ML_airlines_joyce.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Airline Delays\n",
    "## Part 1: Preparing Data for Analysis\n",
    "In this part, we will clean the data and visualize some of the graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from __future__ import division\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path=r'C:\\Users\\antho\\Documents\\airline-delay-master'\n",
    "flt2016 = pd.read_csv(path+r'\\airline_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This part of the code is taken from http://ddowey.github.io/cs109-Final-Project/#!index.md\n",
    "\n",
    "######################################\n",
    "######################################\n",
    "# Remove border function taken from CS109 2013 \n",
    "######################################\n",
    "def remove_border(axes=None, top=False, right=False, left=True, bottom=True):\n",
    "    \"\"\"\n",
    "    Minimize chartjunk by stripping out unnecessary plot borders and axis ticks\n",
    "    \n",
    "    The top/right/left/bottom keywords toggle whether the corresponding plot border is drawn\n",
    "    \"\"\"\n",
    "    ax = axes or plt.gca()\n",
    "    ax.spines['top'].set_visible(top)\n",
    "    ax.spines['right'].set_visible(right)\n",
    "    ax.spines['left'].set_visible(left)\n",
    "    ax.spines['bottom'].set_visible(bottom)\n",
    "    \n",
    "    #turn off all ticks\n",
    "    ax.yaxis.set_ticks_position('none')\n",
    "    ax.xaxis.set_ticks_position('none')\n",
    "    \n",
    "    #now re-enable visibles\n",
    "    if top:\n",
    "        ax.xaxis.tick_top()\n",
    "    if bottom:\n",
    "        ax.xaxis.tick_bottom()\n",
    "    if left:\n",
    "        ax.yaxis.tick_left()\n",
    "    if right:\n",
    "        ax.yaxis.tick_right()\n",
    "        \n",
    "pd.set_option('display.width', 500)\n",
    "pd.set_option('display.max_columns', 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is massive. Let's look at the first 5 rows to determine the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>ArrTime</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>ActualElapsedTime</th>\n",
       "      <th>CRSElapsedTime</th>\n",
       "      <th>AirTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Distance</th>\n",
       "      <th>TaxiIn</th>\n",
       "      <th>TaxiOut</th>\n",
       "      <th>Cancelled</th>\n",
       "      <th>Diverted</th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7009728.0</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>6.873482e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>6.858079e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>6.855029e+06</td>\n",
       "      <td>7.008884e+06</td>\n",
       "      <td>6.855029e+06</td>\n",
       "      <td>6.855029e+06</td>\n",
       "      <td>6.873482e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>6.858079e+06</td>\n",
       "      <td>6.872670e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>7.009728e+06</td>\n",
       "      <td>1.524735e+06</td>\n",
       "      <td>1.524735e+06</td>\n",
       "      <td>1.524735e+06</td>\n",
       "      <td>1.524735e+06</td>\n",
       "      <td>1.524735e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.375130e+00</td>\n",
       "      <td>1.572801e+01</td>\n",
       "      <td>3.924182e+00</td>\n",
       "      <td>1.333830e+03</td>\n",
       "      <td>1.326086e+03</td>\n",
       "      <td>1.481258e+03</td>\n",
       "      <td>1.494801e+03</td>\n",
       "      <td>2.224200e+03</td>\n",
       "      <td>1.273224e+02</td>\n",
       "      <td>1.288668e+02</td>\n",
       "      <td>1.040186e+02</td>\n",
       "      <td>8.168452e+00</td>\n",
       "      <td>9.972570e+00</td>\n",
       "      <td>7.263870e+02</td>\n",
       "      <td>6.860852e+00</td>\n",
       "      <td>1.645305e+01</td>\n",
       "      <td>1.960618e-02</td>\n",
       "      <td>2.463006e-03</td>\n",
       "      <td>1.577206e+01</td>\n",
       "      <td>3.039031e+00</td>\n",
       "      <td>1.716462e+01</td>\n",
       "      <td>7.497434e-02</td>\n",
       "      <td>2.077098e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.406737e+00</td>\n",
       "      <td>8.797068e+00</td>\n",
       "      <td>1.988259e+00</td>\n",
       "      <td>4.780689e+02</td>\n",
       "      <td>4.642509e+02</td>\n",
       "      <td>5.052251e+02</td>\n",
       "      <td>4.826728e+02</td>\n",
       "      <td>1.961716e+03</td>\n",
       "      <td>7.018731e+01</td>\n",
       "      <td>6.940974e+01</td>\n",
       "      <td>6.743980e+01</td>\n",
       "      <td>3.850194e+01</td>\n",
       "      <td>3.531127e+01</td>\n",
       "      <td>5.621018e+02</td>\n",
       "      <td>4.933649e+00</td>\n",
       "      <td>1.133280e+01</td>\n",
       "      <td>1.386426e-01</td>\n",
       "      <td>4.956753e-02</td>\n",
       "      <td>4.009912e+01</td>\n",
       "      <td>1.950287e+01</td>\n",
       "      <td>3.189495e+01</td>\n",
       "      <td>1.837940e+00</td>\n",
       "      <td>3.925964e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>-1.410000e+02</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-5.190000e+02</td>\n",
       "      <td>-5.340000e+02</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>9.280000e+02</td>\n",
       "      <td>9.250000e+02</td>\n",
       "      <td>1.107000e+03</td>\n",
       "      <td>1.115000e+03</td>\n",
       "      <td>6.220000e+02</td>\n",
       "      <td>7.700000e+01</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>5.500000e+01</td>\n",
       "      <td>-1.000000e+01</td>\n",
       "      <td>-4.000000e+00</td>\n",
       "      <td>3.250000e+02</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.000000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>1.325000e+03</td>\n",
       "      <td>1.320000e+03</td>\n",
       "      <td>1.512000e+03</td>\n",
       "      <td>1.517000e+03</td>\n",
       "      <td>1.571000e+03</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>1.100000e+02</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>-2.000000e+00</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>5.810000e+02</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.400000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>2.300000e+01</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>1.728000e+03</td>\n",
       "      <td>1.715000e+03</td>\n",
       "      <td>1.909000e+03</td>\n",
       "      <td>1.907000e+03</td>\n",
       "      <td>3.518000e+03</td>\n",
       "      <td>1.570000e+02</td>\n",
       "      <td>1.590000e+02</td>\n",
       "      <td>1.320000e+02</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>9.540000e+02</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>1.900000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.600000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.100000e+01</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>2.600000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2008.0</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>3.100000e+01</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>2.400000e+03</td>\n",
       "      <td>2.359000e+03</td>\n",
       "      <td>2.400000e+03</td>\n",
       "      <td>2.400000e+03</td>\n",
       "      <td>9.743000e+03</td>\n",
       "      <td>1.379000e+03</td>\n",
       "      <td>1.435000e+03</td>\n",
       "      <td>1.350000e+03</td>\n",
       "      <td>2.461000e+03</td>\n",
       "      <td>2.467000e+03</td>\n",
       "      <td>4.962000e+03</td>\n",
       "      <td>3.080000e+02</td>\n",
       "      <td>4.290000e+02</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.436000e+03</td>\n",
       "      <td>1.352000e+03</td>\n",
       "      <td>1.357000e+03</td>\n",
       "      <td>3.920000e+02</td>\n",
       "      <td>1.316000e+03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Year         Month    DayofMonth     DayOfWeek       DepTime    CRSDepTime       ArrTime    CRSArrTime     FlightNum  ActualElapsedTime  CRSElapsedTime       AirTime      ArrDelay      DepDelay      Distance        TaxiIn       TaxiOut     Cancelled      Diverted  CarrierDelay  WeatherDelay      NASDelay  SecurityDelay  LateAircraftDelay\n",
       "count  7009728.0  7.009728e+06  7.009728e+06  7.009728e+06  6.873482e+06  7.009728e+06  6.858079e+06  7.009728e+06  7.009728e+06       6.855029e+06    7.008884e+06  6.855029e+06  6.855029e+06  6.873482e+06  7.009728e+06  6.858079e+06  6.872670e+06  7.009728e+06  7.009728e+06  1.524735e+06  1.524735e+06  1.524735e+06   1.524735e+06       1.524735e+06\n",
       "mean      2008.0  6.375130e+00  1.572801e+01  3.924182e+00  1.333830e+03  1.326086e+03  1.481258e+03  1.494801e+03  2.224200e+03       1.273224e+02    1.288668e+02  1.040186e+02  8.168452e+00  9.972570e+00  7.263870e+02  6.860852e+00  1.645305e+01  1.960618e-02  2.463006e-03  1.577206e+01  3.039031e+00  1.716462e+01   7.497434e-02       2.077098e+01\n",
       "std          0.0  3.406737e+00  8.797068e+00  1.988259e+00  4.780689e+02  4.642509e+02  5.052251e+02  4.826728e+02  1.961716e+03       7.018731e+01    6.940974e+01  6.743980e+01  3.850194e+01  3.531127e+01  5.621018e+02  4.933649e+00  1.133280e+01  1.386426e-01  4.956753e-02  4.009912e+01  1.950287e+01  3.189495e+01   1.837940e+00       3.925964e+01\n",
       "min       2008.0  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  0.000000e+00  1.000000e+00  0.000000e+00  1.000000e+00       1.200000e+01   -1.410000e+02  0.000000e+00 -5.190000e+02 -5.340000e+02  1.100000e+01  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   0.000000e+00       0.000000e+00\n",
       "25%       2008.0  3.000000e+00  8.000000e+00  2.000000e+00  9.280000e+02  9.250000e+02  1.107000e+03  1.115000e+03  6.220000e+02       7.700000e+01    8.000000e+01  5.500000e+01 -1.000000e+01 -4.000000e+00  3.250000e+02  4.000000e+00  1.000000e+01  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00   0.000000e+00       0.000000e+00\n",
       "50%       2008.0  6.000000e+00  1.600000e+01  4.000000e+00  1.325000e+03  1.320000e+03  1.512000e+03  1.517000e+03  1.571000e+03       1.100000e+02    1.100000e+02  8.600000e+01 -2.000000e+00 -1.000000e+00  5.810000e+02  6.000000e+00  1.400000e+01  0.000000e+00  0.000000e+00  0.000000e+00  0.000000e+00  6.000000e+00   0.000000e+00       0.000000e+00\n",
       "75%       2008.0  9.000000e+00  2.300000e+01  6.000000e+00  1.728000e+03  1.715000e+03  1.909000e+03  1.907000e+03  3.518000e+03       1.570000e+02    1.590000e+02  1.320000e+02  1.200000e+01  8.000000e+00  9.540000e+02  8.000000e+00  1.900000e+01  0.000000e+00  0.000000e+00  1.600000e+01  0.000000e+00  2.100000e+01   0.000000e+00       2.600000e+01\n",
       "max       2008.0  1.200000e+01  3.100000e+01  7.000000e+00  2.400000e+03  2.359000e+03  2.400000e+03  2.400000e+03  9.743000e+03       1.379000e+03    1.435000e+03  1.350000e+03  2.461000e+03  2.467000e+03  4.962000e+03  3.080000e+02  4.290000e+02  1.000000e+00  1.000000e+00  2.436000e+03  1.352000e+03  1.357000e+03   3.920000e+02       1.316000e+03"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we could do use the DataFrame.info() to check the columns. See below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "      <th>DayOfWeek</th>\n",
       "      <th>DepTime</th>\n",
       "      <th>CRSDepTime</th>\n",
       "      <th>ArrTime</th>\n",
       "      <th>CRSArrTime</th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>FlightNum</th>\n",
       "      <th>TailNum</th>\n",
       "      <th>ActualElapsedTime</th>\n",
       "      <th>CRSElapsedTime</th>\n",
       "      <th>AirTime</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Distance</th>\n",
       "      <th>TaxiIn</th>\n",
       "      <th>TaxiOut</th>\n",
       "      <th>Cancelled</th>\n",
       "      <th>CancellationCode</th>\n",
       "      <th>Diverted</th>\n",
       "      <th>CarrierDelay</th>\n",
       "      <th>WeatherDelay</th>\n",
       "      <th>NASDelay</th>\n",
       "      <th>SecurityDelay</th>\n",
       "      <th>LateAircraftDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>1955</td>\n",
       "      <td>2211.0</td>\n",
       "      <td>2225</td>\n",
       "      <td>WN</td>\n",
       "      <td>335</td>\n",
       "      <td>N712SW</td>\n",
       "      <td>128.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>TPA</td>\n",
       "      <td>810</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>754.0</td>\n",
       "      <td>735</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>1000</td>\n",
       "      <td>WN</td>\n",
       "      <td>3231</td>\n",
       "      <td>N772SW</td>\n",
       "      <td>128.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>TPA</td>\n",
       "      <td>810</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>628.0</td>\n",
       "      <td>620</td>\n",
       "      <td>804.0</td>\n",
       "      <td>750</td>\n",
       "      <td>WN</td>\n",
       "      <td>448</td>\n",
       "      <td>N428WN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>515</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>926.0</td>\n",
       "      <td>930</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>1100</td>\n",
       "      <td>WN</td>\n",
       "      <td>1746</td>\n",
       "      <td>N612SW</td>\n",
       "      <td>88.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>515</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1829.0</td>\n",
       "      <td>1755</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>1925</td>\n",
       "      <td>WN</td>\n",
       "      <td>3920</td>\n",
       "      <td>N464WN</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>515</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  DayofMonth  DayOfWeek  DepTime  CRSDepTime  ArrTime  CRSArrTime UniqueCarrier  FlightNum TailNum  ActualElapsedTime  CRSElapsedTime  AirTime  ArrDelay  DepDelay Origin Dest  Distance  TaxiIn  TaxiOut  Cancelled CancellationCode  Diverted  CarrierDelay  WeatherDelay  NASDelay  SecurityDelay  LateAircraftDelay\n",
       "0  2008      1           3          4   2003.0        1955   2211.0        2225            WN        335  N712SW              128.0           150.0    116.0     -14.0       8.0    IAD  TPA       810     4.0      8.0          0              NaN         0           NaN           NaN       NaN            NaN                NaN\n",
       "1  2008      1           3          4    754.0         735   1002.0        1000            WN       3231  N772SW              128.0           145.0    113.0       2.0      19.0    IAD  TPA       810     5.0     10.0          0              NaN         0           NaN           NaN       NaN            NaN                NaN\n",
       "2  2008      1           3          4    628.0         620    804.0         750            WN        448  N428WN               96.0            90.0     76.0      14.0       8.0    IND  BWI       515     3.0     17.0          0              NaN         0           NaN           NaN       NaN            NaN                NaN\n",
       "3  2008      1           3          4    926.0         930   1054.0        1100            WN       1746  N612SW               88.0            90.0     78.0      -6.0      -4.0    IND  BWI       515     3.0      7.0          0              NaN         0           NaN           NaN       NaN            NaN                NaN\n",
       "4  2008      1           3          4   1829.0        1755   1959.0        1925            WN       3920  N464WN               90.0            90.0     77.0      34.0      34.0    IND  BWI       515     3.0     10.0          0              NaN         0           2.0           0.0       0.0            0.0               32.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets see where the no-values or NaNs are located and clean them up. Let's remove rows with NaNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7009728 entries, 0 to 7009727\n",
      "Data columns (total 29 columns):\n",
      "Year                 int64\n",
      "Month                int64\n",
      "DayofMonth           int64\n",
      "DayOfWeek            int64\n",
      "DepTime              float64\n",
      "CRSDepTime           int64\n",
      "ArrTime              float64\n",
      "CRSArrTime           int64\n",
      "UniqueCarrier        object\n",
      "FlightNum            int64\n",
      "TailNum              object\n",
      "ActualElapsedTime    float64\n",
      "CRSElapsedTime       float64\n",
      "AirTime              float64\n",
      "ArrDelay             float64\n",
      "DepDelay             float64\n",
      "Origin               object\n",
      "Dest                 object\n",
      "Distance             int64\n",
      "TaxiIn               float64\n",
      "TaxiOut              float64\n",
      "Cancelled            int64\n",
      "CancellationCode     object\n",
      "Diverted             int64\n",
      "CarrierDelay         float64\n",
      "WeatherDelay         float64\n",
      "NASDelay             float64\n",
      "SecurityDelay        float64\n",
      "LateAircraftDelay    float64\n",
      "dtypes: float64(14), int64(10), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "flt2016.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_total_rows = len(flt2016)\n",
    "flt2016.dropna(subset = ['UniqueCarrier','Origin','Dest','CRSDepTime','CRSArrTime','ArrDelay','CRSElapsedTime','Distance'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data retained: 97.7930812722 %\n"
     ]
    }
   ],
   "source": [
    "df_data_retained = len(flt2016)/data_total_rows * 100\n",
    "print('Data retained: '+ str(df_data_retained)+' %')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6855029 entries, 0 to 7009727\n",
      "Data columns (total 29 columns):\n",
      "Year                 int64\n",
      "Month                int64\n",
      "DayofMonth           int64\n",
      "DayOfWeek            int64\n",
      "DepTime              float64\n",
      "CRSDepTime           int64\n",
      "ArrTime              float64\n",
      "CRSArrTime           int64\n",
      "UniqueCarrier        object\n",
      "FlightNum            int64\n",
      "TailNum              object\n",
      "ActualElapsedTime    float64\n",
      "CRSElapsedTime       float64\n",
      "AirTime              float64\n",
      "ArrDelay             float64\n",
      "DepDelay             float64\n",
      "Origin               object\n",
      "Dest                 object\n",
      "Distance             int64\n",
      "TaxiIn               float64\n",
      "TaxiOut              float64\n",
      "Cancelled            int64\n",
      "CancellationCode     object\n",
      "Diverted             int64\n",
      "CarrierDelay         float64\n",
      "WeatherDelay         float64\n",
      "NASDelay             float64\n",
      "SecurityDelay        float64\n",
      "LateAircraftDelay    float64\n",
      "dtypes: float64(14), int64(10), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "flt2016.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x72e0e1d0>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ptKR7bM6/Hy9RbURkU5TJZOjUqRPTpz9Bbe0SZsx4CoA1a1bTvXuPteUGDfoGmUyGrbf+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\nSttz9rMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1af8d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### plot departure delays\n",
    "sns.set_context('notebook',font_scale=1.2)\n",
    "g = sns.distplot(a=flt2016['DepDelay'],bins=500,kde=False,norm_hist=True,\n",
    "                 hist_kws={'alpha':0.9,'linewidth':.5,'edgecolor':'k','label':'DepDelays'})\n",
    "sns.distplot(a=flt2016['ArrDelay'],bins=500,kde=False,norm_hist=True,\n",
    "                 hist_kws={'alpha':0.4,'linewidth':.5,'edgecolor':'k','label':'ArrDelays'})\n",
    "g.set(xlim=(-50,160))\n",
    "g.set(xlabel='Minutes')\n",
    "g.legend()\n",
    "g.set(title='2008 Departure And Arrival Delays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mean Arrive and Departure Delays\n",
    "# Use value counts to get the top 10 airlines that were displayed\n",
    "top10carriers = flt2016['UniqueCarrier'].value_counts().head(10).index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02Q</td>\n",
       "      <td>Titan Airways</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>04Q</td>\n",
       "      <td>Tradewind Aviation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05Q</td>\n",
       "      <td>Comlux Aviation, AG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>06Q</td>\n",
       "      <td>Master Top Linhas Aereas Ltd.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07Q</td>\n",
       "      <td>Flair Airlines Ltd.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code                    Description\n",
       "0  02Q                  Titan Airways\n",
       "1  04Q             Tradewind Aviation\n",
       "2  05Q            Comlux Aviation, AG\n",
       "3  06Q  Master Top Linhas Aereas Ltd.\n",
       "4  07Q            Flair Airlines Ltd."
