDqH84XwP1YUaiMYZy0Ksuk7TtH2B0TA/J/PJYMbea3T2RjDfOHIspOw4M0do0fVPP1oYKs1RSpENEWgDcv3un2lBVb1DVNaq6prm5OWsC5iMr5lbzw6tOYU9vhMu//yg3PPASFx8/n5Nbc5MHcTBYn/ydn9eeJduV9FNRVVrM9ZefxEfPX8ltT+/iDdc/nNKkwNGxGJHRWFZmqWSaunK3VUuex1Umy/zy8BRNputVdvcMMTIWG+/xNRWtjZUWU0mR24Ar3edXArdm+fgFy8mt9Vx/+cm8uLcfVfh4EinEmWZ1Ghlg63f20NpYQW15cGIPIsL7zz6SH111Cu1dg1z8zQd5YFNy1vN4i5Y8tFS8flX5Pqxr054+QsKkGYx+ZYBtnyHzy2NJYwV7eiM56VyeafxMKf4F8FdgpYjsFJF3AdcB54nIZuA897WRIc5eOYeb3nkq119+UtaGWk1GbUUJ82vLUlIqG3b15DRIPx1nrZzD7/7pFcyrKeOqHz3G9fe/lHCcYXyWSh5aKvUF0qplU0c/S5oqJ+19t6CunMpwEZs7Mlur4rW8n9FSaXLWF0JjSd++4ar6tilWnevXMQ04/cimXIsApBas7xoYYWfXEJeflp3RwanQ2ljJ/73v5Xz8N+v50l0vsGFXD19/64kUzRC7ykXb+0zhzVTpzvP295s6+lg573DXFzjW6Ip51RnPANvWOUC4OMQ8d9TEVHiB/Lb9A5O65/KJwAbqjfxmVUsNL+0bSMqc37DLiafkqj1LolSEi/n6W0/go+ev5PfP7Obmx3fM+J7+LI8SziReU8l8jqlERsdo65z+gr3ShymQbZ0DLG6omDFhZkkB1aqYUjF8YVVLDWMxTar1xcH2LLlNJ04EEeF9Zx3BSYvr+MofNjE4Ep12+3x2f9WVu5ZKHsdUXtzbT0yZ0lIBJ67SOTCS0b5v2zoHp2wkGU9tRQl1FSUFkQFmSsXwBW+2ynO7EneBbWjvYVFD+WGDjIKKiPDJ16xib98w3//L1mm37ctjSyVcHKKqtDivLZVN45lfU7cZ8qyYTM1WUdUpW95PRqFkgJlSMXyhtbGS8pKipCrrN7T3Bt71NZGTWxu44Oh5fPfPL007175vOH+zv8Br1ZK4pfLk9i7e9J2Hx2NJuWZjRx/hotC0F/gVblpxpjLA9vUNMzQ6NmPml8eSxgq2HTBLxTAmpSgkHJXEbJWewVG2HxgMbObXdHzsgpUMR2N87d6pRzGPx1RK8y9QD8m3avnJI9v4W1sXf3xu6oan2WTTnj6WNVdSMk3XiuaqUuorSjIWV2kbTydO0FJpqKC9a2jawX35gCkVwze8DLBE0m7zJUg/Gcuaq7hs7WJ+8diOKWNIfZFRikNCWUl+/uTqKkoSdn+NRGPjyuTODVOPps4mmzr6p42ngJsBNjdzGWBefCSRmAo4yiemsLMrv11g+fkNN/KCVS019Eai7OqZuV1LkNqzpMI15y6nvKSIL00xldPrUJxPUx/jqa8IJ+z++uuWTnojUY5oruSBTfsYGJ4+icFv+iKjtHcPJZSqu3JeNZs7+jPS52x75yDFIWFBXWJjJ5Y0FUYLfFMqhm+sdoP1zycQrF/f3sOCuvLxmoh8o7GqlPeedQT3PNfBY1sPH0XQn6fNJD3qk7BU7tqwm8pwEZ+++GiGozHu2zhlN6assNm1HlcmoFRWzK2mbzjK7gRuhGairXOABfXlCTeKPdgCP7/jKqZUDN9YOS/xdi25mkmfSd55+lLm1ZTxhTueP+xOtzdP29571FWE6Y2MMhab/g5+LKb84dkOzlk1l9OPbKKpKpxzF5iXzTWT+yt+m0wE65PJ/AJorAxTGS4aj8XkK6ZUDN+oKi2mtbGC5/dMr1R6hkbZ1jnIsQvzW6mUh4v40KtX8NSObn6/fvch6/qHR/OymaRHfUUJqszYxfexrQfoHBjhwmPmURQSzls9j/te2JvTnlYbO/qoCBcl5IZaMSczacWqSlvnQMLxFHBiOk5asVkqhjElq+bVzNit+Fk3SJ+PmV8TeeNJCzlqXjVfvmsjw9GDF9J8nfro4bklZ8oAu2vDbspKQpy10uksfuEx8xgcGUu6AWcm2dTRx/K51QmNgaitKGFeTVnalkr34Ch9kWhSlgo4cRWLqRjGNKxqqaGtc2DaYO2G8SB98CvpZ6IoJFx74VFsPzDIzx7ZPr7cmfqYv0rF6xo9XbA+FlPuenYPZ65oHp82+rIjGqktL+GuHLrANu7pZ+U0RY8TWZGBgV1e5td0w7kmo7Wxkh1dgzO6GYOMKRXDV1a1VKMKL0zjTljf3sv82jIap5jhnW+cuaKZVxzZxNf/tHncXZT/gXqv/f3U7q8nd3TR0TvMhce0jC8rKQrxqlVzuef5jpzUX3T2D7O/fzipJo0r5lSxuaM/rQu7Z20smWE410SWNFYwOqbs6h5K+di5xpSK4Sur588crN/QHtx296kgIvzrRUfRMzTKt+9/EXAq6vPa/VUxs/vrzvV7KCkSzll16JTwi46dR18kysMv7fdVxsnY5LayT0qpzKtmOBpLqw19W+cAIiQ9gmJxQ/43ljSlYvjKgrpyasqKp1QqfZFRtu4fyPvMr4kcPb+W15+4gB891MbW/QOMRGPU5HP2V+X0M1VUlTs37OEVRzYd9jlfsbyJqtLinLjAPDdWIplfHl7qcTpFkNs7B5lfWz7p7Jbp8CybfG4saUrF8BUR4ahpZqs869awHJPnmV+T8eFXO9M3P33bs0B+dij2qC4tpjgkU85U2dDeS3v30CGuL4/S4iLOOWoOf3iug+hYdl1gGzv6qC0vYU514q7V5W78JZ24SlvnQMI9v+KZW11GaXEorzPATKkYvrO6pYYX9vQRm8RH7QXpC81SAcdKe+fpS8czn/JZqYjItK1a7tyw200hnjvp+guPmceBgREeazu8MNRPNu3pY+Xc6qQ6GVSEi1ncUJFWBphTo5K8UgmFhNbG/M4AM6Vi+M6qlmoGR8Ym9VGvb++hpbaMpgIJ0k/kfWcfMT6ON5+zv8ApgJws+0tVuWvDHk5b1jBlR4QzVzZTVhLizvXZc4GpKps6+sa7DyfDirnVbE5RqfRGRukcGEk6ndhjcUN+t8A3pWL4zqqWqYP169t7ODpP+30lQk1ZCf90znIAGvK0BY1HfUXJpNlfmzr62bJ/YFLXl0dFuJizVszh7mf3TGqx+kFH7zC9kWhC7VkmsnJeFVv2DaSUsbZ9fC598paK975tBwaydp4yjSkVw3dWzK0mJIcrlf7haEEG6Sdy1cuX8JN3ncpJi+tzLUpa1E3R/v7ODbsRgVcfPbnry+PCY+ext2+YJ7Z3+SXiIWwcH8yVvFJZMbeaaEzZuj/52Ma2JFveT6S1qZLIaIy908znCTKmVAzfKSspYllz1WEDu57b1YsqHLsw/4sepyMUEl65vDmhiu4gU19RMmn2110b9nBKawNzqsumff85R80hXBTKWi8wr9VKKkolnR5gXubW4iQLHz08Cydfg/WmVIys4MxWOfQHOt7uvsAtlUJhskFdW/b188KePi44Zt6M768uK+EVy5u4a8OejLSWn4mNHX3MqS5NqfP1sqYqikOSUg+wbZ0DNFeXUpliYsaSxvyuVTGlYmSF1S01tHcP0RN3p7uhvYe5NaUz3uEawaCuIsxwNMbQyMGeZp7VkYhS8bZr7x4av6Hwk00dfSlZKQDh4hBLmipTtFQGU46nALTUllEckrytVTGlYmSFVd5slbiOxesLoN39bMLLYou3Vu7asIfjF9UxP8FBVOetmktRSHx3gcVimpZSAacIMpVale1JtryfSHFRiEUN+ZtWbErFyAqrJ2SADY5EeWlff0FnfhUadRNatew4MMj69h4uTNBKAafb8cuWNfruAtvRNUhkNMbKFNKJPVbMrWb7gUEGRxKfXDk0Msae3kjSjSQn0upmgOUjplSMrNBcXUpjZXhcqYwH6c1SyRs8S8UL1t/9rGNtJKNUwMkC27p/ICODsKZiYxpBeo+V86pQhRfdyZGJ4NVitTalbqmAE1fZtn8wK7GnTGNKxcgKInJIsN7zqef7YK7ZxMSZKndu2MOqlpqkXT2vXj0PEXwthPTcVsvTUCorUugB5sVB0ompgJM51jcc5cDA9PNrgogpFSNrrGqpZmNHH9GxGOvbe2iuLmVujQXp84W6ci+mMkpHb4THt3UlbaWAY7WesqTB1waTmzr6WVhfnlZrnNbGSsLFofEZ94ngFT62NqRpqYw3lsy/uIopFSNrrGqpYSQaY8v+gYKYST/b8GIq3QMjKbu+PC48Zh4bO/rYsi/xC3YybOroS6mSPp6ikLB8TlXSlkp9RQm1Fel1pG4dTyvOv7iKKRUja3jtWp7Y1sWLe/sLYtLjbCJcHKIyXETX4Ch3rt/DEc2VKbuXvBRkP7LARsdivLSvnxVJtLufimQzwLZ1DrI4jcwvj4X15YQkP2tVTKkYWeOI5irCRSH+74l2YmpFj/lIXUWYLfv7eXRr57S9vmaipbacExbV+eICa9s/wOiYpm2pgDOwa3dPZHyC54zH7hxIO54CzriAltpys1QMYzrCxSGOnFM13v7cgvT5R31lCQ9s2kdMEy94nIoLj5nH+vYedqQxYXEyNo4H6VNPJ/ZY4e4jkY7FI9EYu7qH0qpRiWdJU4XFVBJFRC4QkY0i8qKIXJsLGYzc4LnAmqrCzLMgfd5RXxEmprCooZyj03RfepZOpq2VTXv6CIljGafLeAZYAkplZ9cgMU0/88ujtbHSLJVEEJEi4FvAhcBq4G0isjrbchi5wausP2ZBbVKDk4xg4AXrLzymJe3/3+LGCla31HDnht2ZEG2cjR19LGmqTHqU72QsqCunMlyUUA+wg92JM6NUljRW0DU4mrDrLSjkYmrQqcCLqroFQER+CVwCPJcDWYws41XWW+ZXfuIVQKbr+vK48Jh5fOWeTXz410+TqSbOj249wMuWNWZkXyLCinnV3PNcB4NxPc8mw2uTnyn3l7ef7Z2DeeUqzoVSWQDsiHu9E1g7cSMRuRq4GmDx4sXZkczwneMX1XHasgbOPzozFyUju7xyeTMdvRFOWFiXkf1deuICfvtUO399aX9G9gdQGS7OmNIDuPi4+Xz/L1t46MWZZXzl8iYaMzSMbUljJfNqyuiN5JelItluAyAibwLOV9V3u6+vAE5V1X+a6j1r1qzRdevWZUtEwzCMgkBEHlfVNdk8Zi4C9TuBRXGvFwK7ciCHYRiGkWFyoVT+BiwXkaUiEgbeCtyWAzkMwzCMDJP1mIqqRkXkA8DdQBHwQ1V9NttyGIZhGJknF4F6VPUO4I5cHNswDMPwD6uoNwzDMDKGKRXDMAwjY5hSMQzDMDKGKRXDMAwjY2S9+DEVRGQfsC3FtzcBmSvX9Yegyxh0+SD4MgZdPjAZM0Eu5Zvs2K2q2pxNIfJCqaSDiKzLdkVpsgRdxqDLB8GXMejygcmYCXIpX1DOjbm/DMMwjIxhSsUwDMPIGLNBqdyQawESIOgyBl0+CL6MQZcPTMZMkEv5AnFuCj6mYhiGYWSP2WCpGIZhGFnClIphGIaRMUypFDBiQ+ALHhEpybUMhYD9VjKHKZUkEZEi928gv4Ti8C8islADGjATkeUiUpZrOaZDRI4TkapcyzEV7v/5M8AHvdc5FWgS7LeSPrn8raT6/zOlkiAicpWIPAlck2tZpkJE3g7cB5wI9Abtxywil4jIS8Bnge+LSEOuZZqIiPy9iDwD/AfwK3eQXKAQkctx/s9vBy4HCNJF0X4r6ZPL30q6/z9TKgkgIkcB7wNuB84QkWWqqiISmPMnIqcDNwIfUdW3q2qvd6EJwg/G/VG8G7hMVd8G7AU+KSIrcivZQUTkQuAfgPeq6uuBI4CL3XVBOIdFIvIu4D3Ax1R1GdAuIkfnWLRx7LeSPrn8rWTi/xeYf3TQEJFq77mqvoBzV/j/gOeAD7jLY7mRzmGCjA/hjGpe5a67VkQuFpGqXN3FxsvnLQK8c/ZL4I3ARbm0BjwT3+V+VT1DVR8SkVpgi7uN5NIS8GRU1THgVlU9U1UfE5FVQB/Oec0Z8W7CAP9W4mUM4m9loqs1a7+VTF/rTKlMgohcCzwpIl8SkavcxRtV9QBwC3CEiJzhbpuTczhBxne5i98H3CQiTwF1wD8B/+XefeRKvi+LyGXuuVsPXCki9cAaYB0wD1iQbflcGT8L/LuIeA33ht