Exploring Data with Pandas in Jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d11e1cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "240f22e4",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "weather_data = {\n",
    "    'day': ['1/1/2017','1/2/2017','1/3/2017','1/4/2017','1/5/2017','1/6/2017'],\n",
    "    'temperature': [32,35,28,24,32,31],\n",
    "    'windspeed': [6,7,2,7,4,2],\n",
    "    'event': ['Rain', 'Sunny', 'Snow','Snow','Rain', 'Sunny']\n",
    "}\n",
    "df=pd.DataFrame(weather_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c04d3a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2aad35ce",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0ab395db",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ac865cf",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f89ee9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "00d4c70e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1/1/2017\n",
       "1    1/2/2017\n",
       "2    1/3/2017\n",
       "3    1/4/2017\n",
       "4    1/5/2017\n",
       "5    1/6/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8a006137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df['day'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "30710c21",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature\n",
       "0  1/1/2017           32\n",
       "1  1/2/2017           35\n",
       "2  1/3/2017           28\n",
       "3  1/4/2017           24\n",
       "4  1/5/2017           32\n",
       "5  1/6/2017           31"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['day','temperature']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96a6e60e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#operations on dataframe\n",
    "df['temperature'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3d450958",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['temperature']>32]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c892c4f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1/2/2017\n",
       "Name: day, dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day'][df['temperature'] ==df['temperature'].max()] \n",
    "#kinda doing sql in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7b84a176",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "1  1/2/2017           35          7  Sunny"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['temperature']==df['temperature'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "81d5eb61",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.8297084310253524"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temperature'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "168fd79e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sunny'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['event'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "147f4240",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.333333</td>\n",
       "      <td>4.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.829708</td>\n",
       "      <td>2.338090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.750000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>31.500000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       temperature  windspeed\n",
       "count     6.000000   6.000000\n",
       "mean     30.333333   4.666667\n",
       "std       3.829708   2.338090\n",
       "min      24.000000   2.000000\n",
       "25%      28.750000   2.500000\n",
       "50%      31.500000   5.000000\n",
       "75%      32.000000   6.750000\n",
       "max      35.000000   7.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72b3def5",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/1/2017</th>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/2/2017</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/3/2017</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/4/2017</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/5/2017</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/6/2017</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          temperature  windspeed  event\n",
       "day                                    \n",
       "1/1/2017           32          6   Rain\n",
       "1/2/2017           35          7  Sunny\n",
       "1/3/2017           28          2   Snow\n",
       "1/4/2017           24          7   Snow\n",
       "1/5/2017           32          4   Rain\n",
       "1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#set_index\n",
    "df.set_index('day')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "feb6c5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('day',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0b077f27",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1/1/2017</th>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/2/2017</th>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/3/2017</th>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/4/2017</th>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/5/2017</th>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1/6/2017</th>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          temperature  windspeed  event\n",
       "day                                    \n",
       "1/1/2017           32          6   Rain\n",
       "1/2/2017           35          7  Sunny\n",
       "1/3/2017           28          2   Snow\n",
       "1/4/2017           24          7   Snow\n",
       "1/5/2017           32          4   Rain\n",
       "1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0c181528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1/1/2017', '1/2/2017', '1/3/2017', '1/4/2017', '1/5/2017', '1/6/2017'], dtype='object', name='day')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b0affc02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temperature       35\n",
       "windspeed          7\n",
       "event          Sunny\n",
       "Name: 1/2/2017, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['1/2/2017']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "304552b9",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81358e20",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunny</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sunny</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            day  temperature  windspeed\n",
       "event                                  \n",
       "Rain   1/1/2017           32          6\n",
       "Sunny  1/2/2017           35          7\n",
       "Snow   1/3/2017           28          2\n",
