NY_subway_analysis_notes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# New York Subway and Weather Data \n",
    "\n",
    "Working with 2D data with numpy and pandas.\n",
    "\n",
    "### Questions \n",
    "\n",
    "* What variables area related to subway ridership? \n",
    "    * What stations have the most riders?\n",
    "    * What are the ridership patterns over time? On weekends Holidays? \n",
    "    * How does the weather affect ridership?\n",
    "* What patterns can I find in the weather?\n",
    "    * How does the weather vary across the city? \n",
    "    * How does the temperature rise throughout the month?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# Libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing 2D Data \n",
    "\n",
    "* Pure python: Lists of lists \n",
    "* NumPy: 2D array (more memory efficent, accessing elements differ in syntax) \n",
    "* Pandas : Dataframe \n",
    "\n",
    "### Storing 2D Data : NumPy \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample Numpy 2D array: Subway ridership for 5 stations and 10 days\n",
    "# Columns represent train stations, and rows represent days\n",
    "\n",
    "ridership = np.array([\n",
    "    [   0,    0,    2,    5,    0],\n",
    "    [1478, 3877, 3674, 2328, 2539],\n",
    "    [1613, 4088, 3991, 6461, 2691],\n",
    "    [1560, 3392, 3826, 4787, 2613],\n",
    "    [1608, 4802, 3932, 4477, 2705],\n",
    "    [1576, 3933, 3909, 4979, 2685],\n",
    "    [  95,  229,  255,  496,  201],\n",
    "    [   2,    0,    1,   27,    0],\n",
    "    [1438, 3785, 3589, 4174, 2215],\n",
    "    [1342, 4043, 4009, 4665, 3033]\n",
    "])\n",
    "\n",
    "# Check type of 2D array\n",
    "ridership.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Working with Numpy 2D arrays: \n",
    "\n",
    "* Station with max number of riders on day 1 and the mean riders per day (mean_for_max)\n",
    "* For all days and stations what is the mean ridership overall? (overall_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2342.5999999999999, 3239.9000000000001)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_riders_for_max_station(ridership):\n",
    "    # Max riders on day 1\n",
    "    max_station = ridership[0,:].argmax()            # argmax returns position\n",
    "    # Mean riders per day for max_station\n",
    "    mean_for_max = ridership[:, max_station].mean()\n",
    "    overall_mean = ridership.mean()\n",
    "    return (overall_mean, mean_for_max)\n",
    "\n",
    "mean_riders_for_max_station(ridership)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Station with higher ridership on day1 had a higher ridership than average. This is interesting but not useful.\n",
    "\n",
    "###### Use NumPy's axis function to find more practical results:\n",
    "* Mean ridership per day for each subway station\n",
    "* Maximum and minimum ridership for each day for each subway station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3239.9000000000001, 1071.2)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def min_and_max_riders_per_day(ridership):\n",
    "    # Mean per day for each station \n",
    "    station_riders = ridership.mean(axis = 0)      #selects columns \n",
    "    \n",
    "    max_daily_ridership = station_riders.max()\n",
    "    min_daily_ridership = station_riders.min() \n",
    "    \n",
    "    return (max_daily_ridership, min_daily_ridership)\n",
    "\n",
    "min_and_max_riders_per_day(ridership)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The station with the max daily ridership had 3239 riders.\n",
    "The station with the min daily ridership had 1071 riders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing 2D Data : Pandas Dataframes\n",
    "\n",
    "Recreate the sample 2D numpy array using a pandas dataframe. Advantages of a pandas dataframe over a 2D NumPy array includes the following:\n",
    "* Indexs for rows \n",
    "* Name for columns \n",
    "* Different data types for each column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>R003</th>\n",
       "      <th>R004</th>\n",
       "      <th>R005</th>\n",
       "      <th>R006</th>\n",
       "      <th>R007</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-01-11</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-11</th>\n",
       "      <td>1478</td>\n",
       "      <td>3877</td>\n",
       "      <td>3674</td>\n",
       "      <td>2328</td>\n",
       "      <td>2539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-11</th>\n",
       "      <td>1613</td>\n",
       "      <td>4088</td>\n",
       "      <td>3991</td>\n",
       "      <td>6461</td>\n",
       "      <td>2691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-04-11</th>\n",
       "      <td>1560</td>\n",
       "      <td>3392</td>\n",
       "      <td>3826</td>\n",
       "      <td>4787</td>\n",
       "      <td>2613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-05-11</th>\n",
       "      <td>1608</td>\n",
       "      <td>4802</td>\n",
       "      <td>3932</td>\n",
       "      <td>4477</td>\n",
       "      <td>2705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-06-11</th>\n",
       "      <td>1576</td>\n",
       "      <td>3933</td>\n",
       "      <td>3909</td>\n",
       "      <td>4979</td>\n",
       "      <td>2685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-07-11</th>\n",
       "      <td>95</td>\n",
       "      <td>229</td>\n",
       "      <td>255</td>\n",
       "      <td>496</td>\n",
       "      <td>201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-08-11</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-09-11</th>\n",
       "      <td>1438</td>\n",
       "      <td>3785</td>\n",
       "      <td>3589</td>\n",
       "      <td>4174</td>\n",
       "      <td>2215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-10-11</th>\n",
       "      <td>1342</td>\n",
       "      <td>4043</td>\n",
       "      <td>4009</td>\n",
       "      <td>4665</td>\n",
       "      <td>3033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "          R003  R004  R005  R006  R007\n",
       "05-01-11     0     0     2     5     0\n",
       "05-02-11  1478  3877  3674  2328  2539\n",
       "05-03-11  1613  4088  3991  6461  2691\n",
       "05-04-11  1560  3392  3826  4787  2613\n",
       "05-05-11  1608  4802  3932  4477  2705\n",
       "05-06-11  1576  3933  3909  4979  2685\n",
       "05-07-11    95   229   255   496   201\n",
       "05-08-11     2     0     1    27     0\n",
       "05-09-11  1438  3785  3589  4174  2215\n",
       "05-10-11  1342  4043  4009  4665  3033"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a pandas df\n",
    "\n",
    "ridership_df = pd.DataFrame(\n",
    "    data=[[   0,    0,    2,    5,    0],\n",
    "          [1478, 3877, 3674, 2328, 2539],\n",
    "          [1613, 4088, 3991, 6461, 2691],\n",
    "          [1560, 3392, 3826, 4787, 2613],\n",
    "          [1608, 4802, 3932, 4477, 2705],\n",
    "          [1576, 3933, 3909, 4979, 2685],\n",
    "          [  95,  229,  255,  496,  201],\n",
    "          [   2,    0,    1,   27,    0],\n",
    "          [1438, 3785, 3589, 4174, 2215],\n",
    "          [1342, 4043, 4009, 4665, 3033]],\n",
    "    index=['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',\n",
    "           '05-06-11', '05-07-11', '05-08-11', '05-09-11', '05-10-11'],\n",
    "    columns=['R003', 'R004', 'R005', 'R006', 'R007']\n",
    ")\n",
    "\n",
    "# Print df\n",
    "ridership_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean ridership for each station (column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R003    1071.2\n",
       "R004    2814.9\n",
       "R005    2718.8\n",
       "R006    3239.9\n",
       "R007    1868.2\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of each column \n",
