Assignment 4(Week 2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "flights = pd.read_csv(\"C:/Users/KIIT/Downloads/ClassNotebooks/flights.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>98</td>\n",
       "      <td>N407AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>408.0</td>\n",
       "      <td>-22.0</td>\n",
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       "      <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>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2336</td>\n",
       "      <td>N3KUAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>N171US</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>258</td>\n",
       "      <td>N3HYAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>806</td>\n",
       "      <td>N3730B</td>\n",
       "      <td>SFO</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NK</td>\n",
       "      <td>612</td>\n",
       "      <td>N635NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>509.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>2013</td>\n",
       "      <td>N584UW</td>\n",
       "      <td>LAX</td>\n",
       "      <td>CLT</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>1112</td>\n",
       "      <td>N3LAAA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>DFW</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>532.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>1173</td>\n",
       "      <td>N826DN</td>\n",
       "      <td>LAS</td>\n",
       "      <td>ATL</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>656.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0  2015      1    1            4      AS             98      N407AS   \n",
       "1  2015      1    1            4      AA           2336      N3KUAA   \n",
       "2  2015      1    1            4      US            840      N171US   \n",
       "3  2015      1    1            4      AA            258      N3HYAA   \n",
       "4  2015      1    1            4      AS            135      N527AS   \n",
       "5  2015      1    1            4      DL            806      N3730B   \n",
       "6  2015      1    1            4      NK            612      N635NK   \n",
       "7  2015      1    1            4      US           2013      N584UW   \n",
       "8  2015      1    1            4      AA           1112      N3LAAA   \n",
       "9  2015      1    1            4      DL           1173      N826DN   \n",
       "\n",
       "  ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  \\\n",
       "0            ANC                 SEA                    5  ...         408.0   \n",
       "1            LAX                 PBI                   10  ...         741.0   \n",
       "2            SFO                 CLT                   20  ...         811.0   \n",
       "3            LAX                 MIA                   20  ...         756.0   \n",
       "4            SEA                 ANC                   25  ...         259.0   \n",
       "5            SFO                 MSP                   25  ...         610.0   \n",
       "6            LAS                 MSP                   25  ...         509.0   \n",
       "7            LAX                 CLT                   30  ...         753.0   \n",
       "8            SFO                 DFW                   30  ...         532.0   \n",
       "9            LAS                 ATL                   30  ...         656.0   \n",
       "\n",
       "   ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  \\\n",
       "0          -22.0         0          0                  NaN               NaN   \n",
       "1           -9.0         0          0                  NaN               NaN   \n",
       "2            5.0         0          0                  NaN               NaN   \n",
       "3           -9.0         0          0                  NaN               NaN   \n",
       "4          -21.0         0          0                  NaN               NaN   \n",
       "5            8.0         0          0                  NaN               NaN   \n",
       "6          -17.0         0          0                  NaN               NaN   \n",
       "7          -10.0         0          0                  NaN               NaN   \n",
       "8          -13.0         0          0                  NaN               NaN   \n",
       "9          -15.0         0          0                  NaN               NaN   \n",
       "\n",
       "   SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0             NaN            NaN                  NaN            NaN  \n",
       "1             NaN            NaN                  NaN            NaN  \n",
       "2             NaN            NaN                  NaN            NaN  \n",
       "3             NaN            NaN                  NaN            NaN  \n",
       "4             NaN            NaN                  NaN            NaN  \n",
       "5             NaN            NaN                  NaN            NaN  \n",
       "6             NaN            NaN                  NaN            NaN  \n",
       "7             NaN            NaN                  NaN            NaN  \n",
       "8             NaN            NaN                  NaN            NaN  \n",
       "9             NaN            NaN                  NaN            NaN  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5819074</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>688</td>\n",
       "      <td>N657JB</td>\n",
       "      <td>LAX</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5819075</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>745</td>\n",
       "      <td>N828JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PSE</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>430.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819076</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1503</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819077</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>333</td>\n",
       "      <td>N527JB</td>\n",
       "      <td>MCO</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>330.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819078</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>839</td>\n",
       "      <td>N534JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>442.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
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       "5819075         430.0          -16.0         0          0   \n",
       "5819076         432.0           -8.0         0          0   \n",
       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
       "5819075                  NaN               NaN             NaN            NaN   \n",
       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
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       "\n",
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       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.tail()"
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  },
  {
   "cell_type": "code",
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       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "5819071  2015     12   31            4      B6            802      N589JB   \n",
       "5819072  2015     12   31            4      B6             98      N607JB   \n",
       "5819073  2015     12   31            4      B6             66      N655JB   \n",
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       "\n",
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       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
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       "\n",
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       "\n",
       "[8 rows x 31 columns]"
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     },
     "execution_count": 12,
     "metadata": {},
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   "source": [
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
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       "      <td>...</td>\n",
       "      <td>504.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819061</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>F9</td>\n",
       "      <td>566</td>\n",
       "      <td>N925FR</td>\n",
       "      <td>DEN</td>\n",
       "      <td>RDU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>446.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819062</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>F9</td>\n",
       "      <td>300</td>\n",
       "      <td>N949FR</td>\n",
       "      <td>DEN</td>\n",
       "      <td>TPA</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>509.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819063</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>516</td>\n",
       "      <td>N17245</td>\n",
       "      <td>SEA</td>\n",
       "      <td>IAH</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>600.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819064</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>668</td>\n",
       "      <td>N66848</td>\n",
       "      <td>LAS</td>\n",
       "      <td>IAD</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>652.0</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819065</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>671</td>\n",
       "      <td>N33209</td>\n",
       "      <td>PDX</td>\n",
       "      <td>IAH</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>545.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819066</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>1849</td>\n",
       "      <td>N497UA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>EWR</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>718.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819067</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2342</td>\n",
       "      <td>N3DLAA</td>\n",
       "      <td>DEN</td>\n",
       "      <td>MIA</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>525.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819068</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>219</td>\n",
       "      <td>N3LYAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>ORD</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>544.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819069</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1248</td>\n",
       "      <td>N948JB</td>\n",
       "      <td>LAS</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819070</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>80</td>\n",
       "      <td>N584JB</td>\n",
       "      <td>RNO</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>744.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819071</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>802</td>\n",
       "      <td>N589JB</td>\n",
       "      <td>SLC</td>\n",
       "      <td>MCO</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>625.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819072</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>98</td>\n",
       "      <td>N607JB</td>\n",
       "      <td>DEN</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>519.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819073</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>66</td>\n",
       "      <td>N655JB</td>\n",
       "      <td>ABQ</td>\n",
       "      <td>JFK</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>549.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819074</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>688</td>\n",
       "      <td>N657JB</td>\n",
       "      <td>LAX</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819075</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>745</td>\n",
       "      <td>N828JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PSE</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>430.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819076</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1503</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819077</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>333</td>\n",
       "      <td>N527JB</td>\n",
       "      <td>MCO</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>330.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819078</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>839</td>\n",
       "      <td>N534JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>442.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>25 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "5819054  2015     12   31            4      AS            175      N431AS   \n",
       "5819055  2015     12   31            4      AS            471      N570AS   \n",
       "5819056  2015     12   31            4      AS            850      N536AS   \n",
       "5819057  2015     12   31            4      NK            371      N661NK   \n",
       "5819058  2015     12   31            4      DL           2224      N332NW   \n",
       "5819059  2015     12   31            4      F9            710      N209FR   \n",
       "5819060  2015     12   31            4      F9            682      N701FR   \n",
       "5819061  2015     12   31            4      F9            566      N925FR   \n",
       "5819062  2015     12   31            4      F9            300      N949FR   \n",
       "5819063  2015     12   31            4      UA            516      N17245   \n",
       "5819064  2015     12   31            4      UA            668      N66848   \n",
       "5819065  2015     12   31            4      UA            671      N33209   \n",
       "5819066  2015     12   31            4      UA           1849      N497UA   \n",
       "5819067  2015     12   31            4      AA           2342      N3DLAA   \n",
       "5819068  2015     12   31            4      AA            219      N3LYAA   \n",
       "5819069  2015     12   31            4      B6           1248      N948JB   \n",
       "5819070  2015     12   31            4      B6             80      N584JB   \n",
       "5819071  2015     12   31            4      B6            802      N589JB   \n",
       "5819072  2015     12   31            4      B6             98      N607JB   \n",
       "5819073  2015     12   31            4      B6             66      N655JB   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "5819054            LAX                 ANC                 2355  ...   \n",
       "5819055            LAX                 SEA                 2355  ...   \n",
       "5819056            KOA                 SEA                 2355  ...   \n",
       "5819057            FLL                 BQN                 2358  ...   \n",
       "5819058            PHX                 DTW                 2359  ...   \n",
       "5819059            DEN                 RSW                 2359  ...   \n",
       "5819060            DEN                 MCO                 2359  ...   \n",
       "5819061            DEN                 RDU                 2359  ...   \n",
       "5819062            DEN                 TPA                 2359  ...   \n",
       "5819063            SEA                 IAH                 2359  ...   \n",
       "5819064            LAS                 IAD                 2359  ...   \n",
       "5819065            PDX                 IAH                 2359  ...   \n",
       "5819066            LAS                 EWR                 2359  ...   \n",
       "5819067            DEN                 MIA                 2359  ...   \n",
       "5819068            LAX                 ORD                 2359  ...   \n",
       "5819069            LAS                 JFK                 2359  ...   \n",
       "5819070            RNO                 JFK                 2359  ...   \n",
       "5819071            SLC                 MCO                 2359  ...   \n",
       "5819072            DEN                 JFK                 2359  ...   \n",
       "5819073            ABQ                 JFK                 2359  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "5819054         400.0          -45.0         0          0   \n",
       "5819055         225.0          -19.0         0          0   \n",
       "5819056         712.0          -38.0         0          0   \n",
       "5819057         346.0           28.0         0          0   \n",
       "5819058         512.0          -31.0         0          0   \n",
       "5819059         518.0          -12.0         0          0   \n",
       "5819060         504.0          -21.0         0          0   \n",
       "5819061         446.0          -18.0         0          0   \n",
       "5819062         509.0           -9.0         0          0   \n",
       "5819063         600.0           -6.0         0          0   \n",
       "5819064         652.0          -29.0         0          0   \n",
       "5819065         545.0          -19.0         0          0   \n",
       "5819066         718.0          -24.0         0          0   \n",
       "5819067         525.0          -18.0         0          0   \n",
       "5819068         544.0          -19.0         0          0   \n",
       "5819069        1020.0          159.0         0          0   \n",
       "5819070         744.0          -21.0         0          0   \n",
       "5819071         625.0           17.0         0          0   \n",
       "5819072         519.0          -11.0         0          0   \n",
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       "5819075         430.0          -16.0         0          0   \n",
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       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "5819054                  NaN               NaN             NaN            NaN   \n",
       "5819055                  NaN               NaN             NaN            NaN   \n",
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       "5819064                  NaN               NaN             NaN            NaN   \n",
       "5819065                  NaN               NaN             NaN            NaN   \n",
       "5819066                  NaN               NaN             NaN            NaN   \n",
       "5819067                  NaN               NaN             NaN            NaN   \n",
       "5819068                  NaN               NaN             NaN            NaN   \n",
       "5819069                  NaN               0.0             0.0          159.0   \n",
       "5819070                  NaN               NaN             NaN            NaN   \n",
       "5819071                  NaN               1.0             0.0           16.0   \n",
       "5819072                  NaN               NaN             NaN            NaN   \n",
       "5819073                  NaN               NaN             NaN            NaN   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
       "5819075                  NaN               NaN             NaN            NaN   \n",
       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "5819054                  NaN            NaN  \n",
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       "5819056                  NaN            NaN  \n",
       "5819057                  0.0            0.0  \n",
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       "5819060                  NaN            NaN  \n",
       "5819061                  NaN            NaN  \n",
       "5819062                  NaN            NaN  \n",
       "5819063                  NaN            NaN  \n",
       "5819064                  NaN            NaN  \n",
       "5819065                  NaN            NaN  \n",
       "5819066                  NaN            NaN  \n",
       "5819067                  NaN            NaN  \n",
       "5819068                  NaN            NaN  \n",
       "5819069                  0.0            0.0  \n",
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       "5819072                  NaN            NaN  \n",
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       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[25 rows x 31 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.tail(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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    {
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      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0        2015      1    1            4      AS             98      N407AS   \n",
       "1        2015      1    1            4      AA           2336      N3KUAA   \n",
       "2        2015      1    1            4      US            840      N171US   \n",
       "3        2015      1    1            4      AA            258      N3HYAA   \n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                  ANC                 SEA                    5  ...   \n",
       "1                  LAX                 PBI                   10  ...   \n",
       "2                  SFO                 CLT                   20  ...   \n",
       "3                  LAX                 MIA                   20  ...   \n",
       "4                  SEA                 ANC                   25  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "0               408.0          -22.0         0          0   \n",
       "1               741.0           -9.0         0          0   \n",
       "2               811.0            5.0         0          0   \n",
       "3               756.0           -9.0         0          0   \n",
       "4               259.0          -21.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
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       "5819075         430.0          -16.0         0          0   \n",
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       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
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       "2                        NaN               NaN             NaN            NaN   \n",
       "3                        NaN               NaN             NaN            NaN   \n",
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       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
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       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                        NaN            NaN  \n",
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       "2                        NaN            NaN  \n",
       "3                        NaN            NaN  \n",
       "4                        NaN            NaN  \n",
       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "flights[flights.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>806</td>\n",
       "      <td>N3730B</td>\n",
       "      <td>SFO</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NK</td>\n",
       "      <td>612</td>\n",
       "      <td>N635NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>509.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>2013</td>\n",
       "      <td>N584UW</td>\n",
       "      <td>LAX</td>\n",
       "      <td>CLT</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>1112</td>\n",
       "      <td>N3LAAA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>DFW</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>532.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819074</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>688</td>\n",
       "      <td>N657JB</td>\n",
       "      <td>LAX</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819075</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>745</td>\n",
       "      <td>N828JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PSE</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>430.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819076</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1503</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819077</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>333</td>\n",
       "      <td>N527JB</td>\n",
       "      <td>MCO</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>330.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819078</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>839</td>\n",
       "      <td>N534JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>442.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5819075 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "5        2015      1    1            4      DL            806      N3730B   \n",
       "6        2015      1    1            4      NK            612      N635NK   \n",
       "7        2015      1    1            4      US           2013      N584UW   \n",
       "8        2015      1    1            4      AA           1112      N3LAAA   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "4                  SEA                 ANC                   25  ...   \n",
       "5                  SFO                 MSP                   25  ...   \n",
       "6                  LAS                 MSP                   25  ...   \n",
       "7                  LAX                 CLT                   30  ...   \n",
       "8                  SFO                 DFW                   30  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "4               259.0          -21.0         0          0   \n",
       "5               610.0            8.0         0          0   \n",
       "6               509.0          -17.0         0          0   \n",
       "7               753.0          -10.0         0          0   \n",
       "8               532.0          -13.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
       "5819074         753.0          -26.0         0          0   \n",
       "5819075         430.0          -16.0         0          0   \n",
       "5819076         432.0           -8.0         0          0   \n",
       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "4                        NaN               NaN             NaN            NaN   \n",
       "5                        NaN               NaN             NaN            NaN   \n",
