CapitalOneScript.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Matt Grant \n",
    "# 9/17/2105\n",
    "# Data Analysis: Capital One, Use transaction data to categorize clients\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "%matplotlib inline \n",
    "style.use('ggplot')\n",
    "\n",
    "#Using the transactions data attached below, write a script in \n",
    "#Python,that outputs a list of subscription IDs,their subscription \n",
    "#type (daily, monthly, yearly, one-off), and the duration of their \n",
    "#subscription.\n",
    "# Subscription ID, Type , Duration \n",
    "#    3159        , Monthly, 900 days \n",
    "\n",
    "#Bounus\n",
    "#1. Give annual revenue numbers for all years between 1966 and 2014. \n",
    "#Which years had the highest revenue growth, and highest revenue loss?\n",
    "\n",
    "\n",
    "#2. Predict annual revenue for year 2015 (based on historical retention and new subscribers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Import data into pandas dataframe\n",
    "data = pd.read_csv('../Data Science/CapitalOneScript/subscription_report.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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",
       "    </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>01/01/1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1236</td>\n",
       "      <td>30674</td>\n",
       "      <td>7280</td>\n",
       "      <td>01/01/1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1237</td>\n",
       "      <td>5293</td>\n",
       "      <td>3260</td>\n",
       "      <td>01/01/1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1238</td>\n",
       "      <td>33782</td>\n",
       "      <td>4060</td>\n",
       "      <td>01/02/1966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1239</td>\n",
       "      <td>56714</td>\n",
       "      <td>6370</td>\n",
       "      <td>01/02/1966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  Subscription ID  Amount (USD) Transaction Date\n",
       "0  1235            15447          1900       01/01/1966\n",
       "1  1236            30674          7280       01/01/1966\n",
       "2  1237             5293          3260       01/01/1966\n",
       "3  1238            33782          4060       01/02/1966\n",
       "4  1239            56714          6370       01/02/1966"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Explore data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(779999, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Set Transaction Date as time data type\n",
    "# YYYY - MM - DD\n",
    "data['Transaction Date'] = pd.to_datetime(data['Transaction Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify correct data type \n",
    "data['Transaction Date'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "Subscription ID     0\n",
       "Amount (USD)        0\n",
       "Transaction Date    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Look for any null values\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x115337bd0>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAncAAAGRCAYAAAAHP5VgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIZLcCSGEEEKMIf8Bq6ZAY+4QCV4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cf6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of Subscriber for # of transactions(min:1 (one-off), max 91)\n",
    "data['Subscription ID'].value_counts().plot(kind='hist', bins=20, )\n",
    "ylabel('Subscribers')\n",
    "xlabel('Number of Transactions')\n",
    "title('Distribution of Subscriber per # of transactions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Sort data by 'Subscription ID' and date & re-index \n",
    "data.sort(['Subscription ID','Transaction Date'], inplace=True)\n",
    "data.index = range(1,len(data) + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Create a column for the year in which the transaction occured \n",
    "data['Year'] = [datetime.year for datetime in data['Transaction Date']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>779995</th>\n",
       "      <td>689487</td>\n",
       "      <td>58376</td>\n",
       "      <td>6820</td>\n",
       "      <td>1992-03-01</td>\n",
       "      <td>1992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779996</th>\n",
       "      <td>705627</td>\n",
       "      <td>58376</td>\n",
       "      <td>6820</td>\n",
       "      <td>1993-03-01</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779997</th>\n",
       "      <td>718165</td>\n",
       "      <td>58376</td>\n",
       "      <td>6820</td>\n",
       "      <td>1994-03-01</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779998</th>\n",
       "      <td>727838</td>\n",
       "      <td>58376</td>\n",
       "      <td>6820</td>\n",