    "ridership_df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mean ridership for each day (row)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "05-01-11       1.4\n",
       "05-02-11    2779.2\n",
       "05-03-11    3768.8\n",
       "05-04-11    3235.6\n",
       "05-05-11    3504.8\n",
       "05-06-11    3416.4\n",
       "05-07-11     255.2\n",
       "05-08-11       6.0\n",
       "05-09-11    3040.2\n",
       "05-10-11    3418.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mean of each row \n",
    "ridership_df.mean(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing a station (column) of the dataframe using \"iloc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R003    1608\n",
      "R004    4802\n",
      "R005    3932\n",
      "R006    4477\n",
      "R007    2705\n",
      "Name: 05-05-11, dtype: int64\n",
      "05-01-11       0\n",
      "05-02-11    2539\n",
      "05-03-11    2691\n",
      "05-04-11    2613\n",
      "05-05-11    2705\n",
      "05-06-11    2685\n",
      "05-07-11     201\n",
      "05-08-11       0\n",
      "05-09-11    2215\n",
      "05-10-11    3033\n",
      "Name: R007, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#ridership_df.iloc['R007'] --> This is wrong\n",
    "print ridership_df.iloc[4]\n",
    "\n",
    "print ridership_df['R007'] # prints beginning of column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing a day (row/index) of the dataframe using \"loc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R003    1438\n",
       "R004    3785\n",
       "R005    3589\n",
       "R006    4174\n",
       "R007    2215\n",
       "Name: 05-09-11, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridership_df.loc['05-09-11']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accessing a day/station (row/column) of the dataframe using \"iloc\" or \"loc\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "# Two methods\n",
    "print ridership_df.iloc[0,3]\n",
    "print ridership_df.loc['05-01-11', 'R006']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get a 2D Numpy array from a Pandas Dataframe:\n",
    "\n",
    "This will output a 2D array with no rows or columns. Useful for taking the mean of all the values. Make sure data types are consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    2    5    0]\n",
      " [1478 3877 3674 2328 2539]\n",
      " [1613 4088 3991 6461 2691]\n",
      " [1560 3392 3826 4787 2613]\n",
      " [1608 4802 3932 4477 2705]\n",
      " [1576 3933 3909 4979 2685]\n",
      " [  95  229  255  496  201]\n",
      " [   2    0    1   27    0]\n",
      " [1438 3785 3589 4174 2215]\n",
      " [1342 4043 4009 4665 3033]]\n",
      "2342.6\n"
     ]
    }
   ],
   "source": [
    "print ridership_df.values\n",
    "print ridership_df.values.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'R006'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maxmimum number of riders on the first day \n",
    "ridership_df.iloc[0].argmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We rewrite the mean_riders_for_max_station function, but this time the input is a Pandas DataFrame rather than a 2D NumPy array. The main differences:\n",
    "* iloc[0] instead of [0,:] to access a row\n",
    "* [max_station] instead of [:, max_station] to access a column\n",
    "* .values.mean() instead of .mean() to get the overall mean instead of a series of means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2342.5999999999999, 3239.9000000000001)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mean_riders_for_max_station(ridership):\n",
    "    # Max riders on day 1 station\n",
    "    max_station = ridership.iloc[0].argmax()     \n",
    "    # Mean riders per day for max station\n",
    "    mean_for_max = ridership[max_station].mean()\n",
    "    overall_mean = ridership.values.mean()\n",
    "    return (overall_mean, mean_for_max)\n",
    "\n",
    "mean_riders_for_max_station(ridership_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same result regardless of whether we used numpy arrays or pandas dataframe. \n",
    "\n",
    "Loading Data into a Dataframe\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read in file\n",
    "subway_df = pd.read_csv('nyc_subway_weather.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of printing all the rows of the data, print the first 4 rows only using .head()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UNIT</th>\n",
       "      <th>DATEn</th>\n",
       "      <th>TIMEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>datetime</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "      <th>meanprecipi</th>\n",
       "      <th>meanpressurei</th>\n",
       "      <th>meantempi</th>\n",
       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
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       "      <td>2011-05-01 00:00:00</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.22</td>\n",
       "      <td>0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>04:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 04:00:00</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.25</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>12:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 12:00:00</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.28</td>\n",
       "      <td>0</td>\n",
       "      <td>62.1</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>16:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 16:00:00</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.26</td>\n",
       "      <td>0</td>\n",
       "      <td>57.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R003</td>\n",
       "      <td>05-01-11</td>\n",
       "      <td>20:00:00</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011-05-01 20:00:00</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>30.28</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.258</td>\n",
       "      <td>55.98</td>\n",
       "      <td>7.86</td>\n",
       "      <td>40.700348</td>\n",
       "      <td>-73.887177</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNIT     DATEn     TIMEn  ENTRIESn   EXITSn  ENTRIESn_hourly  \\\n",
       "0  R003  05-01-11  00:00:00   4388333  2911002              0.0   \n",
       "1  R003  05-01-11  04:00:00   4388333  2911002              0.0   \n",
       "2  R003  05-01-11  12:00:00   4388333  2911002              0.0   \n",
       "3  R003  05-01-11  16:00:00   4388333  2911002              0.0   \n",
       "4  R003  05-01-11  20:00:00   4388333  2911002              0.0   \n",
       "\n",
       "   EXITSn_hourly             datetime  hour  day_week     ...       pressurei  \\\n",
       "0            0.0  2011-05-01 00:00:00     0         6     ...           30.22   \n",
       "1            0.0  2011-05-01 04:00:00     4         6     ...           30.25   \n",
       "2            0.0  2011-05-01 12:00:00    12         6     ...           30.28   \n",
       "3            0.0  2011-05-01 16:00:00    16         6     ...           30.26   \n",
       "4            0.0  2011-05-01 20:00:00    20         6     ...           30.28   \n",
       "\n",
       "  rain  tempi  wspdi meanprecipi  meanpressurei  meantempi  meanwspdi  \\\n",
       "0    0   55.9    3.5         0.0         30.258      55.98       7.86   \n",
       "1    0   52.0    3.5         0.0         30.258      55.98       7.86   \n",
       "2    0   62.1    6.9         0.0         30.258      55.98       7.86   \n",
       "3    0   57.9   15.0         0.0         30.258      55.98       7.86   \n",
       "4    0   52.0   10.4         0.0         30.258      55.98       7.86   \n",
       "\n",
       "   weather_lat  weather_lon  \n",
       "0    40.700348   -73.887177  \n",
       "1    40.700348   -73.887177  \n",
       "2    40.700348   -73.887177  \n",
       "3    40.700348   -73.887177  \n",