3lc3Emk3bj/Jhzcg4nyDgHQFX3u8tFVZ8HlgAnuMuy/l0UkY8B97v/57e7Mr4QsN9KvIzvcBcH6bfiyfdfIvJWoIss/VZ8udapqj3iHsA5wAPAUuBsYDdwXNz6Kpzg6M/ilhUFQMaT3HXvB9a4z5uB3wLnB0C+FUAr8FXg98DPgKOB+4ElWZavFPhXnM7XtwCvnmSbWvdvA3AbcFGQZPS+c8A/A9/JpmzucRtxXEi/xlFqfwc8CiyI2yanv5VpZGx1178fONl9nvXfyiTyvcmVrxFY5vdvxa9rnVkqh1MCPKmqW1X1PuBrwBfj1g8ANwP9IvI5Efkyzt1irmX8AoCqfktV17nP9wEHcC6MuZTvG8BXVHWbqn4IeJeq/r2qPgvszIF8ozg+49XAI8DZIrI0fgNV7XH/HsDxadcHSUZ1XGHgWFc94pDN3/MA8AdVfbOqPgXci3N3vXDCNrn8rUwl4wIY/6087j7PxW9lonx/BDYAy1R1SxZ+K75c60ypHE4F0ChuGp+qXge0iMib3NcKRIBjgfcC+1T1pQDIOMeTEZxgn4h8BTgOx3+cS/m+ACwQkbe4r/eIyCIR+RbOD3xjNoVTxz+8SVUHgF/hXAhPFZFSOBisdc/hf+PcRWb1HCYgoxcLegF4hzpkLW6hqhHgd3GLojjnabcrn+T6tzKNjDvjt8vVb2UK+Y4HOuK28fO34su1btYqlamyPFT1Fpysn9fGLf4y8KG4118EngUWq+p/BU1G9472Fzh3Imeq6osBke+Dca+/CRQBr3EvnL4wjYzD7t824EHgTOCouPcdh+OW8M7hpiDJGGepPAx8QUSK/cpcmka+vriXjcBeVd3urvMC3l8it7+VKWV037cMJxCeq9/KtPK5fIs0fivxgfh4a9a3a12m/HP58gAuAW4CTpiwXIBS9/lbcXyNS9zXi3H+sdXu67IAyxgGyoCGgMpX5b6uyOE5DLnPvbhEDY6L7jLgCuC17vLmgMp4OfB6P2VLQb5X4PregfNxY0BAeYBlPMt93hhQ+bxzWJnisS/EqcX5MfDJuOVFCfxOU77WFTML8ExxETkb+ByOv/plIrJNVbviTPVh987lVzi+7H8TpwjoYqBN3bsKdczWoMo44u4yozJmUL5+AFUdzKR8ScqorowHgG5V7RWRzTg/pk6c4Dfq+NkDLWNQ5APOAMIicj2Ou+QTAKo6FFAZjwOudWXsDLh8CVsnrkUUwqllehfwaZzvy6dE5J2q+kN1rNwx3651fmnooDxw2/u7z5cALThZDzfimLveupD7T9wHvBKoBU7Hucv46GyWMejypSjjbpw7OcFxKW0BPjGbZUxRvte4y34GbAeuCeA5zJqMuZRvwrEvApbHvf4g8CH3eZGfv9OCtlRE5APAuSLyAPALdXzTALtF5HzgTBF5UVXbcXLAe4AVqtrlbveQiDyiB/3Xs07GoMuXhoyrPRlFpA04Vv2N7QRaxnTlw4nhvV9Vu/2QLx9kzKV8ccf+C/BjVb1DnA4MxaoaxSn0fMHdfA5+/k790ti5fgCvx8nkOBv4EU5g+Pi49ccDP2US3zSOJpfZLmPQ5cuAjMV58F30XcY05SvJg3Pou4y5lG+KY58Q//3BsZRePsl7M/47LeTsr7XA9erkX38G2EpcgzRVfRrnH3GsiJwjTmWp5w8dU/eMz3IZgy5fujJGsyBfPsiYjnyjWZAvH2TMpXyTHduLC0bFSUNfBDwuIgtF5D1xx87477TglIobqALH/3wZgKpuw6lMrRSR18Vt/gucxm2/wknnMxnzQD6TcXbIlw8y5lK+BI59ibv+KPd41+B0h/C1wDPvlYo4jf+85/GN/24GBuNO7G6cNgerxaEKp4J0PU5rgo+CPy3Egy5j0OUzGWeHfPkgYy7lS+HYq1zFswwnprIUJyngS8keOxnyVqmIyFoRuRX4noi8U0RKVVXlYKVxF07PpPe6/4AenF42Ze7JjOBkWbxGVXfPRhmDLp/JODvkywcZcylfGscud4/9IvAKVX2vX/+/ePJSqYhT7fwtHA19M07K3pFwSKVxOXA3jta+QUTm4wzkGXW3i6rq3tkqY9DlMxlnh3z5IGMu5cvQsderatbaz+SlUgFOBl5U1Z8A9+BUkG93TT1E5HM4mnsu8GGcXjo/xykwus5kzAv5TMbZIV8+yJhL+dI59hcn26HvqM+pdpl44PQ8Whv3eg5Od9b/xGkO9wjwQ+AjOCf958CRE/bhd1uQQMsYdPlMxtkhXz7ImEv5gn5uEvoMuTx4Aie4Gvg/nDYGPwTq49YdhdOs7u1x/4w7cOeKuMtCs13GoMtnMs4O+fJBxlzKF/Rzk8wj6O6vEeBPOA30duEMsQHGx14excE21o+723hmYUiz0wo86DIGXT6TcXbIlw8y5lK+oJ+bhAmcUhGRt4vImSJSp07r7+/jDK/ZBKwRkRVxm/8B+LTrX3wrcAywH/ydiR10GYMun8k4O+TLBxlzKV/Qz02qiGs65VYI50TNw/EPxoCXgEqcFLz97jbLgSuBYVX9nLusHLgBx+9YBPyzqj43G2UMunwm4+yQLx9kzKV8QT83GSHX/jcOzhJYAfzUfV6MMzviNxO2fT3wbWA5bjDK3XbebJYx6PKZjLNDvnyQMZfyBf3cZOqRsy7FIlIMfBYoEpE7cIYQjcF4v5p/BnaJyJmq+md3+S0isgq4E6gSkbNV9Xlgz2yUMejymYyzQ758kDGX8gX93GScXGgynOyFp4HrcYbJPABcgDNL4NS47d4L3Bf3+k3AAPA9YM5sljHo8pmMs0O+fJAxl/IF/dz4cr5zclBnMMwVca+/7Z7Uq4DH3WUhHN/jr4Glce97pckYfPlMxtkhXz7ImEv5gn5ufDnfOTkoVAClHPQx/j3wRff5U8A/uc/X4Ay7MRnzTD6TcXbIlw8y5lK+oJ8bPx45SSlW1UFVHdaDvWvOwxltCfAOnO6at+O0in7CZMw/+UzG2SFfPsiYS/mCfm78IKfjhMXpsqk4fWtucxf3AZ/AycPeqs7ozZwRdBmDLh+YjJkg6PJB8GXMpXxBPzeZJNfFjzGgBKeI5zhXY38KiKnqgwE5yUGXMejygcmYCYIuHwRfxlzKF/Rzkzly7X8DTsM54Q8C78q1PPkoY9DlMxlnh3z5IGMu5Qv6ucnUI+cV9SKyELgC+Ko6rQoCR9BlDLp8YDJmgqDLB8GXMZfyBf3cZIqcKxXDMAyjcMh1TMUwDMMoIEypGIZhGBnDlIphGIaRMUypGIZhGBnDlIphTEBExkTkKRF5VkSeFpEPici0vxURWSIil2VLRsMIKqZUDONwhlT1BFU9GqetxkXAp2d4zxLAlIox67GUYsOYgIj0q2pV3OtlwN+AJqAV+AnOtD6AD6jqwyLyCLAK2ArcBHwduA44C6eh4LdU9btZ+xCGkSNMqRjGBCYqFXdZF3AUTr+mmKpG3LGvv1DVNSJyFvARVX2tu/3VOHMwPi8ipcBDwJtUdWs2P4thZJucNpQ0jDxC3L8lwDdF5ASc6X0rptj+1Tg9nv7OfV2LMxrWlIpR0JhSMYwZcN1fY8BenNhKB3A8TkwyMtXbcGZl3J0VIQ0jIFig3jCmQUSage8A31THV1wL7FbVGE4fpyJ30z6gOu6tdwPvFZESdz8rRKQSwyhwzFIxjMMpF5GncFxdUZzA/Ffddd8GfiMibwLuw5kjDvAMEBWRp4Ebga/hZIQ9ISKCM5jp0uyIbxi5wwL1hmEYRsYw95dhGIaRMUypGIZhGBnDlIphGIaRMUypGIZhGBnDlIphGIaRMUypGIZhGBnDlIphGIaRMUypGIZhGBnj/wMpdhd0Fl5qogAAAABJRU5ErkJggg==\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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\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"
    }
   ],
   "source": [
    "# use function to get insight at cohort groups by age \n",
    "age_r = retention_rate(mkt, ['date_served','age_group'])\n",
    "age_r = pd.DataFrame(age_r.unstack(level = 1))\n",
    "trend_plot(age_r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "e5b13d63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscribing_channel\n",
       "Email        0.838542\n",
       "Facebook     1.000000\n",
       "House Ads    1.000000\n",
       "Instagram    1.000000\n",
       "Push         1.000000\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def conversion_rate(df,col):\n",
    "    converted = mkt[mkt['converted'] == True].groupby(col)['user_id'].nunique()\n",
    "    total = mkt.groupby(col)['user_id'].nunique()\n",
    "    conversion_rate = converted / total\n",
    "    return conversion_rate\n",
    "\n",
    "conversion_rate(mkt, 'subscribing_channel')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a30b577c",
   "metadata": {},
   "source": [
    "## Inconsistence checking"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39840b69",
   "metadata": {},
   "source": [
    "*Which weekday the users like to unscribe the channel??*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8ada10cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mkt['dow'] = mkt['date_subscribed'].dt.dayofweek\n",
    "dow = retention_rate(mkt,['dow'])\n",
    "dow.plot()\n",
    "plt.xlabel('Day of week')\n",
    "plt.ylabel('Retention rate')\n",
    "plt.title('Retention rate by week')\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03b4d95b",
   "metadata": {},
   "source": [
    "*The impact of the users whose displayed language is not their preferred language*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "408ca67e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = mkt[['date_served','language_displayed','language_preferred']]\n",
    "df=pd.DataFrame(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6832a31e",
   "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_served</th>\n",
       "      <th>language_displayed</th>\n",
       "      <th>language_preferred</th>\n",
