ASpanos-Project1.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Exploratory analysis of data set"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Import modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "#import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "import statsmodels.formula.api as smf   \n",
      "# a utility function to only show the coeff section of summary\n",
      "from IPython.core.display import HTML\n",
      "from scipy.stats.stats import pearsonr\n",
      "from patsy import EvalEnvironment, ModelDesc\n",
      "import seaborn as sns\n",
      "import pylab as pl\n",
      "import scipy\n",
      "from IPython.display import display\n",
      "import datetime\n",
      "from mpl_toolkits.basemap import Basemap\n",
      "from IPython import display\n",
      "import time\n",
      "import pandas as pd\n",
      "import statsmodels.api as sm\n",
      "import statsmodels.formula.api as smf\n",
      "from IPython.core.display import HTML\n",
      "from scipy.stats.stats import pearsonr\n",
      "from patsy import EvalEnvironment, ModelDesc\n",
      "import matplotlib.patches as mpatches"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Read data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=pd.read_csv(\"turnstile_weather_v2.csv\")\n",
      "df_orig=df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Get a feel for the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe() #Descriptives of each variable"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\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 \u00d7 21 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "           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]"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "* Extract and define more *granular* temporal variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['DateTime'] = df.apply(\n",
      "    lambda r: pd.datetime.strptime(r['datetime'], '%Y-%m-%d %H:%M:%S'), axis=1)\n",
      "df = df.set_index('DateTime', drop=False)\n",
      "df['station']=df['station'].astype(str)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['day']=df.index.day\n",
      "df['hour_week'] = df.day_week * 24. + df.hour\n",
      "df['week'] = df.index.week\n",
      "df_old=df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Total number of days with recordings is"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df['day'].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "31"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "- Recording from"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "str(min(df['DateTime']).date())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "'2011-05-01'"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "upto "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "str(max(df['DateTime']).date())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "'2011-05-31'"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\n",
      "\n",
      "So it is the full month of May.\n",
      "\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Investigation on missing recordings"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Total number of turnstiles along with statistics on data recordings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "UNIT_rec=df.groupby('UNIT').count()['ENTRIESn_hourly'] #Define number of recordings per turnstile\n",
      "UNIT_rec.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "count    240.000000\n",
        "mean     177.704167\n",
        "std        9.842270\n",
        "min       88.000000\n",
        "25%      172.000000\n",
        "50%      180.000000\n",
        "75%      185.000000\n",
        "max      186.000000\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generate histogram"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sns.distplot(UNIT_rec, kde=False)\n",
      "plt.title('Distribution of individual turnstile recordings', fontsize=15)\n",
      "plt.xlabel('Recordings', fontsize=14)\n",
      "plt.ylabel('Count', fontsize=14)\n",
      "plt.show()\n",
      "plt.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GLppoJStWrP+ABq1v2bJ+66oD1lPnrKvOTHc9DQwMMjS8uu0taycyPLyG3t61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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x48306d8>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can see that there is a variance in the number of recordings by unit. Also:\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Total number of stations is:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df['station'].drop_duplicates())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "207"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Total number of units is"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df['UNIT'].drop_duplicates())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "240"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This means that in some cases there are more than one units assigned to a station"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Stations with more than one units can be seen below"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby('station')['UNIT'].unique()[df.groupby('station')['UNIT'].unique().apply(lambda x: len(x))>1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "station\n",
        "111 ST                   [R008, R310]\n",
        "125 ST                   [R034, R102]\n",
        "145 ST             [R101, R273, R344]\n",
        "167 ST                   [R104, R221]\n",
        "18 AVE                   [R242, R372]\n",
        "23 ST-6 AVE              [R203, R453]\n",
        "25 ST                    [R278, R455]\n",
        "34 ST-HERALD SQ          [R022, R023]\n",
        "34 ST-PENN STA     [R012, R013, R031]\n",
        "42 ST-TIMES SQ           [R032, R033]\n",
        "50 ST              [R116, R188, R234]\n",
        "86 ST              [R179, R186, R215]\n",
        "ATLANTIC AVE             [R057, R348]\n",
        "BOWLING GREEN            [R041, R042]\n",
        "CHAMBERS ST              [R030, R044]\n",
        "CHURCH AVE               [R098, R204]\n",
        "DEKALB AVE               [R099, R236]\n",
        "FORDHAM ROAD             [R112, R119]\n",
        "GRAND ST                 [R240, R250]\n",
        "HOYT ST                  [R225, R456]\n",
        "JAY ST-METROTEC          [R089, R127]\n",
        "LEXINGTON AVE      [R050, R051, R319]\n",
        "LEXINGTON-53 ST          [R016, R017]\n",
        "NOSTRAND AVE             [R061, R198]\n",
        "PROSPECT AVE             [R246, R454]\n",
        "RECTOR ST                [R227, R304]\n",
        "SPRING ST                [R282, R322]\n",
        "WALL ST                  [R027, R043]\n",
        "Name: UNIT, dtype: object"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Therefore it is more natural to group data by station rather than by unit."
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Make new dataframe aggregating remote unit data for single station"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc1=pd.pivot_table(df_old, values=['ENTRIESn','EXITSn','ENTRIESn_hourly','EXITSn_hourly'],index=['station','DateTime'], aggfunc=np.sum, margins=True)\n",
      "nc2=pd.pivot_table(df_old, values=['latitude','longitude','hour','day_week','weekday','fog','precipi','pressurei','rain','tempi','wspdi','meanprecipi','meanpressurei','meantempi','meanwspdi','weather_lat','weather_lon','day','hour_week','week'], index=['station', 'DateTime'],aggfunc=np.mean, margins=True)\n",
      "del df\n",
      "df=nc1.join(nc2)\n",
      "#round values for integer variables\n",
      "def intround(col1):\n",
      "    df[col1]=df[col1].apply(lambda x: int(round(x)))\n",
      "intround('rain')\n",
      "intround('hour')\n",
      "intround('day_week')\n",
      "intround('weekday')\n",
      "intround('fog')\n",
      "intround('day')\n",
      "intround('hour_week')\n",
      "intround('week')\n",
      "#also add station as column\n",
      "df['station']=df.index.get_level_values('station').tolist()\n",
      "df['station']=df['station'].astype(str)\n",
      "#save entry with all stations\n",
      "all_stations=df[df['station']=='All']\n",
      "# Drop entry with all stations\n",
      "df=df.ix[:-1]\n",
      "df['WCI']=35.74+0.6215*df['tempi']-35.75*(df['wspdi']**0.16)+0.4275*(df['tempi']**0.16)\n",
      "df['weekend']=df['day_week'].apply(lambda x: 0 if x in range(0,4) else 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Count missing values in dataframe."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.drop('station', 1)[np.isnan(df.drop('station', 1))].count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "ENTRIESn           0\n",
        "ENTRIESn_hourly    0\n",
        "EXITSn             0\n",
        "EXITSn_hourly      0\n",