       "4    40.700348   -73.887177  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of rows in our dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42649"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(subway_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the describe() function to see some statistics about each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>weekday</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>fog</th>\n",
       "      <th>...</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "      <th>meanprecipi</th>\n",
       "      <th>meanpressurei</th>\n",
       "      <th>meantempi</th>\n",
       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4.264900e+04</td>\n",
       "      <td>4.264900e+04</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "      <td>42649.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.812486e+07</td>\n",
       "      <td>1.986993e+07</td>\n",
       "      <td>1886.589955</td>\n",
       "      <td>1361.487866</td>\n",
       "      <td>10.046754</td>\n",
       "      <td>2.905719</td>\n",
       "      <td>0.714436</td>\n",
       "      <td>40.724647</td>\n",
       "      <td>-73.940364</td>\n",
       "      <td>0.009824</td>\n",
       "      <td>...</td>\n",
       "      <td>29.971096</td>\n",
       "      <td>0.224741</td>\n",
       "      <td>63.103780</td>\n",
       "      <td>6.927872</td>\n",
       "      <td>0.004618</td>\n",
       "      <td>29.971096</td>\n",
       "      <td>63.103780</td>\n",
       "      <td>6.927872</td>\n",
       "      <td>40.728555</td>\n",
       "      <td>-73.938693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.043607e+07</td>\n",
       "      <td>2.028986e+07</td>\n",
       "      <td>2952.385585</td>\n",
       "      <td>2183.845409</td>\n",
       "      <td>6.938928</td>\n",
       "      <td>2.079231</td>\n",
       "      <td>0.451688</td>\n",
       "      <td>0.071650</td>\n",
       "      <td>0.059713</td>\n",
       "      <td>0.098631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137942</td>\n",
       "      <td>0.417417</td>\n",
       "      <td>8.455597</td>\n",
       "      <td>4.510178</td>\n",
       "      <td>0.016344</td>\n",
       "      <td>0.131158</td>\n",
       "      <td>6.939011</td>\n",
       "      <td>3.179832</td>\n",
       "      <td>0.065420</td>\n",
       "      <td>0.059582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.576152</td>\n",
       "      <td>-74.073622</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.550000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>46.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.590000</td>\n",
       "      <td>49.400000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.600204</td>\n",
       "      <td>-74.014870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.039762e+07</td>\n",
       "      <td>7.613712e+06</td>\n",
       "      <td>274.000000</td>\n",
       "      <td>237.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40.677107</td>\n",
       "      <td>-73.987342</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.890000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.913333</td>\n",
       "      <td>58.283333</td>\n",
       "      <td>4.816667</td>\n",
       "      <td>40.688591</td>\n",
       "      <td>-73.985130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.818389e+07</td>\n",
       "      <td>1.331609e+07</td>\n",
       "      <td>905.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.717241</td>\n",
       "      <td>-73.953459</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.958000</td>\n",
       "      <td>60.950000</td>\n",
       "      <td>6.166667</td>\n",
       "      <td>40.720570</td>\n",
       "      <td>-73.949150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.263049e+07</td>\n",
       "      <td>2.393771e+07</td>\n",
       "      <td>2255.000000</td>\n",
       "      <td>1537.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.759123</td>\n",
       "      <td>-73.907733</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.060000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>69.100000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.060000</td>\n",
       "      <td>67.466667</td>\n",
       "      <td>8.850000</td>\n",
       "      <td>40.755226</td>\n",
       "      <td>-73.912033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.357746e+08</td>\n",
       "      <td>1.493782e+08</td>\n",
       "      <td>32814.000000</td>\n",
       "      <td>34828.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.889185</td>\n",
       "      <td>-73.755383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>30.320000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>30.293333</td>\n",
       "      <td>79.800000</td>\n",
       "      <td>17.083333</td>\n",
       "      <td>40.862064</td>\n",
       "      <td>-73.694176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ENTRIESn        EXITSn  ENTRIESn_hourly  EXITSn_hourly  \\\n",
       "count  4.264900e+04  4.264900e+04     42649.000000   42649.000000   \n",
       "mean   2.812486e+07  1.986993e+07      1886.589955    1361.487866   \n",
       "std    3.043607e+07  2.028986e+07      2952.385585    2183.845409   \n",
       "min    0.000000e+00  0.000000e+00         0.000000       0.000000   \n",
       "25%    1.039762e+07  7.613712e+06       274.000000     237.000000   \n",
       "50%    1.818389e+07  1.331609e+07       905.000000     664.000000   \n",
       "75%    3.263049e+07  2.393771e+07      2255.000000    1537.000000   \n",
       "max    2.357746e+08  1.493782e+08     32814.000000   34828.000000   \n",
       "\n",
       "               hour      day_week       weekday      latitude     longitude  \\\n",
       "count  42649.000000  42649.000000  42649.000000  42649.000000  42649.000000   \n",
       "mean      10.046754      2.905719      0.714436     40.724647    -73.940364   \n",
       "std        6.938928      2.079231      0.451688      0.071650      0.059713   \n",
       "min        0.000000      0.000000      0.000000     40.576152    -74.073622   \n",
       "25%        4.000000      1.000000      0.000000     40.677107    -73.987342   \n",
       "50%       12.000000      3.000000      1.000000     40.717241    -73.953459   \n",
       "75%       16.000000      5.000000      1.000000     40.759123    -73.907733   \n",
       "max       20.000000      6.000000      1.000000     40.889185    -73.755383   \n",
       "\n",
       "                fog      ...          pressurei          rain         tempi  \\\n",
       "count  42649.000000      ...       42649.000000  42649.000000  42649.000000   \n",
       "mean       0.009824      ...          29.971096      0.224741     63.103780   \n",
       "std        0.098631      ...           0.137942      0.417417      8.455597   \n",
       "min        0.000000      ...          29.550000      0.000000     46.900000   \n",
       "25%        0.000000      ...          29.890000      0.000000     57.000000   \n",
       "50%        0.000000      ...          29.960000      0.000000     61.000000   \n",
       "75%        0.000000      ...          30.060000      0.000000     69.100000   \n",
       "max        1.000000      ...          30.320000      1.000000     86.000000   \n",
       "\n",
       "              wspdi   meanprecipi  meanpressurei     meantempi     meanwspdi  \\\n",
       "count  42649.000000  42649.000000   42649.000000  42649.000000  42649.000000   \n",
       "mean       6.927872      0.004618      29.971096     63.103780      6.927872   \n",
       "std        4.510178      0.016344       0.131158      6.939011      3.179832   \n",
       "min        0.000000      0.000000      29.590000     49.400000      0.000000   \n",
       "25%        4.600000      0.000000      29.913333     58.283333      4.816667   \n",