       "      <th>consistence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>English</td>\n",
       "      <td>English</td>\n",
       "      <td>0</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>10032</th>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10033</th>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10034</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10035</th>\n",
       "      <td>2018-01-17</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10036</th>\n",
       "      <td>2018-01-06</td>\n",
       "      <td>German</td>\n",
       "      <td>German</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10037 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      date_served language_displayed language_preferred  consistence\n",
       "0      2018-01-01            English            English            0\n",
       "1      2018-01-01            English            English            0\n",
       "2      2018-01-01            English            English            0\n",
       "3      2018-01-01            English            English            0\n",
       "4      2018-01-01            English            English            0\n",
       "...           ...                ...                ...          ...\n",
       "10032  2018-01-17             German             German            0\n",
       "10033  2018-01-17             German             German            0\n",
       "10034  2018-01-05             German             German            0\n",
       "10035  2018-01-17             German             German            0\n",
       "10036  2018-01-06             German             German            0\n",
       "\n",
       "[10037 rows x 4 columns]"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['consistence'] = np.where(df['language_displayed'] == df['language_preferred'], 0, 1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "2262edc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "consistence = df[(df['consistence'] == 1)&(df['date_served']>='2018-01-08')]\\\n",
    ".groupby('date_served')['consistence'].sum()\n",
    "consistence.plot()\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "56c33688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "language_displayed\n",
       "Arabic     0.500000\n",
       "English    0.131260\n",
       "German     0.716216\n",
       "Spanish    0.200000\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate pre-error conversion rate\n",
    "# bug arose sometimes around 2018-01-11\n",
    "lang_bug = mkt[mkt['date_served'] < '2018-01-11']\n",
    "lang_conv = conversion_rate(lang_bug,'language_displayed')\n",
    "lang_conv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "65be5b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spanish index: 1.5236981934112646\n",
      "Arabic index: 3.8092454835281613\n",
      "German index: 5.456486773702502\n"
     ]
    }
   ],
   "source": [
    "# assessing impact\n",
    "# index other language conversion rate aganist English\n",
    "spanish_index = lang_conv['Spanish']/lang_conv['English']\n",
    "arabic_index = lang_conv['Arabic']/lang_conv['English']\n",
    "german_index = lang_conv['German']/lang_conv['English']\n",
    "print('Spanish index:', spanish_index)\n",
    "print('Arabic index:', arabic_index)\n",
    "print('German index:', german_index)\n",
    "# German-speaking users convert 5.46 times the rate of Englsih-speaking users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "5a40d878",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">user_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>4.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>4.0</td>\n",
       "      <td>355.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>3.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2.0</td>\n",
       "      <td>305.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>1.0</td>\n",
       "      <td>303.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id                        converted                 \\\n",
       "language_preferred  Arabic English German Spanish    Arabic English German   \n",
       "date_served                                                                  \n",
       "2018-01-01             4.0   342.0    5.0    11.0       3.0    34.0    1.0   \n",
       "2018-01-02             4.0   355.0    5.0    10.0       0.0    36.0    3.0   \n",
       "2018-01-03             3.0   334.0    3.0     8.0       0.0    35.0    1.0   \n",
       "2018-01-04             2.0   305.0    2.0    14.0       0.0    34.0    0.0   \n",
       "2018-01-05             1.0   303.0    2.0    14.0       1.0    38.0    0.0   \n",
       "\n",
       "                            \n",
       "language_preferred Spanish  \n",
       "date_served                 \n",
       "2018-01-01             0.0  \n",
       "2018-01-02             0.0  \n",
       "2018-01-03             1.0  \n",
       "2018-01-04             3.0  \n",
       "2018-01-05             3.0  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create actual conversion dataframe\n",
    "lang_conversion = mkt.groupby(['date_served','language_preferred'])\\\n",
    ".agg({'user_id':'nunique', 'converted':'sum'})\n",
    "lang_conversion = pd.DataFrame(lang_conversion.unstack(level = 1))\n",
    "lang_conversion.fillna(0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "613b4dfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">user_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">converted</th>\n",
       "      <th>actual_english_rate</th>\n",
       "      <th>exp_spanish_rate</th>\n",
       "      <th>exp_arabic_rate</th>\n",
       "      <th>exp_german_rate</th>\n",
       "      <th>exp_spanish_conv</th>\n",
       "      <th>exp_arabic_conv</th>\n",
       "      <th>exp_german_conv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>9.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.663549</td>\n",
       "      <td>106.658874</td>\n",
       "      <td>152.781630</td>\n",
       "      <td>6.826168</td>\n",
       "      <td>9.599299</td>\n",
       "      <td>3.055633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38.092455</td>\n",
       "      <td>95.231137</td>\n",
