eda_notebook.ipynb

{
 "cells": [
  {
   "source": [
    "## AAVAIL EDA investigation and visualizations\n"
   ],
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from my_modules.data_utils import fetch_data, fetch_ts, engineer_features\n",
    "from my_modules.plot_utils import create_correlation_grid_plot\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_DIR = './data/cs-train'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_aavail_data = fetch_data(DATA_DIR)"
   ]
  },
  {
   "source": [
    "print(df_aavail_data.loc[df_aavail_data.price < 0])\n",
    "#drop rows with negative price values\n",
    "indexNames = df_aavail_data[df_aavail_data['price'] < 0].index\n",
    "df_aavail_data.drop(indexNames, inplace=True)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "               country  customer_id  day invoice  month     price stream_id  \\\n178920  United Kingdom          NaN   26  506401      4 -53594.36         B   \n276274  United Kingdom          NaN   16  516228      7 -44031.79         B   \n405052  United Kingdom          NaN   17  528059     10 -38925.87         B   \n\n        times_viewed  year invoice_date  \n178920             1  2018   2018-04-26  \n276274             1  2018   2018-07-16  \n405052             1  2018   2018-10-17  \n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "... loading ts data from files\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(549, 7)"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "source": [
    "ts_data = fetch_ts(DATA_DIR)\n",
    "\n",
    "all_ts_df = ts_data['all']\n",
    "X, y, dates = engineer_features(all_ts_df)\n",
    "\n",
    "X.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  purchases  unique_invoices  unique_streams  total_views  \\\n",
       "0  2017-11-01          0                0               0            0   \n",
       "1  2017-11-02          0                0               0            0   \n",
       "2  2017-11-03          0                0               0            0   \n",
       "3  2017-11-04          0                0               0            0   \n",
       "4  2017-11-05          0                0               0            0   \n",
       "\n",
       "  year_month  revenue  \n",
       "0    2017-11      0.0  \n",
       "1    2017-11      0.0  \n",
       "2    2017-11      0.0  \n",
       "3    2017-11      0.0  \n",
       "4    2017-11      0.0  "
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>date</th>\n      <th>purchases</th>\n      <th>unique_invoices</th>\n      <th>unique_streams</th>\n      <th>total_views</th>\n      <th>year_month</th>\n      <th>revenue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2017-11-01</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2017-11-02</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2017-11-03</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2017-11-04</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2017-11-05</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2017-11</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "all_ts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "date                object\n",
       "purchases            int64\n",
       "unique_invoices      int64\n",
       "unique_streams       int64\n",
       "total_views          int64\n",
       "year_month          object\n",
       "revenue            float64\n",
       "dtype: object"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "all_ts_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import datetime \n",
    "\n",
    "fig1, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(15, 10))\n",
    "ax1.plot(pd.to_datetime(all_ts_df['date']), all_ts_df['revenue'])\n",
    "ax1.set_title('Revenue all')\n",
    "ax2.plot(pd.to_datetime(all_ts_df['date']), all_ts_df['purchases'])\n",
    "ax2.set_title('Purchases all')\n",
    "ax3.plot(pd.to_datetime(all_ts_df['date']), all_ts_df['total_views'])\n",
    "ax3.set_title('Total views')\n",
    "\n",
    "fig1.autofmt_xdate()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "### Revenue by Country"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "country\n",
       "United Kingdom    3.658066e+06\n",
       "EIRE              1.070692e+05\n",
       "Germany           4.927182e+04\n",
       "France            4.056514e+04\n",
       "Norway            3.849475e+04\n",
       "Spain             1.604099e+04\n",
       "Hong Kong         1.445257e+04\n",
       "Portugal          1.352867e+04\n",
       "Singapore         1.317592e+04\n",
       "Netherlands       1.232280e+04\n",
       "Name: price, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "\n",
    "result_s = df_aavail_data.groupby(['country'])['price'].agg('sum')\n",
    "result_sorted = result_s.sort_values(ascending=False)\n",
    "result_sorted = result_sorted[:10]\n",
    "result_sorted\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[Text(0, 0.5, 'Revenue')]"
      ]
     },
     "metadata": {},
     "execution_count": 18
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "fig2, (ax21) = plt.subplots(1, 1, figsize=(15, 10))\n",
    "ax21.bar(result_sorted.index, result_sorted.values)\n",
    "ax21.set_title('Revenue by country, top 10')\n",
    "ax21.set(ylabel='Revenue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_columns = ['purchases', 'unique_invoices', 'unique_streams', 'total_views', 'revenue']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 purchases  unique_invoices  unique_streams  total_views  \\\n",
       "purchases         1.000000         0.820700        0.904121     0.963792   \n",
       "unique_invoices   0.820700         1.000000        0.883705     0.873395   \n",
