Flight Tickets Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2412d8dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "931db5c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>9/06/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>14:25</td>\n",
       "      <td>16:55</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>5644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>6/03/2019</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → DEL → HYD</td>\n",
       "      <td>08:45</td>\n",
       "      <td>09:25 07 Mar</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>3/04/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>06:05</td>\n",
       "      <td>08:50</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>12/06/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>17:00</td>\n",
       "      <td>23:25</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>10:05</td>\n",
       "      <td>13:00</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>10067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>18/03/2019</td>\n",
       "      <td>Chennai</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>MAA → CCU</td>\n",
       "      <td>19:35</td>\n",
       "      <td>21:55</td>\n",
       "      <td>2h 20m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>15/05/2019</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BOM → BLR</td>\n",
       "      <td>20:00</td>\n",
       "      <td>04:40 16 May</td>\n",
       "      <td>8h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jet Airways</td>\n",
       "      <td>01/03/2019</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → BOM → DEL</td>\n",
       "      <td>22:50</td>\n",
       "      <td>00:45 03 Mar</td>\n",
       "      <td>25h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>26890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Multiple carriers</td>\n",
       "      <td>12/06/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>13:00</td>\n",
       "      <td>01:30 13 Jun</td>\n",
       "      <td>12h 30m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>13377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>6/05/2019</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>Cochin</td>\n",
       "      <td>DEL → BOM → COK</td>\n",
       "      <td>10:35</td>\n",
       "      <td>01:30 07 May</td>\n",
       "      <td>14h 55m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>6979</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Airline Date_of_Journey    Source Destination            Route  \\\n",
       "0             IndiGo       9/06/2019   Kolkata    Banglore        CCU → BLR   \n",
       "1            Vistara       6/03/2019    Mumbai   Hyderabad  BOM → DEL → HYD   \n",
       "2             IndiGo       3/04/2019  Banglore       Delhi        BLR → DEL   \n",
       "3            Vistara      12/06/2019   Kolkata    Banglore  CCU → DEL → BLR   \n",
       "4             IndiGo      01/03/2019  Banglore   New Delhi        BLR → DEL   \n",
       "5             IndiGo      18/03/2019   Chennai     Kolkata        MAA → CCU   \n",
       "6        Jet Airways      15/05/2019   Kolkata    Banglore  CCU → BOM → BLR   \n",
       "7        Jet Airways      01/03/2019  Banglore   New Delhi  BLR → BOM → DEL   \n",
       "8  Multiple carriers      12/06/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "9             IndiGo       6/05/2019     Delhi      Cochin  DEL → BOM → COK   \n",
       "\n",
       "  Dep_Time  Arrival_Time Duration Total_Stops Additional_Info  Price  \n",
       "0    14:25         16:55   2h 30m    non-stop         No info   5644  \n",
       "1    08:45  09:25 07 Mar  24h 40m      1 stop         No info  12395  \n",
       "2    06:05         08:50   2h 45m    non-stop         No info   4077  \n",
       "3    17:00         23:25   6h 25m      1 stop         No info   8610  \n",
       "4    10:05         13:00   2h 55m    non-stop         No info  10067  \n",
       "5    19:35         21:55   2h 20m    non-stop         No info   4332  \n",
       "6    20:00  04:40 16 May   8h 40m      1 stop         No info  13941  \n",
       "7    22:50  00:45 03 Mar  25h 55m      1 stop         No info  26890  \n",
       "8    13:00  01:30 13 Jun  12h 30m      1 stop         No info  13377  \n",
       "9    10:35  01:30 07 May  14h 55m      1 stop         No info   6979  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"Train set.csv\")\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44902128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8012, 11)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2a271dff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            object\n",
       "Date_of_Journey    object\n",
       "Source             object\n",
       "Destination        object\n",
       "Route              object\n",
       "Dep_Time           object\n",
       "Arrival_Time       object\n",
       "Duration           object\n",
       "Total_Stops        object\n",
       "Additional_Info    object\n",
       "Price               int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c690a87e",
   "metadata": {},
   "source": [
    "### Date_of_Journey  should be converted to Date data type\n",
    "### Arrival Time has month in the values when it should be just time\n",
    "### Additional info looks unneccessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74fe55f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8012 entries, 0 to 8011\n",
      "Data columns (total 11 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Airline          8012 non-null   object\n",
      " 1   Date_of_Journey  8012 non-null   object\n",
      " 2   Source           8012 non-null   object\n",
      " 3   Destination      8012 non-null   object\n",
      " 4   Route            8011 non-null   object\n",
      " 5   Dep_Time         8012 non-null   object\n",
      " 6   Arrival_Time     8012 non-null   object\n",
      " 7   Duration         8012 non-null   object\n",
      " 8   Total_Stops      8011 non-null   object\n",
      " 9   Additional_Info  8012 non-null   object\n",
      " 10  Price            8012 non-null   int64 \n",
      "dtypes: int64(1), object(10)\n",
      "memory usage: 688.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb3ef37b",
   "metadata": {},
   "source": [
    "### Route & Total_Stops are 8011 non-null meaning there is one null value in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78465720",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              1\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        1\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9c54b1",
   "metadata": {},
   "source": [
    "### There is in fact a null value in both columns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be01c598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a85f489",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b1baa641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8011, 11)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fcd1510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline            0\n",
       "Date_of_Journey    0\n",
       "Source             0\n",
       "Destination        0\n",
       "Route              0\n",
       "Dep_Time           0\n",
       "Arrival_Time       0\n",
       "Duration           0\n",
       "Total_Stops        0\n",
       "Additional_Info    0\n",
       "Price              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "923970aa",
   "metadata": {},
   "source": [
    "### No more null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e0fdf8c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4e24cee3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "823cda42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7886, 11)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b0ec90f",