        "day                0\n",
        "day_week           0\n",
        "fog                0\n",
        "hour               0\n",
        "hour_week          0\n",
        "latitude           0\n",
        "longitude          0\n",
        "meanprecipi        0\n",
        "meanpressurei      0\n",
        "meantempi          0\n",
        "meanwspdi          0\n",
        "precipi            0\n",
        "pressurei          0\n",
        "rain               0\n",
        "tempi              0\n",
        "weather_lat        0\n",
        "weather_lon        0\n",
        "week               0\n",
        "weekday            0\n",
        "wspdi              0\n",
        "WCI                0\n",
        "weekend            0\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "No missing values"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Plot location of stations on map"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = Basemap(projection='mill',llcrnrlat=40.5,urcrnrlat=40.9,\\\n",
      "            llcrnrlon=-74.1,urcrnrlon=-73.7,resolution='h')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.drawcoastlines()\n",
      "lons=df.groupby('station')['longitude'].mean().tolist()\n",
      "lats=df.groupby('station')['latitude'].mean().tolist()\n",
      "x,y = m(lons,lats)\n",
      "sizes=20\n",
      "mapplot=plt.scatter(x,y, s=sizes, color=\"blue\", alpha=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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L6dGjl7ydVCqld+9udOjQmbFjJ2jnQ2mZ06dPMHPmVAYNGvqvYDRSGojl6bkE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\nTvGvAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19a123c8>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "Measurements per station"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Previously we saw that the number of measurements per unit is variable. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Percentage of recordings"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N_records_station=df.groupby('station')['ENTRIESn'].count()\n",
      "max_rec_station=len(df['day'].unique())*6\n",
      "PRC_records_station=N_records_station/max_rec_station"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(PRC_records_station)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(array([   1.,    0.,    0.,    0.,    0.,    1.,    1.,   14.,   60.,  130.]),\n",
        " array([ 0.47311828,  0.52580645,  0.57849462,  0.6311828 ,  0.68387097,\n",
        "         0.73655914,  0.78924731,  0.84193548,  0.89462366,  0.94731183,  1.        ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KMtKSJBVlpCVJKspIS5JUlJGWJKkoIy1JUlFGWpKkooy0JElFGWlJkooy0pIkFWWkJUkq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\nkiQV9X+pbt6zP7CUpAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19a04eb8>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For most stations, data is above 90% complete in regards to the maximum possible measurements. For some data is not being recorded as frequently."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Use function (found on internet) that computes distance between points on earth, assuming sphericity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import math\n",
      "def distance_on_unit_sphere(lat1, long1, lat2, long2):\n",
      " \n",
      "    # Convert latitude and longitude to \n",
      "    # spherical coordinates in radians.\n",
      "    degrees_to_radians = math.pi/180.0\n",
      "         \n",
      "    # phi = 90 - latitude\n",
      "    phi1 = (90.0 - lat1)*degrees_to_radians\n",
      "    phi2 = (90.0 - lat2)*degrees_to_radians\n",
      "         \n",
      "    # theta = longitude\n",
      "    theta1 = long1*degrees_to_radians\n",
      "    theta2 = long2*degrees_to_radians\n",
      "         \n",
      "    # Compute spherical distance from spherical coordinates.\n",
      "         \n",
      "    # For two locations in spherical coordinates \n",
      "    # (1, theta, phi) and (1, theta, phi)\n",
      "    # cosine( arc length ) = \n",
      "    #    sin phi sin phi' cos(theta-theta') + cos phi cos phi'\n",
      "    # distance = rho * arc length\n",
      "     \n",
      "    cos = (math.sin(phi1)*math.sin(phi2)*math.cos(theta1 - theta2) + \n",
      "           math.cos(phi1)*math.cos(phi2))\n",
      "    arc = math.acos( cos )\n",
      "    length= arc*3963.1676\n",
      " \n",
      "    # Remember to multiply arc by the radius of the earth \n",
      "    # in your favorite set of units to get length.\n",
      "    return length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We define the central point of NYC as the 42 street main station. Let's see if we have this station in the data so we can extract its position."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.station.astype(str).str.contains('42')].station.unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "array(['242 ST', '42 ST-BRYANT PK', '42 ST-GRD CNTRL', '42 ST-PA BUS TE',\n",
        "       '42 ST-TIMES SQ'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So it is 42 ST-GRD CNTRL. Its longitude and latitude are:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lon_CNTR, lat_CNTR=df[df.station.astype(str).str.contains('42 ST-GRD CNTRL')].longitude.unique(),df[df.station.astype(str).str.contains('42 ST-GRD CNTRL')].latitude.unique()\n",
      "lon_CNTR, lat_CNTR"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "(array([-73.976945]), array([ 40.751849]))"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Examine the hypothesis that recording percentage is correlated with distance from centre"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist_CNTR=[0]*36833\n",
      "for i in range(0, len(df)):\n",
      "    dist_CNTR[i]=distance_on_unit_sphere(df['longitude'].iloc[i],df['latitude'].iloc[i],lon_CNTR,lat_CNTR)\n",
      "df['dist_CNTR']=dist_CNTR"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter((df.groupby('station')['dist_CNTR'].mean()), (PRC_records_station))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "<matplotlib.collections.PathCollection at 0x1ee27358>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ruPWs49azjtujt7enq579XWBEkqSEmJglSUqIiVmSpISYmCVJSoiJWZKkhJiY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\nmCVJSoiJWZKkhJiYJUlKyP8HfdgHBk9dYr8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19ca5828>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Two outlier stations can be seen but overall the hypothesis does not appear to stand."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Weather variables"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.drawcoastlines()\n",
      "#m.drawstates()\n",
      "#m.fillcontinents(color='#04BAE3', lake_color='#FFFFFF')\n",
      "#m.shadedrelief()\n",
      "#m.drawlsmask(land_color='coral',ocean_color='aqua',lakes=True)\n",
      "lons=df.groupby('station')['longitude'].mean().tolist()\n",
      "lats=df.groupby('station')['latitude'].mean().tolist()\n",
      "lons_ws1=df.groupby('weather_lon')['weather_lon'].unique().tolist()\n",
      "lats_ws1=df.groupby('weather_lat')['weather_lat'].unique().tolist()\n",
      "x,y=m(lons,lats)\n",
      "x1,y1=m(lons_ws1,lats_ws1)\n",
      "mapplot=plt.scatter(x,y, s=15, color=\"blue\")\n",
      "mapplot2=plt.scatter(x1,y1, s=20, color=\"red\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QHmJiYjhx4phCaNy6dRNLS0tq1apDrVp1qF27DjVr1qJ3767Mnj2PJk2aKo59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\n7NmLlC1bLv19XV3ddAH5mFWR11rKWX3H+vopSKUy5HI5crkMmUyGXC5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bi5VVNaytrbGy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\n/ocanwWxXgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1ee27eb8>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Get distance of each subway station from weather station "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dist=[0]*36833\n",
      "for i in range(0, len(df)):\n",
      "    dist[i]=distance_on_unit_sphere(df['longitude'].iloc[i],df['latitude'].iloc[i],df['weather_lon'].iloc[i],df['weather_lat'].iloc[i])\n",
      "df['dist_ws']=dist"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "View histogram of distances"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.hist(df['dist_ws'].unique())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "(array([ 117.,   59.,   33.,   16.,    5.,    4.,    1.,    1.,    3.,    4.]),\n",
        " array([ 0.04274057,  0.56181764,  1.08089471,  1.59997178,  2.11904885,\n",
        "         2.63812592,  3.157203  ,  3.67628007,  4.19535714,  4.71443421,\n",