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# read full name of the airlines\n",
    "carrier_key = pd.read_csv('L_UNIQUE_CARRIERS.csv')\n",
    "carrier_key.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt2016UC = flt2016[flt2016['UniqueCarrier'].isin(top10carriers)][['UniqueCarrier','ArrDelay','DepDelay','Origin','Dest']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>ArrDelay</th>\n",
       "      <th>DepDelay</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dest</th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WN</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>TPA</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>IAD</td>\n",
       "      <td>TPA</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WN</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>IND</td>\n",
       "      <td>BWI</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  UniqueCarrier  ArrDelay  DepDelay Origin Dest Code          Description\n",
       "0            WN     -14.0       8.0    IAD  TPA   WN  Southwest Airlines \n",
       "1            WN       2.0      19.0    IAD  TPA   WN  Southwest Airlines \n",
       "2            WN      14.0       8.0    IND  BWI   WN  Southwest Airlines \n",
       "3            WN      -6.0      -4.0    IND  BWI   WN  Southwest Airlines \n",
       "4            WN      34.0      34.0    IND  BWI   WN  Southwest Airlines "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016FN = pd.merge(left=flt2016UC,right=carrier_key,how='left',left_on='UniqueCarrier',right_on='Code')\n",
    "flt2016FN.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt2016temp = flt2016FN.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# greater than 30 minutees late and early are changed to 30 minutes to keep data on same scale\n",
    "flt2016temp['ArrDelay'] = flt2016temp['ArrDelay'].apply(lambda x:30 if x>=30 else x )\n",
    "flt2016temp['ArrDelay'] = flt2016temp['ArrDelay'].apply(lambda x:-30 if x<=-30 else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3d2a9b70>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JNAOusrksXPUA85fvREiWKZs4SyRi3Dz/Ls2XD6ZWZlqtVqqrN1Bbu3nK6+Tz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aGIAhlfKR+7dFzPWF5eRqNeRaV0uQhzNk0sn3odI8uy2NraRiadRKVyVUuq3W7j4vwEy8urqmVV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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1af8d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show violin plot\n",
    "sns.set_context('notebook',font_scale=1.2)\n",
    "plt.figure(figsize=(10,4))\n",
    "g = sns.violinplot(x='Description',y='ArrDelay',data=flt2016temp,palette='coolwarm')\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "g.set_ylim(-40,40)\n",
    "g.set_xlabel(xlabel='Airline')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percentage of flights delayed 30 minutes or more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt2016CD = pd.DataFrame()\n",
    "for name in list(flt2016FN['UniqueCarrier'].unique()):\n",
    "    perc_delay = len(flt2016FN[(flt2016FN['UniqueCarrier']==name) & (flt2016FN['ArrDelay']>=30)].index) / len(flt2016FN[flt2016FN['UniqueCarrier']==name].index) * 100\n",
    "    flt2016CD = flt2016CD.append({'UniqueCarrier':name, 'percent_delayed':perc_delay},ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <th>percent_delayed</th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WN</td>\n",
       "      <td>10.764329</td>\n",
       "      <td>WN</td>\n",
       "      <td>Southwest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>XE</td>\n",
       "      <td>15.549759</td>\n",
       "      <td>XE</td>\n",
       "      <td>ExpressJet Airlines  (1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OO</td>\n",
       "      <td>11.837293</td>\n",
       "      <td>OO</td>\n",
       "      <td>SkyWest Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UA</td>\n",
       "      <td>17.270463</td>\n",
       "      <td>UA</td>\n",
       "      <td>United Air Lines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US</td>\n",
       "      <td>10.354730</td>\n",
       "      <td>US</td>\n",
       "      <td>US Airways</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  UniqueCarrier  percent_delayed Code               Description\n",
       "0            WN        10.764329   WN       Southwest Airlines \n",
       "1            XE        15.549759   XE  ExpressJet Airlines  (1)\n",
       "2            OO        11.837293   OO         SkyWest Airlines \n",
       "3            UA        17.270463   UA         United Air Lines \n",
       "4            US        10.354730   US               US Airways "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016CD = pd.merge(left=flt2016CD,right=carrier_key,how='left',left_on='UniqueCarrier',right_on='Code')\n",
    "flt2016CD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt2016CD.drop(labels='UniqueCarrier',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_delayed</th>\n",
       "      <th>Code</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.650153</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.270463</td>\n",
       "      <td>UA</td>\n",
       "      <td>United Air Lines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.617379</td>\n",
       "      <td>MQ</td>\n",
       "      <td>Envoy Air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.585918</td>\n",
       "      <td>CO</td>\n",
       "      <td>Continental Air Lines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.549759</td>\n",
       "      <td>XE</td>\n",
       "      <td>ExpressJet Airlines  (1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percent_delayed Code               Description\n",
       "8        17.650153   AA        American Airlines \n",
       "3        17.270463   UA         United Air Lines \n",
       "6        15.617379   MQ                 Envoy Air\n",
       "9        15.585918   CO    Continental Air Lines \n",
       "1        15.549759   XE  ExpressJet Airlines  (1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016CD.sort_values(by='percent_delayed',ascending=False,inplace=True)\n",
    "flt2016CD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_delayed</th>\n",
       "      <th>Code</th>\n",
       "      <th>Airline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.650153</td>\n",
       "      <td>AA</td>\n",
       "      <td>American Airlines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17.270463</td>\n",
       "      <td>UA</td>\n",
       "      <td>United Air Lines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.617379</td>\n",
       "      <td>MQ</td>\n",
       "      <td>Envoy Air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>15.585918</td>\n",
       "      <td>CO</td>\n",
       "      <td>Continental Air Lines</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15.549759</td>\n",
       "      <td>XE</td>\n",
       "      <td>ExpressJet Airlines  (1)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   percent_delayed Code                   Airline\n",
       "8        17.650153   AA        American Airlines \n",
       "3        17.270463   UA         United Air Lines \n",
       "6        15.617379   MQ                 Envoy Air\n",
       "9        15.585918   CO    Continental Air Lines \n",
       "1        15.549759   XE  ExpressJet Airlines  (1)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016CD.rename(columns={'Description':'Airline'},inplace=True)\n",
    "flt2016CD.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3e27e940>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AFpSkfsBkYCfgeGAtYAowNCJuzkOtFwHLRMR/8jW9gdeBYyJitKT+wChgQ6AZuBU4KiLe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\nQBEREZE68//FCbKQLOtm9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3e254f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context('notebook',font_scale=1.5)\n",
    "color_pal=sns.color_palette(palette='OrRd_r',n_colors=len(flt2016CD.index))\n",
    "g = sns.barplot(x='percent_delayed',y='Airline',data=flt2016CD,palette=color_pal,edgecolor='black',linewidth=0.5)\n",
    "g.set_xlabel(xlabel='% Delayed')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Delays by month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6855029 entries, 0 to 7009727\n",
      "Data columns (total 29 columns):\n",
      "Year                 int64\n",
      "Month                int64\n",
      "DayofMonth           int64\n",
      "DayOfWeek            int64\n",
      "DepTime              float64\n",
      "CRSDepTime           int64\n",
      "ArrTime              float64\n",
      "CRSArrTime           int64\n",
      "UniqueCarrier        object\n",
      "FlightNum            int64\n",
      "TailNum              object\n",
      "ActualElapsedTime    float64\n",
      "CRSElapsedTime       float64\n",
      "AirTime              float64\n",
      "ArrDelay             float64\n",
      "DepDelay             float64\n",
      "Origin               object\n",
      "Dest                 object\n",
      "Distance             int64\n",
      "TaxiIn               float64\n",
      "TaxiOut              float64\n",
      "Cancelled            int64\n",
      "CancellationCode     object\n",
      "Diverted             int64\n",
      "CarrierDelay         float64\n",
      "WeatherDelay         float64\n",
      "NASDelay             float64\n",
      "SecurityDelay        float64\n",
      "LateAircraftDelay    float64\n",
      "dtypes: float64(14), int64(10), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "flt2016.info()\n",
    "flt2016CP = flt2016.copy()\n",
    "flt2016CP.drop(labels=['FlightNum','TailNum','ActualElapsedTime','TaxiIn','TaxiOut','Cancelled',\n",
    "                       'CancellationCode','Diverted','LateAircraftDelay'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_Arrdelay = pd.DataFrame()\n",
    "m_names = 'January','February','March','April','May','June','July','August','September','October','November','December'\n",
    "\n",
    "for m in list(flt2016CP['Month'].unique()):\n",
    "    delay_mean = flt2016CP[flt2016CP['Month']==m]['ArrDelay'].mean(axis=0)\n",