   "metadata": {},
   "source": [
    "### Duplicated values have been dropped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f88d2d2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7886.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9048.862795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4581.736678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5277.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8293.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12293.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>79512.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Price\n",
       "count   7886.000000\n",
       "mean    9048.862795\n",
       "std     4581.736678\n",
       "min     1759.000000\n",
       "25%     5277.000000\n",
       "50%     8293.000000\n",
       "75%    12293.000000\n",
       "max    79512.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7d74baa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/03/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/05/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/06/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/03/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/05/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/03/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/03/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/06/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/05/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/05/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/03/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/06/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/04/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/06/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '27/06/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/05/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '18/04/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '15/04/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '24/04/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n",
      "C:\\Users\\mr-m-\\anaconda3\\lib\\site-packages\\pandas\\core\\tools\\datetimes.py:1047: UserWarning: Parsing '21/04/2019' in DD/MM/YYYY format. Provide format or specify infer_datetime_format=True for consistent parsing.\n",
      "  cache_array = _maybe_cache(arg, format, cache, convert_listlike)\n"
     ]
    }
   ],
   "source": [
    "df['Date_of_Journey']=pd.to_datetime(df['Date_of_Journey'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "003b2733",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time                   object\n",
       "Arrival_Time               object\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b38e78c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Journey_day\"] =pd.to_datetime(df[\"Date_of_Journey\"],format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "10a7b9ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Journey_month\"] =pd.to_datetime(df[\"Date_of_Journey\"],format=\"%d/%m/%Y\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "405a784d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['Date_of_Journey'] = df['Date_of_Journey'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d3507768",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Airline                    object\n",
       "Date_of_Journey    datetime64[ns]\n",
       "Source                     object\n",
       "Destination                object\n",
       "Route                      object\n",
       "Dep_Time                   object\n",
       "Arrival_Time               object\n",
       "Duration                   object\n",
       "Total_Stops                object\n",
       "Additional_Info            object\n",
       "Price                       int64\n",
       "Journey_day        datetime64[ns]\n",
       "Journey_month      datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e499520",
   "metadata": {},
   "source": [
    "### Date of Journey is now of datetime64 data type\n",
    "### We extracted the months and days from the Date of Journey column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "73ab68ca",
   "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>Airline</th>\n",
       "      <th>Date_of_Journey</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>14:25</td>\n",
       "      <td>16:55</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → DEL → HYD</td>\n",
       "      <td>08:45</td>\n",
       "      <td>09:25 07 Mar</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>06:05</td>\n",
       "      <td>08:50</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>17:00</td>\n",
       "      <td>23:25</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>10:05</td>\n",
       "      <td>13:00</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline Date_of_Journey    Source Destination            Route Dep_Time  \\\n",
       "0   IndiGo      2019-09-06   Kolkata    Banglore        CCU → BLR    14:25   \n",
       "1  Vistara      2019-06-03    Mumbai   Hyderabad  BOM → DEL → HYD    08:45   \n",
       "2   IndiGo      2019-03-04  Banglore       Delhi        BLR → DEL    06:05   \n",
       "3  Vistara      2019-12-06   Kolkata    Banglore  CCU → DEL → BLR    17:00   \n",
       "4   IndiGo      2019-01-03  Banglore   New Delhi        BLR → DEL    10:05   \n",
       "\n",
       "   Arrival_Time Duration Total_Stops Additional_Info  Price Journey_day  \\\n",
       "0         16:55   2h 30m    non-stop         No info   5644  2019-09-06   \n",
       "1  09:25 07 Mar  24h 40m      1 stop         No info  12395  2019-06-03   \n",
       "2         08:50   2h 45m    non-stop         No info   4077  2019-03-04   \n",
       "3         23:25   6h 25m      1 stop         No info   8610  2019-12-06   \n",
       "4         13:00   2h 55m    non-stop         No info  10067  2019-01-03   \n",
       "\n",
       "  Journey_month  \n",
       "0    2019-09-06  \n",
       "1    2019-06-03  \n",
       "2    2019-03-04  \n",
       "3    2019-12-06  \n",
       "4    2019-01-03  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48ee46a",
   "metadata": {},
   "source": [
    "### We can now drop the Date of Journey column as it is of no use anymore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ee970834",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Date_of_Journey'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0ee0b760",
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Dep_Time</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>14:25</td>\n",
       "      <td>16:55</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → DEL → HYD</td>\n",
       "      <td>08:45</td>\n",
       "      <td>09:25 07 Mar</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>06:05</td>\n",
       "      <td>08:50</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>17:00</td>\n",
       "      <td>23:25</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>10:05</td>\n",
       "      <td>13:00</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination            Route Dep_Time  Arrival_Time  \\\n",
       "0   IndiGo   Kolkata    Banglore        CCU → BLR    14:25         16:55   \n",
       "1  Vistara    Mumbai   Hyderabad  BOM → DEL → HYD    08:45  09:25 07 Mar   \n",
       "2   IndiGo  Banglore       Delhi        BLR → DEL    06:05         08:50   \n",
       "3  Vistara   Kolkata    Banglore  CCU → DEL → BLR    17:00         23:25   \n",
       "4   IndiGo  Banglore   New Delhi        BLR → DEL    10:05         13:00   \n",
       "\n",
       "  Duration Total_Stops Additional_Info  Price Journey_day Journey_month  \n",
       "0   2h 30m    non-stop         No info   5644  2019-09-06    2019-09-06  \n",
       "1  24h 40m      1 stop         No info  12395  2019-06-03    2019-06-03  \n",