       "6                        NaN               NaN             NaN            NaN   \n",
       "7                        NaN               NaN             NaN            NaN   \n",
       "8                        NaN               NaN             NaN            NaN   \n",
       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
       "5819075                  NaN               NaN             NaN            NaN   \n",
       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "4                        NaN            NaN  \n",
       "5                        NaN            NaN  \n",
       "6                        NaN            NaN  \n",
       "7                        NaN            NaN  \n",
       "8                        NaN            NaN  \n",
       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819075 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.tail(-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>98</td>\n",
       "      <td>N407AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>408.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2336</td>\n",
       "      <td>N3KUAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>N171US</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>258</td>\n",
       "      <td>N3HYAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>806</td>\n",
       "      <td>N3730B</td>\n",
       "      <td>SFO</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>610.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NK</td>\n",
       "      <td>612</td>\n",
       "      <td>N635NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MSP</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>509.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>2013</td>\n",
       "      <td>N584UW</td>\n",
       "      <td>LAX</td>\n",
       "      <td>CLT</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>1112</td>\n",
       "      <td>N3LAAA</td>\n",
       "      <td>SFO</td>\n",
       "      <td>DFW</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>532.0</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>1173</td>\n",
       "      <td>N826DN</td>\n",
       "      <td>LAS</td>\n",
       "      <td>ATL</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>656.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>2336</td>\n",
       "      <td>N958DN</td>\n",
       "      <td>DEN</td>\n",
       "      <td>ATL</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>453.0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>1674</td>\n",
       "      <td>N853AA</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MIA</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>1434</td>\n",
       "      <td>N547US</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MSP</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>605.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>2324</td>\n",
       "      <td>N3751B</td>\n",
       "      <td>SLC</td>\n",
       "      <td>ATL</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>553.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>2440</td>\n",
       "      <td>N651DL</td>\n",
       "      <td>SEA</td>\n",
       "      <td>MSP</td>\n",
       "      <td>40</td>\n",
       "      <td>...</td>\n",
       "      <td>557.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>108</td>\n",
       "      <td>N309AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>455.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>1560</td>\n",
       "      <td>N3743H</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>451.0</td>\n",
       "      <td>-24.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>UA</td>\n",
       "      <td>1197</td>\n",
       "      <td>N78448</td>\n",
       "      <td>SFO</td>\n",
       "      <td>IAH</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>619.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>122</td>\n",
       "      <td>N413AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>PDX</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>507.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>DL</td>\n",
       "      <td>1670</td>\n",
       "      <td>N806DN</td>\n",
       "      <td>PDX</td>\n",
       "      <td>MSP</td>\n",
       "      <td>50</td>\n",
       "      <td>...</td>\n",
       "      <td>551.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NK</td>\n",
       "      <td>520</td>\n",
       "      <td>N525NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>MCI</td>\n",
       "      <td>55</td>\n",
       "      <td>...</td>\n",
       "      <td>543.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>371</td>\n",
       "      <td>N3GXAA</td>\n",
       "      <td>SEA</td>\n",
       "      <td>MIA</td>\n",
       "      <td>100</td>\n",
       "      <td>...</td>\n",
       "      <td>939.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>NK</td>\n",
       "      <td>214</td>\n",
       "      <td>N632NK</td>\n",
       "      <td>LAS</td>\n",
       "      <td>DFW</td>\n",
       "      <td>103</td>\n",
       "      <td>...</td>\n",
       "      <td>529.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>115</td>\n",
       "      <td>N3CTAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>839.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>24 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0   2015      1    1            4      AS             98      N407AS   \n",
       "1   2015      1    1            4      AA           2336      N3KUAA   \n",
       "2   2015      1    1            4      US            840      N171US   \n",
       "3   2015      1    1            4      AA            258      N3HYAA   \n",
       "4   2015      1    1            4      AS            135      N527AS   \n",
       "5   2015      1    1            4      DL            806      N3730B   \n",
       "6   2015      1    1            4      NK            612      N635NK   \n",
       "7   2015      1    1            4      US           2013      N584UW   \n",
       "8   2015      1    1            4      AA           1112      N3LAAA   \n",
       "9   2015      1    1            4      DL           1173      N826DN   \n",
       "10  2015      1    1            4      DL           2336      N958DN   \n",
       "11  2015      1    1            4      AA           1674      N853AA   \n",
       "12  2015      1    1            4      DL           1434      N547US   \n",
       "13  2015      1    1            4      DL           2324      N3751B   \n",
       "14  2015      1    1            4      DL           2440      N651DL   \n",
       "15  2015      1    1            4      AS            108      N309AS   \n",
       "16  2015      1    1            4      DL           1560      N3743H   \n",
       "17  2015      1    1            4      UA           1197      N78448   \n",
       "18  2015      1    1            4      AS            122      N413AS   \n",
       "19  2015      1    1            4      DL           1670      N806DN   \n",
       "20  2015      1    1            4      NK            520      N525NK   \n",
       "21  2015      1    1            4      AA            371      N3GXAA   \n",
       "22  2015      1    1            4      NK            214      N632NK   \n",
       "23  2015      1    1            4      AA            115      N3CTAA   \n",
       "\n",
       "   ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  \\\n",
       "0             ANC                 SEA                    5  ...         408.0   \n",
       "1             LAX                 PBI                   10  ...         741.0   \n",
       "2             SFO                 CLT                   20  ...         811.0   \n",
       "3             LAX                 MIA                   20  ...         756.0   \n",
       "4             SEA                 ANC                   25  ...         259.0   \n",
       "5             SFO                 MSP                   25  ...         610.0   \n",
       "6             LAS                 MSP                   25  ...         509.0   \n",
       "7             LAX                 CLT                   30  ...         753.0   \n",
       "8             SFO                 DFW                   30  ...         532.0   \n",
       "9             LAS                 ATL                   30  ...         656.0   \n",
       "10            DEN                 ATL                   30  ...         453.0   \n",
       "11            LAS                 MIA                   35  ...         753.0   \n",
       "12            LAX                 MSP                   35  ...         605.0   \n",
       "13            SLC                 ATL                   40  ...         553.0   \n",
       "14            SEA                 MSP                   40  ...         557.0   \n",
       "15            ANC                 SEA                   45  ...         455.0   \n",
       "16            ANC                 SEA                   45  ...         451.0   \n",
       "17            SFO                 IAH                   48  ...         619.0   \n",
       "18            ANC                 PDX                   50  ...         507.0   \n",
       "19            PDX                 MSP                   50  ...         551.0   \n",
       "20            LAS                 MCI                   55  ...         543.0   \n",
       "21            SEA                 MIA                  100  ...         939.0   \n",
       "22            LAS                 DFW                  103  ...         529.0   \n",
       "23            LAX                 MIA                  105  ...         839.0   \n",
       "\n",
       "    ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  \\\n",
       "0           -22.0         0          0                  NaN               NaN   \n",
       "1            -9.0         0          0                  NaN               NaN   \n",
       "2             5.0         0          0                  NaN               NaN   \n",
       "3            -9.0         0          0                  NaN               NaN   \n",
       "4           -21.0         0          0                  NaN               NaN   \n",
       "5             8.0         0          0                  NaN               NaN   \n",
       "6           -17.0         0          0                  NaN               NaN   \n",
       "7           -10.0         0          0                  NaN               NaN   \n",
       "8           -13.0         0          0                  NaN               NaN   \n",
       "9           -15.0         0          0                  NaN               NaN   \n",
       "10          -30.0         0          0                  NaN               NaN   \n",
       "11          -10.0         0          0                  NaN               NaN   \n",
       "12           -4.0         0          0                  NaN               NaN   \n",
       "13          -22.0         0          0                  NaN               NaN   \n",
       "14            8.0         0          0                  NaN               NaN   \n",
       "15          -14.0         0          0                  NaN               NaN   \n",
       "16          -24.0         0          0                  NaN               NaN   \n",
       "17           -7.0         0          0                  NaN               NaN   \n",
       "18          -18.0         0          0                  NaN               NaN   \n",
       "19          -12.0         0          0                  NaN               NaN   \n",
       "20            6.0         0          0                  NaN               NaN   \n",
       "21            1.0         0          0                  NaN               NaN   \n",
       "22           -1.0         0          0                  NaN               NaN   \n",
       "23          -12.0         0          0                  NaN               NaN   \n",
       "\n",
       "    SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0              NaN            NaN                  NaN            NaN  \n",
       "1              NaN            NaN                  NaN            NaN  \n",
       "2              NaN            NaN                  NaN            NaN  \n",
       "3              NaN            NaN                  NaN            NaN  \n",
       "4              NaN            NaN                  NaN            NaN  \n",
       "5              NaN            NaN                  NaN            NaN  \n",
       "6              NaN            NaN                  NaN            NaN  \n",
       "7              NaN            NaN                  NaN            NaN  \n",
       "8              NaN            NaN                  NaN            NaN  \n",
       "9              NaN            NaN                  NaN            NaN  \n",
       "10             NaN            NaN                  NaN            NaN  \n",
       "11             NaN            NaN                  NaN            NaN  \n",
       "12             NaN            NaN                  NaN            NaN  \n",
       "13             NaN            NaN                  NaN            NaN  \n",
       "14             NaN            NaN                  NaN            NaN  \n",
       "15             NaN            NaN                  NaN            NaN  \n",
       "16             NaN            NaN                  NaN            NaN  \n",
       "17             NaN            NaN                  NaN            NaN  \n",
       "18             NaN            NaN                  NaN            NaN  \n",
       "19             NaN            NaN                  NaN            NaN  \n",
       "20             NaN            NaN                  NaN            NaN  \n",
       "21             NaN            NaN                  NaN            NaN  \n",
       "22             NaN            NaN                  NaN            NaN  \n",
       "23             NaN            NaN                  NaN            NaN  \n",
       "\n",
       "[24 rows x 31 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<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>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>98</td>\n",
       "      <td>N407AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>408.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2336</td>\n",
       "      <td>N3KUAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>741.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>N171US</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>811.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>258</td>\n",
       "      <td>N3HYAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>756.0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0  2015      1    1            4      AS             98      N407AS   \n",
       "1  2015      1    1            4      AA           2336      N3KUAA   \n",
       "2  2015      1    1            4      US            840      N171US   \n",
       "3  2015      1    1            4      AA            258      N3HYAA   \n",
       "4  2015      1    1            4      AS            135      N527AS   \n",
       "\n",
       "  ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  \\\n",
       "0            ANC                 SEA                    5  ...         408.0   \n",
       "1            LAX                 PBI                   10  ...         741.0   \n",
       "2            SFO                 CLT                   20  ...         811.0   \n",
       "3            LAX                 MIA                   20  ...         756.0   \n",
       "4            SEA                 ANC                   25  ...         259.0   \n",
       "\n",
       "   ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  \\\n",
       "0          -22.0         0          0                  NaN               NaN   \n",
       "1           -9.0         0          0                  NaN               NaN   \n",
       "2            5.0         0          0                  NaN               NaN   \n",
       "3           -9.0         0          0                  NaN               NaN   \n",
       "4          -21.0         0          0                  NaN               NaN   \n",
       "\n",
       "   SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0             NaN            NaN                  NaN            NaN  \n",
       "1             NaN            NaN                  NaN            NaN  \n",
       "2             NaN            NaN                  NaN            NaN  \n",
       "3             NaN            NaN                  NaN            NaN  \n",
       "4             NaN            NaN                  NaN            NaN  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>98</td>\n",
       "      <td>N407AS</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>408.0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0  2015      1    1            4      AS             98      N407AS   \n",
       "\n",
       "  ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  \\\n",
       "0            ANC                 SEA                    5  ...         408.0   \n",
       "\n",
       "   ARRIVAL_DELAY  DIVERTED  CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  \\\n",
       "0          -22.0         0          0                  NaN               NaN   \n",
       "\n",
       "   SECURITY_DELAY  AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0             NaN            NaN                  NaN            NaN  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK', 'AIRLINE', 'FLIGHT_NUMBER',\n",
       "       'TAIL_NUMBER', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT',\n",
       "       'SCHEDULED_DEPARTURE', 'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'TAXI_OUT',\n",
       "       'WHEELS_OFF', 'SCHEDULED_TIME', 'ELAPSED_TIME', 'AIR_TIME', 'DISTANCE',\n",
       "       'WHEELS_ON', 'TAXI_IN', 'SCHEDULED_ARRIVAL', 'ARRIVAL_TIME',\n",
       "       'ARRIVAL_DELAY', 'DIVERTED', 'CANCELLED', 'CANCELLATION_REASON',\n",
       "       'AIR_SYSTEM_DELAY', 'SECURITY_DELAY', 'AIRLINE_DELAY',\n",
       "       'LATE_AIRCRAFT_DELAY', 'WEATHER_DELAY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'rows'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-19-1afdb431f704>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5485\u001b[0m         ):\n\u001b[0;32m   5486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'rows'"
     ]
    }
   ],
   "source": [
    "flights.rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK', 'AIRLINE', 'FLIGHT_NUMBER',\n",
       "       'TAIL_NUMBER', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT',\n",
       "       'SCHEDULED_DEPARTURE', 'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'TAXI_OUT',\n",
       "       'WHEELS_OFF', 'SCHEDULED_TIME', 'ELAPSED_TIME', 'AIR_TIME', 'DISTANCE',\n",
       "       'WHEELS_ON', 'TAXI_IN', 'SCHEDULED_ARRIVAL', 'ARRIVAL_TIME',\n",
       "       'ARRIVAL_DELAY', 'DIVERTED', 'CANCELLED', 'CANCELLATION_REASON',\n",
       "       'AIR_SYSTEM_DELAY', 'SECURITY_DELAY', 'AIRLINE_DELAY',\n",
       "       'LATE_AIRCRAFT_DELAY', 'WEATHER_DELAY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>YEAR</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2336</td>\n",
       "      <td>N3KUAA</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>741.0</td>\n",
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       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
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       "      <td>811.0</td>\n",
       "      <td>5.0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
       "      <td>2015</td>\n",
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       "      <td>AA</td>\n",
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       "      <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>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>N527AS</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>...</td>\n",
       "      <td>259.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819074</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>688</td>\n",
       "      <td>N657JB</td>\n",
       "      <td>LAX</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>753.0</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819075</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>745</td>\n",
       "      <td>N828JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PSE</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>430.0</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819076</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1503</td>\n",
       "      <td>N913JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819077</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>333</td>\n",
       "      <td>N527JB</td>\n",
       "      <td>MCO</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>330.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819078</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>839</td>\n",
       "      <td>N534JB</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>2359</td>\n",
       "      <td>...</td>\n",
       "      <td>442.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</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",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5819079 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0        2015      1    1            4      AS             98      N407AS   \n",
       "1        2015      1    1            4      AA           2336      N3KUAA   \n",
       "2        2015      1    1            4      US            840      N171US   \n",
       "3        2015      1    1            4      AA            258      N3HYAA   \n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                  ANC                 SEA                    5  ...   \n",
       "1                  LAX                 PBI                   10  ...   \n",
       "2                  SFO                 CLT                   20  ...   \n",
       "3                  LAX                 MIA                   20  ...   \n",
       "4                  SEA                 ANC                   25  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "0               408.0          -22.0         0          0   \n",
       "1               741.0           -9.0         0          0   \n",
       "2               811.0            5.0         0          0   \n",
       "3               756.0           -9.0         0          0   \n",
       "4               259.0          -21.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
       "5819074         753.0          -26.0         0          0   \n",
       "5819075         430.0          -16.0         0          0   \n",
       "5819076         432.0           -8.0         0          0   \n",
       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0                        NaN               NaN             NaN            NaN   \n",
       "1                        NaN               NaN             NaN            NaN   \n",
       "2                        NaN               NaN             NaN            NaN   \n",
       "3                        NaN               NaN             NaN            NaN   \n",
       "4                        NaN               NaN             NaN            NaN   \n",
       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
       "5819075                  NaN               NaN             NaN            NaN   \n",
       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                        NaN            NaN  \n",
       "1                        NaN            NaN  \n",
       "2                        NaN            NaN  \n",
       "3                        NaN            NaN  \n",
       "4                        NaN            NaN  \n",
       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[flights.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-27964b25fb07>:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  flights[flights.isnull().any(axis=0)]\n"
     ]
    },
    {
     "ename": "IndexingError",
     "evalue": "Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexingError\u001b[0m                             Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-27964b25fb07>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mflights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3444\u001b[0m         \u001b[1;31m# Do we have a (boolean) 1d indexer?\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3446\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_bool_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3448\u001b[0m         \u001b[1;31m# We are left with two options: a single key, and a collection of keys,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_bool_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3497\u001b[0m         \u001b[1;31m# check_bool_indexer will throw exception if Series key cannot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3498\u001b[0m         \u001b[1;31m# be reindexed to match DataFrame rows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3499\u001b[1;33m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3500\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3501\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_take_with_is_copy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36mcheck_bool_indexer\u001b[1;34m(index, key)\u001b[0m\n\u001b[0;32m   2386\u001b[0m         \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2387\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2388\u001b[1;33m             raise IndexingError(\n\u001b[0m\u001b[0;32m   2389\u001b[0m                 \u001b[1;34m\"Unalignable boolean Series provided as \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2390\u001b[0m                 \u001b[1;34m\"indexer (index of the boolean Series and of \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexingError\u001b[0m: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match)."
