MindSumo - Capital One Transaction Data Analysis

mindsumo_capital_one_transaction_data_analysis

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MindSumo - Capital One - Use transaction data to categorize clients  \n",
    "##### Yilun Zhang\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# data\n",
    "path = \"data/subscription_report.csv\"\n",
    "df = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Subscription ID</th>\n",
       "      <th>Amount (USD)</th>\n",
       "      <th>Transaction Date</th>\n",
       "      <th>Transaction Year</th>\n",
       "      <th>Transaction Day</th>\n",
       "      <th>Transaction Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1235</td>\n",
       "      <td>15447</td>\n",
       "      <td>1900</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1236</td>\n",
       "      <td>30674</td>\n",
       "      <td>7280</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1237</td>\n",
       "      <td>5293</td>\n",
       "      <td>3260</td>\n",
       "      <td>1966-01-01</td>\n",
       "      <td>1966</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1238</td>\n",
       "      <td>33782</td>\n",
       "      <td>4060</td>\n",
       "      <td>1966-01-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239</td>\n",
       "      <td>56714</td>\n",
       "      <td>6370</td>\n",
       "      <td>1966-01-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  Subscription ID  Amount (USD) Transaction Date  Transaction Year  \\\n",
       "0  1235            15447          1900       1966-01-01              1966   \n",
       "1  1236            30674          7280       1966-01-01              1966   \n",
       "2  1237             5293          3260       1966-01-01              1966   \n",
       "3  1238            33782          4060       1966-01-02              1966   \n",
       "4  1239            56714          6370       1966-01-02              1966   \n",
       "\n",
       "   Transaction Day  Transaction Month  \n",
       "0                1                  1  \n",
       "1                1                  1  \n",
       "2                1                  1  \n",
       "3                2                  1  \n",
       "4                2                  1  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# head of data\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# split date to year, month and day\n",
    "df[\"Transaction Year\"] = df[\"Transaction Date\"].map(lambda x: x[-4:])\n",
    "df[\"Transaction Day\"] = df[\"Transaction Date\"].map(lambda x: x[-7:-5])\n",
    "df[\"Transaction Month\"] = df[\"Transaction Date\"].map(lambda x: x[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# change data type\n",
    "df[\"Transaction Year\"] = df[\"Transaction Year\"].astype(\"int\")\n",
    "df[\"Transaction Day\"] = df[\"Transaction Day\"].astype(\"int\")\n",
    "df[\"Transaction Month\"] = df[\"Transaction Month\"].astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# change Transaction Date to date\n",
    "df[\"Transaction Date\"] = df[\"Transaction Date\"].map(lambda x: pd.to_datetime(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Subscription Type and Duration for each Subscription ID\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique subscription IDs:  27609\n"
     ]
    }
   ],
   "source": [
    "# get all unique subscription ids\n",
    "unique_sub_ids = df[\"Subscription ID\"].unique()\n",
    "print \"Number of unique subscription IDs: \", len(unique_sub_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# dictionary to store subscription type\n",
    "sub_dict = {sub_id:0 for sub_id in unique_sub_ids}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# function to determine subscription type and the subscription duration\n",
    "# type:\n",
    "    # daily\n",
    "    # monthly\n",
    "    # yearly\n",
    "    # one-off\n",
    "# duration:\n",
    "    # datetime format\n",
    "def check_sub_type(subscription_id):\n",
    "    \n",
    "    # prepare data for this sucscription_id\n",
    "    df_sub = df[(df[\"Subscription ID\"] == subscription_id)]\n",
    "    df_sub = df_sub.sort(\"Id\")\n",
    "    trans_year = df_sub[\"Transaction Year\"]\n",
    "    trans_month = df_sub[\"Transaction Month\"]\n",
    "    trans_day = df_sub[\"Transaction Day\"]\n",
    "    \n",
    "    # check duration of subscription\n",
    "    start_date = df_sub[\"Transaction Date\"].irow(0)\n",
    "    end_date = df_sub[\"Transaction Date\"].irow(-1)\n",
    "    duration = end_date - start_date\n",
    "    \n",
    "    # check type\n",
    "    if len(df_sub) == 1: # only one record\n",
    "        return [\"one-off\", duration]\n",
    "    \n",
    "    if len(trans_day.unique()) == 1:        # happened on the same day\n",
    "        if len(trans_month.unique()) == 1:  # happened in the same month \n",
    "            return [\"yearly\", duration]\n",
    "        else:                               # happened in different months\n",
    "            return [\"monthly\", duration]  \n",
    "    else:                                   # happened in different months and on different days\n",
    "        return [\"daily\", duration]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# check sub type for all ids\n",
    "for key in sub_dict.keys():\n",