       "      <td>136.412169</td>\n",
       "      <td>4.190170</td>\n",
       "      <td>3.809245</td>\n",
       "      <td>5.456487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-13</th>\n",
       "      <td>8.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>39.616153</td>\n",
       "      <td>99.040383</td>\n",
       "      <td>141.868656</td>\n",
       "      <td>6.734746</td>\n",
       "      <td>7.923231</td>\n",
       "      <td>7.093433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id                        converted                 \\\n",
       "language_preferred  Arabic English German Spanish    Arabic English German   \n",
       "date_served                                                                  \n",
       "2018-01-11             9.0   283.0    2.0    16.0       0.0    28.0    0.0   \n",
       "2018-01-12             4.0   282.0    4.0    11.0       0.0    25.0    0.0   \n",
       "2018-01-13             8.0   276.0    5.0    17.0       1.0    26.0    1.0   \n",
       "\n",
       "                           actual_english_rate exp_spanish_rate  \\\n",
       "language_preferred Spanish                                        \n",
       "date_served                                                       \n",
       "2018-01-11             0.0                28.0        42.663549   \n",
       "2018-01-12             1.0                25.0        38.092455   \n",
       "2018-01-13             0.0                26.0        39.616153   \n",
       "\n",
       "                   exp_arabic_rate exp_german_rate exp_spanish_conv  \\\n",
       "language_preferred                                                    \n",
       "date_served                                                           \n",
       "2018-01-11              106.658874      152.781630         6.826168   \n",
       "2018-01-12               95.231137      136.412169         4.190170   \n",
       "2018-01-13               99.040383      141.868656         6.734746   \n",
       "\n",
       "                   exp_arabic_conv exp_german_conv  \n",
       "language_preferred                                  \n",
       "date_served                                         \n",
       "2018-01-11                9.599299        3.055633  \n",
       "2018-01-12                3.809245        5.456487  \n",
       "2018-01-13                7.923231        7.093433  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create english conversion rate column for affected period\n",
    "lang_conversion = lang_conversion.loc[ '2018-01-11' : '2018-01-31' ]\n",
    "lang_conversion['actual_english_rate'] =\\\n",
    "lang_conversion[('converted','English')]\n",
    "lang_conversion.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "f8225db9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">user_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">converted</th>\n",
       "      <th>actual_english_rate</th>\n",
       "      <th>exp_spanish_rate</th>\n",
       "      <th>exp_arabic_rate</th>\n",
       "      <th>exp_german_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>9.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.663549</td>\n",
       "      <td>106.658874</td>\n",
       "      <td>152.781630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>38.092455</td>\n",
       "      <td>95.231137</td>\n",
       "      <td>136.412169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-13</th>\n",
       "      <td>8.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>39.616153</td>\n",
       "      <td>99.040383</td>\n",
       "      <td>141.868656</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id                        converted                 \\\n",
       "language_preferred  Arabic English German Spanish    Arabic English German   \n",
       "date_served                                                                  \n",
       "2018-01-11             9.0   283.0    2.0    16.0       0.0    28.0    0.0   \n",
       "2018-01-12             4.0   282.0    4.0    11.0       0.0    25.0    0.0   \n",
       "2018-01-13             8.0   276.0    5.0    17.0       1.0    26.0    1.0   \n",
       "\n",
       "                           actual_english_rate exp_spanish_rate  \\\n",
       "language_preferred Spanish                                        \n",
       "date_served                                                       \n",
       "2018-01-11             0.0                28.0        42.663549   \n",
       "2018-01-12             1.0                25.0        38.092455   \n",
       "2018-01-13             0.0                26.0        39.616153   \n",
       "\n",
       "                   exp_arabic_rate exp_german_rate  \n",
       "language_preferred                                  \n",
       "date_served                                         \n",
       "2018-01-11              106.658874      152.781630  \n",
       "2018-01-12               95.231137      136.412169  \n",
       "2018-01-13               99.040383      141.868656  "
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create expected conversion rates for each langauge\n",
    "lang_conversion['exp_spanish_rate'] = \\\n",
    "lang_conversion['actual_english_rate'] * spanish_index\n",
    "\n",
    "lang_conversion['exp_arabic_rate'] = \\\n",
    "lang_conversion['actual_english_rate'] * arabic_index\n",
    "\n",
    "lang_conversion['exp_german_rate'] = \\\n",
    "lang_conversion['actual_english_rate'] * german_index\n",
    "\n",
    "lang_conversion.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "e353dba6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">user_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">converted</th>\n",
       "      <th>actual_english_rate</th>\n",
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       "      <th>exp_german_rate</th>\n",
       "      <th>exp_spanish_conv</th>\n",
       "      <th>exp_arabic_conv</th>\n",
       "      <th>exp_german_conv</th>\n",
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       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-11</th>\n",
