timeseries_workbook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "% matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "users = pd.read_csv('/Users/harishprabhala/Downloads/timeseries_users.csv')\n",
    "events = pd.read_csv('/Users/harishprabhala/Downloads/timeseries_events.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "user_events=users.merge(events, left_on='user_id', right_on='user_id', how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-10 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-03-30 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51.0</td>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>2015-02-18 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender  user_id           event_date\n",
       "0  51.0      f        0  2015-02-26 00:00:00\n",
       "1  51.0      f        0  2015-03-10 00:00:00\n",
       "2  51.0      f        0  2015-03-30 00:00:00\n",
       "3  51.0      f        0  2015-02-15 00:00:00\n",
       "4  51.0      f        0  2015-02-18 00:00:00"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_events.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "male = user_events[user_events.gender == 'm']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "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",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-06 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age gender  user_id           event_date\n",
       "657  35.0      m        2  2015-03-06 00:00:00\n",
       "658  35.0      m        2  2015-03-19 00:00:00\n",
       "659  35.0      m        2  2015-03-15 00:00:00\n",
       "660  35.0      m        2  2015-02-27 00:00:00\n",
       "661  35.0      m        2  2015-02-26 00:00:00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_30 = male[male.age >= 30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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",
       "      <th>event_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>657</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-06 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-19 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-03-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-27 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>35.0</td>\n",
       "      <td>m</td>\n",
       "      <td>2</td>\n",
       "      <td>2015-02-26 00:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age gender  user_id           event_date\n",
       "657  35.0      m        2  2015-03-06 00:00:00\n",
       "658  35.0      m        2  2015-03-19 00:00:00\n",
       "659  35.0      m        2  2015-03-15 00:00:00\n",
       "660  35.0      m        2  2015-02-27 00:00:00\n",
       "661  35.0      m        2  2015-02-26 00:00:00"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_30.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id\n",
       "84     1947\n",
       "12     1705\n",
       "45      758\n",
       "47      473\n",
       "38      391\n",
       "74      349\n",
       "89      278\n",
       "31      271\n",
       "21      254\n",
       "66      224\n",
       "60      208\n",
       "17      147\n",
       "23      129\n",
       "2       126\n",
       "72      123\n",
       "67      100\n",
       "71       95\n",
       "3        65\n",
       "33       51\n",
       "78       41\n",
       "64       17\n",
       "40       10\n",
       "226       0\n",
       "200       0\n",
       "220       0\n",
       "225       0\n",
       "227       0\n",
       "Name: event_date, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_30.groupby('user_id')['event_date'].count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10ed20d68>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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85jZ68K+r20Ik2QTcG/js0PTs4dfwt039yjbvmgs4P8m2TD4lDXCXqrp2WP4GcJc1qm23\n07jlP771MG4w+zjddVieZ427/SGTs73djhmmKz6V5EFD27zrm+V5XIuxexBwXVVdOdU293HbIzfm\n8prb6MG/biS5PfAh4Iyq2gW8gckU1AnAtUx+pVwLD6yqE5jcJfWZSR48/cPhLGHNLu3K5MN9pwIf\nGJrWy7jdwlqP03KSvBi4CTh7aLoWWBye9+cC705yxJzLWpfP4x5O55YnHHMft73kxk+0fM1t9OAf\ndVuI1pLcmsmTd3ZVfRigqq6rqpur6sfAm/n/aYm51lxVO4c/rwfOGeq4bvgVcfevsdevRW2DRwAX\nVdV1Q53rYtwGs47TTm453dK8xiRPBR4JPGkICobpgG8Ny9uYzAf/wjzrW8HzONexS3Iw8DjgfVM1\nz3Xc9pYbzOk1t9GDf81vCzHME74VuKyqXjXVfvTUZo8Fdl9V8FHgtCS3TXIMcE8mb860qO2wJIfv\nXmbyZuDFQw1PGTZ7CvCP865tyi3OutbDuE2ZaZyGX9F3Jbn/8Lr4g6l9Vl2Sk4DnA6dW1fen2hcy\n+T4Mkhw71PfledY36/M477EDfhu4vKp+Mk0yz3HbV24wr9fcgbwzvR4ewMlM3hG/GnjxGvT/QCa/\njn0R2D48TgbeCXxpaP8ocPTUPi8e6r2CVbrqYx+1HcvkSoAvAJfsHh/gTsC/AVcC5wN3nHdtQ1+H\nAd8CjpxqW5NxY/Kfz7XAj5jMkz59JeMELDEJuauBv2P4kGSj+q5iMu+7+3X3xmHbxw/P93bgIuBR\nLevbR20zP4/zqm1ofzvwJ3tsO7dxY9+5MZfXnJ/claTObPSpHknSjAx+SeqMwS9JnTH4JakzBr8k\ndcbgl6TOGPyS1BmDX5I6839jIpRXvai0jAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ecb3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "male_30.groupby('user_id')['event_date'].count().sort_values(ascending=False).plot(kind='hist', bins = 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def delta(time):\n",
    "    initial_time=pd.to_datetime(time).sort_values().reset_index(drop=True)\n",
    "    final_time=initial_time[1:].reset_index(drop=True)\n",
    "    return (final_time-initial_time[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "user_id     \n",
       "0        0     0 days\n",
       "         1     1 days\n",
       "         2     0 days\n",
       "         3     1 days\n",
       "         4     0 days\n",
       "         5     0 days\n",
       "         6     1 days\n",
       "         7     0 days\n",
       "         8     1 days\n",
       "         9     0 days\n",
       "         10    1 days\n",
       "         11    0 days\n",
       "         12    1 days\n",
       "         13    1 days\n",
       "         14    1 days\n",
       "         15    1 days\n",
       "         16    0 days\n",
       "         17    1 days\n",
       "         18    0 days\n",
       "         19    1 days\n",
       "         20    0 days\n",
       "         21    1 days\n",
       "         22    0 days\n",
       "         23    1 days\n",
       "         24    0 days\n",
       "         25    1 days\n",
       "         26    0 days\n",
       "         27    1 days\n",
