Time-Series-Event-Exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " The data set has 30593 rows\n",
      " The data set has 96 rows\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd ## Import all the packages\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "color = sns.color_palette() ## seaborn palette for beautiful plots\n",
    "df = pd.read_csv(\"/Users/Rishi/Desktop/timeseries_events.csv\") ## \n",
    "df1 = pd.read_csv(\"/Users/Rishi/Desktop/timeseries_users.csv\") ## \n",
    "print(\" The data set has\", len(df), \"rows\") \n",
    "print(\" The data set has\", len(df1), \"rows\")\n"
   ]
  },
  {
   "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>user_id</th>\n",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id           event_date\n",
       "0        0  2015-02-26 00:00:00\n",
       "1        0  2015-03-10 00:00:00\n",
       "2        0  2015-03-30 00:00:00\n",
       "3        0  2015-02-15 00:00:00\n",
       "4        0  2015-02-18 00:00:00"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>m</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>f</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender  user_id\n",
       "0   51      f        0\n",
       "1   49      f        1\n",
       "2   35      m        2\n",
       "3   63      m        3\n",
       "4   27      f        4"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "event_date = df['event_date'].astype('datetime64[ns]') ## convert object to datatime format "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>user_id</th>\n",
       "      <th>event_date</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-10 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id           event_date   age gender\n",
       "0        0  2015-02-26 00:00:00  51.0      f\n",
       "1        0  2015-03-10 00:00:00  51.0      f\n",
       "2        0  2015-03-30 00:00:00  51.0      f\n",
       "3        0  2015-02-15 00:00:00  51.0      f\n",
       "4        0  2015-02-18 00:00:00  51.0      f"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.merge(df, df1, how ='left', on=['user_id'])  ## merge casedf and demodf \n",
    "# s1[\"age\"] = \"\" ## create an empty variable age and set it to null\n",
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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f9HaNAAAAAFDsPNqz9Oijj2rRokX6+uuv5e/vrzfffFNHjx7V8uXL9ec//9nbNQIAAABA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\nAJigWQIAAAAAEzRLAAAAAGDi/wNw+N/9s9l0WwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1141eb358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "convictage = s1['age'].value_counts()\n",
    "convictage.head()\n",
    "\n",
    "plt.figure(figsize=(14,4))\n",
    "sns.barplot(convictage.index, convictage.values, alpha=0.8, color=color[1])\n",
    "plt.ylabel('No of occurences', fontsize=15)\n",
    "plt.xlabel('Age', fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>user_id</th>\n",
       "      <th>event_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>2015-01-15 00:00:00</th>\n",
       "      <td>51.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16 00:00:00</th>\n",
       "      <td>51.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-17 00:00:00</th>\n",
       "      <td>51.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-18 00:00:00</th>\n",
       "      <td>51.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-19 00:00:00</th>\n",
       "      <td>51.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              age      \n",
       "                             mean count\n",
       "user_id event_date                     \n",
       "0       2015-01-15 00:00:00  51.0     2\n",
       "        2015-01-16 00:00:00  51.0     2\n",
       "        2015-01-17 00:00:00  51.0     3\n",
       "        2015-01-18 00:00:00  51.0     2\n",
       "        2015-01-19 00:00:00  51.0     2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2 = s1[['user_id', 'event_date', 'age', 'gender']].groupby(['user_id', 'event_date']).agg(['mean', 'count'])\n",
    "s2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>USER-ID</th>\n",
       "      <th>TOTAL-EVENTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   USER-ID  TOTAL-EVENTS\n",
       "0        0           148\n",
       "1        1           509\n",
       "2        2           126\n",
       "3        3            65\n",
       "4        4           247"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " counts = s1.groupby('user_id').size()\n",
