Anls_TimeSeries_hour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import pylab as pl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>startdate</th>\n",
       "      <th>stopdate</th>\n",
       "      <th>Event_type</th>\n",
       "      <th>End_Time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>O_date</th>\n",
       "      <th>O_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>16:03:10</td>\n",
       "      <td>16:06:03</td>\n",
       "      <td>243</td>\n",
       "      <td>Fulton St &amp; Rockwell Pl</td>\n",
       "      <td>40.688226</td>\n",
       "      <td>-73.979382</td>\n",
       "      <td>241</td>\n",
       "      <td>DeKalb Ave &amp; S Portland Ave</td>\n",
       "      <td>40.68981</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration starttime  stoptime  start station id  \\\n",
       "0           173  16:03:10  16:06:03               243   \n",
       "\n",
       "        start station name  start station latitude  start station longitude  \\\n",
       "0  Fulton St & Rockwell Pl               40.688226               -73.979382   \n",
       "\n",
       "   end station id             end station name  end station latitude  ...    \\\n",
       "0             241  DeKalb Ave & S Portland Ave              40.68981  ...     \n",
       "\n",
       "     usertype  birth year gender   startdate    stopdate Event_type End_Time  \\\n",
       "0  Subscriber      1971.0      2  2016-01-01  2016-01-01   no-event      NaN   \n",
       "\n",
       "  weekday      O_date O_hour  \n",
       "0     NaN  2016-01-01     16  \n",
       "\n",
       "[1 rows x 22 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('merged2_event_CB2016_800m.csv')\n",
    "df.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O_date</th>\n",
       "      <th>Event_type</th>\n",
       "      <th>O_hour</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       O_date Event_type  O_hour  Count\n",
       "0  2016-01-01   no-event      16      8\n",
       "1  2016-01-01   no-event      17      7\n",
       "2  2016-01-01   no-event      18      4\n",
       "3  2016-01-01   no-event      19      2\n",
       "4  2016-01-01   no-event      20      2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame(df.groupby(['O_date','Event_type','O_hour'],as_index=False).size())\n",
    "d.reset_index(inplace=True)\n",
    "d.columns = ['O_date','Event_type','O_hour','Count']\n",
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d['month'] = pd.to_datetime(d['O_date']).dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O_date</th>\n",
       "      <th>Event_type</th>\n",
       "      <th>O_hour</th>\n",
       "      <th>Count</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       O_date Event_type  O_hour  Count  month\n",
       "0  2016-01-01   no-event      16      8      1\n",
       "1  2016-01-01   no-event      17      7      1\n",
       "2  2016-01-01   no-event      18      4      1\n",
       "3  2016-01-01   no-event      19      2      1\n",
       "4  2016-01-01   no-event      20      2      1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O_hour</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19.5</td>\n",
       "      <td>12.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.5</td>\n",
       "      <td>58.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>19.5</td>\n",
       "      <td>39.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19.5</td>\n",
       "      <td>40.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19.5</td>\n",
       "      <td>22.265625</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       O_hour      Count\n",
       "month                   \n",
       "3        19.5  12.375000\n",
       "9        19.5  58.875000\n",
       "10       19.5  39.857143\n",
       "11       19.5  40.718750\n",
       "12       19.5  22.265625"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[d['Event_type']=='hockey'].groupby('month').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Event_type</th>\n",
       "      <th>O_hour</th>\n",
       "      <th>Count</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>basketball</td>\n",
       "      <td>16</td>\n",
       "      <td>19.916667</td>\n",
       "      <td>6.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>basketball</td>\n",
       "      <td>17</td>\n",
       "      <td>23.877551</td>\n",
       "      <td>6.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>basketball</td>\n",
       "      <td>18</td>\n",
       "      <td>32.530612</td>\n",
       "      <td>6.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>basketball</td>\n",
       "      <td>19</td>\n",
       "      <td>25.448980</td>\n",
       "      <td>6.244898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>basketball</td>\n",
       "      <td>20</td>\n",