       "      <td>1995-03-01</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>779999</th>\n",
       "      <td>735242</td>\n",
       "      <td>58376</td>\n",
       "      <td>6820</td>\n",
       "      <td>1996-03-01</td>\n",
       "      <td>1996</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  Subscription ID  Amount (USD) Transaction Date  Year\n",
       "779995  689487            58376          6820       1992-03-01  1992\n",
       "779996  705627            58376          6820       1993-03-01  1993\n",
       "779997  718165            58376          6820       1994-03-01  1994\n",
       "779998  727838            58376          6820       1995-03-01  1995\n",
       "779999  735242            58376          6820       1996-03-01  1996"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([ 3159,  3160,  3164, ..., 58372, 58374, 58376])] 27609\n"
     ]
    }
   ],
   "source": [
    "#Create a list of Subscription IDs  27,609 Unique IDs\n",
    "IDs = [data['Subscription ID'].unique()]\n",
    "print IDs, len(IDs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3159"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IDs[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155   1978-08-25\n",
       "156   1978-08-26\n",
       "157   1978-08-27\n",
       "158   1978-08-28\n",
       "159   1978-08-29\n",
       "160   1978-08-30\n",
       "161   1978-08-31\n",
       "162   1978-09-01\n",
       "163   1978-09-02\n",
       "164   1978-09-03\n",
       "165   1978-09-04\n",
       "166   1978-09-05\n",
       "167   1978-09-06\n",
       "168   1978-09-07\n",
       "169   1978-09-08\n",
       "170   1978-09-09\n",
       "171   1978-09-10\n",
       "172   1978-09-11\n",
       "173   1978-09-12\n",
       "174   1978-09-13\n",
       "175   1978-09-14\n",
       "176   1978-09-15\n",
       "177   1978-09-16\n",
       "178   1978-09-17\n",
       "179   1978-09-18\n",
       "180   1978-09-19\n",
       "181   1978-09-20\n",
       "182   1978-09-21\n",
       "183   1978-09-22\n",
       "184   1978-09-23\n",
       "185   1978-09-24\n",
       "Name: Transaction Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Code Check (Find all rows in DataFrame where \n",
    "ID = data[data['Subscription ID'] == IDs[0][3]]\n",
    "ID['Transaction Date']#.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID: 3165 Duration: 30 days 00:00:00  Number of Transactions: 31  Transaction Type: 0 days 23:13:32.903225\n"
     ]
    }
   ],
   "source": [
    "# Test logic  \n",
    "# ex ID 3168  == one transaction 'One off'\n",
    "# ex ID 3180  == 34 transaction over 12054 days == 'Yearly' \n",
    "# ex ID 3159  == 84 transaction over 2557 days == 'Monthly' \n",
    "# ex ID xxxx  == ?? transaction over ?? days == 'daily' \n",
    "\n",
    "\n",
    "IDdata = data[data['Subscription ID'] == 3165] #ID \n",
    "IDID = IDdata['Subscription ID'].iloc[0]\n",
    "nrow = len(IDdata)\n",
    "deltaT = IDdata['Transaction Date'].iloc[nrow-1]-IDdata['Transaction Date'].iloc[0]\n",
    "duration = deltaT/nrow\n",
    "#print ID\n",
    "\n",
    "# add this to a new data frame? \n",
    "print \"ID:\",IDID , \"Duration:\", deltaT, \" Number of Transactions:\", nrow, \" Transaction Type:\",duration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subscription ID</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Subscription ID, Duration, Type]\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(columns =['Subscription ID','Duration','Type',])\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x114c207d0>"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_3160 = data.groupby(data['Subscription ID'] == 3160)\n",
    "_3160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complete\n"
     ]
    }
   ],
   "source": [
    "IDCol = [0]*27609\n",
    "nrowCol = [0]*27609\n",
    "deltaTcol=[0]*27609\n",
    "durationCol=[0]*27609\n",
    "TypeCol=[0]*27609\n",
    "\n",
    "for item in range(27609): #27609 elements\n",
    "    \n",
    "    IDdata = data[data['Subscription ID'] == IDs[0][item]]\n",
    "    ID = IDdata['Subscription ID'].iloc[0]#IDs[0][item] \n",
    "    nrow = IDdata.shape[0]\n",
    "    \n",
    "    if nrow > 1:  \n",
    "        deltaT = IDdata['Transaction Date'].iloc[nrow-1] - IDdata['Transaction Date'].iloc[0]\n",
    "        duration = deltaT/nrow\n",
    "    else:\n",
    "        deltaT = pd.to_timedelta('0 days')\n",
    "        duration = pd.to_timedelta('0 days')\n",
    "        tType = 'One Off'\n",
    "        \n",
    "    if duration > pd.to_timedelta('250 days'):\n",
    "        tType = 'Yearly'\n",
    "        \n",
    "    elif duration >= pd.to_timedelta('27 days') and duration <= pd.to_timedelta('32 days'):\n",
    "        tType = 'Monthly'                      \n",
    "        \n",
