amadeus_challenge.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Amadeus challenge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'/home/frallain/workspace/amadeus_challenge'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import pandas as pd\n",
    "\n",
    "import sys\n",
    "import csv\n",
    "import datetime as dt\n",
    "import itertools\n",
    "import time\n",
    "\n",
    "from GeoBases import GeoBase\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First exercise : count the number of lines in Python for each file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000011\n"
     ]
    }
   ],
   "source": [
    "with open('bookings.csv') as f:\n",
    "    for i,line in enumerate(f):\n",
    "        pass\n",
    "print(i+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20390199\n"
     ]
    }
   ],
   "source": [
    "with open('searches.csv') as f:\n",
    "    for i,line in enumerate(f):\n",
    "        pass\n",
    "print(i+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second exercise : top 10 arrival airports in the world in 2013 (using the bookings file)\n",
    "\n",
    "Arrival airport is the column arr_port. It is the IATA code for the airport\n",
    "\n",
    "To get the total number of passengers for an airport, you can sum the column pax, \n",
    "grouping by arr_port. Note that there is negative pax. That corresponds to cancelations. \n",
    "So to get the total number of passengers that have actually booked, \n",
    "you should sum including the negatives (that will remove the cancelled bookings).\n",
    "\n",
    "Print the top 10 arrival airports in the standard output, including the number of passengers.\n",
    "\n",
    "Bonus point: Get the name of the city or airport corresponding to that airport (programatically, we suggest to have a look at GeoBases in Github)\n",
    "\n",
    "Bonus point: Solve this problem using pandas (instead of any other approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# loading only the relevant columns\n",
    "bookings = pd.read_csv('bookings.csv', sep='^', usecols=('arr_port','pax'))\n",
    "bookings.columns = bookings.columns.str.strip()\n",
    "bookings['arr_port'] = bookings['arr_port'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9e42e2ecd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEYCAYAAAB7twADAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e42fba210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bookings.pax.hist()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('pax variable')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer2 = pd.DataFrame( bookings.groupby('arr_port')['pax'].sum() )\n",
    "\n",
    "sum(bookings[bookings.arr_port=='ZSE']['pax'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9e33bf7fd0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e42ea4b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Just to check that there is a difference of 10 between +1 and -1 bookings\n",
    "bookings[bookings.arr_port=='ZSE']['pax'].hist()\n",
    "plt.xlabel('pax variable for ZSE airport')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pax</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arr_port</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LHR</th>\n",
       "      <td>88809</td>\n",
       "      <td>London Heathrow Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCO</th>\n",
       "      <td>70930</td>\n",
       "      <td>Orlando International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAX</th>\n",
       "      <td>70530</td>\n",
       "      <td>Los Angeles International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LAS</th>\n",
       "      <td>69630</td>\n",
       "      <td>Mc Carran International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JFK</th>\n",
       "      <td>66270</td>\n",
       "      <td>John F Kennedy International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDG</th>\n",
       "      <td>64490</td>\n",
       "      <td>Paris - Charles-de-Gaulle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BKK</th>\n",
       "      <td>59460</td>\n",
       "      <td>Suvarnabhumi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MIA</th>\n",
       "      <td>58150</td>\n",
       "      <td>Miami International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SFO</th>\n",
       "      <td>58000</td>\n",
       "      <td>San Francisco International Airport</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DXB</th>\n",
       "      <td>55590</td>\n",
       "      <td>Dubai International Airport</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pax                                  Name\n",
       "arr_port                                             \n",
       "LHR       88809               London Heathrow Airport\n",
       "MCO       70930         Orlando International Airport\n",
       "LAX       70530     Los Angeles International Airport\n",
       "LAS       69630       Mc Carran International Airport\n",
       "JFK       66270  John F Kennedy International Airport\n",
       "CDG       64490             Paris - Charles-de-Gaulle\n",
       "BKK       59460                          Suvarnabhumi\n",
       "MIA       58150           Miami International Airport\n",
       "SFO       58000   San Francisco International Airport\n",
       "DXB       55590           Dubai International Airport"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sudo pip install geobases\n",
    "geo_o = GeoBase(data='ori_por', verbose=False)\n",
    "iata2city = {}\n",
    "for iata in answer2.index:\n",
    "    try:\n",
    "        iata2city[iata] = geo_o.get(iata)['name']\n",
    "    except KeyError:\n",
    "        pass\n",