       "Snow   1/4/2017           24          7\n",
       "Rain   1/5/2017           32          4\n",
       "Sunny  1/6/2017           31          2"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('event',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a029b8ad",
   "metadata": {
    "scrolled": true
   },
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>event</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Snow</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            day  temperature  windspeed\n",
       "event                                  \n",
       "Snow   1/3/2017           28          2\n",
       "Snow   1/4/2017           24          7"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Snow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2fd2eb6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#different ways of creating DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "35443cb7",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using csv\n",
    "import pandas as pd\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "704afcea",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-01-03</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed  event\n",
       "0 2017-01-01           32          6   Rain\n",
       "1 2017-01-02           35          7  Sunny\n",
       "2 2017-01-03           28          2   Snow"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using excel\n",
    "df = pd.read_excel(r\"C:\\Users\\thiru\\Downloads\\weather_data.xlsx\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "74c26be1",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using dictionary \n",
    "weather_data = {\n",
    "    'day': ['1/1/2017','1/2/2017','1/3/2017'],\n",
    "    'temperature': [32,35,28],\n",
    "    'windspeed': [6,7,2],\n",
    "    'event': ['Rain', 'Sunny', 'Snow']\n",
    "}\n",
    "df = pd.DataFrame(weather_data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1e6a6d7d",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  sunny\n",
       "2  1/3/2017           28          2   snow"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using tuple list\n",
    "weather_data = [\n",
    "    ('1/1/2017',32,6,'Rain'),\n",
    "    ('1/2/2017',35,7,'sunny'),\n",
    "    ('1/3/2017',28,2,'snow')\n",
    "]\n",
    "\n",
    "df=pd.DataFrame(weather_data,columns = ['day','temperature','windspeed','event'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a12e8946",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using list of dictionaries\n",
    "weather_data = [\n",
    "    {'day': '1/1/2017', 'temperature': 32, 'windspeed': 6, 'event': 'Rain'},\n",
    "    {'day': '1/2/2017', 'temperature': 35, 'windspeed': 7, 'event': 'Sunny'},\n",
    "    {'day': '1/3/2017', 'temperature': 28, 'windspeed': 2, 'event': 'Snow'},\n",
    "    \n",
    "]\n",
    "df = pd.DataFrame(data=weather_data, columns=['day','temperature','windspeed','event'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0042f0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read write excel csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "d9a552a7",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers            eps  revenue price         people\n",
       "0   GOOGL          27.82       87   845     larry page\n",
       "1     WMT           4.61      484    65           n.a.\n",
       "2    MSFT             -1       85    64     bill gates\n",
       "3    RIL   not available       50  1023  mukesh ambani\n",
       "4    TATA            5.6       -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "3a980116",
   "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>GOOGL</th>\n",
       "      <th>27.82</th>\n",
       "      <th>87</th>\n",
       "      <th>845</th>\n",
       "      <th>larry page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GOOGL          27.82   87   845     larry page\n",
       "0   WMT           4.61  484    65           n.a.\n",
       "1  MSFT             -1   85    64     bill gates\n",
       "2  RIL   not available   50  1023  mukesh ambani\n",
       "3  TATA            5.6   -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",header=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6297235c",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tickers</td>\n",
       "      <td>eps</td>\n",
       "      <td>revenue</td>\n",
       "      <td>price</td>\n",
       "      <td>people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0              1        2      3              4\n",
       "0  tickers            eps  revenue  price         people\n",
       "1    GOOGL          27.82       87    845     larry page\n",
       "2      WMT           4.61      484     65           n.a.\n",
       "3     MSFT             -1       85     64     bill gates\n",
       "4     RIL   not available       50   1023  mukesh ambani\n",
       "5     TATA            5.6       -1   n.a.     ratan tata"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",header = None)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "30a7483f",
   "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>ticker</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tickers</td>\n",
       "      <td>eps</td>\n",
       "      <td>revenue</td>\n",
       "      <td>price</td>\n",
       "      <td>people</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ticker            eps  revenue  price         people\n",
       "0  tickers            eps  revenue  price         people\n",
       "1    GOOGL          27.82       87    845     larry page\n",
       "2      WMT           4.61      484     65           n.a.\n",
       "3     MSFT             -1       85     64     bill gates\n",
       "4     RIL   not available       50   1023  mukesh ambani\n",
       "5     TATA            5.6       -1   n.a.     ratan tata"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",names =['ticker','eps','revenue','price','people'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e31df746",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers    eps  revenue  price      people\n",
       "0   GOOGL  27.82       87    845  larry page\n",
       "1     WMT   4.61      484     65        n.a.\n",
       "2    MSFT  -1.00       85     64  bill gates"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",nrows=3)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "19e44853",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845.0</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>85</td>\n",