     ]
    }
   ],
   "source": [
    "flights[flights.isnull().any(axis=0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [YEAR, MONTH, DAY, DAY_OF_WEEK, AIRLINE, FLIGHT_NUMBER, TAIL_NUMBER, ORIGIN_AIRPORT, DESTINATION_AIRPORT, SCHEDULED_DEPARTURE, DEPARTURE_TIME, DEPARTURE_DELAY, TAXI_OUT, WHEELS_OFF, SCHEDULED_TIME, ELAPSED_TIME, AIR_TIME, DISTANCE, WHEELS_ON, TAXI_IN, SCHEDULED_ARRIVAL, ARRIVAL_TIME, ARRIVAL_DELAY, DIVERTED, CANCELLED, CANCELLATION_REASON, AIR_SYSTEM_DELAY, SECURITY_DELAY, AIRLINE_DELAY, LATE_AIRCRAFT_DELAY, WEATHER_DELAY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 31 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[flights.isnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-82.000000</td>\n",
       "      <td>-87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.370158</td>\n",
       "      <td>4.407057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1988.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  ARRIVAL_DELAY\n",
       "min        -82.000000     -87.000000\n",
       "mean         9.370158       4.407057\n",
       "max       1988.000000    1971.000000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['DEPARTURE_DELAY','ARRIVAL_DELAY']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [YEAR, MONTH, DAY, DAY_OF_WEEK, AIRLINE, FLIGHT_NUMBER, TAIL_NUMBER, ORIGIN_AIRPORT, DESTINATION_AIRPORT, SCHEDULED_DEPARTURE, DEPARTURE_TIME, DEPARTURE_DELAY, TAXI_OUT, WHEELS_OFF, SCHEDULED_TIME, ELAPSED_TIME, AIR_TIME, DISTANCE, WHEELS_ON, TAXI_IN, SCHEDULED_ARRIVAL, ARRIVAL_TIME, ARRIVAL_DELAY, DIVERTED, CANCELLED, CANCELLATION_REASON, AIR_SYSTEM_DELAY, SECURITY_DELAY, AIRLINE_DELAY, LATE_AIRCRAFT_DELAY, WEATHER_DELAY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 31 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[flights.isnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['YEAR', 'MONTH', 'DAY', 'DAY_OF_WEEK', 'AIRLINE', 'FLIGHT_NUMBER',\n",
       "       'TAIL_NUMBER', 'ORIGIN_AIRPORT', 'DESTINATION_AIRPORT',\n",
       "       'SCHEDULED_DEPARTURE', 'DEPARTURE_TIME', 'DEPARTURE_DELAY', 'TAXI_OUT',\n",
       "       'WHEELS_OFF', 'SCHEDULED_TIME', 'ELAPSED_TIME', 'AIR_TIME', 'DISTANCE',\n",
       "       'WHEELS_ON', 'TAXI_IN', 'SCHEDULED_ARRIVAL', 'ARRIVAL_TIME',\n",
       "       'ARRIVAL_DELAY', 'DIVERTED', 'CANCELLED', 'CANCELLATION_REASON',\n",
       "       'AIR_SYSTEM_DELAY', 'SECURITY_DELAY', 'AIRLINE_DELAY',\n",
       "       'LATE_AIRCRAFT_DELAY', 'WEATHER_DELAY'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.006189</td>\n",
       "      <td>1329.60247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>766.000000</td>\n",
       "      <td>2359.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ELAPSED_TIME  SCHEDULED_DEPARTURE\n",
       "min      14.000000              1.00000\n",
       "mean    137.006189           1329.60247\n",
       "max     766.000000           2359.00000"
      ]
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     "execution_count": 34,
     "metadata": {},
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   "source": [
    "flights[['ELAPSED_TIME','SCHEDULED_DEPARTURE']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ELAPSED_TIME</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>AIR_TIME</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>137.006189</td>\n",
       "      <td>1329.60247</td>\n",
       "      <td>113.511628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>766.000000</td>\n",
       "      <td>2359.00000</td>\n",
       "      <td>690.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ELAPSED_TIME  SCHEDULED_DEPARTURE    AIR_TIME\n",
       "min      14.000000              1.00000    7.000000\n",
       "mean    137.006189           1329.60247  113.511628\n",
       "max     766.000000           2359.00000  690.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['ELAPSED_TIME','LATE_AIRCRAFT_DELAY','AIR_TIME']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.015446</td>\n",
       "      <td>0.076154</td>\n",
       "      <td>23.472838</td>\n",
       "      <td>2.91529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>573.000000</td>\n",
       "      <td>1331.000000</td>\n",
       "      <td>1211.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CANCELLED  SECURITY_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY\n",
       "min    0.000000        0.000000             0.000000        0.00000\n",
       "mean   0.015446        0.076154            23.472838        2.91529\n",
       "max    1.000000      573.000000          1331.000000     1211.00000"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['CANCELLED','SECURITY_DELAY','LATE_AIRCRAFT_DELAY','WEATHER_DELAY']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPARTURE_DELAY</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-82.000000</td>\n",
       "      <td>-87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.370158</td>\n",
       "      <td>4.407057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1988.000000</td>\n",
       "      <td>1971.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      DEPARTURE_DELAY  ARRIVAL_DELAY\n",
       "min        -82.000000     -87.000000\n",
       "mean         9.370158       4.407057\n",
       "max       1988.000000    1971.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights[['DEPARTURE_DELAY','ARRIVAL_DELAY']].agg(['min','mean','max'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights.dropna(axis=1, how='all',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_flights = flights.dropna(axis=1, how='all',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'columns'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-54-0dc9ac0dd7e6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_flights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'columns'"
     ]
    }
   ],
   "source": [
    "new_flights.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'isnull'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-56-04ba3cf0792b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnew_flights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'isnull'"
     ]
    }
   ],
   "source": [
    "new_flights.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>FLIGHT_NUMBER</th>\n",
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       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
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       "      <th>ARRIVAL_TIME</th>\n",
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       "      <th>CANCELLATION_REASON</th>\n",
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       "      <th>1</th>\n",
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       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "          YEAR  MONTH    DAY  DAY_OF_WEEK  AIRLINE  FLIGHT_NUMBER  \\\n",
       "0        False  False  False        False    False          False   \n",
       "1        False  False  False        False    False          False   \n",
       "2        False  False  False        False    False          False   \n",
       "3        False  False  False        False    False          False   \n",
       "4        False  False  False        False    False          False   \n",
       "...        ...    ...    ...          ...      ...            ...   \n",
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       "5819077  False  False  False        False    False          False   \n",
       "5819078  False  False  False        False    False          False   \n",
       "\n",
       "         TAIL_NUMBER  ORIGIN_AIRPORT  DESTINATION_AIRPORT  \\\n",
       "0              False           False                False   \n",
       "1              False           False                False   \n",
       "2              False           False                False   \n",
       "3              False           False                False   \n",
       "4              False           False                False   \n",
       "...              ...             ...                  ...   \n",
       "5819074        False           False                False   \n",
       "5819075        False           False                False   \n",
       "5819076        False           False                False   \n",
       "5819077        False           False                False   \n",
       "5819078        False           False                False   \n",
       "\n",
       "         SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  \\\n",
       "0                      False  ...         False          False     False   \n",
       "1                      False  ...         False          False     False   \n",
       "2                      False  ...         False          False     False   \n",
       "3                      False  ...         False          False     False   \n",
       "4                      False  ...         False          False     False   \n",
       "...                      ...  ...           ...            ...       ...   \n",
       "5819074                False  ...         False          False     False   \n",
       "5819075                False  ...         False          False     False   \n",
       "5819076                False  ...         False          False     False   \n",
       "5819077                False  ...         False          False     False   \n",
       "5819078                False  ...         False          False     False   \n",
       "\n",
       "         CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  \\\n",
       "0            False                 True              True            True   \n",
       "1            False                 True              True            True   \n",
       "2            False                 True              True            True   \n",
       "3            False                 True              True            True   \n",
       "4            False                 True              True            True   \n",
       "...            ...                  ...               ...             ...   \n",
       "5819074      False                 True              True            True   \n",
       "5819075      False                 True              True            True   \n",
       "5819076      False                 True              True            True   \n",
       "5819077      False                 True              True            True   \n",
       "5819078      False                 True              True            True   \n",
       "\n",
       "         AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                 True                 True           True  \n",
       "1                 True                 True           True  \n",
       "2                 True                 True           True  \n",
       "3                 True                 True           True  \n",
       "4                 True                 True           True  \n",
       "...                ...                  ...            ...  \n",
       "5819074           True                 True           True  \n",
       "5819075           True                 True           True  \n",
       "5819076           True                 True           True  \n",
       "5819077           True                 True           True  \n",
       "5819078           True                 True           True  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
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       "          YEAR  MONTH    DAY  DAY_OF_WEEK  AIRLINE  FLIGHT_NUMBER  \\\n",
       "0        False  False  False        False    False          False   \n",
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       "\n",
       "         TAIL_NUMBER  ORIGIN_AIRPORT  DESTINATION_AIRPORT  \\\n",
       "0              False           False                False   \n",
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       "\n",
       "         SCHEDULED_DEPARTURE  ...  ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  \\\n",
       "0                      False  ...         False          False     False   \n",
       "1                      False  ...         False          False     False   \n",
       "2                      False  ...         False          False     False   \n",
       "3                      False  ...         False          False     False   \n",
       "4                      False  ...         False          False     False   \n",
       "...                      ...  ...           ...            ...       ...   \n",
       "5819074                False  ...         False          False     False   \n",
       "5819075                False  ...         False          False     False   \n",
       "5819076                False  ...         False          False     False   \n",
       "5819077                False  ...         False          False     False   \n",
       "5819078                False  ...         False          False     False   \n",
       "\n",
       "         CANCELLED  CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  \\\n",
       "0            False                 True              True            True   \n",
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       "...            ...                  ...               ...             ...   \n",
       "5819074      False                 True              True            True   \n",
       "5819075      False                 True              True            True   \n",
       "5819076      False                 True              True            True   \n",
       "5819077      False                 True              True            True   \n",
       "5819078      False                 True              True            True   \n",
       "\n",
       "         AIRLINE_DELAY  LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
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       "3                 True                 True           True  \n",
       "4                 True                 True           True  \n",
       "...                ...                  ...            ...  \n",
       "5819074           True                 True           True  \n",
       "5819075           True                 True           True  \n",
       "5819076           True                 True           True  \n",
       "5819077           True                 True           True  \n",
       "5819078           True                 True           True  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
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      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0        2015      1    1            4      AS             98      N407AS   \n",
       "1        2015      1    1            4      AA           2336      N3KUAA   \n",
       "2        2015      1    1            4      US            840      N171US   \n",
       "3        2015      1    1            4      AA            258      N3HYAA   \n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                  ANC                 SEA                    5  ...   \n",
       "1                  LAX                 PBI                   10  ...   \n",
       "2                  SFO                 CLT                   20  ...   \n",
       "3                  LAX                 MIA                   20  ...   \n",
       "4                  SEA                 ANC                   25  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "0               408.0          -22.0         0          0   \n",
       "1               741.0           -9.0         0          0   \n",
       "2               811.0            5.0         0          0   \n",
       "3               756.0           -9.0         0          0   \n",
       "4               259.0          -21.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
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       "\n",
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       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
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       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
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       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.dropna(axis=1, how='all',inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
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       "      <th>DISTANCE</th>\n",
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       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>98</td>\n",
       "      <td>ANC</td>\n",
       "      <td>SEA</td>\n",
       "      <td>5</td>\n",
       "      <td>1448</td>\n",
       "      <td>430</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>2336</td>\n",
       "      <td>LAX</td>\n",
       "      <td>PBI</td>\n",
       "      <td>10</td>\n",
       "      <td>2330</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>US</td>\n",
       "      <td>840</td>\n",
       "      <td>SFO</td>\n",
       "      <td>CLT</td>\n",
       "      <td>20</td>\n",
       "      <td>2296</td>\n",
       "      <td>806</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AA</td>\n",
       "      <td>258</td>\n",
       "      <td>LAX</td>\n",
       "      <td>MIA</td>\n",
       "      <td>20</td>\n",
       "      <td>2342</td>\n",
       "      <td>805</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>AS</td>\n",
       "      <td>135</td>\n",
       "      <td>SEA</td>\n",
       "      <td>ANC</td>\n",
       "      <td>25</td>\n",
       "      <td>1448</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819074</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>688</td>\n",
       "      <td>LAX</td>\n",
       "      <td>BOS</td>\n",
       "      <td>2359</td>\n",
       "      <td>2611</td>\n",
       "      <td>819</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819075</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>745</td>\n",
       "      <td>JFK</td>\n",
       "      <td>PSE</td>\n",
       "      <td>2359</td>\n",
       "      <td>1617</td>\n",
       "      <td>446</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819076</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>1503</td>\n",
       "      <td>JFK</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>1598</td>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819077</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>333</td>\n",
       "      <td>MCO</td>\n",
       "      <td>SJU</td>\n",
       "      <td>2359</td>\n",
       "      <td>1189</td>\n",
       "      <td>340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5819078</th>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>4</td>\n",
       "      <td>B6</td>\n",
       "      <td>839</td>\n",
       "      <td>JFK</td>\n",
       "      <td>BQN</td>\n",
       "      <td>2359</td>\n",
       "      <td>1576</td>\n",
       "      <td>440</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5819079 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER ORIGIN_AIRPORT  \\\n",
       "0        2015      1    1            4      AS             98            ANC   \n",
       "1        2015      1    1            4      AA           2336            LAX   \n",
       "2        2015      1    1            4      US            840            SFO   \n",
       "3        2015      1    1            4      AA            258            LAX   \n",
       "4        2015      1    1            4      AS            135            SEA   \n",
       "...       ...    ...  ...          ...     ...            ...            ...   \n",
       "5819074  2015     12   31            4      B6            688            LAX   \n",
       "5819075  2015     12   31            4      B6            745            JFK   \n",
       "5819076  2015     12   31            4      B6           1503            JFK   \n",
       "5819077  2015     12   31            4      B6            333            MCO   \n",
       "5819078  2015     12   31            4      B6            839            JFK   \n",
       "\n",
       "        DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  DISTANCE  SCHEDULED_ARRIVAL  \\\n",
       "0                       SEA                    5      1448                430   \n",
       "1                       PBI                   10      2330                750   \n",
       "2                       CLT                   20      2296                806   \n",
       "3                       MIA                   20      2342                805   \n",
       "4                       ANC                   25      1448                320   \n",