    "answer2['Name'] = answer2.index.map(lambda x: iata2city.get(x,'') )\n",
    "answer2.sort_values(by='pax',ascending=False, inplace=True)\n",
    "answer2[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Third exercise: plot the monthly number of searches for flights arriving at Málaga, Madrid or Barcelona\n",
    "\n",
    "For the arriving airport, you can use the Destination column in the searches file. \n",
    "Plot a curve for Málaga, another one for Madrid, and another one for Barcelona, \n",
    "in the same figure.\n",
    "\n",
    "Bonus point: Solving this problem using pandas (instead of any other approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20390198, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loading only the relevant columns\n",
    "searches = pd.read_csv('searches.csv', sep='^', usecols=('Date','Destination'))\n",
    "searches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.0, 'AGP@1'), (0.9, 'AGP')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filtering data by keeping data only for Málaga, Madrid and Barcelona airports \n",
    "geo_o = GeoBase(data='ori_por', verbose=False)\n",
    "malaga_iatas = geo_o.fuzzyFind('Málaga', 'name')\n",
    "malaga_iatas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.0, 'MAD@1'),\n",
       " (0.9, 'XTI'),\n",
       " (0.9, 'XOC'),\n",
       " (0.9, 'TOJ'),\n",
       " (0.9, 'MAD'),\n",
       " (0.7692307692307693, 'IXM@1')]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "madrid_iatas = geo_o.fuzzyFind('Madrid', 'name')\n",
    "madrid_iatas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1.0, 'BLA@1'),\n",
       " (1.0, 'BCN@1'),\n",
       " (0.9, 'YJD'),\n",
       " (0.9, 'YJB'),\n",
       " (0.9, 'XJB'),\n",
       " (0.8235294117647058, 'BAZ@1'),\n",
       " (0.7777777777777778, 'QAK@1'),\n",
       " (0.7777777777777778, 'QAK'),\n",
       " (0.75, 'BCA@1')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barcelona_iatas = geo_o.fuzzyFind('Barcelona', 'name')\n",
    "barcelona_iatas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dests = searches.Destination.unique()\n",
    "\n",
    "[ dest for dest in dests if \"@\" in str(dest) ]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Barcelona': ['BLA', 'BCN', 'YJD', 'YJB', 'XJB'],\n",
       " 'Madrid': ['MAD', 'XTI', 'XOC', 'TOJ', 'MAD'],\n",
       " 'M\\xc3\\xa1laga': ['AGP', 'AGP']}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city2iatas = {}\n",
    "city2iatas['Málaga'] = [ iata.rstrip('@1') for score,iata in malaga_iatas if score>=0.9 ]\n",
    "city2iatas['Madrid'] = [ iata.rstrip('@1') for score,iata in madrid_iatas if score>=0.9 ]\n",
    "city2iatas['Barcelona'] = [ iata.rstrip('@1') for score,iata in barcelona_iatas if score>=0.9 ]\n",
    "city2iatas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MAD',\n",
       " 'XTI',\n",
       " 'XOC',\n",
       " 'TOJ',\n",
       " 'MAD',\n",
       " 'AGP',\n",
       " 'AGP',\n",
       " 'BLA',\n",
       " 'BCN',\n",
       " 'YJD',\n",
       " 'YJB',\n",
       " 'XJB']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iatas_of_interest = city2iatas.values()\n",
    "iatas_of_interest = list(itertools.chain.from_iterable(iatas_of_interest))\n",
    "iatas_of_interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(677631, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searches = searches[searches.Destination.isin(iatas_of_interest)]\n",
    "searches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "searches['Date'] = pd.to_datetime(searches['Date'],format='%Y-%m-%d')\n",
    "searches.index= searches['Date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Destination</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">AGP</th>\n",
       "      <th rowspan=\"12\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>9633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>6499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">BCN</th>\n",
       "      <th rowspan=\"12\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>29469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>20276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"9\" valign=\"top\">BLA</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">MAD</th>\n",
       "      <th rowspan=\"12\" valign=\"top\">2013</th>\n",
       "      <th>1</th>\n",
       "      <td>24258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>21147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>20272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TOJ</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>8</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XTI</th>\n",
       "      <th>2013</th>\n",
       "      <th>2</th>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">YJB</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>10</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Date\n",
       "Destination               \n",
       "AGP         2013 1    9633\n",
       "                 2    8379\n",
       "                 3   10659\n",
       "                 4    8265\n",
       "                 5   10830\n",
       "                 6    7923\n",
       "                 7    8892\n",
       "                 8    7866\n",
       "                 9    8151\n",
       "                 10   6499\n",
       "                 11   6384\n",
       "                 12   3696\n",
       "BCN         2013 1   29469\n",
       "                 2   28329\n",
       "                 3   30552\n",
       "                 4   31236\n",
       "                 5   28728\n",
       "                 6   26505\n",
       "                 7   29241\n",
       "                 8   27075\n",
       "                 9   23427\n",
       "                 10  20276\n",