       "      <td>64.0</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.60</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers    eps  revenue   price         people\n",
       "0   GOOGL  27.82       87   845.0     larry page\n",
       "1     WMT   4.61      484    65.0            NaN\n",
       "2    MSFT  -1.00       85    64.0     bill gates\n",
       "3    RIL     NaN       50  1023.0  mukesh ambani\n",
       "4    TATA   5.60       -1     NaN     ratan tata"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",na_values=['not available','n.a.'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4fb2514a",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87.0</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484.0</td>\n",
       "      <td>65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>85.0</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers    eps  revenue price         people\n",
       "0   GOOGL  27.82     87.0   845     larry page\n",
       "1     WMT   4.61    484.0    65            NaN\n",
       "2    MSFT  -1.00     85.0    64     bill gates\n",
       "3    RIL     NaN     50.0  1023  mukesh ambani\n",
       "4    TATA   5.60      NaN  n.a.     ratan tata"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\stock_data.csv\",  na_values={\n",
    "        'eps': ['not available'],\n",
    "        'revenue': [-1],\n",
    "        'people': ['not available','n.a.']\n",
    "    })\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "3e19fe4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#write to csv\n",
    "df.to_csv(\"new.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9f0ccdf1",
   "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>GOOGL</th>\n",
       "      <th>27.82</th>\n",
       "      <th>87</th>\n",
       "      <th>845</th>\n",
       "      <th>larry page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GOOGL          27.82   87   845     larry page\n",
       "0   WMT           4.61  484    65           n.a.\n",
       "1  MSFT             -1   85    64     bill gates\n",
       "2  RIL   not available   50  1023  mukesh ambani\n",
       "3  TATA            5.6   -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "58a31650",
   "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>GOOGL</th>\n",
       "      <th>27.82</th>\n",
       "      <th>87</th>\n",
       "      <th>845</th>\n",
       "      <th>larry page</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  GOOGL          27.82   87   845     larry page\n",
       "0   WMT           4.61  484    65           n.a.\n",
       "1  MSFT             -1   85    64     bill gates\n",
       "2  RIL   not available   50  1023  mukesh ambani\n",
       "3  TATA            5.6   -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_csv(\"new.csv\",index=False)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b59e9739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['GOOGL', '27.82', '87', '845', 'larry page'], dtype='object')"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "0e94af96",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04          NaN        9.0   Sunny\n",
       "2017-01-05         28.0        NaN    Snow\n",
       "2017-01-06          NaN        7.0     NaN\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-08          NaN        NaN   Sunny\n",
       "2017-01-09          NaN        NaN     NaN\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "9f17aff1",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['tickers', 'eps'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_6404\\819262830.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"new.csv\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'tickers'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'eps'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, sep, na_rep, float_format, columns, header, index, index_label, mode, encoding, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, decimal, errors, storage_options)\u001b[0m\n\u001b[0;32m   3549\u001b[0m         )\n\u001b[0;32m   3550\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3551\u001b[1;33m         return DataFrameRenderer(formatter).to_csv(\n\u001b[0m\u001b[0;32m   3552\u001b[0m             \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3553\u001b[0m             \u001b[0mline_terminator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mline_terminator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\format.py\u001b[0m in \u001b[0;36mto_csv\u001b[1;34m(self, path_or_buf, encoding, sep, columns, index_label, mode, compression, quoting, quotechar, line_terminator, chunksize, date_format, doublequote, escapechar, errors, storage_options)\u001b[0m\n\u001b[0;32m   1159\u001b[0m             \u001b[0mcreated_buffer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1160\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1161\u001b[1;33m         csv_formatter = CSVFormatter(\n\u001b[0m\u001b[0;32m   1162\u001b[0m             \u001b[0mpath_or_buf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpath_or_buf\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1163\u001b[0m             \u001b[0mline_terminator\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mline_terminator\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, formatter, path_or_buf, sep, cols, index_label, mode, encoding, errors, compression, quoting, line_terminator, chunksize, quotechar, date_format, doublequote, escapechar, storage_options)\u001b[0m\n\u001b[0;32m     87\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mline_terminator\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mline_terminator\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinesep\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     88\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdate_format\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdate_format\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 89\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize_columns\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     90\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchunksize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initialize_chunksize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     91\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\io\\formats\\csvs.py\u001b[0m in \u001b[0;36m_initialize_columns\u001b[1;34m(self, cols)\u001b[0m\n\u001b[0;32m    154\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    