       "...                     ...                  ...       ...                ...   \n",
       "5819074                 BOS                 2359      2611                819   \n",
       "5819075                 PSE                 2359      1617                446   \n",
       "5819076                 SJU                 2359      1598                440   \n",
       "5819077                 SJU                 2359      1189                340   \n",
       "5819078                 BQN                 2359      1576                440   \n",
       "\n",
       "         DIVERTED  CANCELLED  \n",
       "0               0          0  \n",
       "1               0          0  \n",
       "2               0          0  \n",
       "3               0          0  \n",
       "4               0          0  \n",
       "...           ...        ...  \n",
       "5819074         0          0  \n",
       "5819075         0          0  \n",
       "5819076         0          0  \n",
       "5819077         0          0  \n",
       "5819078         0          0  \n",
       "\n",
       "[5819079 rows x 13 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.dropna(axis=1, how='any',inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2015</td>\n",
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       "      <td>N828JB</td>\n",
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       "    <tr>\n",
       "      <th>5819076</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5819079 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         YEAR  MONTH  DAY  DAY_OF_WEEK AIRLINE  FLIGHT_NUMBER TAIL_NUMBER  \\\n",
       "0        2015      1    1            4      AS             98      N407AS   \n",
       "1        2015      1    1            4      AA           2336      N3KUAA   \n",
       "2        2015      1    1            4      US            840      N171US   \n",
       "3        2015      1    1            4      AA            258      N3HYAA   \n",
       "4        2015      1    1            4      AS            135      N527AS   \n",
       "...       ...    ...  ...          ...     ...            ...         ...   \n",
       "5819074  2015     12   31            4      B6            688      N657JB   \n",
       "5819075  2015     12   31            4      B6            745      N828JB   \n",
       "5819076  2015     12   31            4      B6           1503      N913JB   \n",
       "5819077  2015     12   31            4      B6            333      N527JB   \n",
       "5819078  2015     12   31            4      B6            839      N534JB   \n",
       "\n",
       "        ORIGIN_AIRPORT DESTINATION_AIRPORT  SCHEDULED_DEPARTURE  ...  \\\n",
       "0                  ANC                 SEA                    5  ...   \n",
       "1                  LAX                 PBI                   10  ...   \n",
       "2                  SFO                 CLT                   20  ...   \n",
       "3                  LAX                 MIA                   20  ...   \n",
       "4                  SEA                 ANC                   25  ...   \n",
       "...                ...                 ...                  ...  ...   \n",
       "5819074            LAX                 BOS                 2359  ...   \n",
       "5819075            JFK                 PSE                 2359  ...   \n",
       "5819076            JFK                 SJU                 2359  ...   \n",
       "5819077            MCO                 SJU                 2359  ...   \n",
       "5819078            JFK                 BQN                 2359  ...   \n",
       "\n",
       "         ARRIVAL_TIME  ARRIVAL_DELAY  DIVERTED  CANCELLED  \\\n",
       "0               408.0          -22.0         0          0   \n",
       "1               741.0           -9.0         0          0   \n",
       "2               811.0            5.0         0          0   \n",
       "3               756.0           -9.0         0          0   \n",
       "4               259.0          -21.0         0          0   \n",
       "...               ...            ...       ...        ...   \n",
       "5819074         753.0          -26.0         0          0   \n",
       "5819075         430.0          -16.0         0          0   \n",
       "5819076         432.0           -8.0         0          0   \n",
       "5819077         330.0          -10.0         0          0   \n",
       "5819078         442.0            2.0         0          0   \n",
       "\n",
       "         CANCELLATION_REASON  AIR_SYSTEM_DELAY  SECURITY_DELAY  AIRLINE_DELAY  \\\n",
       "0                        NaN               NaN             NaN            NaN   \n",
       "1                        NaN               NaN             NaN            NaN   \n",
       "2                        NaN               NaN             NaN            NaN   \n",
       "3                        NaN               NaN             NaN            NaN   \n",
       "4                        NaN               NaN             NaN            NaN   \n",
       "...                      ...               ...             ...            ...   \n",
       "5819074                  NaN               NaN             NaN            NaN   \n",
       "5819075                  NaN               NaN             NaN            NaN   \n",
       "5819076                  NaN               NaN             NaN            NaN   \n",
       "5819077                  NaN               NaN             NaN            NaN   \n",
       "5819078                  NaN               NaN             NaN            NaN   \n",
       "\n",
       "         LATE_AIRCRAFT_DELAY  WEATHER_DELAY  \n",
       "0                        NaN            NaN  \n",
       "1                        NaN            NaN  \n",
       "2                        NaN            NaN  \n",
       "3                        NaN            NaN  \n",
       "4                        NaN            NaN  \n",
       "...                      ...            ...  \n",
       "5819074                  NaN            NaN  \n",
       "5819075                  NaN            NaN  \n",
       "5819076                  NaN            NaN  \n",
       "5819077                  NaN            NaN  \n",
       "5819078                  NaN            NaN  \n",
       "\n",
       "[5819079 rows x 31 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.dropna(axis=0, how='all',inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YEAR</th>\n",
       "      <th>MONTH</th>\n",
       "      <th>DAY</th>\n",
       "      <th>DAY_OF_WEEK</th>\n",
       "      <th>AIRLINE</th>\n",
       "      <th>FLIGHT_NUMBER</th>\n",
       "      <th>TAIL_NUMBER</th>\n",
       "      <th>ORIGIN_AIRPORT</th>\n",
       "      <th>DESTINATION_AIRPORT</th>\n",
       "      <th>SCHEDULED_DEPARTURE</th>\n",
       "      <th>...</th>\n",
       "      <th>ARRIVAL_TIME</th>\n",
       "      <th>ARRIVAL_DELAY</th>\n",
       "      <th>DIVERTED</th>\n",
       "      <th>CANCELLED</th>\n",
       "      <th>CANCELLATION_REASON</th>\n",
       "      <th>AIR_SYSTEM_DELAY</th>\n",
       "      <th>SECURITY_DELAY</th>\n",
       "      <th>AIRLINE_DELAY</th>\n",
       "      <th>LATE_AIRCRAFT_DELAY</th>\n",
       "      <th>WEATHER_DELAY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [YEAR, MONTH, DAY, DAY_OF_WEEK, AIRLINE, FLIGHT_NUMBER, TAIL_NUMBER, ORIGIN_AIRPORT, DESTINATION_AIRPORT, SCHEDULED_DEPARTURE, DEPARTURE_TIME, DEPARTURE_DELAY, TAXI_OUT, WHEELS_OFF, SCHEDULED_TIME, ELAPSED_TIME, AIR_TIME, DISTANCE, WHEELS_ON, TAXI_IN, SCHEDULED_ARRIVAL, ARRIVAL_TIME, ARRIVAL_DELAY, DIVERTED, CANCELLED, CANCELLATION_REASON, AIR_SYSTEM_DELAY, SECURITY_DELAY, AIRLINE_DELAY, LATE_AIRCRAFT_DELAY, WEATHER_DELAY]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 31 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.dropna(axis=0, how='any',inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5819079, 31)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights.dropna(axis=1, how='all',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tiger\n",
       "1     Bear\n",
       "2    Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = ['Tiger', 'Bear', 'Moose']\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Tiger\n",
       "1        Bear\n",
       "2       Moose\n",
       "3        Lion\n",
       "4    Elephant\n",
       "dtype: object"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "animals = ['Tiger', 'Bear', 'Moose','Lion','Elephant']\n",
    "pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "3 is not in list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-75-1bbe9bb4544d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0manimals\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: 3 is not in list"
     ]
    }
   ],
   "source": [
    "animals.index(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-78-71341b2c82db>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-78-71341b2c82db>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    animals = (['Tiger', 'Bear', 'Moose', index=['India', 'America', 'Canada'])\u001b[0m\n\u001b[1;37m                                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "animals = (['Tiger', 'Bear', 'Moose', index=['India', 'America', 'Canada'])\n",
    "           pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "k=pd.Series(['Tiger', 'Bear', 'Moose'], index=['India', 'America', 'Canada'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India      Tiger\n",
       "America     Bear\n",
       "Canada     Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'India'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'America'"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.index[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Canada'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k.index[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "numbers = [1, 2, 3]\n",
    "m=pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "animals = ['Tiger', 'Bear', None]\n",
    "n=pd.Series(animals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Tiger\n",
       "1     Bear\n",
       "2     None\n",
       "dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numbers = [1, 2, None]\n",
    "pd.Series(numbers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.nan == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.nan == np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Taekwondo'"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Golf'"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Archery'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "India      Tiger\n",
       "America     Bear\n",
       "Canada     Moose\n",
       "dtype: object"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['Tiger', 'Bear', 'Moose'], index=['India', 'America', 'Canada'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golf      Scotland\n",
       "Sumo         Japan\n",
       "Hockey         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports, index=['Golf', 'Sumo', 'Hockey','Cricket'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'}\n",
    "s = pd.Series(sports)\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golf       Scotland\n",
       "Sumo          Japan\n",
       "Hockey          NaN\n",
       "Cricket         NaN\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea'\n",
    "         'cricket':'India'}\n",
    "s = pd.Series(sports, index=['Golf', 'Sumo', 'Hockey','Cricket'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Golf       Scotland\n",
       "Sumo          Japan\n",
       "Hockey          NaN\n",
       "Cricket       India\n",
       "dtype: object"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sports = {'Archery': 'Bhutan',\n",
    "          'Golf': 'Scotland',\n",
    "          'Sumo': 'Japan',\n",
    "          'Taekwondo': 'South Korea',\n",
    "         'Cricket': 'India'}\n",
    "s = pd.Series(sports, index=['Golf', 'Sumo', 'Hockey','Cricket'])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'India'"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.iloc[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.loc['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'India'"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Scotland'"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['Golf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "sports = {99: 'Bhutan',\n",
    "          100: 'Scotland',\n",
    "          101: 'Japan',\n",
    "          102: 'South Korea'}\n",
    "s = pd.Series(sports)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    100.0\n",
       "1    120.0\n",
       "2    101.0\n",
       "3      3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([100.00, 120.00, 101.00, 3.00])\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324.0\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "for item in s:\n",
    "    total+=item\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "324.0\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "total = np.sum(s)\n",
    "print(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      8\n",
       "1    928\n",
       "2    697\n",
       "3    513\n",
       "4    179\n",
       "dtype: int32"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this creates a big series of random numbers\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.38 ms ± 28.6 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 100\n",
    "summary = 0\n",
    "for item in s:\n",
    "    summary+=item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "68.1 µs ± 9.16 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 100\n",
    "summary = np.sum(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10\n",
       "1    930\n",
       "2    699\n",
       "3    515\n",
       "4    181\n",
       "dtype: int32"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s+=2 #adds two to each item in s using broadcasting\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'set_value'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-134-5a98af01d3f1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miteritems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5485\u001b[0m         ):\n\u001b[0;32m   5486\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5487\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5488\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5489\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'set_value'"
     ]
    }
   ],
   "source": [
    "for label, value in s.iteritems():\n",
    "    s.set_value(label, value+2)\n",
    "s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "384 ms ± 14.2 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "for label, value in s.iteritems():\n",
    "    s.loc[label]= value+2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "217 µs ± 26.8 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n 10\n",
    "s = pd.Series(np.random.randint(0,1000,10000))\n",
    "s+=2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             1\n",
       "1             2\n",
       "2             3\n",
       "Animal    Bears\n",
       "dtype: object"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 2, 3])\n",
    "s.loc['Animal'] = 'Bears'\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_sports = pd.Series({'Archery': 'Bhutan',\n",
    "                             'Golf': 'Scotland',\n",
    "                             'Sumo': 'Japan',\n",
    "                             'Taekwondo': 'South Korea'})\n",
    "cricket_loving_countries = pd.Series(['Australia',\n",
    "                                      'Barbados',\n",
    "                                      'Pakistan',\n",
    "                                      'England'], \n",
    "                                   index=['Cricket',\n",
    "                                          'Cricket',\n",
    "                                          'Cricket',\n",
    "                                          'Cricket'])\n",
    "all_countries = original_sports.append(cricket_loving_countries)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_sports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cricket_loving_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Archery           Bhutan\n",
       "Golf            Scotland\n",
       "Sumo               Japan\n",
       "Taekwondo    South Korea\n",
       "Cricket        Australia\n",
       "Cricket         Barbados\n",
       "Cricket         Pakistan\n",
       "Cricket          England\n",
       "dtype: object"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cricket    Australia\n",
       "Cricket     Barbados\n",
       "Cricket     Pakistan\n",
       "Cricket      England\n",
       "dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_countries.loc['Cricket']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost\n",
       "Store 1  Chris       Dog Food  22.5\n",
       "Store 1  Kevyn   Kitty Litter   2.5\n",
       "Store 2  Vinod      Bird Seed   5.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "purchase_1 = pd.Series({'Name': 'Chris',\n",
    "                        'Item Purchased': 'Dog Food',\n",
    "                        'Cost': 22.50})\n",
    "purchase_2 = pd.Series({'Name': 'Kevyn',\n",
    "                        'Item Purchased': 'Kitty Litter',\n",
    "                        'Cost': 2.50})\n",
    "purchase_3 = pd.Series({'Name': 'Vinod',\n",
    "                        'Item Purchased': 'Bird Seed',\n",
    "                        'Cost': 5.00})\n",
    "df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store 1', 'Store 1', 'Store 2'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  Vinod\n",
       "Item Purchased    Bird Seed\n",
       "Cost                    5.0\n",
       "Name: Store 2, dtype: object"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name                  Vinod\n",
       "Item Purchased    Bird Seed\n",
       "Cost                    5.0\n",
       "Name: Store 2, dtype: object"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.loc['Store 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 3)"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost\n",
       "Store 1  Chris       Dog Food  22.5\n",
       "Store 1  Kevyn   Kitty Litter   2.5"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1', 'Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store 1</th>\n",
       "      <th>Store 1</th>\n",
       "      <th>Store 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Vinod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Item Purchased</th>\n",
       "      <td>Dog Food</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>Bird Seed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cost</th>\n",
       "      <td>22.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Store 1       Store 1    Store 2\n",
       "Name               Chris         Kevyn      Vinod\n",
       "Item Purchased  Dog Food  Kitty Litter  Bird Seed\n",
       "Cost                22.5           2.5        5.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2     5.0\n",
       "Name: Cost, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.loc['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2     5.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Store 1']['Cost']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name  Cost\n",
       "Store 1  Chris  22.5\n",
       "Store 1  Kevyn   2.5\n",
       "Store 2  Vinod   5.0"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:,['Name', 'Cost']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost\n",
       "Store 2  Vinod      Bird Seed   5.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('Store 1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>22.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost\n",
       "Store 1  Chris       Dog Food  22.5\n",
       "Store 1  Kevyn   Kitty Litter   2.5\n",
       "Store 2  Vinod      Bird Seed   5.0"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost\n",
       "Store 2  Vinod      Bird Seed   5.0"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_df = df.copy()\n",
    "copy_df = copy_df.drop('Store 1')\n",
    "copy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy_df.drop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Item Purchased  Cost\n",
       "Store 2      Bird Seed   5.0"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del copy_df['Name']\n",
    "copy_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>22.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>2.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>5.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost Location\n",