       "         28    0 days\n",
       "         29    1 days\n",
       "                ...  \n",
       "96       147   0 days\n",
       "         148   1 days\n",
       "         149   0 days\n",
       "         150   1 days\n",
       "         151   0 days\n",
       "         152   1 days\n",
       "         153   0 days\n",
       "         154   1 days\n",
       "         155   0 days\n",
       "         156   1 days\n",
       "         157   0 days\n",
       "         158   1 days\n",
       "         159   0 days\n",
       "         160   1 days\n",
       "         161   0 days\n",
       "         162   1 days\n",
       "         163   0 days\n",
       "         164   1 days\n",
       "         165   0 days\n",
       "         166   1 days\n",
       "         167   1 days\n",
       "         168   1 days\n",
       "         169   1 days\n",
       "         170   1 days\n",
       "         171   1 days\n",
       "         172   1 days\n",
       "         173   1 days\n",
       "         174   1 days\n",
       "         175   1 days\n",
       "         176   1 days\n",
       "Name: event_date, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_events.groupby('user_id')['event_date'].apply(delta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "label=events.groupby('user_id')['event_date'].apply(days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'user_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2133\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2134\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2135\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4433)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'user_id'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-80-4df3f0aa2461>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtime_delta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'delta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mhow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'outer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mleft_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'user_id'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m   4605\u001b[0m                      \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4606\u001b[0m                      \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4607\u001b[0;31m                      copy=copy, indicator=indicator)\n\u001b[0m\u001b[1;32m   4608\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4609\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecimals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36mmerge\u001b[0;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m     59\u001b[0m                          \u001b[0mright_on\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_on\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleft_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mleft_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                          \u001b[0mright_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mright_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msuffixes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                          copy=copy, indicator=indicator)\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m__debug__\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator)\u001b[0m\n\u001b[1;32m    541\u001b[0m         (self.left_join_keys,\n\u001b[1;32m    542\u001b[0m          \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 543\u001b[0;31m          self.join_names) = self._get_merge_keys()\n\u001b[0m\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    545\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/tools/merge.py\u001b[0m in \u001b[0;36m_get_merge_keys\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    808\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_rkey\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mrk\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 810\u001b[0;31m                             \u001b[0mright_keys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mright\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mrk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    811\u001b[0m                         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m                             \u001b[0;31m# work-around for merge_asof(right_index=True)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2057\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2058\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2059\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2060\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2061\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2064\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2065\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2066\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2067\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2068\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1384\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1385\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1386\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1387\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1388\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   3541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3542\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3543\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3544\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3545\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/harishprabhala/anaconda/lib/python3.6/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2134\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2135\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2136\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2138\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4433)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'user_id'"
     ]
    }
   ],
   "source": [
    "time_delta=users.merge(label.to_frame(name='delta'),how='outer',left_on='user_id', right_on='user_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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