    "    sub_dict[key] = check_sub_type(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# create data frame\n",
    "submission_df = pd.DataFrame(columns=[\"Subscription Id\",\"Subscription Type\",\"Subscription Duration\"])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# add data to it\n",
    "i = 0\n",
    "for key in sub_dict.keys():\n",
    "    submission_df.loc[i] = [key,sub_dict[key][0],sub_dict[key][1]]\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# submission\n",
    "submission_df.to_csv(\"data/submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subscription Id</th>\n",
       "      <th>Subscription Type</th>\n",
       "      <th>Subscription Duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3159</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2557 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3160</td>\n",
       "      <td>monthly</td>\n",
       "      <td>2038 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3164</td>\n",
       "      <td>one-off</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3165</td>\n",
       "      <td>daily</td>\n",
       "      <td>30 days</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3168</td>\n",
       "      <td>one-off</td>\n",
       "      <td>0 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription Id Subscription Type  Subscription Duration\n",
       "0             3159           monthly              2557 days\n",
       "1             3160           monthly              2038 days\n",
       "2             3164           one-off                 0 days\n",
       "3             3165             daily                30 days\n",
       "4             3168           one-off                 0 days"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# just a head\n",
    "submission_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue for Each Year\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_by_year = df.groupby(\"Transaction Year\").sum()\n",
    "df_by_year = df_by_year[[\"Amount (USD)\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x114f76c90>"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ThkEXXgiXXgqLLZZ1VJJUeeatdEqSVO2c6VSjffEFHHYYjB8Pd90Fa6+ddUSS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\nUkq/2+V9jgG+Sx6W+NOU0h/WPXipl/YkzyNiH/KX/SzyH79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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f974d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_by_year[\"Amount (USD)\"].plot(figsize=(16,8),title=\"Revenue by Year\",grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# calculate revenue change for each year\n",
    "# % change = (this year - last year) / last year\n",
    "df_by_year[\"Revenue Change\"] = 0.0\n",
    "for index_year in df_by_year.index:\n",
    "    if min(df_by_year.index) == index_year:\n",
    "        continue\n",
    "    else:\n",
    "        this_revenue = df_by_year[\"Amount (USD)\"][index_year]\n",
    "        last_revenue = df_by_year[\"Amount (USD)\"][index_year-1]\n",
    "        this_change = (this_revenue - last_revenue) / float(last_revenue)\n",
    "        df_by_year[\"Revenue Change\"][index_year] = this_change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Amount (USD)</th>\n",
       "      <th>Revenue Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Transaction Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>36431250</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1967</th>\n",
       "      <td>55206230</td>\n",
       "      <td>0.515354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968</th>\n",
       "      <td>68890920</td>\n",
       "      <td>0.247883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969</th>\n",
       "      <td>77045920</td>\n",
       "      <td>0.118376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>84949330</td>\n",
       "      <td>0.102581</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Amount (USD)  Revenue Change\n",
       "Transaction Year                              \n",
       "1966                  36431250        0.000000\n",
       "1967                  55206230        0.515354\n",
       "1968                  68890920        0.247883\n",
       "1969                  77045920        0.118376\n",
       "1970                  84949330        0.102581"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_by_year.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amount (USD)      55206230.000000\n",
       "Revenue Change           0.515354\n",
       "Name: 1967, dtype: float64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the maximum revenue change\n",
    "df_by_year.sort(\"Revenue Change\").irow(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Amount (USD)      874770.000000\n",
       "Revenue Change        -0.508203\n",
       "Name: 2014, dtype: float64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the lowest revenue change\n",
    "df_by_year.sort(\"Revenue Change\").irow(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Revenue in 2015\n",
    "---\n",
    "- ignore and one-off subscriptions\n",
    "- check if end year = 2014  \n",
    "    if yearly ->  yes  \n",
    "    if monthly -> check if month = 12  \n",
    "    if daily -> check if date = 28 and month = 12  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# find subscription ids that have not ended yet\n",
    "sub_id_cont = []\n",