       "50%        6.900000      0.000000      29.958000     60.950000      6.166667   \n",
       "75%        9.200000      0.000000      30.060000     67.466667      8.850000   \n",
       "max       23.000000      0.157500      30.293333     79.800000     17.083333   \n",
       "\n",
       "        weather_lat   weather_lon  \n",
       "count  42649.000000  42649.000000  \n",
       "mean      40.728555    -73.938693  \n",
       "std        0.065420      0.059582  \n",
       "min       40.600204    -74.014870  \n",
       "25%       40.688591    -73.985130  \n",
       "50%       40.720570    -73.949150  \n",
       "75%       40.755226    -73.912033  \n",
       "max       40.862064    -73.694176  \n",
       "\n",
       "[8 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subway_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Linear correlation between two variables using pearson's r\n",
    "\n",
    "* Standardize each variable by converting it to a number of standard deviations above or below the mean. This will help scale the data. The further a data point is from the x,y axis, the larger the contribution to the pearson's r value.\n",
    "* Multiply each pair of values and take the average of the products. This will split the data into four quadrants where the data is either both above/below.\n",
    "* Value should be from -1 to +1\n",
    "\n",
    "Note: Numpy has a corrcoef() function as awell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to compute linear correlation between 2 variables\n",
    "def correlation(x, y):       # input can be a numpy 2d array or panda series \n",
    "    # Standardize data\n",
    "    std_x = (x - x.mean()) / x.std(ddof=0)           #\"ddof=0\" for panda's uncorrected std function \n",
    "    std_y = (y - y.mean()) / y.std(ddof=0)\n",
    "    return (std_x * std_y).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Get columns of data\n",
    "entries = subway_df['ENTRIESn_hourly']\n",
    "cum_entries = subway_df['ENTRIESn']\n",
    "rain = subway_df['meanprecipi']\n",
    "temp = subway_df['meantempi']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How to interpret pearson's r results:\n",
    "\n",
    "* Postive \"r\" value. As one value increases, the other value increases:\n",
    "    * Both values above the mean, product is positive from (+,+)\n",
    "    * Both values below the mean, product is positive from (-,-)\n",
    "\n",
    "* Negative \"r\" value. As one value increases, the other value decreases:\n",
    "    * One value above the mean, other value below the mean, product is negative from (-, +) \n",
    "    * One value above the mean, other value below the mean, product is negative from (+, -) \n",
    "    \n",
    "* If value is near 0, then poor correlation between two variables\n",
    "\n",
    "\n",
    "Applying pearson's r to subway data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.035648515772244052"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(entries, rain)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subway entries and rain \n",
    "* small positive correlation\n",
    "* more rain correlated with more subway entries\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.026693348321570803"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(entries, temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subway entries and temperature \n",
    "* small negative correlation\n",
    "* higher temperature correlated with lower subway entries\n",
    "* lower temperature correlated with higher subway entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.22903432340843488"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(rain, temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rain and temperature \n",
    "* medium negative correlation \n",
    "* higher rain correlated with lower temperature \n",
    "* lower rain correlated with higher temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.58589547076620752"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation(entries, cum_entries)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Subway daily entries and cummulative subway entries \n",
    "* high positive correlation \n",
    "* high cummulative subway entries correlated with high daily entries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Means and Axis Names \n",
    "* For axis = 0 or axis = 'index', the mean of each column is taken\n",
    "* For axis = 1 or axis = 'columns', the mean of each row is taken\n",
    "\n",
    "\n",
    "Pandas and Vectorized Operations \n",
    "* Match up elements by index and columns instead of position ( as in numpy 2d arrays)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert Total Entries and Exits to Hourly Entries and Exits\n",
    "\n",
    "The subway data included the cummulative number of entries and exits by riders. We do not want the cummulative data, instead we want the hourly number of entries and exits. This can be done using two methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Sample data: DF with total entries (column1) and total exits (column2)\n",
    "entries_and_exits = pd.DataFrame({\n",
    "    'ENTRIESn': [3144312, 3144335, 3144353, 3144424, 3144594,\n",
    "                 3144808, 3144895, 3144905, 3144941, 3145094],\n",
    "    'EXITSn': [1088151, 1088159, 1088177, 1088231, 1088275,\n",
    "               1088317, 1088328, 1088331, 1088420, 1088753]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170.0</td>\n",
       "      <td>44.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>214.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>87.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>36.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>153.0</td>\n",
       "      <td>333.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENTRIESn  EXITSn\n",
       "0       NaN     NaN\n",
       "1      23.0     8.0\n",
       "2      18.0    18.0\n",
       "3      71.0    54.0\n",
       "4     170.0    44.0\n",
       "5     214.0    42.0\n",
       "6      87.0    11.0\n",
       "7      10.0     3.0\n",
       "8      36.0    89.0\n",
       "9     153.0   333.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Method 1 : Write a function \n",
    "\n",
    "# Use shift(1) which shifts index by period of 1\n",
    "def get_hourly_entries_and_exits(entries_and_exits):\n",
    "    # Subtract value from each previous row \n",
    "    return entries_and_exits - entries_and_exits.shift(1)\n",
    "get_hourly_entries_and_exits(entries_and_exits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ENTRIESn  EXITSn\n",
      "0       NaN     NaN\n",
      "1      23.0     8.0\n",
      "2      18.0    18.0\n",
      "3      71.0    54.0\n",
      "4     170.0    44.0\n",
      "5     214.0    42.0\n",
      "6      87.0    11.0\n",
      "7      10.0     3.0\n",
      "8      36.0    89.0\n",
      "9     153.0   333.0\n"
     ]
    }
   ],
   "source": [
    "# Method 2: Builtin .diff() function\n",
    "print entries_and_exits.diff() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas and non built-in functions \n",
    "* write a helper function\n",
    "* applymap() function maps a function element to element\n",
    "\n",
    "For dataframes the applymap() and apply() functions do different things. \n",
    "* apply(): applies helper on each column/series. \n",
    "    * Can get max value from each column using df.apply(np.max) \n",
    "* applymap(): applies helper to each element \n",
    "\n",
    "Example: Get the second largest value in each column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Helper for single column\n",
    "def second_largest_in_column(column):\n",
    "    # Sort column in descending order. Find second value.\n",
    "    sorted_column = column.sort_values(ascending=False)\n",