       "      <td>9.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.663549</td>\n",
       "      <td>106.658874</td>\n",
       "      <td>152.781630</td>\n",
       "      <td>6.826168</td>\n",
       "      <td>9.599299</td>\n",
       "      <td>3.055633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-12</th>\n",
       "      <td>4.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>136.412169</td>\n",
       "      <td>4.190170</td>\n",
       "      <td>3.809245</td>\n",
       "      <td>5.456487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-13</th>\n",
       "      <td>8.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>39.616153</td>\n",
       "      <td>99.040383</td>\n",
       "      <td>141.868656</td>\n",
       "      <td>6.734746</td>\n",
       "      <td>7.923231</td>\n",
       "      <td>7.093433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-14</th>\n",
       "      <td>5.0</td>\n",
       "      <td>285.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.663549</td>\n",
       "      <td>106.658874</td>\n",
       "      <td>152.781630</td>\n",
       "      <td>5.119626</td>\n",
       "      <td>5.332944</td>\n",
       "      <td>4.583449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-15</th>\n",
       "      <td>6.0</td>\n",
       "      <td>729.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>124.943252</td>\n",
       "      <td>312.358130</td>\n",
       "      <td>447.431915</td>\n",
       "      <td>26.238083</td>\n",
       "      <td>18.741488</td>\n",
       "      <td>49.217511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id                        converted                 \\\n",
       "language_preferred  Arabic English German Spanish    Arabic English German   \n",
       "date_served                                                                  \n",
       "2018-01-11             9.0   283.0    2.0    16.0       0.0    28.0    0.0   \n",
       "2018-01-12             4.0   282.0    4.0    11.0       0.0    25.0    0.0   \n",
       "2018-01-13             8.0   276.0    5.0    17.0       1.0    26.0    1.0   \n",
       "2018-01-14             5.0   285.0    3.0    12.0       0.0    28.0    0.0   \n",
       "2018-01-15             6.0   729.0   11.0    21.0       0.0    82.0    4.0   \n",
       "\n",
       "                           actual_english_rate exp_spanish_rate  \\\n",
       "language_preferred Spanish                                        \n",
       "date_served                                                       \n",
       "2018-01-11             0.0                28.0        42.663549   \n",
       "2018-01-12             1.0                25.0        38.092455   \n",
       "2018-01-13             0.0                26.0        39.616153   \n",
       "2018-01-14             0.0                28.0        42.663549   \n",
       "2018-01-15             5.0                82.0       124.943252   \n",
       "\n",
       "                   exp_arabic_rate exp_german_rate exp_spanish_conv  \\\n",
       "language_preferred                                                    \n",
       "date_served                                                           \n",
       "2018-01-11              106.658874      152.781630         6.826168   \n",
       "2018-01-12               95.231137      136.412169         4.190170   \n",
       "2018-01-13               99.040383      141.868656         6.734746   \n",
       "2018-01-14              106.658874      152.781630         5.119626   \n",
       "2018-01-15              312.358130      447.431915        26.238083   \n",
       "\n",
       "                   exp_arabic_conv exp_german_conv  \n",
       "language_preferred                                  \n",
       "date_served                                         \n",
       "2018-01-11                9.599299        3.055633  \n",
       "2018-01-12                3.809245        5.456487  \n",
       "2018-01-13                7.923231        7.093433  \n",
       "2018-01-14                5.332944        4.583449  \n",
       "2018-01-15               18.741488       49.217511  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lang_conversion['exp_spanish_conv'] = \\\n",
    "lang_conversion['exp_spanish_rate'] / 100\\\n",
    "* lang_conversion[('user_id', 'Spanish')]\n",
    "\n",
    "lang_conversion['exp_arabic_conv'] = \\\n",
    "lang_conversion['exp_arabic_rate'] / 100\\\n",
    "* lang_conversion[('user_id', 'Arabic')]\n",
    "\n",
    "lang_conversion['exp_german_conv'] = \\\n",
    "lang_conversion['exp_german_rate'] / 100\\\n",
    "* lang_conversion[('user_id', 'German')]\n",
    "\n",
    "lang_conversion.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "25218ca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "614.4382972685759\n",
      "76.0\n",
      "Expected lost conversion rate: 538.4382972685759\n"
     ]
    }
   ],
   "source": [
    "# sum expecteed subscribers for each language\n",
    "expected_subs = lang_conversion['exp_spanish_conv'].agg('sum') +\\\n",
    "lang_conversion['exp_arabic_conv'].agg('sum') +\\\n",
    "lang_conversion['exp_german_conv'].agg('sum') \n",
    "\n",
    "print(expected_subs)\n",
    "\n",
    "# calculate how many subscribers we actually got\n",
    "actual_subs = lang_conversion[('converted', 'Spanish')].agg('sum')+\\\n",
    "lang_conversion[('converted', 'Arabic')].agg('sum') +\\\n",
    "lang_conversion[('converted', 'German')].agg('sum')\n",
    "\n",
    "print(actual_subs)\n",
    "\n",
    "lost_subs = expected_subs - actual_subs\n",
    "print('Expected lost conversion rate:', lost_subs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d580bdce",
   "metadata": {},
   "source": [
    "**House ads conversion rate**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "05decef5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_served  marketing_channel\n",
      "2018-01-01   Email                1.000000\n",
      "             Facebook             0.117647\n",
      "             House Ads            0.084656\n",
      "             Instagram            0.106667\n",
      "             Push                 0.083333\n",