    " counts\n",
    " d1f = counts.to_frame().reset_index()\n",
    " d1f.head()\n",
    " d1f = d1f.rename(columns={'user_id': 'USER-ID', 0: 'TOTAL-EVENTS'})\n",
    " d1f.head()\n",
    "# counts_df = pd.DataFrame(df.groupby('user_id').size().rename('counts'))\n",
    "# counts_df.head()\n",
    "# counts_df.column()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>AGE</th>\n",
       "      <th>GENDER</th>\n",
       "      <th>USER-ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>f</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>63</td>\n",
       "      <td>m</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27</td>\n",
       "      <td>f</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AGE GENDER  USER-ID\n",
       "0   51      f        0\n",
       "1   49      f        1\n",
       "2   35      m        2\n",
       "3   63      m        3\n",
       "4   27      f        4"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2f = pd.read_csv(\"/Users/Rishi/Desktop/timeseries_users.csv\") ## \n",
    "# df2.head()\n",
    "d2f = d2f.rename(columns={'age': 'AGE', 'gender': 'GENDER', 'user_id': 'USER-ID'})\n",
    "d2f.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>USER-ID</th>\n",
       "      <th>TOTAL-EVENTS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>GENDER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>148</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>509</td>\n",
       "      <td>49.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>63.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>247</td>\n",
       "      <td>27.0</td>\n",
       "      <td>f</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   USER-ID  TOTAL-EVENTS   AGE GENDER\n",
       "0        0           148  51.0      f\n",
       "1        1           509  49.0      f\n",
       "2        2           126  35.0      m\n",
       "3        3            65  63.0      m\n",
       "4        4           247  27.0      f"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = pd.merge(d1f, d2f, how ='left', on=['USER-ID'])  ## merge casedf and demodf \n",
    "d1.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot a histogram of total number of events per user for all male users who are 30+ years old."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>USER-ID</th>\n",
       "      <th>TOTAL-EVENTS</th>\n",
       "      <th>AGE</th>\n",
       "      <th>GENDER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>126</td>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>65</td>\n",
       "      <td>63.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>1705</td>\n",
       "      <td>45.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>147</td>\n",
       "      <td>51.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>254</td>\n",
       "      <td>41.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>129</td>\n",
       "      <td>56.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>31</td>\n",
       "      <td>271</td>\n",
       "      <td>42.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>33</td>\n",
       "      <td>51</td>\n",
       "      <td>52.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>391</td>\n",
       "      <td>34.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>10</td>\n",
       "      <td>41.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>45</td>\n",
       "      <td>758</td>\n",
       "      <td>39.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>473</td>\n",
       "      <td>61.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>208</td>\n",
       "      <td>53.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>17</td>\n",
       "      <td>62.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>224</td>\n",
       "      <td>51.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>100</td>\n",
       "      <td>33.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>71</td>\n",
       "      <td>95</td>\n",
       "      <td>42.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>72</td>\n",
       "      <td>123</td>\n",
       "      <td>33.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>74</td>\n",
       "      <td>349</td>\n",
       "      <td>54.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>41</td>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>84</td>\n",
       "      <td>1947</td>\n",