       "Store 1  Chris       Dog Food  22.5     None\n",
       "Store 1  Kevyn   Kitty Litter   2.5     None\n",
       "Store 2  Vinod      Bird Seed   5.0     None"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Location'] = None\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    22.5\n",
       "Store 1     2.5\n",
       "Store 2     5.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs = df['Cost']\n",
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Store 1    24.5\n",
       "Store 1     4.5\n",
       "Store 2     7.0\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "costs+=2\n",
    "costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Item Purchased</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Location</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Chris</td>\n",
       "      <td>Dog Food</td>\n",
       "      <td>24.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 1</th>\n",
       "      <td>Kevyn</td>\n",
       "      <td>Kitty Litter</td>\n",
       "      <td>4.5</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Store 2</th>\n",
       "      <td>Vinod</td>\n",
       "      <td>Bird Seed</td>\n",
       "      <td>7.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Name Item Purchased  Cost Location\n",
       "Store 1  Chris       Dog Food  24.5     None\n",
       "Store 1  Kevyn   Kitty Litter   4.5     None\n",
       "Store 2  Vinod      Bird Seed   7.0     None"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>№ Summer</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Total</td>\n",
       "      <td>№ Winter</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Total</td>\n",
       "      <td>№ Games</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
       "      <td>Combined total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria (ALG)</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina (ARG)</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia (ARM)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0         1     2     3     4      5         6     7     8  \\\n",
       "0                NaN  № Summer  01 !  02 !  03 !  Total  № Winter  01 !  02 !   \n",
       "1  Afghanistan (AFG)        13     0     0     2      2         0     0     0   \n",
       "2      Algeria (ALG)        12     5     2     8     15         3     0     0   \n",
       "3    Argentina (ARG)        23    18    24    28     70        18     0     0   \n",
       "4      Armenia (ARM)         5     1     2     9     12         6     0     0   \n",
       "\n",
       "      9     10       11    12    13    14              15  \n",
       "0  03 !  Total  № Games  01 !  02 !  03 !  Combined total  \n",
       "1     0      0       13     0     0     2               2  \n",
       "2     0      0       15     5     2     8              15  \n",
       "3     0      0       41    18    24    28              70  \n",
       "4     0      0       11     1     2     9              12  "
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/KIIT/Downloads/ClassNotebooks/olympics.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <th>15</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>№ Summer</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
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       "      <td>№ Winter</td>\n",
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       "      <td>Combined total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria (ALG)</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>15</td>\n",
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       "      <td>15</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina (ARG)</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia (ARM)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Australia (AUS) [AUS] [Z]</td>\n",
       "      <td>25</td>\n",
       "      <td>139</td>\n",
       "      <td>152</td>\n",
       "      <td>177</td>\n",
       "      <td>468</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>43</td>\n",
       "      <td>144</td>\n",
       "      <td>155</td>\n",
       "      <td>181</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Austria (AUT)</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>86</td>\n",
       "      <td>22</td>\n",
       "      <td>59</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>218</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>111</td>\n",
       "      <td>116</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Azerbaijan (AZE)</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
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       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bahamas (BAH)</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bahrain (BRN)</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Barbados (BAR) [BAR]</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Belarus (BLR)</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Belgium (BEL)</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>142</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>53</td>\n",
       "      <td>56</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Bermuda (BER)</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Bohemia (BOH) [BOH] [Z]</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Botswana (BOT)</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Brazil (BRA)</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>108</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>55</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>British West Indies (BWI) [BWI]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Bulgaria (BUL) [H]</td>\n",
       "      <td>19</td>\n",
       "      <td>51</td>\n",
       "      <td>85</td>\n",
       "      <td>78</td>\n",
       "      <td>214</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>38</td>\n",
       "      <td>52</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Burundi (BDI)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Cameroon (CMR)</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Canada (CAN)</td>\n",
       "      <td>25</td>\n",
       "      <td>59</td>\n",
       "      <td>99</td>\n",
       "      <td>121</td>\n",
       "      <td>279</td>\n",
       "      <td>22</td>\n",
       "      <td>62</td>\n",
       "      <td>56</td>\n",
       "      <td>52</td>\n",
       "      <td>170</td>\n",
       "      <td>47</td>\n",
       "      <td>121</td>\n",
       "      <td>155</td>\n",
       "      <td>173</td>\n",
       "      <td>449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Chile (CHI) [I]</td>\n",
       "      <td>22</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0         1     2     3     4      5  \\\n",
       "0                               NaN  № Summer  01 !  02 !  03 !  Total   \n",
       "1                 Afghanistan (AFG)        13     0     0     2      2   \n",
       "2                     Algeria (ALG)        12     5     2     8     15   \n",
       "3                   Argentina (ARG)        23    18    24    28     70   \n",
       "4                     Armenia (ARM)         5     1     2     9     12   \n",
       "5           Australasia (ANZ) [ANZ]         2     3     4     5     12   \n",
       "6         Australia (AUS) [AUS] [Z]        25   139   152   177    468   \n",
       "7                     Austria (AUT)        26    18    33    35     86   \n",
       "8                  Azerbaijan (AZE)         5     6     5    15     26   \n",
       "9                     Bahamas (BAH)        15     5     2     5     12   \n",
       "10                    Bahrain (BRN)         8     0     0     1      1   \n",
       "11             Barbados (BAR) [BAR]        11     0     0     1      1   \n",
       "12                    Belarus (BLR)         5    12    24    39     75   \n",
       "13                    Belgium (BEL)        25    37    52    53    142   \n",
       "14                    Bermuda (BER)        17     0     0     1      1   \n",
       "15          Bohemia (BOH) [BOH] [Z]         3     0     1     3      4   \n",
       "16                   Botswana (BOT)         9     0     1     0      1   \n",
       "17                     Brazil (BRA)        21    23    30    55    108   \n",
       "18  British West Indies (BWI) [BWI]         1     0     0     2      2   \n",
       "19               Bulgaria (BUL) [H]        19    51    85    78    214   \n",
       "20                    Burundi (BDI)         5     1     0     0      1   \n",
       "21                   Cameroon (CMR)        13     3     1     1      5   \n",
       "22                     Canada (CAN)        25    59    99   121    279   \n",
       "23                  Chile (CHI) [I]        22     2     7     4     13   \n",
       "\n",
       "           6     7     8     9     10       11    12    13    14  \\\n",
       "0   № Winter  01 !  02 !  03 !  Total  № Games  01 !  02 !  03 !   \n",
       "1          0     0     0     0      0       13     0     0     2   \n",
       "2          3     0     0     0      0       15     5     2     8   \n",
       "3         18     0     0     0      0       41    18    24    28   \n",
       "4          6     0     0     0      0       11     1     2     9   \n",
       "5          0     0     0     0      0        2     3     4     5   \n",
       "6         18     5     3     4     12       43   144   155   181   \n",
       "7         22    59    78    81    218       48    77   111   116   \n",
       "8          5     0     0     0      0       10     6     5    15   \n",
       "9          0     0     0     0      0       15     5     2     5   \n",
       "10         0     0     0     0      0        8     0     0     1   \n",
       "11         0     0     0     0      0       11     0     0     1   \n",
       "12         6     6     4     5     15       11    18    28    44   \n",
       "13        20     1     1     3      5       45    38    53    56   \n",
       "14         7     0     0     0      0       24     0     0     1   \n",
       "15         0     0     0     0      0        3     0     1     3   \n",
       "16         0     0     0     0      0        9     0     1     0   \n",
       "17         7     0     0     0      0       28    23    30    55   \n",
       "18         0     0     0     0      0        1     0     0     2   \n",
       "19        19     1     2     3      6       38    52    87    81   \n",
       "20         0     0     0     0      0        5     1     0     0   \n",
       "21         1     0     0     0      0       14     3     1     1   \n",
       "22        22    62    56    52    170       47   121   155   173   \n",
       "23        16     0     0     0      0       38     2     7     4   \n",
       "\n",
       "                15  \n",
       "0   Combined total  \n",
       "1                2  \n",
       "2               15  \n",
       "3               70  \n",
       "4               12  \n",
       "5               12  \n",
       "6              480  \n",
       "7              304  \n",
       "8               26  \n",
       "9               12  \n",
       "10               1  \n",
       "11               1  \n",
       "12              90  \n",
       "13             147  \n",
       "14               1  \n",
       "15               4  \n",
       "16               1  \n",
       "17             108  \n",
       "18               2  \n",
       "19             220  \n",
       "20               1  \n",
       "21               5  \n",
       "22             449  \n",
       "23              13  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>3</th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>№ Summer</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
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       "      <td>№ Winter</td>\n",
       "      <td>01 !</td>\n",
       "      <td>02 !</td>\n",
       "      <td>03 !</td>\n",
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       "      <td>03 !</td>\n",
       "      <td>Combined total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria (ALG)</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina (ARG)</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia (ARM)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Australia (AUS) [AUS] [Z]</td>\n",
       "      <td>25</td>\n",
       "      <td>139</td>\n",
       "      <td>152</td>\n",
       "      <td>177</td>\n",
       "      <td>468</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>43</td>\n",
       "      <td>144</td>\n",
       "      <td>155</td>\n",
       "      <td>181</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Austria (AUT)</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>86</td>\n",
       "      <td>22</td>\n",
       "      <td>59</td>\n",
       "      <td>78</td>\n",
       "      <td>81</td>\n",
       "      <td>218</td>\n",
       "      <td>48</td>\n",
       "      <td>77</td>\n",
       "      <td>111</td>\n",
       "      <td>116</td>\n",
       "      <td>304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Azerbaijan (AZE)</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Bahamas (BAH)</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Bahrain (BRN)</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Barbados (BAR) [BAR]</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Belarus (BLR)</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "      <td>39</td>\n",
       "      <td>75</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>44</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Belgium (BEL)</td>\n",
       "      <td>25</td>\n",
       "      <td>37</td>\n",
       "      <td>52</td>\n",
       "      <td>53</td>\n",
       "      <td>142</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>45</td>\n",
       "      <td>38</td>\n",
       "      <td>53</td>\n",
       "      <td>56</td>\n",
       "      <td>147</td>\n",
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      ],
      "text/plain": [
       "                            0         1     2     3     4      5         6  \\\n",
       "0                         NaN  № Summer  01 !  02 !  03 !  Total  № Winter   \n",
       "1           Afghanistan (AFG)        13     0     0     2      2         0   \n",
       "2               Algeria (ALG)        12     5     2     8     15         3   \n",
       "3             Argentina (ARG)        23    18    24    28     70        18   \n",
       "4               Armenia (ARM)         5     1     2     9     12         6   \n",
       "5     Australasia (ANZ) [ANZ]         2     3     4     5     12         0   \n",
       "6   Australia (AUS) [AUS] [Z]        25   139   152   177    468        18   \n",
       "7               Austria (AUT)        26    18    33    35     86        22   \n",
       "8            Azerbaijan (AZE)         5     6     5    15     26         5   \n",
       "9               Bahamas (BAH)        15     5     2     5     12         0   \n",
       "10              Bahrain (BRN)         8     0     0     1      1         0   \n",
       "11       Barbados (BAR) [BAR]        11     0     0     1      1         0   \n",
       "12              Belarus (BLR)         5    12    24    39     75         6   \n",
       "13              Belgium (BEL)        25    37    52    53    142        20   \n",
       "\n",
       "       7     8     9     10       11    12    13    14              15  \n",
       "0   01 !  02 !  03 !  Total  № Games  01 !  02 !  03 !  Combined total  \n",
       "1      0     0     0      0       13     0     0     2               2  \n",
       "2      0     0     0      0       15     5     2     8              15  \n",
       "3      0     0     0      0       41    18    24    28              70  \n",
       "4      0     0     0      0       11     1     2     9              12  \n",
       "5      0     0     0      0        2     3     4     5              12  \n",
       "6      5     3     4     12       43   144   155   181             480  \n",
       "7     59    78    81    218       48    77   111   116             304  \n",
       "8      0     0     0      0       10     6     5    15              26  \n",
       "9      0     0     0      0       15     5     2     5              12  \n",
       "10     0     0     0      0        8     0     0     1               1  \n",
       "11     0     0     0      0       11     0     0     1               1  \n",
       "12     6     4     5     15       11    18    28    44              90  \n",
       "13     1     1     3      5       45    38    53    56             147  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(14)"
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  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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       "     0         1     2     3     4      5         6     7     8     9     10  \\\n",
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  {
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   "execution_count": 175,
   "metadata": {},
   "outputs": [
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       "      <td>28</td>\n",
       "      <td>59</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>United Arab Emirates (UAE)</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>United States (USA) [P] [Q] [R] [Z]</td>\n",
       "      <td>26</td>\n",
       "      <td>976</td>\n",
       "      <td>757</td>\n",
       "      <td>666</td>\n",
       "      <td>2399</td>\n",
       "      <td>22</td>\n",
       "      <td>96</td>\n",
       "      <td>102</td>\n",
       "      <td>84</td>\n",
       "      <td>282</td>\n",
       "      <td>48</td>\n",
       "      <td>1072</td>\n",
       "      <td>859</td>\n",
       "      <td>750</td>\n",
       "      <td>2681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>Uruguay (URU)</td>\n",
       "      <td>20</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>Uzbekistan (UZB)</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>Venezuela (VEN)</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Vietnam (VIE)</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>Virgin Islands (ISV)</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>Yugoslavia (YUG) [YUG]</td>\n",
       "      <td>16</td>\n",
       "      <td>26</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>83</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>29</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Independent Olympic Participants (IOP) [IOP]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Zambia (ZAM) [ZAM]</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Zimbabwe (ZIM) [ZIM]</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Mixed team (ZZX) [ZZX]</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Totals</td>\n",
       "      <td>27</td>\n",
       "      <td>4809</td>\n",
       "      <td>4775</td>\n",
       "      <td>5130</td>\n",
       "      <td>14714</td>\n",
       "      <td>22</td>\n",
       "      <td>959</td>\n",
       "      <td>958</td>\n",
       "      <td>948</td>\n",
       "      <td>2865</td>\n",
       "      <td>49</td>\n",
       "      <td>5768</td>\n",
       "      <td>5733</td>\n",
       "      <td>6078</td>\n",
       "      <td>17579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                0   1     2     3     4  \\\n",
       "134                                 Ukraine (UKR)   5    33    27    55   \n",
       "135                    United Arab Emirates (UAE)   8     1     0     0   \n",
       "136           United States (USA) [P] [Q] [R] [Z]  26   976   757   666   \n",
       "137                                 Uruguay (URU)  20     2     2     6   \n",
       "138                              Uzbekistan (UZB)   5     5     5    10   \n",
       "139                               Venezuela (VEN)  17     2     2     8   \n",
       "140                                 Vietnam (VIE)  14     0     2     0   \n",
       "141                          Virgin Islands (ISV)  11     0     1     0   \n",
       "142                        Yugoslavia (YUG) [YUG]  16    26    29    28   \n",
       "143  Independent Olympic Participants (IOP) [IOP]   1     0     1     2   \n",
       "144                            Zambia (ZAM) [ZAM]  12     0     1     1   \n",
       "145                          Zimbabwe (ZIM) [ZIM]  12     3     4     1   \n",
       "146                        Mixed team (ZZX) [ZZX]   3     8     5     4   \n",
       "147                                        Totals  27  4809  4775  5130   \n",