       "unique_streams    0.904121         0.883705        1.000000     0.907361   \n",
       "total_views       0.963792         0.873395        0.907361     1.000000   \n",
       "revenue           0.575842         0.454396        0.472225     0.537586   \n",
       "\n",
       "                  revenue  \n",
       "purchases        0.575842  \n",
       "unique_invoices  0.454396  \n",
       "unique_streams   0.472225  \n",
       "total_views      0.537586  \n",
       "revenue          1.000000  "
      ],
      "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>purchases</th>\n      <th>unique_invoices</th>\n      <th>unique_streams</th>\n      <th>total_views</th>\n      <th>revenue</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>purchases</th>\n      <td>1.000000</td>\n      <td>0.820700</td>\n      <td>0.904121</td>\n      <td>0.963792</td>\n      <td>0.575842</td>\n    </tr>\n    <tr>\n      <th>unique_invoices</th>\n      <td>0.820700</td>\n      <td>1.000000</td>\n      <td>0.883705</td>\n      <td>0.873395</td>\n      <td>0.454396</td>\n    </tr>\n    <tr>\n      <th>unique_streams</th>\n      <td>0.904121</td>\n      <td>0.883705</td>\n      <td>1.000000</td>\n      <td>0.907361</td>\n      <td>0.472225</td>\n    </tr>\n    <tr>\n      <th>total_views</th>\n      <td>0.963792</td>\n      <td>0.873395</td>\n      <td>0.907361</td>\n      <td>1.000000</td>\n      <td>0.537586</td>\n    </tr>\n    <tr>\n      <th>revenue</th>\n      <td>0.575842</td>\n      <td>0.454396</td>\n      <td>0.472225</td>\n      <td>0.537586</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "corr = all_ts_df[all_columns].corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "TypeError",
     "evalue": "'float' object cannot be interpreted as an integer",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-44-d73919dfbcdb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcreate_correlation_grid_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_ts_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mall_columns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Correlations'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/aavail/ai-workflow-capstone/my_modules/plot_utils.py\u001b[0m in \u001b[0;36mcreate_correlation_grid_plot\u001b[0;34m(df, columns, title)\u001b[0m\n\u001b[1;32m     55\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfacecolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'white'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m         \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msuptitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     58\u001b[0m         \u001b[0;31m# Generate a custom diverging colormap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0mcmap\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdiverging_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m220\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mdiverging_palette\u001b[0;34m(h_neg, h_pos, s, l, sep, n, center, as_cmap)\u001b[0m\n\u001b[1;32m    742\u001b[0m     \"\"\"\n\u001b[1;32m    743\u001b[0m     \u001b[0mpalfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdark_palette\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcenter\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"dark\"\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mlight_palette\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 744\u001b[0;31m     \u001b[0mneg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpalfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_neg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"husl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    745\u001b[0m     \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpalfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m128\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msep\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"husl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    746\u001b[0m     midpoint = dict(light=[(.95, .95, .95, 1.)],\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mlight_palette\u001b[0;34m(color, n_colors, reverse, as_cmap, input)\u001b[0m\n\u001b[1;32m    639\u001b[0m     \u001b[0mlight\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset_hls_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.95\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# noqa\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    640\u001b[0m     \u001b[0mcolors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlight\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mreverse\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 641\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mblend_palette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/seaborn/palettes.py\u001b[0m in \u001b[0;36mblend_palette\u001b[0;34m(colors, n_colors, as_cmap, input)\u001b[0m\n\u001b[1;32m    775\u001b[0m     \u001b[0mpal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLinearSegmentedColormap\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mas_cmap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m         \u001b[0mpal\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ColorPalette\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_colors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/core/function_base.py\u001b[0m in \u001b[0;36mlinspace\u001b[0;34m(start, stop, num, endpoint, retstep, dtype, axis)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m     \"\"\"\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnum\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of samples, %s, must be non-negative.\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'float' object cannot be interpreted as an integer"
     ]
    }