       "2   2h 45m    non-stop         No info   4077  2019-03-04    2019-03-04  \n",
       "3   6h 25m      1 stop         No info   8610  2019-12-06    2019-12-06  \n",
       "4   2h 55m    non-stop         No info  10067  2019-01-03    2019-01-03  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20630a1c",
   "metadata": {},
   "source": [
    "### We can now extract the hours and minutes from Dep_Time column\n",
    "### and then drop the Dep_Time column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6d3e3096",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Dep_hour\"] = pd.to_datetime(df[\"Dep_Time\"]).dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8966a6da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Dep_min\"] = pd.to_datetime(df[\"Dep_Time\"]).dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "548beb49",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Dep_Time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f67341bf",
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Arrival_Time</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>16:55</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → DEL → HYD</td>\n",
       "      <td>09:25 07 Mar</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>08:50</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>23:25</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>13:00</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination            Route  Arrival_Time Duration  \\\n",
       "0   IndiGo   Kolkata    Banglore        CCU → BLR         16:55   2h 30m   \n",
       "1  Vistara    Mumbai   Hyderabad  BOM → DEL → HYD  09:25 07 Mar  24h 40m   \n",
       "2   IndiGo  Banglore       Delhi        BLR → DEL         08:50   2h 45m   \n",
       "3  Vistara   Kolkata    Banglore  CCU → DEL → BLR         23:25   6h 25m   \n",
       "4   IndiGo  Banglore   New Delhi        BLR → DEL         13:00   2h 55m   \n",
       "\n",
       "  Total_Stops Additional_Info  Price Journey_day Journey_month  Dep_hour  \\\n",
       "0    non-stop         No info   5644  2019-09-06    2019-09-06        14   \n",
       "1      1 stop         No info  12395  2019-06-03    2019-06-03         8   \n",
       "2    non-stop         No info   4077  2019-03-04    2019-03-04         6   \n",
       "3      1 stop         No info   8610  2019-12-06    2019-12-06        17   \n",
       "4    non-stop         No info  10067  2019-01-03    2019-01-03        10   \n",
       "\n",
       "   Dep_min  \n",
       "0       25  \n",
       "1       45  \n",
       "2        5  \n",
       "3        0  \n",
       "4        5  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fa52f17",
   "metadata": {},
   "source": [
    "### Now, we also extract hours and minutes from the Arrival_Time column\n",
    "### and also drop it at last"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "86da52cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Arr_hour\"] = pd.to_datetime(df[\"Arrival_Time\"]).dt.hour\n",
    "df[\"Arr_min\"] = pd.to_datetime(df[\"Arrival_Time\"]).dt.minute\n",
    "df.drop(['Arrival_Time'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "33d88fea",
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Route</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Additional_Info</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arr_hour</th>\n",
       "      <th>Arr_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → BLR</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>BOM → DEL → HYD</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>CCU → DEL → BLR</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>BLR → DEL</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>No info</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination            Route Duration Total_Stops  \\\n",
       "0   IndiGo   Kolkata    Banglore        CCU → BLR   2h 30m    non-stop   \n",
       "1  Vistara    Mumbai   Hyderabad  BOM → DEL → HYD  24h 40m      1 stop   \n",
       "2   IndiGo  Banglore       Delhi        BLR → DEL   2h 45m    non-stop   \n",
       "3  Vistara   Kolkata    Banglore  CCU → DEL → BLR   6h 25m      1 stop   \n",
       "4   IndiGo  Banglore   New Delhi        BLR → DEL   2h 55m    non-stop   \n",
       "\n",
       "  Additional_Info  Price Journey_day Journey_month  Dep_hour  Dep_min  \\\n",
       "0         No info   5644  2019-09-06    2019-09-06        14       25   \n",
       "1         No info  12395  2019-06-03    2019-06-03         8       45   \n",
       "2         No info   4077  2019-03-04    2019-03-04         6        5   \n",
       "3         No info   8610  2019-12-06    2019-12-06        17        0   \n",
       "4         No info  10067  2019-01-03    2019-01-03        10        5   \n",
       "\n",
       "   Arr_hour  Arr_min  \n",
       "0        16       55  \n",
       "1         9       25  \n",
       "2         8       50  \n",
       "3        23       25  \n",
       "4        13        0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8c15499",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8595073",
   "metadata": {},
   "outputs": [],
   "source": [
    "h = df['Duration'].str.extract('(\\d+)h', expand=False).astype(float)*60\n",
    "m = df['Duration'].str.extract('(\\d+)m', expand=False).astype(float)\n",
    "\n",
    "df['Duration_Minutes'] = h.add(m, fill_value=0).astype(int).astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b200d04f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df=df.drop(['new','Duration'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "946d5bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7f2106",
   "metadata": {},
   "source": [
    "### I extracted the Duration column as minutes and put it into a new column called Duration_Minutes\n",
    "### then I dropped the Duration column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d9e5c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Airline'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66d5f353",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sns.set()\n",
    "sns.catplot(y= \"Price\", x = \"Airline\", data = df.sort_values(\"Price\", ascending = False), kind=\"boxen\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "104f997c",
   "metadata": {},
   "source": [
    "### Jet Airways Business has the highest prices."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ac014da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e6764b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Source'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59988686",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.catplot(y= \"Price\", x = \"Source\", data = df.sort_values(\"Price\", ascending = False), kind=\"violin\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57aa7e04",
   "metadata": {},
   "source": [
    "Im not entirely sure but i think that Bangalore has the most outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c7d69a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cochin       3309\n",
       "Banglore     2167\n",
       "Delhi         928\n",
       "New Delhi     699\n",
       "Hyderabad     510\n",
       "Kolkata       273\n",