       "                 11  19824\n",
       "                 12  15400\n",
       "BLA         2013 1      57\n",
       "                 3      57\n",
       "                 4      57\n",
       "                 6      57\n",
       "                 7      57\n",
       "                 8     285\n",
       "                 9     228\n",
       "                 11     56\n",
       "                 12    112\n",
       "MAD         2013 1   24258\n",
       "                 2   22800\n",
       "                 3   24681\n",
       "                 4   25251\n",
       "                 5   26334\n",
       "                 6   22800\n",
       "                 7   22971\n",
       "                 8   21831\n",
       "                 9   21147\n",
       "                 10  22294\n",
       "                 11  20272\n",
       "                 12  14504\n",
       "TOJ         2013 8      57\n",
       "                 9      57\n",
       "XTI         2013 2      57\n",
       "YJB         2013 10     56\n",
       "                 11     56"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tableau2 = searches.groupby(by=[searches.Destination, searches.index.year, searches.index.month ]).count()\n",
    "tableau2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f9e08b1f050>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAE8CAYAAAAMvfwgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e08b44f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, a = plt.subplots(1,1)\n",
    "tableau2.xs('AGP').plot(style='-or', ax=a)\n",
    "tableau2.xs('MAD').plot(style='-ob', ax=a)\n",
    "tableau2.xs('BCN').plot(style='-og', ax=a)\n",
    "plt.legend(['AGP','MAD','BCN'])\n",
    "plt.ylabel('monthly number of searches for flights arriving at')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fourth exercise: match searches with bookings\n",
    "\n",
    "For every search in the searches file, find out whether the search ended up in a booking or not \n",
    "(using the info in the bookings file). For instance, search and booking origin and destination \n",
    "should match. \n",
    "For the bookings file, origin and destination are the columns dep_port and arr_port, respectively. \n",
    "Generate a CSV file with the search data, and an additional field, containing 1 if the search \n",
    "ended up in a booking, and 0 otherwise.\n",
    "\n",
    "Bonus point: Solving this problem using pandas (instead of any other approach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "used_columns = ['Date',\n",
    "                 'Origin',\n",
    "                 'Destination',\n",
    "                 'NbSegments',\n",
    "                 'Seg1Departure',\n",
    "                 'Seg1Arrival',\n",
    "                 'Seg2Departure',\n",
    "                 'Seg2Arrival',\n",
    "                 'Seg3Departure',\n",
    "                 'Seg3Arrival',\n",
    "                 'Seg4Departure',\n",
    "                 'Seg4Arrival',\n",
    "                 'Seg5Departure',\n",
    "                 'Seg5Arrival',\n",
    "                 'Seg6Departure',\n",
    "                 'Seg6Arrival' ]\n",
    "searches = pd.read_csv('searches.csv', sep='^', usecols=used_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>NbSegments</th>\n",
       "      <th>Seg1Departure</th>\n",
       "      <th>Seg1Arrival</th>\n",
       "      <th>Seg2Departure</th>\n",
       "      <th>Seg2Arrival</th>\n",
       "      <th>Seg3Departure</th>\n",
       "      <th>Seg3Arrival</th>\n",
       "      <th>Seg4Departure</th>\n",
       "      <th>Seg4Arrival</th>\n",
       "      <th>Seg5Departure</th>\n",
       "      <th>Seg5Arrival</th>\n",
       "      <th>Seg6Departure</th>\n",
       "      <th>Seg6Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>TXL</td>\n",
       "      <td>AUH</td>\n",
       "      <td>2</td>\n",
       "      <td>TXL</td>\n",
       "      <td>AUH</td>\n",
       "      <td>AUH</td>\n",
       "      <td>TXL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2013-01-01</td>\n",
       "      <td>ICT</td>\n",
       "      <td>SFO</td>\n",
       "      <td>2</td>\n",
       "      <td>ICT</td>\n",
       "      <td>SFO</td>\n",
       "      <td>SFO</td>\n",
       "      <td>ICT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2013-01-01</td>\n",
       "      <td>RNB</td>\n",
       "      <td>ARN</td>\n",
       "      <td>1</td>\n",
       "      <td>RNB</td>\n",
       "      <td>ARN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>2013-01-01</td>\n",
       "      <td>OSL</td>\n",
       "      <td>MAD</td>\n",
       "      <td>2</td>\n",
       "      <td>OSL</td>\n",
       "      <td>MAD</td>\n",
       "      <td>MAD</td>\n",
       "      <td>OSL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Origin Destination  NbSegments Seg1Departure Seg1Arrival  \\\n",
       "0  2013-01-01    TXL         AUH           2           TXL         AUH   \n",
       "1  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "2  2013-01-01    ICT         SFO           2           ICT         SFO   \n",
       "3  2013-01-01    RNB         ARN           1           RNB         ARN   \n",
       "4  2013-01-01    OSL         MAD           2           OSL         MAD   \n",
       "\n",
       "  Seg2Departure Seg2Arrival Seg3Departure Seg3Arrival Seg4Departure  \\\n",
       "0           AUH         TXL           NaN         NaN           NaN   \n",
       "1           NaN         NaN           NaN         NaN           NaN   \n",
       "2           SFO         ICT           NaN         NaN           NaN   \n",
       "3           NaN         NaN           NaN         NaN           NaN   \n",
