{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тьюториалы:\n",
    "- http://byumcl.bitbucket.org/bootcamp2013/labs/pandas.html\n",
    "- http://pandas.pydata.org/pandas-docs/stable/10min.html\n",
    "- https://www.kaggle.com/c/titanic/details/getting-started-with-python-ii\n",
    "\n",
    "Примеры:\n",
    "- http://pandas.pydata.org/pandas-docs/stable/cookbook.html#cookbook\n",
    "\n",
    "Если нужно что-то более специальное -- смотрите в документации\n",
    "- http://pandas.pydata.org/pandas-docs/stable/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассмотрим сэмпл из данных по авиарейсам в США за январь-апрель 2008 года.\n",
    "\n",
    "\n",
    "Полный датасет можно найти здесь: http://stat-computing.org/dataexpo/2009/2008.csv.bz2\n",
    "\n",
    "Описание: http://stat-computing.org/dataexpo/2009/the-data.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "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>Unnamed: 0</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>...</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>6987797</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>1929</td>\n",
       "      <td>1852</td>\n",
       "      <td>2352</td>\n",
       "      <td>2329</td>\n",
       "      <td>CO</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4060381</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>1014</td>\n",
       "      <td>1006</td>\n",
       "      <td>1249</td>\n",
       "      <td>1258</td>\n",
       "      <td>NW</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>10</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>3336246</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>915</td>\n",
       "      <td>915</td>\n",
       "      <td>1007</td>\n",
       "      <td>1017</td>\n",
       "      <td>EV</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>10</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>115014</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2016</td>\n",
       "      <td>1930</td>\n",
       "      <td>2140</td>\n",
       "      <td>2058</td>\n",
       "      <td>XE</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>398869</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>1119</td>\n",
       "      <td>1120</td>\n",
       "      <td>1224</td>\n",
       "      <td>1240</td>\n",
       "      <td>MQ</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>8</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Year  Month  DayofMonth  DayOfWeek  DepTime  CRSDepTime  \\\n",
       "0     6987797  2008     12           9          2     1929        1852   \n",
       "1     4060381  2008      7          24          4     1014        1006   \n",
       "2     3336246  2008      6           3          2      915         915   \n",
       "3      115014  2008      1          11          5     2016        1930   \n",
       "4      398869  2008      1          11          5     1119        1120   \n",
       "\n",
       "   ArrTime  CRSArrTime UniqueCarrier        ...          TaxiIn TaxiOut  \\\n",
       "0     2352        2329            CO        ...               5      23   \n",
       "1     1249        1258            NW        ...               8      10   \n",
       "2     1007        1017            EV        ...              11      10   \n",
       "3     2140        2058            XE        ...               7      26   \n",
       "4     1224        1240            MQ        ...               3       8   \n",
       "\n",
       "   Cancelled  CancellationCode  Diverted  CarrierDelay  WeatherDelay NASDelay  \\\n",
       "0          0               NaN         0            23             0        0   \n",
       "1          0               NaN         0           NaN           NaN      NaN   \n",
       "2          0               NaN         0           NaN           NaN      NaN   \n",
       "3          0               NaN         0            42             0        0   \n",
       "4          0               NaN         0           NaN           NaN      NaN   \n",
       "\n",
       "  SecurityDelay  LateAircraftDelay  \n",
       "0             0                  0  \n",
       "1           NaN                NaN  \n",
       "2           NaN                NaN  \n",
       "3             0                  0  \n",
       "4           NaN                NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('flights.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Unnamed: 0', u'Year', u'Month', u'DayofMonth', u'DayOfWeek',\n",
       "       u'DepTime', u'CRSDepTime', u'ArrTime', u'CRSArrTime', u'UniqueCarrier',\n",
       "       u'FlightNum', u'TailNum', u'ActualElapsedTime', u'CRSElapsedTime',\n",
       "       u'AirTime', u'ArrDelay', u'DepDelay', u'Origin', u'Dest', u'Distance',\n",
       "       u'TaxiIn', u'TaxiOut', u'Cancelled', u'CancellationCode', u'Diverted',\n",
       "       u'CarrierDelay', u'WeatherDelay', u'NASDelay', u'SecurityDelay',\n",
       "       u'LateAircraftDelay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100000, 30)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1416\n",
       "1     528\n",
       "2     152\n",
       "3     213\n",
       "4     354\n",
