Explore Turnstile Using Interact.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Explore subway data using simple interactive widgets "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy as np\n",
      "\n",
      "# Seaborn is a visualization library on top of matplotlib.\n",
      "import seaborn as sns\n",
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# read input data - version 2\n",
      "df = pd.read_csv(\"turnstile_data_master_with_weather_v2.csv\", parse_dates=['datetime'])\n",
      "df.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>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": 2,
       "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": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "station_group = df.groupby([\"station\"], as_index=False)\n",
      "# group by station and aggregate to obtain average ridership per station\n",
      "ordered = station_group[\"station\", \"ENTRIESn_hourly\"].aggregate(np.mean).sort(\"ENTRIESn_hourly\")\n",
      "ordered.head()"
     ],
     "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>station</th>\n",
        "      <th>ENTRIESn_hourly</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>76 </th>\n",
        "      <td> AQUEDUCT TRACK</td>\n",
        "      <td>   0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>86 </th>\n",
        "      <td>    BEACH 36 ST</td>\n",
        "      <td>  27.080460</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>172</th>\n",
        "      <td>  ORCHARD BEACH</td>\n",
        "      <td>  85.897727</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>88 </th>\n",
        "      <td>    BEACH 60 ST</td>\n",
        "      <td> 117.939024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>87 </th>\n",
        "      <td>    BEACH 44 ST</td>\n",
        "      <td> 175.267857</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "            station  ENTRIESn_hourly\n",
        "76   AQUEDUCT TRACK         0.000000\n",
        "86      BEACH 36 ST        27.080460\n",
        "172   ORCHARD BEACH        85.897727\n",
        "88      BEACH 60 ST       117.939024\n",
        "87      BEACH 44 ST       175.267857"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_stations = list(ordered.tail()[\"station\"])\n",
      "print top_stations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['ROOSEVELT AVE', '34 ST-HERALD SQ', 'MAIN ST', '42 ST-GRD CNTRL', '59 ST-COLUMBUS']\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Define procedures to produce visualization"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_station_data(station_name):\n",
      "    \"\"\"Filter subway dataframe for a particular station.\n",
      "    Return two Pandas Series with ridership data - one for rain and one for no-rain.\n",
      "    Prints number of elements in each of the series. \n",
      "    This is a helper function for visualization.\n",
      "    \"\"\"\n",
      "    station_rain = df[ (df[\"station\"] == station_name) & (df[\"rain\"] == 1) ][\"ENTRIESn_hourly\"]\n",
      "    station_no_rain = df[ (df[\"station\"] == station_name) & (df[\"rain\"] == 0) ][\"ENTRIESn_hourly\"]\n",
      "    \n",
      "    print \"RAIN number of elements: \", str(len(station_rain))\n",
      "    print \"NO RAIN number of elements: \", str(len(station_no_rain))\n",
      "    return station_rain, station_no_rain\n",
      "\n",
      "def histogram_vis(station_name):\n",
      "    \"\"\"Produce a figure with two histograms for the particular station. \n",
      "    One histogram for rain and one for no-rain.\n",
      "    \"\"\"\n",
      "    station_rain, station_no_rain = get_station_data(station_name)\n",
      "    \n",
      "    # find maximum ridership value for this station - used for x-axis limits\n",
      "    max_value = max(station_rain.max(), station_no_rain.max())\n",
      "    \n",
      "    # create figure and axes\n",
      "    fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(14, 6))\n",
      "    \n",
      "    # produce histogram\n",
      "    # use ax argument to specify axis explicitly\n",
      "    station_rain.hist(bins=20, ax=ax0)\n",
      "    ax0.set_xlim(-1, max_value*1.1)\n",
      "    ax0.set_title(\"Ridership with RAIN for \" + station_name)\n",
      "    \n",
      "    station_no_rain.hist(bins=20, ax=ax1)\n",
      "    ax1.set_xlim(-1, max_value*1.1)\n",
      "    ax1.set_title(\"Ridership with NO RAIN for \" + station_name)\n",
      "\n",
      "histogram_vis(\"WORLD TRADE CTR\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "RAIN number of elements:  42\n",
        "NO RAIN number of elements:  144\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GTtpZl9hnqt/rsi5s5+BYDG2UJKlZu4Tl48AnI+Ib1fOTHA4mSZIkaa5Mm7Bk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\nSZIkSZIkSZIkSZIkSZIWp/8fNt3H8qvw0CYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1098b0c50>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Using IPython interactive features to explore the data in an interactive way"
     ]
    },
    {
     "cell_type": "heading",
     "level": 6,
     "metadata": {},
     "source": [
      "In order to use interactive features, you need to download this notebook and run it on your computer. Running it online is not going to give you access to interactive features. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.html.widgets import interact\n",
      "import IPython\n",
      "%matplotlib inline\n",
      "\n",
      "# Interactive components available starting with IPython 2.0\n",
      "IPython.__version__"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "'2.3.0'"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i1 = interact(histogram_vis, station_name=top_stations)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "RAIN number of elements:  42\n",
        "NO RAIN number of elements:  144\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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1s29FxPGZeS1wMvDFdixkamqa0dGd7Zh1y42NTTTdvt6+NrPMRpbXqOHhwabX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\nJEmSJEmSJEmSJEmSJEmSJGn5/X/XKbKJFNtOagAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1038956d0>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def compare_vis(station_name, type_vis):\n",
      "    \"\"\"Produce two visualizations comparing ridership for a given station.\n",
      "    type_vis == \"violin\", then the two visualizations are violin plots. \n",
      "    type_vis == \"box\", then the two visualizations are box plots\n",
      "    Uses Seaborn to produce plots. \n",
      "    \"\"\"\n",
      "    station_rain, station_no_rain = get_station_data(station_name)\n",
      "    \n",
      "    fig, ax0 = plt.subplots(ncols=1, figsize=(7, 5))\n",
      "    ax0.set_title(\"Compare Ridership for \" + station_name)\n",
      "    if type_vis == \"box\":\n",
      "        sns.boxplot([station_rain, station_no_rain], names=[\"RAIN\", \"NO RAIN\"], ax=ax0)\n",
      "    elif type_vis == \"violin\":\n",
      "        # bw sets the binwidth of the kernel density estimator for each of the violins\n",
      "        sns.violinplot([station_rain, station_no_rain], names=[\"RAIN\", \"NO RAIN\"], bw=0.3, ax=ax0)\n",
      "\n",
      "compare_vis(\"WORLD TRADE CTR\", \"violin\")   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "RAIN number of elements:  42\n",
        "NO RAIN number of elements:  144\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3ux2oKGXcmHHExsb6OTIhnHMuuXUtpBlG74lPOKbX5Ka1btBa19sJ6QWsklfn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\nfFR4jklxgstw8e2pc6itr+eF55c5HY7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       "text": [
        "<matplotlib.figure.Figure at 0x10e66f350>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "i2 = interact(compare_vis, station_name = top_stations, type_vis = [\"box\", \"violin\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "RAIN number of elements:  42\n",
        "NO RAIN number of elements:  144\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10cc9f3d0>"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}