       "Name: Destination, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Destination'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f415249a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6ivE3QwgjYoytMcbmEMLIEMLFQ12cJEmSJEmSpKE10DkI3xJjbOx5EGNcD5w6JBWpbMUYi12CJEmSJElS2RloQJgMIVT1PAgh1ABVW9lf2m4hhGKXIEmSJEmSVHYGukjJVcCdIYTLya1k/BHgiiGrSpIkSZIkSVJBDHSRku+GEOYApwAB+EaM8fYhrUySJEmSJEnSkBtoD0JijLcCtw5hLSpTzj0oSZIkSZJUPFudgzCEcF/+vjmEsKHPrTmEsGEgFwghJEMIj4cQ/pp/PCqE8LcQwov5+5F99r0ohDAvhPB8COFNfdqPCCHMyT/3o5CfrC6EUBVCuDbf/lAIYdoO/DeQJEmSJEmSytZWA8IY4wn5+4YY47A+t4YY47ABXuMCYG6fxxcCd8YYZwB35h8TQjgAOAc4EHgz8LMQQjJ/zKXAecCM/O3N+fZzgfUxxn2AHwDfGWBNKiEuTiJJkiRJklQ821zFOISQCCE8vSMnDyFMBk4Dftmn+Qw2LnByBXBmn/bfxxg7Y4wLgHnA0SGECcCwGOOsmBuLeuVmx/Sc63rglGDatMtyqLEkSZIkSVLhbTMgjDFmgSdDCFN24Pz/C3wRyPZpGx9jXJ4/93JgXL59ErC4z35L8m2T8tubt29yTIwxDTQBozcvIoRwXghhdghh9urVq3fgx1AhmO1KkiRJkiQV3kAXKZkAPBNCeBho7WmMMb5tSweEEE4HVsUYHw0hnDSAa/SXDsWttG/tmE0bYrwMuAzgyCOPtJtaibHnoCRJkiRJUvEMNCD8zx049/HA20IIpwLVwLAQwlXAyhDChBjj8vzw4VX5/ZcAe/Y5fjKwLN8+uZ/2vscsCSGkgOHAuh2oVSXAHoSSJEmSJEmFt61VjKtDCJ8F3gXsB9wfY7yn57a1Y2OMF8UYJ8cYp5FbfOSuGOP7gT8DH8zv9kHgpvz2n4Fz8isT70VuMZKH88OQm0MIx+TnF/zAZsf0nOus/DXsjraL8n+dJEmSJElS4W2rB+EVQDfwT+AtwAHkViXeGd8GrgshnAu8TC58JMb4TAjhOuBZIA18KsaYyR/zCeA3QA1wa/4G8CvgtyGEeeR6Dp6zk7WpiOxBKEmSJEmSVHjbCggPiDEeDBBC+BXw8I5cJMZ4N3B3fnstcMoW9rsEuKSf9tnAQf20d5APGCVJkiRJkiRtv22tYtzds5FfJVgaMg4xliRJkiRJKrxt9SA8NISwIb8dgJr84wDEGOOwIa1OZcUhxpIkSZIkSYW31YAwxpgsVCGSJEmSJEmSCm9bQ4wlSZIkSZIk7cYMCFUynINQkiRJkiSp8AwIJUmSJEmSpDJmQKiic3ESSZIkSZKk4jEgVMlwiLEkSZIkSVLhGRCqZBgQSpIkSZIkFZ4BoUpGNpstdgmSJEmSJEllx4BQJcOAUJIkSZIkqfAMCFUyMplMsUuQJEmSJEkqOwaEKhn2IJQkSZIkSSo8A0KVDHsQSpIkSZIkFZ4BoUqGAaEkSZIkSVLhGRCq6GKMgAGhJEmSJElSMRgQqmQYEEqSJEmSJBWeAaGKLoQAGBBKkiRJkiQVgwGhSkY6nS52CZIkSZIkSWXHgFBF1zMHoQGhJEmSJElS4RkQqmR0d3cXuwRJ0m6isbGRCy64gKVLlxa7FEmSJKnkGRCq6HrmILQHoSRpsNx99908/vjjXH/99cUuRZIkSSp5BoQqGfYglCQNFqevkCRJkgbOgFBFl8lkAQNCSdLg6QkIe3qpS5IkSdoyA0IVXU8waEAoSZIkSZJUeAaEKrqu7q7cfVdXkSuRJEmSJEkqPwaEKrrOzlwwaA9CSZIkSZKkwjMgVNH19By0B6EkSZIkSVLhGRCq6Lq6OgHo6OgociWSJEmSJEnlx4BQRdcTDBoQSpIkSZIkFZ4BoYoqnU6Tzs892NbWXuRqJEmSJEmSyo8BoYqqvX1jKNja1lrESiRJkiRJksqTAaGKqqWlpXe7ubllK3tKkjRwIQQAYoxFrkSSJEkqfQaEKqrGxkYAslXD2NDUVNxiJEm7nZ6gUJIkSdKWGRCqqJryoWC2Zjjt7W105+cjlCRpMNiDUJIkSdo2A0IV1fr16wHI1ozY5LEkSYPBHoSSJEnSthkQqqhWrVoFQLZ+HACrV68uZjmSJEmSJEllx4BQRbVq1SpCVS3Z6uEArFy5ssgVSZIkSZIklRcDQhXVypUryVbUESvrex9LkiRJkiSpcAwIVVRLli4jU1kPqUpCRRXLly8vdkmSJEmSJEllxYBQRZPJZFi1cgXZqgYAspUNLFu2rMhVSZIkSZIklRcDQhXNmjVrSKfTxOphAGSqGli8ZGmRq5IkSZIkSSovBoQqmp7hxL09CKsaWLVyJdlstphlSZIkSZIklRUDQhXNqlWrAIiVdb33mUyaxsbGIlYlSZIkSZJUXgwIVTRr1qwBIFbW5u9zQeHq1auLVpMkSZIkSVK5MSBU0axevZqQqoRkJWBAKEmSVAy/+93vuPDCC4tdhiRJKqJUsQtQ+WppaYGK6t7HMVW1sV2SJEkFcemllxa7BEmSVGT2IFTRdHR0EBN9Mur8dmdnZ5EqkiRJkiRJKj8GhCqajo4OYkj2Pu4JCzs6OopVkiRJkiRJUtkxIFTRdHd3kw19fgUTubCwq6urSBVJkiRJkiSVHwNCFU1FRQWJGDc2xExvuyRJkgorm80WuwRJklQkBoQqmoqKCkI+FAQga0AoSZJULI7ikCSpfBkQqmgqKysJ2XTv45APCCsrK4tVkiRJUtlyHmhJksqXAaGKpra2FrLdGxsy3RvbJUmSVFBtbW3FLkGSJBWJAaGKpqGhgdjd2fs4ZDp72yVJklRYBoSSJJUvA0IVTUNDQ27ewfww45A2IJQkSSoWA0JJksqXAaGKpicIDOmuTe4NCCVJkgqj78Ikra2tRaxEkiQVkwGhiqauri63kcl/MM3PQVhfX1+kiiRJkspL316D9iCUJKl8GRCqaHoCwpAPCHvuXaREkiSpMNrb2/vdliRJ5cWAUEXTGwTmew6S6SKZTFFZWVm8oiRJksqIAaEkSQJIFbsAla+eILBixTPExpdz24aDkiRJBdPR0dG73dnZWcRKJElSMdmDUEWTSuXy6UTnBhKtayFGUqlkkauSJEkqH30XKem7LUmSyosBoYommcyHgTHfELMb2yRJkjTkMplM73Z3d3cRK5EkScVkQKiiSSQ2+/WLkRD8lZS2JMa47Z0kSdoOfQPCvtuSJKm8mMaoaHo/hIZ8Qwh+MJW2wteHJGmw9f3yyS+iJEkqXwaEKpp0Op3fyieEIUEmk97i/lK52/iakSRp8IUQtr2TJEnaLRkQqmheEXaEhAGItBU//elPi12CJGk30zcUNCCUJKl8GRCqaDo6OgCI+Q+jMZGiu6vL4S3SFsybN6932+HGkqTB0HeBOBeLkySpfBkQqmja2tryW/lvq5MVxBjp7OwsWk1SKctms73bXV1dRaxEkrS76LtonAGhJEnly4BQRdPe3p7byK9cHJMVm7ZL2kTf3rUG6ZKkwdA3IHSIsSRJ5cuAUEXT2tqa2+j5MJrIBYQtLS1FqkgqbX17EDpfpyRpMPQNBfuGhZIkqbz4KUBFs2HDhtxGTw/CimoAmpqailWSVNL69iB0iLEkabA5D7QkSeXLgFBF09jYSEhV9T6OKQNCaWucg1CSNNj6LnrV931GkiSVlyELCEMIe4YQ/hFCmBtCeCaEcEG+fVQI4W8hhBfz9yP7HHNRCGFeCOH5EMKb+rQfEUKYk3/uRyE/FiKEUBVCuDbf/lAIYdpQ/TwafE1NTVDxyoCwsbGxSBVJpa3vH249q4BLkrQz+k5Z4ZdPkiSVr6HsQZgGPh9j3B84BvhUCOEA4ELgzhjjDODO/GPyz50DHAi8GfhZCKFnKbVLgfOAGfnbm/Pt5wLrY4z7AD8AvjOEP48G2cpVq0inansfx8oaAFavXl2skqSS1reXh3N1SpIGQ99QsLu7u4iVSJKkYhqygDDGuDzG+Fh+uxmYC0wCzgCuyO92BXBmfvsM4Pcxxs4Y4wJgHnB0CGECMCzGOCvmJka5crNjes51PXBKcPm1Xcby5cuJlfUbGxIpQlUtK1asKF5RUgnr28vDnraSpMHQt0d6Z2dnESuRJEnFVJA5CPNDfw8HHgLGxxiXQy5EBMbld5sELO5z2JJ826T89ubtmxwTY0wDTcDofq5/Xghhdghhtr3TSkM6nWbd2rVkq+o3ac9U1LNy5coiVSWVtr49O/y3TJI0GHpCwYDTV0iSVM6GPCAMIdQDNwCfjTFu2Nqu/bTFrbRv7ZhNG2K8LMZ4ZIzxyLFjx26rZBXAypUryWazxKqGTdqzVfW8vHjJFo6SyltXVxeViUhdZWD58uXFLkfaJbgqq7R17e3tANSFYEAoSVIZG9KAMIRQQS4cvDrGeGO+eWV+2DD5+1X59iXAnn0Onwwsy7dP7qd9k2NCCClgOLBu8H8SDbaFCxcCkK0ZsUl7tmYka1av6v2wKmmjjo4OKpORCTVpXn55UbHLkSTtBnp6ENZhD0JJksrZUK5iHIBfAXNjjP/T56k/Ax/Mb38QuKlP+zn5lYn3IrcYycP5YcjNIYRj8uf8wGbH9JzrLOCuaFeBXcKWA8IRxBh5+eWXC1+UVMLS6TSdnZ1UJmBCbZqFCxYUuySppPV8HHBqYmnrekLB2hjp8AtaSZLK1lD2IDwe+Bfg5BDCE/nbqcC3gTeEEF4E3pB/TIzxGeA64FngNuBTMcaeJTs/AfyS3MIl84Fb8+2/AkaHEOYB/0p+RWSVvoULFxKq6iBVtUl7tmYkAC+99FIxypJK1pIluaH3VcnIpLoM69Y3smHD1mZtkMpbT0Do94bS1rW1tVEZApVAe1tbscuRJElFkhqqE8cY76P/OQIBTtnCMZcAl/TTPhs4qJ/2DuBdO1GmiuT5F16gu2bUK9pj9TBCMsX8+fOLUJVUunp63VYlI5PrM71thxxySBGrkiTt6trb23MBYYysNSCUJKlsFWQVY6mvzs5OXl60iGztKxachpAgUzuK559/vvCFSSVsQX5IcVUyMrkuC9jTVtqanqHFDjGWtq69vZ0qoAqcA1qSpDJmQKiCmz9/PtlslmxdPwEhkKkZzQsvvEg2my1wZVLpeuaZZ6hMRgIwujrLsKrAs88+W+yyJEm7uLa2NipjpCq/LUmSypMBoQqup3dgtm5Mv89n68bQ3t7G0qVLC1mWVLI6Ozt5es5T1CZ7Fl2AGcM6efzR2Qbp0hbcfffdxS5B2iW0tbZSlQ8IO7u7SafTxS5JkiQVgQGhCu7ZZ58lVNYSK+v7fT5TP7Z3P0nwj3/8g7b2DoZVbgwDjxnfxcrVa5g9e3YRK5NK1+rVqwEXKZG2pa2tjUqgMv+4Z1VjSZJUXgwIVXBPP/MM3bVjct2g+hFrRhBSFcydO7fAlUmlp7u7m2uu+i0T6iK1qY1Bx5HjuhlWBVdc8Rt7EUr9cBVjaWA62tupACryjzs7O4tZjiRJKhIDQhVUc3MzS5csIZvvJdivkCBdO4Y5Tz9duMKkEnXNNdew8OXFvHdGyybtFQl49/RW5sx5mr/+9a9Fqk4qXT3DJLu7u4tciVTa0uk0SSCZf+xrRpKk8mRAqIJ68sknAcg07LHV/TINezDvxRdpbW0tRFlSSXrkkUe4/PLLOWZ8F4ePeeWcUK+Z2MUBI9P8+Ec/5LnnnitChVLp6hkmuWDhgiJXIpW2GHMLYPWM67BXuiRJ5cmAUAX1xBNPEBKprfcgBDINE4gx8tRTTxWoMqm0zJ8/n698+ctMqkvzkf37D8pDgE8d1MKwVDcX/vsXWb58eYGrlEpTR0cHzS3NALzw/AusW7euyBVJpSuEQAR6BuMnk8mt7S5JknZTBoQqqEcfe4x0/VhIpLa6X7Z+HCSSPP744wWqTCodTz/9NOd/5tNUxXY+f+gGarfychleFfnCIU10tjTyqU9+ggUL7C0lXX/99cRsJNZHstks11xzTbFLkkpWKpUiA2Tyjw0IJUkqTwaEKpjVq1czf948MsMmbnvnZIpM/TgeeGDW0BcmlZD777+ff/3cZ6mNLXzliCbGVG97gYVJ9Vn+41UbSLeu49Of+mTvUH6pHD3wwAP86le/IlZGqIbs9CzX/eE6br/99mKXJpWkyqoquoGeiSyqqqqKWY4kSSoSA0IVzL333gtAetReA9o/PXIaL7+8iIULFw5hVVJpyGaz/PrXv+aiiy5iQlUHX31VE+NqBj4P1JSGDF89oon62MJnP3tBrgeVq7eqjHR1dXHllVdy0UUXkR2ehQYgQDw0wli45JJL+NnPfkZ7e3uxS5VKSk1NDd1AV/5xdXV1McuRJElFYkCogrnrrn9A3ShizYgB7Z8ZNQ2Au+++e8hqkkpBW1sbF110Ib/5zW84cUInXzmiieFV2x/ujavJ8p9HNXLoyE5+9KMfcckll9DV1bXtA6VdWCaT4a677uJ9738fv/zlL8lOypJ+bXrjJ5wUZE7MkJ2e5fe//z3nvOccbr755t5VjqVyV1VdTXcIdAOJRIKKiopilyRJkorAgFAFsXLlSp5+eg5dI6YN+JhYWUe2YQ/+9ve/2xNKu61169Zx/vmf4aEHH+SD+7Zx3gFtVO7E9E+1KfjsoS28c3o7d9xxB1/8t39zNXDtllpbW7nuuut4z3vfw9e//nVWdawi85oM2WOzsPm8nQmIR0Qyr8uwPqznO9/5Dme96yyuuuoqmpqailK/VCoqKyvJEMgAFamtzxEtSZJ2X34KUEHcdNNNRCA9dsZ2Hdc9diaLX7qXxx57jCOOOGJoipOKpLm5mU998hOsXrmCzx3SwuFjuwflvIkAb5/ewdiaLL944nHO/8yn+dmlP3deKe0Wnn/+ef7yl79w+x2309nRCWMge0yWODlC2MbBYyDzugwsh3Xz1nHZZZdx+eWXc/LJJ/O2t72Ngw46iBC2dRJp95JMJokBsjHXg1CSJJUnA0INuc7OTv50059Jj5hCrGrYrmPTo6dTvfgRbrjhBgNC7XZ+9rOfsXz5cv7jiA3sOyKz7QO20wkTuqhORv73qflceeWVfOxjHxv0a0iF0N3dzd///neuv+F6XnzhRUIqkJmUIe4TYdR2niwAEyEzMQNNkJ2X5Y677uD2229n6rSpnPXOs3jTm97kPGwqG+l0mkSMJMgN2ZckSeXJrwk15O68805amjfQPf7A7T84kaJz7Ezuv/9+li1bNvjFSUUyd+5cbr75Zt4ypWNIwsEeR47r5oQJnVxzzdUsXbp0yK4jDYWOjg6uueYazjr7LL71rW8xb+U8sodnSZ+WJh69A+Hg5obnhh6nT0+TPTLLog2L+P73v887z3onv/71r2lpaRmUn0MqZa2trVTGSBXQ1d3t/JySJJUpA0INqWw2y1VXXw11o8gOm7BD50iPO4AYAtdee+0gVycVz6233kplMnDmXkO/ourZe7eTzWT529/+NuTXkgbLs88+y4c/8mF+/vOfsz61nsyJGdJvSOd6DVYO8sVSEPeKZE7OkDkpw4b6DfzmN7/hAx/8AI8++uggX0wqLWtWr6YeqM8/Xrt2bTHLkSRJRWJAqCF1zz33sGTxYjomHAr9zOtUuWgWiba1JNrWUv3sX6lcNOsV+8SqOrpHz+Avf/0ra9asKUTZ0pBqb2/nH3fdyWGjO6kpwEQPo6oj+45Mc9utt9gzRLuEOXPm8MlPfpJl65eReU2GzGsysAfbnmNwZwVgLGSPz5I5JcPazrV87nOf47777hviC0vF0dXVxcqVKxnFxg65ixcvLmZJkiSpSAwINWRijPzmiiugZgSZUXv1u0+idS0h003IdJNsXkGitf9vrbsnHko6neb3v//9UJYsFcR1111H04Zm3jylo2DXfNOeHSxbvoJbb721YNeUdtQvfvkLYlUk/fo0jC9SEaMgfUqaMCzwf5f9X5GKkIbW/PnzyWSz7AH0jPN47rnnilmSJEkqEgNCDZlZs2ax4KWX6JxwKISd+1WL1cPoHr03f7rpJhobGwenQKkIli1bxjVXX8URY7uYOYRzD27uyLHdzBiR4ZeX/Z+vIZW8tWvXkh2WHfyhxNsrBZkRGdatW1fkQqSh8fjjjwMwDaglMC4keCLfJkmSyosBoYbM1VdfA9UNpEfvPSjn655wKF2dnfzpT38alPNJhZbJZPjmJZdApot/mbl9cw/+9vkaFjUnWdSc5OLZ9fz2+ZrtOj4E+NC+rWxo3sD3v/c9YozbdbxUSMcfdzxhVYBizyrRBMllSY4/7vgiFyINjQdnzWKPEGjIj9/fO2Z54okn6OgoXA93