       "      <td>33.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>89</td>\n",
       "      <td>278</td>\n",
       "      <td>37.0</td>\n",
       "      <td>m</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    USER-ID  TOTAL-EVENTS   AGE GENDER\n",
       "2         2           126  35.0      m\n",
       "3         3            65  63.0      m\n",
       "12       12          1705  45.0      m\n",
       "17       17           147  51.0      m\n",
       "21       21           254  41.0      m\n",
       "23       23           129  56.0      m\n",
       "31       31           271  42.0      m\n",
       "33       33            51  52.0      m\n",
       "38       38           391  34.0      m\n",
       "40       40            10  41.0      m\n",
       "45       45           758  39.0      m\n",
       "47       47           473  61.0      m\n",
       "60       60           208  53.0      m\n",
       "64       64            17  62.0      m\n",
       "66       66           224  51.0      m\n",
       "67       67           100  33.0      m\n",
       "71       71            95  42.0      m\n",
       "72       72           123  33.0      m\n",
       "74       74           349  54.0      m\n",
       "78       78            41  35.0      m\n",
       "84       84          1947  33.0      m\n",
       "89       89           278  37.0      m"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2m = d1[(d1[\"AGE\"] > 30) & (d1[\"GENDER\"] == 'm')]\n",
    "d2m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1151c32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "x = d2m['TOTAL-EVENTS']\n",
    "num_bins = 30\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "## TOTAL-EVENTS - X Label\n",
    "## Frequency - Y Label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the list of inter-event intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>user_id</th>\n",
       "      <th>event_date</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>time_f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-10 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id           event_date   age gender     time_f\n",
       "0        0  2015-02-26 00:00:00  51.0      f 2015-02-26\n",
       "1        0  2015-03-10 00:00:00  51.0      f 2015-03-10\n",
       "2        0  2015-03-30 00:00:00  51.0      f 2015-03-30\n",
       "3        0  2015-02-15 00:00:00  51.0      f 2015-02-15\n",
       "4        0  2015-02-18 00:00:00  51.0      f 2015-02-18"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## we first convert timestamps from string to date / time format and store the value in a new column \"time_f\"\n",
    "from datetime import datetime as dt\n",
    "time_t = lambda x: dt.strptime(x, \"%Y-%m-%d %H:%M:%S\")\n",
    "s1[\"time_f\"] = s1[\"event_date\"].map(time_t)\n",
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# we do that again store the value in the new column \"time_i\" and shift the row of 1 step\n",
    "s1[\"time_i\"] = s1[\"event_date\"].map(time_t)\n",
    "s1[\"time_i\"] = s1[\"time_i\"].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>user_id</th>\n",
       "      <th>event_date</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>time_f</th>\n",
       "      <th>time_i</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>f</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>2015-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>2015-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-15</td>\n",
       "      <td>2015-03-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>2015-02-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id           event_date   age gender     time_f     time_i\n",
       "0        0  2015-02-26 00:00:00  51.0      f 2015-02-26        NaT\n",
       "1        0  2015-03-10 00:00:00  51.0      f 2015-03-10 2015-02-26\n",
       "2        0  2015-03-30 00:00:00  51.0      f 2015-03-30 2015-03-10\n",
       "3        0  2015-02-15 00:00:00  51.0      f 2015-02-15 2015-03-30\n",
       "4        0  2015-02-18 00:00:00  51.0      f 2015-02-18 2015-02-15"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# we now define a function to compute the time interval between two timestamps in second and apply it to our dataframe\n",
    "\n",
    "def time_interval(df):\n",
    "    \"\"\"\n",
    "    INPUTS:\n",
    "    - df: dataframe with columns 'time_f' and 'time_i'\n",
    "    \n",
    "    OUTPUTS:\n",
    "    - time in second between the two timestamps 'time_f' and 'time_i'\n",