    "    month_Arrdelay = month_Arrdelay.append({'Month':m_names[m-1], 'Delay':delay_mean},ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x620424e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z6ScD4yWNAb4HXAz8GzgR+ECbYjMzM7MmWk3i6wOrAysB6wIfAp4BprcpLjMzMxtBq8Ve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\nchI3MzOrKSdxMzOzmnISNzMzq6n/D+k62eKpQlKIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5b980dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(context='notebook',font_scale=1.2)\n",
    "g=sns.barplot(x='Month',y='Delay',data=month_Arrdelay,color='salmon',edgecolor='black',linewidth=0.5)\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "g.set_ylabel('Delay (minutes)')\n",
    "plt.title('2016 Arrival Delays')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "month_Depdelay = pd.DataFrame()\n",
    "m_names = 'January','February','March','April','May','June','July','August','September','October','November','December'\n",
    "\n",
    "for m in list(flt2016CP['Month'].unique()):\n",
    "    delay_mean = flt2016CP[flt2016CP['Month']==m]['DepDelay'].mean(axis=0)\n",
    "    month_Depdelay = month_Depdelay.append({'Month':m_names[m-1], 'Delay':delay_mean},ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x5ba91470>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mVnOjcgcgaRfgUKAfmAUcEBG3Nd3zDWAHYEZxKiLiY10N1MzMbITKmuwlCZgE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\ntHWAt5UGwp1NWszmbWRcdlX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      "text/plain": [
       "<matplotlib.figure.Figure at 0x41fca4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_context(context='notebook',font_scale=1.2)\n",
    "g=sns.barplot(x='Month',y='Delay',data=month_Depdelay,color='salmon',edgecolor='black',linewidth=0.5)\n",
    "g.set_xticklabels(g.get_xticklabels(),rotation=90)\n",
    "g.set_ylabel('Delay (minutes)')\n",
    "plt.title('2016 Departure Delays')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Departure delays at the major cities by month - **This is also the long way to do it**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**This is the easiest way to do it!!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us focus on major airports only - ORD, BOS, SFO and LGA\n",
    "# for 2008\n",
    "flt2016ORD = flt2016CP[(flt2016CP['Origin'] == 'ORD') | (flt2016CP['Dest']=='ORD')]\n",
    "flt2016BOS = flt2016CP[(flt2016CP['Origin'] == 'BOS') | (flt2016CP['Dest']=='BOS')]\n",
    "flt2016SFO = flt2016CP[(flt2016CP['Origin'] == 'SFO') | (flt2016CP['Dest']=='SFO')]\n",
    "flt2016LGA = flt2016CP[(flt2016CP['Origin'] == 'LGA') | (flt2016CP['Dest']=='LGA')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>94059</th>\n",
       "      <th>102746</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>2008</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayofMonth</th>\n",
       "      <td>8</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayOfWeek</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepTime</th>\n",
       "      <td>1711</td>\n",
       "      <td>1813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRSDepTime</th>\n",
       "      <td>1600</td>\n",
       "      <td>1742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrTime</th>\n",
       "      <td>2031</td>\n",
       "      <td>2040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRSArrTime</th>\n",
       "      <td>1945</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UniqueCarrier</th>\n",
       "      <td>XE</td>\n",
       "      <td>XE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRSElapsedTime</th>\n",
       "      <td>165</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AirTime</th>\n",
       "      <td>106</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ArrDelay</th>\n",
       "      <td>46</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DepDelay</th>\n",
       "      <td>71</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Origin</th>\n",
       "      <td>ORD</td>\n",
       "      <td>ORD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dest</th>\n",
       "      <td>EWR</td>\n",
       "      <td>CLE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Distance</th>\n",
       "      <td>719</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CarrierDelay</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WeatherDelay</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASDelay</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SecurityDelay</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               94059  102746\n",
       "Year             2008   2008\n",
       "Month               1      1\n",
       "DayofMonth          8     25\n",
       "DayOfWeek           2      5\n",
       "DepTime          1711   1813\n",
       "CRSDepTime       1600   1742\n",
       "ArrTime          2031   2040\n",
       "CRSArrTime       1945   2004\n",
       "UniqueCarrier      XE     XE\n",
       "CRSElapsedTime    165     82\n",
       "AirTime           106     49\n",
       "ArrDelay           46     36\n",
       "DepDelay           71     31\n",
       "Origin            ORD    ORD\n",
       "Dest              EWR    CLE\n",
       "Distance          719    316\n",
       "CarrierDelay        0      0\n",
       "WeatherDelay        0      0\n",
       "NASDelay            2      5\n",
       "SecurityDelay       0      0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016ORD.head(2).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     27.146565\n",
       "1     28.316219\n",
       "2     19.577626\n",
       "3     14.475421\n",
       "4     12.085624\n",
       "5     24.961450\n",
       "6     15.545762\n",
       "7     10.956220\n",
       "8      9.643021\n",
       "9      4.818503\n",
       "10     6.815212\n",
       "11    27.155092\n",
       "Name: DepDelay, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt2016ORD[['Month','DepDelay']].groupby(by='Month').mean().reset_index().DepDelay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\seaborn\\categorical.py:2885: UserWarning: The `hline` parameter has been removed\n",
      "  warnings.warn(\"The `hline` parameter has been removed\", UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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i1q1ba+jQocrNzVVGRobeeustPfLIIwoICND69etdmRUAAABAOXK6DNSoUUML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\n6n/yl9vmZnpkOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3d2b9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", font='StixGeneral', context=\"talk\")\n",
    "Months = np.array(['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', \n",
    "                   'September', 'October', 'November', 'December'])\n",
    "\n",
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(11, 12), sharex=True, sharey=True)\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016ORD[['Month','DepDelay']].groupby('Month').mean().reset_index().DepDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax1); ax1.set_ylabel('Chicago')\n",
    "ax1.set_title('Departure delays by Month from 4 airports')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016LGA[['Month','DepDelay']].groupby('Month').mean().reset_index().DepDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax2); ax2.set_ylabel('New York')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016SFO[['Month','DepDelay']].groupby('Month').mean().reset_index().DepDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax3); ax3.set_ylabel('San Francisco')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016BOS[['Month','DepDelay']].groupby('Month').mean().reset_index().DepDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax4); ax4.set_ylabel('Boston')\n",
    "sns.despine(bottom=True)\n",
    "plt.setp(f.axes, xticklabels=Months)\n",
    "plt.tight_layout(h_pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dJF9fX+Xm5sput5d6vqSkRCtXruToAAAAAHAdKrMMDBw4UMOHD9fw4cO1cuVK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\nAJfwdnbipe4lcPLkSWVkXN/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tU5cuXdSuXTtNnz5dlmXJbrdrxowZ+uSTTzRt2jRlZGRc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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x18d0b048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(11, 12), sharex=True, sharey=True)\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016ORD[['Month','ArrDelay']].groupby('Month').mean().reset_index().ArrDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax1); ax1.set_ylabel('Chicago');\n",
    "ax1.set_title('Arrival delays by Month from 4 airports')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016LGA[['Month','ArrDelay']].groupby('Month').mean().reset_index().ArrDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax2); ax2.set_ylabel('New York')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016SFO[['Month','ArrDelay']].groupby('Month').mean().reset_index().ArrDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax3); ax3.set_ylabel('San Francisco')\n",
    "sns.barplot(x=np.arange(len(Months)),y=flt2016BOS[['Month','ArrDelay']].groupby('Month').mean().reset_index().ArrDelay, \n",
    "            ci=None, palette=\"coolwarm\", hline=0, ax=ax4); ax4.set_ylabel('Boston')\n",
    "sns.despine(bottom=True)\n",
    "plt.setp(f.axes, xticklabels=Months)\n",
    "plt.tight_layout(h_pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "flt2016ORD['DepHr'] = flt2016ORD['CRSDepTime']//100\n",
    "flt2016SFO['DepHr'] = flt2016SFO['CRSDepTime']//100\n",
    "flt2016BOS['DepHr'] = flt2016BOS['CRSDepTime']//100\n",
    "flt2016LGA['DepHr'] = flt2016LGA['CRSDepTime']//100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "ORD = flt2016ORD[['DepHr','DepDelay']].groupby('DepHr').mean().reset_index()\n",
    "SFO = flt2016SFO[['DepHr','DepDelay']].groupby('DepHr').mean().reset_index()\n",
    "BOS = flt2016BOS[['DepHr','DepDelay']].groupby('DepHr').mean().reset_index()\n",
    "LGA = flt2016LGA[['DepHr','DepDelay']].groupby('DepHr').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAICFnN69n5KSopiYGHl4eEiSChcurLZt26pChQqWhQMAAABgLacLwWeffaZu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\nubm5OnjwoCZPnmw6ilvz5s3Vvn171apl72klKSl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gxnbTFIL9+/dr5cqVeuutt/Tyyy+r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5b967860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", font='StixGeneral', context=\"talk\")\n",