SZJUGgwINSSeffZZ5sx5is5xB0JicH7NYu1IMiP25Prrb6Czs3NQzikV0oMPPshTc+bw/hktjKnJbtexi5qTtGcStGcSPNdYwaLm5HZff2pDhndOb+Oee+9l7ty52328VCgf+MAHGD9+PKmHUjD06/j0rwtSD6ZoqG/g4x//eJGKkIZOc3MzT82Zw4w+XxjtS24l40ceeaR4hUklKp1O8/l//TynnHIKbz39rTzxxBPFLkmSBpUBoYbENdf8jlBRRXrcvoN63q4Jh7BhQxO33XbboJ5XKoTHHnuMiiQct0dX0Wo4aWJXby1Sqaqvr+eSiy+hIlNB6oEUFG40fk6E5INJQmvgG//5DUaPHl3gAqSh9/DDD5PNZtmvT9tUoCoEHnzwwWKVJZWkTCbDd7/7XR6Z/Qjj6/eiva2Lr3z5KyxcuLDYpUnSoDEg1KBrbGzkvvv+SeeYmZCsGNRzZxv2INaN4c9//sugnlcqhOXLl1OXGvqFWLemIhGpTMLKlSuLWIW0bTNnzuSrX/kqcV0kPL19r5rwRIBGoBESdydyj7fn+OcDrIR//dy/cvjhh2/XsdKu4rHHHqM6kWByn7YUgb1i5LHZs4tWl1Rqmpubueiii7jttts4cOLxHLfPGZy833vp6kjzyU980h63knYbBoQadHfddRfZbJb0mBmDf/IQ6Bq9Ny+++AIvv/zy4J9fGkKnn346jZ1w/4rirbzwt8XVdGXg1FNPLVoN0kC95jWv4fTTTyfxYmK7hhqHxkDozt9WB0LjdgSEaUg+l+SEE07grW996/YXLe0i5j77LJOzWRKbfW21J7B0+XI2bNhQnMKkEjJ//nw+eu5Hefihh3nV1Ndz4KTjABhWM4rX7ftekrGGL3zhC1x55ZXO7yxpl2dAqEF3x9/+BrWjiLWjhuT8mdHTAfj73/8+JOeXhsqxxx7L/vvtyzXz6lnVVvh/fhduSPLHhbUce+wx7L///gW/vrQjzj77bIgQlhWo7+1KiN2Rs88+mxCK2d9XGlrLly2jv8HzY3qeX768kOVIJWfOnDl84hOfpGl9Cyftew77jNu0R3l99QhO2e+97DlyP375y1/y3e9+15BQ0i7NgFCDavXq1Tz7zDN0jZo+ZNeIlXVkhk3kzjvvGrJrSEMhhMBXv/Z1EpW1/GBOA10FnFettTvwv3OGMWLkKC688KLCXVjaSb2rqQ7ujBVblsrdtbcXa3UUqTDaOzup6qe9p83XgMpZV1cXX/3q16gI1Zy83/sY0zCp3/1SyUpePf009p9wDDfffLMdGCTt0gwINaiefPJJADIjJm9jz52TGT6JxYtfZv369UN6HWmwTZo0iX/74r+zuDnBnLWFSjzgoZUVrGmHr//nfzFy5MiCXVfaGW1tbfz39/6bUBWI4wrUK2MUhNrAD/73BzQ1NRXmmlIRVFVW0t1Pe09bVVV/8aFUHpYsWcLatWvYd/yrqa1s2Oq+IQQOmnQCFakqHn/88QJVKEmDz4BQg+qpp54ipCrIDtHw4h6Zhj16ryftao4//nhqqqt4soAB4ZNrKxg/dgwHHXRQwa4p7YyVK1fyyU99khdffJH0kWmoLtCFKyD96jQrV67kvI+fx6JFiwp0Yamwxo4dS2M/7T1t48aNK1wxUomZNGkSDQ3DmL/6cbozndvcf96qx+lOdzqFi6RdmgGhBtUTTz5Jum4chKH91crWjSEkUgaE2iU9+uijtHd00lCRLdg1Gyoia9at47nnnivYNaUd9cQTT3Dux85lweIFZE7IwMQCFzAG0q9Ns2LdCs77+Hk88MADBS5AGnp7TZ/OqsQrP6+tBOpraxk1ami/7JVKWVVVFf/xH1+iqX01dz//e9q6mvvdL8bIs8tm8fjLd3LMMce4CJykXZoBoQZNR0cHixYuJFNfgG+cE0kydaN57rnnh/5a0iB68cUX+eYlF7NnQ5Yz9uoo2HXfM6OdkZVZvv61r7JkyZKCXVfaXnfffTef/dxnac40k35dGvYoUiFjIH1Kmo6qDi688EL+/Oc/F6kQaWjMmDGDtdksHWw6fH95CMyYOdNFelT2jjvuOL7zne/QkW3mzrlX0di2apPns9kMjyy4laeX3scb3vAGLr74YpLJZJGqlaSdZ0CoQbNw4UJijNs3vDjTRU1NDWeddRY1NTWQ6Rr4oTWjmDd/vquFaZdxxx138In/9/8InRv49EHNVBbwM2RdReTTB22ged0qPvbRc+0RpZI0b948vnHxN8iOyJI+JQ3DilxQLaRflybuEfmf//kfnnjiiSIXJA2eGTNmALkegz2yRFYBM2bOLEpNUql59atfzaWXXkrdsGrueeFamtrXALmegw8tuIWFa5/hIx/5CF/+8peprKwscrWStHMMCDVo5s2bB7BdAWFId3Haaadx/vnnc9pppxHSAw8Is7WjaG9rZdWqVdveWSqya6+9losvvpjp9e1cfFQjk+oKN7y4xz7DM/zXUY2MTbVy4YUXcscddxS8Bmlrbr75ZtLZNJnjMoVbtXhbkpA9Jkusitx0003FrkYaNJMn5xaU67vcWwvQHWPvc5Jg+vTp/OQnP6GmroaHXvor2ZjlpdVPsnjdc5x33nl86EMfssetpN2CAaEGzfz58wnJCmLVwLt8xFQlN998Mz/60Y+4+eabiamBf/PWE0T2BJNSqVqyZAmXXfZ/HDG2i4sOb2Z41fb3em1Ph01627and+yD6LiaLF89oomZI9L88H9/4ErgKikrV67MLUZSqAVJBqoCYm1k+Yrlxa5EGjT19fUAdAC3ELmFSM/EFw0NW1+1VSo3EydO5F//9XM0tq1mWeM8nlv5MAcffDDve9/7il2aJA0aA0INmueef55M7SjYnm/QkpW0t7dz/fXX097eDsntDwhfeOGF7S1VKqjf//73ZNNpPrRfG8kd/Fe3LR026W3btoMBIUBlEj68XyvNLa388Y9/3OHzSIPtVa96FbElbtqlqRS0AuvgyCOOLHYl0qDp6MjFgRXA8vytp+Nue3t7kaqSStcJJ5xAVVUVi9Y8S2tHE2984xvtOShpt2JAqEGRyWR48cUXydSOLtxFkxVQO9JVWVXy9tlnHzIR5jeldvgctam4SW/b2tTOzb35QmOulr333nunziMNpje96U3UN9STfCoJJTS9bJgTqEhV8Na3vrXYpUiDZuHChQD0/eQ2DEiF0PucpI1SqRQN9Q20dW0AYPjw4UWuSJIGlwGhBsXixYvp6uwkWzemoNdN14xmrisZq8SdfvrpTJs6hSteqGdl2479s1uTipv0tq3ZiYBwUXOS6+bXc9ihh/Ca17xmh88jDbaGhgY+8uGPwCpytx3VzaYLYHXvxLnWQ2JxgnPOOYfx48fvxImk0jJr1iwqQmBin7YkgSkRHnzgAReBkzaTzWZp2tBEQ3VuFJPTtEja3RgQalA8++yzAGTqxhb0upn6MTSuX5ebt0oqUalUiq99/T9Jp+r41uPDWd1evH96F7ck+Pbjw6gbMYoLL/qSQ2NUct72trcxZuwYks/txDLf3WwyJH9nAsLwXKC2rpZzzjlnx08ilZi2tjb+dscd7BsjlWz6PnAwkZeXLOHJJ58sUnVSaVq8eDHd3d2MGzaVylQ1L730UrFLkqRBZUCoQTFnzhxCRTWxZkRBr5tt2AOAp59+uqDXlbbX3nvvzQ/+94d0JGr59XN1Ravj0mcaqKwfyf/+8MdMnDhx2wdIBVZZWck73v6OXA/CDTt4kgo2GZK/wysid0BiWYLTTzvdRRu0W/nTn/5Ea1sbx/Xz3CFAXSLBb3/720KXJZW0OXPmADC6fgIja8fz5JNPFbkiqbRdddVVnHrqqSxbtqzYpWiADAg1KJ586im668Zu3wIlgyBbO4qQrOh9w5ZK2cyZM3n7O97JM+tSbOgqfM+9pS0JXm5O8P5/+QCTJ08u+PWlgTr99NNJppKEeTv4Oqlg0wWwdjAgDPMDZOGMM87YsRNIJai5uZmrr7qKGcCevPI1VknghGyWRx55hMcff7zwBUol6r777qO2qoFh1aMZP2waCxcuYOnSpcUuSypJjz76KJdddhktLS1897vfdUj+LsKAUDutubmZJYsXk60fV/iLhwTpujE8/fQzhb+2tANGjx5NNsKy1p0YPrmDluavOXp0ARcTknbAiBEjOO3U00i8lIDGIhXRCskXkpxw4gnsueeeRSpCGny/+93vaGlp4Q1b2efVwPBEgkt/9jPnIpSAuXPnMmvWLKaOOpAQAlNG708ikeTyyy8vdmlSSYgxsnDhQq677jo+/vGP87nPfY6KRILhlSmeePxx3vWud3HxxRdz991309TUVOxytQU7vqSmlNez0l22kCsY95GtHc2ChS+QzWZJJMy8VbpWr17Nr375C2aOyDBzRLrg1z98bDcT6yM/+fGPOOKIIxwyqZJ23nnncc+997Bh1gbSJ6WhpoAX74LUAykqU5VccP4FBbywNLTWrVvHH669loOBCf30HuxRQeB12Sx/ev557rvvPk488cTCFSmVmIULF/KlL/0HNZX17LvHUQDUVjaw7/ijueOOO9hzzz35wAc+4LzOKiuNjY0899xzPP/88zz77LM88/TTbGhuBqChsoJ9R9Szsq2TEOC4PUaycEMb/7jzTu644w4ApkyZwkEHHcR+++3Hfvvtx957701FxY7OCaPBYkCondYzQW+2dmRRrp+tGUl3VxfLly9n0qRJRalB2pbZs2fzjf/6T7raW/no0S0kivAZsiIBH9uvmYsfDZz7kQ/z9f/8Lw444IDCFyINwLBhw/jOt7/DBZ+9AO6F9AlpKMT0nR2QvD9JojnBN7/7TVcu1m7l2muvpau7m5MHsO9hwL2JBL+5/HJOOOEEww+VnUwmw0033cSll/6cRExy4ox3UZmq7n3+wEnH0dbVxK9+9SueeeYZPv/5z/ueod3WmjVrePjhh3n88cd56sknWb5iRe9z9ZUVDKtIsueoBkZVV1Kbyo1aWtXeCUBdRYoDRw9j/xhp6uxmXWc3jatWcOcdS7nllluA3KKOM2fO5NBDD+WII47g0EMPpaqqqvA/aJkzINROW7BgASFVQaysL8r1szW5YPKll14yIFTJ6erq4oorruCqq37LxLrIvx+1gYl12aLVM2NEhq8cuYGfPA2f+tQnOe+8j3P22WeTTBZ+yLO0LQcccADf++/v8e8X/jvt/2gnfVwaRg3hBZshdV+KVFeK/7r4vzjqqKOG8GJSYTU3N/OnG2/kIGD0VnoP9kgSODGb5aZ585g9e7avB5WNGCMPPvgg//fz/+OlBS8xftg0jtrrzdRWNvD4y3cBcPiUk0mEBEfvdSoja/dg9iP/5H3vex9nn30273nPexyloV1ec3MzL7zwAk888QSzZs3ihRdeAKAqlWR4RYqZI+oYXlnBsMoUqQGO4kuEwMjqSkZWVwK511pHJktTVzdNnWmWz3+R5+bO5Xe/+x1VVVUcffTRHH300Rx00EFMnTqVVMr4aqj5X1g7raWlBSpqCr5ASY9YUb2xDqmEPPvss3z7W99k4aKXee3ETj6wbxtVJZDD7TM8wyVHN/LLZ2u59NJLufvuf/Dv/34h06dPL3Zp0isceuih/PzSn/P5L3yeNfesIX1MGiYMwYXW5IYVN9Q08N3vfdfetdrt3HDDDbR3drI9g4UPA/6RSHDlFVcYEGq3F2PkkUce4Ve//BVzn5tLffUIjt37bUweObO3B21j26pNjgkhMHOPI5g0ch/mLPknV111FTfe+EfOPvtdnH322dTXF6cDhbQ12WyWDRs2sGbNGtauXcuaNWtYtWoVK1euZNmyZSx++WXWrlsHQACGV1UyY3gdY2uqqK9IDlqP8hACNakkNakke9Tm2tLZyPrOLla3dzH7wVn885//BHI9DCdOnMjkyZOZMGEC48aNY9y4cYwePbr3VltbOyh1lTMDQu20yspKyGaKV0DMbKxDKgHZbJZf/OIXXHPN1Yyshi8c1sxhYwo/5+DW1FVEzj+klVkru/jtC8/x0XPP5WPnncc555zjMDKVnGnTpnHZ/13GF//9i8x7YB7pE9MwmOtirc/1HJw4fiLf/973mThx4iCeXCq+tWvX8rtrrmE/tj734OZS+V6ENz/1FLNmzeLYY48duiKlIpo7dy4//elPeeqpp6irGsYR097IXqMPIpEY2De7dVXDOWbv09lvwtE8s+wBfvOb33D9H67nAx/8AO985zudW00Fk8lkWLlyJUuWLGHlypWsXr16YxC4ejVr166lsamJbPaVI5qqK1JUJwK1ySQzRtQxrCLF8KoKKgo4z38qERhbU8XYmipijLSlMzR1pWnuStO2egVzVi7nkXSG7swr66+qqmLUqFGMGTOmNzQcO3Ys48aNY/z48UydOpVhw4YV7GfZFRkQaqdVVFQUNSAMWQNClY7u7m6++c1vcuedd3LSxE7eO7ON2hL9lzY3aXA3B41q5PLncr0JV65cyfnnn++CPyo5o0eP5gf/8wM++alPsvjBxaTfnIbB+Gc/A6lZKUaPGM2Pfvgjxo4dOwgnlUpHNpvlu9/5Dl2dnbx5B44/Eng4JPjed7/Lry6/nBEjRgxyhVLxdHZ28vOf/5wbb7yRqopaXjX19ew15hCSAwwGNzeidhzH73Mm61tXMmfpP/nZz37GzX+9mS9/5cvsu+++g1y9tKnLL7+835W1qytSVCYClSFQk0wwor6aymSCqt5bkupkgkSJdRIIIVBXkaKuIvWKeai7s1k60lk6M1k6Mxm6svntpnUsWbeGBRE6M1m6M5vmFBWpFD/92c/Yb7/9CviT7Dr8C1A7rba2lpjpgmxxekiF7g4AamoKucSl1L9vfSsXDr57nzbO3b90w8G+hlVGPnNwK2+Z0sGNN97IT3/602KXJPVr2LBhfP1rXyd2RsL8QRresigQWyNfuuhLhoPa7cQY+clPfsKsBx/kLTEOaO7BzaUInBWzrF+3josuvJDW1tYhqFQqvJaWFi644AJuuOEG9h57GG856Fz2GXf4DoeDfY2sG89rZp7FCTPeweqV6/jkJz/J/fffPwhVS1u2aNGiV7Q1VFZQn0zQkErSUJmiPh+41aWS1KaSVJdoOLgtqRCoTiWoSSWorUhRl8r9bA0VKYZVpqhPJWhIJajZbN7C7nSaDRs2FKnq0rcL/OmqUnfEEUdw9dVXk2xcSmbU1O06Nls3mkTb2tx27WiydaO3+/rJxpepqKzkwAMP3O5jpcH22OzZHDu+i7dO6yx2KdslEeC9M9pZ1JzksUdnF7scaYv22WcfRo8ZzeqW1YNzwpZcT/gjjjhicM4nlYjOzk6+//3vc9ttt3EscPROnGsigbNi5Lpn53L+Zz7DJd/8JnvsscdglSoVxXe+8x2em/scx+79NvYcNTS9+yaO2JtRdRO478Ub+NrXvsbll1/OnnvuOSTXkr761a/ywQ9+kBdeeIEVK1awYsUK1q5dy9o1a1izdi0rNjSR6WdoLkBFMkllMkFFgKpErmdhLoDLBYn1FamChohdmSwt3Wna0xna0xk6Mrkegt0x0pWNdKYzZGPs99jq6mpGDB/BxLFjGT16NGPGjGHChAlMnjyZ/fffn5EjRxbs59jVGBBqpx122GHU1dfTvX7BdgeEXVOPJdGaCwg7Djh9+y8eI5WNizjm1a+2B6FKQjKZZG1noCMN1YP4L+zUhgyLmpO921MbBn9Yf2s6sKErQa0rhKmELVy4kPXr1sOYQTphdW5qgDlz5nDIIYcM0kml4po/fz6XXHwx8+bP53XA64CwA70H+zqQwHuJXD9/Ph/9yEf4whe/yEknnTQY5UoFt2DBAu655x4OnHj8kIWDPaorajl+n7dz69O/5Pe//z3/9m//NqTXU/lKJBLstdde7LXXXv0+