        "         5.23351128]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x17dcbf28>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Looking for stations too far from weather station; likely to give inaccurate weather readings.Displaying list for distance > 2 miles"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['station'][df['dist_ws']>=2].unique()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "array(['18 AVE', '20 AVE', '46 ST-BLISS ST', '55 ST', '71 ST', '77 ST',\n",
        "       '79 ST', 'BAY RIDGE AVE', 'BAY RIDGE-95 ST', 'BEACH 25 ST',\n",
        "       'BEACH 36 ST', 'BEACH 44 ST', 'BEACH 60 ST', 'BEACH 67 ST',\n",
        "       'FAR ROCKAWAY', 'JUNIUS ST', 'ST. GEORGE', 'SUTPHIN BLVD'], dtype=object)"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We might consider dropping this data when investigating weather effects on ridership"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There exist indications that weather variables are not always reliable. For example precipitation by definition cannot be zero when it is raining, but... "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum(df[df['precipi']==0]['rain']!=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "5926"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One method of dealing with this ambiguity is substituting the problematic zero-valued precipitation entries on rainy days with the average - i.e. imputation:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['precipi'].ix[((df.rain==1) & (df['precipi']==0))]=df['precipi'][((df['precipi']!=0)) & (df['rain']==1)].mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Investigate effect of rain on ridership"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Visualise subway ridership and rain incidence in NYC"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m.drawcoastlines()\n",
      "#m.drawstates()\n",
      "#m.fillcontinents(color='#04BAE3', lake_color='#FFFFFF')\n",
      "#m.shadedrelief()\n",
      "#m.drawlsmask(land_color='coral',ocean_color='aqua',lakes=True)\n",
      "lons=df.groupby('station')['longitude'].mean().tolist()\n",
      "lats=df.groupby('station')['latitude'].mean().tolist()\n",
      "#lons_ws=df.groupby('station')['weather_lon'].mean().tolist()\n",
      "#lats_ws=df.groupby('station')['weather_lat'].mean().tolist()\n",
      "stat_entries=df.groupby('station')['ENTRIESn'].sum().tolist()\n",
      "#Simple function that assigns variable for coloring\n",
      "def colmean(col1):\n",
      "    return df.groupby('station')[col1].mean().tolist()\n",
      "x,y=m(lons,lats)\n",
      "min_mark=200\n",
      "max_stat_entries=max(stat_entries)\n",
      "sizes=min_mark*(stat_entries/max_stat_entries)\n",
      "colors=map(lambda x: x*100, colmean('rain'))\n",
      "mapplot=plt.scatter(x,y, s=sizes, c=colors, cmap=\"Blues\")\n",
      "plt.colorbar(mapplot, label='Generic')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "<matplotlib.colorbar.Colorbar instance at 0x000000001ED28DC8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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pzOIrbLIoI+w8eJAqJvWo3bwDAD63/Yh6Fk1Lp/cJ8klJSRw4c4EmHbIKoXt4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\nUSSep31xOgtnVr+w9JBz58Jxdx9c4+1VXNw9LC2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66XlWrnydlStX1Tgrrw/2dk9HfKtV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\nvgKBQKADhPgKBAKBDhDiKxAIBDrg/wGlE7AXgMH3ogAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1a145978>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins1=bins1=np.linspace(0,10000, 5).tolist()\n",
      "plt.hist([df[df['rain']==0].ENTRIESn_hourly, df[df['rain']==1].ENTRIESn_hourly],normed=False, color=[\"blue\",\"green\"])\n",
      "plt.xlabel('Hourly Entries')\n",
      "plt.ylabel('Frequencies')\n",
      "plt.title('Histogram of hourly entries',fontsize=14)\n",
      "blue_patch = mpatches.Patch(color='blue', label='No rain')\n",
      "green_patch = mpatches.Patch(color='green', label='Rain')\n",
      "plt.legend(handles=[blue_patch, green_patch])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 33,
       "text": [
        "<matplotlib.legend.Legend at 0x1eda0828>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AOAnYDTgAODci2oETgPvLba8GTivruBQ4LDP3AHaOiG1auQ+SJI10rT4N8Qjw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\nDAuSJKnS/weR72gks8YD5gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19ca56a0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bins1=bins1=np.linspace(0,10000, 5).tolist()\n",
      "plt.hist([df[df['rain']==0].ENTRIESn_hourly, df[df['rain']==1].ENTRIESn_hourly],normed=True, color=[\"blue\",\"green\"])\n",
      "plt.xlabel('Hourly Entries')\n",
      "plt.ylabel('Frequencies')\n",
      "plt.title('Normalised Histogram of hourly entries',fontsize=14)\n",
      "blue_patch = mpatches.Patch(color='blue', label='No rain')\n",
      "green_patch = mpatches.Patch(color='green', label='Rain')\n",
      "plt.legend(handles=[blue_patch, green_patch])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "<matplotlib.legend.Legend at 0x1baa93c8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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llUseP6GMZnkaMIMyUNRWwGfoP2RIkqQlTCuBYpfM3Ljp/WkRcWBm/q1ThZIk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\nSbUZKCRJUm3/H/ga9BzPoWclAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x17dcb518>"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The normalised histogram suggests that distributions are similar."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#sns.distplot(df[df['rain']==0].ENTRIESn_hourly, hist=False,kde_kws={\"color\": \"green\", \"lw\": 3, \"label\": \"rain\"})\n",
      "#sns.distplot(df[df['rain']==1].ENTRIESn_hourly, hist=False,kde_kws={\"color\": \"blue\", \"lw\": 3, \"label\": \"norain\"})\n",
      "sns.kdeplot(df[df['rain']==0].ENTRIESn_hourly, color=\"green\", shade=True)\n",
      "sns.kdeplot(df[df['rain']==1].ENTRIESn_hourly, color=\"blue\", shade=True)\n",
      "plt.xlim(-1000, 6000)\n",
      "plt.xlabel('Hourly entries')\n",
      "plt.ylabel('Density')\n",
      "plt.title('KDE of hourly entries',fontsize=14)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.text.Text at 0x1bd10160>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s8PDWWykt9toiIslIyUISqaiABQu89Orlkh2FRQs1wSoWbZtBe293ClL2jrzB\nJspN6cy+GeMo2+92QDs6iojEmpKFJPLJJ1BTE739FRZv/4DKYBmD2o3GcVq20PDQrNOhYAnpfRbx\n4YcprFypQkcRkVhRspBE/ve/0N/RqleYVxK7XRt3p8DXm0Hpo6kcfgcAjz8e3S2mRURkJyULSeR/\n/yNq9QpBN8i80jfIcHLpmTosCtHtudHZk2DQS3gzt/LMMz4qK+MShohIm6dkIUmUl8PcudCnT5DM\nzMjbW16+gGL/Bga2G4XHic9OinulDqFPxjAC+06luNjhtddU6CgiEgtKFpLE/PleamqiNwUxt+R1\nAAbGeNfG3RmdfQaMeACcoKYiRERiRMlCkth5HkT0tnhOIY2+aftHpb3m6pu2P10LUqHvDObP91JY\nqG9pEZFo00/WJDF7dgoeDwweHPnIwtrKZaytWkb/9ANJ9aRHIbrmcxyH0TlnwMj7AC2jFBGJBSUL\nSWD7dvj0Uw8DBkBGRuTt1R4cNTAOqyDqMzh9DO0HLoac1Tz7XArbt8c7IhGRtkXJQhKYN8+L3++w\n777RaW9uyes4eDDph0anwQh5HC+j806DEQ9QUe7hxRc1uiAiEk1KFpLAnDmheoX99ou8ra0161lW\nPp9eqT8h05sXeYNRsm/GMWSOeBk8fh56xIPrxjsiEZG2Q8lCEpg1KwWPx2VYFLZDmF86HReXQTE+\njnpPpTipHNbtCDCvYJeksnChvrVFRKJFP1HbuO3b4fPPPfTrF6Rdu8jb27lkMjHqFeoamTkR3/5P\nAPDQI/rWFhGJFv1EbePmzvUSCDhR2V+hIrCNxds/oCClLx1SukchuuhK82RyyLB86LCMV1/1sXVr\nvCMSEWkblCy0cbNmhXY1jMb+Cp9uexe/W8XgFjyOek8dnH0SnpEPE6hJ4amn47OzpIhIW6NkoY2b\nNcuL1+sycGDkIwu1UxCD4rxrY2Myve3Z95AN4K3kvodrVOgoIhIFMdtM3xjjAe4F9gGqgHOttSvq\nXB8P/AnwA49Yax9q6B5jTD/gMSAIfAlcYq11jTGXAGcCLvA3a+3zsepPa1RaCl984WHAgCDpEe6d\n5HdrWFD6FjmeArr6BkQnwBg5vPMEFg15no2LJ/Hhh2WMGROdLa5FRJJVLEcWTgBSrbWHANcCd9Ze\nMMb4gLuAscAY4HxjTEH4nrR67rkLuM5aOxpwgInGmE7AhcDBwJF125eQTz7xEgxGp16haPssyoMl\nDGo3Gsf5LPCPAAAgAElEQVRxohBd7LRP6UrfUQsBuOM+FS6IiEQqlsnCocBbANbaucDIOtcGAV9Z\na0ustTXALGB0+J7p9dwz3Fr7Yfjj6cBR1tpNwL7W2gDQFdABxbuIZr3CvNI3ARiUnlhLJhty9JAR\n0Plz5n3Qhe+/T+zkRkQk0cUyWcgBSut8HghPM9ReK6lzbRuQ28A9XkKjCbW2h5+LtTYQnor4GHgy\nuuG3frNne0lJcTEmspEF13X5pOQ10pwseqcNj1J0sdUttT/5B8+AYAp/nfp9vMMREWnVYpkslALZ\ndV/LWlv7rlWyy7VsoLiBewKEahV2fS4A1tp/ExpZGGOMOTxq0bdyxcXw5ZeheoW0tMjaWlnxGZtr\n1mLSD8XrxKzMJeqOGdUeUrfx/NM5BKJz2KaISFKK5U/+2cB44HljzEHA4jrXlgL9jTHtgTJCUxB3\nECpUrO+eT40xY6y1M4FjgfeMMQOA26y1PydUJFkF7PYtIT8/e3dPaRPmzAHXhZEjveTl7Tw9qu7H\nTbW4+G0A9mt/JFlZ8T1lck/slzmSl/abRtncX/Lye2u46FfZSfPv35Bk7n8y9x3U/2Tvf6RimSy8\nDIw1xswOf362MeZUIMta+6Ax5gpgBqHRjYetteuMMT+6J/z3lcCDxphUoAh4Ibwa4jNjzMeEkoxp\n1tqPdhfUxo3botfDBDZ9ehqQSv/+lRQXhwZm8vIyKC4u3+O2Zn7/El589HCHs3176yoNGXX4dmbM\nhVv+tpmLfrVX0vz71yc/Pztp+5/MfQf1X/2PPFFy3ORaiO4myzfM4YdnsHy5h//8p4LU1NBjzUkW\nvq9axYVLh9A/7WDO6HRXDCKNraAb4JZb0vCvGc7Hn2+kb5conNHdSiXzD8xk7juo/+p/dsRV3tqU\nqQ3asgWKirwMGBDckSg017zSNwAYlMC7NjbG43gZdthKcD1cfmthvMMREWmVlCy0QR9/HJpdGjYs\n8qq+0K6NDgNbyZLJ+hxzSFdot4V5bwxj47bi3d8gIiI/oGShDZo9O3QmQqSbMZX6N1NUNoe9fIPJ\n9naMRmhxkZHmo+cBn+KW5XP9g3PjHY6ISKujZKENWrgwdB5E//6RJQsLSqfjEmRQu8Q9C6Kpjjsy\ndD736891pdLfuoo0RUTiTclCGxMMwtKlHrp3d/H5ImtrXkm4XqEVT0HU6t7VQ/sBhfhXHso9M2bE\nOxwRkVZFyUIbs2qVQ0WFQ+/ekY0qVAXL+XTbO3T09iTf1zs6wcXZ2GP8AEx9pIZAULs0iYg0lZKF\nNqaoKFSv0KtXZMnC59v+R7VbyeA2MAVR64D9XXzZW9g270ReLpoe73BERFoNJQttzJIloX/SSJOF\n2iWTrXkVxK68KbDfqHVQlcdfHl5Gku0xIiLSbEoW2piiotA/ae/ezX8jDLgB5pdMI9PTgb1Sh0Qr\ntIQw+vAqcAKs+d9xzP5utxt+iogIShbanMJCL1lZLh06ND9ZsGVzKQ1sYlD6YXictvUtktuhml5D\n18B3+3PrS2/GOxwRkVahbb0TJLmyMvjmG4devYI4EWzuOa/0daD17tq4O6N/WgHAwjeG88Wmxbt5\ntoiI7DZZMMb83hjTpSWCkchY68F1I1sJ4boun5S8TqrTjj5pI6IYXeLoN6SYzA4l8OWp/H3WA/EO\nR0Qk4TVlZKEdMNMYM80Yc7IxJsLV+xIr0VgJsbpqCd9Xf03/tIPwOWnRCi2heDxw8JhiqMnkzZdz\n+aZ0VbxDEhFJaLtNFqy1NwEDgSnAT4HPjTH/MsbsG+vgZM/sXAnR/HqFubUbMbWhJZP1GTFqI443\ngLvgAu799J/xDkdEJKE1tWahHdAH6AsEgS3A3caY22IVmOy5wkIPjuPSo0fzRxbmlbyOg5cB6QdH\nMbLEk5XjZ8jwLbBxCE+9tZJNFZviHZKISMJqSs3CU8BK4HDgFmvtUGvtDcDRwPmxDU+aynVD0xCd\nO7u0a9e8NjZVr+WrikX0Sd2Pdp6c6AaYgA48fAMA1XPP4aEv7o9zNCIiiaspIwvvAf2stedYa2cB\nGGNSrbVVQNtahN+Kff+9Q3GxE1G9wvzS0FLCtroKYle9+m+jU9cyWHISD855ke012+MdkohIQmpK\nsnCetXbHT1FjjBdYCGCtXRerwGTPRGMzpkWl7wBta9fGxjgOHDhmAwRS2TbvFzxV9Hi8QxIRSUgN\nJgvGmP8ZY4LAgcaYYO0foBKwLRahNElhYeQrIb6p/JIsTwfyUpJnpexPDt6ELzUACy/k3kX/piZQ\nE++QREQSTkpDF6y1PwUwxtxtrf1ty4UkzRHpmRCVgTI21nxLn9Th0Qwr4bXLCLDPAZtZOKsP6xYP\n4aXlz/PLgafFOywRkYTSYLJgjDneWvsGsMgYc8au1621T8Q0MtkjhYUe0tJcOndu3jTE2qplABT4\n+kQzrFZh/zHfs3BWASy4mH8dcA0nm1Pa3DbXIiKRaOwn4v7hv3/awB9JEDU1sHy5hx49gni9zWtj\nddVSAPJTekcvsFaie+8yuvXaDst+hl21nfe+eTveIYmIJJTGpiFuDP99Vu1jxphcoIe19svYhyZN\n9dVXHvx+J6LixjWVoTKU/CQcWQA4YMz3vPJEX5j7G+4Z+HfG9j4m3iGJiCSMpuyzcK4x5hFjTAFQ\nCLxgjLk19qFJU9WuhIikuHFNZfKOLADsc9AmsvOqcBb8hrnLv2L++rnxDklEJGE0ZWL2YuAq4BTg\nVWAooF+7EsjOZZORnAmxlHQnmyxPh2iF1ar4fC5jjvsOt7odzP49/1z0j3iHJCKSMJpUxWWt3QIc\nB0yz1vqB9JhGJXtkyZJQoULPns1LFmqC1ayvWkl+Sm+cSM62buVGjNpATl4VzoJLeOuLBSzbohXC\nIiLQtGSh0BjzBqFzId4xxjwHzI9tWLInCgs9tG8fJKeZOzSvq/6KIIGkXAlRV4rPZczP1uLWtIPZ\n1/Dvz+6Od0giIgmhKcnCOcBfgQOttdXA48C5MY1Kmqy4GNat80RU3Lg6yesV6ho+aiO5HapgwUU8\nt+BDvtu+Nt4hiYjEXVOShSxgH+BSY8yNwEjguphGJU1WOwURWXFjaLi9wNc7GiG1aikpLof/bC34\n0wl8eCVTP7833iGJiMRdU5KF5wmdOFn3uck7sZ1gorISIon3WKjPfodsJK9jBSw6n0fnvEVx5dZ4\nhyQiElcN7rNQR2dr7VExj0SaJSorISqX4nPakePtHK2wWjVvisvhx3/HK4/3pfKD3/LYEQ9z+Yir\n4h2WiEjcNGVk4VNjzE9iHok0S1GRF6/XpXv35tUsBNwAa6uW0ymlp7Y4rmPfgzaRl18Oi87l3pmv\nUuGviHdIIiJx05R3h2GEzodYZ4z5OvxnZawDk90LBkMHSHXr5uLzNa+NDdWr8LtVFKQk90qIXXlT\nXI44/jsIpFH8zoU8u/TpeIckIhI3TUkWTiS0bPIgdp4LcUQsg5Km+eYbh/Jyhz59IpuCAMhXceOP\n7HPgJtrnl8Fn53D3+y8QCAbiHZKISFzsNlmw1q4CDgXOBzYBo8OPSZxFuhkT7Cxu1MjCj3m9cMSE\ndRBIZe20M3hz5WvxDklEJC6acjbE7YR2b/w54APONsbcFevAZPeiVdwIWgnRkH0O2EReQSl8djZ/\ne+dZXLf5+1mIiLRWTZmGGAdMAiqttVuBscCxMY1KmmTnsslITptcihcf7VO6RSusNsXjgbETvoeg\nj6Wv/JyP1s6Md0giIi2uKcnCrhO1afU8JnFQVOQlM9OlY8fmJQuu67KmytIxpQdepymraJPT0P03\nk9e5GD47k79Ofzbe4YiItLimbsr0X6CDMeZ3wEfAMzGNSnarvBy+/tqhV68gzT37aXPNWiqDZUl/\nJsTueDxw9MQN4KYw77mjWLzxs3iHJCLSopqSLLwJvA5sBEYBN1hrb41pVLJb1npwXSfCeoUlgOoV\nmmLIiC3kdd0EiycxZZpGF0QkuTSYLBhjCowxHwIzgUsJTT0cAVxsjMlrofikAUuWRGOb59CZEEoW\nds/jgXETNoPr5b0nD+LrEm01IiLJo7GRhX8Bswht93ygtfZAoDPwOfCPlghOGlZUVHuAVBROm9Qe\nC00yePhWcruthy9OZcqbz8c7HBGRFtNYsrCPtfY6a21N7QPhI6r/CAyPeWTSqMLC0D9dRHssVC7F\nwUOnlJ7RCqtN83jgmIlbwfXy2sPD2Fi+Md4hiYi0iMaShXo3w7fWBtFqiLhy3dCyyc6dg7Rr19w2\nXFZXLaW9txspTmp0A2zDhuxXQk73NQS/OJkpb7wY73BERFqETg5qhTZscNi61RNRcWOJfyPbA1sp\n8O0dxcjaPscJjy7g4en7e7Oq5Ot4hyQiEnONLa4fYoxp6CehdvCJo9opiIg2Y6qq3bmxV1RiSiZD\n9y3j7b2+o/jLX3DZMxfx6gV34DR3/aqISCvQ2MjCAHYeHLXrHxP70KQhUVkJURlaCaE9Fvac48DP\nTigBPHzyzFimf/1mvEMSEYmpBkcWdFhU4tq5EkJnQsSL2aeEzr028/2Sk7j62bGMueqnZPoy4x2W\niEhMqGahFSos9JCa6tKlS+TTEJ00DdEsjgPjTtgAwMbpF3PXgr/GOSIRkdhRstDK1NTA8uUeevYM\n4vU2v51vK5eQ6+lMmicjesElmX5DSujepwSWnsi90z/Cblka75BERGJCyUIrs2KFh5oaJ6LixrJA\nMcX+78lXvUJEHAeOOuE7AAL/u57ff/g7HWEtIm2SkoVWZuex1JHUK4SLG1WvELG+g0ro2bcU7EQ+\nnl/JC8t0boSItD1KFlqZ2pUQkeyxsGbHNs8aWYiU48CRE9eEPp55MzfMvo6SquI4RyUiEl1KFlqZ\nwsLIV0Joj4Xo6jOwlF79S3GXHcfmr/Zmytxb4h2SiEhUNbYpU0SMMR7gXmAfoAo411q7os718cCf\nAD/wiLX2oYbuMcb0Ax4DgsCXwCXWWtcY8zvgl+Emp1lrb45VfxJFUZGH9u2D5OQ0v43aaQgdIBUd\nodGF1TzytyGkfXQbj+51JKcOPJ2fFOwX79BERKIiliMLJwCp1tpDgGuBO2svGGN8wF3AWGAMcL4x\npiB8T1o999wFXGetHQ04wERjTB/gNOBga+1BwNHGmGEx7E/clZTAd995IipuBFhduYRMTwcyPLlR\nikz6mG30MSVU2Z/irj6Qq2f+jkBQR6iISNsQy2ThUOAtAGvtXGBknWuDgK+stSXhUy1nAaPD90yv\n557h1toPwx9PB44CVgPHWGtr3zl9NHD4VVuxZEnkUxCVgTI21axWcWMMHDEhVLuQ9/E9fLZxEU8W\nPRbfgEREoiSWyUIOUFrn80B4mqH2Wkmda9uA3Abu8RIaTai1Hci11vqttZuNMY4x5m/AImvtV1Hv\nRQKpPRMikuLGtVXLcHE1BREDvQdsY+9BxRQX7U/qmiP58yeTdYy1iLQJMatZIPSmn13nc0/4eGsI\nJQp1r2UDxQ3cEzDGBOt5LsaYdOCRcHsXNyWo/Pzs3T8pQa1aFfp76NA08vKa18YWb6iRHpkDyMpK\nj0pcrUms+3z8KRu458Y8Cubfy5q9DLd/ehOPn/B4TF9zT7Tm7/9IJXPfQf1P9v5HKpbJwmxgPPC8\nMeYgYHGda0uB/saY9kAZoSmIOwC3gXs+NcaMsdbOBI4F3jPGOMCrwHvW2ibvtbtx47YIuxU/Cxdm\n4PF4yM2toLgZq/Py8jKwWz4HICfYne3bK6McYWLLykqPeZ/zu1XSb3AxX30+gC6HnsITnz/Bz3uf\nwiHdR8X0dZsUW352q/7+j0Qy9x3Uf/U/8kQpltMQLwOVxpjZhAoVf2eMOdUYc164TuEKYAYwB3jY\nWruuvnvCbV0J3GSMmUMowXmRUDHkaOAYY8z/wn8OimF/4ioYDK2E6N7dxedrfjtrqsIrIVK0x0Ks\nHBHed6HdrL/i4PD7D39HTaAmzlGJiDRfzEYWwoWHF+3y8LI6198A3mjCPVhrlwOH7/Lwy0C7aMTa\nGqxe7VBe7jBiRGQV9qsrl5DuZJPl6RClyGRXPfbeTv+hW1m+uAcjj/09C7idqYvv5dL9fhvv0ERE\nmkWbMrUS0TiWuiZYzfqqleSn9MZxnN3fIM1WuzKi5K0ryfLlcMf8v7B225o4RyUi0jxKFlqJaJwJ\nsaZiOUECFGib55jbq08ZZp8tLP88nyP9t1Hhr+D6WdfEOywRkWZRstBK7DwTovkbMn1bvgSAfO2x\n0CJqRxeWvvhLBrYfzJtfv86738yIc1QiIntOyUIrUVjoISPDpWPH5icLq8qKACjQHgstoluvcgb+\nZAt2cQfGVN+Gx/Fw7YdXUeFv03uHiUgbpGShFaiogK+/9tCrV5BISg00stDyakcX3n98FMf3mci3\n277hnkV3xTkqEZE9o2ShFVi2zEMw6ES0cyPAqrJCfE47crydoxSZ7E7XnuUM3m8zy79sj9l8BR3S\nO3LPortYWdymNxsVkTZGyUIrsLO4sflTEAE3wOqKZXRK6YnH0T97S/ppeHThpQeHcs7Q86kJ1nDt\nR1fhupEdCCYi0lL0rtEKRGPZ5IbqVdS4VRRoM6YW12WvCoaM2MyKojx8y09k3/zhfLD6fV5f8Uq8\nQxMRaRIlC61ANJZNrq5cCqADpOLkp+PX4Dgu/71/AOcNuwifx8cfZ13D9urk3YJWRFoPJQsJznVD\nKyEKCoK0i2C/yjVVoWRBIwvx0bl7BUNHbuZrm8vqBfvx8/4n8335ev46f0q8QxMR2S0lCwluwwaH\nLVs8ERc37hhZ0EqIuNkxunBffyb2PYkuGV14cPF9FG76Mt6hiYg0SslCgqvdjCmS4kaANZVL8eKj\nfUq3aIQlzZDftZJhB2xi1fIcnr93H3497CICboDfz7ycoBtZMigiEktKFhJcNOoVXNdlTZUlP7Un\nXieWp5LL7hxz8rd07FzBy4/15ctnTufALocw//t5PLv06XiHJiLSICULCa52JUQk0xCba9ZSGSyj\nS1rfaIUlzZSdW8OvryqiY+cKXn1ib3I+eIBUTzqT5/yRLZWb4x2eiEi9lCwkuKIiDz6fS5cuzZ+G\nWF0Z2rmxc6qKGxNBdl4Nv766iE5dKnjnv0Po+8lrbK3cyq2f3Bzv0ERE6qVkIYH5/WCthx49gni9\nzW9nTZUFoHPq3lGKTCKVnRtKGPK7lrNk2liy3nuEJwsfY8H6efEOTUTkR5QsJLAVKzzU1DgRnTQJ\nO1dCdE7TyEIiycqp4ZyriijoVs72WWfDtHu4euYV+IP+eIcmIvIDShYS2M5jqSOrlF9TuRQHDwW+\nXtEIS6IoK8fPOVcV0bl7Gcy/lMLHL+SRxQ/FOywRkR9QspDAorUSYnXVUtp7u5HiSY1WaBJFmdl+\nzr5yCQV7lcKCi5j8x46s27Y+3mGJiOygZCGBReNMiBL/RrYHtlLgU71CIsvM9vPrK5eR030t/vnn\nMPHX3xLU1gsikiCULCSwwkIPeXkuubnNb6N2m+f8FE1BJLqMLD8XXbUGX7cvWfXBkZx2/lYCgXhH\nJSKiZCFhlZbC2rWeiEYVANZUhlZCFPhU3NgaZGW5nP67xdBtHu+/1pPLfutTwiAicadkIUFFYzMm\n0JkQrdHeeXsz4sJ/Q/e5PP9cOpddlqaEQUTiSslCgopGcSPsnIbopGmIVmVc59PJO+sc2Otjnn8+\nlUsuScevFZUiEidKFhLUzgOkIksWvq1cQq6nM2mejGiEJS2knSeb83rcRvszz4W95vDSSz4uvlgJ\ng4jEh5KFBFVU5MXjcdlrr+ZvyFQWKKbY/z35qldolXK8+Zzf4w46nn0B9JjFK6/4uPDCNGpq4h2Z\niCQbJQsJyHVD0xDdurmkRrA1wura4kbVK7RaWd4OnNf9rxSc9Rvo+SGvvZbK+eenK2EQkRalZCEB\nrV7tUFbmRGXnRkAjC61cprc9v97rNrqc9Tvo9QFvvunj3HPTqK6Od2QikiyULCSgaBc3ao+F1i/D\nk8s53W+j21lXQ+/3mT49lV//WgmDiLQMJQsJKBo7N8LOaYh8X+9IQ5IE0M6TzdndbqP7mddCn3eZ\nMSOVs89Oo6oq3pGJSFunZCEB7TxAKtLTJpeQ6elAhieCLSAloaR7Mjm72230OOt62Ptt3nknlTOV\nMIhIjClZSECFhR4yMlw6dWp+slAZKGNTzWoVN7ZBaZ4Mzuoyhd5n3gx9Z/D+u6lMOjOVysp4RyYi\nbZWShQRTUQErV4a2eXac5reztmoZLq6mINqoVE87zuj6Z/Y+8y/QbxofvJ/G6ZOUMIhIbChZSDDL\nl3sIBp0oFDfWLpvUSoi2yuekM6nLLfQ/82/Q/w0+nJnGab9KpaIi3pGJSFujZCHB7FwJEWm9Qu2y\nyd6RhiQJLMVJ5bTON2HOvBsGvMasj9I49bRUysvjHZmItCVKFhJMYWF0DpCqHVnI18hCm5fi+Di1\n4EYGn3EvmFeYMzuNU071UVYW78hEpK1QspBgaldC9OwZ6bLJJaQ7WWR5OkQjLElwXieF/yu4nqFn\nPAQDX+KTj9M56f98rFwZQeGLiEiYkoUEU1joIT8/SEYE5z7VBKtZX7WS/JQ+OJFUSUqr4nVSODn/\nD+x75uMw+DkWzk9n1KhMbrwxjZKSeEcnIq2ZkoUEsmGDw+bNnoinINZVf0WQAAWqV0g6HsfLiR2v\nYcSZT8PJJ+Nmfcd996Vy4IGZTJ2KTq0UkWZRspBAorUZ05pK1SskM4/jYWL7azjwAAhc0peccXdR\nVg4XXghHHpnBhx964x2iiLQyShYSSLTOhNBKCHEch5/lXsEheSdSevCV5F4+ip8eXcbSpR5OOimD\nSZPSVc8gIk2mZCGBROtMiNoDpLR7Y3JzHIdjci/jsKxJbEj/mKVH7MN1f/mGQYMCzJjhUz2DiDSZ\nkoUEUlTkwedz6do18j0WfE46Od7OUYpMWivHcRibcxGHZ5/NusqVTA2O5rIbLFdfXUWHDu6OeobH\nHvOpnkFEGqRkIUH4/WCthx49XLwRTCkH3ABrq5bTKaUXHkf/vBJKGI7MOZ9jO13MpprV/HHFWPqM\nWMY//1nJ6adXU17u8Pvfp3PEEapnEJH66d0kQaxc6aG62ol4JcSG6lX43Spt8yw/cnTH8zg65xK2\n+L/juq/GsiFo+cUv/Nx7bwVHHeXHWtUziEj9lCwkiNqVEBHXK9SuhFBxo9TjsOzTOTb3txT7v+f6\nr47h28oi2reHSy6p5o47Khk8eGc9ww03qJ5BREKULCSIqK2EqFoCQL6KG6UBh2SdwvG5V1IS2MD1\nXx3D3JLXCbpB+vZ1+fOfq/j970P1DPffr3oGEQlRspAgorYSolKnTcruHZh1EhPzrmV7YAu3rTqF\n3ywdztubH6XGreTggwP885+VTJr0w3qGmTNVzyCSrJQsJIjCQg+5uS55eZG1s7pyCV58tE/pFp3A\npM0amTmRSwueYnjG8XxfvYr71lzKeUUDef7726n0bObnP/9hPcPJJ2dw+untVM8gkoSULCSA0lJY\ns8YT8aiC67qsqbJ0TOmB10mJUnTSlhX4+nBi+z9yZZeXOCxrElXBCp5efzPnFRkeXHMlVRlfc8kl\n1dx5ZyVDhgR4++0U1TOIJCElCwlgyZLoHEu9uWYtlcEyFTfKHsv2duLo3Iu5ussrHJv7W9p5cpi2\n+X4uXroPd6w6HX/nBdxyy4/rGR59VPUMIslAyUICiNpKiB07N6peQZonzZPJIVmn8LvOL3BS+8l0\nTunLnJKX+f3y0fxpxThShrzB3feUc8YZ1VRUOFxzTToHHpjJ7benanpCpA1TspAAon4mhFZCSIS8\nTgo/yRjHxQWPc1bHe+iXdiCFZbP4y9cncdXXI8ke/TB3/6uEceNq2LDB4c470zjooCyOPTaDRx7x\nsXmzEgeRtiTmE9vGGA9wL7APUAWca61dUef6eOBPgB94xFr7UEP3GGP6AY8BQeBL4BJrrRtuJx+Y\nDQy11lbHul/RVFTkwXFcevSI0mmTmoaQKHEch77p+9M3fX/W13zF7G1Ps7jibf695mLyUm7i+BMv\n4l+/Oo8vF3bkgw9SWLTIw8KF6Vx/vcuRR/r5v//zM3asn/T0ePdERCLREiMLJwCp1tpDgGuBO2sv\nGGN8wF3AWGAMcL4xpiB8T1o999wFXGetHQ04wMRwO+OAt4GCFuhPVLluaNlkt24uqamRtbW6cgkO\nHjql9IxOcCJ1dPH14xcdbuCKLi9xaNZplAe28Z/1k7n06/6s6H8FF1+3nIcequCss6rp0cNlxgwf\nv/51O4YMyeKKK9L4+GMvwcgGz0QkTloiWTgUeAvAWjsXGFnn2iDgK2ttibW2BpgFjA7fM72ee4Zb\naz8MfzwdOCr8cQA4Etgaw37ExJo1Dtu3R77Ns+u6rK5aSntvN1KcCLMOkUbkegs4Jvc3XN3lVcbl\nXEqqk8kbm+7loiVDeXTbmQwdO5+77qrkH/+o4MQTa/D5XP7zn1QmTsxg5MhMpkxJZflyzYCKtCYt\n8T82Byit83kgPM1Qe63uAqxtQG4D93gJjSbU2h5+Ltbad621W6IdeEuIVr1CiX8j2wNbKfDtHY2w\nRHYr3ZPFqOxfcUWXF/lF+xvIT+nNrOIXuGr5KG5YcRyb289g0qRqHnigkptuquSII/xs2uTw97+n\nceihmYwdm8GDD/rYuFH1DSKJriUW45cC2XU+91hra98ZS3a5lg0UN3BPwBgTrOe5eyQ/P3v3T2pB\n334b+nvIkFTy8po/IrCq+BsAumf0JSur4Qnixq4lA/U/Fv1PZ1T2iRyafwK2/GPe3/IEX2yfyRfb\nZ9I7Ywi/7HE1R4w6hdGjU6ishNmz4Z13YP58L59/7uWGG2DcOJg0CSZOhHbtYhAiifd/v6Wp/8nd\n/0i1RLIwGxgPPG+MOQhYXOfaUqC/MaY9UEZoCuIOwG3gnk+NMWOstTOBY4H39jSYjRu3NbsjsTB/\nfvg4K54AACAASURBVDrgo1OnCoqLm1/guHTTZwDkuT3Yvr2y3udkZaU3eC0ZqP+x7/9eDOeM9sP5\nLtMye/vTfFH+Hrfbs7jvq6s4quOZjOt4LiNG9GTECCguho8+SuGDD7xMm+Zl2jTIzHSZMMHPSSfV\ncOihATxRGvvMz89OuP/7LUn9V/8j1RLJwsvAWGPM7PDnZxtjTgWyrLUPGmOuAGYQmhJ52Fq7zhjz\no3vCf18JPGiMSQWKgBd2ea3IlhPEQWGhh3btXPLzI1wJURVeCaFlk5IAuqUaTu5wE2P9F/FJ2fMs\nKnuDlzbcycsb7mJE9rEc2+k89s09ivHj/Ywf72f1aoeZM1OYOdPLM8/4eOYZH127BjnppBpOPtnP\nwIGqjBSJJ8d1W937ayTcRMouKyuhT58s+vcPMmVKVURt3bjiZyze/gHXd32PNE9Gvc/Rb9bqf7z6\nX+NW8mX5e8wte4m1NUUAdE7tw7Edz+OIDpPITukAQDAYquOZOTOFOXO8lJeH6hmGDAlw1lk1nHJK\nDWlpe/76+s1S/U/y/kdcGKRkIY6++MLDkUdmMm5cDRdeWBNRW+cU9iUYdLmq6ysNPkdvlup/IvR/\nbfUS5pW9yOLyd/BTjc9J57C8kzi20wX0yxi+43nV1aG6hg8+8PLpp14CAYdu3YJcdlk1p51Ws0d7\nN+jNQv1P8v5HnCxo/VIcFRaGvvy9e0eWsJUFStjqX0++T9s8S+Lrnvr/7d15fJTVucDx3ztLMpOV\n7CsBInACYZN9lUURa6vWrRW14NpqW9Fbq3WrtrWLt7Vea2stUiy21nprvRWw1+VWXNkUWYosh0UC\nSdgSspBtlszM/eOdgYSGIMxMQjLP9/OZD5l3PzOTzMN5n/OcIVye9hD35C3jopQFJFkyWFH7Avfs\nnMY9O85jRc2fcPtbiIuDKVN8PPigh2efbeGSS7xUVxvcd5+DceMSWbTITktLd7dGiNggwUI3Ck0g\nFfacEMHKjdmSryB6kARLKlOS53JXzl+Zl/FflDimsrtlA78uv41btg5iyf4HOOD+DID0dLjpJi8L\nF7bw5S97qaszePBBB2PGJPLMM3aam7u5MUL0chIsdKNQjYWionDnhNgGID0LokeyGBYGOSZyXcYv\n+E7O3zgvaR7+QIClVb/iW9tH8Ohnl/Px0dfxBXz06QPz55tBw5VXemlsNHjkETNo+M1v7DQ2dndr\nhOidJFjoRlu2WMjK8pOYGN5xjo+E6BeBqxKi+/Sx5TE79XbuyV3KVWk/oNA+jPUNb/HTPVdx+7ZS\nXjn0OPWtVaSmwvXXm0HD1Vd7aW42+NGPHIwdm8hTT8VJ0CBEhEmw0E2qqgyqqy1hl3mGNrNNygRS\nopewGXGMTJjD17Of5ZtZzzM24TLqWqt44eAj3LJ1ME/uu5ntTWtJTg5w7bVenn22hWuu8eB2G/z4\nx/GMHp3EE0/EcfToqc8lhDg1CRa6ybZtoVsQ4Y9GqXBtJ9GSToIlNexjCXG2yYsbzGVp93Fv7nIu\nTv0PUi25vFf7EvfvmsXdO6bwf0eWYHM28dWvtrJwYQvXXuvB64XHHjODhp//PI7aHjdrjBBnFwkW\nukkoXyHcngW3v5kq7z5JbhS9nsOSxKSkr3BnzkvcmPlrhjpmUOb6lN9WfIubtp7D42Xz+Nj9InMu\nP8iiRS1cf72HQAAefzyefv3gscfiqOmRM8gI0f26ooKj6ECkRkJUunYQICC3IETMMAyD4vixFMeP\npd53mHVNS9nY/Dor619hZf0rGBgMThjHmKlf4IFZF7Pt3VEsWxbHE0/Es3BhHDff7OG227xkZsZU\njRkhwiLBQjfZssWC3R4gPz+8P1jlbjNfIdsmIyFE7Em1ZnN+yq3MSr6Fw6172OFahXZ9yI7mT9DN\nHwE/JH1wPpMevRzvqlv5+PXhPPVUPL//fRw33ujl9ts9ZGdL0CDEqUiw0A18PtDaQmFhAKs1vGOF\naixIz4KIZYZhkGMvJsdezLTk62nxH2Wnay3atZKdrtW8fuRpUE9jPSeVwk9/Qs2783n66SQWL7Yz\nf76Xb3/bQ06OBA1CnIwEC91gzx4Dt9ugf39f2McK9SxkSc+CEMc4LSmMSJjNiITZ+AM+qq072VT7\nDttdK6kY9W0ovRs23IR35fdZuDCP5/5gZd48LwvuaCUvT4IGIU4kwUI32Lo1MvkKYI6EcBhJJFnS\nwz6WEL2RxbBSnDCKbH8Js1Nvp671IDvcq9DTVrJ79GDYOBfvBw+w+Pf9+cMSL6On7+Pm65K4dE4C\ndnt3X70QZwcJFrpBaCREuMGC1+/hgHs3BfahGEbY84QIERP62HIZb7uC8YlX4El3sSfnE7ZPvZct\nawpo+eAbrHu7hHVvwx1JtYyauZMb5yZw5awiLBb5HROxS4KFbhCpYZMHPbvx4yNb8hWEOCNxFgfK\nMQXlmMKlFwc4OHsd63YuZevaQho3zWbd8vGsWw4LMsoYPmszC+bn8MVxJRKci5gjwUI32LLFSmpq\ngD59wjvOscqNkq8gRNgMwyAvbiCXlA7kklJo9Gxk5ebDfLo2j7pPJ7Lx5Uu46WVwFm3m/EuqeOCm\nwQzsK4XQRGyQYKGLNTZCebmFESPCT248NieE9CwIEXFJcSnMGZPCnDHQ3LKJ99fVsXFNBk07x/La\n01Zee6aV3OGbmfsVC3fMHUBSkvQ2iN5LgoUuFirzHInkxtBsk1K9UYjoSnBauGhaOhdNC3CwdgX/\nXFXHro8HcXDTufzXJnjyh00Mm7abb30tk0svTMYmf1lFLyMf6S4WGgkRmQmkNHbDQYo1J+xjCSE+\nn9y0ZK7/YjKBi1v4V/mLfLAqwKH1k9j89ghuexsWpNQx6+Iq7rwhl9HngqQ3iN5AgoUuFqmREL6A\nj0r3DrJs/bEYMsWHEF3NMAxGFhUzsgiar97F+9v+wYY1aTT/6yLeeGkQb7wEaflVfPUrAW66NoH+\n/aV+g+i5JFjoYtu2WTCMAIWF4f3hOOzZS2vALWWehTgLJFhTuGjYOOaUBih3vca76yvZvW4wtdu/\nxO+edPK7J+Gc4Ye56doErvgyZGRI4CB6FgkWulAgYI6EyM8PEB8f3rEqQiMhJLlRiLOGYRgUOUuY\nN6UEz6QWNtQ+zqqP3dRsmM7uT2fx4P1WHnrIx6SpDXxpjoPJk32UlPixSOegOMtJsNCFKisNGhoM\nhg+PxEiI0LDJ/mEfSwgReXEWJxMyZjPhIjh8/h5WH7iDjR+l0brpSla9N5pV75nbpaX5mTLFx5Qp\nPiZP9qGUBA/i7CPBQhcKjYSITHKjzDYpRE+RbR/AZUUD+GJfD9su/l/WVPyIfTvSoGwGdWWzeO21\nvrz2mllbOj3dfyxwmDLFDB4kSVJ0NwkWutCKFebLXVQU/v3KCpfGip00W37YxxJCdA2bEcfwhAsY\nPvgCaov380nzcj5p/C6NR5KhbAbO8ktp3DON5cszWL78ePAwderx4GHwYAkeRNeTYKGLLF9uY/Hi\nOHJz/WEXZAoEApS7t5Nh64vVkLdQiJ4ozZbPBSnfYGbyzexKX8u6/KXsds/F63dBbTGUzYA9M6nd\nO4tly/JZtswMHvqke5k2NcDUKWYPxKBBEjyI6JNvmi6wbZuFO+5w4HAEuO8+N05neMc74t2Py9/I\nOfHjI3OBQohuYzVsx+an8Ad81LRWcjBjJwf77eLg5N+x3303DdVmzwNlM6krm8HyZQUsX2bun9in\niXMnHOXC6Q7Onx7HwIESPIjIk2AhyurqYN48J83NBvfe66ZfvwjcgnCHKjdKvoIQvYnFsJJpLyLT\nXsQw5/nHljfn1nNQ7eKg92MOeP5CxQEPR3YXE9hzHk1lM/jwzXw+fBMeBuJSaxgwch8TJnm4eHoq\n00bldl+DRK8hwUIU+XzwjW842bvXwpVXepk0KfxREGBWbgQZCSFErEiwpFIcP4bi+DHmgnTwDW2l\nqrWMA55nKTvQQPmODGp2KTx7JqPfH4V+H/74n4C9maSCreSfU83QUj/Tx6Zx8cR+pPWxdmubRM8i\nwUIU/fSncbzzjo3Ro33MneuN2HGlxoIQwmrYyLUPJNc+kHMHAgPN5Udb17Ojsood2+M5sCeDhsoC\nGvcNYkfZUHa8Da8C/wE4Mg/Sf3A940Y6mDEuk+HDAhQVBWTYpuiQBAtRsnSpjV//Op68PD/f+Y4b\nawSD+HL3dgwsZNqKIndQIUSvkGLLYGy/DMb2Cy1pwBK3gY36ALv3eqgst3OkMgPXgRK2r1JsXwV/\nesbcMs7pZmCJi3Ej4xk+DEpLfQwZ4ichobtaI84WEixEwZYtFhYsMBMa77/fTWJiZI9f7tpOmjUf\nmxEX2QMLIXqlhLh4RgzIZUSbNKe61jVsr9qJ3ttA5b44Wg70x3NoJFs3lrB1w/H/3RhGgAHFfoYP\n81Na6qe01EdpqZ+8vIAkUsYQCRYirLbWTGhsaTH43vfc9O0b2Rrw9a1VNPpqKHGURvS4QojY0seW\nzcS8bCbmARPhqO8we9x/47PGLewub6Z+fw4cHEng0Eg+qxzJZ7v7sHRpm/37BCgt9TFsmJ/iYj9Z\nWQEyMwNkZfnJzAyQkiIzbvYmEixEUGsrfP3rTsrLLVx9tZeJEyOT0NhWqHKjJDcKISIpxZrNyIQ5\njEyYA9lwdFQVZe6NlLmfZLdrPTU1wMGRcGgklkNjaT40lpUrC1i5suPj2e1m8GAGEMd/zsz0d7As\n/PlyRHRJsBBBP/lJPO+9Z2PsWB/XXBO5hMa2KkIjISS5UQgRRSnWLEYkzGZEwmwAGrKrKRuwkT2e\n9exx/Ylq315wJ8Gh4djqFdnecaR5huNsOQd/UzoN9Xbq6gy0trB586m7GJKT2/dMdBRo9Ovnp6BA\nbn90BwkWIuTvf7fx9NNx5Of7uesud9QyikMTSEmNBSFEV0q2ZpqlqhMuAKDBd4Qy9wbK0jfwmftd\n9rcuYf8J+zgsiSRaksnw5RDX3Bd7c18szfkYTTkEmrLwN2XQ2tgHb0MKroYkDtckULY3noD/5H9A\nExMDlJT4KSkxZ+xUys+QIX6ysyWIiCYJFiJg82YLd97pwOmMTkJjW6HbEJm2fqfYUgghoifZmtEu\neGj01VDm2UCZewN1voO4/c24/E24A00cNfbhdm6j1emBjFMc2G+AKw2aso89jKZcbE1FGDUKz+ES\nPtlQzCeftE/wTk0NMGSIOfGWGUyYj4yMyOaNxSoJFsJUUwPz5ztxuQzuv99NYWF0P5jlrm2kWnKI\nt8hYJiHE2SPJms4w5/ntKk+eqDXgxeNvxh1oxh1owu1vMn8OBhVuf3B5chPuzNC6Clx+3W691+uF\nmmI4XAqHh2FUDae5ajRr1vZlzZr2X2sZGX5GjoTi4vhjPRElJT5SU6P9ivQuEiyEobUVbr3VSUWF\nhWuu8TB+fOQTGttq8tVT23qQgfETonoeIYSIBpthx2ZNJYHwvqn9AT9H8vdR4dlChXcT5Z4XOeTd\nBV4bVJdAVSn2qrE4j0zEdUixYkU6K1a074nIzTVvX7S9pTFokJ+kpLAurdeSYCEMjz4azwcf2Bg/\nvpWrr26N+vlCyY2SryCEiGUWw0KWvT9Z9v6cyxcB8AZcHPDspCJzCxXFW6nwPE6tL5hF4U6E6iEk\n10wnpeY8ODyMhgP5vPOOg3feaX/swkI/hYXtkyxDiZZmsqW5LjU1toaGSrBwhl55xcYzz8RRUODn\nzjs9XVIitdxlTiAlIyGEEKI9u+GgKH44RfHDjy1r8tVR6d3KYXawO2UTlYWLqAz88th6iyud3PpL\nSKudha3qXFoO9uPw/iTWrrUSCHQeCdhsATIyzEAiO/vEwMLfLsjIyAjgcESt6V1CgoUzsHmzhbvu\ncpCQYCY0dlUp1Aq3TCAlhBCfV6K1D4OtkxmdNItGh4tAIECNr4IKz1YqPFspt3/KQceL7M95HkrM\nfZyWZIY5xtEvcB5JLoWtpQAas/E1ZuBpTOZovZX6eqirM6ivN9i928KWLafuYkhKajsU1N+utyI7\nO/SvWdwqKens67WQYOE0VVcbzJvnxOOB++93U1DQdZm25TKBlBBCnDHDMMiw9SXD1tcsPoWZdHnQ\nu5NKz1bKPVuo8Gxlc/MKNrPC3MkRfGSCgUGyNZM0ew7p9lwKbDn0seWQ7C8grqUIa0s+NObgb8yg\npSGB+nrj2KOuznzs22fg93c+WVB8/PEgIhRAnBhYZGWZy5OTuyawkGDhNJgJjQ4qKy3Mneth3Dh/\nl56/wrWdREs6CRZJ4xVCiEiwGXYK44ZSGDeUCVwFQIv/KPs926n3HabBf4RGXw2N/iM0+I7Q4Kvm\ngPsz9ro+7fiAiebDnuugjy2bNFsOafZcSuw5pNly6GPNJd7TF2vT8R6Lhnp7u4Cithbq6w02bbLg\n832+wOJ4IOFv9zwzM8Bll0XgdQr/ELHjhz+MZ+VKM6Hxqquin9DYltvfTJV3H/3izu3S8wohRKxx\nWlI4xzG+0208/hYa/TU0+o7QcOzf6naBRaPvCLu86/G3nGSknBVIhaT0dNLtuaTb8kiz59Iv+HMf\nWy5ObyHWxgL8jdk0HY0/FlCEgovaWvOWyObNFlpbT9rFMDAQYFc4r4kEC5/TX/9qY+HCOAoL/dx1\nV9ckNLZV6dpBgADZcgtCCCG6XZzFSbqlgHRbQafb+QN+XP6GYCBxPLBo21PR4DvCIfc+9rm2dnqs\nhNRU0jPzSLfnkmbPpa8tjxH2XNJsuaTZ8nB48zEa82hpSGgXVLz8sr0QJFiIuk2bLNx99/GERqez\n66+h3C0TSAkhRE9jMSwkWFNJsKaSYz+n0209fheNfjN4OOqrOhZQHPVVmYGFv5pqT+Wxsv8n47Ak\nkZ6RR3pOLn3sOfDyy2G3Q4KFU6iqMpg/30xofPBBN/n5XVs6NBAIsLN5He/VvgRAtl1qLAghRG8U\nZ3GQbikk3VbY6XbegJtGXw0Nvioa/KHeiep2P9d5DrPfvdPcYfxTo2DBu+FcmwQLnfB64ZZbHOzf\nb+G66zyMGdM1CY2+gI9tTatYU7+U1XVLqWk1C4skWPqQZ1ddcg1CCCHOTnYjnjRbHmm2vE63aw14\nafbX8Qv2hH1OCRY68YMfxLN6tY2JE1u58sroJjS2Brx82vgeq+uWsqZ+OUd9VQA4jCRGOb/AUOdM\nBjrGYzdk0nchhBCnZjPspFizQIKF6HnpJRuLFsXRt6+fBQs8URnH6vG72NjwNqvrX+Wj+n/Q7K8H\nINGSxtiEyyh1zmRA/BishrxNQgghuo98C3VgwwYL3/2ug8TEyCc0tvgaWd/wJqvrlvJJwxu4/E0A\npFiymZj4FUqdMyiKG4HF6HxsrRBCCNFVJFg4weHDBjfc4MTrhe99z01eXvgJjY2ttaw7+jqr65ey\noeGfeAMuANKsBYxNupyhzhkU2IdgMbp4PKYQQgjxOUiw0IbHYyY0Hjhg4frrPYwefeYJjfWtVayt\nf43Vda+yufFdfJg5D1m2AZQ6ZzLUOYNc20CMs60AuBBCCHGCqAULSikL8FtgBOAGbtFa726z/hLg\n+0Ar8JzW+vcn20cpNRBYAviBT4Fvaa0DSqlbga8Hj/FjrfU/wrnmhx+OZ80aG5Mnt3LFFaef0HjE\nu581dUtZXf8qW5tWEcAMNvJsitKEGQx1zCTL3i+cSxRCCCG6XDR7Fr4MxGmtJyulJgC/DC5DKWUH\nngDGAs3ASqXUMmAqEN/BPk8AD2it31dKPQNcppRaA9wBjAGcwIdKqf/TWnvO5GL/8hcbzz0XR1GR\nnzvuOL2ExnLXdp4uvx3d/NGxZX3tw4M9CNNJs+WfySUJIYQQZ4VoBgtTgDcAtNZrlVJj26wbAuzS\nWtcDKKU+BM4DJgGvd7DPaK31+8GfXwcuBHzASq21F/AqpXZh9kisO90L3bvXaJfQeLrzjle4NLua\n1zMgbgylzhkMcU4PDlcRQggher5oBgspwNE2z31KKYvW2h9cV99mXQOQepJ9rIBxkm07OsZps9lg\n6FA/V1zhISfHT+A0cxonpl7KT/uuxe13H1sWoGsrPX4+gbP0urqKtD922x/LbQdpf6y3n7ArCkYz\nWDgKJLd5HgoUwPySb7suGag7yT4+pVTbhqacZNtkoPYU12RkZSX/28KsLNi4Ecy7GWdqdBj7CiGE\nENHx/VsmhX2MaI7VWwlcDKCUmgj8q8267cAgpVSaUioO8xbEqk722aCUmh78+QvA+8BHwDSlVLxS\nKhXz1sZJJhgXQgghxJkyAqfb5/45KaUMjo9sALgRMxkxSWu9SCn1JeBhzIBlsdb6mY720VrvUEoN\nAhYBccBW4NbgaIhbMEdDWICfaK3/HpXGCCGEEDEsasGCEEIIIXoHKRkohBBCiE5JsCCEEEKITkmw\nIIQQQohO9cq5IZRSlwNXaa2vCz6fCDyJWRb6La31j4LLH8EcfdEK3KW1/lgplQm8CDiA/ZhJli3d\n0IyIOVXp7Z4uWO3zMa31zNMpDa6UcgIvAFmYdTrma62ru6URZyhYDfU5oB8QD/wY2EaMvAbBOiyL\ngMFAALgN8zO+hBhoP4BSKhv4BDgfs81LiJ22r+d4vZ3PgJ8RW+2/H7gEsAO/wRxRuIQotL/X9Swo\npX4F/JT2hZyeAeZqracCE5RSo5RSo4HztNYTgGuAp4PbPgy8oLU+D9gAfKPrrj5qjpXeBu7DLKPd\nKyil7sX8sogPLgqVBj8P8zNwmVIqF7M0+GRgDvCz4JDd24FNwW3/CDzU1dcfAdcBVcE2XIT5Of4l\nsfMafAnwB3+3H8L83Y+Z9geDxYVAE2ZbY+bzr5RyAGitZwYfNxNb7Z8BTAr+XZ8BFBPFz36vCxYw\nI6vbCQYLSqkUzPkm9gTXvwlcgFmO+i0ArXU5YAv2KhwrU41ZWvqCrrv0qGlXehtzTo7eYhdwBceD\nwxNLg18AjCNYGlxrfTS4zwjav9dv0DPf65cxA1wwf5+9xNBroLVeyvGAvj9mYbYxsdJ+4BeY/xk6\nEHweM+89MBJIUEq9qZR6O9iDHEvtvxDYrJR6FVgOLCOKn/0eGywopW5WSm0+4TFGa/3XEzY9sYT0\nqcpFt13eyBmWkD7LdFh6u7suJpK01v8DtJ0i9HRKg7d9Xc64XHh30lo3aa0blVLJmIHDQ7T/vY6F\n18CnlFoC/Ar4MzHyGVBK3YDZq/RWcJFBjLQ9qAn4hdZ6Dubtpz+fsL63tz8Ls3bRVZjtf5Eovv89\nNmdBa70YWPw5Nj2xLHSoXLSHk5ecTgGq2izr6Torvd3bfN7S4Ccu77HvtVKqL/A/wNNa678opX7e\nZnVMvAZa6xuUUjmYlV3bTgXXm9t/IxBQSl0AjAKex/wCCenNbQfYgfm/ZLTWO5VSR4Bz26zv7e2v\nBrZprVuBHUopF1DQZn1E298r/nfZmWC3i0cpVRysEHkhZrnolcAcpZShlCoCDK31EdqUnOZ4aeme\nrrPS273N6ZQG7/HvdfAL8i3gXq31kuDimHkNlFJfCyZ5AbRgzka7Lhbar7WerrWeobWeCWwE5gFv\nxELbg24kmH+llMrH/MJ7K4ba/yFmnlKo/QnA29Fqf4/tWTiFQPAREuqisgJvaq0/BlBKfQCsxgya\nvhXc9sfA88Hs0Srg2q666Cj6OzBbKbUy+PzG7ryYKAm933cDi4IJPFuBvwWzgZ8CPsB8rx/QWruV\nUs9gvtcfYGbQ98T3+gHM7sOHlVKh3IU7gadi5DX4G7BEKfUeZkb4nZhzz8TSZyAkQGx9/hcDf1BK\nhb7kbgSOECPtD45oOE8p9RFmu74JlBGl9ku5ZyGEEEJ0qtffhhBCCCFEeCRYEEIIIUSnJFgQQggh\nRKckWBBCCCFEpyRYEEIIIUSnJFgQQgghRKckWBCil1NK9VdK7elgeUSqeCqlblBK/SESxzrJ8d85\nyfIxSqlF0TqvEOK43lqUSQjRdaJdrGV6Rwu11p8At0b53EIIJFgQIuYFJxV7EpiF+cX/J631z4NT\n4D4SLCdMcLKmd4B3MWdvrQJcwAvB9bOAR7XWU4LP5wMTtNbfbHMuK+ZMidMxK6ou0Vo/GTzXA5iT\nAw0BNmNWlAuV812ttZ6klKoC1gG5wD3Ag1rrmUqpgcBvgQygGbhDa71RKXVtcDsfsAe4XmvtjugL\nKEQMkNsQQsSGfKXUhrYPjvcI3IY5Ac1wYDxwpVLqYv69xyBURt0ABgPXaa1nB5+jtV4B5CqlBgS3\nnweceHviViCgtR4DTAAuU0pNDa6bhFl2fQhQBFyotV4QPPak4DYZwM+01ufSfrbR5zHnxxiDOWX1\nS8HljwKztdZjMctAl3y+l0sI0Zb0LAgRG/YHv2CPaZOzMBPzf/gBoEUp9WfgfGBZJ8c7rLXe18Hy\n54GvBXshckLzsLRxATAy2AsBkAgMA7YBn2qt9wevbRuQfpJzrz2hHYnAOMx5AkKLE5VS6cByYJVS\n6lXgFa31pk7aJIQ4CQkWhBAWgr0DbZ7bON6LEGJv83NLm5/b9kAsAd7AvD3x/EnOdY/W+lUApVQW\n0ABMDO7T9pjGv+8OHdxGsAItbYMhpVRfrXUNcJdSajHwReAFpdQPtNZ/7ui4QoiTk9sQQogVwHyl\nlEUplYCZK7ACqAaKg1PbpgPTTrL/sS/1YG9DBXA78KeTnOvrSimbUioZcya88ae4Pl8w16FDwWno\ndyqlrgNQSs0G3g22RwPVWuvHgD8Co05xLiFEByRYECI2dDRiIbRsIeYX/CZgPbBUa71Ua70V+Aew\nBfgrx+e7P3EK+BOf/zewRWt9sINz/g7YCWwAPgIWa63f7+AYbS0FNiql4js573XALUqpTcBPgK9o\nrf3AI8A/lVIfYwY7T5zkHEKITsgU1UKIiFFK2TB7FP47dKtBCNHzSc+CECIilFIGUAm0SqAgRO8i\nPQtCCCGE6JT0LAghhBCiUxIsCCGEEKJTEiwIIYQQolMSLAghhBCiUxIsCCGEEKJTEiwIIYQQr8KC\nCAAAAAhJREFUolP/D79OOap1G0sLAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x19e8e630>"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Define a function that returns only the first part of the statsmodels summary result"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def short_summary(est):\n",
      "    return HTML(est.summary().tables[0].as_html())\n",
      "dummy_units = pd.get_dummies(df['station'], prefix='station')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# C() operator used when treating ordinal as categorical\n",
      "#est1=smf.ols(formula='ENTRIESn_hourly ~ WCI*station*rain', data=df).fit()\n",
      "#short_summary(est1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "env = EvalEnvironment.capture()\n",
      "desc = ModelDesc.from_formula('ENTRIESn_hourly ~ C(hour)*station+weekend', env)\n",
      "desc.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "'ENTRIESn_hourly ~ C(hour) + station + C(hour):station + weekend'"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "est=smf.ols(formula='ENTRIESn_hourly ~ C(hour)*station+weekend', data=df).fit()\n",
      "short_summary(est)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table class=\"simpletable\">\n",
        "<caption>OLS Regression Results</caption>\n",
        "<tr>\n",
        "  <th>Dep. Variable:</th>     <td>ENTRIESn_hourly</td> <th>  R-squared:         </th>  <td>   0.822</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.816</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   132.5</td>  \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Date:</th>             <td>Sun, 01 Mar 2015</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Time:</th>                 <td>14:56:45</td>     <th>  Log-Likelihood:    </th> <td>-3.2415e+05</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td> 36833</td>      <th>  AIC:               </th>  <td>6.508e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Residuals:</th>          <td> 35590</td>      <th>  BIC:               </th>  <td>6.614e+05</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>  1242</td>      <th>                     </th>      <td> </td>     \n",
        "</tr>\n",
        "</table>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 38,
       "text": [
        "<IPython.core.display.HTML at 0x1f5e5860>"
       ]
      }
     ],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = scipy.stats.probplot(df.ENTRIESn_hourly - est.fittedvalues)\n",
      "plt.figure(figsize=[12,12])\n",
      "ax1 = plt.subplot(221)\n",
      "plt.plot(c[0][0],c[1][1] + c[0][0]*c[1][0], 'b-', alpha=0.5)\n",
      "plt.plot(c[0][0], c[0][1], 'o', mfc='w', mec='b', mew=1, alpha=0.4)\n",
      "ax1.set_xlabel('Quantiles', fontsize=14)\n",
      "ax1.set_ylabel('Ordered Residuals', fontsize=14)\n",
      "ax1.set_title('Normal Prob. Plot of Residuals', fontsize=14)\n",
      "ax2 = plt.subplot(222)\n",
      "d = plt.hist(df.ENTRIESn_hourly - est.fittedvalues, bins=50)\n",
      "ax2.set_xlabel('Residuals', fontsize=14)\n",
      "ax2.set_ylabel('Counts', fontsize=14)\n",
      "ax2.set_title('Residual distribution', fontsize=14)\n",
      "ax3 = plt.subplot(223)\n",
      "plt.plot(est.fittedvalues, np.abs(df.ENTRIESn_hourly-est.fittedvalues), 'b.', ms=1)\n",
      "ax3.set_xlabel('Predicted Ridership', fontsize=14)\n",
      "ax3.set_ylabel('Residuals (absolute values)', fontsize=14)\n",
      "ax3.set_title('Residuals abs. values vs predicted values', fontsize=14)\n",
      "ax4 = plt.subplot(224)\n",
      "plt.plot(df.ENTRIESn_hourly-est.fittedvalues, 'b.', ms=1)\n",
      "ax4.set_xlabel('Dataset row number', fontsize=14)\n",
      "ax4.set_ylabel('Residuals', fontsize=14)\n",
      "ax4.set_title('Residuals', fontsize=14)\n",
      "plt.suptitle('Residuals Analysis', fontsize=16)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 39,
       "text": [
        "<matplotlib.text.Text at 0x3aaa7208>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l9PK+AsyMtvtvwH3RAaojbepjZkcSdrr3Z84YHZR+Qzjw7EJ4Lx6KQvIVwHTgbmDPLL9b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\nddz3kHoNHdqB7t2hogK6dIFFiyKxT7fOr1gBnTu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gAR9/DO3apZg3D8rKCth552rmzKmm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\n16KF4wlgZ9b8H+oCLCL62GeWlyXlS5Jt5mWVZW+7sFEqQsPPc0nLdou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JnOvuE939OeAK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\nGfNHjkwgHqeImJsDl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5VX8u+RETcgFBegRILl3L6dwvuOO3ju4cNs52uucW3T1sY2eegh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\nozJ7gJOBGSzVcLw9u7r3RM62uKzjOI5TAy7oC0LW6xgLxkDszc1mvoHy8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IqBIHZjkX/n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       "text": [
        "<matplotlib.figure.Figure at 0x3ae12470>"
       ]
      }
     ],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Subset data: select only the station which has the highest total value for Entries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_34STPS=df.ix[df.groupby('station')['ENTRIESn'].max().idxmax()]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_34STPS.groupby('rain').describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>ENTRIESn</th>\n",
        "      <th>ENTRIESn_hourly</th>\n",
        "      <th>EXITSn</th>\n",
        "      <th>EXITSn_hourly</th>\n",
        "      <th>WCI</th>\n",
        "      <th>day</th>\n",
        "      <th>day_week</th>\n",
        "      <th>dist_CNTR</th>\n",
        "      <th>dist_ws</th>\n",
        "      <th>fog</th>\n",
        "      <th>...</th>\n",
        "      <th>meanwspdi</th>\n",
        "      <th>precipi</th>\n",