   ],
   "source": [
    "create_correlation_grid_plot(df=all_ts_df, columns=all_columns, title='Correlations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>united_states</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Kasen Todd</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>singapore</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Ensley Garza</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>united_states</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Lillian Carey</td>\n",
       "      <td>False</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>united_states</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Beau Christensen</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_basic</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>singapore</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Ernesto Gibson</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id   country_name   age     customer_name  is_subscriber  \\\n",
       "0            1  united_states  21.0        Kasen Todd           True   \n",
       "1            2      singapore  31.0      Ensley Garza          False   \n",
       "2            3  united_states  22.0     Lillian Carey          False   \n",
       "3            4  united_states  21.0  Beau Christensen           True   \n",
       "4            5      singapore  22.0    Ernesto Gibson           True   \n",
       "\n",
       "  subscriber_type  num_streams  \n",
       "0  aavail_premium         23.0  \n",
       "1             NaN         12.0  \n",
       "2  aavail_premium         22.0  \n",
       "3    aavail_basic         19.0  \n",
       "4  aavail_premium         23.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_aavail_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               country    customer_id            day invoice          month  \\\n",
       "count           815011  625249.000000  815011.000000  815011  815011.000000   \n",
       "unique              43            NaN            NaN   42646            NaN   \n",
       "top     United Kingdom            NaN            NaN  537434            NaN   \n",
       "freq            751228            NaN            NaN    1350            NaN   \n",
       "first              NaN            NaN            NaN     NaN            NaN   \n",
       "last               NaN            NaN            NaN     NaN            NaN   \n",
       "mean               NaN   15333.415068      15.064819     NaN       6.592718   \n",
       "std                NaN    1698.360788       8.788845     NaN       3.598781   \n",
       "min                NaN   12346.000000       1.000000     NaN       1.000000   \n",
       "25%                NaN   13956.000000       7.000000     NaN       3.000000   \n",
       "50%                NaN   15279.000000      15.000000     NaN       6.000000   \n",
       "75%                NaN   16813.000000      23.000000     NaN      10.000000   \n",
       "max                NaN   18287.000000      31.000000     NaN      12.000000   \n",
       "\n",
       "                price stream_id   times_viewed           year  \\\n",
       "count   815011.000000    815011  815011.000000  815011.000000   \n",
       "unique            NaN      5007            NaN            NaN   \n",
       "top               NaN    85123A            NaN            NaN   \n",
       "freq              NaN      5017            NaN            NaN   \n",
       "first             NaN       NaN            NaN            NaN   \n",
       "last              NaN       NaN            NaN            NaN   \n",
       "mean         4.802631       NaN       5.231106    2018.247654   \n",
       "std        135.058707       NaN       5.109982       0.545261   \n",
       "min     -53594.360000       NaN       0.000000    2017.000000   \n",
       "25%          1.250000       NaN       1.000000    2018.000000   \n",
       "50%          2.100000       NaN       3.000000    2018.000000   \n",
       "75%          4.210000       NaN       8.000000    2019.000000   \n",
       "max      38970.000000       NaN      24.000000    2019.000000   \n",
       "\n",
       "               invoice_date  \n",
       "count                815011  \n",
       "unique                  495  \n",
       "top     2018-12-03 00:00:00  \n",
       "freq                   7756  \n",
       "first   2017-11-28 00:00:00  \n",
       "last    2019-07-31 00:00:00  \n",
       "mean                    NaN  \n",
       "std                     NaN  \n",
       "min                     NaN  \n",
       "25%                     NaN  \n",
       "50%                     NaN  \n",
       "75%                     NaN  \n",
       "max                     NaN  "
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     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "df_aavail_data.describe(include='all')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Missing Value Summary\n",
      "-----------------------------------\n",
      "country              0\n",
      "customer_id     189759\n",
      "day                  0\n",
      "invoice              0\n",
      "month                0\n",
      "price                0\n",
      "stream_id            0\n",
      "times_viewed         0\n",
      "year                 0\n",
      "invoice_date         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "## missing values summary\n",
    "print(\"Missing Value Summary\\n{}\".format(\"-\"*35))\n",
    "print(df_aavail_data.isnull().sum(axis = 0))"
   ]
  },
  {
   "source": [
    "missing_values_perc = df_aavail_data.isnull().sum(axis=0)*100/len(df_aavail_data)\n",
    "plt.rcParams[\"figure.figsize\"] = (15, 10)\n",
    "plt.title(label=\"Missing values in %\")\n",
    "plt.bar(missing_values_perc.index, missing_values_perc.values)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 38,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<BarContainer object of 10 artists>"
      ]
     },
     "metadata": {},
     "execution_count": 38
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['United Kingdom',\n",
       " 'France',\n",
       " 'USA',\n",
       " 'Belgium',\n",
       " 'Australia',\n",
       " 'EIRE',\n",
       " 'Germany',\n",
       " 'Portugal',\n",
       " 'Japan',\n",
       " 'Denmark',\n",
       " 'Nigeria',\n",
       " 'Netherlands',\n",
       " 'Poland',\n",
       " 'Spain',\n",
       " 'Channel Islands',\n",
       " 'Italy',\n",
       " 'Cyprus',\n",
       " 'Greece',\n",
       " 'Norway',\n",
       " 'Austria',\n",
       " 'Sweden',\n",
       " 'United Arab Emirates',\n",
       " 'Finland',\n",
       " 'Switzerland',\n",
       " 'Unspecified',\n",
       " 'Malta',\n",
       " 'Bahrain',\n",
       " 'RSA',\n",
       " 'Bermuda',\n",
       " 'Hong Kong',\n",
       " 'Singapore',\n",
       " 'Thailand',\n",
       " 'Israel',\n",
       " 'Lithuania',\n",
       " 'West Indies',\n",
       " 'Lebanon',\n",
       " 'Korea',\n",
       " 'Brazil',\n",
       " 'Canada',\n",
       " 'Iceland',\n",
       " 'Saudi Arabia',\n",
       " 'Czech Republic',\n",
       " 'European Community']"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "country_list=df_aavail_data['country'].unique().tolist()\n",
    "country_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "error",
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File b'./data/cs-train/ts-all.csv' does not exist: b'./data/cs-train/ts-all.csv'",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-30ec862aab91>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mts_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./data/cs-train/ts-all.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[1;32m    700\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[1;32m    701\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 702\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    703\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    427\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m    893\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'has_index_names'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    896\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, engine)\u001b[0m\n\u001b[1;32m   1120\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'c'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1121\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'c'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1122\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1123\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1124\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'python'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/io/parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, src, **kwds)\u001b[0m\n\u001b[1;32m   1851\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'usecols'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1852\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1853\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1854\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] File b'./data/cs-train/ts-all.csv' does not exist: b'./data/cs-train/ts-all.csv'"
     ]
    }
   ],
   "source": [
    "ts_df = pd.read_csv(\"./data/cs-train/ts-all.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_USA = df_aavail_data.loc[df_aavail_data['country_name']=='united_states']\n",
    "df_singapore = df_aavail_data.loc[df_aavail_data['country_name']=='singapore']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1650x1050 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_correlation_grid_plot(df_USA, ['age', 'is_subscriber', 'num_streams'], '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1650x1050 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_correlation_grid_plot(df_singapore, ['age', 'is_subscriber', 'num_streams'], '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>700.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>700</td>\n",
       "      <td>700</td>\n",
       "      <td>649</td>\n",
       "      <td>676.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>700</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>united_states</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diego Mueller</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>592</td>\n",
       "      <td>232</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>498.297143</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.711429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.853550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>292.087867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.114414</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.200617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>244.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>755.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customer_id   country_name         age  customer_name is_subscriber  \\\n",
       "count    700.000000            700  700.000000            700           700   \n",
       "unique          NaN              1         NaN            700             2   \n",
       "top             NaN  united_states         NaN  Diego Mueller          True   \n",
       "freq            NaN            700         NaN              1           592   \n",
       "mean     498.297143            NaN   27.711429            NaN           NaN   \n",
       "std      292.087867            NaN    9.114414            NaN           NaN   \n",