       "\n",
       "         5   6    7    8    9    10  11    12    13    14     15  \n",
       "134    115   6    2    1    4     7  11    35    28    59    122  \n",
       "135      1   0    0    0    0     0   8     1     0     0      1  \n",
       "136   2399  22   96  102   84   282  48  1072   859   750   2681  \n",
       "137     10   1    0    0    0     0  21     2     2     6     10  \n",
       "138     20   6    1    0    0     1  11     6     5    10     21  \n",
       "139     12   4    0    0    0     0  21     2     2     8     12  \n",
       "140      2   0    0    0    0     0  14     0     2     0      2  \n",
       "141      1   7    0    0    0     0  18     0     1     0      1  \n",
       "142     83  14    0    3    1     4  30    26    32    29     87  \n",
       "143      3   0    0    0    0     0   1     0     1     2      3  \n",
       "144      2   0    0    0    0     0  12     0     1     1      2  \n",
       "145      8   1    0    0    0     0  13     3     4     1      8  \n",
       "146     17   0    0    0    0     0   3     8     5     4     17  \n",
       "147  14714  22  959  958  948  2865  49  5768  5733  6078  17579  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(148, 16)"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria (ALG)</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Argentina (ARG)</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Armenia (ARM)</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>Independent Olympic Participants (IOP) [IOP]</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>Zambia (ZAM) [ZAM]</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>Zimbabwe (ZIM) [ZIM]</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>Mixed team (ZZX) [ZZX]</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>Totals</td>\n",
       "      <td>27</td>\n",
       "      <td>4809</td>\n",
       "      <td>4775</td>\n",
       "      <td>5130</td>\n",
       "      <td>14714</td>\n",
       "      <td>22</td>\n",
       "      <td>959</td>\n",
       "      <td>958</td>\n",
       "      <td>948</td>\n",
       "      <td>2865</td>\n",
       "      <td>49</td>\n",
       "      <td>5768</td>\n",
       "      <td>5733</td>\n",
       "      <td>6078</td>\n",
       "      <td>17579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>147 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                0   1     2     3     4  \\\n",
       "1                               Afghanistan (AFG)  13     0     0     2   \n",
       "2                                   Algeria (ALG)  12     5     2     8   \n",
       "3                                 Argentina (ARG)  23    18    24    28   \n",
       "4                                   Armenia (ARM)   5     1     2     9   \n",
       "5                         Australasia (ANZ) [ANZ]   2     3     4     5   \n",
       "..                                            ...  ..   ...   ...   ...   \n",
       "143  Independent Olympic Participants (IOP) [IOP]   1     0     1     2   \n",
       "144                            Zambia (ZAM) [ZAM]  12     0     1     1   \n",
       "145                          Zimbabwe (ZIM) [ZIM]  12     3     4     1   \n",
       "146                        Mixed team (ZZX) [ZZX]   3     8     5     4   \n",
       "147                                        Totals  27  4809  4775  5130   \n",
       "\n",
       "         5   6    7    8    9    10  11    12    13    14     15  \n",
       "1        2   0    0    0    0     0  13     0     0     2      2  \n",
       "2       15   3    0    0    0     0  15     5     2     8     15  \n",
       "3       70  18    0    0    0     0  41    18    24    28     70  \n",
       "4       12   6    0    0    0     0  11     1     2     9     12  \n",
       "5       12   0    0    0    0     0   2     3     4     5     12  \n",
       "..     ...  ..  ...  ...  ...   ...  ..   ...   ...   ...    ...  \n",
       "143      3   0    0    0    0     0   1     0     1     2      3  \n",
       "144      2   0    0    0    0     0  12     0     1     1      2  \n",
       "145      8   1    0    0    0     0  13     3     4     1      8  \n",
       "146     17   0    0    0    0     0   3     8     5     4     17  \n",
       "147  14714  22  959  958  948  2865  49  5768  5733  6078  17579  \n",
       "\n",
       "[147 rows x 16 columns]"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0, how='any',inplace = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(axis=1, how='all',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>№ Summer</th>\n",
       "      <th>01 !</th>\n",
       "      <th>02 !</th>\n",
       "      <th>03 !</th>\n",
       "      <th>Total</th>\n",
       "      <th>№ Winter</th>\n",
       "      <th>01 !.1</th>\n",
       "      <th>02 !.1</th>\n",
       "      <th>03 !.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th>№ Games</th>\n",
       "      <th>01 !.2</th>\n",
       "      <th>02 !.2</th>\n",
       "      <th>03 !.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         № Summer  01 !  02 !  03 !  Total  № Winter  01 !.1  \\\n",
       "Afghanistan (AFG)              13     0     0     2      2         0       0   \n",
       "Algeria (ALG)                  12     5     2     8     15         3       0   \n",
       "Argentina (ARG)                23    18    24    28     70        18       0   \n",
       "Armenia (ARM)                   5     1     2     9     12         6       0   \n",
       "Australasia (ANZ) [ANZ]         2     3     4     5     12         0       0   \n",
       "\n",
       "                         02 !.1  03 !.1  Total.1  № Games  01 !.2  02 !.2  \\\n",
       "Afghanistan (AFG)             0       0        0       13       0       0   \n",
       "Algeria (ALG)                 0       0        0       15       5       2   \n",
       "Argentina (ARG)               0       0        0       41      18      24   \n",
       "Armenia (ARM)                 0       0        0       11       1       2   \n",
       "Australasia (ANZ) [ANZ]       0       0        0        2       3       4   \n",
       "\n",
       "                         03 !.2  Combined total  \n",
       "Afghanistan (AFG)             2               2  \n",
       "Algeria (ALG)                 8              15  \n",
       "Argentina (ARG)              28              70  \n",
       "Armenia (ARM)                 9              12  \n",
       "Australasia (ANZ) [ANZ]       5              12  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/KIIT/Downloads/ClassNotebooks/olympics.csv', index_col = 0, skiprows=1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['№ Summer', '01 !', '02 !', '03 !', 'Total', '№ Winter', '01 !.1',\n",
       "       '02 !.1', '03 !.1', 'Total.1', '№ Games', '01 !.2', '02 !.2', '03 !.2',\n",
       "       'Combined total'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)              13     0       0       2      2         0   \n",
       "Algeria (ALG)                  12     5       2       8     15         3   \n",
       "Argentina (ARG)                23    18      24      28     70        18   \n",
       "Armenia (ARM)                   5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]         2     3       4       5     12         0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)             0         0         0        0       13       0   \n",
       "Algeria (ALG)                 0         0         0        0       15       5   \n",
       "Argentina (ARG)               0         0         0        0       41      18   \n",
       "Armenia (ARM)                 0         0         0        0       11       1   \n",
       "Australasia (ANZ) [ANZ]       0         0         0        0        2       3   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)               0         2               2  \n",
       "Algeria (ALG)                   2         8              15  \n",
       "Argentina (ARG)                24        28              70  \n",
       "Armenia (ARM)                   2         9              12  \n",
       "Australasia (ANZ) [ANZ]         4         5              12  "
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    if col[:2]=='01':\n",
    "        df.rename(columns={col:'Gold' + col[4:]}, inplace=True)\n",
    "    if col[:2]=='02':\n",
    "        df.rename(columns={col:'Silver' + col[4:]}, inplace=True)\n",
    "    if col[:2]=='03':\n",
    "        df.rename(columns={col:'Bronze' + col[4:]}, inplace=True)\n",
    "    if col[:1]=='№':\n",
    "        df.rename(columns={col:'#' + col[1:]}, inplace=True) \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan (AFG)                               False\n",
       "Algeria (ALG)                                    True\n",
       "Argentina (ARG)                                  True\n",
       "Armenia (ARM)                                    True\n",
       "Australasia (ANZ) [ANZ]                          True\n",
       "                                                ...  \n",
       "Independent Olympic Participants (IOP) [IOP]    False\n",
       "Zambia (ZAM) [ZAM]                              False\n",
       "Zimbabwe (ZIM) [ZIM]                             True\n",
       "Mixed team (ZZX) [ZZX]                           True\n",
       "Totals                                           True\n",
       "Name: Gold, Length: 147, dtype: bool"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan (AFG)                               False\n",
       "Algeria (ALG)                                   False\n",
       "Argentina (ARG)                                 False\n",
       "Armenia (ARM)                                   False\n",
       "Australasia (ANZ) [ANZ]                         False\n",
       "                                                ...  \n",
       "Independent Olympic Participants (IOP) [IOP]    False\n",
       "Zambia (ZAM) [ZAM]                              False\n",
       "Zimbabwe (ZIM) [ZIM]                            False\n",
       "Mixed team (ZZX) [ZZX]                          False\n",
       "Totals                                           True\n",
       "Name: Silver.1, Length: 147, dtype: bool"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Silver.1'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan (AFG)                               True\n",
       "Algeria (ALG)                                   True\n",
       "Argentina (ARG)                                 True\n",
       "Armenia (ARM)                                   True\n",
       "Australasia (ANZ) [ANZ]                         True\n",
       "                                                ... \n",
       "Independent Olympic Participants (IOP) [IOP]    True\n",
       "Zambia (ZAM) [ZAM]                              True\n",
       "Zimbabwe (ZIM) [ZIM]                            True\n",
       "Mixed team (ZZX) [ZZX]                          True\n",
       "Totals                                          True\n",
       "Name: Bronze.2, Length: 147, dtype: bool"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Bronze.2'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
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       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)             NaN   NaN     NaN     NaN    NaN       NaN   \n",
       "Algeria (ALG)                12.0   5.0     2.0     8.0   15.0       3.0   \n",
       "Argentina (ARG)              23.0  18.0    24.0    28.0   70.0      18.0   \n",
       "Armenia (ARM)                 5.0   1.0     2.0     9.0   12.0       6.0   \n",
       "Australasia (ANZ) [ANZ]       2.0   3.0     4.0     5.0   12.0       0.0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)           NaN       NaN       NaN      NaN      NaN     NaN   \n",
       "Algeria (ALG)               0.0       0.0       0.0      0.0     15.0     5.0   \n",
       "Argentina (ARG)             0.0       0.0       0.0      0.0     41.0    18.0   \n",
       "Armenia (ARM)               0.0       0.0       0.0      0.0     11.0     1.0   \n",
       "Australasia (ANZ) [ANZ]     0.0       0.0       0.0      0.0      2.0     3.0   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)             NaN       NaN             NaN  \n",
       "Algeria (ALG)                 2.0       8.0            15.0  \n",
       "Argentina (ARG)              24.0      28.0            70.0  \n",
       "Armenia (ARM)                 2.0       9.0            12.0  \n",
       "Australasia (ANZ) [ANZ]       4.0       5.0            12.0  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df.where(df['Gold'] > 0)\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold['Gold'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold['Silver.1'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Silver.1'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gold'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia (AUS) [AUS] [Z]</th>\n",
       "      <td>25.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>181.0</td>\n",
       "      <td>480.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           # Summer   Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Algeria (ALG)                  12.0    5.0     2.0     8.0   15.0       3.0   \n",
       "Argentina (ARG)                23.0   18.0    24.0    28.0   70.0      18.0   \n",
       "Armenia (ARM)                   5.0    1.0     2.0     9.0   12.0       6.0   \n",
       "Australasia (ANZ) [ANZ]         2.0    3.0     4.0     5.0   12.0       0.0   \n",
       "Australia (AUS) [AUS] [Z]      25.0  139.0   152.0   177.0  468.0      18.0   \n",
       "\n",
       "                           Gold.1  Silver.1  Bronze.1  Total.1  # Games  \\\n",
       "Algeria (ALG)                 0.0       0.0       0.0      0.0     15.0   \n",
       "Argentina (ARG)               0.0       0.0       0.0      0.0     41.0   \n",
       "Armenia (ARM)                 0.0       0.0       0.0      0.0     11.0   \n",
       "Australasia (ANZ) [ANZ]       0.0       0.0       0.0      0.0      2.0   \n",
       "Australia (AUS) [AUS] [Z]     5.0       3.0       4.0     12.0     43.0   \n",
       "\n",
       "                           Gold.2  Silver.2  Bronze.2  Combined total  \n",
       "Algeria (ALG)                 5.0       2.0       8.0            15.0  \n",
       "Argentina (ARG)              18.0      24.0      28.0            70.0  \n",
       "Armenia (ARM)                 1.0       2.0       9.0            12.0  \n",
       "Australasia (ANZ) [ANZ]       3.0       4.0       5.0            12.0  \n",
       "Australia (AUS) [AUS] [Z]   144.0     155.0     181.0           480.0  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = only_gold.dropna()\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)              13     0       0       2      2         0   \n",
       "Algeria (ALG)                  12     5       2       8     15         3   \n",
       "Argentina (ARG)                23    18      24      28     70        18   \n",
       "Armenia (ARM)                   5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]         2     3       4       5     12         0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)             0         0         0        0       13       0   \n",
       "Algeria (ALG)                 0         0         0        0       15       5   \n",
       "Argentina (ARG)               0         0         0        0       41      18   \n",
       "Armenia (ARM)                 0         0         0        0       11       1   \n",
       "Australasia (ANZ) [ANZ]       0         0         0        0        2       3   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)               0         2               2  \n",
       "Algeria (ALG)                   2         8              15  \n",
       "Argentina (ARG)                24        28              70  \n",
       "Armenia (ARM)                   2         9              12  \n",
       "Australasia (ANZ) [ANZ]         4         5              12  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia (AUS) [AUS] [Z]</th>\n",
       "      <td>25</td>\n",
       "      <td>139</td>\n",
       "      <td>152</td>\n",
       "      <td>177</td>\n",
       "      <td>468</td>\n",
       "      <td>18</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>43</td>\n",
       "      <td>144</td>\n",
       "      <td>155</td>\n",
       "      <td>181</td>\n",
       "      <td>480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Algeria (ALG)                    12     5       2       8     15         3   \n",
       "Argentina (ARG)                  23    18      24      28     70        18   \n",
       "Armenia (ARM)                     5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]           2     3       4       5     12         0   \n",
       "Australia (AUS) [AUS] [Z]        25   139     152     177    468        18   \n",
       "\n",
       "                           Gold.1  Silver.1  Bronze.1  Total.1  # Games  \\\n",
       "Algeria (ALG)                   0         0         0        0       15   \n",
       "Argentina (ARG)                 0         0         0        0       41   \n",
       "Armenia (ARM)                   0         0         0        0       11   \n",
       "Australasia (ANZ) [ANZ]         0         0         0        0        2   \n",
       "Australia (AUS) [AUS] [Z]       5         3         4       12       43   \n",
       "\n",
       "                           Gold.2  Silver.2  Bronze.2  Combined total  \n",
       "Algeria (ALG)                   5         2         8              15  \n",
       "Argentina (ARG)                18        24        28              70  \n",
       "Armenia (ARM)                   1         2         9              12  \n",
       "Australasia (ANZ) [ANZ]         3         4         5              12  \n",
       "Australia (AUS) [AUS] [Z]     144       155       181             480  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_gold = df[df['Gold'] > 0]\n",
    "only_gold.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[(df['Gold'] > 0) | (df['Gold.1'] > 0)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
       "      <th>Total.1</th>\n",
       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Liechtenstein (LIE)</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     # Summer  Gold  Silver  Bronze  Total  # Winter  Gold.1  \\\n",
       "Liechtenstein (LIE)        16     0       0       0      0        18       2   \n",
       "\n",
       "                     Silver.1  Bronze.1  Total.1  # Games  Gold.2  Silver.2  \\\n",
       "Liechtenstein (LIE)         2         5        9       34       2         2   \n",
       "\n",
       "                     Bronze.2  Combined total  \n",
       "Liechtenstein (LIE)         5               9  "
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['Gold.1'] > 0) & (df['Gold'] == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th># Summer</th>\n",
       "      <th>Gold</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
       "      <th>Gold.1</th>\n",
       "      <th>Silver.1</th>\n",
       "      <th>Bronze.1</th>\n",
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       "      <th># Games</th>\n",
       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan (AFG)</th>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Algeria (ALG)</th>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Argentina (ARG)</th>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Armenia (ARM)</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australasia (ANZ) [ANZ]</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         # Summer  Gold  Silver  Bronze  Total  # Winter  \\\n",
       "Afghanistan (AFG)              13     0       0       2      2         0   \n",
       "Algeria (ALG)                  12     5       2       8     15         3   \n",
       "Argentina (ARG)                23    18      24      28     70        18   \n",
       "Armenia (ARM)                   5     1       2       9     12         6   \n",
       "Australasia (ANZ) [ANZ]         2     3       4       5     12         0   \n",
       "\n",
       "                         Gold.1  Silver.1  Bronze.1  Total.1  # Games  Gold.2  \\\n",
       "Afghanistan (AFG)             0         0         0        0       13       0   \n",
       "Algeria (ALG)                 0         0         0        0       15       5   \n",
       "Argentina (ARG)               0         0         0        0       41      18   \n",