    "    elif duration > pd.to_timedelta('0 days') and  duration < pd.to_timedelta('2 days'):\n",
    "        tType = 'Daily'\n",
    "    #elif duration == pd.to_timedelta('0 days'):\n",
    "    #    tType ='One Off-v2'\n",
    "    \n",
    "\n",
    "    IDCol[item] = ID\n",
    "    nrowCol[item] = nrow\n",
    "    deltaTcol[item]= deltaT\n",
    "    durationCol[item]= duration\n",
    "    TypeCol[item]= tType\n",
    "    #print ID, nrow, deltaT, duration, tType     \n",
    "    \n",
    "\n",
    "print 'complete'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27609"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(IDCol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del ID[:]\n",
    "del nrow[:]\n",
    "del deltaT[:]\n",
    "del duration[:]\n",
    "del tType[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subscription ID</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3159</td>\n",
       "      <td>2557 days</td>\n",
       "      <td>Monthly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3160</td>\n",
       "      <td>2038 days</td>\n",
       "      <td>Monthly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3164</td>\n",
       "      <td>0 days</td>\n",
       "      <td>One Off</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3165</td>\n",
       "      <td>30 days</td>\n",
       "      <td>Daily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3168</td>\n",
       "      <td>0 days</td>\n",
       "      <td>One Off</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Subscription ID  Duration     Type\n",
       "0             3159 2557 days  Monthly\n",
       "1             3160 2038 days  Monthly\n",
       "2             3164    0 days  One Off\n",
       "3             3165   30 days    Daily\n",
       "4             3168    0 days  One Off"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results['Subscription ID'] = IDCol\n",
    "results['Duration'] = deltaTcol\n",
    "results['Type'] = TypeCol\n",
    "results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Output to csv file\n",
    "results.to_csv(\"CapitalOneTransactions.csv\", index=False, cols=('ID','Type','Duration(Days)'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Year\n",
      "1966     36431250\n",
      "1967     55206230\n",
      "1968     68890920\n",
      "1969     77045920\n",
      "1970     84949330\n",
      "1971     91189830\n",
      "1972     95586580\n",
      "1973     99383650\n",
      "1974     98744820\n",
      "1975    105504600\n",
      "1976    104622260\n",
      "1977    101857340\n",
      "1978    106251790\n",
      "1979    108166430\n",
      "1980    108528090\n",
      "1981    110106310\n",
      "1982    111565530\n",
      "1983    108256270\n",
      "1984    104525580\n",
      "1985    110577480\n",
      "1986    110139420\n",
      "1987    115754290\n",
      "1988    109618750\n",
      "1989    115033300\n",
      "1990    112381870\n",
      "1991     79165380\n",
      "1992     62355060\n",
      "1993     48719610\n",
      "1994     38032150\n",
      "1995     29052430\n",
      "1996     22347710\n",
      "1997     17567700\n",
      "1998     15106790\n",
      "1999     14109370\n",
      "2000     13263950\n",
      "2001     12343040\n",
      "2002     11398870\n",
      "2003     10515720\n",
      "2004      9611030\n",
      "2005      8762010\n",
      "2006      7859160\n",
      "2007      7068410\n",
      "2008      6140160\n",
      "2009      5344260\n",
      "2010      4590540\n",
      "2011      3694750\n",
      "2012      2736530\n",
      "2013      1778720\n",
      "2014       874770\n",
      "Name: Amount (USD), dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Total revenue by year in $ \n",
    "revenue_by_year = data['Amount (USD)'].groupby(data['Year']).sum()#\n",
    "print revenue_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlMAAAGRCAYAAABBiTbKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEekEhSkRERGRTvh/skcQl/QihZcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115488bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "revenue_by_year.plot(title='Revenue Over Time ($100 Million)')\n",
    "rcParams['figure.figsize'] = (10, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlsAAAGfCAYAAABoVBdOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114cdfd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart of revenue chanege per year \n",
    "revenue_by_year.pct_change().plot(kind='bar',title='Percent Revenue Change Per Year')\n",
    "rcParams['figure.figsize'] = (10, 6)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greatest Revenue Growth: 1967\n",
      "Greatest Revenue Loss: 2014\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Year\n",
       "2014   -0.508203\n",
       "2013   -0.350009\n",
       "1991   -0.295568\n",