       "4           MAD         OSL           NaN         NaN           NaN   \n",
       "\n",
       "  Seg4Arrival Seg5Departure Seg5Arrival Seg6Departure Seg6Arrival  \n",
       "0         NaN           NaN         NaN           NaN         NaN  \n",
       "1         NaN           NaN         NaN           NaN         NaN  \n",
       "2         NaN           NaN         NaN           NaN         NaN  \n",
       "3         NaN           NaN         NaN           NaN         NaN  \n",
       "4         NaN           NaN         NaN           NaN         NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "time data '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV' does match format specified",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-28a40aafb710>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msearches\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msearches\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Date'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'%Y-%m-%d'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/util/decorators.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     87\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     90\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/tseries/tools.pyc\u001b[0m in \u001b[0;36mto_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, coerce, unit, infer_datetime_format)\u001b[0m\n\u001b[0;32m    274\u001b[0m     return _to_datetime(arg, errors=errors, dayfirst=dayfirst, yearfirst=yearfirst,\n\u001b[0;32m    275\u001b[0m                         \u001b[0mutc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mutc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbox\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbox\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 276\u001b[1;33m                         unit=unit, infer_datetime_format=infer_datetime_format)\n\u001b[0m\u001b[0;32m    277\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/tseries/tools.pyc\u001b[0m in \u001b[0;36m_to_datetime\u001b[1;34m(arg, errors, dayfirst, yearfirst, utc, box, format, exact, unit, freq, infer_datetime_format)\u001b[0m\n\u001b[0;32m    388\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0marg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 390\u001b[1;33m         \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_listlike\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    391\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    392\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mABCIndexClass\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/tseries/tools.pyc\u001b[0m in \u001b[0;36m_convert_listlike\u001b[1;34m(arg, box, format, name)\u001b[0m\n\u001b[0;32m    381\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mDatetimeIndex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_simple_new\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtz\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtz\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    382\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 383\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    384\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    385\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: time data '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV' does match format specified"
     ]
    }
   ],
   "source": [
    "searches['Date'] = pd.to_datetime(searches['Date'],format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "errors = searches[searches.Date.str.contains(',')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV',\n",
       " '2013-01-01,10:15:33,MPT,b0af35b31588dc4ab06d5cf2986e8e02,MD,ATH,MIL,0,1,ATH,MIL,2013-01-04,,,,,,,,,,,,,,,,,,,,,,,,,,,,1ASIWS,0,0,0,d41d8cd98f00b204e9800998ecf8427e,KIV']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "errors.Date.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Origin</th>\n",
       "      <th>Destination</th>\n",
       "      <th>NbSegments</th>\n",
       "      <th>Seg1Departure</th>\n",
       "      <th>Seg1Arrival</th>\n",
       "      <th>Seg2Departure</th>\n",
       "      <th>Seg2Arrival</th>\n",
       "      <th>Seg3Departure</th>\n",
       "      <th>Seg3Arrival</th>\n",
       "      <th>Seg4Departure</th>\n",
       "      <th>Seg4Arrival</th>\n",
       "      <th>Seg5Departure</th>\n",
       "      <th>Seg5Arrival</th>\n",
       "      <th>Seg6Departure</th>\n",
       "      <th>Seg6Arrival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td>1</td>\n",
       "      <td>ATH</td>\n",
       "      <td>MIL</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date Origin Destination  NbSegments Seg1Departure Seg1Arrival  \\\n",
       "0   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "1   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "2   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "3   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "4   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "5   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "6   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "7   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "8   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "9   2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "10  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "11  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "12  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "13  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "14  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "15  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "16  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "17  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "18  