RFM Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RFM Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This iPython notebook explains how to perform RFM analysis from customer purchase history data. The sample orders file is Sample - Superstore dataset from Tableau Software.\n",
    "\n",
    "If you have suggestions or improvements please contribute on https://github.com/joaolcorreia/RFM-analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read the sample orders file, containing all past purchases for all customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "orders = pd.read_csv('sample-orders.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>order_date</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer</th>\n",
       "      <th>grand_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9/7/11</td>\n",
       "      <td>CA-2011-100006</td>\n",
       "      <td>Dennis Kane</td>\n",
       "      <td>378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/8/11</td>\n",
       "      <td>CA-2011-100090</td>\n",
       "      <td>Ed Braxton</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3/14/11</td>\n",
       "      <td>CA-2011-100293</td>\n",
       "      <td>Neil Franz�sisch</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/29/11</td>\n",
       "      <td>CA-2011-100328</td>\n",
       "      <td>Jasper Cacioppo</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4/8/11</td>\n",
       "      <td>CA-2011-100363</td>\n",
       "      <td>Jim Mitchum</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  order_date        order_id          customer  grand_total\n",
       "0     9/7/11  CA-2011-100006       Dennis Kane          378\n",
       "1     7/8/11  CA-2011-100090        Ed Braxton          699\n",
       "2    3/14/11  CA-2011-100293  Neil Franz�sisch           91\n",
       "3    1/29/11  CA-2011-100328   Jasper Cacioppo            4\n",
       "4     4/8/11  CA-2011-100363       Jim Mitchum           21"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orders.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the RFM Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since recency is calculated for a point in time and the Tableau Super Store dataset last order date is Dec 31 2014, that is the date we will use to calculate recency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set this date to the current day and extract all orders until yesterday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import datetime as dt\n",
    "NOW = dt.datetime(2014,12,31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Make the date_placed column datetime\n",
    "orders['order_date'] = pd.to_datetime(orders['order_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the RFM Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rfmTable = orders.groupby('customer').agg({'order_date': lambda x: (NOW - x.max()).days, # Recency\n",
    "                                        'order_id': lambda x: len(x),      # Frequency\n",
    "                                        'grand_total': lambda x: x.sum()}) # Monetary Value\n",
    "\n",
    "rfmTable['order_date'] = rfmTable['order_date'].astype(int)\n",
    "rfmTable.rename(columns={'order_date': 'recency', \n",
    "                         'order_id': 'frequency', \n",
    "                         'grand_total': 'monetary_value'}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Validating the RFM Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron Bergman</th>\n",
       "      <td>3</td>\n",
       "      <td>415</td>\n",
       "      <td>887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Hawkins</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Smayling</th>\n",
       "      <td>7</td>\n",
       "      <td>88</td>\n",
       "      <td>3050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Bellavance</th>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>7756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Hart</th>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>3249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 frequency  recency  monetary_value\n",
       "customer                                           \n",
       "Aaron Bergman            3      415             887\n",
       "Aaron Hawkins            7       12            1744\n",
       "Aaron Smayling           7       88            3050\n",
       "Adam Bellavance          8       54            7756\n",
       "Adam Hart               10       34            3249"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmTable.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Customer **Aaron Bergman** has frequency:3, monetary value:$887 and recency:415 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>order_date</th>\n",
       "      <th>order_id</th>\n",
       "      <th>customer</th>\n",
       "      <th>grand_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>2011-02-19</td>\n",
       "      <td>CA-2011-152905</td>\n",
       "      <td>Aaron Bergman</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>2011-03-07</td>\n",
       "      <td>CA-2011-156587</td>\n",
       "      <td>Aaron Bergman</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2336</th>\n",
       "      <td>2013-11-11</td>\n",
       "      <td>CA-2013-140935</td>\n",
       "      <td>Aaron Bergman</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     order_date        order_id       customer  grand_total\n",
       "624  2011-02-19  CA-2011-152905  Aaron Bergman           13\n",
       "665  2011-03-07  CA-2011-156587  Aaron Bergman          310\n",
       "2336 2013-11-11  CA-2013-140935  Aaron Bergman          564"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aaron = orders[orders['customer']=='Aaron Bergman']\n",
    "aaron"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inserting the date of Aaron purchase and comparing it to the recency in the rfmTable we verify our RFM table is correct."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(NOW - dt.datetime(2013,11,11)).days==415"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determining RFM Quartiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "quantiles = rfmTable.quantile(q=[0.25,0.5,0.75])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25</th>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50</th>\n",
       "      <td>6.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.75</th>\n",
       "      <td>8.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>3784.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      frequency  recency  monetary_value\n",