       "min        1.000000            NaN   14.000000            NaN           NaN   \n",
       "25%      244.750000            NaN   21.000000            NaN           NaN   \n",
       "50%      500.500000            NaN   24.000000            NaN           NaN   \n",
       "75%      755.250000            NaN   34.000000            NaN           NaN   \n",
       "max     1000.000000            NaN   57.000000            NaN           NaN   \n",
       "\n",
       "       subscriber_type  num_streams  \n",
       "count              649   676.000000  \n",
       "unique               3          NaN  \n",
       "top     aavail_premium          NaN  \n",
       "freq               232          NaN  \n",
       "mean               NaN    18.853550  \n",
       "std                NaN     4.200617  \n",
       "min                NaN     2.000000  \n",
       "25%                NaN    17.000000  \n",
       "50%                NaN    20.000000  \n",
       "75%                NaN    22.000000  \n",
       "max                NaN    27.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_USA.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>300</td>\n",
       "      <td>300</td>\n",
       "      <td>279</td>\n",
       "      <td>278.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>singapore</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wilson Henson</td>\n",
       "      <td>False</td>\n",
       "      <td>aavail_basic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>109</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>505.640000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.113333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.172662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>281.458915</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.134840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.006030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>259.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>734.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>996.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customer_id country_name         age  customer_name is_subscriber  \\\n",
       "count    300.000000          300  300.000000            300           300   \n",
       "unique          NaN            1         NaN            300             2   \n",
       "top             NaN    singapore         NaN  Wilson Henson         False   \n",
       "freq            NaN          300         NaN              1           181   \n",
       "mean     505.640000          NaN   27.113333            NaN           NaN   \n",
       "std      281.458915          NaN    9.134840            NaN           NaN   \n",
       "min        2.000000          NaN   14.000000            NaN           NaN   \n",
       "25%      259.750000          NaN   21.000000            NaN           NaN   \n",
       "50%      500.500000          NaN   23.000000            NaN           NaN   \n",
       "75%      734.250000          NaN   33.000000            NaN           NaN   \n",
       "max      996.000000          NaN   55.000000            NaN           NaN   \n",
       "\n",
       "       subscriber_type  num_streams  \n",
       "count              279   278.000000  \n",
       "unique               3          NaN  \n",
       "top       aavail_basic          NaN  \n",
       "freq               109          NaN  \n",
       "mean               NaN    15.172662  \n",
       "std                NaN     5.006030  \n",
       "min                NaN     1.000000  \n",
       "25%                NaN    12.000000  \n",
       "50%                NaN    16.000000  \n",
       "75%                NaN    19.000000  \n",
       "max                NaN    29.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_singapore.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_s_s = df_singapore.loc[df_singapore['is_subscriber'] == True]\n",
    "df_s_ns = df_singapore.loc[df_singapore['is_subscriber'] == False]\n",
    "df_USA_s = df_USA.loc[df_USA['is_subscriber'] == True]\n",
    "df_USA_ns = df_USA.loc[df_USA['is_subscriber'] == False]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result = df_aavail_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(16.0, 15.0, 19.0, 20.0)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nr_s_s = round(df_s_s['num_streams'].mean(), 0)\n",
    "nr_s_ns = round(df_s_ns['num_streams'].mean(), 0)\n",
    "nr_u_s = round(df_USA_s['num_streams'].mean(), 0)\n",
    "nr_u_ns = round(df_USA_ns['num_streams'].mean(), 0)\n",
    "t_s_s = 'aavail_basic'\n",
    "t_s_ns = 'aavail_unlimited'\n",
    "t_u_s ='aavail_basic'\n",
    "t_u_ns = 'aavail_premium'\n",
    "\n",
    "nr_s_s, nr_s_ns, nr_u_s, nr_u_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "v1 = {'subscriber_type': t_s_s, 'num_streams': nr_s_s}\n",
    "v2 = {'subscriber_type': t_s_ns, 'num_streams': nr_s_ns}\n",
    "v3 = {'subscriber_type': t_u_s, 'num_streams': nr_u_s}\n",
    "v4 = {'subscriber_type': t_u_ns, 'num_streams': nr_u_ns}\n",
    "df_s_s_r = df_s_s.fillna(value=v1)\n",
    "df_s_ns_r = df_s_ns.fillna(value=v2)\n",
    "df_USA_s_r = df_USA_s.fillna(value=v3)\n",
    "df_USA_ns_r = df_USA_ns.fillna(value=v4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_result_2 = pd.concat([df_s_s_r, df_s_ns_r, df_USA_s_r, df_USA_ns_r])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>united_states</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Diego Mueller</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_basic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>700</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>711</td>\n",