       "Name: Distance, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Distance'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'A', 'B', 'C'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CancellationCode'].unique()"
   ]
  },
  {
   "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>DepDelay</th>\n",
       "      <th>ArrDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8</td>\n",
       "      <td>-9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1</td>\n",
       "      <td>-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-3</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-4</td>\n",
       "      <td>-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>44</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DepDelay  ArrDelay\n",
       "0        37        23\n",
       "1         8        -9\n",
       "2         0       -10\n",
       "3        46        42\n",
       "4        -1       -16\n",
       "5        -3        -2\n",
       "6        -4       -12\n",
       "7        44        39\n",
       "8         0         6\n",
       "9        12         9"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['DepDelay', 'ArrDelay']][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Слайсы по столбцам и строкам:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>DayofMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  DayofMonth\n",
       "0  2008     12           9\n",
       "1  2008      7          24\n",
       "2  2008      6           3\n",
       "3  2008      1          11\n",
       "4  2008      1          11\n",
       "5  2008      9          13\n",
       "6  2008      9          25\n",
       "7  2008      4          26\n",
       "8  2008      7           8\n",
       "9  2008      6          20"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:10, 1:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Группировка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x000000000CEFB668>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('UniqueCarrier')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cancelled        \n",
       "0          count     98038\n",
       "           unique      297\n",
       "           top         ATL\n",
       "           freq       5784\n",
       "1          count      1962\n",
       "           unique      204\n",
       "           top         ORD\n",
       "           freq        216\n",
       "dtype: object"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Cancelled')['Origin'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cancelled  CancellationCode\n",
       "1          A                   793\n",
       "           B                   787\n",
       "           C                   382\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.groupby('Cancelled')['CancellationCode'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Объединение данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "carrier = data[data['CancellationCode'] == 'A']\n",
    "weather = data[data['CancellationCode'] == 'B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Unnamed: 0</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>...</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>66</th>\n",
       "      <td>140022</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2216</td>\n",
       "      <td>YV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>197</th>\n",
       "      <td>991373</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045</td>\n",
       "      <td>MQ</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>287</th>\n",
       "      <td>1659482</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2057</td>\n",
       "      <td>9E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>340</th>\n",
       "      <td>2379893</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1335</td>\n",
       "      <td>DL</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>402</th>\n",
       "      <td>2280003</td>\n",
       "      <td>2008</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>730</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1225</td>\n",
       "      <td>AA</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Year  Month  DayofMonth  DayOfWeek  DepTime  CRSDepTime  \\\n",
       "66       140022  2008      1          16          3      NaN        2000   \n",
       "197      991373  2008      2          12          2      NaN         900   \n",
       "287     1659482  2008      3           8          6      NaN        1940   \n",
       "340     2379893  2008      4           9          3      NaN        1045   \n",
       "402     2280003  2008      4          10          4      NaN         730   \n",
       "\n",
       "     ArrTime  CRSArrTime UniqueCarrier        ...          TaxiIn TaxiOut  \\\n",
       "66       NaN        2216            YV        ...             NaN     NaN   \n",
       "197      NaN        1045            MQ        ...             NaN     NaN   \n",
       "287      NaN        2057            9E        ...             NaN     NaN   \n",
       "340      NaN        1335            DL        ...             NaN     NaN   \n",
       "402      NaN        1225            AA        ...             NaN     NaN   \n",
       "\n",
       "     Cancelled  CancellationCode  Diverted  CarrierDelay  WeatherDelay  \\\n",
       "66           1                 A         0           NaN           NaN   \n",