       "Armenia (ARM)                 0         0         0        0       11       1   \n",
       "Australasia (ANZ) [ANZ]       0         0         0        0        2       3   \n",
       "\n",
       "                         Silver.2  Bronze.2  Combined total  \n",
       "Afghanistan (AFG)               0         2               2  \n",
       "Algeria (ALG)                   2         8              15  \n",
       "Argentina (ARG)                24        28              70  \n",
       "Armenia (ARM)                   2         9              12  \n",
       "Australasia (ANZ) [ANZ]         4         5              12  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th># Summer</th>\n",
       "      <th>Silver</th>\n",
       "      <th>Bronze</th>\n",
       "      <th>Total</th>\n",
       "      <th># Winter</th>\n",
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       "      <th>Silver.1</th>\n",
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       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gold</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>Algeria (ALG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>Argentina (ARG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>Armenia (ARM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      # Summer  Silver  Bronze  Total  # Winter  Gold.1  Silver.1  Bronze.1  \\\n",
       "Gold                                                                          \n",
       "0           13       0       2      2         0       0         0         0   \n",
       "5           12       2       8     15         3       0         0         0   \n",
       "18          23      24      28     70        18       0         0         0   \n",
       "1            5       2       9     12         6       0         0         0   \n",
       "3            2       4       5     12         0       0         0         0   \n",
       "\n",
       "      Total.1  # Games  Gold.2  Silver.2  Bronze.2  Combined total  \\\n",
       "Gold                                                                 \n",
       "0           0       13       0         0         2               2   \n",
       "5           0       15       5         2         8              15   \n",
       "18          0       41      18        24        28              70   \n",
       "1           0       11       1         2         9              12   \n",
       "3           0        2       3         4         5              12   \n",
       "\n",
       "                      country  \n",
       "Gold                           \n",
       "0           Afghanistan (AFG)  \n",
       "5               Algeria (ALG)  \n",
       "18            Argentina (ARG)  \n",
       "1               Armenia (ARM)  \n",
       "3     Australasia (ANZ) [ANZ]  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['country'] = df.index\n",
    "df = df.set_index('Gold')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Gold.2</th>\n",
       "      <th>Silver.2</th>\n",
       "      <th>Bronze.2</th>\n",
       "      <th>Combined total</th>\n",
       "      <th>country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Afghanistan (AFG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>Algeria (ALG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41</td>\n",
       "      <td>18</td>\n",
       "      <td>24</td>\n",
       "      <td>28</td>\n",
       "      <td>70</td>\n",
       "      <td>Argentina (ARG)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>Armenia (ARM)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Australasia (ANZ) [ANZ]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gold  # Summer  Silver  Bronze  Total  # Winter  Gold.1  Silver.1  \\\n",
       "0     0        13       0       2      2         0       0         0   \n",
       "1     5        12       2       8     15         3       0         0   \n",
       "2    18        23      24      28     70        18       0         0   \n",
       "3     1         5       2       9     12         6       0         0   \n",
       "4     3         2       4       5     12         0       0         0   \n",
       "\n",
       "   Bronze.1  Total.1  # Games  Gold.2  Silver.2  Bronze.2  Combined total  \\\n",
       "0         0        0       13       0         0         2               2   \n",
       "1         0        0       15       5         2         8              15   \n",
       "2         0        0       41      18        24        28              70   \n",
       "3         0        0       11       1         2         9              12   \n",
       "4         0        0        2       3         4         5              12   \n",
       "\n",
       "                   country  \n",
       "0        Afghanistan (AFG)  \n",
       "1            Algeria (ALG)  \n",
       "2          Argentina (ARG)  \n",
       "3            Armenia (ARM)  \n",
       "4  Australasia (ANZ) [ANZ]  "
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>...</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4779736</td>\n",
       "      <td>4780127</td>\n",
       "      <td>4785161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002295</td>\n",
       "      <td>-0.193196</td>\n",
       "      <td>0.381066</td>\n",
       "      <td>0.582002</td>\n",
       "      <td>-0.467369</td>\n",
       "      <td>1.030015</td>\n",
       "      <td>0.826644</td>\n",
       "      <td>1.383282</td>\n",
       "      <td>1.724718</td>\n",
       "      <td>0.712594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>54571</td>\n",
       "      <td>54571</td>\n",
       "      <td>54660</td>\n",
       "      <td>...</td>\n",
       "      <td>7.242091</td>\n",
       "      <td>-2.915927</td>\n",
       "      <td>-3.012349</td>\n",
       "      <td>2.265971</td>\n",
       "      <td>-2.530799</td>\n",
       "      <td>7.606016</td>\n",
       "      <td>-2.626146</td>\n",
       "      <td>-2.722002</td>\n",
       "      <td>2.592270</td>\n",
       "      <td>-2.187333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>182265</td>\n",
       "      <td>182265</td>\n",
       "      <td>183193</td>\n",
       "      <td>...</td>\n",
       "      <td>14.832960</td>\n",
       "      <td>17.647293</td>\n",
       "      <td>21.845705</td>\n",
       "      <td>19.243287</td>\n",
       "      <td>17.197872</td>\n",
       "      <td>15.844176</td>\n",
       "      <td>18.559627</td>\n",
       "      <td>22.727626</td>\n",
       "      <td>20.317142</td>\n",
       "      <td>18.293499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>27457</td>\n",
       "      <td>27457</td>\n",
       "      <td>27341</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.728132</td>\n",
       "      <td>-2.500690</td>\n",
       "      <td>-7.056824</td>\n",
       "      <td>-3.904217</td>\n",
       "      <td>-10.543299</td>\n",
       "      <td>-4.874741</td>\n",
       "      <td>-2.758113</td>\n",
       "      <td>-7.167664</td>\n",
       "      <td>-3.978583</td>\n",
       "      <td>-10.543299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Bibb County</td>\n",
       "      <td>22915</td>\n",
       "      <td>22919</td>\n",
       "      <td>22861</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.527043</td>\n",
       "      <td>-5.068871</td>\n",
       "      <td>-6.201001</td>\n",
       "      <td>-0.177537</td>\n",
       "      <td>0.177258</td>\n",
       "      <td>-5.088389</td>\n",
       "      <td>-4.363636</td>\n",
       "      <td>-5.403729</td>\n",
       "      <td>0.754533</td>\n",
       "      <td>1.107861</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SUMLEV  REGION  DIVISION  STATE  COUNTY   STNAME         CTYNAME  \\\n",
       "0      40       3         6      1       0  Alabama         Alabama   \n",
       "1      50       3         6      1       1  Alabama  Autauga County   \n",
       "2      50       3         6      1       3  Alabama  Baldwin County   \n",
       "3      50       3         6      1       5  Alabama  Barbour County   \n",
       "4      50       3         6      1       7  Alabama     Bibb County   \n",
       "\n",
       "   CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  ...  RDOMESTICMIG2011  \\\n",
       "0        4779736            4780127          4785161  ...          0.002295   \n",
       "1          54571              54571            54660  ...          7.242091   \n",
       "2         182265             182265           183193  ...         14.832960   \n",
       "3          27457              27457            27341  ...         -4.728132   \n",
       "4          22915              22919            22861  ...         -5.527043   \n",
       "\n",
       "   RDOMESTICMIG2012  RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  \\\n",
       "0         -0.193196          0.381066          0.582002         -0.467369   \n",
       "1         -2.915927         -3.012349          2.265971         -2.530799   \n",
       "2         17.647293         21.845705         19.243287         17.197872   \n",
       "3         -2.500690         -7.056824         -3.904217        -10.543299   \n",
       "4         -5.068871         -6.201001         -0.177537          0.177258   \n",
       "\n",
       "   RNETMIG2011  RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  \n",
       "0     1.030015     0.826644     1.383282     1.724718     0.712594  \n",
       "1     7.606016    -2.626146    -2.722002     2.592270    -2.187333  \n",
       "2    15.844176    18.559627    22.727626    20.317142    18.293499  \n",
       "3    -4.874741    -2.758113    -7.167664    -3.978583   -10.543299  \n",
       "4    -5.088389    -4.363636    -5.403729     0.754533     1.107861  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/KIIT/Downloads/ClassNotebooks/census.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([40, 50], dtype=int64)"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SUMLEV'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4779736,   54571,  182265, ...,   21118,    8533,    7208],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CENSUS2010POP'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4785161,   54660,  183193, ...,   21102,    8545,    7181],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['POPESTIMATE2010'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 9, 8, 7, 1, 5, 3, 4, 2], dtype=int64)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DIVISION'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['STNAME', 'CTYNAME'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-226-2132ea0636a9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     13\u001b[0m                    \u001b[1;34m'POPESTIMATE2014'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m                    'POPESTIMATE2015']\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcolumns_to_keep\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3459\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3460\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3461\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3462\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3463\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1314\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis)\u001b[0m\n\u001b[0;32m   1375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1377\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{not_found} not in index\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1378\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1379\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['STNAME', 'CTYNAME'] not in index\""
     ]
    }
   ],
   "source": [
    "columns_to_keep = ['STNAME',\n",
    "                   'CTYNAME',\n",
    "                   'BIRTHS2010',\n",
    "                   'BIRTHS2011',\n",
    "                   'BIRTHS2012',\n",
    "                   'BIRTHS2013',\n",
    "                   'BIRTHS2014',\n",
    "                   'BIRTHS2015',\n",
    "                   'POPESTIMATE2010',\n",
    "                   'POPESTIMATE2011',\n",
    "                   'POPESTIMATE2012',\n",
    "                   'POPESTIMATE2013',\n",
    "                   'POPESTIMATE2014',\n",
    "                   'POPESTIMATE2015']\n",
    "df = df[columns_to_keep]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['STNAME', 'CTYNAME'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-238-15b70ea7c035>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'STNAME'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'CTYNAME'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\util\\_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstacklevel\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m                 )\n\u001b[1;32m--> 311\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[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mset_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m   5444\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5445\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5446\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of {missing} are in the columns\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5447\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5448\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of ['STNAME', 'CTYNAME'] are in the columns\""
     ]
    }
   ],
   "source": [
    "df = df.set_index(['STNAME', 'CTYNAME'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Michigan'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.index.Int64Engine._check_type\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Michigan'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-239-948eb76ccb27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Michigan'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Washtenaw County'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    923\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    926\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1098\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1099\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIndexingError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1100\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1102\u001b[0m         \u001b[1;31m# no multi-index, so validate all of the indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m    836\u001b[0m                 \u001b[1;31m# We don't need to check for tuples here because those are\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    837\u001b[0m                 \u001b[1;31m#  caught by the _is_nested_tuple_indexer check above.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 838\u001b[1;33m                 \u001b[0msection\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    839\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    840\u001b[0m                 \u001b[1;31m# We should never have a scalar section here, because\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1162\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1163\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1164\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1166\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m         \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1113\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3768\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mMultiIndex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3769\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3770\u001b[1;33m                 loc, new_index = index._get_loc_level(\n\u001b[0m\u001b[0;32m   3771\u001b[0m                     \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdrop_level\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3772\u001b[0m                 )\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36m_get_loc_level\u001b[1;34m(self, key, level, drop_level)\u001b[0m\n\u001b[0;32m   3110\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaybe_mi_droplevels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0milevels\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3111\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3112\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_level_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3113\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaybe_mi_droplevels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdrop_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36m_get_level_indexer\u001b[1;34m(self, key, level, indexer)\u001b[0m\n\u001b[0;32m   3202\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3204\u001b[1;33m             \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc_single_level_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlevel_index\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3205\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3206\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlevel\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lexsort_depth\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexes\\multi.py\u001b[0m in \u001b[0;36m_get_loc_single_level_index\u001b[1;34m(self, level_index, key)\u001b[0m\n\u001b[0;32m   2853\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2854\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2855\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mlevel_index\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2856\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2857\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'Michigan'"
     ]
    }
   ],
   "source": [
    "df.loc['Michigan', 'Washtenaw County']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [MultiIndex([('Michigan', 'Washtenaw County'),\\n            ('Michigan',     'Wayne County')],\\n           names=['time', 'user'])] are in the [index]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-240-e3f1f971b84a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m df.loc[ [('Michigan', 'Washtenaw County'),\n\u001b[0m\u001b[0;32m      2\u001b[0m          ('Michigan', 'Wayne County')] ]\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 931\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1151\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1153\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1154\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1155\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1091\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1092\u001b[0m         \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1093\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1094\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[0;32m   1095\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1312\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1314\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_read_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1316\u001b[0m         if needs_i8_conversion(ax.dtype) or isinstance(\n",
      "\u001b[1;32m~\\anaconda3\\envs\\Data Science\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis)\u001b[0m\n\u001b[0;32m   1372\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1374\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1375\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [MultiIndex([('Michigan', 'Washtenaw County'),\\n            ('Michigan',     'Wayne County')],\\n           names=['time', 'user'])] are in the [index]\""
     ]
    }
   ],
   "source": [
    "df.loc[ [('Michigan', 'Washtenaw County'),\n",
    "         ('Michigan', 'Wayne County')] ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>cheryl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>10</th>\n",
       "      <td>1469974724</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>16</th>\n",
       "      <td>1469974654</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1469974484</td>\n",
       "      <td>cheryl</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>19</th>\n",
       "      <td>1469974514</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>20</th>\n",
       "      <td>1469974754</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1469974824</td>\n",
       "      <td>sue</td>\n",
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       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1469974854</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1469974924</td>\n",
       "      <td>sue</td>\n",
       "      <td>advanced.html</td>\n",