       "      <td>386</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.532000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.759000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>288.819436</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.120096</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.659467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>250.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>500.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>750.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customer_id   country_name          age  customer_name is_subscriber  \\\n",
       "count   1000.000000           1000  1000.000000           1000          1000   \n",
       "unique          NaN              2          NaN           1000             2   \n",
       "top             NaN  united_states          NaN  Diego Mueller          True   \n",
       "freq            NaN            700          NaN              1           711   \n",
       "mean     500.500000            NaN    27.532000            NaN           NaN   \n",
       "std      288.819436            NaN     9.120096            NaN           NaN   \n",
       "min        1.000000            NaN    14.000000            NaN           NaN   \n",
       "25%      250.750000            NaN    21.000000            NaN           NaN   \n",
       "50%      500.500000            NaN    24.000000            NaN           NaN   \n",
       "75%      750.250000            NaN    33.000000            NaN           NaN   \n",
       "max     1000.000000            NaN    57.000000            NaN           NaN   \n",
       "\n",
       "       subscriber_type  num_streams  \n",
       "count             1000  1000.000000  \n",
       "unique               3          NaN  \n",
       "top       aavail_basic          NaN  \n",
       "freq               386          NaN  \n",
       "mean               NaN    17.759000  \n",
       "std                NaN     4.659467  \n",
       "min                NaN     1.000000  \n",
       "25%                NaN    15.000000  \n",
       "50%                NaN    19.000000  \n",
       "75%                NaN    21.000000  \n",
       "max                NaN    29.000000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result_2.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'file_name': 'imputed_data.csv',\n",
       " 'message': 'File saved to project storage.',\n",
       " 'bucket_name': 'dataanalysisandhypothesistestingc-donotdelete-pr-v62gpcgrtbkdwv',\n",
       " 'asset_id': '244231fa-57c4-4f4d-ab00-a641b746cca5'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "project.save_data(file_name = \"imputed_data.csv\",data = df_result_2.to_csv(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('seaborn-deep')\n",
    "df_result_USA = df_result_2.loc[df_result_2['country_name']=='united_states']\n",
    "df_result_singapore = df_result_2.loc[df_result_2['country_name']=='singapore']\n",
    "\n",
    "age_s_USA = df_result_USA.loc[df_result_USA['is_subscriber'] == True]['age'];\n",
    "age_ns_USA = df_result_USA.loc[df_result_USA['is_subscriber'] == False]['age'];\n",
    "age_s_S = df_result_singapore.loc[df_result_singapore['is_subscriber'] == True]['age'];\n",
    "age_ns_S = df_result_singapore.loc[df_result_singapore['is_subscriber'] == False]['age'];\n",
    "                                        \n",
    "charn_rate_usa = len(age_ns_USA)/len(df_result_USA)\n",
    "charn_rate_s = len(age_ns_S)/len(df_result_singapore)\n",
    "charn_rate_s, charn_rate_usa\n",
    "\n",
    "bins = np.linspace(10, 60, 24)\n",
    "\n",
    "fig6, (ax3, ax1, ax2) = plt.subplots(3, 1, figsize=(11,10))\n",
    "# plt.bar(['Singapore', 'USA'], [charn_rate_s*100, charn_rate_usa*100])\n",
    "ax3.bar(['Singapore', 'USA'], [charn_rate_s*100, charn_rate_usa*100])\n",
    "ax3.set_title('Churn rate % by country')\n",
    "ax3.set(ylabel='Churn rate %')\n",
    "# fig6.suptitle()\n",
    "ax1.hist([age_s_USA, age_ns_USA], bins, label=['Active', 'Churned'])\n",
    "ax1.set_title('Subscriber by age and type in USA')\n",
    "ax1.set(ylabel='Count')\n",
    "ax2.hist([age_s_S, age_ns_S], bins, label=['Active', 'Churned'])\n",
    "ax2.set_title('Subscriber by age and type in Singapore')\n",
    "ax2.set(xlabel='Age', ylabel='Count')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.6033333333333334, 0.15428571428571428)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "charn_rate_usa = len(age_ns_USA)/len(df_result_USA)\n",
    "charn_rate_s = len(age_ns_S)/len(df_result_singapore)\n",
    "charn_rate_s, charn_rate_usa\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n8LFm5X0F7WPgDwJ/Q/uxejzws0xbbWfmt2kvNH5+oQc/mvhW+iNMsyL5TmZORcTlwIbMnPWDpmd6Ta6kY4PHwI88Z9N+ad8S2k89r17keSQdoVyBS1JRHgOXpKIMuCQVZcAlqSgDLklFGXBJKur/AYisWGji5uN9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['Singapore', 'USA'], [charn_rate_s*100, charn_rate_usa*100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "scrolled": true
   },
   "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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>108</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>107.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>108</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>united_states</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Josue Casey</td>\n",
       "      <td>False</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>108</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>108</td>\n",