    "    return sorted_column.iloc(1) # Second value at position 1\n",
    "\n",
    "# Apply function to a df \n",
    "def second_largest(df):\n",
    "    return df.apply(second_largest_in_column)\n",
    "\n",
    "# second_largest(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas addition of dataframes and series \n",
    "\n",
    "* dataframe + dataframe = dataframe \n",
    "* series + series = series \n",
    "* dataframe + series = ???\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1    2    3\n",
      "0  11  52   93  134\n",
      "1  21  62  103  144\n",
      "2  31  72  113  154\n",
      "3  41  82  123  164\n"
     ]
    }
   ],
   "source": [
    "# Series + Square dataFrame 0,1,2,3 column/index names \n",
    "s = pd.Series([1, 2, 3, 4])\n",
    "df = pd.DataFrame({\n",
    "    0: [10, 20, 30, 40],\n",
    "    1: [50, 60, 70, 80],\n",
    "    2: [90, 100, 110, 120],\n",
    "    3: [130, 140, 150, 160]\n",
    "})\n",
    "\n",
    "# Each value of the series was added to each column of the dataframe\n",
    "print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1   2   3\n",
      "0  11  22  33  44\n"
     ]
    }
   ],
   "source": [
    "# Series + one-row DataFrame \n",
    "s = pd.Series([1, 2, 3, 4])\n",
    "df = pd.DataFrame({0: [10], 1: [20], 2: [30], 3: [40]})\n",
    "\n",
    "# Each value of the series was added to each column of the dataframe\n",
    "# Same as above \n",
    "print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1   2   3\n",
      "0  11 NaN NaN NaN\n",
      "1  21 NaN NaN NaN\n",
      "2  31 NaN NaN NaN\n",
      "3  41 NaN NaN NaN\n"
     ]
    },
    {
     "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44</td>\n",
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       "  </tbody>\n",
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       "    0\n",
       "0  11\n",
       "1  22\n",
       "2  33\n",
       "3  44"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series + one-column DataFrame\n",
    "s = pd.Series([1, 2, 3, 4])\n",
    "df = pd.DataFrame({0: [10, 20, 30, 40]}) # one column\n",
    "\n",
    "# First column had 1 added, the rest were NaNs\n",
    "print df + s\n",
    "\n",
    "# df.add(s, axis = 'columns') \n",
    "df.add(s, axis = 'rows') # Each value of series added to one row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    a   b    c    d\n",
      "0  11  52   93  134\n",
      "1  21  62  103  144\n",
      "2  31  72  113  154\n",
      "3  41  82  123  164\n"
     ]
    }
   ],
   "source": [
    "# DataFrame column with name + Series index with name ( matching names)\n",
    "\n",
    "s = pd.Series([1, 2, 3, 4], index=['a', 'b', 'c', 'd'])\n",
    "df = pd.DataFrame({\n",
    "    'a': [10, 20, 30, 40],\n",
    "    'b': [50, 60, 70, 80],\n",
    "    'c': [90, 100, 110, 120],\n",
    "    'd': [130, 140, 150, 160]\n",
    "})\n",
    "print df + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1   2   3   a   b   c   d\n",
      "0 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "1 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
      "3 NaN NaN NaN NaN NaN NaN NaN NaN\n"
     ]
    }
   ],
   "source": [
    "# DataFrame column with name + Series index with name ( non- matching names)\n",
    "s = pd.Series([1, 2, 3, 4])\n",
    "df = pd.DataFrame({\n",
    "    'a': [10, 20, 30, 40],\n",
    "    'b': [50, 60, 70, 80],\n",
    "    'c': [90, 100, 110, 120],\n",
    "    'd': [130, 140, 150, 160]\n",
    "})\n",
    "\n",
    "# No match between indexes --> NaN\n",
    "print df + s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Standardizing rows/columns using vector operations without apply() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>exam1</th>\n",
       "      <th>exam2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andre</th>\n",
       "      <td>43</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barry</th>\n",
       "      <td>81</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>78</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dan</th>\n",
       "      <td>75</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emilio</th>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fred</th>\n",
       "      <td>70</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greta</th>\n",
       "      <td>91</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humbert</th>\n",
       "      <td>65</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivan</th>\n",
       "      <td>98</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>87</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         exam1  exam2\n",
       "Andre       43     24\n",
       "Barry       81     63\n",
       "Chris       78     56\n",
       "Dan         75     56\n",
       "Emilio      89     67\n",
       "Fred        70     51\n",
       "Greta       91     79\n",
       "Humbert     65     46\n",
       "Ivan        98     72\n",
       "James       87     60"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example dataset using grades and names of students \n",
    "grades_df = pd.DataFrame(\n",
    "    data={'exam1': [43, 81, 78, 75, 89, 70, 91, 65, 98, 87],\n",
    "          'exam2': [24, 63, 56, 56, 67, 51, 79, 46, 72, 60]},\n",
    "    index=['Andre', 'Barry', 'Chris', 'Dan', 'Emilio', \n",
    "           'Fred', 'Greta', 'Humbert', 'Ivan', 'James']\n",
    ")\n",
    "grades_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "exam1    77.7\n",
       "exam2    57.4\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grades_df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Andre</th>\n",
       "      <td>-2.196525</td>\n",
       "      <td>-2.186335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Barry</th>\n",
       "      <td>0.208891</td>\n",
       "      <td>0.366571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chris</th>\n",
       "      <td>0.018990</td>\n",
       "      <td>-0.091643</td>\n",
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       "    <tr>\n",
       "      <th>Dan</th>\n",
       "      <td>-0.170911</td>\n",
       "      <td>-0.091643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emilio</th>\n",
       "      <td>0.715295</td>\n",
       "      <td>0.628408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fred</th>\n",
       "      <td>-0.487413</td>\n",
       "      <td>-0.418938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greta</th>\n",
       "      <td>0.841896</td>\n",
       "      <td>1.413917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humbert</th>\n",
       "      <td>-0.803916</td>\n",
       "      <td>-0.746234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivan</th>\n",
       "      <td>1.284999</td>\n",
       "      <td>0.955703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>0.588694</td>\n",
       "      <td>0.170194</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            exam1     exam2\n",
       "Andre   -2.196525 -2.186335\n",
       "Barry    0.208891  0.366571\n",
       "Chris    0.018990 -0.091643\n",
       "Dan     -0.170911 -0.091643\n",
       "Emilio   0.715295  0.628408\n",
       "Fred    -0.487413 -0.418938\n",
       "Greta    0.841896  1.413917\n",
       "Humbert -0.803916 -0.746234\n",
       "Ivan     1.284999  0.955703\n",
       "James    0.588694  0.170194"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subtract mean1 from column1 and mean2 from column2\n",
    "# Divide each column by it's standard deviation \n",
    "\n",
    "(grades_df - grades_df.mean() )/ grades_df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "      <th>exam2</th>\n",
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       "  <tbody>\n",
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       "      <td>0.707107</td>\n",