       "      <td>33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1469977424</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1469977454</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1469977484</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1469977634</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1469977664</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1469974634</td>\n",
       "      <td>cheryl</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>1469974664</td>\n",
       "      <td>cheryl</td>\n",
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       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>31</th>\n",
       "      <td>1469977694</td>\n",
       "      <td>bob</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1469977724</td>\n",
       "      <td>bob</td>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          time    user          video  playback position paused  volume\n",
       "0   1469974424  cheryl     intro.html                  5  False    10.0\n",
       "1   1469974454  cheryl     intro.html                  6    NaN     NaN\n",
       "2   1469974544  cheryl     intro.html                  9    NaN     NaN\n",
       "3   1469974574  cheryl     intro.html                 10    NaN     NaN\n",
       "4   1469977514     bob     intro.html                  1    NaN     NaN\n",
       "5   1469977544     bob     intro.html                  1    NaN     NaN\n",
       "6   1469977574     bob     intro.html                  1    NaN     NaN\n",
       "7   1469977604     bob     intro.html                  1    NaN     NaN\n",
       "8   1469974604  cheryl     intro.html                 11    NaN     NaN\n",
       "9   1469974694  cheryl     intro.html                 14    NaN     NaN\n",
       "10  1469974724  cheryl     intro.html                 15    NaN     NaN\n",
       "11  1469974454     sue  advanced.html                 24    NaN     NaN\n",
       "12  1469974524     sue  advanced.html                 25    NaN     NaN\n",
       "13  1469974424     sue  advanced.html                 23  False    10.0\n",
       "14  1469974554     sue  advanced.html                 26    NaN     NaN\n",
       "15  1469974624     sue  advanced.html                 27    NaN     NaN\n",
       "16  1469974654     sue  advanced.html                 28    NaN     5.0\n",
       "17  1469974724     sue  advanced.html                 29    NaN     NaN\n",
       "18  1469974484  cheryl     intro.html                  7    NaN     NaN\n",
       "19  1469974514  cheryl     intro.html                  8    NaN     NaN\n",
       "20  1469974754     sue  advanced.html                 30    NaN     NaN\n",
       "21  1469974824     sue  advanced.html                 31    NaN     NaN\n",
       "22  1469974854     sue  advanced.html                 32    NaN     NaN\n",
       "23  1469974924     sue  advanced.html                 33    NaN     NaN\n",
       "24  1469977424     bob     intro.html                  1   True    10.0\n",
       "25  1469977454     bob     intro.html                  1    NaN     NaN\n",
       "26  1469977484     bob     intro.html                  1    NaN     NaN\n",
       "27  1469977634     bob     intro.html                  1    NaN     NaN\n",
       "28  1469977664     bob     intro.html                  1    NaN     NaN\n",
       "29  1469974634  cheryl     intro.html                 12    NaN     NaN\n",
       "30  1469974664  cheryl     intro.html                 13    NaN     NaN\n",
       "31  1469977694     bob     intro.html                  1    NaN     NaN\n",
       "32  1469977724     bob     intro.html                  1    NaN     NaN"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/KIIT/Downloads/ClassNotebooks/log.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "              user          video  playback position paused  volume\n",
       "time                                                               \n",
       "1469974424  cheryl     intro.html                  5  False    10.0\n",
       "1469974424     sue  advanced.html                 23  False    10.0\n",
       "1469974454  cheryl     intro.html                  6    NaN     NaN\n",
       "1469974454     sue  advanced.html                 24    NaN     NaN\n",
       "1469974484  cheryl     intro.html                  7    NaN     NaN\n",
       "1469974514  cheryl     intro.html                  8    NaN     NaN\n",
       "1469974524     sue  advanced.html                 25    NaN     NaN\n",
       "1469974544  cheryl     intro.html                  9    NaN     NaN\n",
       "1469974554     sue  advanced.html                 26    NaN     NaN\n",
       "1469974574  cheryl     intro.html                 10    NaN     NaN\n",
       "1469974604  cheryl     intro.html                 11    NaN     NaN\n",
       "1469974624     sue  advanced.html                 27    NaN     NaN\n",
       "1469974634  cheryl     intro.html                 12    NaN     NaN\n",
       "1469974654     sue  advanced.html                 28    NaN     5.0\n",
       "1469974664  cheryl     intro.html                 13    NaN     NaN\n",
       "1469974694  cheryl     intro.html                 14    NaN     NaN\n",
       "1469974724  cheryl     intro.html                 15    NaN     NaN\n",
       "1469974724     sue  advanced.html                 29    NaN     NaN\n",
       "1469974754     sue  advanced.html                 30    NaN     NaN\n",
       "1469974824     sue  advanced.html                 31    NaN     NaN\n",
       "1469974854     sue  advanced.html                 32    NaN     NaN\n",
       "1469974924     sue  advanced.html                 33    NaN     NaN\n",
       "1469977424     bob     intro.html                  1   True    10.0\n",
       "1469977454     bob     intro.html                  1    NaN     NaN\n",
       "1469977484     bob     intro.html                  1    NaN     NaN\n",
       "1469977514     bob     intro.html                  1    NaN     NaN\n",
       "1469977544     bob     intro.html                  1    NaN     NaN\n",
       "1469977574     bob     intro.html                  1    NaN     NaN\n",
       "1469977604     bob     intro.html                  1    NaN     NaN\n",
       "1469977634     bob     intro.html                  1    NaN     NaN\n",
       "1469977664     bob     intro.html                  1    NaN     NaN\n",
       "1469977694     bob     intro.html                  1    NaN     NaN\n",
       "1469977724     bob     intro.html                  1    NaN     NaN"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('time')\n",
    "df = df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                           video  playback position  paused  volume\n",
       "time       user                                                    \n",
       "1469974424 cheryl     intro.html                  5   False    10.0\n",
       "           sue     advanced.html                 23   False    10.0\n",
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       "           sue     advanced.html                 24   False    10.0\n",
       "1469974484 cheryl     intro.html                  7   False    10.0\n",
       "1469974514 cheryl     intro.html                  8   False    10.0\n",
       "1469974524 sue     advanced.html                 25   False    10.0\n",
       "1469974544 cheryl     intro.html                  9   False    10.0\n",
       "1469974554 sue     advanced.html                 26   False    10.0\n",
       "1469974574 cheryl     intro.html                 10   False    10.0\n",
       "1469974604 cheryl     intro.html                 11   False    10.0\n",
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       "1469974634 cheryl     intro.html                 12   False    10.0\n",
       "1469974654 sue     advanced.html                 28   False     5.0\n",
       "1469974664 cheryl     intro.html                 13   False     5.0\n",
       "1469974694 cheryl     intro.html                 14   False     5.0\n",
       "1469974724 cheryl     intro.html                 15   False     5.0\n",
       "           sue     advanced.html                 29   False     5.0\n",
       "1469974754 sue     advanced.html                 30   False     5.0\n",
       "1469974824 sue     advanced.html                 31   False     5.0\n",
       "1469974854 sue     advanced.html                 32   False     5.0\n",
       "1469974924 sue     advanced.html                 33   False     5.0\n",
       "1469977424 bob        intro.html                  1    True    10.0\n",
       "1469977454 bob        intro.html                  1    True    10.0\n",
       "1469977484 bob        intro.html                  1    True    10.0\n",
       "1469977514 bob        intro.html                  1    True    10.0\n",
       "1469977544 bob        intro.html                  1    True    10.0\n",
       "1469977574 bob        intro.html                  1    True    10.0\n",
       "1469977604 bob        intro.html                  1    True    10.0\n",
       "1469977634 bob        intro.html                  1    True    10.0\n",
       "1469977664 bob        intro.html                  1    True    10.0\n",
       "1469977694 bob        intro.html                  1    True    10.0\n",
       "1469977724 bob        intro.html                  1    True    10.0"
      ]
     },
     "execution_count": 247,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [
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       "      <td>11</td>\n",
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       "      <th>10.0</th>\n",
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       "      <th>10.0</th>\n",
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       "      <th>10.0</th>\n",
       "      <td>intro.html</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                video  playback position  paused\n",
       "volume                                          \n",
       "5.0        intro.html                 15   False\n",
       "5.0        intro.html                 14   False\n",
       "5.0        intro.html                 13   False\n",
       "5.0     advanced.html                 28   False\n",
       "5.0     advanced.html                 31   False\n",
       "5.0     advanced.html                 32   False\n",
       "5.0     advanced.html                 33   False\n",
       "5.0     advanced.html                 30   False\n",
       "5.0     advanced.html                 29   False\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  5   False\n",
       "10.0       intro.html                 12   False\n",
       "10.0    advanced.html                 27   False\n",
       "10.0       intro.html                 11   False\n",
       "10.0       intro.html                 10   False\n",
       "10.0    advanced.html                 26   False\n",
       "10.0       intro.html                  9   False\n",
       "10.0    advanced.html                 25   False\n",
       "10.0       intro.html                  8   False\n",
       "10.0       intro.html                  7   False\n",
       "10.0    advanced.html                 24   False\n",
       "10.0       intro.html                  6   False\n",
       "10.0    advanced.html                 23   False\n",
       "10.0       intro.html                  1    True\n",
       "10.0       intro.html                  1    True"
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('volume')\n",
    "df = df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>intro.html</td>\n",
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       "      <td>advanced.html</td>\n",
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       "      <th>33</th>\n",
       "      <td>advanced.html</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           video  paused\n",
       "playback position                       \n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "1                     intro.html    True\n",
       "5                     intro.html   False\n",
       "6                     intro.html   False\n",
       "7                     intro.html   False\n",
       "8                     intro.html   False\n",
       "9                     intro.html   False\n",
       "10                    intro.html   False\n",
       "11                    intro.html   False\n",
       "12                    intro.html   False\n",
       "13                    intro.html   False\n",
       "14                    intro.html   False\n",
       "15                    intro.html   False\n",
       "23                 advanced.html   False\n",
       "24                 advanced.html   False\n",
       "25                 advanced.html   False\n",
       "26                 advanced.html   False\n",
       "27                 advanced.html   False\n",
       "28                 advanced.html   False\n",
       "29                 advanced.html   False\n",
       "30                 advanced.html   False\n",
       "31                 advanced.html   False\n",
       "32                 advanced.html   False\n",
       "33                 advanced.html   False"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('playback position')\n",
    "df = df.sort_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
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       "      <td>28</td>\n",
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       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <th>False</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <th>False</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <th>False</th>\n",
       "      <td>31</td>\n",
       "      <td>31</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <th>False</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          level_0  index          video\n",
       "playback position paused                               \n",
       "1                 True          0      0     intro.html\n",
       "                  True          1      1     intro.html\n",
       "                  True          2      2     intro.html\n",
       "                  True          3      3     intro.html\n",
       "                  True          4      4     intro.html\n",
       "                  True          5      5     intro.html\n",
       "                  True          6      6     intro.html\n",
       "                  True          7      7     intro.html\n",
       "                  True          8      8     intro.html\n",
       "                  True          9      9     intro.html\n",
       "                  True         10     10     intro.html\n",
       "5                 False        11     11     intro.html\n",
       "6                 False        12     12     intro.html\n",
       "7                 False        13     13     intro.html\n",
       "8                 False        14     14     intro.html\n",
       "9                 False        15     15     intro.html\n",
       "10                False        16     16     intro.html\n",
       "11                False        17     17     intro.html\n",
       "12                False        18     18     intro.html\n",
       "13                False        19     19     intro.html\n",
       "14                False        20     20     intro.html\n",
       "15                False        21     21     intro.html\n",
       "23                False        22     22  advanced.html\n",
       "24                False        23     23  advanced.html\n",
       "25                False        24     24  advanced.html\n",
       "26                False        25     25  advanced.html\n",
       "27                False        26     26  advanced.html\n",
       "28                False        27     27  advanced.html\n",
       "29                False        28     28  advanced.html\n",
       "30                False        29     29  advanced.html\n",
       "31                False        30     30  advanced.html\n",
       "32                False        31     31  advanced.html\n",
       "33                False        32     32  advanced.html"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.reset_index()\n",
    "df = df.set_index(['playback position', 'paused'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>intro.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          level_0  index       video\n",
       "playback position paused                            \n",
       "1                 True          0      0  intro.html\n",
       "                  True          1      1  intro.html\n",
       "                  True          2      2  intro.html\n",
       "                  True          3      3  intro.html\n",
       "                  True          4      4  intro.html"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.fillna(method='ffill')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>29</th>\n",
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       "      <th>31</th>\n",
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       "      <th>32</th>\n",
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       "      <th>33</th>\n",
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      ],
      "text/plain": [
       "                          level_0  index          video\n",
       "playback position paused                               \n",
       "29                False        28     28  advanced.html\n",
       "30                False        29     29  advanced.html\n",
       "31                False        30     30  advanced.html\n",
       "32                False        31     31  advanced.html\n",
       "33                False        32     32  advanced.html"
      ]
     },
     "execution_count": 255,
     "metadata": {},
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    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>30</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <th>False</th>\n",
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       "      <td>30</td>\n",
       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <th>False</th>\n",
       "      <td>31</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>advanced.html</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          level_0  index          video\n",
       "playback position paused                               \n",
       "1                 True          0      0     intro.html\n",
       "                  True          1      1     intro.html\n",
       "                  True          2      2     intro.html\n",
       "                  True          3      3     intro.html\n",
       "                  True          4      4     intro.html\n",
       "                  True          5      5     intro.html\n",
       "                  True          6      6     intro.html\n",
       "                  True          7      7     intro.html\n",
       "                  True          8      8     intro.html\n",
       "                  True          9      9     intro.html\n",
       "                  True         10     10     intro.html\n",
       "5                 False        11     11     intro.html\n",
       "6                 False        12     12     intro.html\n",
       "7                 False        13     13     intro.html\n",
       "8                 False        14     14     intro.html\n",
       "9                 False        15     15     intro.html\n",
       "10                False        16     16     intro.html\n",
       "11                False        17     17     intro.html\n",
       "12                False        18     18     intro.html\n",
       "13                False        19     19     intro.html\n",
       "14                False        20     20     intro.html\n",
       "15                False        21     21     intro.html\n",
       "23                False        22     22  advanced.html\n",
       "24                False        23     23  advanced.html\n",
       "25                False        24     24  advanced.html\n",
       "26                False        25     25  advanced.html\n",
       "27                False        26     26  advanced.html\n",
       "28                False        27     27  advanced.html\n",
       "29                False        28     28  advanced.html\n",
       "30                False        29     29  advanced.html\n",
       "31                False        30     30  advanced.html\n",
       "32                False        31     31  advanced.html\n",
       "33                False        32     32  advanced.html"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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