    "    \"\"\"\n",
    "    return (df[\"time_f\"] - df[\"time_i\"]).total_seconds() ## This can be changed to convert to Days instead of seconds.\n",
    "\n",
    "s1[\"time_interval in Seconds\"] = s1.apply(time_interval, axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>user_id</th>\n",
       "      <th>event_date</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>time_f</th>\n",
       "      <th>time_i</th>\n",
       "      <th>time_interval</th>\n",
       "      <th>time_interval in Seconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-10 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>2015-02-26</td>\n",
       "      <td>1036800.0</td>\n",
       "      <td>1036800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>2015-03-10</td>\n",
       "      <td>1728000.0</td>\n",
       "      <td>1728000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-15</td>\n",
       "      <td>2015-03-30</td>\n",
       "      <td>-3715200.0</td>\n",
       "      <td>-3715200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>2015-02-15</td>\n",
       "      <td>259200.0</td>\n",
       "      <td>259200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-22 00:00:00</td>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>2015-02-22</td>\n",
       "      <td>2015-02-18</td>\n",
       "      <td>345600.0</td>\n",
       "      <td>345600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id           event_date   age gender     time_f     time_i  \\\n",
       "1        0  2015-03-10 00:00:00  51.0      f 2015-03-10 2015-02-26   \n",
       "2        0  2015-03-30 00:00:00  51.0      f 2015-03-30 2015-03-10   \n",
       "3        0  2015-02-15 00:00:00  51.0      f 2015-02-15 2015-03-30   \n",
       "4        0  2015-02-18 00:00:00  51.0      f 2015-02-18 2015-02-15   \n",
       "5        0  2015-02-22 00:00:00  51.0      f 2015-02-22 2015-02-18   \n",
       "\n",
       "   time_interval  time_interval in Seconds  \n",
       "1      1036800.0                 1036800.0  \n",
       "2      1728000.0                 1728000.0  \n",
       "3     -3715200.0                -3715200.0  \n",
       "4       259200.0                  259200.0  \n",
       "5       345600.0                  345600.0  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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v3jw9/PDDKi4uDtruhRdeUEREhBYvXtz9sfR8+AAAnPk0ao/Ho+TkZNXX16u2\ntla1tbU6efKkrrvuOrndblVXV7fb5uTJk3rppZc0e/ZsXXrppd1+LWZWAIBe+c9//iNJevzxxzu8\nNV2SPvnkE8XFxQX+++2331ZTU5PmzJnTo9cirAAAvXL69GlJ0t13362rr766w5oJEya0++833nhD\nkZGRmjVrVo9ei7ACAPRK22U8m82mpKSkdn3vv/++6uvrNWzYsHbt5eXlSkhIUHR0dI9ei/esAAC9\nkpCQoNjYWG3fvl2NjY2B9uPHj2v16tVat25d4KYLSTp16pQOHTqkK6+8ssevxcwKANArQ4cO1X33\n3ac1a9bo+9//vhYuXKioqCgVFRXpk08+0RNPPNHu2az//ve/OnXqlC655JIevxZhBQDotTlz5mjk\nyJHavHmznnvuOQ0ePFiTJk3S5s2bdd1117Wr/fzzzyWpx5cAJWmQ3+/3WzJiAAD6CO9ZAQCMR1gB\nAIxHWAEAjEdYAQCMR1gBAIxHWAEAjEdYAQCMR1gBAIxHWAEAjEdYAQCM9/8A0jOMDu18sfAAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a2fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.mlab as mlab\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "x = s1['time_interval in Seconds']\n",
    "num_bins = 30\n",
    "n, bins, patches = plt.hist(x, num_bins, facecolor='blue', alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "## Time Interval in Seconds - X Label\n",
    "## Frequency - Y Label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# # normalized histogram\n",
    "# sns.set_style(\"whitegrid\")\n",
    "# sns.set_context(\"paper\", rc=c)\n",
    "\n",
    "# fig = plt.figure(figsize=(10, 20))\n",
    "\n",
    "# bins = np.linspace(0, 600000, 30)\n",
    "\n",
    "# for j in range(5):\n",
    "#     ax = plt.subplot2grid((5, 1), (j, 0))\n",
    "#     i = s1[j]\n",
    "#     events_i = s1[s1.user_id == i]\n",
    "#     s1[\"time_f\"] = s1[\"timestamp\"].map(time_t)\n",
    "#     s1[\"time_i\"] = s1[\"timestamp\"].map(time_t)\n",
    "#     s1[\"time_i\"] = s1[\"time_i\"].shift(1)\n",
    "#     s1[\"time_interval\"] = s1.apply(time_interval, axis=1)\n",
    "#     s1 = s1.dropna()\n",
    "#     time_int_i = s1[\"time_interval\"].values\n",
    "#     h = ax.hist(time_int_i, bins, normed=1, label=str(i), alpha=0.5)\n",
    "#     ax.legend()\n",
    "#     ax.set_xlabel(\"time in s\")\n",
    "#     ax.set_ylim([0., 0.00003])\n",
    "\n",
    "# fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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