       "197          1                 A         0           NaN           NaN   \n",
       "287          1                 A         0           NaN           NaN   \n",
       "340          1                 A         0           NaN           NaN   \n",
       "402          1                 A         0           NaN           NaN   \n",
       "\n",
       "    NASDelay SecurityDelay  LateAircraftDelay  \n",
       "66       NaN           NaN                NaN  \n",
       "197      NaN           NaN                NaN  \n",
       "287      NaN           NaN                NaN  \n",
       "340      NaN           NaN                NaN  \n",
       "402      NaN           NaN                NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([carrier, weather]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>...</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>66</th>\n",
       "      <td>140022</td>\n",
       "      <td>2008</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2216</td>\n",
       "      <td>YV</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</td>\n",
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       "      <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>197</th>\n",
       "      <td>991373</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1045</td>\n",
       "      <td>MQ</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>287</th>\n",
       "      <td>1659482</td>\n",
       "      <td>2008</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2057</td>\n",
       "      <td>9E</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>A</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>115</th>\n",
       "      <td>754677</td>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1710</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1935</td>\n",
       "      <td>OH</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>B</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>144</th>\n",
       "      <td>5346966</td>\n",
       "      <td>2008</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2242</td>\n",
       "      <td>CO</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>B</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0  Year  Month  DayofMonth  DayOfWeek  DepTime  CRSDepTime  \\\n",
       "66       140022  2008      1          16          3      NaN        2000   \n",
       "197      991373  2008      2          12          2      NaN         900   \n",
       "287     1659482  2008      3           8          6      NaN        1940   \n",
       "115      754677  2008      2          12          2      NaN        1710   \n",
       "144     5346966  2008      9          14          7      NaN        1755   \n",
       "\n",
       "     ArrTime  CRSArrTime UniqueCarrier        ...          TaxiIn TaxiOut  \\\n",
       "66       NaN        2216            YV        ...             NaN     NaN   \n",
       "197      NaN        1045            MQ        ...             NaN     NaN   \n",
       "287      NaN        2057            9E        ...             NaN     NaN   \n",
       "115      NaN        1935            OH        ...             NaN     NaN   \n",
       "144      NaN        2242            CO        ...             NaN     NaN   \n",
       "\n",
       "     Cancelled  CancellationCode  Diverted  CarrierDelay  WeatherDelay  \\\n",
       "66           1                 A         0           NaN           NaN   \n",
       "197          1                 A         0           NaN           NaN   \n",
       "287          1                 A         0           NaN           NaN   \n",
       "115          1                 B         0           NaN           NaN   \n",
       "144          1                 B         0           NaN           NaN   \n",
       "\n",
       "    NASDelay SecurityDelay  LateAircraftDelay  \n",
       "66       NaN           NaN                NaN  \n",
       "197      NaN           NaN                NaN  \n",
       "287      NaN           NaN                NaN  \n",
       "115      NaN           NaN                NaN  \n",
       "144      NaN           NaN                NaN  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([carrier[:3], weather]).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пропуски в данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    23\n",
       "1   NaN\n",
       "2   NaN\n",
       "3    42\n",
       "4   NaN\n",
       "5   NaN\n",
       "6   NaN\n",
       "7    39\n",
       "8   NaN\n",
       "9   NaN\n",
       "Name: CarrierDelay, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CarrierDelay'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21545.000000\n",
       "mean        15.759155\n",
       "std         39.728067\n",
       "min          0.000000\n",
       "25%          0.000000\n",
       "50%          0.000000\n",
       "75%         16.000000\n",
       "max       1092.000000\n",
       "Name: CarrierDelay, dtype: float64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CarrierDelay'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "5     True\n",
       "6     True\n",
       "7    False\n",
       "8     True\n",
       "9     True\n",
