capstone1_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cslib import fetch_data, engineer_features, convert_to_ts, fetch_ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = \"data/cs-train/\"\n",
    "df = fetch_data(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>day</th>\n",
       "      <th>invoice</th>\n",
       "      <th>month</th>\n",
       "      <th>price</th>\n",
       "      <th>stream_id</th>\n",
       "      <th>times_viewed</th>\n",
       "      <th>year</th>\n",
       "      <th>invoice_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>6.95</td>\n",
       "      <td>85048</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>6.75</td>\n",
       "      <td>79323W</td>\n",
       "      <td>12</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>2.10</td>\n",
       "      <td>22041</td>\n",
       "      <td>21</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>1.25</td>\n",
       "      <td>21232</td>\n",
       "      <td>5</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>13085.0</td>\n",
       "      <td>28</td>\n",
       "      <td>489434</td>\n",
       "      <td>11</td>\n",
       "      <td>1.65</td>\n",
       "      <td>22064</td>\n",
       "      <td>17</td>\n",
       "      <td>2017</td>\n",
       "      <td>2017-11-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  customer_id  day invoice  month  price stream_id  \\\n",
       "0  United Kingdom      13085.0   28  489434     11   6.95     85048   \n",
       "1  United Kingdom      13085.0   28  489434     11   6.75    79323W   \n",
       "2  United Kingdom      13085.0   28  489434     11   2.10     22041   \n",
       "3  United Kingdom      13085.0   28  489434     11   1.25     21232   \n",
       "4  United Kingdom      13085.0   28  489434     11   1.65     22064   \n",
       "\n",
       "   times_viewed  year invoice_date  \n",
       "0            12  2017   2017-11-28  \n",
       "1            12  2017   2017-11-28  \n",
       "2            21  2017   2017-11-28  \n",
       "3             5  2017   2017-11-28  \n",
       "4            17  2017   2017-11-28  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>"
      ],
      "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  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfts = convert_to_ts(df)\n",
    "dfts.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#the columns in the dfts are: date, purchases, unique_invoices, unique_streams, total_views, year_month, revenue\n",
    "#plot\n",
    "plt.plot(dfts['date'], dfts['revenue'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#show outliers\n",
    "plt.boxplot(dfts['revenue'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export to csv\n",
    "dfts.to_csv('data/ts-data.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}