        "      <th>pressurei</th>\n",
        "      <th>tempi</th>\n",
        "      <th>weather_lat</th>\n",
        "      <th>weather_lon</th>\n",
        "      <th>week</th>\n",
        "      <th>weekday</th>\n",
        "      <th>weekend</th>\n",
        "      <th>wspdi</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rain</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">0</th>\n",
        "      <th>count</th>\n",
        "      <td> 1.440000e+02</td>\n",
        "      <td>   144.000000</td>\n",
        "      <td> 1.440000e+02</td>\n",
        "      <td>   144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 1.440000e+02</td>\n",
        "      <td> 1.440000e+02</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td>...</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "      <td> 144.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 2.803489e+08</td>\n",
        "      <td> 15027.416667</td>\n",
        "      <td> 1.954442e+08</td>\n",
        "      <td> 12519.861111</td>\n",
        "      <td>  36.768546</td>\n",
        "      <td>  16.125000</td>\n",
        "      <td>   3.041667</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   5.277778</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  29.998264</td>\n",
        "      <td>  64.652083</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  19.583333</td>\n",
        "      <td>   0.666667</td>\n",
        "      <td>   0.458333</td>\n",
        "      <td>   5.277778</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 2.997932e+07</td>\n",
        "      <td> 11142.103948</td>\n",
        "      <td> 2.308281e+07</td>\n",
        "      <td> 10779.394741</td>\n",
        "      <td>  17.673077</td>\n",
        "      <td>   9.833491</td>\n",
        "      <td>   2.138148</td>\n",
        "      <td> 1.993756e-08</td>\n",
        "      <td> 2.492195e-09</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   1.430721</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.126349</td>\n",
        "      <td>   8.978957</td>\n",
        "      <td>   0.000001</td>\n",
        "      <td>   0.000001</td>\n",
        "      <td>   1.445998</td>\n",
        "      <td>   0.473050</td>\n",
        "      <td>   0.500000</td>\n",
        "      <td>   3.078835</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.543819e+08</td>\n",
        "      <td>   864.000000</td>\n",
        "      <td> 1.010966e+08</td>\n",
        "      <td>   512.000000</td>\n",
        "      <td>  18.901155</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   1.550000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  29.760000</td>\n",
        "      <td>  46.900000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  17.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 2.810828e+08</td>\n",
        "      <td>  6589.750000</td>\n",
        "      <td> 1.946169e+08</td>\n",
        "      <td>  3951.750000</td>\n",
        "      <td>  25.703154</td>\n",
        "      <td>   7.750000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   4.616667</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  29.910000</td>\n",
        "      <td>  57.900000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  18.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   3.500000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 2.941029e+08</td>\n",
        "      <td> 12927.500000</td>\n",
        "      <td> 2.060031e+08</td>\n",
        "      <td>  9055.500000</td>\n",
        "      <td>  30.615331</td>\n",
        "      <td>  13.500000</td>\n",
        "      <td>   3.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   5.200000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  29.980000</td>\n",
        "      <td>  63.000000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  19.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   5.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 2.962915e+08</td>\n",
        "      <td> 20311.500000</td>\n",
        "      <td> 2.073193e+08</td>\n",
        "      <td> 18042.500000</td>\n",
        "      <td>  37.161365</td>\n",
        "      <td>  25.250000</td>\n",
        "      <td>   5.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   6.154167</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  30.080000</td>\n",
        "      <td>  71.100000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  21.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   8.100000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 3.271478e+08</td>\n",
        "      <td> 37567.000000</td>\n",
        "      <td> 2.353659e+08</td>\n",
        "      <td> 37544.000000</td>\n",
        "      <td>  86.945062</td>\n",
        "      <td>  31.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   7.700000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>  30.310000</td>\n",
        "      <td>  86.000000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  22.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>  15.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"8\" valign=\"top\">1</th>\n",
        "      <th>count</th>\n",
        "      <td> 4.200000e+01</td>\n",
        "      <td>    42.000000</td>\n",
        "      <td> 4.200000e+01</td>\n",
        "      <td>    42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td> 4.200000e+01</td>\n",
        "      <td> 4.200000e+01</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "      <td>  42.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 2.784744e+08</td>\n",
        "      <td> 17859.476190</td>\n",
        "      <td> 1.948843e+08</td>\n",
        "      <td> 14570.714286</td>\n",
        "      <td>  36.497482</td>\n",
        "      <td>  15.571429</td>\n",
        "      <td>   2.571429</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.047619</td>\n",
        "      <td>...</td>\n",
        "      <td>   6.402381</td>\n",
        "      <td>   0.072908</td>\n",
        "      <td>  29.886905</td>\n",
        "      <td>  59.080952</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  19.571429</td>\n",
        "      <td>   0.857143</td>\n",
        "      <td>   0.285714</td>\n",
        "      <td>   6.402381</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 3.453773e+07</td>\n",
        "      <td> 12932.239799</td>\n",
        "      <td> 2.487884e+07</td>\n",
        "      <td> 12584.057483</td>\n",
        "      <td>  23.448195</td>\n",
        "      <td>   5.041984</td>\n",
        "      <td>   1.862896</td>\n",
        "      <td> 1.316446e-08</td>\n",
        "      <td> 2.327170e-09</td>\n",
        "      <td>   0.215540</td>\n",
        "      <td>...</td>\n",
        "      <td>   4.176175</td>\n",
        "      <td>   0.044892</td>\n",
        "      <td>   0.137406</td>\n",
        "      <td>   5.061921</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000001</td>\n",
        "      <td>   0.737261</td>\n",
        "      <td>   0.354169</td>\n",
        "      <td>   0.457230</td>\n",
        "      <td>   5.252896</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td> 1.804137e+08</td>\n",
        "      <td>   808.000000</td>\n",
        "      <td> 1.173701e+08</td>\n",
        "      <td>   633.000000</td>\n",
        "      <td>  13.449804</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.010000</td>\n",
        "      <td>  29.560000</td>\n",
        "      <td>  48.200000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  18.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td> 2.740716e+08</td>\n",
        "      <td>  8746.500000</td>\n",
        "      <td> 1.908343e+08</td>\n",
        "      <td>  4418.250000</td>\n",
        "      <td>  20.838928</td>\n",
        "      <td>  15.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   3.650000</td>\n",
        "      <td>   0.068291</td>\n",
        "      <td>  29.775000</td>\n",
        "      <td>  55.900000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  19.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   0.875000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td> 2.952422e+08</td>\n",
        "      <td> 13774.500000</td>\n",
        "      <td> 2.057093e+08</td>\n",
        "      <td>  8843.500000</td>\n",
        "      <td>  25.507070</td>\n",
        "      <td>  17.000000</td>\n",
        "      <td>   2.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   4.816667</td>\n",
        "      <td>   0.068291</td>\n",
        "      <td>  29.945000</td>\n",
        "      <td>  59.000000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  20.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>   5.800000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 2.959409e+08</td>\n",
        "      <td> 31920.000000</td>\n",
        "      <td> 2.071767e+08</td>\n",