H2OkpaWF9evX09TURGNjI42Njaxfv57169fT2NjYO1fh6rVraW/Z2GM8hMCIygr2HVHH8Kqhm1NzwYZWlrR20ta9cXRiCIHhw4YxZkIu8Bs1ahQjR45k5MiRjBgxghEjRjB8+PDex1VVVUNW3+7OvwK101KpFCeecAK3//0fdGXSkCzcr1WiZSWxs5XXvva1BbumtDXvevc5XHrpz/iPR0bwyQOa2Xv44AR5/7Jve29A+OUjB3/F7mfWpfi/ZxvY0J3k39551qCfXxoMCxcu5N8v/HdiKhJn9v+t8faK0yPhxcBXvvYVvv3Nb7P//vsPynmlYmhvb+eqq67imquvpgZ4P7DvTgaDfe1L4P/FyHWtrXz1q1/lxBNP5DOf+Yy9CbXLefTRRwHYa+zBBbleTWU9ewzbi0cefqQg15P6E0KgoaGBhoaGAe3f3NzMnDlz+Mtf/sL999/P+s4uXm5JcPAAAsK565tp7sqFfA+vXE9DZYr9R279upls5MWmVmLMje4466yzeOMb38iUKVNc6KdADAg1KE4//XRuu+02KpY9QfeeRxbmojFStfgRhg0bzvHHH1+Ya0rbcM4557DvvvtyycXf4D9nB44a18UpkzrZf2SaUpvaIxvhqbUp7lxSzRNrKpg8eRLf+drXnURbJaejIzc/5uW/uZyu0EX6+DQM1pfDKUgfn6ZxViOf+vSneN9738e73/1u6uvrB+kC0tDLZDL87W9/4/8u/Tlr16/jUOBUoHYA4eAtRJbnt39FZAJw6laOG03gvBi5H7j7vvt46MEHOec97+E973kPdXV1WzxOKiWPP/44tVUN1FYOLCgZDKPqJvDUkhdYuXIl48ePL9h1pYFqbm7mxRdf5Pnnn2fu3Lk8/fTTrFmzBoDKVJJx1RXsM3xg/843d6VJ54cAr+/sHtAxyUTgVWOGs6C5nfWd3fzud7/jumuvZe+99+aAAw9k//33Z+bMmUydOpWUI56GRIhbGLe9uzryyCPj7NnOrzUUvvGNb/D3O++i9aB3EGuGD/i46mf/Cmz/EOPUquepWvBPLrroIt7ylrds17HSUGtubuaKK67g1ltuprmllQl1kVMmtXPihC7qKnbs392LZ+cCi53tQdjUGbhnWRX/WF7D6jYYOWI4bzvjTN73vvdRXV29U+eWBlNLSwu33HILV119FY3rG4kTItlXZaF268cl7k4QVm8MOOLYSPak/ufc6dUJ4bFAYkmC+oZ63nPOe3jrW9/qiq0qaTFG7rvvPn5x2WUsXLSISSFwaoxM2Y5eg78isrDP42nAuQM8vonIHcBTwLD6ev7lgx/kzDPPdHiXSlYmk+HGG2/kxz/+MfvtcTSH7DnwUUiPv3wXC9c8DeRWKx5RO47Dp5w84ONbO5u49elfccAB+/P1r3+dcePGbXf90s5Kp9OsXr2aZcuWsWzZMl5++WUWLVrE/HnzWJ0PAwFqKyoYVpFgRGUFI6sraKhIEbajt8PDK9dvEgyOrKrg6PEDn/svnc2yvrOb9Z3dNHWmaU6n6c7Pn5hKpZgyZQrTp09n6tSpTJ48mYkTJzJhwgSGDx++XXWWsX7/IxkQatCsXbuW9773fbRUjqRj3zcz0O5SOxQQdndQP+d6Dtx3Bj/96U/8R0Alq7Ozk7vuuos//emPzJ37HBUJOHxMF8dP6OLQ0d2ktmMt+Z0JCLsy8NiaCu5fXsWTayvIRjj88MM444wzOfHEE+22r5Ly0ksv8cc//pHbbr+Nzo5OGAuZAzMwwEWGdygg7LEeEk8nCCsCqYoUp5x8Cu94xzsceqyS0hMMXv7rXzNv/nzGhASnxCwHAIntHFK8MwFhj6VE/kZgPpFRI0bw3ve/n7e97W1+6aSSEGPk5Zdf5r777uPmv97MkqVLmDhib47d+20kEwPvhfSP537P6ubFvY/HNuzJ6/Y7Z7tqWbzueR5ZeCshwCmvP4VTTjmFww47zFBdOyzGSHt7O83NzWzYsIENGzb0zi3Y2NjIunXrWLduHWvXrmXVypWsb2ykbwaUTCSoq0hSm0zQUJliWEWKYZUVVCa344+UfuxsQNjfz9mazrChK01zV5qW7jRtmUhb96a9EysrKxkzZgxjx45lzJgxvfMVDh8+vHe+wp5h1sOGDSvnv4EMCMGAcKjdcMMN/PCHP6Rz6rGk9xjYqsLbHRDGSNWLf6eyaTG/+tWv2HvvvXe0XKmgXnjhBW699Vb+/rc7aNrQTEMlHDOugxMmdDF9WGabmfr2BoQxwvONKf65vJKHV1fT3h0ZPWokb3zTmzn11FOZOnX7FhWShlJ3dzf33nsvN/7xRuY8NYeQDGT2zBD3jjBq+861UwFhjyYI8wPJRUliOjJz35m84+3v4OSTTzb0UNGk02n+8Y9/cNWVV7Jg0SJGJRK8NpvlUCC5g3MNDkZA2GMBkX+EwIIYGT5sGGe/+92ceeaZA57vStpZMUZWrVrFokWLePHFF3nuued4+ulnWLs21zNqTMMkZow7gskjZ253B4PBCAgBWjobeW75wyxZ/xxd6U4qKirYb7/9eodP7rXXXkyZMsXQsAx0dXXR1tb2iltrayvt7e20tra+4tbS0kJLczPNzc20tLTQ2ta6xZWJAapSKaqSgYoA1ckk1ckE1alk7+rE1cnEkHS2GeyAcEvS2di70nFbOkNnZuOKx10ROtMZ0tmt/PeprKSuro76fGjY0NBAfX09tbW11NXV9d5qa2t723q2e241NTUkk8lB/9mGmAEhGBAOtWw2y0Vf+hKzZj1I+35vITtswjaP2d6AsGLp41QueZTPfOYzvOtd79qpeqViSKfTPPzww9x+++3cd98/6e5OM7Uhy8mT2jl+j64trn480ICwtTvwz+WV3Lm0huWtgeqqKl570km86U1v4vDDD98V38C0G0un09x88838+vJfs37dekJ9IDM9Q5wWd3iewUEJCHt0Q1gUSL6UJDZF6hvq+Zf3/wvveMc7/ONNBdPW1sYtt9zCdddey4qVKxkXEpwYsxzMjgeDPQYzIOyxkMi9wItAbU0Nb33b2zjrrLOcd007LcZIU1MTq1atYvXq1axatYrly5ezbNkyli5dxtKlS+noaO/dv6F6BCNq92BcwxT2GD6NuqqBT4O0ucEKCHtksmlWbXiZlRsWsbZ1GY1tq8hkc4s6hBAYP248kyZNYuKk3NDJPfbYg7Fjx/b2jKqsrNzha2vnZLNZWltbac4Hdb1h3WZhXk/Y13Pf2tJCaz4EbG9vJ5MZ2GKGFckEqUSSVCKQJJIKgVQiUJFI5O9z27lboDKRoCKZ204UaaTdA8vXkk5Vctppp3HzzTeTSndx3ITRRaklk410ZbN0ZbN0Z7J0ZyPd2Y336WykOxtJZ7NkCGRiJB0j3Zksma2Ei31VVVbmwsLaWurr6qjtEyz23G8ePPYNJIcNG1boL6ANCMGAsBBaWlr42MfOY9nqdbQecAaxausTmW5PQJhc/zLVL/yNN7zh9Xz5y192aLF2ec3Nzdx5553c9Kc/Mv+lBdSkAsft0c4b9+xkUt2mb0jbCggXbEhyx+IqHlxVTXcmcsD++3PGmWdy0kknUVNTM+Q/i7S9XnzxRb72ta+xZMkSGAOZ/TMwni18ZBm4QQ0Ie08CrIHkc0lYAWPHjeWrX/kqhx566M6dV9qK5cuX86c//Ym/3HQTLW1tTAmBE2JkX7Z/KPGWDEVA2GM5kX8CzwAkEpx00kmcddZZHHjggX6GU68YIy0tLTQ2NrJ+/XrWr1/fu90zNHLt2rWsXbOWdevX0b3ZkMJkIkV99XBqK4ZTXzWChprRDK8ZzbCaMVSlBu/zz2AHhJvLZjNs6FhHc8daNrSvZUPHOtq6mmjtaqKjq+0V+w8fNpzRo0czesxoRo0axejRufsRI0YwcuTI3vvhw4eX8zDKHdbZ2cns2bN54YXcwjIrV6xgzdq1NDaup7m5hew2gqNUMkEqkSAVAskASSCVCKRCgmQi5LdDbrv3Phf4pRKBZNi4z6747+U9S9dw+tvfwfnnn8+PfvQj/vrHG3ntpDHFLmu7ZWMknc0Fhulslkzvdu4+kw8We7f73gOZmOvl2J3JkN1G9lZZWcnwYcMYOXIkY8eNY/z48UyePJlDDjmEmTNnDvaPZkAIBoSFsnDhQj523nl0pBpo2/802Mr8HgMNCEN7I3XP/oXp0/bk0p/9zCFe2q3EGHn22We56aabuOvOO0mnuzlxQifvnN7OqOrcv9NbCghXtiX4w/waHlxZSU11FW9805t529vexowZMwr+c0gDlU6n+fBHPszLK18mfXgaJrLTwWCP8EQgLMyfbATEEZF42CB+3lkJqcdSjKoexdVXXU1t7TZWTZG2Q4yRxx57jBtuuIEH7r8fYmR/4Hhgz8F6kfQxlAFhj0Yis4DHQqAjRvadOZN3vPOdnHzyyfbE3c30hH0bNmygqamJpqamfrd75kdramxiQ/OGLfakqqqoobqijqpULTUV9VRX1FFT2UBtRW4F4prKBqor6goSoAx1QLg13Zku2ruaaetq3njf3UxHdxud6VY60q20d7aSjf3/d6ytrWP48GG9c7ANHz58k9uwYcNesV3uq8ReeOGFPPDAA72P6ypS1KUSVCYSVCY39tbb2ItvY7i3q4Z6g6mUehCWikxPsNi312LM92TM9PRwjHRmszR1dZPObvzseskll3DiiScOZjn9/oKW96teQ2batGl89Stf4T++/GWq5t9L5z6vG/CiJf3q7qD2xb/RUFvNNy+5xHBQu50QAgceeCAHHnggn/zkJ7nqqqu48YYbmLWymrfs2cbbp3e84piONPxhfg1/X1pNRUUlH/zgezjnnHOoq9t6r12pFMybN49FCxeR3TM7qOEgQDwsEhpzJ9zpnoP9GQ+ZPTKsnbeWJ598kmOPPXbwr6Gy09LSwu23386NN9zA4iVLqE0kOCFGjgaGD0EwWEgjCLwFODlGngAeenEe3/rWt/jpj3/MaW99K2eeeSYTJmx7WhoVXmdnZ2+Pvt5QLx/w9YR+uce5+5aW5i2GfSEEqipqqUrVUJmspjJZw8iKPRk/toaqilqqUzX552t775MJp0UBqEhWUlEzmmE1Ww5YYox0ZTro7G6nM91GZ3cbHfn7znQ7XZ3trFvazsqX19GZ6aCzu410pnuL56utqaVh2DBGjMgt7tA3ROwJGnvCxlGjRtHQ0LBbhWKvfe1rNwkIW7vTZElRm4RECFQloTqVpC6VpGqI5vHblaUSCZrb27n++uuB3ByE5S4ZAslkoCq/AEw2RtrSGVq703RloCuTpS2TpTWd2SQcnDJlCvvss09BarQHoYbU7373Oy699FK6Jh1O9+Qj+t1nmz0Isxlqnr+NyrY1/PCH/8vBBx88VOVKJWXZsmX84he/4M477+Sg0Wm60pFEyPUg3NAV+N6Tw1i4Icmpp53GRz7yEcaM2fW67at8xRj5yU9+wh/+8AcYA9m9s8RJMTcGZxAk7s5/+BrMgDALLIfES7lVjt/4xjdy0UUXOa+ndsrChQu58cYbue3WW+no7GRyCBwdIwcBFQUIBgvRg3BzkcgC4CHgOSCGwLHHHss73/lOjjzySP/QLpB0Os2SJUtYvnw5K1asYMWKFaxatap3OO/adetoa2vt99hESFBVWUtVsoaKZHUu9EtVU5mqoSp/q+xzX5mqpjJZvUv/v73jmSvojM29PaKqQgNvPPCDxS5rp6Sz3XSlO+hMt+Xuu9vpyrTn7tPtuWAx00FXup2ubO757nRnv+dKJlOMGjmSUaNHM2bMaMaMGcP48eN750+cMmXKLrdgUc+iN/PmzWPBggUsWrSIhQsX8vKiRbR3bPzyPpVMUJdKUpNMUJNKUpNMUp1KUJ3MhYcVifLrUVioRUpKWTZGOvMLpnSkM7Rnsr0LqrRnI21d3fRN48aMHs3UadOYNm0aU6dOZe+992bvvfceqpEq9iBU4Z1zzjksWrSIW265hWz1cDJjtjP5jpHKBfeR2LCci77yFcNBlZWJEyfyta99jaOOOorvfve71KcyHDm2i/WdgW8+Npy1XZVcfMl/csIJJxS7VGm7hRD49Kc/zYQJE7juD9ex4qEVhKpAZnKGOCHCWErjU0oGWANheSC5JElsj4waPYp3fPQdvPe97zUc1A576qmnuOaaa3jggQdIhcDBMfJqYFKEQe1SW4ICgenAdKCJyCMx8uiDD/LAAw+w9/TpvOe97+Xkk08u+yGOQ+XOO+/k2muvZcGCBXR2bgx7kokUtVUNVKfqqErVMbF+LNUj66ju7dVXk7tP1VCRrCq7wKM73clpbzuN888/H4Bb//y3Ile081KJClKVFdRWDjy4y2TTdKZ7einm7ju62+jsbqWju5Wm5a2sWvIi7V2P09G96dyJEydO5NBDD+WCCy7YJabnCCEwfvx4xo8fz/HHH9/bHmNk7dq1LFq0iEWLFrF48WIWL17MkiVLeHnlylf0ok2EQHUqRUUCKkKgMpmgMj8sefPhypXJQCqRIFlmr69SF2MkE3PDgnuGAnf3WfikK7/gSVcmSzfQlYl0ptOvOE99XR0TJk/ikClTmDx5MlOmTGHq1KnsueeeJfGasAehhlx3dzf/+vnP89RTc2g74G1k6zbtGr+1HoSpFc9StegBPvShD/GRj3ykIPVKpej+++/nS1+6iNOmdNDUFXhwTR3/8z8/cIEE7Ray2SyPPfYYf/7zn3lg1gN0dXYRkoHs2Cxxj0jcI0I925WZ7FQPwlYIKwJhRSCxKkFMR1IVKY4+6mje+ta38upXv9rgQjts8eLF/OTHP2bWgw9Sm0jw6myWVwN1RQoFf0qkqaamt1fU8PZ2PlWEWtJEngLuTyRYlc0ydcoUPnP++Rx99NEFr2V31t3dzRvf+MbeAKO2chgTRkxnfMNURtSNo6ainuRW5g4vZ7tjD8KhEmOkM91OS8d61rQsYUXTQlY1v9z7/AUXXMA73/nOIlY4dDKZDOvWrWPVqlUbe+Tmb+vXr2f9unWsW7+OpqYNr1hwp69kPjzMrVzMK+Y7rOizknFFIhcq9qxonCyBORBLsQdhts8cgN357Y2rGGfzKxn32Y6RTCQf/GXYWnRWV1fH8OG5hYNGjhzJyJEjGT0615N29OjRjBs3jnHjxpVSL1oXKQEDwmJpbGzkQx/+MOva0rQecAakKnufq1w0C4CuqZvO4ZRoWU3N3L9wzNFH8+1vf5tEIlHQmqVS81//9V/8/e9/B+Dss8/m05/+dJErkgZfZ2cnTz75JA899BAPzHqApUuWAhDqAplxmVxYOA6o3Pp5whO5zz0DWpykG1idCwWTq5LE5twxY8eN5fjjjueYY47h8MMPdzVw7bQ77riDb3/rWySzWV6b7zFYWeTegt8n8vqzzupdafLv11/P54tYU5bIXOCORIJ12SxnnHEGn/vc5/wcOIh6vpCZO/c5VqxYQYybfpHSuzBIsnbjkOGKzYYMJ6uoTFVTkaymMllFogzmCizmIiXFFGMkne2iK91BV6aD7nRnflhyfghyuo3OdEfvkOTOTCvtnS1kN/u9aqhvYNpe0zj++ON5+9vf7nsq0NHR0TuPZ3Nzc+9CPs3NzTQ3N79ye8MGWlpaNhne3J8QoDKZzK+iTP62MUjcNGzcLHgcpOHQc9c3s6wlV2dDZYqGyhT7j9z5cCwb8wFeZmOQ1xvyxU0Dv3Q2t5JwOgvd2Qzdma1/aZxIJKivq6O+oYFhw4YxbNgwGhoaem/19fUMHz6choaGTRb0aWho2BW/ODYgBAPCYnrqqac4//zz6Roxlc59Tt76oiXpTuqe+RNjGmq4/Ne/Yvjw4YUrVCpRc+fO5eMf/zgAf/jDHxg/fnyRK5KG3tKlS5k9ezYPPfQQjz72KO1t7bmPNGMhOyk/b+GO/