       "      <td>19.795918</td>\n",
       "      <td>6.244898</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Event_type  O_hour      Count     month\n",
       "0  basketball      16  19.916667  6.333333\n",
       "1  basketball      17  23.877551  6.244898\n",
       "2  basketball      18  32.530612  6.244898\n",
       "3  basketball      19  25.448980  6.244898\n",
       "4  basketball      20  19.795918  6.244898"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = pd.DataFrame(d.groupby(['Event_type','O_hour']).mean())\n",
    "m.reset_index(inplace=True)\n",
    "m.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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MGc/f8LGxsX9x5Mhv1dYEa+lkAibd1So1Ws78eYkzESloK/XYO1rQ8z5fvP2c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V7rG+NqOI4ASJ4Eoxd9KCWBR+9v5OltrZgNU9e/i1+6yPDffY9cKIRxw0bqvvRbKE0z9wVZCnkO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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1175a0e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure(figsize=(10,6))\n",
    "sns.set(style=\"darkgrid\")\n",
    "#titanic = sns.load_dataset(\"titanic\")\n",
    "#ax = sns.barplot(x=\"Month\", y='Count',hue='Type',data=e)\n",
    "for i in m['Event_type'].unique():\n",
    "    if i == 'other':\n",
    "        break\n",
    "    #sns.barplot(x=\"O_hour\", y='Count',data=m[m['Event_type']==i])\n",
    "    pl.plot(m[m['Event_type']==i]['O_hour'],m[m['Event_type']==i]['Count'],'o-',label=i)\n",
    "pl.legend()\n",
    "pl.xlabel('Hour',size=15)\n",
    "pl.ylabel('Number of Trip',size=15)\n",
    "pl.title('Average Trip Counts from 16 pm to 23 pm on 2016',size=15)\n",
    "pl.savefig('AvgTripHour_2016.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tripduration</th>\n",
       "      <th>starttime</th>\n",
       "      <th>stoptime</th>\n",
       "      <th>start station id</th>\n",
       "      <th>start station name</th>\n",
       "      <th>start station latitude</th>\n",
       "      <th>start station longitude</th>\n",
       "      <th>end station id</th>\n",
       "      <th>end station name</th>\n",
       "      <th>end station latitude</th>\n",
       "      <th>...</th>\n",
       "      <th>usertype</th>\n",
       "      <th>birth year</th>\n",
       "      <th>gender</th>\n",
       "      <th>startdate</th>\n",
       "      <th>stopdate</th>\n",
       "      <th>Event_type</th>\n",
       "      <th>End_Time</th>\n",
       "      <th>weekday</th>\n",
       "      <th>O_date</th>\n",
       "      <th>O_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>173</td>\n",
       "      <td>16:03:10</td>\n",
       "      <td>16:06:03</td>\n",
       "      <td>243</td>\n",
       "      <td>Fulton St &amp; Rockwell Pl</td>\n",
       "      <td>40.688226</td>\n",
       "      <td>-73.979382</td>\n",
       "      <td>241</td>\n",
       "      <td>DeKalb Ave &amp; S Portland Ave</td>\n",
       "      <td>40.689810</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>136</td>\n",
       "      <td>16:05:54</td>\n",
       "      <td>16:08:11</td>\n",
       "      <td>420</td>\n",
       "      <td>Clermont Ave &amp; Lafayette Ave</td>\n",
       "      <td>40.687645</td>\n",
       "      <td>-73.969689</td>\n",
       "      <td>270</td>\n",
       "      <td>Adelphi St &amp; Myrtle Ave</td>\n",
       "      <td>40.693083</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>653</td>\n",
       "      <td>16:13:47</td>\n",
       "      <td>16:24:40</td>\n",
       "      <td>83</td>\n",
       "      <td>Atlantic Ave &amp; Fort Greene Pl</td>\n",
       "      <td>40.683826</td>\n",
       "      <td>-73.976323</td>\n",
       "      <td>278</td>\n",
       "      <td>Concord St &amp; Bridge St</td>\n",
       "      <td>40.697666</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>659</td>\n",
       "      <td>16:13:47</td>\n",
       "      <td>16:24:46</td>\n",
       "      <td>83</td>\n",
       "      <td>Atlantic Ave &amp; Fort Greene Pl</td>\n",
       "      <td>40.683826</td>\n",
       "      <td>-73.976323</td>\n",
       "      <td>278</td>\n",
       "      <td>Concord St &amp; Bridge St</td>\n",
       "      <td>40.697666</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1419</td>\n",
       "      <td>16:20:39</td>\n",
       "      <td>16:44:19</td>\n",
       "      <td>83</td>\n",
       "      <td>Atlantic Ave &amp; Fort Greene Pl</td>\n",
       "      <td>40.683826</td>\n",
       "      <td>-73.976323</td>\n",
       "      <td>532</td>\n",
       "      <td>S 5 Pl &amp; S 4 St</td>\n",
       "      <td>40.710451</td>\n",
       "      <td>...</td>\n",
       "      <td>Subscriber</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tripduration starttime  stoptime  start station id  \\\n",
       "0           173  16:03:10  16:06:03               243   \n",