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "19  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "20  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "21  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "22  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "23  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "24  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "25  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "26  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "27  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "28  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "29  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "30  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "31  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "32  2013-01-01    ATH         MIL           1           ATH         MIL   \n",
       "\n",
       "   Seg2Departure Seg2Arrival Seg3Departure Seg3Arrival Seg4Departure  \\\n",
       "0                                                                      \n",
       "1                                                                      \n",
       "2                                                                      \n",
       "3                                                                      \n",
       "4                                                                      \n",
       "5                                                                      \n",
       "6                                                                      \n",
       "7                                                                      \n",
       "8                                                                      \n",
       "9                                                                      \n",
       "10                                                                     \n",
       "11                                                                     \n",
       "12                                                                     \n",
       "13                                                                     \n",
       "14                                                                     \n",
       "15                                                                     \n",
       "16                                                                     \n",
       "17                                                                     \n",
       "18                                                                     \n",
       "19                                                                     \n",
       "20                                                                     \n",
       "21                                                                     \n",
       "22                                                                     \n",
       "23                                                                     \n",
       "24                                                                     \n",
       "25                                                                     \n",
       "26                                                                     \n",
       "27                                                                     \n",
       "28                                                                     \n",
       "29                                                                     \n",
       "30                                                                     \n",
       "31                                                                     \n",
       "32                                                                     \n",
       "\n",
       "   Seg4Arrival Seg5Departure Seg5Arrival Seg6Departure Seg6Arrival  \n",
       "0                                                                   \n",
       "1                                                                   \n",
       "2                                                                   \n",
       "3                                                                   \n",
       "4                                                                   \n",
       "5                                                                   \n",
       "6                                                                   \n",
       "7                                                                   \n",
       "8                                                                   \n",
       "9                                                                   \n",
       "10                                                                  \n",
       "11                                                                  \n",
       "12                                                                  \n",
       "13                                                                  \n",
       "14                                                                  \n",
       "15                                                                  \n",
       "16                                                                  \n",
       "17                                                                  \n",
       "18                                                                  \n",
       "19                                                                  \n",
       "20                                                                  \n",
       "21                                                                  \n",
       "22                                                                  \n",
       "23                                                                  \n",