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       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dan</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Emilio</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fred</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
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       "    <tr>\n",
       "      <th>Greta</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humbert</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ivan</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>James</th>\n",
       "      <td>0.707107</td>\n",
       "      <td>-0.707107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            exam1     exam2\n",
       "Andre    0.707107 -0.707107\n",
       "Barry    0.707107 -0.707107\n",
       "Chris    0.707107 -0.707107\n",
       "Dan      0.707107 -0.707107\n",
       "Emilio   0.707107 -0.707107\n",
       "Fred     0.707107 -0.707107\n",
       "Greta    0.707107 -0.707107\n",
       "Humbert  0.707107 -0.707107\n",
       "Ivan     0.707107 -0.707107\n",
       "James    0.707107 -0.707107"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Standardize by rows, set axis = columns to get mean of each row \n",
    "# Subtract means from rows using sub() and axis = 'index'\n",
    "mean_diffs = grades_df.sub(grades_df.mean(axis='columns'), axis='index')\n",
    "\n",
    "# Divide by standard deviation - same values since only 2 columns \n",
    "mean_diffs.div(grades_df.std(axis='columns'), axis ='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas Groupby() function \n",
    "\n",
    "General idea: Group data then aggregate data by taking an average within the group. Pandas has the following built-in functions:\n",
    "* group_data()\n",
    "* sum_grouped_items()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>above_three</th>\n",
       "      <th>even</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
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       "      <th>b</th>\n",
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       "      <th>d</th>\n",
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       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   above_three   even  value\n",
       "a        False  False      1\n",
       "b        False  False      3\n",
       "c        False   True      2\n",
       "d         True   True      4\n",
       "e        False  False      1\n",
       "f         True   True      6\n",
       "g         True   True      4"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example dataframe\n",
    "values = np.array([1, 3, 2, 4, 1, 6, 4])\n",
    "example_df = pd.DataFrame({\n",
    "    'value': values,\n",
    "    'even': values % 2 == 0,\n",
    "    'above_three': values > 3 \n",
    "}, index=['a', 'b', 'c', 'd', 'e', 'f', 'g'])\n",
    "\n",
    "example_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{False: Index([u'a', u'b', u'e'], dtype='object'), True: Index([u'c', u'd', u'f', u'g'], dtype='object')}\n"
     ]
    }
   ],
   "source": [
    "# Groups\n",
    "grouped_data = example_df.groupby('even')\n",
    "# The groups attribute is a dictionary mapping keys to lists of row indexes\n",
    "print grouped_data.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(True, False): Index([u'c'], dtype='object'), (False, False): Index([u'a', u'b', u'e'], dtype='object'), (True, True): Index([u'd', u'f', u'g'], dtype='object')}\n"
     ]
    }
   ],
   "source": [
    "# Group by multiple columns\n",
    "grouped_data = example_df.groupby(['even', 'above_three'])\n",
    "print grouped_data.groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       above_three  value\n",
      "even                     \n",
      "False          0.0      5\n",
      "True           3.0     16\n"
     ]
    }
   ],
   "source": [
    "# Get sum of each group\n",
    "grouped_data = example_df.groupby('even')\n",
    "print grouped_data.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "even\n",
      "False     5\n",
      "True     16\n",
      "Name: value, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Limit columns in result\n",
    "grouped_data = example_df.groupby('even')\n",
    "print grouped_data.sum()['value']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "even\n",
      "False     5\n",
      "True     16\n",
      "Name: value, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Subset of columns from the grouped data before collapsing to a dataframe\n",
    "# Same results\n",
    "print grouped_data['value'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas groupby() function applied to NYC subway data \n",
    "\n",
    "Question1: How does ridership vary by hour of the day?\n",
    "* For each hour of the day, group the data \n",
    "* For each hour of the day, find average ridership \n",
    "\n",
    "Question2 : How does rain affect subway ridership?\n",
    "* Break data into two groups 1) raining 2) non-raining \n",
    "* Compare ridership between two groups\n",
    "\n",
    "Question3: How does ridership vary by day of week?\n",
    "* For each day of the week, group the data\n",
    "* For each day of the week, find average ridership "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Question3 Solution: How does ridership vary by day of week?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   UNIT     DATEn     TIMEn  ENTRIESn   EXITSn  ENTRIESn_hourly  \\\n",
       "0  R003  05-01-11  00:00:00   4388333  2911002              0.0   \n",
       "1  R003  05-01-11  04:00:00   4388333  2911002              0.0   \n",
       "2  R003  05-01-11  12:00:00   4388333  2911002              0.0   \n",
       "3  R003  05-01-11  16:00:00   4388333  2911002              0.0   \n",
       "4  R003  05-01-11  20:00:00   4388333  2911002              0.0   \n",
       "\n",
       "   EXITSn_hourly             datetime  hour  day_week     ...       pressurei  \\\n",
       "0            0.0  2011-05-01 00:00:00     0         6     ...           30.22   \n",
       "1            0.0  2011-05-01 04:00:00     4         6     ...           30.25   \n",
       "2            0.0  2011-05-01 12:00:00    12         6     ...           30.28   \n",
       "3            0.0  2011-05-01 16:00:00    16         6     ...           30.26   \n",
       "4            0.0  2011-05-01 20:00:00    20         6     ...           30.28   \n",
       "\n",
       "  rain  tempi  wspdi meanprecipi  meanpressurei  meantempi  meanwspdi  \\\n",
       "0    0   55.9    3.5         0.0         30.258      55.98       7.86   \n",
       "1    0   52.0    3.5         0.0         30.258      55.98       7.86   \n",
       "2    0   62.1    6.9         0.0         30.258      55.98       7.86   \n",
       "3    0   57.9   15.0         0.0         30.258      55.98       7.86   \n",
       "4    0   52.0   10.4         0.0         30.258      55.98       7.86   \n",
       "\n",
       "   weather_lat  weather_lon  \n",
       "0    40.700348   -73.887177  \n",
       "1    40.700348   -73.887177  \n",
       "2    40.700348   -73.887177  \n",
       "3    40.700348   -73.887177  \n",
       "4    40.700348   -73.887177  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check headers first\n",
    "subway_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hourly data of the dataframe is taken in four hour intervals. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hour\n",
      "0     1403.248441\n",
      "4      311.717653\n",
      "8      879.029929\n",
      "12    3010.949693\n",
      "16    2306.527816\n",
      "20    3226.240093\n",
      "Name: ENTRIESn_hourly, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Question 1\n",
    "ridership_by_hour = subway_df.groupby('hour').mean()['ENTRIESn_hourly']\n",