    "for subscription_id in submission_df[submission_df[\"Subscription Type\"] != \"one-off\"][\"Subscription Id\"]:\n",
    "    this_type = submission_df[submission_df[\"Subscription Id\"] == subscription_id].irow(0)[\"Subscription Type\"]\n",
    "    # get data for this sub_id\n",
    "    df_sub = df[df[\"Subscription ID\"] == subscription_id]\n",
    "    df_sub = df_sub.sort(\"Id\")\n",
    "    \n",
    "    # check duration of subscription\n",
    "    end_date = df_sub[\"Transaction Date\"].irow(-1)\n",
    "    \n",
    "    # check end_date\n",
    "    if this_type == \"yearly\":\n",
    "        if end_date.year == 2014:\n",
    "            sub_id_cont.append(int(subscription_id))\n",
    "    elif this_type == \"monthly\":\n",
    "        if end_date.year == 2014 and end_date.month == 12:\n",
    "            sub_id_cont.append(int(subscription_id))\n",
    "    elif this_type == \"daily\":\n",
    "        if end_date.year == 2014 and end_date.month == 12 and end_date.day == 28:\n",
    "            sub_id_cont.append(int(subscription_id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 239 different subscriptions that are still going on.\n",
      "[3377, 3455, 3587, 3843, 4429, 4640, 4685, 4804, 5507, 5557] ...\n"
     ]
    }
   ],
   "source": [
    "print \"There are\",len(sub_id_cont), \"different subscriptions that are still going on.\"\n",
    "print sub_id_cont[:10], \"...\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Subscription ID</th>\n",
       "      <th>Amount (USD)</th>\n",
       "      <th>Transaction Date</th>\n",
       "      <th>Transaction Year</th>\n",
       "      <th>Transaction Day</th>\n",
       "      <th>Transaction Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>2285</td>\n",
       "      <td>41333</td>\n",
       "      <td>7280</td>\n",
       "      <td>1966-03-03</td>\n",
       "      <td>1966</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2911</th>\n",
       "      <td>4146</td>\n",
       "      <td>47252</td>\n",
       "      <td>5230</td>\n",
       "      <td>1966-05-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2922</th>\n",
       "      <td>4157</td>\n",
       "      <td>30148</td>\n",
       "      <td>5510</td>\n",
       "      <td>1966-05-02</td>\n",
       "      <td>1966</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5590</th>\n",
       "      <td>6825</td>\n",
       "      <td>9318</td>\n",
       "      <td>5510</td>\n",
       "      <td>1966-08-06</td>\n",
       "      <td>1966</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6342</th>\n",
       "      <td>7577</td>\n",
       "      <td>24463</td>\n",
       "      <td>390</td>\n",
       "      <td>1966-09-06</td>\n",
       "      <td>1966</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  Subscription ID  Amount (USD) Transaction Date  Transaction Year  \\\n",
       "1050  2285            41333          7280       1966-03-03              1966   \n",
       "2911  4146            47252          5230       1966-05-02              1966   \n",
       "2922  4157            30148          5510       1966-05-02              1966   \n",
       "5590  6825             9318          5510       1966-08-06              1966   \n",
       "6342  7577            24463           390       1966-09-06              1966   \n",
       "\n",
       "      Transaction Day  Transaction Month  \n",
       "1050                3                  3  \n",
       "2911                2                  5  \n",
       "2922                2                  5  \n",
       "5590                6                  8  \n",
       "6342                6                  9  "
      ]
     },
     "execution_count": 343,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot using only these 239 subscriptions\n",
    "df_ongoing = df[df[\"Subscription ID\"].isin(sub_id_cont)]\n",
    "df_ongoing.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transaction Year\n",
      "1966     29930\n",
      "1967     53940\n",
      "1968     87450\n",
      "1969    105440\n",
      "1970    144520\n",
      "Name: Amount (USD), dtype: int64\n",
      "......\n",
      "Transaction Year\n",
      "2010    874770\n",
      "2011    874770\n",
      "2012    874770\n",
      "2013    874770\n",
      "2014    874770\n",
      "Name: Amount (USD), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# group transactions by year and sum the amount\n",
    "df_ongoing_by_year = df_ongoing.groupby(\"Transaction Year\").sum()\n",
    "print df_ongoing_by_year[\"Amount (USD)\"].head(5)\n",
    "print \"......\"\n",
    "print df_ongoing_by_year[\"Amount (USD)\"].tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1131a9e10>"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KDjus7fMlSZKqWUSQUlp11x1gzbbtkSR1soULYf/9YcQIw6wkSVJbDLSSVCZe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\n2KKrkSRJkqqHgVbqJE1NcME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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1198844d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the amount\n",
    "df_ongoing_by_year[\"Amount (USD)\"].plot(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Comment:\n",
    "- The annual revenue for ongoing subscriptions have flattened after year 1990.\n",
    "- Meaning that there are no new or terminated subscriptions after 1990.\n",
    "- The best guess for the revenue in 2015 is 874770."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}