      "Name: user_id, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Calculate conversion rate by date served and channel\n",
    "daily_conv_channel = conversion_rate(mkt, ['date_served', 'marketing_channel'])\n",
    "\n",
    "print(daily_conv_channel.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "65378279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate conversion rate by date served and channel\n",
    "daily_conv_channel = conversion_rate(mkt, ['date_served', \n",
    "                                                 'marketing_channel'])\n",
    "\n",
    "# Unstack daily_conv_channel and convert it to a DataFrame\n",
    "daily_conv_channel = pd.DataFrame(daily_conv_channel.unstack(level = 1))\n",
    "\n",
    "# Plot results of daily_conv_channel\n",
    "daily_conv_channel.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "b881f48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add day of week column to marketing\n",
    "mkt['DoW_served'] = mkt['date_served'].dt.dayofweek\n",
    "\n",
    "# Calculate conversion rate by day of week\n",
    "DoW_conversion = conversion_rate(mkt, ['DoW_served', 'marketing_channel'])\n",
    "\n",
    "\n",
    "# Unstack channels\n",
    "DoW_df = pd.DataFrame(DoW_conversion.unstack(level=1))\n",
    "\n",
    "# Plot conversion rate by day of week\n",
    "DoW_df.plot()\n",
    "plt.title('Conversion rate by day of week\\n')\n",
    "plt.ylim(0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "f868f7af",
   "metadata": {},
   "outputs": [
    {
     "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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\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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# House ads conversion by language\n",
    "# Isolate the rows where marketing channel is House Ads\n",
    "house_ads = mkt[mkt['marketing_channel'] == 'House Ads']\n",
    "\n",
    "# Calculate conversion by date served, and language displayed\n",
    "conv_lang_channel = conversion_rate(house_ads, ['date_served','language_displayed'])\n",
    "\n",
    "# Unstack conv_lang_channel\n",
    "conv_lang_df = pd.DataFrame(conv_lang_channel.unstack(level=1))\n",
    "\n",
    "# Use your plotting function to display results\n",
    "trend_plot(conv_lang_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "54443ebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "is_correct_lang   No    Yes\n",
      "date_served                \n",
      "2018-01-01       2.0  189.0\n",
      "2018-01-02       3.0  247.0\n",
      "2018-01-03       0.0  220.0\n",
      "2018-01-04       0.0  168.0\n",
      "2018-01-05       0.0  160.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\QXJ\\AppData\\Local\\Temp\\ipykernel_2688\\894601515.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  house_ads['is_correct_lang'] = np.where(\n"
     ]
    }
   ],
   "source": [
    "# Creating a DataFrame for house ads\n",
    "# Add the new column is_correct_lang\n",
    "house_ads['is_correct_lang'] = np.where(\n",
    "    house_ads['language_displayed'] == house_ads['language_preferred'], \n",
    "    'Yes', \n",
    "    'No')\n",
    "\n",
    "# Groupby date_served and correct_language\n",
    "language_check = house_ads.groupby(['date_served','is_correct_lang'])['marketing_channel'].count()\n",
    "\n",
    "# Unstack language_check and fill missing values with 0's\n",
    "language_check_df = pd.DataFrame(language_check.unstack(level=1)).fillna(0)\n",
    "\n",
    "# Print results\n",
    "print(language_check_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "24fdd598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confirming house ads error\n",
    "# Divide the count where language is correct by the row sum\n",
    "language_check_df['pct'] = language_check_df['Yes']/(language_check_df['Yes'] + language_check_df['No'])\n",
    "\n",
    "# Plot and show your results\n",
    "plt.plot(language_check_df.index.values, language_check_df['pct'])\n",
    "plt.xticks(rotation = 30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f2fd1c8",
   "metadata": {},
   "source": [
    "**Setting up conversion indexes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "5ca37653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spanish index: 1.5236981934112646\n",
      "Arabic index: 3.8092454835281613\n",
      "German index: 5.456486773702502\n"
     ]
    }
   ],
   "source": [
    "# Calculate pre-error conversion rate\n",
    "house_ads_bug = house_ads[house_ads['date_served'] < '2018-01-11']\n",
    "lang_conv = conversion_rate(house_ads_bug, 'language_displayed')\n",
    "\n",
    "\n",
    "# Index other language conversion rate against English\n",
    "spanish_index = lang_conv['Spanish'] /lang_conv['English']\n",
    "arabic_index = lang_conv['Arabic']/lang_conv['English']\n",
    "german_index = lang_conv['German'] /lang_conv['English']\n",
    "\n",
    "print(\"Spanish index:\", spanish_index)\n",
    "print(\"Arabic index:\", arabic_index)\n",
    "print(\"German index:\", german_index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "07f948e3",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">user_id</th>\n",
       "      <th colspan=\"4\" halign=\"left\">converted</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>language_preferred</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "      <th>Arabic</th>\n",
       "      <th>English</th>\n",
       "      <th>German</th>\n",
       "      <th>Spanish</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_served</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>2.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>3.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>143.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   user_id                        converted                 \\\n",
       "language_preferred  Arabic English German Spanish    Arabic English German   \n",
       "date_served                                                                  \n",
       "2018-01-01             2.0   171.0    5.0    11.0       2.0    13.0    1.0   \n",
       "2018-01-02             3.0   200.0    5.0    10.0       0.0    14.0    3.0   \n",