       "2012   -0.259346\n",
       "1995   -0.236109\n",
       "1996   -0.230780\n",
       "1994   -0.219367\n",
       "1993   -0.218674\n",
       "1997   -0.213893\n",
       "1992   -0.212344\n",
       "2011   -0.195138\n",
       "2010   -0.141034\n",
       "1998   -0.140082\n",
       "2008   -0.131324\n",
       "2009   -0.129622\n",
       "2006   -0.103041\n",
       "2007   -0.100615\n",
       "2005   -0.088338\n",
       "2004   -0.086032\n",
       "2003   -0.077477\n",
       "2002   -0.076494\n",
       "2001   -0.069430\n",
       "1999   -0.066025\n",
       "2000   -0.059919\n",
       "1988   -0.053005\n",
       "1984   -0.034462\n",
       "1983   -0.029662\n",
       "1977   -0.026428\n",
       "1990   -0.023049\n",
       "1976   -0.008363\n",
       "1974   -0.006428\n",
       "1986   -0.003962\n",
       "1980    0.003344\n",
       "1982    0.013253\n",
       "1981    0.014542\n",
       "1979    0.018020\n",
       "1973    0.039724\n",
       "1978    0.043143\n",
       "1972    0.048215\n",
       "1989    0.049394\n",
       "1987    0.050980\n",
       "1985    0.057899\n",
       "1975    0.068457\n",
       "1971    0.073461\n",
       "1970    0.102581\n",
       "1969    0.118376\n",
       "1968    0.247883\n",
       "1967    0.515354\n",
       "1966         NaN\n",
       "Name: Amount (USD), dtype: float64"
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generates percent change and orders series from highest revenue loss to highest revenue growth\n",
    "# 2014 - greatest revenue loss (-58.0%)\n",
    "# 1967 - greatest revenue growth (51.5%)\n",
    "print 'Greatest Revenue Growth:',revenue_by_year.pct_change().idxmax()\n",
    "print 'Greatest Revenue Loss:', revenue_by_year.pct_change().idxmin()\n",
    "revenue_by_year.pct_change().order()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1966    1136\n",
       "1967    1783\n",
       "1968    2417\n",
       "1969    2912\n",
       "1970    3406\n",
       "1971    3884\n",
       "1972    4219\n",
       "1973    4565\n",
       "1974    4825\n",
       "1975    5117\n",
       "1976    5297\n",
       "1977    5618\n",
       "1978    5933\n",
       "1979    6172\n",
       "1980    6418\n",
       "1981    6667\n",
       "1982    6896\n",
       "1983    7034\n",
       "1984    7140\n",
       "1985    7382\n",
       "1986    7545\n",
       "1987    7685\n",
       "1988    7875\n",
       "1989    8040\n",
       "1990    8107\n",
       "1991    7187\n",
       "1992    6607\n",
       "1993    6052\n",
       "1994    5571\n",
       "1995    5123\n",
       "1996    4755\n",
       "1997    4380\n",
       "1998    4059\n",
       "1999    3798\n",
       "2000    3573\n",
       "2001    3328\n",
       "2002    3087\n",
       "2003    2851\n",
       "2004    2609\n",
       "2005    2364\n",
       "2006    2100\n",
       "2007    1879\n",
       "2008    1650\n",
       "2009    1434\n",
       "2010    1221\n",
       "2011     976\n",
       "2012     728\n",
       "2013     479\n",
       "2014     239\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total Number of Subscribers per year\n",
    "data['Subscription ID'].groupby(data['Year']).nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1966    10044\n",
       "1967    14270\n",
       "1968    18681\n",
       "1969    20862\n",
       "1970    23230\n",
       "1971    24898\n",
       "1972    26110\n",
       "1973    27567\n",
       "1974    27220\n",
       "1975    28755\n",
       "1976    27422\n",
       "1977    27605\n",
       "1978    28748\n",
       "1979    29098\n",
       "1980    29376\n",
       "1981    29560\n",
       "1982    29905\n",
       "1983    29498\n",
       "1984    28876\n",
       "1985    30202\n",
       "1986    29907\n",
       "1987    30598\n",
       "1988    29941\n",
       "1989    31043\n",
       "1990    30353\n",
       "1991    21369\n",
       "1992    16830\n",
       "1993    13096\n",
       "1994    10115\n",
       "1995     7720\n",
       "1996     5990\n",
       "1997     4721\n",
       "1998     4073\n",
       "1999     3798\n",
       "2000     3573\n",
       "2001     3328\n",
       "2002     3087\n",
       "2003     2851\n",
       "2004     2609\n",
       "2005     2364\n",
       "2006     2100\n",
       "2007     1879\n",
       "2008     1650\n",
       "2009     1434\n",
       "2010     1221\n",
       "2011      976\n",
       "2012      728\n",
       "2013      479\n",
       "2014      239\n",
       "Name: Subscription ID, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of transactions per year\n",
    "data['Subscription ID'].groupby(data['Year']).count()"
   ]
  },
  {
   "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": "2.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}