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(11, 12), sharex=False, sharey=True)\n",
    "\n",
    "sns.barplot(x=ORD.DepHr, y=ORD.DepDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax1); ax1.set_ylabel('Chicago');\n",
    "ax1.set_title('Average Departure Delay by hour of day for 4 airports');ax1.set_xlabel('')\n",
    "sns.barplot(x=SFO.DepHr, y=SFO.DepDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax2); ax2.set_ylabel('San Francisco')\n",
    "ax2.set_xlabel('')\n",
    "sns.barplot(x=BOS.DepHr, y=BOS.DepDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax3); ax3.set_ylabel('Boston')\n",
    "ax3.set_xlabel('')\n",
    "sns.barplot(x=LGA.DepHr, y=LGA.DepDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax4); ax4.set_ylabel('New York')\n",
    "ax4.set_xlabel('Departure Hour')\n",
    "\n",
    "sns.despine(bottom=True)\n",
    "plt.setp(f.axes,yticklabels='')\n",
    "plt.tight_layout(h_pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\antho\\Anaconda2\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "flt2016ORD['ArrHr'] = flt2016ORD['CRSArrTime']//100\n",
    "flt2016SFO['ArrHr'] = flt2016SFO['CRSArrTime']//100\n",
    "flt2016BOS['ArrHr'] = flt2016BOS['CRSArrTime']//100\n",
    "flt2016LGA['ArrHr'] = flt2016LGA['CRSArrTime']//100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ORD = flt2016ORD[['ArrHr','ArrDelay']].groupby('ArrHr').mean().reset_index()\n",
    "SFO = flt2016SFO[['ArrHr','ArrDelay']].groupby('ArrHr').mean().reset_index()\n",
    "BOS = flt2016BOS[['ArrHr','ArrDelay']].groupby('ArrHr').mean().reset_index()\n",
    "LGA = flt2016LGA[['ArrHr','ArrDelay']].groupby('ArrHr').mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAACA9dwOBOXKlVPevHnTrdu/f7+io6OzvSkAAAAAnuH2KUMFChTQ5MmTFRQU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\nAXQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x400da2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(style=\"white\", font='StixGeneral', context=\"talk\")\n",
    "f, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1, figsize=(11, 12), sharex=False, sharey=True)\n",
    "\n",
    "sns.barplot(x=ORD.ArrHr, y=ORD.ArrDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax1); ax1.set_ylabel('Chicago');\n",
    "ax1.set_title('Average Arrival Delay by hour of day for 4 airports');ax1.set_xlabel('')\n",
    "sns.barplot(x=SFO.ArrHr, y=SFO.ArrDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax2); ax2.set_ylabel('San Francisco')\n",
    "ax2.set_xlabel('')\n",
    "sns.barplot(x=BOS.ArrHr, y=BOS.ArrDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax3); ax3.set_ylabel('Boston')\n",
    "ax3.set_xlabel('')\n",
    "sns.barplot(x=LGA.ArrHr, y=LGA.ArrDelay, ci=None, palette=\"coolwarm\", hline=0, ax=ax4); ax4.set_ylabel('New York')\n",
    "ax4.set_xlabel('Arrival Hour')\n",
    "\n",
    "sns.despine(bottom=True)\n",
    "plt.setp(f.axes,yticklabels='')\n",
    "plt.tight_layout(h_pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x400c6e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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BAACYHIEPAADA5Ah8AAAAJkfgAwAAMDkCHwAAgMkR+AAAAEyOwAcAAGByBD4A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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6204eba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dont reset index! Need the UniqueCarrier values!\n",
    "plt.figure(figsize=(20, 11), dpi=300)\n",
    "flt2016[['UniqueCarrier','ArrDelay']].groupby('UniqueCarrier').mean().plot(kind='bar',edgecolor='black',linewidth=0.5,color='green')\n",
    "plt.ylabel('Average delay in minutes');plt.xlabel('Carrier');\n",
    "plt.axhline(y=0, xmin=200, xmax=0, hold=None,color='black',linewidth=1,ls='--');\n",
    "plt.title('Mean arrival delays by carrier 2016');plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f332320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eeeUVLVu2TMHBwRo/frySkpK0Y8cOnT17VkeOHFGzZs00e/ZsjR49Wp06ddLv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\nhGig/g+lvyCsMQW4GwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4815e198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dont reset index! Need the UniqueCarrier values!\n",
    "plt.figure(figsize=(20, 11), dpi=300)\n",
    "flt2016ORD[['UniqueCarrier','ArrDelay']].groupby('UniqueCarrier').mean().plot(kind='bar',edgecolor='black',linewidth=0.5,color='green')\n",
    "plt.ylabel('Average delay in minutes');plt.xlabel('Carrier');\n",
    "plt.axhline(y=0, xmin=200, xmax=0, hold=None,color='black',linewidth=1,ls='--');\n",
    "plt.title('Mean arrival delays by carrier 2016 in Chicago');plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3f332860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2OnbsmFJSUvTzzz/LMAz16tVLtWrVKo44AAAAt4RiKXr33ntvlp/T09N1zz33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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x49230ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 11), dpi=300)\n",
    "flt2016LGA[['UniqueCarrier','ArrDelay']].groupby('UniqueCarrier').mean().plot(kind='bar',edgecolor='black',linewidth=0.5,color='pink')\n",
    "plt.ylabel('Average delay in minutes');plt.xlabel('Carrier');\n",
    "plt.axhline(y=0, xmin=200, xmax=0, hold=None,color='black',linewidth=1,ls='--');\n",
    "plt.title('Mean arrival delays by carrier 2016 in New York');plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b99a550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qfv36kv46qhcWFmbL6mwUPQAA4Li6dTXWjqPaxVUxIMCu5Tp37qzOnTsrPT1d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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x40507b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 11), dpi=300)\n",
    "flt2016SFO[['UniqueCarrier','ArrDelay']].groupby('UniqueCarrier').mean().plot(kind='bar',edgecolor='black',linewidth=0.5,color='red')\n",
    "plt.ylabel('Average delay in minutes');plt.xlabel('Carrier');\n",
    "plt.axhline(y=0, xmin=200, xmax=0, hold=None,color='black',linewidth=1,ls='--');\n",
    "plt.title('Mean arrival delays by carrier 2016 in San Francisco');plt.grid();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b9e5588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8AAAAXo6GDwAAwMvR8AEAAHg5Gj4AAAAvR8MHAADg5Wj4AAAAvBwNHwAAgJej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\nf6en/lEvhqFcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b9e54e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 11), dpi=300)\n",
    "flt2016BOS[['UniqueCarrier','ArrDelay']].groupby('UniqueCarrier').mean().plot(kind='bar',edgecolor='black',linewidth=0.5,color='purple')\n",
    "plt.ylabel('Average delay in minutes');plt.xlabel('Carrier');\n",
    "plt.axhline(y=0, xmin=200, xmax=0, hold=None,color='black',linewidth=1,ls='--');\n",
    "plt.title('Mean arrival delays by carrier 2016 in Boston');plt.grid();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive Model for Flights in Chicago O'hare Airport (ORD)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 6855029 entries, 0 to 7009727\n",
      "Data columns (total 29 columns):\n",
      "Year                 int64\n",
      "Month                int64\n",
      "DayofMonth           int64\n",
      "DayOfWeek            int64\n",
      "DepTime              float64\n",
      "CRSDepTime           int64\n",
      "ArrTime              float64\n",
      "CRSArrTime           int64\n",
      "UniqueCarrier        object\n",
      "FlightNum            int64\n",
      "TailNum              object\n",
      "ActualElapsedTime    float64\n",
      "CRSElapsedTime       float64\n",
      "AirTime              float64\n",
      "ArrDelay             float64\n",
      "DepDelay             float64\n",
      "Origin               object\n",
      "Dest                 object\n",
      "Distance             int64\n",
      "TaxiIn               float64\n",
      "TaxiOut              float64\n",
      "Cancelled            int64\n",
      "CancellationCode     object\n",
      "Diverted             int64\n",
      "CarrierDelay         float64\n",
      "WeatherDelay         float64\n",
      "NASDelay             float64\n",
      "SecurityDelay        float64\n",
      "LateAircraftDelay    float64\n",
      "dtypes: float64(14), int64(10), object(5)\n",
      "memory usage: 1.5+ GB\n"
     ]
    }
   ],
   "source": [
    "flt2016.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict whether a flight is on time or delayed. To do this, we use the aviation defintion of late and early departure/arrivals.\n",
    "\n",
    "* A flight is on-time if the departure delay is within 15 minutes of the scheduled departure time (CRSDepTime).\n",
    "* A flight is delayed if the departure delay is more than 15 minutes of the schedule departure time (CRSDepTime).\n",
    "\n",
    "* The features listed below may be of interest for predicting whether a flight is on-time or late:\n",
    "    * Month\n",
    "    * Day of Month\n",
    "    * Day of Week\n",
    "    * CRSDepTime\n",
    "    * CRSArrTime\n",
    "    * Origin (departure airport)\n",
    "    * Destination (arrival airport)\n",
    "    * CRSElapsedTime (total flight time)\n",
    "    * Distance\n",
    "    * Carrier name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we decide to use a delay threshold of 15 minutes based on standard aviation guidlines. We will use the Origin of Chicago O'hare Airport (ORD). CRSArrTime can be dropped as we have the hour extracted from this feature.\n",
    "\n",
    "TLDR; We are predicting departure delay (late or on-time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "flt2016['DepHr'] = flt2016['CRSDepTime']/100\n",
    "flt2016['ArrHr'] = flt2016['CRSArrTime']/100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modeling packages\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build feature sets for our analysis\n",
    "# separate classes based on 15 minute delay\n",
    "delay_threshold = 15.\n",
    "# choose direction\n",
    "direction = 'Origin'\n",
    "\n",
    "Ycol = 'DepDelay'\n",
    "\n",
    "if direction == 'Origin':\n",
    "    # Features for departure delays\n",
    "    Xcols = ['Month','DayOfWeek','DayofMonth','DepHr','UniqueCarrier','Dest','Distance','CRSElapsedTime']\n",