    "print ridership_by_hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11643f790>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GnDjMzKwSJw4zM6vEicPMzCpx4jAzs0qcOMzMrJJWDTliLTB87LU9ur/Zp+/V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115923390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot data in four hour intervals \n",
    "# ridership_by_hour.plot()\n",
    "ridership_by_hour.plot.bar()\n",
    "title('4-Hour Intervals vs Subway Ridership')\n",
    "ylabel('Number of Subway Riders')\n",
    "xlabel('Time (4hr intervals)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above is a plot of the ridership data vs four hour interval units. Highest ridership between the following intervals:\n",
    "* 12pm - 4pm \n",
    "* 4pm - 8pm\n",
    "* 8pm - 12am  \n",
    "\n",
    "More useful to plot hourly entries and exits for each station (UNIT)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ex_ridership_df2 = pd.DataFrame({\n",
    "    'UNIT': ['R051', 'R079', 'R051', 'R079', 'R051', 'R079', 'R051', 'R079', 'R051'],\n",
    "    'TIMEn': ['00:00:00', '02:00:00', '04:00:00', '06:00:00', '08:00:00', '10:00:00', '12:00:00', '14:00:00', '16:00:00'],\n",
    "    'ENTRIESn': [3144312, 8936644, 3144335, 8936658, 3144353, 8936687, 3144424, 8936819, 3144594],\n",
    "    'EXITSn': [1088151, 13755385,  1088159, 13755393,  1088177, 13755598, 1088231, 13756191,  1088275]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   ENTRIESn  EXITSn\n",
      "0       NaN     NaN\n",
      "1       NaN     NaN\n",
      "2      23.0     8.0\n",
      "3      14.0     8.0\n",
      "4      18.0    18.0\n",
      "5      29.0   205.0\n",
      "6      71.0    54.0\n",
      "7     132.0   593.0\n",
      "8     170.0    44.0\n"
     ]
    }
   ],
   "source": [
    "# Hourly entries and exits for each station (UNIT)\n",
    "def hourly_for_group(entries_and_exits):\n",
    "    return entries_and_exits - entries_and_exits.shift(1)\n",
    "\n",
    "ridership_by_station = ex_ridership_df2.groupby('UNIT')['ENTRIESn', 'EXITSn'].apply(hourly_for_group)\n",
    "print ridership_by_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11bc7f250>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MzKxwlS6UuwB4F3AJ8KKkVenxqqRVLROemZkVpdIYRJOvsjYzs/bDScDMzMpy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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174d1e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# By station:\n",
    "# ridership_by_station.plot()\n",
    "ridership_by_station.plot.bar()\n",
    "title('Stations vs Ridership')\n",
    "xlabel('Station Number')\n",
    "ylabel('Number of Subway Riders')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joining multiple Pandas Dataframes \n",
    "\n",
    "Note: this is unrelated to the NY subway dataset\n",
    "\n",
    "* Pandas merge() function joins multiple tables \n",
    "* Types of joins: inner, left, right \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is unrelated to the NY subway dataset\n",
    "ex_subway_df = pd.DataFrame({\n",
    "    'UNIT': ['R003', 'R003', 'R003', 'R003', 'R003', 'R004', 'R004', 'R004',\n",
    "             'R004', 'R004'],\n",
    "    'DATEn': ['05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11',\n",
    "              '05-01-11', '05-02-11', '05-03-11', '05-04-11', '05-05-11'],\n",
    "    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'ENTRIESn': [ 4388333,  4388348,  4389885,  4391507,  4393043, 14656120,\n",
    "                 14656174, 14660126, 14664247, 14668301],\n",
    "    'EXITSn': [ 2911002,  2911036,  2912127,  2913223,  2914284, 14451774,\n",
    "               14451851, 14454734, 14457780, 14460818],\n",
    "    'latitude': [ 40.689945,  40.689945,  40.689945,  40.689945,  40.689945,\n",
    "                  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ,  40.69132 ],\n",
    "    'longitude': [-73.872564, -73.872564, -73.872564, -73.872564, -73.872564,\n",
    "                  -73.867135, -73.867135, -73.867135, -73.867135, -73.867135]\n",
    "})\n",
    "\n",
    "ex_weather_df = pd.DataFrame({\n",
    "    'DATEn': ['05-01-11', '05-01-11', '05-02-11', '05-02-11', '05-03-11',\n",
    "              '05-03-11', '05-04-11', '05-04-11', '05-05-11', '05-05-11'],\n",
    "    'hour': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'latitude': [ 40.689945,  40.69132 ,  40.689945,  40.69132 ,  40.689945,\n",
    "                  40.69132 ,  40.689945,  40.69132 ,  40.689945,  40.69132 ],\n",
    "    'longitude': [-73.872564, -73.867135, -73.872564, -73.867135, -73.872564,\n",
    "                  -73.867135, -73.872564, -73.867135, -73.872564, -73.867135],\n",
    "    'pressurei': [ 30.24,  30.24,  30.32,  30.32,  30.14,  30.14,  29.98,  29.98,\n",
    "                   30.01,  30.01],\n",
    "    'fog': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'rain': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "    'tempi': [ 52. ,  52. ,  48.9,  48.9,  54. ,  54. ,  57.2,  57.2,  48.9,  48.9],\n",
    "    'wspdi': [  8.1,   8.1,   6.9,   6.9,   3.5,   3.5,  15. ,  15. ,  15. ,  15. ]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>DATEn</th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>UNIT</th>\n",
       "      <th>hour</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>fog</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>4388333</td>\n",
       "      <td>2911002</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>4388348</td>\n",
       "      <td>2911036</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>30.32</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>4389885</td>\n",
       "      <td>2912127</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>4391507</td>\n",
       "      <td>2913223</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>4393043</td>\n",
       "      <td>2914284</td>\n",
       "      <td>R003</td>\n",
       "      <td>0</td>\n",
       "      <td>40.689945</td>\n",
       "      <td>-73.872564</td>\n",
       "      <td>0</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-01-11</td>\n",
       "      <td>14656120</td>\n",
       "      <td>14451774</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.24</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>05-02-11</td>\n",
       "      <td>14656174</td>\n",
       "      <td>14451851</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.32</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>05-03-11</td>\n",
       "      <td>14660126</td>\n",
       "      <td>14454734</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.14</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>05-04-11</td>\n",
       "      <td>14664247</td>\n",
       "      <td>14457780</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>29.98</td>\n",
       "      <td>0</td>\n",
       "      <td>57.2</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>05-05-11</td>\n",
       "      <td>14668301</td>\n",
       "      <td>14460818</td>\n",
       "      <td>R004</td>\n",
       "      <td>0</td>\n",
       "      <td>40.691320</td>\n",
       "      <td>-73.867135</td>\n",
       "      <td>0</td>\n",
       "      <td>30.01</td>\n",
       "      <td>0</td>\n",
       "      <td>48.9</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DATEn  ENTRIESn    EXITSn  UNIT  hour   latitude  longitude  fog  \\\n",
       "0  05-01-11   4388333   2911002  R003     0  40.689945 -73.872564    0   \n",
       "1  05-02-11   4388348   2911036  R003     0  40.689945 -73.872564    0   \n",
       "2  05-03-11   4389885   2912127  R003     0  40.689945 -73.872564    0   \n",
       "3  05-04-11   4391507   2913223  R003     0  40.689945 -73.872564    0   \n",
       "4  05-05-11   4393043   2914284  R003     0  40.689945 -73.872564    0   \n",
       "5  05-01-11  14656120  14451774  R004     0  40.691320 -73.867135    0   \n",
       "6  05-02-11  14656174  14451851  R004     0  40.691320 -73.867135    0   \n",