       "      <td>39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>468.629630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.342593</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.607477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>296.141877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.079259</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.041663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>194.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>748.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>992.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customer_id   country_name         age customer_name is_subscriber  \\\n",
       "count    108.000000            108  108.000000           108           108   \n",
       "unique          NaN              1         NaN           108             1   \n",
       "top             NaN  united_states         NaN   Josue Casey         False   \n",
       "freq            NaN            108         NaN             1           108   \n",
       "mean     468.629630            NaN   27.342593           NaN           NaN   \n",
       "std      296.141877            NaN    9.079259           NaN           NaN   \n",
       "min        3.000000            NaN   17.000000           NaN           NaN   \n",
       "25%      194.250000            NaN   21.000000           NaN           NaN   \n",
       "50%      446.000000            NaN   24.000000           NaN           NaN   \n",
       "75%      748.000000            NaN   33.000000           NaN           NaN   \n",
       "max      992.000000            NaN   53.000000           NaN           NaN   \n",
       "\n",
       "       subscriber_type  num_streams  \n",
       "count              108   107.000000  \n",
       "unique               4          NaN  \n",
       "top     aavail_premium          NaN  \n",
       "freq                39          NaN  \n",
       "mean               NaN    19.607477  \n",
       "std                NaN     4.041663  \n",
       "min                NaN     5.000000  \n",
       "25%                NaN    18.000000  \n",
       "50%                NaN    20.000000  \n",
       "75%                NaN    22.000000  \n",
       "max                NaN    27.000000  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_USA_ns.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>customer_id</th>\n",
       "      <th>country_name</th>\n",
       "      <th>age</th>\n",
       "      <th>customer_name</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>subscriber_type</th>\n",
       "      <th>num_streams</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>united_states</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Kasen Todd</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>singapore</td>\n",
       "      <td>31.0</td>\n",
       "      <td>Ensley Garza</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>united_states</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Lillian Carey</td>\n",
       "      <td>False</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>united_states</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Beau Christensen</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_basic</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>singapore</td>\n",
       "      <td>22.0</td>\n",
       "      <td>Ernesto Gibson</td>\n",
       "      <td>True</td>\n",
       "      <td>aavail_premium</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customer_id   country_name   age     customer_name  is_subscriber  \\\n",
       "0            1  united_states  21.0        Kasen Todd           True   \n",
       "1            2      singapore  31.0      Ensley Garza          False   \n",
       "2            3  united_states  22.0     Lillian Carey          False   \n",
       "3            4  united_states  21.0  Beau Christensen           True   \n",
       "4            5      singapore  22.0    Ernesto Gibson           True   \n",
       "\n",
       "  subscriber_type  num_streams  \n",
       "0  aavail_premium         23.0  \n",
       "1             NaN         12.0  \n",
       "2  aavail_premium         22.0  \n",
       "3    aavail_basic         19.0  \n",
       "4  aavail_premium         23.0  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ax = df_s_s['num_streams'].plot(kind='hist')\n",
    "#df_s_ns['num_streams'].plot(kind='hist', ax=ax)\n",
    "\n",
    "\n",
    "plt.style.use('seaborn-deep')\n",
    "#fig, ax = plt.subplots(figsize=(10,10))\n",
    "#plt.figure(figsize=(10,10))\n",
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10,10))\n",
    "fig.suptitle('Distribution of num_streams in countries, for active and churned users.')\n",
    "#ax1.plot(x, y)\n",
    "#ax2.plot(x, -y)\n",
    "\n",
    "sub_streams_s = df_s_s['num_streams'];\n",
    "no_sub_streams_s = df_s_ns['num_streams'];\n",
    "sub_streams_usa = df_USA_s['num_streams'];\n",
    "no_sub_streams_usa = df_USA_ns['num_streams'];\n",
    "bins = np.linspace(0, 40, 15)\n",
    "\n",
    "ax1.hist([sub_streams_s, sub_streams_usa], bins, label=['Singapore', 'USA'])\n",
    "ax1.set_title('Distribution for active users in Singapore and USA')\n",
    "# ax1.set(xlabel='Number of streams (num_streams)')\n",
    "ax2.hist([no_sub_streams_s, no_sub_streams_usa], bins, label=['Singapore', 'USA'])\n",
    "ax2.set_title('Distribution for churned users in Singapore and USA')\n",
    "ax2.set(xlabel='Number of streams (num_streams)')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ax = df_s_s['num_streams'].plot(kind='hist')\n",
    "#df_s_ns['num_streams'].plot(kind='hist', ax=ax)\n",
    "\n",
    "#subscription type\n",
    "\n",
    "# plt.style.use('seaborn-deep')\n",
    "#fig, ax = plt.subplots(figsize=(10,10))\n",
    "#plt.figure(figsize=(10,10))\n",
    "fig3, (ax5, ax6) = plt.subplots(2, 1, figsize=(10,16))\n",
    "fig3.suptitle('subscriber_type')\n",