       "0.25        5.0     30.0          1145.0\n",
       "0.50        6.0     75.0          2257.0\n",
       "0.75        8.0    183.0          3784.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Send quantiles to a dictionary, easier to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "quantiles = quantiles.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'frequency': {0.25: 5.0, 0.5: 6.0, 0.75: 8.0},\n",
       " 'monetary_value': {0.25: 1145.0, 0.5: 2257.0, 0.75: 3784.0},\n",
       " 'recency': {0.25: 30.0, 0.5: 75.0, 0.75: 183.0}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the RFM segmentation table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfmSegmentation = rfmTable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create two classes for the RFM segmentation since, being high recency is bad, while high frequency and monetary value is good. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def RClass(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 1\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 2\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 3\n",
    "    else:\n",
    "        return 4\n",
    "    \n",
    "# Arguments (x = value, p = recency, monetary_value, frequency, k = quartiles dict)\n",
    "def FMClass(x,p,d):\n",
    "    if x <= d[p][0.25]:\n",
    "        return 4\n",
    "    elif x <= d[p][0.50]:\n",
    "        return 3\n",
    "    elif x <= d[p][0.75]: \n",
    "        return 2\n",
    "    else:\n",
    "        return 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rfmSegmentation['R_Quartile'] = rfmSegmentation['recency'].apply(RClass, args=('recency',quantiles,))\n",
    "rfmSegmentation['F_Quartile'] = rfmSegmentation['frequency'].apply(FMClass, args=('frequency',quantiles,))\n",
    "rfmSegmentation['M_Quartile'] = rfmSegmentation['monetary_value'].apply(FMClass, args=('monetary_value',quantiles,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rfmSegmentation['RFMClass'] = rfmSegmentation.R_Quartile.map(str) \\\n",
    "                            + rfmSegmentation.F_Quartile.map(str) \\\n",
    "                            + rfmSegmentation.M_Quartile.map(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aaron Bergman</th>\n",
       "      <td>3</td>\n",
       "      <td>415</td>\n",
       "      <td>887</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Hawkins</th>\n",
       "      <td>7</td>\n",
       "      <td>12</td>\n",
       "      <td>1744</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aaron Smayling</th>\n",
       "      <td>7</td>\n",
       "      <td>88</td>\n",
       "      <td>3050</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Bellavance</th>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>7756</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Adam Hart</th>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "      <td>3249</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 frequency  recency  monetary_value  R_Quartile  F_Quartile  \\\n",
       "customer                                                                      \n",
       "Aaron Bergman            3      415             887           4           4   \n",
       "Aaron Hawkins            7       12            1744           1           2   \n",
       "Aaron Smayling           7       88            3050           3           2   \n",
       "Adam Bellavance          8       54            7756           2           2   \n",
       "Adam Hart               10       34            3249           2           1   \n",
       "\n",
       "                 M_Quartile RFMClass  \n",
       "customer                              \n",
       "Aaron Bergman             4      444  \n",
       "Aaron Hawkins             3      123  \n",
       "Aaron Smayling            2      322  \n",
       "Adam Bellavance           1      221  \n",
       "Adam Hart                 2      212  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmSegmentation.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Uncomment any of the following lines to: copy data to clipboard or save it to a CSV file.\n",
    "# rfmSegmentation.to_clipboard()\n",
    "# rfmSegmentation.to_csv('rfm-table.csv', sep=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Who are the top 5 best customers? by RFM Class (111), high spenders who buy recently and frequently?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>frequency</th>\n",
       "      <th>recency</th>\n",
       "      <th>monetary_value</th>\n",
       "      <th>R_Quartile</th>\n",
       "      <th>F_Quartile</th>\n",
       "      <th>M_Quartile</th>\n",
       "      <th>RFMClass</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sanjit Engle</th>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>12210</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>John Lee</th>\n",
       "      <td>11</td>\n",
       "      <td>21</td>\n",
       "      <td>9801</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pete Kriz</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>8647</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Harry Marie</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8237</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lena Creighton</th>\n",
       "      <td>12</td>\n",
       "      <td>16</td>\n",
       "      <td>7661</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                frequency  recency  monetary_value  R_Quartile  F_Quartile  \\\n",
       "customer                                                                     \n",
       "Sanjit Engle           11        9           12210           1           1   \n",
       "John Lee               11       21            9801           1           1   \n",
       "Pete Kriz              12        9            8647           1           1   \n",
       "Harry Marie            10        2            8237           1           1   \n",
       "Lena Creighton         12       16            7661           1           1   \n",
       "\n",
       "                M_Quartile RFMClass  \n",
       "customer                             \n",
       "Sanjit Engle             1      111  \n",
       "John Lee                 1      111  \n",
       "Pete Kriz                1      111  \n",
       "Harry Marie              1      111  \n",
       "Lena Creighton           1      111  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfmSegmentation[rfmSegmentation['RFMClass']=='111'].sort('monetary_value', ascending=False).head(5)"
   ]
  }
 ],
 "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": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}