       "Name: CarrierDelay, dtype: bool"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['CarrierDelay'].isnull()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "      <td>4060381</td>\n",
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       "      <th>2</th>\n",
       "      <td>3336246</td>\n",
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       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "      <td>915</td>\n",
       "      <td>1007</td>\n",
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       "      <td>EV</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>11</td>\n",
       "      <td>5</td>\n",
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       "      <td>1240</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>5</th>\n",
       "      <td>5140949</td>\n",
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       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>948</td>\n",
       "      <td>951</td>\n",
       "      <td>1122</td>\n",
       "      <td>1124</td>\n",
       "      <td>EV</td>\n",
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       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4917190</td>\n",
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       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>1746</td>\n",
       "      <td>1750</td>\n",
       "      <td>1953</td>\n",
       "      <td>2005</td>\n",
       "      <td>WN</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4192170</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1950</td>\n",
       "      <td>1950</td>\n",
       "      <td>2053</td>\n",
       "      <td>2047</td>\n",
       "      <td>CO</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>25</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>9</th>\n",
       "      <td>3054633</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "      <td>5</td>\n",
       "      <td>2142</td>\n",
       "      <td>2130</td>\n",
       "      <td>2254</td>\n",
       "      <td>2245</td>\n",
       "      <td>WN</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>10</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>10</th>\n",
       "      <td>6797973</td>\n",
       "      <td>2008</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>554</td>\n",
       "      <td>600</td>\n",
       "      <td>741</td>\n",
       "      <td>755</td>\n",
       "      <td>FL</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</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>11</th>\n",
       "      <td>3080727</td>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>28</td>\n",
       "      <td>6</td>\n",
       "      <td>1005</td>\n",
       "      <td>1000</td>\n",
       "      <td>1048</td>\n",
       "      <td>1050</td>\n",
       "      <td>WN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>7</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>13</th>\n",
       "      <td>3858318</td>\n",
       "      <td>2008</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>817</td>\n",
       "      <td>806</td>\n",
       "      <td>936</td>\n",
       "      <td>929</td>\n",
       "      <td>UA</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>32</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",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0  Year  Month  DayofMonth  DayOfWeek  DepTime  CRSDepTime  \\\n",
       "1      4060381  2008      7          24          4     1014        1006   \n",
       "2      3336246  2008      6           3          2      915         915   \n",
       "4       398869  2008      1          11          5     1119        1120   \n",
       "5      5140949  2008      9          13          6      948         951   \n",
       "6      4917190  2008      9          25          4     1746        1750   \n",
       "8      4192170  2008      7           8          2     1950        1950   \n",
       "9      3054633  2008      6          20          5     2142        2130   \n",
       "10     6797973  2008     12          14          7      554         600   \n",
       "11     3080727  2008      6          28          6     1005        1000   \n",
       "13     3858318  2008      7          22          2      817         806   \n",
       "\n",
       "    ArrTime  CRSArrTime UniqueCarrier        ...          TaxiIn TaxiOut  \\\n",
       "1      1249        1258            NW        ...               8      10   \n",
       "2      1007        1017            EV        ...              11      10   \n",
       "4      1224        1240            MQ        ...               3       8   \n",
       "5      1122        1124            EV        ...               4      28   \n",
       "6      1953        2005            WN        ...               8       9   \n",
       "8      2053        2047            CO        ...               5      25   \n",
       "9      2254        2245            WN        ...               7      10   \n",
       "10      741         755            FL        ...               5       8   \n",
       "11     1048        1050            WN        ...               3       7   \n",
       "13      936         929            UA        ...               5      32   \n",
       "\n",
       "    Cancelled  CancellationCode  Diverted  CarrierDelay  WeatherDelay  \\\n",
       "1           0               NaN         0           NaN           NaN   \n",