       "1           136  16:05:54  16:08:11               420   \n",
       "2           653  16:13:47  16:24:40                83   \n",
       "3           659  16:13:47  16:24:46                83   \n",
       "4          1419  16:20:39  16:44:19                83   \n",
       "\n",
       "              start station name  start station latitude  \\\n",
       "0        Fulton St & Rockwell Pl               40.688226   \n",
       "1   Clermont Ave & Lafayette Ave               40.687645   \n",
       "2  Atlantic Ave & Fort Greene Pl               40.683826   \n",
       "3  Atlantic Ave & Fort Greene Pl               40.683826   \n",
       "4  Atlantic Ave & Fort Greene Pl               40.683826   \n",
       "\n",
       "   start station longitude  end station id             end station name  \\\n",
       "0               -73.979382             241  DeKalb Ave & S Portland Ave   \n",
       "1               -73.969689             270      Adelphi St & Myrtle Ave   \n",
       "2               -73.976323             278       Concord St & Bridge St   \n",
       "3               -73.976323             278       Concord St & Bridge St   \n",
       "4               -73.976323             532              S 5 Pl & S 4 St   \n",
       "\n",
       "   end station latitude  ...      usertype  birth year gender   startdate  \\\n",
       "0             40.689810  ...    Subscriber      1971.0      2  2016-01-01   \n",
       "1             40.693083  ...    Subscriber      1980.0      1  2016-01-01   \n",
       "2             40.697666  ...    Subscriber      1976.0      1  2016-01-01   \n",
       "3             40.697666  ...    Subscriber      1985.0      2  2016-01-01   \n",
       "4             40.710451  ...    Subscriber      1993.0      1  2016-01-01   \n",
       "\n",
       "     stopdate Event_type End_Time weekday      O_date O_hour  \n",
       "0  2016-01-01   no-event      NaN     NaN  2016-01-01     16  \n",
       "1  2016-01-01   no-event      NaN     NaN  2016-01-01     16  \n",
       "2  2016-01-01   no-event      NaN     NaN  2016-01-01     16  \n",
       "3  2016-01-01   no-event      NaN     NaN  2016-01-01     16  \n",
       "4  2016-01-01   no-event      NaN     NaN  2016-01-01     16  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = df[df['O_hour']>18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tem = df[['O_date','Event_type']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_bydate = pd.DataFrame(df.groupby(['O_date','start station id']).size())\n",
    "d_bydate.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: left;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O_date</th>\n",
       "      <th>start station id</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>83</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>274</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>353</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>397</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       O_date  start station id  0\n",
       "0  2016-01-01                83  1\n",
       "1  2016-01-01               243  1\n",
       "2  2016-01-01               274  1\n",
       "3  2016-01-01               353  1\n",
       "4  2016-01-01               397  2"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_bydate.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "362"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_bydate.O_date.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(d_bydate['start station id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stat = d_bydate['start station id'].unique()\n",
    "dat = d_bydate.O_date.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "M = np.zeros([20,362])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  5., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_bydate[(d_bydate['O_date'] == '2016-01-01') & (d_bydate['start station id'] == 83)][0].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "(0, 83, 0, '2016-01-01')\n",
      "2\n",
      "(0, 83, 1, '2016-01-02')\n",
      "5\n",
      "(0, 83, 2, '2016-01-03')\n",
      "4\n",
      "(0, 83, 3, '2016-01-04')\n",
      "4\n",
      "(0, 83, 4, '2016-01-05')\n",
      "5\n",
      "(0, 83, 5, '2016-01-06')\n",
      "9\n",
      "(0, 83, 6, '2016-01-07')\n",
      "7\n",
      "(0, 83, 7, '2016-01-08')\n",
      "3\n",
      "(0, 83, 8, '2016-01-09')\n",
      "4\n",
      "(0, 83, 9, '2016-01-10')\n",
      "4\n",
      "(0, 83, 10, '2016-01-11')\n",
      "8\n",
      "(0, 83, 11, '2016-01-12')\n",
      "10\n",
      "(0, 83, 12, '2016-01-13')\n",
      "14\n",
      "(0, 83, 13, '2016-01-14')\n",
      "5\n",
      "(0, 83, 14, '2016-01-15')\n",
      "6\n",
      "(0, 83, 15, '2016-01-16')\n",
      "3\n",
      "(0, 83, 16, '2016-01-17')\n",
      "1\n",
      "(0, 83, 17, '2016-01-18')\n",
      "2\n",