        "      <td> 24543.250000</td>\n",
        "      <td>  63.648545</td>\n",
        "      <td>  19.000000</td>\n",
        "      <td>   4.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   0.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>   9.416667</td>\n",
        "      <td>   0.068291</td>\n",
        "      <td>  29.980000</td>\n",
        "      <td>  62.475000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  20.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   9.200000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 3.465691e+08</td>\n",
        "      <td> 39938.000000</td>\n",
        "      <td> 2.460307e+08</td>\n",
        "      <td> 40668.000000</td>\n",
        "      <td>  76.347620</td>\n",
        "      <td>  20.000000</td>\n",
        "      <td>   6.000000</td>\n",
        "      <td> 1.087667e+00</td>\n",
        "      <td> 1.561150e-01</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>...</td>\n",
        "      <td>  13.266667</td>\n",
        "      <td>   0.210000</td>\n",
        "      <td>  30.040000</td>\n",
        "      <td>  73.000000</td>\n",
        "      <td>  40.743515</td>\n",
        "      <td> -73.993007</td>\n",
        "      <td>  20.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>   1.000000</td>\n",
        "      <td>  17.300000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>16 rows \u00d7 27 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "                ENTRIESn  ENTRIESn_hourly        EXITSn  EXITSn_hourly  \\\n",
        "rain                                                                     \n",
        "0    count  1.440000e+02       144.000000  1.440000e+02     144.000000   \n",
        "     mean   2.803489e+08     15027.416667  1.954442e+08   12519.861111   \n",
        "     std    2.997932e+07     11142.103948  2.308281e+07   10779.394741   \n",
        "     min    1.543819e+08       864.000000  1.010966e+08     512.000000   \n",
        "     25%    2.810828e+08      6589.750000  1.946169e+08    3951.750000   \n",
        "     50%    2.941029e+08     12927.500000  2.060031e+08    9055.500000   \n",
        "     75%    2.962915e+08     20311.500000  2.073193e+08   18042.500000   \n",
        "     max    3.271478e+08     37567.000000  2.353659e+08   37544.000000   \n",
        "1    count  4.200000e+01        42.000000  4.200000e+01      42.000000   \n",
        "     mean   2.784744e+08     17859.476190  1.948843e+08   14570.714286   \n",
        "     std    3.453773e+07     12932.239799  2.487884e+07   12584.057483   \n",
        "     min    1.804137e+08       808.000000  1.173701e+08     633.000000   \n",
        "     25%    2.740716e+08      8746.500000  1.908343e+08    4418.250000   \n",
        "     50%    2.952422e+08     13774.500000  2.057093e+08    8843.500000   \n",
        "     75%    2.959409e+08     31920.000000  2.071767e+08   24543.250000   \n",
        "     max    3.465691e+08     39938.000000  2.460307e+08   40668.000000   \n",
        "\n",
        "                   WCI         day    day_week     dist_CNTR       dist_ws  \\\n",
        "rain                                                                         \n",
        "0    count  144.000000  144.000000  144.000000  1.440000e+02  1.440000e+02   \n",
        "     mean    36.768546   16.125000    3.041667  1.087667e+00  1.561150e-01   \n",
        "     std     17.673077    9.833491    2.138148  1.993756e-08  2.492195e-09   \n",
        "     min     18.901155    1.000000    0.000000  1.087667e+00  1.561150e-01   \n",
        "     25%     25.703154    7.750000    1.000000  1.087667e+00  1.561150e-01   \n",
        "     50%     30.615331   13.500000    3.000000  1.087667e+00  1.561150e-01   \n",
        "     75%     37.161365   25.250000    5.000000  1.087667e+00  1.561150e-01   \n",
        "     max     86.945062   31.000000    6.000000  1.087667e+00  1.561150e-01   \n",
        "1    count   42.000000   42.000000   42.000000  4.200000e+01  4.200000e+01   \n",
        "     mean    36.497482   15.571429    2.571429  1.087667e+00  1.561150e-01   \n",
        "     std     23.448195    5.041984    1.862896  1.316446e-08  2.327170e-09   \n",
        "     min     13.449804    4.000000    0.000000  1.087667e+00  1.561150e-01   \n",
        "     25%     20.838928   15.000000    1.000000  1.087667e+00  1.561150e-01   \n",
        "     50%     25.507070   17.000000    2.000000  1.087667e+00  1.561150e-01   \n",
        "     75%     63.648545   19.000000    4.000000  1.087667e+00  1.561150e-01   \n",
        "     max     76.347620   20.000000    6.000000  1.087667e+00  1.561150e-01   \n",
        "\n",
        "                   fog     ...       meanwspdi     precipi   pressurei  \\\n",
        "rain                       ...                                           \n",
        "0    count  144.000000     ...      144.000000  144.000000  144.000000   \n",
        "     mean     0.000000     ...        5.277778    0.000000   29.998264   \n",
        "     std      0.000000     ...        1.430721    0.000000    0.126349   \n",
        "     min      0.000000     ...        1.550000    0.000000   29.760000   \n",
        "     25%      0.000000     ...        4.616667    0.000000   29.910000   \n",
        "     50%      0.000000     ...        5.200000    0.000000   29.980000   \n",
        "     75%      0.000000     ...        6.154167    0.000000   30.080000   \n",
        "     max      0.000000     ...        7.700000    0.000000   30.310000   \n",
        "1    count   42.000000     ...       42.000000   42.000000   42.000000   \n",
        "     mean     0.047619     ...        6.402381    0.072908   29.886905   \n",
        "     std      0.215540     ...        4.176175    0.044892    0.137406   \n",
        "     min      0.000000     ...        0.000000    0.010000   29.560000   \n",
        "     25%      0.000000     ...        3.650000    0.068291   29.775000   \n",
        "     50%      0.000000     ...        4.816667    0.068291   29.945000   \n",
        "     75%      0.000000     ...        9.416667    0.068291   29.980000   \n",
        "     max      1.000000     ...       13.266667    0.210000   30.040000   \n",
        "\n",
        "                 tempi  weather_lat  weather_lon        week     weekday  \\\n",
        "rain                                                                       \n",
        "0    count  144.000000   144.000000   144.000000  144.000000  144.000000   \n",
        "     mean    64.652083    40.743515   -73.993007   19.583333    0.666667   \n",
        "     std      8.978957     0.000001     0.000001    1.445998    0.473050   \n",
        "     min     46.900000    40.743515   -73.993007   17.000000    0.000000   \n",
        "     25%     57.900000    40.743515   -73.993007   18.000000    0.000000   \n",
        "     50%     63.000000    40.743515   -73.993007   19.000000    1.000000   \n",
        "     75%     71.100000    40.743515   -73.993007   21.000000    1.000000   \n",
        "     max     86.000000    40.743515   -73.993007   22.000000    1.000000   \n",
        "1    count   42.000000    42.000000    42.000000   42.000000   42.000000   \n",
        "     mean    59.080952    40.743515   -73.993007   19.571429    0.857143   \n",
        "     std      5.061921     0.000000     0.000001    0.737261    0.354169   \n",
        "     min     48.200000    40.743515   -73.993007   18.000000    0.000000   \n",
        "     25%     55.900000    40.743515   -73.993007   19.000000    1.000000   \n",
        "     50%     59.000000    40.743515   -73.993007   20.000000    1.000000   \n",
        "     75%     62.475000    40.743515   -73.993007   20.000000    1.000000   \n",
        "     max     73.000000    40.743515   -73.993007   20.000000    1.000000   \n",
        "\n",
        "               weekend       wspdi  \n",
        "rain                                \n",
        "0    count  144.000000  144.000000  \n",
        "     mean     0.458333    5.277778  \n",
        "     std      0.500000    3.078835  \n",
        "     min      0.000000    0.000000  \n",
        "     25%      0.000000    3.500000  \n",
        "     50%      0.000000    5.800000  \n",
        "     75%      1.000000    8.100000  \n",
        "     max      1.000000   15.000000  \n",
        "1    count   42.000000   42.000000  \n",
        "     mean     0.285714    6.402381  \n",
        "     std      0.457230    5.252896  \n",
        "     min      0.000000    0.000000  \n",
        "     25%      0.000000    0.875000  \n",
        "     50%      0.000000    5.800000  \n",
        "     75%      1.000000    9.200000  \n",
        "     max      1.000000   17.300000  \n",
        "\n",
        "[16 rows x 27 columns]"
       ]
      }
     ],
     "prompt_number": 42
    }
   ],
   "metadata": {}
  }
 ]
}