       "2           0               NaN         0           NaN           NaN   \n",
       "4           0               NaN         0           NaN           NaN   \n",
       "5           0               NaN         0           NaN           NaN   \n",
       "6           0               NaN         0           NaN           NaN   \n",
       "8           0               NaN         0           NaN           NaN   \n",
       "9           0               NaN         0           NaN           NaN   \n",
       "10          0               NaN         0           NaN           NaN   \n",
       "11          0               NaN         0           NaN           NaN   \n",
       "13          0               NaN         0           NaN           NaN   \n",
       "\n",
       "   NASDelay SecurityDelay  LateAircraftDelay  \n",
       "1       NaN           NaN                NaN  \n",
       "2       NaN           NaN                NaN  \n",
       "4       NaN           NaN                NaN  \n",
       "5       NaN           NaN                NaN  \n",
       "6       NaN           NaN                NaN  \n",
       "8       NaN           NaN                NaN  \n",
       "9       NaN           NaN                NaN  \n",
       "10      NaN           NaN                NaN  \n",
       "11      NaN           NaN                NaN  \n",
       "13      NaN           NaN                NaN  \n",
       "\n",
       "[10 rows x 30 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['CarrierDelay'].isnull()][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Избавляемся от пропусков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21545, 30)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.dropna(subset=['CarrierDelay'])\n",
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 30)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_data = data.dropna()\n",
    "new_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_data.to_csv('has_carrier_delay.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тьюториалы:\n",
    "- http://matplotlib.org/users/pyplot_tutorial.html\n",
    "- http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb\n",
    "- http://matplotlib.org/users/beginner.html\n",
    "\n",
    "Примеры (просто делайте ctrl+f тип нужного графика):\n",
    "- http://matplotlib.org/examples/pylab_examples/index.html\n",
    "\n",
    "Документация:\n",
    "- http://matplotlib.org/contents.html\n",
    "\n",
    "Matplotlib + Pandas:\n",
    "- http://pandas.pydata.org/pandas-docs/stable/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = linspace(1, 10, 20)\n",
    "y = np.log(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xd379940>]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XXQQbN4a56/vtF7siEckHGpZJsJkzw0Kkzp3hhRcU7CKSPXXuCeQeblA9bBg88AAMHBi7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\nu4hIAfo/43Sa4luLJCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcefb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0xda5fe10>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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0OMz5EfFj26Nq/BLYHRHf67i3AIBcOKJVXrfxw/ZuNWr5B22fIWlrRPzaPD+zUdL/RsTf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd8d52b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatter(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xdc29d30>]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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A8SHHhLs/6u5Hu3sRsB5YGHJIta3Z1q/LzDoCHzf0F1oyuUdt1AfwP8B8dx8T\ndiAAZrbPtlXw5Mf5U4GKMGNy91vdfX93706w6DXN3YeEGROAmbVJfurCzNoCpxF8tA5N8mPzcjM7\nKPnUyURnEfoSIjAlk7QM6GNmu5qZEXydQl14BjCzfZN/7g+cCzwVZjh8PV9OAIYl7w8FGhyMtsg1\nVM3sKaAI2NvMlgGJbYtOYTGzvsBlwPvJOW4HbnX3/w0xrP2Ax5JtlguAZ919YojxRFkH4I/Jlhat\ngPHuPiXkmAB+DoxPToN8RAQ29CXnkE8Brgw7FgB3f9vMXiCY+tic/HNcuFEB8KKZ7UUQ0zVhLYan\nypfAncDzZvYjYCnwgwbPo01MIiLxk9dz7iIicaXkLiISQ0ruIiIxpOQuIhJDSu4iIjGk5C4iEkNK\n7iIiMaTkLiISQ/8PDNDqEToShl0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdaec668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(x, y, 'bo-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f1c8d0>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qHM8BOAt4CvhALvmBRcCjtN65sF0ossieMvwD8C9PGcsiP/AO4P9MMp5F/lMy\nrwD+10znz2XpKdcH8uZFxARARBwF5qXxU8/nCEN0PpIatK6OvkvrBy2Lc0hLN3uAo8CjEbGbfPL/\nKfD7tN74qS2X7NDK/aik3ZI+k8ZyyX8h8I+SvpaWb/6rpLPIJ3/Zx4AH0vaM5c+lULxZDP2dA5Le\nDjwEfC4iXuGNmYf2HCLiRES8n9Zv58slvZcM8kv6LWAiIsaATve6D132kssjYhnwUeB2Sb9JBn/2\nyWxgGfDn6Rz+H63funPJD4CktwDXAX+RhmYsfy6F4ghwfml/URobdhOS5gNIWgD8OI0fobWG2DYU\n5yNpNq0i8fWIeDgNZ3UOABHxz7TeKvYa8sh/OXCdpL8H/jvwYUlfB45mkB2AiHgxff4JrWXL5eTx\nZw+tFYoXIuKptP8/aBWOXPK3XQs8HRH/mPZnLH8uhWI3cJGkCyTNAVYCW2rONBnx+t8ItwAjafsW\n4OHS+EpJcyRdCFxE6yHDun0V2B8R95TGsjgHSee17+qQ9DbgI8ABMsgfEX8QEedHxLtp/WzvjIhP\nAY8w5NkBJJ2VrkSR9Gu01smfJYM/e4C0PPOCpMVp6Erg+2SSv+TjtH7RaJu5/HU3X7po0lxD606c\ncWB13XkmyfcArbsNXgV+CHwaOAd4LOXeAfyL0vFraN1tcABYMQT5Lwdeo3VH2R7gmfRnfm4O5wBc\nmjKPAfuA/5jGs8hfynQFJ5vZWWSntcbf/rl5tv33M5f8Kc+/pvUL6RjwP2nd9ZRT/rOAnwBnl8Zm\nLL8fuDMzs45yWXoyM7OauFCYmVlHLhRmZtaRC4WZmXXkQmFmZh25UJiZWUcuFGZm1pELhZmZdfT/\nAZsIGe4M9kErAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d107b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['AirTime'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10a00eb8>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GVgH7O8b3F2NDqNV0gEqyr9Nmzp85Ozh/dksrHn9+RDwv6deB+yU9zevXRnq6BrJhwwZG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf1b7828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['AirTime'].hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "С группировкой:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000000104614E0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x0000000010021F98>], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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L7iRNlbRCyWUm90p6TdL2NO1tNWa7DngdKEXE2yPiBODDadq6OqpbRL6ua3H5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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadf7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmp_data = pd.concat([data[data['Origin'] == 'ATL'], \n",
    "                      data[data['Origin'] == 'ORD']])\n",
    "cmp_data['AirTime'].hist(by=cmp_data['Origin'], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000000103E9FD0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000000102080B8>], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x103e9ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmp_data['AirTime'].hist(by=cmp_data['Origin'], \n",
    "                         bins=30, sharey=True, sharex=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как сделать то же самое в Matplotlib?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xca4d780>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xd1ece80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 30\n",
    "fig, axis = plt.subplots(1, 2, sharey=True, sharex=True)\n",
    "axis[0].hist(cmp_data[cmp_data['Origin'] == 'ATL']['AirTime'].dropna(),\n",
    "             bins=bins)\n",
    "axis[0].set_xlabel('AirTime')\n",
    "axis[0].set_ylabel('Scores')\n",
    "axis[1].hist(cmp_data[cmp_data['Origin'] == 'ORD']['AirTime'].dropna(),\n",
    "             bins=bins)\n",
    "axis[1].set_xlabel('AirTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fig.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4XdJPgDOBy4AzgLvTRWYAN0u6kqR7ay/gya6Ou9LWrYd+/T4D28+jpuaaovUa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\nggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc7cedd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 30\n",
    "index = np.arange(bins)\n",
    "plt.hist(cmp_data[cmp_data['Origin'] == 'ATL']['AirTime'].dropna(),\n",
    "         bins=bins, alpha=0.6)\n",
    "plt.hist(cmp_data[cmp_data['Origin'] == 'ORD']['AirTime'].dropna(),\n",
    "         bins=bins, alpha=0.6)\n",
    "plt.xlabel('AirTime')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Comparison of ATL and ORD airports')\n",
    "plt.legend(['ATL', 'ORD'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7FlnXmzr1IDo5JjOzWudjLGZmliknFjMzy5QTi5mZZcqJxczMMuXEYmZmmXJi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x37873198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = 30\n",
    "index = np.arange(bins)\n",
    "plt.hist(cmp_data[cmp_data['Origin'] == 'ATL']['AirTime'].dropna(),\n",
    "         bins=bins, alpha=0.6, normed=True)\n",
    "plt.hist(cmp_data[cmp_data['Origin'] == 'ORD']['AirTime'].dropna(),\n",
    "         bins=bins, alpha=0.6, normed=True)\n",
    "plt.xlabel('AirTime')\n",
    "plt.ylabel('Scores')\n",
    "plt.title('Comparison of ATL and ORD airports')\n",
    "plt.legend(['ATL', 'ORD'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x4d4e3b38>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1EokpNDU9rxaLSIFRF1kflO2xmLq68/npT+8GRtHSshY4mhA0rgMuBr5BGDdJ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\nMCIikhUKMCIikhX/D3j9c+TAYtDjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3adbf550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['DepDelay'], data['ArrDelay'])\n",
    "plt.xlabel('DepDelay')\n",
    "plt.ylabel('ArrDelay')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Component 1 explains 0.97 of total variance\n",
      "Component 2 explains 0.03 of total variance\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x4edf89b0>"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O2/9MMMZ8CzQAjhpjQrJVN8XbDz8IhGU7vbx9W66GDh3q+DkyMpLIyEhnZFkV\nkVmfz6H71OdJuyMFSgjus8qQdjQB8OIMPoBh0KBhgMH657kfKMtXX33Pf+66nbui76B7RhrfG1d+\nHjqSDoMGsjw/o6uVuo5FRUURFRVVIGlfdZuEMcYbcBGR08YYH6weTcOA5sBxERlrjOmL1bDdzxhT\nE/gcuAOrmulnoEpujQ/aJnH9SM9M58PVH/LKt68iR+6A5X/B0VXAEeBhrBrIrLENdwKZgA2IIogy\nvEo/ejCDtP/8h/iuXSnTqJEGBKXy4Mw2CWdUN4UAq40xG7GW8FokIkuxeje1NMbswAoYYwBEJAb4\nAojBWtmll0aC65eI8GXMl9T+qDazNs/C+8fKMG8oHC0DuAPzgDJkXyLU+t2XYMozls/YQTUC8KA+\n4fz06KPUadtWA4RShURHXKsL5GdyvLwGwmVtCwoK4ue9PzNg2QAyJZPRzUdT378+5crdRHq6KxAM\nHAL8gFOcWyL0T0JoyBuk0oUM5tKOsYzjAIlAQ2JifqdGjRqF8TUodc0qjr2b1HXi/Ckxpk+fTIcO\n7S55DEC3br1wcwsnOWgXFbuXBT8Y0XQETYKbsHnTZkYtHk16eiawlnNVS42B14GGlKE0fTnC07gx\nh3bU5v84xCJgA3CIF1/srgFCqUKmJYnr3KVKBefPfXT+es82W1NrcrxspYXcjhHJJNl/IjSbCWW2\nwcp/efHu5/Gx+TJx4gekpWVgDcL3who/maUy5WlCX77kSU7zKT0Yz2COUAY/v3r06/c4fn5+tGjR\nQgOEUvnkzJKEU0bkFdSHG2TEdXx8vERHR0t8fLxT081r6ou89o8YMcqaEiPbKGZ//3oSHR3tOMcx\nbUa2YzxDq4p51Ca8boQ7ywluJQUqCHjYPwH20dXxAiUdI60r8IN8hJv8AzIWFwnGK9dR2Eqpy0Nx\nm5ajoD43QpC41IP8Sp2b+mKFQLTAihwP3dymxvDyCsh1uoyffvrJcV5MTIx4evpb6foeElq1E95E\niHQTPNdkm0fJZv+ECVTOFlTmSwReMg0/OYaR0bSRIFaIq6ufjBs3QWy2QPH3r+fU70KpG40GiWvI\nxUoJF5vD6GrTjo6OFpvtJoFAgfoCgeLlFeEoFeRWIvD3rycjRoxyPKjd3f3F1dVHfH3riqdngHTt\n