       "7  05-03-11  14660126  14454734  R004     0  40.691320 -73.867135    0   \n",
       "8  05-04-11  14664247  14457780  R004     0  40.691320 -73.867135    0   \n",
       "9  05-05-11  14668301  14460818  R004     0  40.691320 -73.867135    0   \n",
       "\n",
       "   pressurei  rain  tempi  wspdi  \n",
       "0      30.24     0   52.0    8.1  \n",
       "1      30.32     0   48.9    6.9  \n",
       "2      30.14     0   54.0    3.5  \n",
       "3      29.98     0   57.2   15.0  \n",
       "4      30.01     0   48.9   15.0  \n",
       "5      30.24     0   52.0    8.1  \n",
       "6      30.32     0   48.9    6.9  \n",
       "7      30.14     0   54.0    3.5  \n",
       "8      29.98     0   57.2   15.0  \n",
       "9      30.01     0   48.9   15.0  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is unrelated to the NY subway dataset\n",
    "# JOIN two tables\n",
    "ex_subway_df.merge(ex_weather_df, on =['DATEn', 'hour', 'latitude', 'longitude'], how = 'inner' )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing Data \n",
    "* Data1: A scatterplot of subway stations with latitude and longitude as the x and y axes and ridership as the bubble size\n",
    "* Data2: Histograms of subway ridership for days with rain and days without rain\n",
    "* Data3: A scatterplot with subway ridership on one axis and precipitation or temperature on the other"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>ENTRIESn</th>\n",
       "      <th>EXITSn</th>\n",
       "      <th>ENTRIESn_hourly</th>\n",
       "      <th>EXITSn_hourly</th>\n",
       "      <th>hour</th>\n",
       "      <th>day_week</th>\n",
       "      <th>weekday</th>\n",
       "      <th>fog</th>\n",
       "      <th>precipi</th>\n",
       "      <th>pressurei</th>\n",
       "      <th>rain</th>\n",
       "      <th>tempi</th>\n",
       "      <th>wspdi</th>\n",
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       "      <th>meanwspdi</th>\n",
       "      <th>weather_lat</th>\n",
       "      <th>weather_lon</th>\n",
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       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
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       "      <th>40.576152</th>\n",
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       "      <td>5.517838</td>\n",
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       "      <td>40.603489</td>\n",
       "      <td>-73.958763</td>\n",
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       "      <th>40.577961</th>\n",
       "      <th>-73.961806</th>\n",
       "      <td>4.552910e+07</td>\n",
       "      <td>4.612408e+07</td>\n",
       "      <td>1950.295699</td>\n",
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      ],
      "text/plain": [
       "                          ENTRIESn        EXITSn  ENTRIESn_hourly  \\\n",
       "latitude  longitude                                                 \n",
       "40.576152 -73.975925  9.659049e+06  8.641132e+06       403.896175   \n",
       "40.576298 -73.968523  8.306897e+06  6.646823e+06       526.697297   \n",
       "40.577961 -73.961806  4.552910e+07  4.612408e+07      1950.295699   \n",
       "40.589547 -73.974295  7.268214e+06  7.961334e+06       485.382353   \n",
       "40.590867 -73.797011  6.477945e+06  5.994957e+06       500.725610   \n",
       "\n",
       "                      EXITSn_hourly       hour  day_week   weekday       fog  \\\n",
       "latitude  longitude                                                            \n",
       "40.576152 -73.975925     325.956284  10.032787  2.907104  0.715847  0.010929   \n",
       "40.576298 -73.968523     419.562162   9.989189  2.951351  0.708108  0.010811   \n",
       "40.577961 -73.961806    1930.483871  10.000000  2.935484  0.709677  0.010753   \n",
       "40.589547 -73.974295     362.941176  10.164706  2.905882  0.705882  0.011765   \n",
       "40.590867 -73.797011     374.628049  10.097561  2.951220  0.719512  0.024390   \n",
       "\n",
       "                       precipi  pressurei      rain      tempi     wspdi  \\\n",
       "latitude  longitude                                                        \n",
       "40.576152 -73.975925  0.006284  29.972568  0.229508  63.383607  5.553005   \n",
       "40.576298 -73.968523  0.006216  29.973297  0.227027  63.375135  5.517838   \n",
       "40.577961 -73.961806  0.006183  29.973118  0.225806  63.394086  5.531720   \n",
       "40.589547 -73.974295  0.006118  29.971176  0.200000  63.650588  5.630588   \n",
       "40.590867 -73.797011  0.002744  29.981098  0.195122  61.721341  9.945122   \n",
       "\n",
       "                      meanprecipi  meanpressurei  meantempi  meanwspdi  \\\n",
       "latitude  longitude                                                      \n",
       "40.576152 -73.975925     0.006284      29.972568  63.383607   5.553005   \n",
       "40.576298 -73.968523     0.006216      29.973297  63.375135   5.517838   \n",
       "40.577961 -73.961806     0.006183      29.973118  63.394086   5.531720   \n",
       "40.589547 -73.974295     0.006118      29.971176  63.650588   5.630588   \n",
       "40.590867 -73.797011     0.002744      29.981098  61.721341   9.945122   \n",
       "\n",
       "                      weather_lat  weather_lon  \n",
       "latitude  longitude                             \n",
       "40.576152 -73.975925    40.603489   -73.958763  \n",
       "40.576298 -73.968523    40.603489   -73.958763  \n",
       "40.577961 -73.961806    40.603489   -73.958763  \n",
       "40.589547 -73.974295    40.603489   -73.958763  \n",
       "40.590867 -73.797011    40.660004   -73.844849  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data1 : Group data by longitude and latitude. Then find the mean\n",
    "data_by_location = subway_df.groupby(['latitude', 'longitude']).mean()\n",
    "data_by_location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    40.576152\n",
      "1    40.576298\n",
      "2    40.577961\n",
      "3    40.589547\n",
      "4    40.590867\n",
      "Name: latitude, dtype: float64\n",
      "0   -73.975925\n",
      "1   -73.968523\n",
      "2   -73.961806\n",
      "3   -73.974295\n",
      "4   -73.797011\n",
      "Name: longitude, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# Only want longitude and latitude (row indexes) --> as_index=False\n",
    "data_by_location = subway_df.groupby(['latitude', 'longitude'], as_index=False).mean()\n",
    "print data_by_location.head()['latitude']\n",
    "print data_by_location.head()['longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10b9ab850>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RYQ6mDUjk2HbmhWcInt+cegx5JZXsOlDKHaceE/Jo6MH2PhcYDywDUNWtItKl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\ndQVpCdH4m5hv5oHpw8kr8fDKij1U1vgZnBbPAxcN7xKXEZU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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10b989610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot with (x,y) as latitude,longitude and bubblesize = ridership\n",
    "# Scale bubble markers by dividing by std\n",
    "scaled_hourly_entries = (10 * data_by_location['ENTRIESn_hourly'] / data_by_location['ENTRIESn_hourly'].std() )\n",
    "plt.scatter(data_by_location['latitude'], data_by_location['longitude'], s=scaled_hourly_entries )\n",
    "title('Location and Ridership as Markers')\n",
    "xlabel('Latitude')\n",
    "ylabel('Longitude')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can compare to a NYC subway map to get exact stations. High ridership in the following clusters.\n",
    "* longitude from - 73.95 to -74.03  \n",
    "* latitude from 40.70 to 40.79"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [cats]",
   "language": "python",
   "name": "Python [cats]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}