       "2018-01-03             2.0   179.0    3.0     8.0       0.0    15.0    1.0   \n",
       "2018-01-04             2.0   149.0    2.0    14.0       0.0    12.0    0.0   \n",
       "2018-01-05             NaN   143.0    1.0    14.0       NaN    17.0    0.0   \n",
       "\n",
       "                            \n",
       "language_preferred Spanish  \n",
       "date_served                 \n",
       "2018-01-01             0.0  \n",
       "2018-01-02             0.0  \n",
       "2018-01-03             1.0  \n",
       "2018-01-04             3.0  \n",
       "2018-01-05             3.0  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# analyzing user preferences\n",
    "# Group house_ads by date and language\n",
    "converted = house_ads.groupby(['date_served','language_preferred'])\\\n",
    "                        .agg({'user_id':'nunique',\n",
    "                              'converted':'sum'})\n",
    "\n",
    "# Unstack converted\n",
    "converted_df = pd.DataFrame(converted.unstack(level = 1))\n",
    "\n",
    "converted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "ef3ec793",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a DataFrame based on indexes\n",
    "# Create English conversion rate column for affected period\n",
    "converted_df['english_conv_rate'] = converted_df.loc['2018-01-11' : '2018-01-31']\\\n",
    "[('converted','English')]\n",
    "\n",
    "\n",
    "# Create expected conversion rates for each language\n",
    "converted_df['expected_spanish_rate'] =spanish_index * converted_df['english_conv_rate'] \n",
    "converted_df['expected_arabic_rate'] = arabic_index * converted_df['english_conv_rate'] \n",
    "converted_df['expected_german_rate'] = german_index * converted_df['english_conv_rate'] \n",
    "\n",
    "# Multiply number of users by the expected conversion rate\n",
    "converted_df['expected_spanish_conv'] = converted_df['expected_spanish_rate']\\\n",
    "* converted_df[('user_id','Spanish')] /100\n",
    "converted_df['expected_arabic_conv'] = converted_df['expected_arabic_rate']\\\n",
    "* converted_df[('user_id','Arabic')] /100\n",
    "converted_df['expected_german_conv'] = converted_df['expected_german_rate']\\\n",
    "* converted_df[('user_id','German')] /100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "18e8b2a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.4409128299394\n"
     ]
    }
   ],
   "source": [
    "# assessing bug impact\n",
    "# Use .loc to slice only the relevant dates\n",
    "converted_df = converted_df.loc['2018-01-11':'2018-01-31']\n",
    "\n",
    "# Sum expected subscribers for each language\n",
    "expected_subs = converted_df['expected_spanish_conv'].sum()\\\n",
    "+ converted_df['expected_arabic_conv'].sum()\\\n",
    "+ converted_df['expected_german_conv'].sum()\n",
    "\n",
    "# Calculate how many subscribers we actually got\n",
    "actual_subs = converted_df[('converted','Spanish')].sum()\\\n",
    "+ converted_df[('converted','Arabic')].sum()\\\n",
    "+ converted_df[('converted','German')].sum()\n",
    "\n",
    "# Subtract how many subscribers we got despite the bug\n",
    "lost_subs = expected_subs - actual_subs\n",
    "print(lost_subs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f7e3f41",
   "metadata": {},
   "source": [
    "## A/B test\n",
    "\n",
    "<br>\n",
    "\n",
    "*Prior to running the test determine:*\n",
    "\n",
    "- What is the desired outcome of the test? what is our hypothesis? \n",
    "- What is the metric we are trying to impact? (i.e: page view, conversoin)? \n",
    "- Will we get enough traffic to our site to reach statistical siginificane and make a decision in a timely manner?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bf679b7",
   "metadata": {},
   "source": [
    "*A/B test: half of emails are generoc upsells to our product whiel the other half contanied personalized messaging around individual usage of our site*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a4a69bc",
   "metadata": {},
   "source": [
    "**Step I: check if the test was run proporply ?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "637eb7b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variant\n",
      "control            270\n",
      "personalization    284\n",
      "Name: user_id, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "allocation = mkt[mkt['marketing_channel'] == 'Email']\\\n",
    ".groupby(['variant'])['user_id'].nunique()\n",
    "print(allocation)\n",
    "allocation.plot(kind = 'bar')\n",
    "plt.xticks(rotation = 0)\n",
    "plt.show()\n",
    "# there is statistical test to check if the difference is due to random chance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e735f55f",
   "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>variant</th>\n",
       "      <th>control</th>\n",
       "      <th>personalization</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a100000526</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100000530</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100000534</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100000538</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100000542</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100007288</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100007289</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100007290</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100007291</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a100007293</th>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>553 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "variant    control personalization\n",
       "user_id                           \n",
       "a100000526     NaN            True\n",
       "a100000530     NaN            True\n",
       "a100000534     NaN           False\n",
       "a100000538     NaN            True\n",
       "a100000542     NaN            True\n",
       "...            ...             ...\n",
       "a100007288    True             NaN\n",
       "a100007289    True             NaN\n",
       "a100007290    True             NaN\n",