       "24                                                                  \n",
       "25                                                                  \n",
       "26                                                                  \n",
       "27                                                                  \n",
       "28                                                                  \n",
       "29                                                                  \n",
       "30                                                                  \n",
       "31                                                                  \n",
       "32                                                                  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_err = pd.DataFrame([line.split(',') for line in errors.Date.tolist()])\n",
    "df_err.columns = 'Date,Time,TxnCode,OfficeID,Country,Origin,Destination,RoundTrip,NbSegments,Seg1Departure,Seg1Arrival,Seg1Date,Seg1Carrier,Seg1BookingCode,Seg2Departure,Seg2Arrival,Seg2Date,Seg2Carrier,Seg2BookingCode,Seg3Departure,Seg3Arrival,Seg3Date,Seg3Carrier,Seg3BookingCode,Seg4Departure,Seg4Arrival,Seg4Date,Seg4Carrier,Seg4BookingCode,Seg5Departure,Seg5Arrival,Seg5Date,Seg5Carrier,Seg5BookingCode,Seg6Departure,Seg6Arrival,Seg6Date,Seg6Carrier,Seg6BookingCode,From,IsPublishedForNeg,IsFromInternet,IsFromVista,TerminalID,InternetOffice'.split(',')\n",
    "df_err = df_err[used_columns]\n",
    "df_err.NbSegments = df_err.NbSegments.astype(int)\n",
    "df_err"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20390231, 16)\n"
     ]
    }
   ],
   "source": [
    "searches = searches.append(df_err)\n",
    "print searches.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20390231, 16)\n",
      "(20390198, 16)\n"
     ]
    }
   ],
   "source": [
    "print searches.shape\n",
    "errors.index\n",
    "searches.drop(errors.index, inplace=True)\n",
    "print searches.shape # 20390198 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "searches['Date'] = pd.to_datetime(searches['Date'],format='%Y-%m-%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                20390198\n",
       "unique                    359\n",
       "top       2013-01-01 00:00:00\n",
       "freq                    57330\n",
       "first     2013-01-01 00:00:00\n",
       "last      2013-12-25 00:00:00\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "searches.Date.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55eacd73d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEECAYAAAAs+JM2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55eacd7a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "searches.Date.hist(bins=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Origin\n",
      "Destination\n",
      "Seg1Departure\n",
      "Seg1Arrival\n",
      "Seg2Departure\n",
      "Seg2Arrival\n",
      "Seg3Departure\n",
      "Seg3Arrival\n",
      "Seg4Departure\n",
      "Seg4Arrival\n",
      "Seg5Departure\n",
      "Seg5Arrival\n",
      "Seg6Departure\n",
      "Seg6Arrival\n"
     ]
    }
   ],
   "source": [
    "# searches['Seg1Date'] = pd.to_datetime(searches['Seg1Date'],format='%Y-%m-%d')\n",
    "used_columns_to_strip = used_columns\n",
    "used_columns_to_strip.remove('Date')\n",
    "used_columns_to_strip.remove('NbSegments')\n",
    "for column in used_columns_to_strip:\n",
    "    print column\n",
    "    searches[column] = searches[column].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f55ea6287d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEYCAYAAAByXKB5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55ea9531d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "searches.NbSegments.hist()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('NbSegments variable')\n",
    "plt.ylabel('Count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bookings = pd.read_csv('bookings.csv', \n",
    "                       sep='^', \n",
    "                       usecols=('act_date           ','dep_port','arr_port','route          '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_date</th>\n",
       "      <th>dep_port</th>\n",
       "      <th>arr_port</th>\n",
       "      <th>route</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-05 00:00:00</td>\n",
       "      <td>ZRH</td>\n",
       "      <td>LHR</td>\n",
       "      <td>LHRZRH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-26 00:00:00</td>\n",
       "      <td>SAL</td>\n",
       "      <td>CLT</td>\n",
       "      <td>SALATLCLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-26 00:00:00</td>\n",
       "      <td>SAL</td>\n",
       "      <td>CLT</td>\n",
       "      <td>CLTATLSAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-26 00:00:00</td>\n",
       "      <td>AKL</td>\n",
       "      <td>SVO</td>\n",
       "      <td>AKLHKGSVO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-26 00:00:00</td>\n",
       "      <td>AKL</td>\n",
       "      <td>SVO</td>\n",
       "      <td>SVOHKGAKL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              act_date  dep_port  arr_port            route\n",
       "0  2013-03-05 00:00:00  ZRH       LHR       LHRZRH         \n",
       "1  2013-03-26 00:00:00  SAL       CLT       SALATLCLT      \n",
       "2  2013-03-26 00:00:00  SAL       CLT       CLTATLSAL      \n",
       "3  2013-03-26 00:00:00  AKL       SVO       AKLHKGSVO      \n",
       "4  2013-03-26 00:00:00  AKL       SVO       SVOHKGAKL      "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings.columns = bookings.columns.str.strip()\n",
    "bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>act_date</th>\n",
       "      <th>dep_port</th>\n",
       "      <th>arr_port</th>\n",