      "(0, 83, 18, '2016-01-19')\n",
      "5\n",
      "(0, 83, 19, '2016-01-20')\n",
      "5\n",
      "(0, 83, 20, '2016-01-21')\n",
      "6\n",
      "(0, 83, 21, '2016-01-22')\n",
      "1\n",
      "(0, 83, 22, '2016-01-27')\n",
      "6\n",
      "(0, 83, 23, '2016-01-28')\n",
      "4\n",
      "(0, 83, 24, '2016-01-29')\n",
      "3\n",
      "(0, 83, 25, '2016-01-30')\n",
      "1\n",
      "(0, 83, 26, '2016-01-31')\n",
      "8\n",
      "(0, 83, 27, '2016-02-01')\n",
      "10\n",
      "(0, 83, 28, '2016-02-02')\n",
      "7\n",
      "(0, 83, 29, '2016-02-03')\n",
      "6\n",
      "(0, 83, 30, '2016-02-04')\n",
      "6\n",
      "(0, 83, 31, '2016-02-05')\n",
      "3\n",
      "(0, 83, 32, '2016-02-06')\n",
      "7\n",
      "(0, 83, 33, '2016-02-07')\n",
      "15\n",
      "(0, 83, 34, '2016-02-08')\n",
      "4\n",
      "(0, 83, 35, '2016-02-09')\n",
      "5\n",
      "(0, 83, 36, '2016-02-10')\n",
      "3\n",
      "(0, 83, 37, '2016-02-11')\n",
      "7\n",
      "(0, 83, 38, '2016-02-12')\n",
      "1\n",
      "(0, 83, 39, '2016-02-13')\n",
      "1\n",
      "(0, 83, 40, '2016-02-14')\n",
      "1\n",
      "(0, 83, 41, '2016-02-15')\n",
      "4\n",
      "(0, 83, 42, '2016-02-16')\n",
      "3\n",
      "(0, 83, 43, '2016-02-17')\n",
      "8\n",
      "(0, 83, 44, '2016-02-18')\n",
      "6\n",
      "(0, 83, 45, '2016-02-19')\n",
      "8\n",
      "(0, 83, 46, '2016-02-20')\n",
      "1\n",
      "(0, 83, 47, '2016-02-21')\n",
      "3\n",
      "(0, 83, 48, '2016-02-22')\n",
      "4\n",
      "(0, 83, 49, '2016-02-23')\n",
      "2\n",
      "(0, 83, 50, '2016-02-24')\n",
      "2\n",
      "(0, 83, 51, '2016-02-25')\n",
      "1\n",
      "(0, 83, 52, '2016-02-26')\n",
      "6\n",
      "(0, 83, 53, '2016-02-27')\n",
      "3\n",
      "(0, 83, 54, '2016-02-28')\n",
      "5\n",
      "(0, 83, 55, '2016-02-29')\n",
      "6\n",
      "(0, 83, 56, '2016-03-01')\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 0 is out of bounds for axis 0 with size 0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-76-264c26651ce7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;31m#M[i][j] =\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_bydate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md_bydate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'O_date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0md_bydate\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'start station id'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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      8\u001b[0m         \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0"
     ]
    }
   ],
   "source": [
    "for i in range(20):\n",
    "    for j in range(362):\n",
    "        d = dat[j]\n",
    "        s = stat[i]\n",
    "        \n",
    "        #M[i][j] = \n",
    "        print(d_bydate[(d_bydate['O_date'] == d) & (d_bydate['start station id'] == s)][0].values[0])\n",
    "        print(i,s,j,d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>O_date</th>\n",
       "      <th>Event_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>no-event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        O_date Event_type\n",
       "19  2016-01-01   no-event\n",
       "20  2016-01-01   no-event\n",
       "21  2016-01-01   no-event\n",
       "22  2016-01-01   no-event\n",
       "23  2016-01-01   no-event"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d_bydate = d_bydate.merge(tem,on='O_date',how='left').drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1181aac50>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KlCmjI0eO6JtvvtHbb7+tokWLatOmTXJ1dVW1atUy3dfJyUlDhw7VqFGjJEkvv/yyChcu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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11836a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pl.figure(figsize=(10,6))\n",
    "sns.set(style=\"darkgrid\")\n",
    "#titanic = sns.load_dataset(\"titanic\")\n",
    "#ax = sns.barplot(x=\"Month\", y='Count',hue='Type',data=e)\n",
    "#for i in m['Event_type'].unique():\n",
    "#    if i == 'other':\n",
    "#        break\n",
    "    #sns.barplot(x=\"O_hour\", y='Count',data=m[m['Event_type']==i])\n",
    "#    pl.plot(m[m['Event_type']==i]['O_hour'],m[m['Event_type']==i]['Count'],'o-',label=i)\n",
    "Xl = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']\n",
    "X = range(12)\n",
    "Y = [2653, 2765, 4848, 5643, 6471, 8330, 7257, 9131, 16417, 16179, 12619, 8353]\n",
    "pl.bar(X,Y)\n",
    "#pl.legend()\n",
    "pl.xticks(arange(12), ('Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec') )\n",
    "pl.xlabel('Month',size=15)\n",
    "pl.ylabel('Number of Trip',size=15)\n",
    "pl.title('2016 Trip Counts Sample by 1000 ',size=15)\n",
    "#pl.savefig('AvgTripHour_2016.png',dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}