    "else:\n",
    "    # Features for arrival delays\n",
    "    Xcols = ['Month','DayOfWeek','DayofMonth','ArrHr','UniqueCarrier','Origin','Distance','CRSElapsedTime']\n",
    "    \n",
    "scaler = StandardScaler()\n",
    "X_values = flt2016[flt2016[direction]=='ORD'][Xcols]\n",
    "Y_values = flt2016[flt2016[direction]=='ORD'][Ycol]\n",
    "\n",
    "# Change the categorical values of airline names to numbers\n",
    "X_values['UniqueCarrier'] = pd.factorize(X_values['UniqueCarrier'])[0]\n",
    "\n",
    "if direction == 'Origin':\n",
    "    X_values['Dest'] = pd.factorize(X_values['Dest'])[0]\n",
    "else:\n",
    "    X_values['Origin'] = pd.factorize(X_values['Origin'])[0]\n",
    "    \n",
    "\n",
    "rows = np.random.choice(X_values.index.values, 20000)\n",
    "sampled_X = X_values.loc[rows]\n",
    "sampled_Y = Y_values.loc[rows]\n",
    "\n",
    "TrainX, TestX, TrainY, TestY = train_test_split(\n",
    "    sampled_X, sampled_Y, test_size=0.50, random_state=0)\n",
    "\n",
    "TrainX_scl = scaler.fit_transform(TrainX)\n",
    "TestX_scl = scaler.transform(TestX)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [Month, DayOfWeek, DayofMonth, DepHr, UniqueCarrier, Dest, Distance, CRSElapsedTime]\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [Month, DayOfWeek, DayofMonth, DepHr, UniqueCarrier, Dest, Distance, CRSElapsedTime]\n"
     ]
    }
   ],
   "source": [
    "# check for null values\n",
    "print TestX[pd.isnull(TestX).any(axis=1)].T\n",
    "print TrainX[pd.isnull(TrainX).any(axis=1)].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#i = np.identity(TrainX.shape[1])\n",
    "#pca = PCA().fit(TrainX)\n",
    "#coef = pca.transform(i)\n",
    "#pd.DataFrame(coef, columns=['PC-1', 'PC-2', 'PC-3', 'PC-4', 'PC-5', 'PC-6', 'PC-7', 'PC-8'], index=TrainX.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lUhw8eBCJiYl49dVX4efnh7i4OPTo0QMTJ07E0KFDMXXqVERGRrKTVyRjQzmG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\nIuKiNPREREREXJSGnoiIiIiL+v/EzNKrs2Nx9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3865c8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA, TruncatedSVD\n",
    "pca = PCA().fit(TrainX_scl, TrainY)\n",
    "\n",
    "plt.title(\"Skree Plot for Explained Variance\")\n",
    "plt.ylabel(\"Percentage of explained variance\")\n",
    "plt.xlabel(\"PCA Components\")\n",
    "plt.plot(pca.explained_variance_ratio_*100);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pd.DataFrame(coef, columns=['PC-1', 'PC-2'], index=TrainX_scl.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The 1st Principal Component explains: 28 % of the variance.\n",
      "\n",
      "The 1st and 2nd Principal Components explain: 41 % of the variance.\n",
      "\n",
      "The 1st 2nd and 3rd Principal Components explain: 54 % of the variance.\n",
      "\n",
      "The 1st 2nd 3rd and 4th Principal Components explain: 66 % of the variance.\n",
      "\n",
      "The 1st 2nd 3rd 4th and 5th Principal Components explain: 79 % of the variance.\n"
     ]
    },
    {
     "data": {
      "image/png": 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RUVE6duyYduzYoejo6EqMB7cbNkwKDJTOnJHmzDGdBgAAVBCXS19ISIjatm2r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x296fadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svd = TruncatedSVD(n_components=5)\n",
    "TrainX_2d = svd.fit_transform(TrainX_scl)\n",
    "\n",
    "# we make a scree plot to see how many Principal Components to consider\n",
    "eig = svd.explained_variance_\n",
    "# and calculate the variance explained by the PC analysis\n",
    "var_exp = svd.explained_variance_ratio_.cumsum()*100.\n",
    "\n",
    "plt.plot(np.arange(1,len(eig)+1), eig, color='r')\n",
    "plt.title('Scree plot for the PCA')\n",
    "plt.xlabel('Number of principal components')\n",
    "plt.ylabel('Eigenvalues')\n",
    "remove_border()\n",
    "\n",
    "print 'The 1st Principal Component explains:', var_exp[0].astype(int), '% of the variance.'\n",
    "print ''\n",
    "print 'The 1st and 2nd Principal Components explain:', var_exp[1].astype(int), '% of the variance.'\n",
    "print ''\n",
    "print 'The 1st 2nd and 3rd Principal Components explain:', var_exp[2].astype(int), '% of the variance.'\n",
    "print ''\n",
    "print 'The 1st 2nd 3rd and 4th Principal Components explain:', var_exp[3].astype(int), '% of the variance.'\n",
    "print ''\n",
    "print 'The 1st 2nd 3rd 4th and 5th Principal Components explain:', var_exp[4].astype(int), '% of the variance.'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x3b9b74a8>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4815f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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udri3TST2SiZLn4XG4PjUtMCcT3JeskjsgiDcV0K9a3E84xihCQUMn51A1Qyj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JFy7Pw9YdRBr+dxy5TSAINOfoSXWG4wZ1n15kz6KJFZa9tzydte90YeuNLUpoLDBE9v8lhXfW\n2imp87Loo15UI9L2Jk9i69y+UB8t7hbyLRNrU0vVpXLTzJsndE6SiSPo9Vi+/rXPehhJzgCjbtz9\n5Cc/obGxkZ/97Gfcc889+P1+vve979HTE5FUtdnGbln5otFz5/fwPfssUkMjeDzINbXM21LJz9/L\nZH3RBmatvXJiF1QUvvlIHSt395DbFsDeE2JapYfrnmkir8XPQ7cUs3VDxhkRkVGHFQTgwPwUWvIM\ng9f0WDR8sCiVTRdkoQ/ITDvpGkp9jxOzK4SsKEj9w5x22sNdv6pizdudg8p5Om+Yt9ZncWiOlVcv\nyIoK6gP0WdUxeuySViSnub/wRxDwmNUx43NbNXTatTTm6fnlD8t4/IYC3tiYxUO3FvO720vQ+SU0\nEqhlyOgJDRYPGvwyFrfE1/7cQGm1G0EaW053i3SIzsz4E5/Xzs+ipjj2vhry9fzinqm8tyKNbz9U\nR1pvOKaOIeZZ2LQcmh275RIW4cDcsZ3wRpLSG6I3jpVrh13Dc1f2W6SqBHattvPX6wv45Q/LaMwb\nmux4DCIfLkq8713jrOb3FV8MCeokST5LRl2xHzhwgHvvvZdVq1YBsHr1ajweD4888ghXXDH+HtL2\n9nauvz6xgtHTTz9NRkb89N3niXBTE8Hde2LfkGWse48y+RsXMa9oKVtrt8at9I3H1JMuJp+OFemw\nuCVW7+rm/ttKJz7QBIVik6o9+LUCHyyO8+MsCmxfa6e+wEBPqnpCbU+CJHPnvTXktkenoEVJoTVH\nj7u/Ql9Si0hakUe+UYStO4GqWILPDepEpp1wUVtsRFSEuMcdnJPC8ekWWvOGB1aBxgIj93+nlFsf\nqae0LroyWAC8eoFOu5brn2zi/u+U4EiPH7RVIZmiei91RUZ0fiWuhn9IE7FRvXBTG0UNPkQZmvL1\nvHpBFh6Tmlv+VE9JvY+OrHFU4MsKhhFqhx3pGt5Zl8WOVRMrLMtv8HLnvdUY4xT3fTQ/lb6U6PEo\nKoHGQiNPX5PHj35djSzAkVnWiKjOKFT1nqa2t5aSaNrJkQAAIABJREFU1IgrXZu7jedPP0u7px29\nWs+K3BWsKTx7QmNPkiRJNKMG9vz8fHJycqJeM5lM3HnnnTz55JPjXrFnZWXx1ltvffxRfk4IHjiI\n3B1fUUxubePFXffzRP7fkCewAz7zmCvKRWs49q5xSGoOrFyF6LTu8OI1MSxHWtW2dNCUp4/5ER8g\npFNxYsbE0rtpPUGmnnDFBHWIVO8v39vDqX5v8IHKb11AJtU5sYI1TUhh8Ye91BQb6ciMP/7tXxqa\nPNq6Anz5hVYKmnyowzJtWTp2rUhHkLsoaRgqNJOAly7OYfcqO1ktXlKc4biBXZAUbn60jrlH3ITV\nAl6DSHe6lt3LbexdFq3nHtCreOHKWI2Ckio3+U2RiYVqHGZsZleI2Ueii/FOLivA8+WzoT22whkg\n05BJSA7jGDAy6qepwMD7i9JYOaw9UCZSx7H5vKyEY2goMPD2ahshjcirF45d7BeQAnT6OihJLaHK\nUcWvPvy/tHlbB98/2HGAamc1X5uVLI77LOgN9PJq1St4Qm5mpM9kRf5KROHMe0Mk+XQZNbDfdddd\nPPbYY/z0pz+NsWe9/vrrefrppz/VwX3eUE8uA5MJPLEuXi6Tmj6LBt8IJbKxcI+UlR3GQLGTuTeI\nOzVOMBu+Mh/2/zVBmYtfaaU1R4csCkw75WZOhZOaYiOOVHXCivXxYnaGmHW0jzRnCLMrhMWdODuR\n2htthqIKy6x4rxujT6KmxBS7xZAg21DQ6OPd5bbRCxOH3f+tf6ynoGkogFtdXjLbgzx+Qz65bQGu\neKkVAWgsNEQscQGPWUO3PVFRo0xuWxAR0IYVtC6JVJePnPZWJJXIB4tTyWgP4LBpCWvjZx2y2v1o\n+gN6Ub131NWvGJa54qXWqD1uKc3KRf/6GNr8Au5690ec7q2MOifTmMk9Z/0bv/rwlzGBHUHg+avy\nqJxmYV6FE1FWqCwzcWB+Kue80c72L2XGrXaX1SI71mTw85+dYnK1h1//YPR2NpPGTHn6dACeOfVU\nVFAHCMkhdjS+wwWTLiLLmHhCkeTMs63+LZ468QTd/XLHb9a9wda6Ldyz5N+SxYqfM0YN7MuXL6e0\ntJTNmzdzySWXxLx/7bXXnrF99vr6enbs2EFLSwt79+5lyZIlg9K1nxe05eXo5s+j/tQHVE4xk9Pq\np6zaiwBUlxnxGSemqiZIMgv3x5fUBKgsM2F1Brnzf2u47/YRKWJZwdYdpCej/zVBiCiMCQKXvdTC\nmnd7Yq6X2Rkkv9lPSZ031lo2kfTqCFK7A9z01ybKqj1UlRnZus7OWfvjt3hBZF8/ohYXeTYan0Rh\nvY8FFU7qiowcm24Z7BDQ+iXymn3UFxoHi+NQFEpqvRGVv3FK367Z2RUV1AfH7gqzcncPj95ciN4v\nMa3az1+uyRm8b3cCUR+AaZVe0h2xWQmjT2bFnh58OpGvP9bA7+4opbY0/jjrC00ENAK6kMIVL7bS\nkamjttg49NyViOd7miPIVx+tZ3L90D2EDVos11+PqagUua+PO94UeELrpT5LRFIJ5PTIXCLaMa0w\n4fDH/u0BEAQOzU3h0NyIKqGtK8A9/1VJSl+Yw3NS49Y8ICucvb0TAShq8FFY743b7TDAnIw5WLQW\nZEWmvq8u7jHOoJO36t7k+uk3JLxOkjOLO+jmbyefHgzqABISR7uP8OiRP3Lb/Ds+w9ElmShjtrvl\n5OTEDeoDbNy48YwMpKioiPvuu++MXOuzIiAF+MO/lHGktQ+vDtRBmfxmH0v39PDiFXHkYccgrSdE\nfmuQsAjOFA3pjsjq1q8VOTnNxN8vzeGiV9vI7grys59Xsn2NnaMzzGiDCosOOElzBLn3jklDF+xv\nCSurjt+uZHNGcrC3/qGev12VS12xkYBOJKMzSHpXgA/PGnsSN/+gE5Wk8Nfr8/lgcRrqkExvqobJ\np12k90bnl0MqKK738e2H6jg8y4KtJ8jqd3tQZFABtz5Sz/75KRybbkGUYfGHDlDg/m8Xo/OGKWrw\nMf9gpOf7F6Op+o0gpzVxT3eaI4SsFqmYm8Lm87KJW3Ieh2mn3HEV7gBK3EaW1qvRhhUufK2dP369\nMK7XfGuBgaZcPZPqfZi9EnfeW8OuFTYOzbQQVgusf7OTadWeQcniLpuGpnw9QY3I3qU26mbsZ9pf\nrmXVqzWU72vmppEfoGugoawEjzg+X/irnmshrf87Mbeil+ZcfbRGvaJg7Qsxs1+6Vi1BboI2RoAs\nYxY/WnQXAAIC4ii1uxpxQsaTST4hm2tfj9IKGM4px8m4ryf55yX5r+cM8tChB9nXcwD6F8lhrUhd\niYm6kjgrtARp8kFkhZW7IyurZ7+cxweL01jxXjfWvjCH5lgHV33GfmMTTVhh47ZONm4b0tauj7fC\nIqJJPxomn8TX/tJISC0QVgvo/TIVc6xjBnZ1SGbGCTcldV66MrR8sMRGWKeiLd/If949lcv+3sKy\nvb2IQLdNw6nJJhZ/2MuUKg9TqmKDjajAov1OFu2P+Ef3mVU8dW0+skZFQAPddi25zT62bshM2Fsf\nD88omZOBrIpfL2JzQ88YOj8D9NgSr+b1Njtf+sbP6N71FcpPuUnrDsYN7ACPfrWAf/uv02iDChWz\nLWw+NxNv/3bMI2VmREmhqN7L2p3dZLUH+MM3S4ZOlnxUpPo4fVkKl6i8rNgzItsTCGB/40Psl9np\n8sVvuRtAkBUm1UT+JpIIJ8rjGM8IAn2pWu67rZSvP1qPvStIdZxshEVjYVp6OXfMu3Nwv1YQBEpT\nSmnztsUcb9PZOKfk3KHbkiV2Ne2kxdPCwqyFTLVNG3XsSSaOZxQXvUDSHvdzRzKwnyECYT/Huo6O\n/4QBGVMFVrzXRWuOgYYCAyG9CltXgIJGH2ve6SKkFjg11URQJ8YV/jg608KyvY64a5+ekc5h/XTZ\ntWTGKbyTBKIEXDRhZVDJbLRgOEBRvZfyk24EYG5FH7tWeiITEEHAZ1Lz1LUFHJlpweyROTgvFYMv\n4mSXEs+kRSewf14KBU1+9AGZbruWd9bYOTpzqHiv267j4VuL8ZoTB9V4bD87gwUHnIOr0QGCaqiY\nE7l+am+YXMXEe5bEfdnD2b3Mxqpd3eS2BWLe0y1bjn7ZUvTrzsb/+maCo7SxOTL0/OsvyjnnjTY2\nX5BDaMSxslqgdpKZ9iw9K9+NH5w9ZjU7V9kj34sRdZpGh4/5mct4s/6NUe9HUCIqgBDxg68uTZxe\n78rQ8fp5WSzZ56Aza2g7qMhazK2zv0W2KYd0Q3rMeTfNvJnavlpaPdH77K6gC4ffQaoulZPdJ3jo\n8IPUOWtRUHi16mVm2Gfy40V3oRun5nySsVmas4wttZvxS7HZrDzzxLONST5bkoH9DOEOuXEHExfG\nmdQm/JJ/0C1sYJW+fHc3Vz/XikqB1iwdfVY1RQ0+VKFIMDP4ZbyjBNXDM600FBoobohu0+q1qHln\nrT3uOe+stZPX7CfFFR206ooMZLcHMPmiV/ROs4q3z45/LQC9V6K43sNXnmgazFzrgzKLPuqN3k8W\nBQ7PHWrD0vsk9p2VyobtsZ0EtSVGnrqhMPbDFIXZh/uYd9CJoMCJcjMfLE4bNE8ZD702LS9dmsN5\nWzrIaY8E4l6rio8WpPHuynSQFRYecHL+0svZr96FLxx/62I4YY3Is1/O5coXWshtCSACQkoKutUr\nSbnnbgBsD/6e6v/7E/TBfaNea+O2TtqyDTFBfThes5o9y2OD5QDNuXqa8vQUjqglUBcW8K0530ar\n0nKw4wC9/l4kRYr5QZdVAo0FBibV+iISwmM836oyEy2zctGKIYwaI2Wpk/nm7FvJkIzI7T0oOWYE\nXXQ3QbYpB1ecfzMhJcRPdt/N4xuf5KFDD1LbN+RH4ZN8fNT+IX84/DC3z//uqGNKMn6mpZczP2sB\ne1p2R71u06dzSdmln9GoknxckoH9DJGiS8VusONxxaaUjRoTk1OnUNdXS2+gX+KzP/U+85hrcJWc\n0x4YDDQAXWlqLC4JS18YT4JVqaIWefibRXz5+RYKG3yowwrtWTq2n23ndALv96MzrTxzVS7X/q0Z\nk0dCpUBAI+BI03Bwbgor9vSQ3RFEBiQVqGWFa/7WzLsr0vlwcRoGTziSshYErnmykfJKDxlx+s6l\n0QrrFQVHmobN52UzpdJDcaOfkAjN+Xq2rcugLUsX6+imKHzliUYW7u8d3M9euL+XBQecPPzN4sSK\ne3G2Oj5alEbF3BQWftSLwSfx0YKUiFZ8f3vg7o2FaBdlsKBvAe+1vDvKjQxROdXCf981hVUHA5xv\nWETpZTehKR9KGysqkYdW+Gjqjb9FApHe/llH+jh1eU7CYwZwjVLMp4jw6FcLWb+9czAlryouxnzb\nd1CJKm6ZfSvBcJAOXwc/3ftv+L2xK7WXL8xm6mlPxFJ2DHwGNelmGxcXn8OXitbT52il6RtfxXW8\nAYM7iDovH/25G7H++EeDRbEnuo/jTpAC7gv28Xzls9TFMYABON59HEmWUImfzOY3yRA/XPhjnjrx\nJIe7DuEP+8k2ZXPxpEuYlRHrEZLkn5tkYD9DqEU1S3KW0uRuGlqV96MXdVR0Hox/XjhxT/uUah/H\nyk0EtWJi9zHAmarlj98oRh2SUUlKQhOO4Sz5oBerW+LDBSnsW5yGx6TC2hcmq83P4ZkW0nf2oJEU\nRAk0XpnJ1V5yWgIoIhyYkzI4FhHiBnW3UeTdFQlWlAP3IkR6uh/4dglf+1MdVWVm9qxIHzSAGcm8\ng04WftQ72BIGka2DWcdcrN/WwRvnxGmPSvDcBClizvL+0hF1A/3jaraEefLkE8zLmIdO1BGQY1Ps\n8ZBVAjsW6tnBERb1PstP+Pf+YSj8dv+vOd17etTzze4w1r4Q9s5PuK8pCHRk63nmy3m8tT4Tu2Jm\nxfQLuKCoiJAc4s9H/8SRzsO4gi7cXger3usir9lPX0ok0+M1qqmaaqGuxEhYO47gKUCTp5EXTz9P\nbV8Ns+5+nPKjQ3v84aoq3A89DGo1KT/4PgA1vaM7Q552nCaR64Ev7CUgBTCKyTasM4VKVPGVGTd+\n1sNIcgZIBvYzyLXl14MgsLdlN12+bixaM6UppRzuPJLwnKY8PTOPx0/ha8IKb5yTPW6hlrBGJDyO\n7ea07gCTqj1sOSeDNzZE9ydfVuVh7dvdcb8YZp/EZS+2cmSGhUB/u9nfL8kmr8VPSf3QVoBfK/Le\n8nQ6shPsgY4ItB6LhvvunBxpxxPjLPP7g/PcQ31RQX04kys9vHHO2J8lSgrXP9VES7aOd1eljzkJ\nOt59nFX5q3mv5V184cg9qgMS2qCCzySixBtvPx+1f8jhzkPMzpjDoc4K9rW+n/iD+u/RY1LhMas5\nb2sHe5emjRpU0/UZpOqs1Pc1EFZCcY+RNSIdmTo6CHHa8Ro9xzS0elrY3fIeAKmOIN//Qz0Fjb7B\nOo2zPnDw7JV5HJtuHl9QH4Yz6KRm92YurHLGvhkO49+yFet370BQq5lhnznqtc7KWcqBjv0xE2UA\nuyEDgzpx5iNJki8yycB+BhEEgevKr+eqqVfjDPRi1pjZ07KHva17E57zxjmZlJ90U9Dkiyl0OjXZ\nRFP+GSgQGrFqzW0NoA3J7F1iiwpsoqQw/YRr1C9FWl8YYdgWvNes4X+/O4m1O7ooaPQRVgvsWZrG\n6SnjLCcfTqIgOZAdkBNnN1QKICsIihLf1rWfiza1cta+SLHhpFoP+xan4ev3FT880xqjQx+QA6QZ\nbPzPqt/y9ImnqHJWMqWiiev+eJL6fD3bzrZzeJaVYJwqdwWFJ4//lV+t/g27m98jIMVf9c852Iut\nT2bn8jQktciJaRZW7eomv9FH3aT42yk6UcefNjyKSlTR7evm7nfvotXbkvC+ISL+sr1hG4Fh++lX\nP9tMUWN0fUZGV4hLXmnlRPmUcQofRzPptCtG6nYAub0dubcXld2OHCdgD+e1mlfjBnWtqGVd4brP\nnc5FkiT/KJKB/VNALapJN0SKzYpTijGoDPgkX9xj/QYVv/zhJAw+mRRniCX7HGzod/9ypGnGpQCn\nCUjYu4IjNNCHMeIHsClPz4E51hijErM7TIoz/spvAIVIW9twwZqQVuTNDeO3Nf24VE0yMf+AM24H\nQGOujimVLiqnjb4fPP24e/D8uYddzD0cyZbIwFPX5bFnWez2gUVjptBayF1n/SsAPdt+jI+TFDX5\n+dpfm/jFv5bRkqB9LSBHUuqKM84KFshoD3Dt35qxuCXq87TUlJl5/opctAEZKZF1L6BXG9jesJ31\nRetJN6SzIHsBr9WMHtiBKMU5vU+isCH+9zK3JcDsQ04q5qeOec2RNOfpCaqJK4UspKYiWiN/ozS9\njRRtCs5g7LPRibqEAja55jzOK71gwuNKkuSLQlIE+FOmJKWUKbapox6jqES8ZjWteQY2XZDN1vWR\ntrbyk26svaMHWoAFB3r58f9U8b3fVpHbHP+HejjONC1bN2ZFgvMwvEYVnlEkbCGi15L9Sfd/Pya7\nVqbz8sXZ7J9rJaQemqzUFRiomG0lM4HP+QCCrAz2/Y9EBDLj6NmrBTXri6Jz/NLpyD55SC3w+1uL\naR1F+vWs7LMi/7ujFZ1vxGfLCpe92ILVLSEAq3d1c9v9NfzsZ6coavCOKo7jDPbyh0MP8scjjwBw\nadnlZBgmZqSkDcqD7YwjEQGLe3ytflEoCqemmmlM4CynKixA6JenTtOnUW6bHvc4s9aScH+dCXgt\nJEnyRSQZ2P8BfG/BD5ibMQ+9OHZaPawV+XBRKpIIqX1hZh3tQwjLTD/Wx5XPN3PBa22YB9rUFIXM\ndj9ffqEVXUhhcrWXmx5vHJe1aEe2PqaFKawROZWgkl4SoKYokhFYvruHz+LHVVEJvLUhkz99vYif\n/ttUXrkgi21r07nv9ohMq9eoipmsDLdvVUQBtyn+nnFIBVWTRwQjRWG2Nz1KJ1t2uQjX1QPwwmU5\nHJ1lTZj6T9OlcfmUiE1vaVMwYnc7fHyiwNPX57N7SaQFcMEBJ9NPurF3B8lrDXDha+3ofHGCa/89\nhZQQ7zbtpMPbQYYxgwtKL4o7juGoBTVC/4yhz6qmIyN+oaIjRU3FvMhqPb0zgBCO852KY92rFTTk\ndIb5842FVJYZCfTr4nuMIkEVaGZG76vfPv+7zMucj0EV+W7pVXpm2eewKGtxwnvQq5L960mSjEYy\nFf8PIE2fxs+X/wd/OPQQr9e+Nubx7Zk6GvINlDT4uPaZZta+00lmV3CwxWvp+w5ePzeTPcvTmXW4\nL2o/U++VUIcVQh+zC+j5K3Iw+CWmnnRj9UiEVJEA8MwVucw57qItW8e+xWkITCC0x9GZ1/sk/PEM\nW8ajSS8I9KRr2XpudBV8VamR3BYfLfnGqGP13jBpjhD5zX5SnCEUYhfDnRlaTkwxD9YjCJLMjCMu\nbt0HylUywsD+fzgMcmTlXTWKNn2WIYvfrPkdOlVku0OVk4PXVBlzby6rhm3r7Cz+qDdm9TzrmIuL\nNrWx6cIc/HoRUVZQhZWo/nZn0Mm2+je5tvx63hpDdAYgrAybKAgCO1emk9nZgtkz9B0KqaBibgou\nixpkhe/dW0Nzto5XLs6hpV9W1ugOU1Lr4dgM6+A9qVBxxdSrmPXkE7wwzcnT1+ST2+xjcpWH2Yec\nmMIadIsXRY3HrDXzs2W/oLq3mlM9JylLLWOKbSqd3k72te3FEYj1SphpT7ZfJUkyGsnA/g9kqq2c\nzbWvj5JijCBpRB79aiFLP3BQdtrN1KpogRSbI8T5mzs4MsNKYVN06v29VbZRhU3GIqxV8cJlOagl\nhbIqD23ZehqKjKQ4ghS0+Nm+LmNC17e3+ymt83J6shmHTYsqJJPf7GfG0T42XxBr86kNSAQNH+9r\n2WfTIatDpHcG6E7XDgYcv1FNu05FVnuA7esyMHglZh53kd4dJKBT4TWI3HtbKdKwCnBFJeIzq5Bb\nWgn3OXnPdZAjnYfRiFqmlKdgO95HZ4LVLsCyvOVYdUP7/YFvXEPT7g/jHtuWrefo9Ej6+ni5BZ9B\nJM0RYsXuHs7e2YMMvHRpLrJaQNbFZgd0Kh2KouDwJzYMSsQHS2z4DCpWvddNam8Ir1HN4VlWtq+L\n1Iik9wSxOULYHCEmV1fxwLdKqC824DWrOT7dgjosk9IbpLjBz9lNqax49CpcKzr5+u/uhVD0NpJ2\n8Xz069bFHcek1ElMSp3ErqadPHPyqYjegwIiInJ/CZ8KFQuyF3Jt+XUTvs8kSb5IJAP7P5CVeSt5\n6fQLCUU3htOdqeO1C7K565eVcd+39Ya4eFMrC/dHFx4FRym4Gi991sjX4sOFqZE+e0VBFZTZuco+\noaA+q8LJNc81k+YM4zGqqM3X09UvOar3xN+/DWvEcTvJxcNt1eAPytg7fHRlD0uhq4SoQrDXLszG\n2hdGDMuYPRLulNg+wfoiIwfni7x/5H840n1kMMC8eYUJva8s4bPQqXQszIpemYp2O4LRAEqcynhR\n4J01dk5PNg/ed0eWnoYCA0GNSNCg5tJNbZTWeFFJCm3ZOjadn02PXYvdkME5xeeioKBT6/GEx2fw\nMoCAwJE5KRyZkxLznioMK3cOqQI+dW0+1cO2ahSVSFgFPel6Vu7ppfTt4/jeeBPLHbejeL34tmxB\nqqtHTElBM28uqb/+n4SV7LLDwetVm3iy7dWEhaYSEoFwAHXSICZJklFJ/gv5B6ISVdw651s8VPEg\n9a66cZ2jDSZe3c+tcMa0yBU0ekcVsxkX/WlnhUhaFlmhJ+tj7Gsq8qAe+6kpJl47P4vW/r39lN4g\nBfVeGkc4gcnqiBiPGJKHrFknSFgr0p2hH/U5KKKAM0VNyWn34GQj5joakTfXplPbfSjqdUkt4LEk\nFgyYYZsZo9ZlN9gpSivlRM+JmOPTOwM058XWPPhManasTueyl1qZNswkp7jBR2G9j4e/XcYlay7F\nrI0E22JrMT3+WHne0bDp05mWNo1j3cdwh1yElTBqVBQ0eLnur3XktUYmIn1mFadGWvn2o6gE9ixJ\nY93bXUh1dQiCQMrd/4rlzu8i1dQipttQZcdmZwACBw/S98tfETpxkqn+Pm7N0bD5nExOlcdvlzzU\nVcFTJ57kuvLrJ3SfSZJ8kUgG9n8w09Nn8Nu1v+PyVxNb4Q6nM11LThxjkZA6Ig3rMao4MjuF7nQt\ngqzgsqg+WVCPx8dcPR+Zk8Kpsh6yOgI8f3kuvbah1LUzVYvHqMbUF8IzUhpVEBBRPlYP9QCKSkQV\nkpE0o4+9065NKFIjyuDLsoJv7E6DoQ+Gdm8bDxy8j2/MumXQqEQQBC6fciUPVfw+yvPapGjJ9woc\nyog/gevI1JHXEiv3mtse4OI3e6iaW0Wds5bilBK+POUqGl2NdI6w3xyezh7JLPssvrfwB0DERa3J\n3Ujgv36D6YmXo47rzNAy44SLhR/1YvZIOFPUvLfcxpHZKYPj/Nm/TyO/4DTntexlSe5SRIMBcUb8\nqneAcEcHju/cjlRXF3kWwJTTQczOZv7jnikJixJfrHyeS8suiypqTJIkyRDJwP4ZcKrn1LiPfXd5\nGjNOuFCN+F0OqUWeviafoE4884E8DnpvmKBWjKyox4socv93ilm21xEV1AcIa0VsPcHYwA6E1Z/8\nnsbUABAE3DZdRPEuDpNsk3EG+ib2oQI0e5pp9jTT4evkZ0t/Pph+LrGWkG6w4wj0IisSOlHH5PQZ\n6LO10B5flU4TltEnsNlNbe7lnca3OdJ1hO/O+y6zM+fykyX/xgunX6DD245OFekFH/QniBqmwNyM\nefzL3O8MvqYSVRRZi+lqcjJyKpnX5OfqvzWjGyjwa4TSGg//833dYIdFV7qGLu8pTh/4X5499Qze\nsA+VoKIsdRJfnfl10vRpUdd0//7BwaA+nKyuICa3hDsl/t8vrIR5+sSTfH32LXHfT5Lki06y3e0z\n4I+H/zDuY2ced8cEdQCjX+bsd7o+cVAXpfHVtmuDMuvf7EAc0fakDo6uHiZpVDhSE6etEwbwf6Sq\nWBzFu0kpZVw15Ro6fO0f+7LHu45S0RHxCFAUhf96/z+odJwaVFwLyAEqOg/wfoKgDpDeHUzYax7u\nX9F2+Tp5rvI5AIpTSvjBwh/yq1W/5qYZN+MMxBfGUVCYnDYFrSp2wiWmxu6360PKUFDvJ6BTRSrn\nR+AJe6h2VtPqaaHJ3ciOph3c8fZt/OXon+n0dg4eF6qujjs2lQwFTaNnSY51T8AiOUmSLxjJFftn\nQF+CH9soFIUrn29hyfuJK50LGyeQIh5x7Q1vdDC/wonFFcZlUVMxN4Wt52QmDKheo4rtX8pAFVaQ\nVQoGb5jMjiBXP9PEb78fW0imDkqDOuN6X+KkuhLv4z5pjcAZIN+cx28P/BoAizPIwv1OfAYV+xek\nEtKObz4clINUdB5kXtZ8Hjn8MNV98QNZPAYmULIAPr0YV6L19OShdrtGVwPekBejxojD18OWus30\nBpxoRW1CA5vnK5+lqvc0d591T1SAN33tZgI7dyH39MQ9b4AD81LwjSFoNEBv0MFLVS/ydsN2NhSf\nw3XlNxA+dizh8Uv2OTgxI7GKYLZpbPe7JEm+qCQD+z+QgBTgd/t/G7c3dySCpLB0b0/MKmk4oY+b\nrhYEjk+3sHZnN6l9YWy9YXJb/Oh9Ei9fmosmIMUE6pFmID6ThvoSDU9fX8Btv6vinXWZ9KVoEBUo\nrfWwalc329Zl8M7ZGTTn6clu9dM2QqHN4AnjSB0KKGJYJq/JF1NQ91lQ3XYUb9jD5S+1snB/L6n9\nRYDnvNnJ5o2ZfHhW2hhXiJCms+EJedjR+M64P1sVkrnp8QbeW57OqXILu1ams/Ldboz9wT2kgpPT\nLGwZ1scvCiKiIPJi5Qtsqn6Fnn7pWFGIPwkprXKzdmc3FlcVR7L3MePbP0G/cgUAunnzsN5zN+5H\n/kT41ClQlLi9/1ZXeMIdDL3BXl6tfoUpnWpZVElMAAAgAElEQVTynPG3OWQBAueuJsvgoz1OxkRA\nYHXBmnF/ZpIkXzSSgf0fyIMVDwy6ao2FohY5Md3C/Io+ZGL3TIJq2L9g4jreAzQVGnnxshy+9pdG\nADQSzD/oZPcyGzOPOPGZNThSNVSXmgiPskJtLDQiqkRueawx5r0Nb3Wyf0EKrfkGchu9FNd66LFp\nCKlFTB6J3lR1lOmK0SsRVEdEdvzjXAl+WqQermdas5dVu7qiNM+zOwJc+korpyeb4tYNDCfXlMvG\nknPZVPPqhNrQZFGguMGHOtxFdamRly/JYf/8FFbs7kElKRybbqFibgrKsIBaklJCs7uJFyufxx0e\n8jiXldiVfnaLn/kHeplX4Yxs85w+Ts+Rb2G968eYb4hUm5uuugrjZZcR/OADFFmh9p7vYa1qjbrO\nggNO3tiQQWvuxFzW/JKft5vf4YZA/EyCqDdw5VW/4GxdmLvf+zEdI4oBF2Uv5qzsJRP6zCRJvkgk\nA/unjBIIENx/AJ9O4EhXYvvWGGRl0EVNAAJqYXD17tOLvL84lcMDvcfDUteCLI9qJTqcxoLoH2R7\nd4jb7q9h64ZMLv17K0dmWTk1bWyXNosrfk96al+Y1Tu72XRRDi0FkVX4+q1tvLUhK24KN9UZ4rIX\nW9m9Ip39C8e3Iv60MDuDrNkZHdRPTjGxZ6kNv14kozMwamAXELh55tfRq/XsaR7fZG4ANSqOT7Pw\n+vlZkQJARaGx0MgzCfTXc0253DTja7xS/XJUUB+OQTTgk32gKLTl6nnhijzeX2rjpscbyWsNoPT2\n4nnsL5iuvgpBE6mJCJ06heeJpwjX1SEFAngMIqZh2ypqSeGSl1t5/so8ujL6WwbHuY0STDODXg/+\n2Ip/dUE+otVKplrNf6/6Fc+efIZGVyMaUc1M+2yunPLlpLNbkiSjkAzsnyKuR/6I96mnCVdV0ZZn\nwvnDSTAOv3QAe3dw0KddADZdkElGVwhFjPQMR6Wr+3/kdL4wX/9jPS9flktz/tirKHmkVrwK/nJj\nATWTLaS5wpRVutH6JYJjeJarR9ku0AWGAoEqLLNjTUbC1G2fWY06JNOUO6JnfkCT/Ez/mI8ShHRB\nGUNg6L42b8zkrXV2/Alc3EYiIlKcUoKsyLiDExONsRhTaZ7i5sa/NpLVESCkFqkrNvDclXn4jNF/\nC5PaxP+s/i0WrQV/OHHNhTw4S+y/X1GgqcDI09fk84PfViMA4cpKgvv3o1uyhMDBgzi+9S9IjU0A\npAFN2Tp2L7Ww9P0eLN7I9WYfc1NWc5p3Vtvps6oJagTej+OQN5LCgllo5zUT3DtkaewxqvCa1BSs\nWYOgjjznDEMG35l3+zifXJIkSSAZ2D81vJu34Prt/6K4IsHZ1uEl1RGIsUpNRGpPdDW016Thb+uz\nWPCRg/O3tGP0SjhTNGw/205dSaSI6qJN7cw85aHkdzW8scFOTamJ5lx9RJM9TgDLaY1eLVVOMVMz\nObJCf/38bMqmuLE5grTljD5JaM7VY+uNXSn6tQIHhqm9SWoRaZRvXF+alr98tYgee/QzuvmPdbx6\nae7QqnCCmJ1BzF6JLruOsEZE75MorfaQ2htkzwp7zPFaf5hFHzkQTEbwuXBa1OxcaRt3UIeISpo7\n6CJNl0ZIGduhbzhZR1s496W6wX19iGwBpHcH+d/vTopKweea8zBrBgRqSni3eVfca8ZLyQM0Fuip\nKzJQUu8DlQr6ndfc9z8wGNQHeOdsO902LUs+iK4RMfpkzt/aQUgFj3+lYMz7K7IUccWUK9E/eAG9\n3/s+nZWHePICK/UlFgJGFdmpbaytfIHLplwx5rWSJEkSSzKwf0r4nntuMKgDaEMRJ7bODO24Vp5V\nk83sPSuNORW9GAMK67d1ktviY8XuHvTD1OjKqj08fVUeR+akYO+O2I4afRJ9Vg2N+XqC+v4/8YjV\naVpPkI1bIoVJIRU0FBp58rr8mDGMiiyjCcPr52aS0xbA3jMUwGTg2AwLNZNMiGEZg19GCEu4rdpR\ni62cI6RdZx3sZcEhF1VTXexaOcq5kowggzKid31SpZtrn20mpy3A8XIzzbl6pla6KWr0c2KqKSaw\ni2GZBQf7mNSqYP7ut3A/+md2LxDpSx19P30kWlFLljEbjUpDljFrQopwq95ujQrqA5TWeFn8gYN9\nS2yDr9U6a/jJ7v/D3Wfdw0WTLmJPy3tUO6Or79P1dpxxetkBQloVvakaqPehKS9HO3cuAOGq2Ap+\nVUjmhqebsLrjtzjWz8iImsgB2PV2CqyFtHlaERAoSSnlphlfxay1QKYF2xOP88ttd3DKU9N/hkK9\nq55nTj2NXm3gvNLzEz6nJEmSxCcZ2D8lpO7oViG/TqSh0Dj+dLIo8NevFLBwmoUb/9pATntkxaYd\nkfZOdYZZv72TI7Ot+Prd0nasTueDRWkowwrTRn5uepufbV/KoKDZT12RkUNzrAnHpvdJnLu1nfwm\nP7IoUFukx2NSsfgjJzZHCJ9BRW2RgfpCA/buEAGtyKmpJrZuzMLWFeDiV1pZeLCPjgwtv7utBKct\n8cpbUkWP4fK/tyICVz3fgihHJgu9KWosrjCTT3uYXOVC51fQe8P4DWr8RpEuu44da+xseKuTDW92\noO5frM444WbGiaHMQkGjf3DCk9YdoKDJz+wjfcw54kLzlRtIue02tFOn0v32T8f4Y8USlIO81fAm\nl5RdyvqiDdT11eELR5v5iIJqsKd9OPau+L7yKiUS3IcH9rAS5nDXIR6qeIDvL/oRP1n6U/589E9U\n91YhyTKF1kKumHwl91fcS+P/a+++46MqswaO/+70kl5JQgokhN4CCNIxiKAUQbCBIOqK7Lqi29yi\nu7oirrvrNn1RURSExUVBitKRItJBkA4pEAKk92T6zH3/GDIkZCYJCCaE5/v5vJ93Z+6de08mkXPv\nc5/nnIq6ExyDim20S69CEdUK/xdmcSD/AGvPriF3qh5daVu6HKvgnk0FSMDYNXn4mbzf+ZfGhZL0\n3xX0TVvMubJMXLhICGjDox0epW1Qks/vafelXaSbsuq8b3Va2X5hm0jsgnAdRGK/SZThYdQcgN0x\nIIQKL41GGpLdry0HTpTR83A5Wrv3Z9nRlyz4VzjYMSCELsfKOdbZv3ZSv0pwsY12mVWsu68V3/Wu\n/0JDZ3by3NuZ7qHayzqfqMAluRMNuC8uonKtHOwRwF9+267W58sC1RQHq3lnZgIZiYYrIwi+1Ahn\n5Lo8zyiAQoaHll3CtkqiNEhNQLkDXY3n9zJQEqDCqVYwcHcpqV8XoLO4PEndG4PJSc9DZRzpGoAs\nQUKWmfIAFW/8OpGZ991LBKCKjaVrVQi7XK7GLeuqsfwrvSQNgOHxdwOwKWsj+aZ8DGo9nUO7kluV\nw+GCQ3UOYalnToPJ4H3bqZLT2Jw2QnQh/Kr3b+psH9x6KJ+d/h9215W/SoUMKeXBREycjN8zM9gt\nnWXed/+k3FYOYQoI8yOzjZGiUA2T/3fRZ1IHCIhpQ0xIPL/v+wfPsL+vpXY1nSk5jdPLxQ1AkaWw\nwc8LglCXSOw3iWHyo1j37kMudQ+BVndMu1alpkIWTo0jvW0Rk5deqrOWGNzPOANL7WS082PdyAhM\n3vqcX6Y1O3hqfpZ7/XgjRg/uW5NbK6mDO/cqvVxjdDhdWWe9ulOtYNX90Q0nRVnGUOnAdLm5itbi\npP/u4jrn0dhlIgrq3tFKgFWv4O+/SCQq18q41bm0yzDV2a8mBdDtSBmFIWqyE4ysHutuVKJT6gjX\nRyBbraT9bhbnI4rRV4Vgrtn4xcvEO0OlA63FRUmYe9herbyy//D4uxkefzd2lx2VpEKSJIotxfx9\n/5uklaZhdVrRKrWoFWpOdfCjbaapzhLH4iA1W4Z5n5hmdpiwOMxeK8kBPNT+YfRKHTsu7qDYUkyA\nJoA+re7g4fsfQSEpkGWZNTv+407qNchKicPdAxi5IZ/QEt9zBYzh0Ve+10Yk9GrRfjE+twVofBeo\nEQTBN5HYbxJ9airO375I1aJFOE6foUuGjS2p4LrGIr5WnfsDe+8MIXV7kdeGMBIwdXE2C6bG8fXw\niDplX2sa+G0Rbc+ZsaotSA6QG/gLiL1QdzmSL0azi65Hy+sUomnUna4kobW6MPmDsdLB+BU5hBdd\n46SzPBvtT1dxqFcQn0yJ5ddvpft8HlzNYHIycGcxQevyqTQq2TIsjOAeXWgb1Jb5n/6SLeOhItBd\nCEZhc+JnchJ10YzeKpOWbKTKz528A0tsDNtayM6B7mFyvcrAPfH31DmfWnEl2YfoQnh94F84WniE\ntJIzJAe35/v871lx3zIic610OV6B/vKoREGYhjWjIqkI9J64IwyR7ufW9RibdD9jk+5HluU6y8Wq\n7JXkVOZ4/VxlgJrvegZy9xbvd9BSUBCGqY/Ve25f7opLZU3ml5wrP1f7mEj0uqr1rSAIjSMS+03k\n99gUjI8+guP0GSK0WjZfmsexoobXsuvsEi7ZhU1z5R9fh1rBuhERTFh5iaDyuskqKtdGWJGN3Eit\nz3anxgo7Y9fkc75bNJ891gZZVeF1v5q8lnz1wSlBfsS1TTIL0AQQogshqzyLkhANY1flcMe+UkJL\n7V6rnQFeC/Zw+T2Dxf3dFEZo2TQ8nAdW5vo8twyobDKDd12Z5d39pAnjHx5lX7t9rNecxqq/kohd\nGiVmp8zTH2ZhsEFeuJo9fUNQ22UGf1uEX5UTtUNm1cNtGJs4jg6h3jub5VRcYtHJT7hYeQGX7CIu\nIJ77E8ez4MTHnCo6iR0HHz0eR89DpbQ9a6IsSM23A0Pdqxt8aBeUXOtO+Xjhcdae/YoyaxmB2kBG\ntx1Dx8vxeFsDrlZqao0w1OKS3VXmLpNCQpBLSkCWUbVLwjh9Oro7r69gjFqh5rmeL/Dekblklmbg\nkB2EaEO4I6ovj3R49LqOKQi3O5HYbzJJqUTdqSMAf0p4lX8eeIvv8g9isVtAumo4V5ZJ/bqQHt+X\nYzA7KA1Us7N/CN9drjC3v28wmW0NDN9SiF+lg6hLZmJy3cPSX9zf6krBGm9cMvoqJ+tGhHNsSAwX\nDA0ndaXdRXFw4+cFXGit40i3emLwdg5JhcluQkam774SUrcUeIrC+LqmsOgUnvKqNRUFqznUI5Cu\nR8q4Y38poYU2nxcB1cdPyqyiMEhNWKl7dMBYbkP18Wdsa1eI1cvzBrteRUGEjvgLFiIL7Iz7qnbJ\n07hsMzO7/5TUy8/Va8VnLuKdQ//hcMGhWs+VsyqyOJC7H7OzxiMPpcSh3sEc6h4Aau8JXXK6kCUJ\nFBJ61ZVRks1Zm1hw/KNaw+pHCr5nepcnuSsu1euxtEotScHtKDAX1NkWlWuh13fu/gaKVq0IW7kC\n59lMZIcD3YABSNrrW4ZYLSk4ib8NfovjhccotBTSI6InQdrrr6ooCLc7kdh/RFqllt/2/T0A699+\nnrmxabW2T1x+icHfFKG+/G9+dI6VhCwTepOTnYPcz1ZLg9Qsfcj9XDKswMprr7hbwJ5JbmBpmkKi\nsJWOjXdH4FJ5L+V5tcHbC+i/txSn5J5oVZ1oHQqo8FOiNzvR2d136hdjdHz6cEytNdaNYbJXeZqU\n9D5YWqvSmzc5kRp29gvm7i2FBFZcSY42FezvHcQ9G/IZ8k1RrYmGvu78ATROqAhU8cWEVjyx8AIq\np4zjzBmqijr6jKHSz/edswRYnXXnADhlJ2/sm82ZkjNeP1crqddUT5tclV2m16ES9twZ6mmK4nQ5\nWZ2xss6z8jJbGasyVjIkdihKyXv8M7rNpNBUSHppGu7q8O7ucmO/zEPllFHGxuL/q1+ijm2NOra1\n12NcL0mS6BLe9YYeUxBuV80msb/99tssXboUl8vF9OnT+clPftLUId1UA+6azrIDvyf/8g2usdJB\nyndlnqRezWB2MWhXMbsGhCArJDofLycq18qGkZGUX+5jLuMuM9sYvobp65BlErIs2FTUSrZVegUr\nx0by7aAw2maa6HSinLxIHQd7BeGqZya+L3q1HuvlmuGGerrAFYao2d8niM3DwzEZVJyPNzJ0eyHB\npXZMBiUHU4JITzTym7fS66weaCiqsEIbacn+fHF/Kx5cngOyTCvJ9x3j8U7+JKdV1fldAWTF6jmQ\ntZ572oyslUC3Z28lrSSt7gcaUs8ER4UMcefN5NyTQGrccADSS9PIKq+7fAzgfFkW58rOkRiU6HV7\niC6ENwf/ja/Pbybt3EE067czdFkaRrP7B5XtdhSGpm/QIwhC/ZpFYt+8eTNdu3Zl5syZbN68meef\nf54+ffrQ43KxjJbEevQo5X96FdvRowy808C6Ua0w6xV0PVpOsJeiJADh+VaiL5oJL7QzbdEFzHoF\n3w4MxWxU87/XhlNsLcZivIaJZo2o5x1cbKfDmco6d9BGs4t2GWa+HSyRmWgkM9Ho/QCN5HQ5UaDA\nhYv8MA1tz3qfyb6rXzDr7mvleZ3W3o+09rVHKcavyMGvyvtkORn3xMX8CC16s7NWARi7WoFdLXH6\n8vFUyck8NPBZdnzzHJX2qx5ZuGS23hVOcloVXY9V1Jq1fzZez5djWuGsyCa3KpeYGjO+00vTPXfB\n18zl8tozPjrHQpjLyE+7/8zzfFypUKKQFF6XkEmShLKBGesqhYp7EkbS6/eLsG4/WTuM3FzK3/oH\nuruHe+rJ3yzO4hIq587Fcf48isBA/J58EnWH9jf1nILQUjSLxB4cHEyvXr0AGDlyJB07diQjI6PF\nJXaXyUTpc8/jOOMejr37axOtcq3sGhROkF84LsVFFK66//ir7S5+NvccweXuZKSzuhiws5iN90Sw\nPaS681Xj75Y1Nhc2bf313wd9W1RnRrlDKbHx7jCOd/rhy5Akp4uwAhsSFqoidKCA/z7amrQkI5P/\ndxFFja/hUistW1LDGz6m7DtxbkgNY1/fYPIjtWgtLuKyzTz66QXCi+ycj9Nj1Smx6BRIISH4PfUk\nRv8Ift3nN7y2+1Ucco2rm8uPGpaNb8XRrgF0OV6B0ilzPk7P5tRwrDolStlFmaWsVmKPMEReHVKj\nhRXaKArV1qpN4FduZ+iOEgY990+MNSbptQ1MpE1gG9JL0+scp01gW+IDEho8n7O4GPvxE163OU6f\nxrJlK/p7Rlz7D9JI9uPHKZ75UxwZmZ73LBs2EvDibzBOFhPqBKEhzSKxVyf1ajabjW7dujVRNDdP\n1Ucfe5J6ta7HK+h6vAL1oIHQqRP2Y8frfE7txJPUq3m7AKimRIlSocQpO+vcuUlOmQE7iimI1JIV\nr6fCT+V1OZqyxqi4Wadg011h7BwYSvk1FtlROF24lLXvEo2VDh5Zks3OgWGc7HRliZZDo2DXgBDs\nagWTll0CCfLDNSyaEou1gUY0ALvuCOLO3cV1Cqns7BfMmtGRnp7yJj8Fpzr6M++peB7530WWPuhe\ngx2mCCR04V/RpqQA0DMihUnJD7IyfUXtZ+AumaJIPbsi9ewaUHdduVN2Mu/oe/x9yD9QKdz/iY1K\nGMWmrA1eq7+pJBWdwzpzouhErQIyAIYKOyM25FMeoCIzyQ+zXklgmZ2heypJ6TUG47C7au2vkBQ8\nmPww7x99lyLzlTK2YbpwHu7wSOO6olmsyDbv1e+QZVwV3vuo3yhlc96oldQBXEVFVLz3HoYJ45H0\n19YmVhBuN80isdeUkZFBp06daNeuXcM732Ic58/73CYXFhH4x5cp/f0fcJ49W+9xKvUSO/v7bmvq\nxIlBYWByp6l8c3Eb6aVp2Jw2wvRhdDtWyQMrc1DIUGlU8ufftaMiuO4StV39ghmwqwizTsl7MxIa\n1S3OG0Olk/jsSi5F65BcMrEXLYxfkYPJoCSjrZdjShL77wjiZCd/HCoJWZKxNlStDvcFS1iJg319\nghm4s6jWI4Sd/YM9Sb2mC7F6/v7LRGSlAqPKyH3DZ6KNTam1zyMdJ5MS2YsN59ZzrPAYuaacRq3L\nP1uWyeasTYxsMwoArUrHT7v/nA+PzuNc+VmcsvsRhEJSEKgNpMxS5nVtoclfzZLH4pBcMj0Ol5Hy\nXRkuBVyaMoK7Hpvt9dz9ou8kPjCBFWnLKbWWEqwNZny7B2hlbOV1/6spolqhSkzEfqhuVTxlbGv0\nI27e3bqrvBz7iZNetzkzz2L68iuMD066aecXhJbgR0nseXl5TJkyxef2JUuWEB4ejsPh4NNPP+WV\nV175McL60ala++58pQgLQzd4EBHr11L10QIcOZew7tqNM73ukCoKCYu+/l9dhaOCpaeX8NHIhZwr\nO0ehuYBOoZ3RJRdRvO5pHCdP4lflJLTYViex601Oeh4uozhQzZrRkded1AGsWgUPfX6JkGL3HWD1\nSMDXw0J9l5eVJCr93dukekYmPGSZhLOVTP84i3WjIll9XyTt06rQm10oXDJVXnq/V59HVkpoFBqe\n7PoThsUO87pb+5AOtA/pwLNf/7ThWKpDQq5TdKVzWGfeGvpP9ubs4YOj8yg0F+CSXRRZiihqoEmM\nrJA4lBLEoctNVmL8ypmCe3Rge/Z2ThefxKA2Mi7pfoK0QUQZo/hpj2cbHW9NkiRhfPJJyv/0J1xF\nNeLS69FPGI8i4OZVhJMdTnD6Liok2xq3okMQbmc/SmKPjIxk06ZNDe730UcfMWPGDIzGHzYhq7ky\nPvE4pi+W40i7KlkbDBjG3w+Aws8P/+eeRXY4yBs81Otx/Kpc9N1bwreD6u97XWIp5v1/TeGR81HE\nPPwQuih/aONP+OqVVM3/CHt6Bh2N5ZzjShGXngdLmLAqj7AiGy4JLvyApA5g1ynJjtYSXqOxiV0p\nEZ5nQW11Ym/gWb+skFChxIH3f+yjLpp56LOLxGVb0FllHliZiwuwaRVobC4Usru3en1i/Fp76rn7\njEOWsfhakuZDpCGiznsKSUFWuftC64ewOq1U2CqYs/c1ThadxIX7Z9yevZXHOk1jWNxdDRyhfsbx\n41CGhlC18BOcubkogoLQjxt70++WlSHBqNq1w1ZU90JHGRODYcyYm3p+QWgJms1Q/OLFixk1ahTh\n4eG4XC42b97M8OHDUXiZDXyrUvj5EfTPf1D+yp+xHTsGFguqxET0EydgfOjBWvvKdjuyxXc514CK\nRsyClySOaPIY+9m3mNauxe/xxwn83W9RGAz4//xZ0kvSOP7dP6F64rcsUxSqQXu5epssgaux3ejq\nsfTBGIrDCjFWuYvubEoNR+WQ0dhcDSZ2oG5SrzGrPydKx9p7I/nZu+c80wcV4GkQczrJQEGo92p4\nrS5ZiMy3EDmu4cc+kiQRqW9Fvim/wX0BYv1jubeN985kvpaj1Sf6gpkRmwvwq3RQ7q/izP1hzD/6\nAceLas/JKLQUsuTUf+kb1Q+D+octTdMNHoRu8KAfdIxrUWAu4NOT/+Xc44HIQzrQ5nQpY9bkobHL\nYDRiePghFIHXVgBJEG5HzSKxL168mNmzZzNnzhzAfXc0efJkRtzEZ3lNRduzJ2Erv8B++jRyWTma\n7t2QdLo6+yn0elRtErDl5dXZVmFUsr+X72fs1TRWF8O2FLgLtFRWUfn+PDR39EGfmorL5eKd79/m\nfEWNJCNJnE8wsuSRGGZ8eJ7MNgacDefdBpUHa1j+QLTXbYZKBy6FhMWgbNQyvOo4PRQSZ9r7s+be\nCMavqvtdrRsZicXLULzkdPHA8ovYdSq6/GJso36OpOB2HC060uB+0cZonu0xC62q7u8V3IWKrlWX\n4+X03X+lp3pK+n6+fKQEvFStzTPlsuHcOsa3e+Caz9NUis1FvLrrT1f+HuM0ZMRFkN21Fb/aH4H/\nhAkY7hMtXAWhMZpFYp8yZUq9z+BbGkmS0HTo0OB+hunTKTl+CGPFlWFspwTHugRQEFl/clA4ZX72\n7lmS06quvGm3kztzBseW/In1ZTu9ztAGOJdgYN3dYWy+OwKTr+fTN4hDJTF5yQVcksSyiVFU+V/f\n+ujMhNqPb0w6BWfaGbkQ6/1RgqxUsGxiNA98kcPq9JU8l/J8g+dozPC5AgVvDPwrwXrfF14jEkay\nJ2c3Jkf93edqqtIrWH5/K3dd+h1FBBVX0G9NOls6JHit9nd15bnm7tNTS2pfZF52ppXMoZcmeiYh\nCoLQsJYzzt0COe4eyP8eT+JoJ38uRmnJaGNg7b0RLJrScDnPO3cXk1QzqV92pL2OhRlLfCZ1ALNO\nyY6BoT88qcsQ5x/PU12eRvKxzt6mU1IYpiGtvfG6kzqAfNVf8ncpgbz/TBuqjL6HHPIidey9I4js\nCt+rFWoyOxp+xu7CxftH5ta7T6fQTtyfNL5WPXSj2g+D0vfQ+c6B7gutdfdG8saL7dgwPIxW2VVe\nu/0ZVAYGxQxpMNbmxNfvQEbmWNGxHzkaQbi1NYs7dsE7vUrPhZTWHOjgZSgZic6hncksy6xz56ey\nudunertq+3ZACDZt/ddzepOTkrAf1tgDICHfxb/ufwcZmQ1Z63yu4W4/cBzL+Q5ooFB8PWIuXpmP\nUOavYsuwMPeL+ob2FRLfdw8kWXJ/v5bt32D67HNkswlVUhL+P52JIsidfGVZ5nx54y4AzpScweww\no1f5nnj4cIdHGR43gnXn1uB0ObkrbjibszayMmOFz1irlQep2TAigo5pZvw1AcCVER13u9PetA1q\n26hYmwuVwvdFXXUtAEEQGkf8F9OMaZQaOoZ2ItdUt/VooiGO2QPf4N/f/Yut2V/X2uZfYScxo+7d\nOkBxSANtVWUZQ5WDUh8TzhorLN/CCyslFDPcFxE9IlK8JvbEoCQGjf0jX37zKyg5dV3nis11MHqN\n+/m6XSVhU0sM3lHE8glROFSKepO7Q6OkY2gnyv76Vyo/mA+myxdJGzZiWr/B3Y7U6SQvLpCy1gWN\nGuOyOK1YGkjsAGGGMB7rNM3zOq3Ue4MYb8xGFd+OSeSxMX9k1dnV5FfloVPp6RHeg4c73nrV2bqE\ndeVI4fd13tcqtQxr/cNm+AvC7UYk9jpxD8YAACAASURBVGZuZvefUW4t53jRUSxOK5JTJvaCmYe+\n2kvJsplEP1d31nJJqJaFU2OZ8UEWqqtWeunNvtcIGyoc2DQSl+J+2Gxqpd3F+FW5GKU2nvemd3qC\nAlM+JwqPU24vR6fUkRiUxKyU55EkieTgZE5fR2KPMkbz2pCfYZ33FK6qPNQOmfBiO0O/KSYy18q8\np+K9Tp4DQHbPXp/kP4SyxeOvJPXLXBkZmDIyAPADno/TM3dGPBVB9V/0hBvCCLyOtqOmRgz114qv\nczLdInvSLbLnNZ+ruZmYPIn0kjN8l/+dp4SvTqnj7vh76B7RvYmjE4Rbi0jszZxOpeNP/V9lz7MP\n8b0li1a5FlIOl6OQwXJiLf39NGy6J4J8c+1lWMe6BbJ/THu6bUzDeLlrml0FIcU2zsfp69zFRuRa\nMJicnGv7A2sIyDJ9DpTS83A5ckf3EPHOi9+yPG0ZWeXncLlcRBmiuK/taMYm3e/52JROU8koy+BE\njeVbWqUWP7Wfz+It8f4J/GvYfyj/9Yu4vKweCCiz41L5uFuXZdr6t+Wvd/0D85v/rF2IxQsJSDhv\n5uHPL/HBTxLq3Tc5qD2KBpqteOOnbqD17lXadRh4zedorlQKFb/v9zJ7Lu3mQN5+lJKS1LjhdAj1\n3T5XEATvRGK/BVi/P0Ls6r3EeqnIpd6xj4ee/gNLzi3zJEAlSjqGdkT7i/78vct/GPxtEUqnzNGu\n/hzrHACShITk7jbmkonOsTB8cwGfPhxT5/jXTJY5nWjEqZRQAWdLM5l35D1KrCWeXXJMOazMWEGv\nyD7E+LvPqVfpea3/66w/t5aTxSdRK9QMaT0UrVLLnL2zqbiqy1qgJpDne72AUqHEkVm7rni1b4aE\n+Wx2E6UM4a3Uf6OUlJjl+gvY1BSXbUZpd+H00f5WiZLhcfUXu/FlcOuhHC1seDkdgL/an/sSR1/X\neZorhaSgf8wA+scMaOpQBOGWJhL7LaDs1Vd9ltl0FhUxVNWZXkMG8mXGKqocVXQK7cyg1oNZcvK/\n5Ebr+OzBugnbHy1DVp8ltNhO74OlLH40BrvmBiySUCgoidDyn5nx/GarxMqMFbWSerVCcyEr0pfz\nbM/nPO+pZIm7DlgZeLgcZWgIZQ/qyJTyGZM4jm8ubONC5QXAPXw+LnE8iUFJAEga7xP9bPX8PEb/\nUE+/dMPDD1H13yXIpaU+96+mc6owoqUc7wWCkoKTSA65vvaiw+OHs/zMZ17nVFwtXB9xXevhBUFo\n+URib+ZcJhOOc74rlSmCg1HGRBOi1zOty/Ra2/pE9mZ1xkqszrpLoqJ0rbj32+NQVcXJZCMHU4Ia\nVxymkTKS/HAVtqfU6jtZlliuJHxncTFF05/A/t0hd/9xoHjhXL6eEMWRniG09o/ll71+g0Glp2No\nRxTSlTtx7aABWHfudBe4qSH+oo09Ltlr05bKnPPIsowkSajbtsXwwASqFi0GX13NLlP07Ea3+N5k\nlGZQYi2pVWY2xmJgSn6S+yJMde3/aSklJZM7TmX+sQ8o9XIxVJOf5tqG7QVBuH2IxN7MuUpKoMr7\nDHcAVWJbFF7aWFbZq9iXux+Hq+4SMiVK7u4wFm1KMdYd37LvjuBGlXbVVtmx6pXQiDK/LrWCC0+P\nxb/8gM99ArRXmomU/eFl7AcO1toeVmxn7Je5HOsSwPmKLD4/8z/C9OG8deBv2GU7/mp/Hu7wKPfM\nnIntyDEsX38Nl8vwSoGBDA/ux57sk2TF1/1+8tVW1qV9yb3J7qpzQX9+FU3PnpjXrEGuMuHMzcWR\nkVFrpKQkMZK5k/RcuLSj1rHCy2Hw17kM3l6AxrmP/A9WE/yPv6Pp0qXB7+lqQ2KHEOvfmtUZq7hU\ndYn0krTa/eBxL2nrEXHrT5gTBOHmEIm9mVOGh6OMalW3cQyAVkPg63Vbd+7N2ctHxz4gpyrH6zFd\nuNAqtQT959+UPPscDu2lBuOINcYyY3kObw01UdLIpXBBEa0ZEx7N4YJDdSqhBWmDGZc0HnDXxbcd\nPuz1GFF5NvruK2HngFDOV5znfI1CJsXWYuZ+/w6r0legf9SAZuxwOmVaGV0Qg/+UKVi/2UH8iQNe\nE7tLLbH74reexA5gGH+/pxmPLMtUzpuHZdPXuCrKUcXEsH5iOBdsR+scq0TnotVFk7umOTKO48cp\n/d0fCF+9snH9z6/SNiiR53v9AoAla15nTfkuKi7/CFqHRJ+ofky4hcrFXo+s8iz+e/ITssqzkJBo\nG5jI452fIMJYt7GOIAi1icTezEkaDbqRI6k8+x44at+56VJT0VzVt97mtLHw+Mc+kzq4q3kdzDvI\nkN5DCfzdi4x+dw6RX2WyfUiYp11qTeH6cJ7u/gxdP+hA4JKHKfHxfBkgdVM+g3YWUxpuIEaTgeHe\nUTze+QlWZazwFHiJD0jggXaTSAhIcMdjtyNbfbfj9C+vv3DNxaqLnv99Mk6icEAnftG+PY68fHos\nraDn4TIcKgUnO/qxdWiYpwSrCd/D7pIk4T9jBv4zZnjeu7RtFt4+4tAo+L57IF1OVnresx87hnXv\nXnT9+tUbe30sO3cy7KUv6GIv4ZvBIdhVCnofLKV9iBLpCyf4mMB3qysw5fPG3te5VOP3eqnqEtmV\n2bwx8E3xGEIQGiAS+y0g4MXfAGDZsAFnTi6K4CA0/foRPOf1Ovtuy97KhUrf5WKrKWUo/umzmL/e\nTGRlFaOBAbuK2TAigu1D3VXb9Eo9/aLv5JEOk2llbIVpzVpGrMzkg2kxyMq6SUVlc9HlRAWRBTYi\nC2yU/uZFcDgYPnYMw2Lv4mTxCRQoaB/awTNxDUBhMKBK8N7wptKo5ECvxq8Jl5HZl7OXtLS9BLz6\nZzqeujKbvuPJCtqcNTH/iTiQJMIM4Y0+LlDruX6dbVf3jbfZcJ7PhutI7I6CAir+9nfMX61BLisj\nFGo1uLFnHaJq6VL8Wmh/hc/PfFYrqVfLKj/HF2nLmdp5mpdPCYJQTST2W4AkSQT+9kUCfvECzoJC\nFCHBXp+rg7sHe0M0Cg33bMjHvHp1rQlnwWUOxmwowtS/O0HtOvNIh8mE6d1J3ul0Mnf76+x5NNpr\nUgeIO2+i/Zkr8wHkkhKqFi7EMHYMSoWSLmFdfcZkfGI6jvT0WuvJnRIc6RpAYcS1zf42OUxsWvl3\nxp2qXfBGKUPXI+V0OFVJbo84xiQ2rqtbtXbB7ThTcrrO+zqzk/67a3/vivBwtNfR8tRZXEzRo1Nw\nnDjheydZxn6k7iOBlqK+0abGXLQKwu1OJPZbiKTRoIrx3v602oCYQazKWEXlVeu+q6kUaoa0HkbI\nwm+wXzWLHMBYbuXnx1oR9OCsWu/v/PAVdvX2x6Gpe9eqcLhoe9bEtE+y67R6cZw7h2yzIWnqfy5v\nGH0fkl5H1YKFVGZlcIFijnUwsG5kJBISofpQykzF2KXGrTlXnfa+tl3rkBl+QkHAtJ/Ve6HhzWMd\np3G2NJMTxVeSrsalpP++EuKzr9SqR5LQpd6FqlWrazo+QMW//lV/Ur9MEdRy+5LrlN7b3cL1tbwV\nhNuNSOwtTGv/1vRp1Ydt2VvdBWguU0kq2gUlMyH5Ae5o1ZdTRUsI8HGMfEcpX51YhEqhZFSb+wjU\nBrLDfsJrUgfQ2JyEFVhxKutOFJP0hkYv/dKnpqJPTSUMiJWd2LO3o684z5CYoSQEJfDFP2awOjCT\n4gYm7wUX2xjyje9Kcn1a9yc4+s5GxVSTQW3gtQFzWHduLaeLT6JSqBkWexdJJYcwpa/ClZuLFByE\nbuhQz+OTa2U/VXdE4GqKqCiMTz11Xce/FQyIHsjB/IM4XLXncuiVeobHjWiiqATh1iESewv0XMrz\nhOsjOJx/iCpHFRH6cEa2uZc7o/sDcL48i7MBFq6uwC0Dn02M4mDvbCrOuGukrz+3jtFtx5AT5vv5\nssWgZk//UE538OexRdl0rDEcr+mVgtSI5XFXU0pKhsXVbv4xNMtA9O6zbBoeQVGoBrXdSWGolqJQ\njWetul+FnRGbCvCr8lET32jE+OCka46nmlqpZmziOEgcd+XNJ3vi9+QTyE4nkrLhZYP1kdT1t65V\ntknA/xcvoIpoubPDh8QO5UzpGbZnb/VUHAzSBDGyzShRN14QGkEk9hZIKSmZ0ukxpnR6zOv2dWfX\nciQ1mNZnywktvnJXtLtvMDsGhuJUXJn6XWwpZtnpz5GMEjjkeovYlIRoWDcqgo5nzoJajaZ3b4Jm\nv3bDfi5FYACxF608sfDKc1aHUmLnncFk922LMTyKAX9fT0Shj1n7Wi3GByeh6Xlz1oD/0KQOoB04\nEOu27XU3KJXoH36QoFdeQWH4YU16mjtJkni62wxGtxnNpvObUEoKRiaMuubJjoJwuxKJ/TZkdznI\njjfw0eNx3LMxn1Z5VuxKia1DQ73WQDc5TehlFZJLRvYy3F7T2QQDRyb1Ysg9M9CPHHld67h98Xv6\nJ5i//ApX/pWGNyqnzJC9FQSOuh9VcgeKqjaDl+V4UkAAwf/+N/oRw29YPDeD30+ewnbgoLvYjv3y\nz2EwYJgwnqC/vHFDv8/mLto/hmmdH2/qMAThliMS+20oJSKFLec3k5lo5N2ZV1qrhhb4XksemWfj\nXKvLSd9HmVZw9zfPmHoXo1JG3dCYARytwtjyh3s5d2YP2oJy7tpaSBQB6MeMwThtKgDK+Hicp+q2\nf9Xde2+zT+oAkkpFyIfzMK9fj3XT1yBJ6B+YgK7/tc8JEATh9iQS+23ozuj+9IjowYG82uVeI4uc\nFPkY7WxXoKQ4LoByW5nPpF7tVMkpHC4HKsWN+/PKq8pjzr7ZnFVlQicNEMbhgdE8kvgg9/V4BMD9\njNvH553nfdfbb24kScIwahSGUTf+4kgQhJavZZauEuolSRK/6/sSE9tNolNIJ5KC2jG09TAezm5F\nWH7du/aYC2buvxjJz3s+R8/wFKKNMYRoQ3weP7viPAdy99/QmD869iFny2ovYStX2liZvwmT3QSA\nddcuHGlpXj9vP3Yc5yXf66MFQRBaCnHHfptSK9RMver5ZdXZz3nq339kzYhQLkXrkFwQe8HMhNX5\nhPzuGWKj+tE3yl1JrdBcyE83zcDistQ5toyM2WG6YbE6XA4ySjO8bssz5bEpayPjku5HNpl9treV\n7XZkS91YBUEQWhqR2AUPw8SJdNy1m/hPvsJlMSPJIOl0GMaMxnDVErFQXSjxgfGc9lKJzV/tzx1R\n118j/Wou2YVT9l0v3uJwt07VDh6Esk0CzrPn6uyjTk5GmRB/w2ISBEForsRQvOAhSRLB//wHoZ8s\nwO/RRzE++iihnywg6J//qDMbW5Ikhra+C8nLU22rw0pGqZdudNdJo9TQ2j/O67YgTRB3xbknxSn0\negwPPwR+xlr7KEJCMD4x/brW0wuCINxqxB27UIeuf390/fs3uN/Fqgu1qttVs8k21mauoVv4jSsm\n8kC7iVyszKbQXOh5Ty2pGdh6EOE11jcHPPssqoQEzMuW4youQREZgXHaVHQDB96wWARBEJozkdiF\nayLb7VS+/z7W3XtIKUpHG25l7ahIzIbaxVnKbGU39Lw9Inrw+zte4ov05RSaC9Gr9PSLupORCXVn\njhtGj8YwevQNPb8gCMKtQiR2odFkp5Oip36CdfPXAERd/r92aVX85+dtMBmv/DkFam58k5Kk4Hb8\nps9vb/hxBUEQWhLx0FFoNPOKlVi3bqvzfny2mXvXXukX7q/2Z1Tb+37EyARBEIRq4o5daDTL9m98\nLidLzDQjIREfEM99bUfT/QY+XxcEQRAaTyR2ofHqmVUef9HKv7bGEPuPf6NSij8rQRCEpiKG4oVG\n0993L2i890KXnE40qzZhXrDoR45KEARBqKnZJPaPPvqIqVOnkpqaysqVK5s6HMEL3d3DMYwb67t1\nq92O9evNP25QgiAIQi3NIrHv27ePPn368Mknn/B///d/vP76600dkuCFJEkE/fMfqO+4w+c+cmXl\njxiRIAiCcLVmkdi7detG165dAYiKiiImJqaJIxJ8kSSp3vanyljvFeIEQRCEH0ezSOw6nQ4Am83G\nW2+9xauvvtrEEQn18Zs2DfXlC7GalDEx+P/0mSaISBAEQajWLBI7gNVqZdGiRZw5c4ZnnnmGgoKC\npg5J8EHS6wlZ+DH6cWNRtklA2ToG7V3DCH7736g7d27q8ARBEG5rP8q6pLy8PKZMmeJz+5IlSwgP\nD+fJJ5/kySefZNasWaxfv57HHnvsxwhPuA6qyEhC5v4fsiyDLIsGK4IgCM3Ej5LYIyMj2bRpU6P3\n79u3rzthCM2eJEm+Z8kLgiAIP7pmcZtVUlJCeXm553VGRgb33HNPE0YkCIIgCLemZlEibPXq1Xzw\nwQc8/PDDJCQkMGnSJMLCwpo6LEEQBEG45TSLxD5t2jSmTZvW1GEIgiAIwi2vWQzFC4IgCIJwY4jE\nLgiCIAgtiEjsgiAIgtCCiMQuCIIgCC2ISOyCIAiC0IKIxC4IgiAILUizWO72QzidTgByc3ObOBJB\nEARBuPmq8111/rvaLZ/Yq5vFTJ48uYkjEQRBEIQfT0FBAfHx8XXel+RbvCi7xWLh2LFjhIeHo1Qq\nmzocQRAEQbipnE4nBQUFdOnSxdP2vKZbPrELgiAIgnCFmDwnCIIgCC2ISOyCIAiC0IKIxC4IgiAI\nLYhI7IIgCILQgojELgiCIAgtiEjsgiAILdCFCxc4cuRIU4chNAGR2H8Eb7/9NgMHDqR///588MEH\nTR1Ok9u+fTvvvPMOn376KX/9619xuVxNHVKzsXTpUoYOHcodd9zB66+/jliN6t2ZM2d48sknmzqM\nZunw4cP87Gc/49SpU3To0KGpw2lWli1bxuLFi1m2bBmzZ8/GarU2dUg3hUjsN9nmzZvp2rUr27Zt\n449//CNvvfUWhw8fbuqwmkx2djavvvoqM2bM4JFHHsFms7F06dKmDqtZOHLkCAqFgq+//pq5c+fy\n2WefsXbt2qYOq9lxOBy8/fbb2Gy2pg6l2dmzZw+//OUvefXVVxk+fDgajaapQ2o2srOzWbJkCVOm\nTGHixIlER0ezfPnypg7rphCJ/SYLDg5m6NChqFQqRo4cSceOHcnIyGjqsJrMypUr6dKlC2q1GoA7\n77yTJUuWNHFUzYPdbmfSpEkolUp69+7NoEGDbuu/FV8+//xzRo0a1dRhNDs2m40XX3yRn//854SF\nhTV1OM2O1WolKyuLwsJCACoqKvD392/iqG4Okdhvsl69etV6bbPZ6NatWxNF0/ROnTpFq1atPK+j\noqJIT0/H4XA0YVTNg7e/le7duzdRNM3T6dOnCQ0NJTQ0tKlDaXZ27txJbm4uZ8+e5Xe/+x0PPPAA\nR48ebeqwmo2kpCRSU1OZPHkyixYtwmAwMHr06KYO66YQif1HlJGRQadOnWjXrl1Th9JkTCYTBoPB\n89poNOJyuSgpKWnCqJqfkpISXC4XgwcPbupQmg2Hw8HmzZsZMWJEU4fSLKWnp9O+fXteeOEF3njj\nDSZMmMCvfvWrpg6rWXnttdcIDAzkzTffRJIkJElq6pBuilu+u1tTy8vLY8qUKT63L1myhPDwcBwO\nB59++imvvPLKjxdcMxQYGIjZbPa8NpvNSJJEYGBgE0bV/MyfP585c+a02H94rsfSpUt58MEHmzqM\nZkulUhEdHe15PXToUP785z9TUlJCcHBwE0bWPMiyzKxZs3jrrbc4f/48s2bNIiwsjPvvv7+pQ7vh\nRGL/gSIjI9m0aVOD+3300UfMmDEDo9H4I0TVfHXu3Jnjx497Xufm5pKYmCgm+dSwZMkSxo8fT0RE\nRFOH0qysXr2aRYsWAe6ujiUlJYwcOZIlS5YQEhLSxNE1vcTERL766ivPa71ejyRJ6PX6Joyq+Th5\n8iS5ubnExsYSGxvLCy+8wPr161tkYhdD8T+CxYsXM2rUKMLDw3G5XGzcuPG2XeJ13333ceTIEc8z\n9V27djF+/Pgmjqr5+Oqrr+jSpQuJiYkA7Nixg4qKiiaOqnlYunQp69evZ/369bz55pt069aN9evX\ni6R+Wf/+/SkpKSEvLw+AtLQ0+vfv77Wt5+0oODiY8vJyz7+9ERERREZGNnFUN4do23qTLV68mNmz\nZ6NQuK+hZFlm8uTJvPTSS00cWdPZuHEjx48fJzo6moyMDH796197ZsnfzjZv3sysWbNqrV0fPHgw\n7733XhNG1Tzt3buXd955x3MHL7h9//33LF68mD59+nD27FmmT58uRn5qWL58Oenp6bRr145Tp04x\nY8aMFjkRUyR2QRAEQWhBxFC8IAiCILQgIrELgiAIQgsiErsgCIIgtCAisQuCIAhCCyISuyAIgiC0\nICKxC8J1sNlslJeXN3UYTc5ms/Hdd981dRiCINQgErsgXAOn08ny5csZMWLED26/m5WVxQsvvED7\n9u2ZPn06L7/8MtOnT+f999+vt0/07NmzmT179g8698WLFxkyZAgXL1687mOcPXuWp556StQjF4Rm\nRiR2QbgGJSUl9OvXj5ycnEZ/5r///a/X9+Pj45k0aRIAzzzzDK+99hrvvPMOy5cv5+WXX/Z5vNTU\nVO6+++5rC/wqoaGhPP7444SHh1/3Mdq0acO4ceN+UBwtid1u57PPPmvqMARBJHZBuBZhYWHExMQ0\nev99+/bxxhtv+NyuVCprvTYajQwZMoQtW7b4/Mydd95J3759Gx2DNzqdjunTp//gGv2iSY2bLMvM\nnj1bPJYQmgXRBEYQbpD58+cTHh7OqVOnKCkpYc6cOaxfvx673c7cuXMZOHAg3bp1a/A4FouFgIAA\n0tLS+PTTT/H396e4uJht27axYcMGDh48SElJCWPHjuXIkSO8/fbb9OjRg5ycHLZs2cIjjzzCz3/+\nc8A9wrBw4UICAwPZtWsXDz74IHfffTd5eXl8+eWXTJgwAa1Wy4IFC1i5ciW/+MUv+Ne//oXZbOal\nl15ixIgROBwO3nrrLVq3bk12djYRERE88cQTjfpO9u3bx+7du1GpVHz33Xe88cYbREREkJubyyef\nfEJMTAwHDhwgJSWFxx57jIKCAt5//30yMzNJTU1lw4YN5ObmMm/ePFasWMGWLVvw8/Pjww8/xGw2\n8/HHH3Pw4EFGjx7NBx98gFar5Y033vD0tv/8888pLy/HbDZz/Phx/vCHP9C6dWtWrVrFwoULmTp1\nKuvWreP777/nzTffZMiQIQBs3bqVw4cPc/r0aYxGI3PmzOHChQu8/fbbREREoFAoWLNmDYMGDWLO\nnDlkZmZy5MgRAObOnctTTz0lGhsJTUcWBOGaJScny9u3b/e8Lisrk0ePHu15vWLFClmWZXnPnj1y\ncnKyz+NUb9+zZ48sy7J89OhRuUePHvLChQtlp9Mpz5w5Ux43bpycm5srL1++XE5PT5effPJJ+cUX\nX/QcY9KkSfKMGTNkq9Uqb9u2Te7cubNssVhkl8slT5kyRc7KypJlWZZXrlwp33PPPbLdbpdXrVol\nJycny9nZ2bIsy/KaNWvk5ORkedeuXbLT6ZT/8pe/yD169JCLiorkXbt2yUOGDJFlWZbNZrPcoUMH\nuaysTJZlWV6+fLk8bNgwrz/b6dOn5aeeesrzetq0afL7778vOxwOeezYsXJaWprnmAMHDpRXrlwp\ny7IsL1q0SO7Tp4+ckZEhy7IsP/TQQ/KUKVNks9ks2+12edCgQfKXX34py7Isz5s3T+7Ro4d84sQJ\n2W63y7NmzZKHDBkiW61Wed26dfJzzz3nOf+CBQvkUaNGyTabTbZYLHJycrL87rvvyrIsy2+++ab8\n6KOPyrIsyzk5OfKf//xnWZZl2eFwyAMHDpTnzZsny7IsP//88/IDDzwgl5eXy2lpaXJycrJ8/vx5\nWZZl+cUXX6z1exGEpiKG4gXhBtDpdBQXF/PKK69gMpkYO3bsNX1+wYIF/PrXv+add97hT3/6E1On\nTkWhUBAQEED79u2JjIxkwoQJJCYm0r1791qf1Wq1dO7cGY1GQ9u2bbHb7RQVFXH06FHKy8uJi4sD\nYNy4cXz++eeoVKo68VU3wrjzzjtRKBTMnDkTi8XCwYMHSUlJ4b333sPpdLJ//35cLlejOs4tWbKE\nAQMGeF7PnTuXJ554giNHjnDhwgWSkpI8392YMWM8z6cNBgN+fn60bdsWgLi4OGJiYtDpdKhUKmJj\nYz2T/kJDQwkODqZjx46oVCpmzpxJTk4O6enpfP755/Ts2dNz/okTJ5KZmcmBAwfQarUAnu2JiYnk\n5uYC7vaw+fn5zJs3j/nz55OSkuKZzKjVaklKSsLf39/Tga/6c4LQXIiheEG4ATQaDe+++y6zZs1i\n+/bt/O1vf6N3796N/vzjjz/u87n5tTzHrt7X5XJx/vz5Otv9/f0bdZyAgABCQkKwWCxotVrS0tLY\nunUrEydOBKjVgc6X7OxsEhISPK8NBgMAOTk5WK1WZFn2xBsTE8PGjRu9Hqe6M2LN1zabzeu+1eez\nWCzk5ORgMpk824xGI8HBwZ62pjVJkuT5mS5evEi/fv2YPHlyvT9fze9aEJoTcccuCDdAVVUVCQkJ\nrFmzhtTUVJ5++mksFkuTxhQaGkpmZib5+fme9y5duuQzKV7NZDKRmJjI2rVrWbRoETNnzrymWfSh\noaHs2bOn1ntZWVm0bt0au91Odna2532bzeYZWfghqqqqUCgUJCQk0Lp1a86ePVtru9VqbfA8wcHB\n7N27t9Z71c/PBeFWIBK7IFwjb3doJSUlfPXVVxgMBl566SXUajUOhwO9Xg9AcXExBQUFdT7ndDpr\n/f+rybJc5+7Y5XLVisHX/05JSSEkJIQXXniB48ePc+TIEdauXYtGo/Hsd/Wxq+9w9+7dS+fOnenU\nqRP79u3zHPvQoUMAVFRU4HA4cLlcPu/e7733XrZu3crcuXPJyspixYoV5OXl0bVrV7p3787SpUs9\n++7evZupU6d6jenq967ebrFY/74IjgAAAspJREFUPD/Ptm3bGDt2LCEhIUyePJlNmzZRXFwMuJNz\n27Zt6dmzZ53fYc3vPzU1lU2bNrFgwQIKCgrYsmUL+/fvr/P9Xv070+v1FBcX43K5POcUhKagfOWV\nV15p6iAE4VZRUFDA4sWL2bNnDwqFgvj4eEJDQykvL+eFF15ArVZz8OBBBg8eTEpKCsHBwWzfvp01\na9bQtWtXoqOjPcfKyspi4cKFnDlzBpvNRkJCAmFhYZ7tx44d45NPPiE3N5fk5GRat27NmTNnWLx4\nMefOnaNjx44UFxezcOFCHA4H/fv354svvmDfvn2EhobSr18/7rjjDjZu3MjHH39MWVkZzz33HLIs\ns2zZMrZt24bRaKR9+/YUFRWxYsUKVCoVe/fuZc+ePbz66qv4+flhNBpZunQpq1evJikpiaKiInbu\n3EmnTp1YvHgxp0+fJjk52fNMvFpCQgJ6vZ5FixaxYsUK+vTpw4gRI5AkiaFDh/Lll19y6tQpz0XE\nmDFjPN/vyZMn6d27N3a7ncWLF3Px4kV69+7NpUuX+OSTT7BarfTp04eLFy+yY8cOqqqq2LNnD2lp\nabz00ktoNBoSEhKIiIhgwYIFZGdns3//fl5++WX8/f1ZtWoVmzdvJigoiE6dOjF//nxOnTpFSkoK\nvXr1IiQkhA8//JCFCxdiMBh49tlnycrK4qOPPqK4uJgBAwawbds2Nm/ejJ+fH/369cPPz4/58+eT\nlZXF0KFDxax4oclIcmMelgmC0KLt3buXqVOncvr06aYO5Zp88cUXvPPOO/Wu+xeE240YihcEweNW\nvM6/FWMWhJtJJHZBuM2VlZWxZs0awL1ErTFL2ZqDvLw8tmzZQkFBAV999VWjJwUKQksnhuIFQRAE\noQURd+yCIAiC0IKIxC4IgiAILYhI7IIgCILQgojELgiCIAgtiEjsgiAIgtCCiMQuCIIgCC3I/wMl\nKPh2EABXGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x59dd1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.colors import ListedColormap\n",
    "cmap_set1 = ListedColormap(['#4daf4a', '#377eb8', '#e41a1c' ])\n",
    "\n",
    "plt.set_cmap(plt.cm.Paired)\n",
    "cmap=plt.cm.Paired\n",
    "Pltcolors = cmap([0,1,2,3,4,5])\n",
    "plt.figure(figsize=(8, 6))    \n",
    "plt.scatter(TrainX_2d[:,0], TrainX_2d[:,1], c=(TrainY >= delay_threshold), s = 50, cmap=cmap_set1)\n",
    "plt.ylabel('2nd Principal component' )\n",
    "plt.xlabel('1st Principal component' )\n",
    "plt.title(\"PC2 vs. PC1, classes by delay > 15 Minutes (green)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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