2kNstkDxCqgpNHcT3jTCfW6Ct4dA1RxpQSWBz3OUHiqxS6bTRo6BDCdAAtnuON7Pr67jXgqiVKXU\njUSDxDUahdgdAAAgAElEQVTiUqWEvB7U2at3rjTtmJiYXGZJtUlMTIxjv6dnQK4BKj4+Xnr37iPk\nqP5ZIbh7CXf3Ft4IElo/LPj7CcTYSyvnX8vbHiBEqjBRZuIqCbjIcBcPebRZiwuO16olpZxHg0Qx\nk9vbb35KCVdaksjPedHR0eLldXOOAGSz3SzR0dGOAOPhES5gE0/PmjkCzZQp0wQ8xZptVQTXFOH2\nN4TXXIXHHxdKZZUA6tmrskQgQqCEvQRhE/CQ6nwjn/GkxFNSBrl4yNeffurIozXld4D4+dXVqiWl\nnEyDRDFyyXURsj2kcyslZJ1/OfXw+UnbetBf+LYeExMjHh4l7Nvn2x/s5cXdPq12fHy8vc2hpmBK\nCHWeF/qUEZ6qIJTxzNHGYZ37k/3nQHuporLUopTMw0WOYmSwe4iU9iqZ631p1ZJSBUODRDFxsTf6\nyyklXO7D8lJpn9s/1v7wriNgkylTpslPP/0kVkNyvH1ftuokPKV37z7i6VVBqOYm9HIRunkK4R4C\nIQLu9sBTxVFaOPdzb6nN/8n/4S5HMPKWR4j88s03GgSUKgIaJIqJS73RX0kpIb8ulnZ0dLT4+dWz\n5yleIFq8vWvKggUL5L333rM/1D+3VxeJvUQRKHCTEOEmdEPoaYSqnQV+tAeQADnXlVXsf5YUiJe6\nzJevcZVDIK/iId6M0DYGpYqQBoliIr/tDgX1Np1X2rlVNVltDDbx8Mha9jOrVDDY2lf2VuFpV6G3\nq1DbTTA2e/uCt4CvPYjUyhEQb6OqLKSxHKCs9KaU2PAUuFnAW0aMGOX0+1VK5Y8zg4SOuL5KWVNU\nuLuHk5YWl+s0FgUt+6jpY8eOUbduA1JTWwMLseZfPAC4Ys2n6AOcAR4EUiHIQLMMKC/w68vwxyeQ\n6Ya18lvW1BlNgCSs+SB/4w7OMpg3qM0axjKaT2hOCi2w5l/aiJfXC+zbt1Mn4VOqiDhzxLUGCSfI\nz4R4BXW9r7/+lj59XsfNrQIpKXGkp6diBYQyWBPopQEeQGngJNay4rFQwhUij1nDG9Y2h+gdkDYB\n6A8EAL9nu+ItwCnu4hiDOUMNXHgbwwwgzYTj6noUYwSbrUqRBUql1DkaJG5g48dPZMCAIXh4RJCS\n8rd9wryqWIvzvACMJ/uMqtbaDB5AirXdxw0avwh1VsAGd1hbBpKPAiWwgghkreOQlUZjGjGEJG4i\nk9G4MosXSeMxbLbufPRRX1q1agWgC/8oVUxokCimCrpE0a3bc8yYMQeoCMTZt2bNqBoFPASUAr7E\nKjGMAqYC/uDpBXfdBrd/DVtcYZU7nB6GtexHGLATazbWsfZ0XYmkJEM4QhiZjMKdOUwinSnAJ4Dn\nBZP/KaWKBw0SxVB+pti+lIut0ZCamsrdd7cEhmI9yEOBWGCG/exeQCDWSrDBwD9AKrjZoIEvNDoE\nu1wgygP+HQ58BJwAVpCz7SGAFoQymGhCcGEkwlzqk8Fi4DDQEF/fSmRkHNJqJaWKKQ0SReBipYS8\nps++1Ft29jR/+WU5Xbs+j6trCBkZR/nvf98hNjaWiRPfw80thNTUI6SnlwaOA7+SszrJAN8DkVgl\nilZWG3M9X2hyDA4GwPKzkFAe60Gfbj+nOrDJnhvhPiIYzH5KYhhJGRaQQAbpWNN7l8HD4xjvvTeO\n+vXrarWSUsWYThVeyHIbVZ29+2nO8RLWuARf35svGF2d/ZzsaXp5lRRjPO0jmKvZ/8zqolo528A1\nd8k5o+oDYs2vFGYfw9BbMN7Cze7CSxWETs2FcpNz6Q4bIOBm375JWvG9/MbNshUj7XAVF2oIeMqQ\nIcMkPj5efvrppxwzwSqlijd0nEThyW0shIdHCfHyCnAEjSlTpp03wvkWxwjnLDmDQoC4u/uf9+C2\nCfjZg4RNAIFnBSbYH+qV7QHB0378Invg8BOoLeAvVDHC8wjdPYSKv9jTjhZrVLRk+9QTCJTWuMoG\njGzGQx7FSwxuAmHi6uqTI+9KqWuLBolClNuo6nPTYItjAN24cRMueGP38PCXBQsWSExMTLZAEy8w\n3J5GjMBM+5+V7EHAT85NfeFl/2RNub3Z/rubPYh4CtQSKtiELi7CC+5CdQ8BV4FBcm66jXP5MmyU\nh/GRjbjIH4RKWzzFEGpP112efbaHlhiUusY5M0joGteXEBFhNURb9f9Z7QCHgCPANmAjLi6l2b17\nN1DOfgzA/0hNTadduwG4uyfg5lbKfnxT+3H7gFuxehbtxxrP4ApkcK4La29gOjABeAkYgzUY7iRg\ng9AwaLYTSqfDiuHwZyWQF7B6OI0HPsVqwL4Lwx08SgkGEU8qMBhXFuED/IvVgJ1Jt27P8PHHUwvm\ni1RKXZucFW0K4kMxKEmIZJ/moo69Omms/XcvyTnZXUn7m/sAuWAtBjztpYTNAqtzaSewCbwj1gJB\nIucm4FthrzIaJI6V3gI9hUfvF14LFRr0FVxt9tJI4HlplhAXykt7SspWjPxGCXkAd4G/7McHS8eO\nT8nMmTMd60wopa59aEmicFWsGI7NVpGkpE+wxh8EA9OA4cCTWKWLhsDNQCugLFb3om32Ty+gAlbp\nobv9+GCsUdFglRrKAouxurX+iVVS8QYeBcKB7eDXFJoshZqZsO5uWPR/kOqLNS4ia2yEVZJxpSbt\n8eMtDnIceBVflpICjARqYvXAyuDddydqLyWlVN6cFW0K4kMxKEnMnTtfvLwCxJroLuutfoVkX3nN\n+tQ8r3SwQsDH/vlG4C77/lD7n5XE6sU0Tc7NqOolMExyTsC3WbAdE1q2sdaSbvGwYDt/NlabvaTi\nLa78Lp2YKTuoIL/iIs1xEXjQXtJBXF19daEfpa5z6AR/hSPn+IcZWKWH8lgT5mUC0Zxrp2iEVTLY\nCSzAKjFk2LcdxBqXsASrZLAi23kNsUod7wJDsMYx2At4HgFwZ3u4Yxb8lQK/loNTJ4CngM+w2h7i\ngWTc8KUTfgwgjn14MYwMVpKKNWfTKe64ox6LFn0L6PQZSl3vdJxEITm3LkNMtvaErLd3P7G6pt5s\nf5Mvay8JfGTfXjLb8Z/bSw7R2docsj617aUNr3MlDDcvoaGL8LqL8IiLEOhz3rUD7aUTf3Fnk3Qn\nWPaCLMVLGuMu4CKAgLs8+OBDsnr16iL9HpVShQstSRSOhIQEypSpSEZGBlaPpHVYbQYRWCOd+wGv\nAt8B84A5WG/3gjVFd9ZMqgn2cxaTe0nC3qvJZTXcsgqajICjibD8Jjh6FKs0sDtbzqrgwRG68QT9\nWEIM8QznNtaxCauXlDsuLi5s3foHNWrUKJgvRylVbDmzJKEN1xdx7Ngx+0+uWFVM1bAm1/sbOA28\niDV76iqs7qZfYFU7VQLOcq7bbNZUGK0AX6zAUNa+PRNIhZqB0PR+OHMcvioL+88CXYC77OdZaXmy\nnu7E0Zc0NvMFj5NGNDasaTka4umZjDFnmTFjigYIpdRV05JEHubNW0CXLj1ISfHAesAnY41ZaIkV\nEIZiPegP2veHYfVIugsraGRgxeAywFGsdodn7OeGYI1fGA+VTkDzvlaTxbIA2J0EVMEKSmnAXqA2\nNk7SgwDe4AgbCGEEifZyiitWe0kNbLamfPvtPOrVq6ftDUrdwHSCvwJ2rsF6OvC4fasHVknib6yH\n9zSsFd76YlUx/YNV/TTYfrwr54KIYK3RkIxjdbjy66F5b6tpY4UbxPwA8jg5q6LuxJvR9ORNXiON\nddzCCGLYhMGaFvwbwAMvr0oYc1hnZVVKAVrdVOBiY2Px8IggKWkl1lfkgtUeUQd4B6sU8Q6wA3gW\nKzAcBhpjVR95c26m1qzjvQFPKN0GmgmUOQ0rfWBTJGQeAvzIPs7Bl4r0wotXeZmVuHAfgWxhB1ap\nJQhYQuvWbRgzZhSnT5/W3kpKqQKhQSIXERERpKTsxerO+i7WFBdlgKXA21jTZmwDegI/AnOByVgD\n5I4BJbGqnuYCk4B5UNIbIltBJRusyYAvb4b03fbzvbFKJbH4sZYXieJlxrOMkzQDYsjEKqlAr14v\n0qDBbTRo0EDbHJRSBU6rm/IwcuRoBg2aAYwGOmE9yCsAu4D/Am8CH2K1URzGWsshA0i1p2CvXvIN\ngXv2w82uEO0P6zIgJYpzVUr3AGmUwI3eZPASZ/gJf0aRwnbSsaqqMvn4449p06aNlhaUUpekbRKF\nYNu2bdSseSswDGuQm70tgTNAa6x2iXJYJYZqwGasxXnSAXfwyoBGHeDWb2FzK1j1JZx1wWqU3uS4\nTklq04c4XuAU32MYTTC7+JdzwQbmzp2vbQ1KqXxzZpBwcUYi15upUz/mlltuB5KA/ljdXB8Fnrf/\n6YrVHekwVlvCNqyusRngbuDuVvBSGvgYmLIJfpoDZ8tjBZadwJ8E8g8j6MEu/qI8Z2mIB124K1uA\nMDRq1Jj4+HgNEEqpIqNB4jxTp37M88/3IS0t1L4lg3NrQf8IvI/VS8kAjwGngE/ANQpubwe906BM\nMszwge96w8kwrGqlg8DXBNGTt7mVnZSmNDO4DXee5SH24IJVwhCM8WTcuPGsXv2rVi8ppYpUkVU3\nGWPux6rcdwGmi8jYXI4p1OqmhIQEwsKqkpKyElgDvGDPXgRWu8RIwB9rPQc3oDyY/VDbDZqehX9q\nwLI9cPh/nGvYDgQOUZpkXsefbpxgPlUZy53sYy4QitXYnYmrqxtvvNGbV199RYODUuqKXfNzN2E9\neXdjzYHtjvUKXT2X465o3pIrdW6upniBYPvMqS5irfSWNXNr1toRm4Rq3wo9KwvdXITwEfb5mtxz\nHBuKp0zARf4BeQ+kPJ4CN9lnkXV3zLG0YMGCq14RLjw83J6efvSjnxvhEx4enuuzAJw3d1NRdYFt\nAOwSkTgAY8x8oA2wvYjyA1hdX5OTd2P1YkrGil+ZWD2VlmC1FeyFiD7QvBt4pMKyibDzFawR14GO\n48vyN33x4ilSmI3hZuAw7vYr7bX/6Y+Xlw8zZkzhiSeeuOr8x8XFZQVXpdQNwBjnFBYupqiCRDms\nNTuzHMAKHEUuPT2r2ylYD3ywGq5bQ9lUaO4CJdNgRSBs/RFkK9ZypgAJhJFJXxLogCszyKQmLhwl\nEyvgGCAFDw8/Ro0aSpMmjXUQnFKqWCv2g+mGDh3q+DkyMpLIyMgCu9bGjRsRSQXHG789SAQdt0ZJ\nlw+AX4fBxgaQ0RhrIr9/AX/C6UF/0nmcTD6mPNWZTQIP2tOYDNwGPMFzzz3AiBHDNDAopZwmKiqK\nqKioAkm7SBqujTENgaEicr/9935YdWhjzztOCjN/n3zyCd2798Ya4PYTlHCFSANVM2FtKETvgjRv\n+9GVgb+pyBsMYDwPk8FUDBMR/qESVukiBWgPfI61XGhT4uK2F1iAsDdWFUjaSqniJ6//89fD3E3r\ngcrGmHCswQbtgQ5FlBeHv//+GwgGn+XQ2AXqZMIGF3jfHZKTsNrarZHSlTnAAIT/8A6T8aYKmSSS\njjUWIhar6yy4u3+Pl1c90tP3MX36ZC1BKKWuKUUyTkJEMrAWY1gK/AXMF5FtRZGX7Hx8fMDlIHRN\nB9MGJvvBcg9IzsAaaX0nVanALOqzjhRiaUoVFjIEIdExwM4bcGXixInEx8dz8OBuli2bRlzcdh0U\nd417++236dGjR6Fcq0uXLgwePPjSB6pCs3r16htzvjRndZMqiI+VvcITExNjdS1zryggAvPF6qrq\nJTUoIZ+DHMXIAFzEHy851yW2pICngL94eZWUuXPnF2q+sxT293W5wsPDxWaziZ+fn5QpU0Y6d+4s\nZ86cKepsFbmZM2fK3XffnWNb586dZdCgQVeU3tChQ+Xpp592RtaKhYiICFm2bFlRZ6NYyuv/PE7s\nAqsjrrOpUaMG3bo9C2lHsEZJt+Nm3mc+qazgBH/iRiUiGI0HJ0nFqlbyBv7l9df7EB39C/v27dAS\nQx6MMSxevJiTJ0+yadMmNm7cyNtvv10g18rMzLz0QcWEiBRKV0Z15awljG9Qzoo2BfGhiN6Mx42b\nIHWNl3yJtxwGeR138SFIrEF1WaWGmwVWCNhk3LgJRZLP8xXV95Vf578Rvvnmm/LQQw85fk9JSZHX\nXntNKlSoIKGhodKzZ09JTk527B87dqyUKVNGypUrJ5988okYY2TPnj0iYr159+zZU1q1aiW+vr6y\nbNmyi6Z37NgxeeihhyQgIEACAwPlnnvucVxnzJgxUq5cOfHz85Pq1avL8uXLRcR6Q3/qqaccxy1c\nuFBq1aolJUuWlKZNm8q2bdty3Ov48eOlTp06EhAQIO3bt5eUlJQLvpNt27aJl5eXuLm5ia+vr5Qs\nWdJxPy+88II8+OCD4ufnJw0bNpS9e/c6zuvTp4+EhYWJv7+/3HbbbbJq1SoREfnxxx/Fw8NDPDw8\nxNfXV+rWrZvr38WYMWOkUqVK4ufnJ7Vq1ZJvvvkmx/5p06ZJjRo1HPs3btwoIiL79++XRx55RIKD\ngyUoKEheeuklERHJzMyUESNGSHh4uISEhMgzzzwjJ0+eFBGRqKgoKV++fI70s/9bGDp0qDzxxBPS\nqVMn8fPzk5tvvll+//13ERF5+umnxcXFRby9vcXPz0/GjRsnycnJ0rFjRylVqpQEBARIgwYNch2I\nOnbsWHnsscdybOvdu7f06dNHREQ+/fRTxz1WqlRJpk6d6jguK89jx46V0NBQ6dSp0wX3cbHvMKt0\n+Prrr0vJkiXlpptukh9++MGx//jx49KlSxcpW7asBAYGysMPP+zYt2jRIqlbt64EBARIo0aN5M8/\n/8z171CkcEoSRR4ILpq5onjopaeLPPKIpJcuLV/f3Vi8QcBLoKw9OGAPFjeJq6uvTJkyrfDzmIdr\nKUjs379fateuLa+88opj/8svvyxt2rSRf//9V06fPi2tW7eWAQMGiIjIDz/8IGXKlJFt27ZJUlKS\nPPXUU+Li4pIjSAQEBMi6detERCQ5Ofmi6fXv31969uwpGRkZkp6eLqtXrxYRkR07dkhYWJgcOXJE\nRETi4uIcD+fs1Tg7duwQHx8fWbZsmaSnp8s777wjlStXlrS0NMe93nHHHXLkyBFJTEyUGjVq5HgI\nZTdz5kxp3Lhxjm2dO3eWoKAg2bBhg2RkZEjHjh2lQ4cOjv2ff/65JCYmSkZGhkycOFFCQ0MdQSg/\n1U1ffvml4x6/+OIL8fHxyfF7+fLlHQ/qPXv2yL59+yQjI0NuueUWee211yQpKUlSUlJkzZo1IiIy\nffp0qVKlisTGxsqZM2fkkUceceQhKipKwsLCclz//CBhs9nkxx9/lMzMTOnfv780bNgwx7FZgVpE\nZOrUqdK6dWtJTk6WzMxM+eOPP+TUqVMX3GNcXJz4+PjI6dOnRUQkIyNDypQpI9HR0SIismTJEvn7\n779FROTXX38Vb29vRzCMiooSNzc36d+/v6SmpkpycvIF93Gx73DmzJni4eEh06dPl8zMTPnoo4+k\nbNmyjnNbtWol7du3lxMnTkh6err8+uuvIiLyxx9/SOnSpWX9+vWSmZkps2fPloiICElNTc3171GD\nRFE99BYvFjl7VqKjo8XLq5bABHuAKC3WlBxGateue9XTaDhbfr4vhuKUz5WIiIgQPz8/8fPzE2OM\ntGjRQk6cOOHY7+Pjk+Ntee3atVKxYkUREenatavjAS8isnv37guCxDPPPJPjehdLb/DgwdK2bVvZ\nvXt3jnN2794tISEh8ssvvzge+FmyP3xHjBgh7dq1c+zLzMyUcuXKycqVKx33OnfuXMf+N998U3r2\n7Jnr95JXkOjevbvj9yVLlkiNGjVyPV9EpGTJko43zitpk6hbt6589913IiJy3333yXvvvXfBMevW\nrZPSpUtLRkbGBfuaN28uH330keP3HTt2iIeHh2RkZOQrSLRs2dKxLyYmRry9vXM9VkRkxowZl3zD\nztK4cWOZM2eOiIgsXbpUKleunOexbdu2ddx3VFSUeHp65ng453Yf2WX/DmfOnClVqlRx7Dt79qwY\nY+To0aNy+PBhcXV1zfFvP0vPnj1l8ODBObZVq1bNEUTOVxhBotgPpisSrVoB1jQdxhwGWgBPA18B\nfXj99TcYN+6C+QivCTKkaMdRLFy4kKZNm7Jq1SqefPJJjh07hr+/PwkJCZw9e5Zbb73VcWxmZmbW\nywKHDh3i9ttvd+wLCwtz7Mu+Lcul0nvjjTcYOnQo9957L8YYunfvTt++falUqRL//e9/GTp0KDEx\nMdx3331MnDiR0NDQHNc6dOgQ4eHhjt+NMYSFhXHw4EHHtpCQEMfP3t7eHD58+LK+q+zX9Pb25vTp\n047fx48fz4wZMxxpnjp1imPHjuU77dmzZ/Puu+8SGxsLwJkzZxzn79+/n0qVKl1wzv79+wkPD8fF\n5cKmzPO/j/DwcNLS0jh69Gi+8nP+vSYnJ5OZmZnrtTp16sSBAwdo3749J06c4KmnnmLUqFG4urpe\ncGyHDh2YN28eTz31FPPmzePJJ5907Pv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x5150f0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components = 2)\n",
    "data_new = pca.fit_transform(data[['DepDelay', 'ArrDelay']].dropna())\n",
    "print 'Component 1 explains %.2f of total variance'%pca.explained_variance_ratio_[0]\n",
    "print 'Component 2 explains %.2f of total variance'%pca.explained_variance_ratio_[1]\n",
    "plt.scatter(data['DepDelay'], data['ArrDelay'])\n",
    "tg = pca.components_[0,1]/pca.components_[0,0]\n",
    "plt.plot([data['DepDelay'].min(), data['DepDelay'].max()],\n",
    "         [data['DepDelay'].min() * tg, data['DepDelay'].max() * tg], color='green')\n",
    "plt.plot([data['DepDelay'].min(), data['DepDelay'].max()],\n",
    "         [data['ArrDelay'].min(), data['ArrDelay'].max()], color = 'red')\n",
    "plt.legend(['Regression that accounts variance', 'Obvious regression'], loc=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задания"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По векторным операциям:\n",
    "1. Найдите несколькими способами диагональ матричного произведения\n",
    "2. Вставьте между каждым числом в заданном векторе четыре нуля\n",
    "3. Найдите ближайшее значение в массиве к заданному\n",
    "4. Найдите ближайшую строчку в массиве к заданной\n",
    "\n",
    "По датасету flights:\n",
    "1. Найдите рейс (FlightNum) с максимальной длиной перелетов. Уникален ли такой рейс?\n",
    "2. Найдите для каждого аэропорта среднее время полета (AirTime) по всем вылетевшим из него рейсам. Какой аэропорт имеет наибольшее значение этого показателя?\n",
    "3. Найдите аэропорт, у которого наибольшая доля задержанных (DepDelay > 0) рейсов. Исключите при этом из рассмотрения аэропорты, из которых было отправлено меньше 1000 рейсов (используйте функцию filter после groupby)."
   ]
  }
 ],
 "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": "2.7.11"
  }
 },
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