       "      <th>route</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-03-05</td>\n",
       "      <td>ZRH</td>\n",
       "      <td>LHR</td>\n",
       "      <td>LHRZRH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>SAL</td>\n",
       "      <td>CLT</td>\n",
       "      <td>SALATLCLT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>SAL</td>\n",
       "      <td>CLT</td>\n",
       "      <td>CLTATLSAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>AKL</td>\n",
       "      <td>SVO</td>\n",
       "      <td>AKLHKGSVO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-03-26</td>\n",
       "      <td>AKL</td>\n",
       "      <td>SVO</td>\n",
       "      <td>SVOHKGAKL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    act_date  dep_port  arr_port            route\n",
       "0 2013-03-05  ZRH       LHR       LHRZRH         \n",
       "1 2013-03-26  SAL       CLT       SALATLCLT      \n",
       "2 2013-03-26  SAL       CLT       CLTATLSAL      \n",
       "3 2013-03-26  AKL       SVO       AKLHKGSVO      \n",
       "4 2013-03-26  AKL       SVO       SVOHKGAKL      "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings['act_date'] = pd.to_datetime(bookings['act_date'],format='%Y-%m-%d')\n",
    "bookings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f55ef323050>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEECAYAAADZBhiGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f55ef323f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bookings.act_date.hist(bins=365)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bookings['dep_port'] = bookings['arr_port'].str.strip()\n",
    "bookings['arr_port'] = bookings['arr_port'].str.strip()\n",
    "bookings['route'] = bookings['route'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# bookings['brd_time'] = pd.to_datetime(bookings['brd_time'],format='%Y-%m-%d %H:%M:%S')\n",
    "# bookings[bookings.brd_time == '3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# bookings['brd_time'] = pd.to_datetime(bookings['brd_time'],format='%Y-%m-%d %H:%M:%S', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# bookings['act_date'] = pd.to_datetime(bookings['act_date'],format='%Y-%m-%d %H:%M:%S')\n",
    "# bookings['cre_date'] = pd.to_datetime(bookings['cre_date'],format='%Y-%m-%d %H:%M:%S', errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# bookings['brd_time'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                10000010\n",
       "unique                    365\n",
       "top       2013-01-16 00:00:00\n",
       "freq                   133330\n",
       "first     2013-01-01 00:00:00\n",
       "last      2013-12-31 00:00:00\n",
       "Name: act_date, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bookings['act_date'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# bookings['cre_date'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bookings = bookings.sort_values(by='act_date')\n",
    "# bookings['act_date'].searchsorted('2013-07-19', 'right')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.6759619713\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "search2booking = []\n",
    "\n",
    "for i, Date ,Origin ,Destination ,NbSegments ,Seg1Departure ,Seg1Arrival ,Seg2Departure ,Seg2Arrival ,Seg3Departure ,Seg3Arrival ,Seg4Departure ,Seg4Arrival ,Seg5Departure ,Seg5Arrival ,Seg6Departure ,Seg6Arrival in searches.itertuples():# 20 390 198 rows\n",
    "    if i == 10: break\n",
    "    result = bookings[ (bookings.act_date == Date) &\n",
    "                   (bookings.dep_port == Origin) & \n",
    "                   (bookings.arr_port == Destination) ]\n",
    "    if not result.empty:\n",
    "        search2booking.append(i)\n",
    "print time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "15 second per row... What is wrong with filtering in one pass ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.4883961678\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "search2booking = []\n",
    "# 20 390 198\n",
    "for i, Date ,Origin ,Destination ,NbSegments ,Seg1Departure ,Seg1Arrival ,Seg2Departure ,Seg2Arrival ,Seg3Departure ,Seg3Arrival ,Seg4Departure ,Seg4Arrival ,Seg5Departure ,Seg5Arrival ,Seg6Departure ,Seg6Arrival  in searches.itertuples():# 20 390 198 rows\n",
    "    if i == 1000: break\n",
    "    f1 = bookings[ bookings.act_date == Date ]\n",
    "    f2 = f1[ (f1.dep_port == Origin) ]\n",
    "    f3 = f2[ (f2.arr_port == Destination) ]\n",
    "    if not f3.empty:\n",
    "        print f3\n",
    "        search2booking.append(i)\n",
    "    \n",
    "print time.time() - start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "30 seconds for 1000 rows => 30 * 20 390 / 60 = 10195 minutes = 170 hours..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "searches['index'] = searches.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42.920951128\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "result = pd.merge(bookings, searches, how='inner', left_on=['act_date','dep_port','arr_port'], right_on=['Date','Origin','Destination'], sort=False)\n",
    "print time.time() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134770, 21)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "# result.head(5000)\n",
    "result.to_csv('result.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134770, 11)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus exercise: write a Web Service\n",
    "\n",
    "Wrap the output of the second exercise in a web service that returns the data in JSON format (instead of printing to the standard output). The web service should accept a parameter n>0. For the top 10 airports, n is 10. For the X top airports, n is X.”\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}