       "a100007291    True             NaN\n",
       "a100007293    True             NaN\n",
       "\n",
       "[553 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by user_id and variant\n",
    "email = mkt[mkt['marketing_channel'] == 'Email']\n",
    "subscribers = email.groupby(['user_id', 'variant'])['converted'].max()\n",
    "sub = pd.DataFrame(subscribers.unstack(level = 1))\n",
    "sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d40ea0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create individual series of control and personalization\n",
    "control = sub['control'].dropna()\n",
    "personalization = sub['personalization'].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fe7e83f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Control conversion rate: 0.2814814814814815\n",
      "Personalization conversion rate: 0.3908450704225352\n"
     ]
    }
   ],
   "source": [
    "# calculate conversion rate\n",
    "print('Control conversion rate:', np.mean(control))\n",
    "print('Personalization conversion rate:', np.mean(personalization))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ffae1f3",
   "metadata": {},
   "source": [
    "**Step II: calculating lift & significance testing**\n",
    "- Lift: (treatment conversion rate - control conversion rate) / control conversion rate \n",
    "- statistical significance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "69924522",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lift: 38.85 %\n"
     ]
    }
   ],
   "source": [
    "# calculate lift \n",
    "a_mean = np.mean(control)\n",
    "b_mean = np.mean(personalization)\n",
    "\n",
    "lift = (b_mean - a_mean)/a_mean\n",
    "print('lift:', round(lift*100,2), '%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "890538f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ttest_indResult(statistic=-2.7343299447505074, pvalue=0.006451487844694175)\n"
     ]
    }
   ],
   "source": [
    "# calculate statistical significance \n",
    "from scipy.stats import ttest_ind\n",
    "t = ttest_ind(control, personalization)\n",
    "print(t)\n",
    "# the difference id statistical significant"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7546af96",
   "metadata": {},
   "source": [
    "**Pitfall: A/B test and segementation**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46d1ef96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate lift and statistical significance across multiple segments of users"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "10edadd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arabic\n",
      "lift: 50.0 %\n",
      "Ttest_indResult(statistic=-0.5773502691896255, pvalue=0.5795840000000001)\n",
      "English\n",
      "lift: 39.0 %\n",
      "Ttest_indResult(statistic=-2.2183598646203166, pvalue=0.026991701290720815)\n",
      "German\n",
      "lift: -1.62 %\n",
      "Ttest_indResult(statistic=0.1910083418078718, pvalue=0.8494394170062678)\n",
      "Spanish\n",
      "lift: 166.67 %\n",
      "Ttest_indResult(statistic=-2.3570226039551585, pvalue=0.040156718110477524)\n"
     ]
    }
   ],
   "source": [
    "for lang in np.unique(mkt['language_displayed'].values):\n",
    "    print(lang)\n",
    "    \n",
    "    lang_data = mkt[(mkt['marketing_channel'] == 'Email')\\\n",
    "                    & (mkt['language_displayed'] == lang)]\n",
    "    \n",
    "    subscribers = lang_data.groupby(['user_id', 'variant'])['converted'].max()\n",
    "    sub = pd.DataFrame(subscribers.unstack(level = 1))\n",
    "    \n",
    "    control = sub['control'].dropna()\n",
    "    personalization = sub['personalization'].dropna()\n",
    "    \n",
    "    a = np.mean(control)\n",
    "    b = np.mean(personalization)\n",
    "    lift = (b-a)/a\n",
    "    print('lift:', round(lift*100,2), '%')\n",
    "    t = ttest_ind(control, personalization)\n",
    "    print(t)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6a68683f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-18 years\n",
      "lift: 375.5 %\n",
      "Ttest_indResult(statistic=-4.79410440267648, pvalue=2.779301009264944e-06)\n",
      "19-24 years\n",
      "lift: 641.02 %\n",
      "Ttest_indResult(statistic=-6.703745552570057, pvalue=8.703172251371585e-11)\n",
      "24-30 years\n",
      "lift: 742.33 %\n",
      "Ttest_indResult(statistic=-6.302856383162445, pvalue=1.0980286291676167e-09)\n",
      "30-36 years\n",
      "lift: 19.27 %\n",
      "Ttest_indResult(statistic=-0.3876647512083575, pvalue=0.6986120802081497)\n",
      "36-45 years\n",
      "lift: -29.53 %\n",
      "Ttest_indResult(statistic=0.6341709097448146, pvalue=0.5266434462898644)\n",
      "45-55 years\n",
      "lift: -6.17 %\n",
      "Ttest_indResult(statistic=0.09606018010960825, pvalue=0.9235398169400137)\n",
      "55+ years\n",
      "lift: -8.55 %\n",
      "Ttest_indResult(statistic=0.18071675257359116, pvalue=0.8568014395949484)\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import ttest_ind\n",
    "def mkt_channel_result(df,col, mkt_channel):\n",
    "    \"\"\"return the lift and statistical sigfinicance about the A/B test of a certain marketing channel \"\"\"\n",
    "    for val in np.unique(df[col].values):\n",
    "        print(val)\n",
    "        \n",
    "        val_df = df[(df['marketing_channel'] == mkt_channel)\\\n",
    "        & (df[col] == val)]\n",
    "        \n",
    "        subscribers = val_df.groupby(['user_id','variant'])['converted'].max()\n",
    "        sub = pd.DataFrame(subscribers.unstack(level = 1))\n",
    "        \n",
    "        control = sub['control'].dropna()\n",
    "        personalization = sub['personalization'].dropna()\n",
    "        \n",
    "        a = np.mean(control)\n",
    "        b = np.mean(personalization)\n",
    "        lift = (b-a)/a\n",
    "        print('lift:', round(lift*100,2), '%')\n",
    "        t = ttest_ind(control, personalization)\n",
    "        print(t)\n",
    "        \n",
    "mkt_channel_result(mkt, 'age_group', 'Facebook')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9327b55c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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