I3RCWFpICzN9xLMRiqrKnnV4a/iqKOO4qijjmLq1KlF/wZeu4+HH36Yf/u3f2MacHaM1JfIMOJS6UG4uTSRvwEPAO9///s577zzil3Sbqm7u5ulS5eyYsUK1q5dy5o1azbp7dTY2MSGpiaaW5rZ2t+NFclKKitygWFForI3OKxIVVORrOrdrkxWUdFzS23cToTSD4Aff/kuGttW9T4eUTuOw6ecXMSKBibGSCbbTVemk+5MJ93p/H2mc2Pgl+mke5PtTtLZzvyiJx2vCJH7qqqqZviwYQzPL2YyevToTXpOjRkzhj333NO/5wZROp2mtbWVlpaW3gCx7+PN75ubm3vDxZaWFrr7GfraV2Uqme+lmBsSXZHcuHpzZSK3yEbVAOZXnLu+GWCrwWDfefo6M7khul3Zjffd2SzdMRfydWUypLNbD/lqampeEfLV19dTX1/fG/L1vfW0NTQ0UFNTU06f+wwIwYCw2HoWLemcdjzp8ftvcb+qF/9OVdNifvrTn3LAAQcUsEKpdHV1dfH6178egHvuuaec3sAkIPeBeO7cucyaNYu7776bJUuW5J7oWeRkcoSt/Y0ZgWWQnJeE1bnH4/cYz2tf81qOO+44DjroICort9E1UdpBl156Kdf+7nd8CagqgQCuRzEWKRmoSOSXQM3Mmfzil78sdjllLZPJ0NLS8ooeTpv3dOoJIJo3NNPc0kJrSwtt7W3bPH9FqorKfGCYSuQDxd6QMb+dX+gktxjKxu1yGBKdjdl8uNfR25Nv430+3MvkevZ1ZzrpznbSnemiO9NBV3fHK3rzbS6VSlFf3xOUbBqabOnWs6JxVVVVgf4raLB0dna+IkDseQ1v2LCh99azYvn69evZ0NTUb7CYTOQWZqlOBOoqktRXpBheWUF9RXKTv1XS2UhTVzcburpp7c7Qls7QkY20d/cfVtbV1jK8z2rZPWFfT8+9/n4/6+rqdsWefMXiIiUqvnPOOYeHHnqIx+fMpmXUXlBR/Yp9ko1LSK1byLnnnWc4KPXRN7gwHFQ5SqVSHHzwwRx88MGcd955LFy4kHvvvZfbbr+NJQ8tITwdyOyTIe6zWVAYISwIJF/IDR8eN34cb/nAW3jNa17DPvvs4+tJBTF9+nSywE3A6URqSySEK1VpIg8BLwNv3muvYpdT9pLJZG8gtL0ymQxtbW2b9HLa2i0XWLTQ0ryG1S0tW1xJuUcqWZEbAp2syYeGG1dSrqqopTqVX2iloo7qijpSiYqi/7ufzWboTLfT0d1KR3rjAh8bh+3m7rsz+aG83R3AVnpwVlTmApP6esY0NFDfsEe/vaXq6up62+vq6nrbDfnKS1VVFVVVVYwZM2bAx8QYaW9vZ/369b29jNesWcPKlStZuXJl7yItXc3tANRUpBhekZt6IJ2NrOvsJpvvnDZi+HCm7D2ViRMnssceezB27FjGjBnDqFGjGDVqFCNGjKCiomLwf3BtkwGhCiqEwGc/+1k+9KEPUblkNl17bbb6ajZL9eKHmDBxImeffXZxipQk7RKmTZvGtGnTeP/7389DDz3E1ddczVNPPgXLIPPqTG7ocRckHk4Qlgdm7DuD9733fZx44ol+w6yCe9Ob3sSqVav4xS9+wfMhcGSMHA2MNijcRGufxUqaslmOOeYYPv+FLxS7LO2EZDLZ28NnR/QNGDefl63vvG1NTU00NTaxvrGRFU1LaGlt6fd8FalKaisbqE7VU1PZQG1lA/VVI6irGkF91QiqK+p2OkBMZ7pp6WyktbMxf99Ee1cz7d0ttKdbaO9spb/Ar6KiMjdcd/QIJoyY0Ntzavjw4dTX129xXjQDPg21EAK1tbXU1tYyadKkfvfJZrMsXbqUOXPm8M9/3svCBQsBqEylOOnoozn66KPZb7/9GDFiROEK13ZxiLGK4oc//CE33Hgj7QeeucmqxqnlT1P18oN861vf2mQpeUk5t99+O3V1dZxwwgnb3lkqQ3fccQff/e/v0l3RTfqUNKl7UyQ2JPj0pz/NO97xjqL3GpFeeuklrrnmGv7+97+TzWbZIwT2j5EDgPFAKHBgWApDjDcQeQ54hsBCIlngkIMP5v3/8i+8+tWv9nWrHZJOp2lqamLdunWsX7++937NmjWsXr2aVSvzq8yuW7fJHHuVFdUMrx7D8JpxjKmfyKi6PUhsYxhza2cTq5uXsL5tBRs61tDc3kjfALC2ppax48YxfnxuJdOe+fl6VjsdNWoUI0eOpLa2doj+a0jSJpyDEAwIS0VzczNnv/vdNFWOpXPGKbnGbJb6J3/H4QcfwA/+53/8MChJ2iFPPvkk559/PrEqQgdcfPHFvOY1ryl2WdImVq1axT333MM9d9/NnKefJsZIQyLB9GyW6cDewPACBHW3EHksvz0hfzt1iK/bQWQB8BLwUkiwKh/O7DlpEiedfDKvfe1rmTlz5pDWIPXo7u5mxYoVLFu2jCVLlrBw4ULmzZvH/Hnz6ejc+mqxm5s0cRIzZs5g+vTp7LnnnkyaNIlJkybtcO9JSRoiBoRgQFhKfvjDH3LjH/9Ey+HvgVQ1yXWLqH7xb/YelCTttL/85S/cd999HHbYYbznPe8pdjnSVq1du5ZZs2bx6KOP8ugjj9C4YQMAoxMJpmSzTCPXs28kQ9PD8JZ8T6ehCgZbiSwCFgKLQmB5jESgqrKSQw89lCOOPJJjjjmGadOm+QWxSkY6nc4FhfPnk93GyqkjR47k4IMPdqVeSbsKA0IwICwlL774Iueeey6d044jPf4Aql74G6NjE3+88UbnhpIkSWUpxshLL73Eo48+yhNPPMFTTzzBhpbcXGoNiQRT+wSGY4FECc5h2JQftrwIWNSnh2BlRQUHHHgghx12GK961as44IADXDlckqTCcxVjlZYZM2YwffrezF81j/To6aQaF/Pms99lOChJkspWCIG9996bvffem7PPPptsNsuiRYt46qmneOKJJ3jy8cd5et06AGrzPQynA9OBcRR+DkPIzSH4Erkhw4sSCdZlcx0QaqqrOfiQQzjzsMM49NBD2XfffQ0EJUkqUSYxKqoTTzyBl668kmTTMohZhxZLkiT1kUgk2Guvvdhrr70444wziDGyfPlynnzySZ588kkef/RRblm5Esj1MNwrm2UfYF+gdojCwu78HIIvkJtDcHV+RNKw+noOPfxwDjvsMA455BD22WcfksnkkNQgSZIG1y4fEIYQ3gz8EEgCv4wxfrvIJWk77LfffhAjFaueI4TghNSSJElbEUJg4sSJTJw4kbe85S0ALF++nEcffZTHHnuM2Q8/zFMbNhCAqUT2Aw5i5xc86SQyF5gLzAuBrhhzcwgedhhnHXEERxxxBPvssw+JRGInf0JJklQMu/QchCGEJLkvL98ALAEeAd4TY3x2S8c4B2FpWbt2LW9/+9sBmDJ1Glf99soiVyRJkrTrymazPP/889x///3cd++9vLRwIQHYB3gVsB+QGmBYGIksBh4Fns6HgqNGjuSEE0/khBNO4PDDD6eqqmrIfhZJkjQkdss5CI8G5sUYXwIIIfweOAPYYkCo0jJ69GiGjxhJU+N69tvX3oOSJEk7I5FIsP/++7P//vvz0Y9+lCVLlnDbbbdx6803c+3atYxMJDgpm+VQILmVoHABkTtDYFGM1FRV8YbXv55TTz2VAw880F6CkiTthnb1gHASsLjP4yXAqzffKYRwHnAewJQpUwpTmQZs7NgxNDWuZ+zYscUuRZIkabcyefJkPvrRj/LhD3+YBx98kN9cfjl/fOEFZoUE74pZxm0WErYT+TPwNDBqxAgu+MAHeMtb3kJtbW1R6pckSYWxqweE/X3t+Yox0zHGy4DLIDfEeKiL0vapyq9mN2rUqCJXIkmStHtKJpMcf/zxHHfccdxzzz18/3vf4+fNzZwTIzPzH6kbiVyeSNAInPvhD3POOec4hFiSpDKxqweES4A9+zyeDCwrUi3aQQ0NDYABoSRJ0lALIXDSSSdx0EEH8e9f/CK/nz+f6fk5yVeEQKa6mp9873scdNBBRa5UkiQV0q4eED4CzAgh7AUsBc4B3lvckrS9zjvvPPbaay+OPvroYpciSZJUFsaMGcP3vv99vv2tb7F69WoAJldV8clPfcpwUJKkMrRLr2IMEEI4FfhfIAn8OsZ4ydb2dxVjSZIkSZIklandchVjYoy3ALcUuw5JkiRJkiRpV5QodgGSJEmSJEmSiseAUJIkSZIkSSpjBoSSJEmSJElSGTMglCRJkiRJksqYAaEkSZIkSZJUxgwIJUmSJEmSpDJmQChJkiRJkiSVMQNCSZIkSZIkqYwZEEqSJEmSJEllzIBQkiRJkiRJKmMGhJIkSZIkSVIZMyCUJEmSJEmSypgBoSRJkiRJklTGDAglSZIkSZKkMhZijMWuoaBCCKuBRcWuQ68wBlhT7CKkXYCvFWngfL1IA+frRRoYXyvSwPl6KU1rYoxv3ryx7AJClaYQwuwY45HFrkMqdb5WpIHz9SINnK8XaWB8rUgD5+tl1+IQY0mSJEmSJKmMGRBKkiRJkiRJZcyAUKXismIXIO0ifK1IA+frRRo4Xy/SwPhakQbO18suxDkIJUmSJEmSpDJmD0JJkiRJkiSpjBkQSpIkSZIkSWXMgFDbFEKIIYTv93n8hRDC1wfx/NNCCO0hhMdDCHNDCA+HED44gONOCiH8Nb/99RDCF7aw3wODVas0GEIIe4QQfh9CmB9CeDaEcEsIYeZ2nuPuEMKR/bQfGUL40eBVKxVGCCETQngihPBkCOGxEMJxQ3CN3vcNaVcQQmjZ7PGHQgg/2Y7jp4UQnh6qegp9vLSz+v4OhhBODSG8GEKYsq39t/f9I7//oL+PScXW5/PaM/nPbP8aQthqrtT3vWhr72P5v4lGDEHZGqBUsQvQLqETeEcI4VsxxjVDdI35McbDAUII04EbQwiJGOPlO3viGKNvzioZIYQA/BG4IsZ4Tr7tMGA88MLOnj/GOBuYvbPnkYqgPcZ4GEAI4U3At4DXFrOgEEIqxpguZg1SIYUQkjHGTLHrkIZaCOEU4MfAG2OMLw/BJU4CWgA7Kmh30/fz2jjgGmA48LWdPXGM8dSdPYd2jj0INRBpcqsPfW7zJ0IIY0MIN4QQHsnfjs+3zwkhjAg5a0MIH8i3/zaE8PqtXSzG+BLwr8D5+WPqQgi/zp//8RDCGVs49IB8r6qXQgjn96nRb6tVSl4HdMcYf97TEGN8ArgvhPDfIYSn86+fd/c8H0L4Yr7tyRDCt/uc6135HrcvhBBOzO+7ec/aX/f3upBK3DBgPUAIoT6EcGe+V+GcnveA/LfRc0MIv8h/i31HCKEm/9xRIYSnQgizel5Xm18ghDAqhPCn/H4PhhAOybd/PYRwWQjhDuDKLb3PScUUQmgIISwIIVTkHw8LISwMIVSEEI7Iv1/MAj7V55hk/vXwSP73/uP59pNCCP8IIVwDzMm3/SmE8Gj+tXXeZtf+fv71eGcIYWy+7WP58z6Zf73U5tv3yr8OHwkhfKMw/3Wkrct/ZvoFcFqMcX6+7V/zn8GeDiF8dhvHH5X/m2R6COGtIYSH8o//HkIYH0KYBvw/4HMh19PqxP72G+qfUxpqMcZVwHnAp/N/9/f7PtOPiSGE20KuB+93exrz72NjClK8+mVAqIH6KfC+EMLwzdp/CPwgxngU8E7gl/n2+4HjgQOBl4AT8+3HAA8O4HqPAfvlt/8DuCt/jdcB/x1CqOvnmP2ANwFHA1/r+dAslZiDgEf7aX8HcBhwKPB6cr/nE0IIbwHOBF4dYzwU+G6fY1IxxqOBz7Llb+18XWhXUZP/Q+o5cu8lPWFCB/D2GOOryL0HfD+EEPLPzQB+GmM8EGgk9z4EcDnw/2KMxwJb6g31n8DjMcZDgC8BV/Z57gjgjBjje9ny+5xUCD2viydCCE8A/wUQY2wG7gZOy+93DnBDjLGb3O//+fnf/77OBZryv8tHAR8LIeyVf+5o4D9ijAfkH38kxngEcCRwfghhdL69Dngs/3q8h43vPTfGGI/Kv0/NzV8Lcq+fS/PXXLGz/zGkQVAF3AScGWN8DiCEcATwYeDV5P5W+VgI4fD+Dg65YcM/J/ce8RJwH3BMfiTU74EvxhgX5vf5QYzxsBjjP/vbbwh/Rqlg8q+DBDCOrb/P9HUY8G7gYODdIYQ9C1SutsEhxhqQGOOGEMKV5Hr1tfd56vXkeu71PB4WQmgA/gm8BlgEXAqcF0KYBKyLMQ6kR1/os/1G4G1h4xyD1UB/c4XcHGPsBDpDCKvIDdlcMqAfUCq+E4Df5Yd2rQwh3EPujfW1wOUxxjaAGOO6PsfcmL9/FJi2hfP6utCuou+QlWPJ9d47iNz7wTdDCK8BssAkcr/HAAvyPXAh/zoIublrGmKMPcO6rgFO7+d6J5APFGOMd4UQRvf5EuzPMcae97p+3+fyAY001HpfF5Cbu4lcaAe5sPqLwJ/IhRsfy/8Oj4gx3pPf57fAW/LbbwQOCSGclX88nFzI3gU8HGNc0Oe654cQ3p7f3jO/31pyr8Fr8+1XsfF96KAQwsXACKAeuD3ffjwbg/vfAt/Zrp9eGnzd5Ib9ngtckG87AfhjjLEVIIRwI7nODY9vduz+5EZVvTHGuCzfNhm4NoQwAagEFtC/ge4n7Yp6PiRt6X1m82mU7owxNgGEEJ4FpgKLC1Gots4ehNoe/0vuzbRv770EcGz+27HDYoyT8n803UvujfVEct9wrwbOIhccDsTh5L6Bhtw/OO/sc40pMca5/RzT2Wc7gwG4StMz5HonbS7009bTHrfwXM/v/NZ+331daJcTY5wFjAHGAu/L3x+RD0pWkvuiCPr//d7Sa2lz/e3X81pr7dO2pfc5qahijPeTC8VfCyRjjE+z9feMAHymz+/yXjHGO/LP9f7OhxBOIheMH5vvEfg4G19zrygjf/8b4NMxxoPJ9c6t7mcfqRRkgbOBo0IIX8q3DfR9Yzm5Xu19exf+GPhJ/nf/42z5tTLQ/aRdSsitH5ABVrH195m+/PukRBkQasDyPZeuY+OwEYA7gE/3PAi5xRaIMS4m98fdjD7d77/AAALC/Lwd3yP3Rgq5b6E/0zOkbEtd/qVdxF1AVQjhYz0NIYSjyM239u783B1jyfXAfZjca+wjfeZzGlWEmqWCCiHsByTJ9VgaDqyKMXaHEF5H7lvmLYoxrgeaQwjH5JvO2cKu95ILH3sCkTUxxg397Nfv+5xUIq4EfkduWDExxkagKYRwQv759/XZ93bgE2HjvIUztzBly3BgfYyxLf9aPKbPcwlyX/gCvJfc5zuABmB5/tx9r3k/G1+DfdulosmPyjid3PRJ55J7PzgzhFCbf028nf7/ZmkkN6z/m/n3Dci9Xpbmtz/YZ99mcq8LtrGftMvK/83yc3Lhd2Tg7zMqUQaE2l7fJxf89TgfODI/Cemz5Cbk7fEQG7sT/5PcsLD76N/e+Ul755ILIX/cZwXjbwAVwFMhN9G8k1xrl5V/83w78IYQwvwQwjPA18kNg3wKeJJciPjFGOOKGONtwJ+B2fn5p77Q74mlXV/vXGvkhjB+MD/k/mpy7zOzyQUMzw3gXOcCl4XcIg0BaOpnn6/nz/sU8G22/Afb1t7npGK7GhhJLiTs8WHgp/nf/77TwvwSeBZ4LP956v/ov9fGbUAq/9r4BpvOHd0KHBhCeBQ4mfyciMBXyH3u+xubvkYvAD4VQniEXEAilYR8x4c3A18mN4z+N+S+mH0I+GWMcfPhxT3HrQTeSu419mpy7yV/CCH8E1jTZ9e/AG/vWaRkK/tJu5qez2vPAH8n90Xqf+afG+j7jEpUyP2tKkmStHsIIdT3zHcbQrgQmBBjvGAbh0m7nPw8T2fEGP+l2LVIkqRdm2muJEna3ZwWQriI3OecRcCHiluONPhCCD8mtwDJqcWuRZIk7frsQShJkiRJkiSVMecglCRJkiRJksqYAaEkSZIkSZJUxgwIJUmSJEmSpDJmQChJklTGQgiZEMITIYRnQghPhhD+NYSwQ58RQwhf2uzxAztR14dCCBP7PP5lCOGAHT2fJEmStsxFSiRJkspYCKElxlif3x4HXAPcH2P82s6caxDquhv4Qoxx9mCcT5IkSVtmD0JJkiQBEGNcBZwHfDrkJEMI/x1CeCSE8FQI4eMAIYQJIYR78z0Pnw4hnBhC+DZQk2+7Or9fS/7+pBDC3SGE60MIz4UQrg4hhPxzX82f/+kQwmX5654FHAlcnT9fTf74I/PHvCeEMCd/zHd66g8htIQQLsn3hHwwhDC+oP8BJUmSdlEGhJIkSeoVY3yJ3GfEccC5QFOM8SjgKOBjIYS9gPcCt8cYDwMOBZ6IMV4ItMcYD4sxvq+fUx8OfBY4AJgOHJ9v/0mM8agY40FADXB6jPF6YDbwvvz52ntOkh92/B3gZOAw4KgQwpn5p+uAB2OMhwL3Ah8bhP8kkiRJuz0DQkmSJG0u5O/fCHwghPAE8BAwGpgBPAJ8OITwdeDgGGPzAM75cIxxSYwxCzwBTMu3vy6E8FAIYQ650O/AbZznKODuGOPqGGMauBp4Tf65LuCv+e1H+1xDkiRJW5EqdgGSJEkqHSGE6UAGWEUuKPxMjPH2fvZ7DXAa8NsQwn/HGK/cxqk7+2xngFQIoRr4GXBkjHFxPnCs3laJW3muO26cYDuDn3UlSZIGxB6EkiRJAiCEMBb4OblhvxG4HfhECKEi//zMEEJdCGEqsCrG+AvgV8Cr8qfo7tl3gHrCwDUhhHrgrD7PNQMN/RzzEPDaEMKYEEISeA9wz3ZcU5IkSZvxW1VJkqTyVpMfQlwBpIHfAv+Tf+6X5IbpPpZfVGQ1cCZwEvBvIYRuoAX4QH7/y4CnQgiPbWEewk3EGBtDCL8A5gALyQ1d7vEb4OchhHbg2D7HLA8hXAT8g1xvwltijDdt7w8tSZKkjcLGURiSJEmSJEmSyo1DjCVJkiRJkqQyZkAoSZIkSZIklTEDQkmSJEmSJKmMGRBKkiRJkiRJZcyAUJIkSZIkSSpjBoSSJEmSJElSGTMglCRJkiRJksrY/wd+NaXAnFursAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(y= \"Price\", x = \"Destination\", data = df.sort_values(\"Price\", ascending = False), kind=\"violin\", height = 6, aspect = 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1101c4e1",
   "metadata": {},
   "source": [
    "### The Route and Total_Stops columns give us the same info so we will drop the Route column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "75205b3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Route'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea330b87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No info                         6152\n",
       "In-flight meal not included     1455\n",
       "No check-in baggage included     249\n",
       "1 Long layover                    17\n",
       "Change airports                    5\n",
       "No Info                            3\n",
       "Business class                     2\n",
       "Red-eye flight                     1\n",
       "1 Short layover                    1\n",
       "2 Long layover                     1\n",
       "Name: Additional_Info, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Additional_Info'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfef4abe",
   "metadata": {},
   "source": [
    "### Most of the Additional_Info column has no info and the other values do not correlate to the price of tickets so we will also drop this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "587b6fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Additional_Info'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0b9399a6",
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arr_hour</th>\n",
       "      <th>Arr_min</th>\n",
       "      <th>Duration_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1 stop</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>non-stop</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination Duration Total_Stops  Price Journey_day  \\\n",
       "0   IndiGo   Kolkata    Banglore   2h 30m    non-stop   5644  2019-09-06   \n",
       "1  Vistara    Mumbai   Hyderabad  24h 40m      1 stop  12395  2019-06-03   \n",
       "2   IndiGo  Banglore       Delhi   2h 45m    non-stop   4077  2019-03-04   \n",
       "3  Vistara   Kolkata    Banglore   6h 25m      1 stop   8610  2019-12-06   \n",
       "4   IndiGo  Banglore   New Delhi   2h 55m    non-stop  10067  2019-01-03   \n",
       "\n",
       "  Journey_month  Dep_hour  Dep_min  Arr_hour  Arr_min Duration_Minutes  \n",
       "0    2019-09-06        14       25        16       55              150  \n",
       "1    2019-06-03         8       45         9       25             1480  \n",
       "2    2019-03-04         6        5         8       50              165  \n",
       "3    2019-12-06        17        0        23       25              385  \n",
       "4    2019-01-03        10        5        13        0              175  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "786d4eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1 stop      4271\n",
       "non-stop    2567\n",
       "2 stops     1015\n",
       "3 stops       32\n",
       "4 stops        1\n",
       "Name: Total_Stops, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Total_Stops'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72e028d",
   "metadata": {},
   "source": [
    "### We can replace the strings to integers by replacing these values to 1,0,2,3,4 respectively"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "85a93555",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.replace({\"non-stop\":0, \"1 stop\": 1, \"2 stops\": 2, \"3 stops\":3, \"4 stops\": 4}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6367d4b1",
   "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>Airline</th>\n",
       "      <th>Source</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Total_Stops</th>\n",
       "      <th>Price</th>\n",
       "      <th>Journey_day</th>\n",
       "      <th>Journey_month</th>\n",
       "      <th>Dep_hour</th>\n",
       "      <th>Dep_min</th>\n",
       "      <th>Arr_hour</th>\n",
       "      <th>Arr_min</th>\n",
       "      <th>Duration_Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>2h 30m</td>\n",
       "      <td>0</td>\n",
       "      <td>5644</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>2019-09-06</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>55</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>24h 40m</td>\n",
       "      <td>1</td>\n",
       "      <td>12395</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>2019-06-03</td>\n",
       "      <td>8</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>25</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>2h 45m</td>\n",
       "      <td>0</td>\n",
       "      <td>4077</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>2019-03-04</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>50</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vistara</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>6h 25m</td>\n",
       "      <td>1</td>\n",
       "      <td>8610</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "      <td>25</td>\n",
       "      <td>385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IndiGo</td>\n",
       "      <td>Banglore</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>2h 55m</td>\n",
       "      <td>0</td>\n",
       "      <td>10067</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Airline    Source Destination Duration  Total_Stops  Price Journey_day  \\\n",
       "0   IndiGo   Kolkata    Banglore   2h 30m            0   5644  2019-09-06   \n",
       "1  Vistara    Mumbai   Hyderabad  24h 40m            1  12395  2019-06-03   \n",
       "2   IndiGo  Banglore       Delhi   2h 45m            0   4077  2019-03-04   \n",
       "3  Vistara   Kolkata    Banglore   6h 25m            1   8610  2019-12-06   \n",
       "4   IndiGo  Banglore   New Delhi   2h 55m            0  10067  2019-01-03   \n",
       "\n",
       "  Journey_month  Dep_hour  Dep_min  Arr_hour  Arr_min Duration_Minutes  \n",
       "0    2019-09-06        14       25        16       55              150  \n",
       "1    2019-06-03         8       45         9       25             1480  \n",
       "2    2019-03-04         6        5         8       50              165  \n",
       "3    2019-12-06        17        0        23       25              385  \n",
       "4    2019-01-03        10        5        13        0              175  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03b44ef6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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