155\u001b[0m                 \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 156\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    157\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    158\u001b[0m         \u001b[1;31m# update columns to include possible multiplicity of dupes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    959\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    960\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 961\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    962\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    963\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1147\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1148\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1149\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple_same_dim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1150\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1151\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple_same_dim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m    825\u001b[0m                 \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    826\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 827\u001b[1;33m             \u001b[0mretval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    828\u001b[0m             \u001b[1;31m# We should never have retval.ndim < self.ndim, as that should\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    829\u001b[0m             \u001b[1;31m#  be handled by the _getitem_lowerdim call above.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1192\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot index with multidimensional key\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1194\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_iterable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1196\u001b[0m             \u001b[1;31m# nested tuple slicing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_iterable\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1130\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1131\u001b[0m         \u001b[1;31m# A collection of keys\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1132\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1133\u001b[0m         return self.obj._reindex_with_indexers(\n\u001b[0;32m   1134\u001b[0m             \u001b[1;33m{\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1328\u001b[0m         \u001b[0maxis_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1329\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1330\u001b[1;33m         \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_indexer_strict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1331\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1332\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_get_indexer_strict\u001b[1;34m(self, key, axis_name)\u001b[0m\n\u001b[0;32m   5794\u001b[0m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnew_indexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reindex_non_unique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5796\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_raise_if_missing\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5797\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5798\u001b[0m         \u001b[0mkeyarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_raise_if_missing\u001b[1;34m(self, key, indexer, axis_name)\u001b[0m\n\u001b[0;32m   5854\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0muse_interval_msg\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5855\u001b[0m                     \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5856\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"None of [{key}] are in the [{axis_name}]\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5857\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5858\u001b[0m             \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmissing_mask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnonzero\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"None of [Index(['tickers', 'eps'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "df.to_csv(\"new.csv\",columns=['tickers','eps'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "00c275c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"new.csv\",header=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e78fa92d",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>n.a.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>not available</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.6</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers            eps  revenue price         people\n",
       "0   GOOGL          27.82       87   845     larry page\n",
       "1     WMT           4.61      484    65           n.a.\n",
       "2    MSFT             -1       85    64     bill gates\n",
       "3    RIL   not available       50  1023  mukesh ambani\n",
       "4    TATA            5.6       -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read excel\n",
    "df = pd.read_excel(r\"C:\\Users\\thiru\\Downloads\\stock_data.xlsx\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "1f8bfcd1",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>sam walton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.60</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers    eps  revenue price         people\n",
       "0   GOOGL  27.82       87   845     larry page\n",
       "1     WMT   4.61      484    65     sam walton\n",
       "2    MSFT  -1.00       85    64     bill gates\n",
       "3    RIL     NaN       50  1023  mukesh ambani\n",
       "4    TATA   5.60       -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def convert_people_cell(cell):\n",
    "    if cell == 'n.a.':\n",
    "        return 'sam walton'\n",
    "    return cell\n",
    "\n",
    "def convert_eps_cell(cell):\n",
    "    if cell == 'not available':\n",
    "        return None\n",
    "    return cell\n",
    "\n",
    "df = pd.read_excel(r\"C:\\Users\\thiru\\Downloads\\stock_data.xlsx\",converters = {\n",
    "    'people': convert_people_cell,\n",
    "    'eps' : convert_eps_cell\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "f577377c",
   "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>tickers</th>\n",
       "      <th>eps</th>\n",
       "      <th>revenue</th>\n",
       "      <th>price</th>\n",
       "      <th>people</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GOOGL</td>\n",