    "#ax1.plot(x, y)\n",
    "#ax2.plot(x, -y)\n",
    "values = {'subscriber_type': 'subscriber_type_missing'}\n",
    "df_s_s = df_s_s.fillna(value=values)\n",
    "df_s_ns = df_s_ns.fillna(value=values)\n",
    "df_USA_s = df_USA_s.fillna(value=values)\n",
    "df_USA_ns = df_USA_ns.fillna(value=values)\n",
    "\n",
    "sub_stype_s = df_s_s['subscriber_type'];\n",
    "no_sub_stype_s = df_s_ns['subscriber_type'];\n",
    "sub_stype_usa = df_USA_s['subscriber_type'];\n",
    "no_sub_stype_usa = df_USA_ns['subscriber_type'];\n",
    "bins = np.linspace(0, 40, 15)\n",
    "\n",
    "ax5.hist([sub_stype_s, sub_stype_usa], label=['Singapore active', 'USA active'])\n",
    "ax6.hist([no_sub_stype_s, no_sub_stype_usa], label=['Singapore churned', 'USA churned'])\n",
    "\n",
    "plt.style.use('seaborn-deep')\n",
    "fig4, (ax7, ax8) = plt.subplots(2, 1, figsize=(11,10))\n",
    "fig4.suptitle('Distribution of subscriber_type for active and churned users')\n",
    "ax7.hist([sub_stype_s, no_sub_stype_s], label=['Active', 'Churned'])\n",
    "ax7.set_title('Singapore')\n",
    "ax8.hist([sub_stype_usa, no_sub_stype_usa], label=['Active', 'Churned'])\n",
    "ax8.set_title('USA')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_aavail_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "values = {'subscriber_type': 'subscriber_type_missing'}\n",
    "df = df.fillna(value=values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customer_id         0\n",
       "country_name        0\n",
       "age                 0\n",
       "customer_name       0\n",
       "is_subscriber       0\n",
       "subscriber_type     0\n",
       "num_streams        46\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>age</th>\n",
       "      <th>is_subscriber</th>\n",
       "      <th>num_streams</th>\n",
       "      <th>aavail_basic</th>\n",
       "      <th>aavail_premium</th>\n",
       "      <th>aavail_unlimited</th>\n",
       "      <th>subscriber_type_missing</th>\n",
       "      <th>singapore</th>\n",
       "      <th>united_states</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>True</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31</td>\n",
       "      <td>False</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>False</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>True</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>True</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age is_subscriber num_streams aavail_basic aavail_premium aavail_unlimited  \\\n",
       "0  21          True          23            0              1                0   \n",
       "1  31         False          12            0              0                0   \n",
       "2  22         False          22            0              1                0   \n",
       "3  21          True          19            1              0                0   \n",
       "4  22          True          23            0              1                0   \n",
       "\n",
       "  subscriber_type_missing singapore united_states  \n",
       "0                       0         0             1  \n",
       "1                       1         1             0  \n",
       "2                       0         0             1  \n",
       "3                       0         0             1  \n",
       "4                       0         1             0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import numpy as np\n",
    "\n",
    "## one hot encode the subscriber\n",
    "ohe1 = OneHotEncoder()\n",
    "column = df['subscriber_type'].values.reshape(-1,1)\n",
    "ohe1.fit(column)\n",
    "labels1 = ohe1.categories_[0].tolist()\n",
    "X1 = ohe1.transform(column).toarray()\n",
    "## one hot encode the country\n",
    "ohe2 = OneHotEncoder()\n",
    "column = df['country_name'].values.reshape(-1,1)\n",
    "ohe2.fit(column)\n",
    "labels2 = ohe2.categories_[0].tolist()\n",
    "X2 = ohe2.transform(column).toarray()\n",
    "## concat all of the data\n",
    "#labels = ['customer_id','age','customer_name', 'is_subscriber', 'num_streams']\n",
    "labels = ['age', 'is_subscriber', 'num_streams']\n",
    "X = df.loc[:,labels].to_numpy()\n",
    "labels = labels + labels1 + labels2\n",
    "X = np.hstack([X,X1,X2])\n",
    "df1 = pd.DataFrame({label:X[:,i] for i,label in enumerate(labels)})\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_types = { 'age': 'float64', 'is_subscriber': 'bool', 'num_streams': 'float64', 'aavail_basic': 'bool', 'aavail_premium': 'bool', 'aavail_unlimited': 'bool', 'subscriber_type_missing': 'bool', 'singapore': 'bool', 'united_states': 'bool' }\n",
    "df2 = df1.astype(data_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                        float64\n",
       "is_subscriber                 bool\n",
       "num_streams                float64\n",
       "aavail_basic                  bool\n",
       "aavail_premium                bool\n",
       "aavail_unlimited              bool\n",
       "subscriber_type_missing       bool\n",
       "singapore                     bool\n",
       "united_states                 bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1650x1050 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "create_correlation_grid_plot(df2, ['age', 'is_subscriber', 'num_streams', 'aavail_basic', 'aavail_premium', 'aavail_unlimited', 'subscriber_type_missing', 'singapore', 'united_states'], 'Correlation between variables after Hot Encoder')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.9 64-bit ('Python32_WML': conda)",
   "metadata": {
    "interpreter": {
     "hash": "935598c70c3351f5ef0d704d389fd632b51b6c169df11182902173ee51ae3ca5"
    }
   }
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}