       "      <td>27.82</td>\n",
       "      <td>87</td>\n",
       "      <td>845</td>\n",
       "      <td>larry page</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WMT</td>\n",
       "      <td>4.61</td>\n",
       "      <td>484</td>\n",
       "      <td>65</td>\n",
       "      <td>sam walton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>85</td>\n",
       "      <td>64</td>\n",
       "      <td>bill gates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>RIL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
       "      <td>1023</td>\n",
       "      <td>mukesh ambani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TATA</td>\n",
       "      <td>5.60</td>\n",
       "      <td>-1</td>\n",
       "      <td>n.a.</td>\n",
       "      <td>ratan tata</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  tickers    eps  revenue price         people\n",
       "0   GOOGL  27.82       87   845     larry page\n",
       "1     WMT   4.61      484    65     sam walton\n",
       "2    MSFT  -1.00       85    64     bill gates\n",
       "3    RIL     NaN       50  1023  mukesh ambani\n",
       "4    TATA   5.60       -1  n.a.     ratan tata"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_excel(\"new.xlsx\", sheet_name=\"stocks\", index=False, startrow=2, startcol=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "ffaad129",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write two dataframes to two separate sheets in excel\n",
    "\n",
    "\n",
    "df_stocks = pd.DataFrame({\n",
    "    'tickers': ['GOOGL', 'WMT', 'MSFT'],\n",
    "    'price': [845, 65, 64 ],\n",
    "    'pe': [30.37, 14.26, 30.97],\n",
    "    'eps': [27.82, 4.61, 2.12]\n",
    "})\n",
    "\n",
    "df_weather =  pd.DataFrame({\n",
    "    'day': ['1/1/2017','1/2/2017','1/3/2017'],\n",
    "    'temperature': [32,35,28],\n",
    "    'event': ['Rain', 'Sunny', 'Snow']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "16e74d95",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.ExcelWriter('stocks_weather.xlsx') as writer:\n",
    "    df_stocks.to_excel(writer, sheet_name=\"stocks\")\n",
    "    df_weather.to_excel(writer, sheet_name=\"weather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "dd193ec4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handling missing data : fillna,dropna,interpolate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "146810ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "#fillna - fillna to fill missing values using different ways\n",
    "#interpolate - it make a guess on missing values using interpolation\n",
    "#dropna - it drop rows with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a5195277",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/7/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed   event\n",
       "0   1/1/2017         32.0        6.0    Rain\n",
       "1   1/4/2017          NaN        9.0   Sunny\n",
       "2   1/5/2017         28.0        NaN    Snow\n",
       "3   1/6/2017          NaN        7.0     NaN\n",
       "4   1/7/2017         32.0        NaN    Rain\n",
       "5   1/8/2017          NaN        NaN   Sunny\n",
       "6   1/9/2017          NaN        NaN     NaN\n",
       "7  1/10/2017         34.0        8.0  Cloudy\n",
       "8  1/11/2017         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_data (1).csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9b073da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas._libs.tslibs.timestamps.Timestamp"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_data (1).csv\",parse_dates= ['day'])\n",
    "type(df.day[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d5a92655",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04          NaN        9.0   Sunny\n",
       "2017-01-05         28.0        NaN    Snow\n",
       "2017-01-06          NaN        7.0     NaN\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-08          NaN        NaN   Sunny\n",
       "2017-01-09          NaN        NaN     NaN\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_data (1).csv\",parse_dates= ['day'])\n",
    "df.set_index('day',inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "20e9e74c",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04          0.0        9.0   Sunny\n",
       "2017-01-05         28.0        0.0    Snow\n",
       "2017-01-06          0.0        7.0       0\n",
       "2017-01-07         32.0        0.0    Rain\n",
       "2017-01-08          0.0        0.0   Sunny\n",
       "2017-01-09          0.0        0.0       0\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill all NaN with one specific value\n",
    "\n",
    "new_df= df.fillna(0)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "95ee1aca",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No Event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>No Event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed     event\n",
       "day                                         \n",
       "2017-01-01         32.0        6.0      Rain\n",
       "2017-01-04          0.0        9.0     Sunny\n",
       "2017-01-05         28.0        0.0      Snow\n",
       "2017-01-06          0.0        7.0  No Event\n",
       "2017-01-07         32.0        0.0      Rain\n",
       "2017-01-08          0.0        0.0     Sunny\n",
       "2017-01-09          0.0        0.0  No Event\n",
       "2017-01-10         34.0        8.0    Cloudy\n",
       "2017-01-11         40.0       12.0     Sunny"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fill na using column names and dict\n",
    "\n",
    "new_df = df.fillna({\n",
    "        'temperature': 0,\n",
    "        'windspeed': 0,\n",
    "        'event': 'No Event'\n",
    "    })\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "f4a61f35",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>32.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04         32.0        9.0   Sunny\n",
       "2017-01-05         28.0        9.0    Snow\n",
       "2017-01-06         28.0        7.0    Snow\n",
       "2017-01-07         32.0        7.0    Rain\n",
       "2017-01-08         32.0        7.0   Sunny\n",
       "2017-01-09         32.0        7.0   Sunny\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#ffill - forward fill\n",
    "#it works from top to bottom\n",
    "#Example: If you have [1, NaN, NaN, 2],ffill will fill the missing values with 1, resulting in [1, 1, 1, 2].\n",
    "new_df = df.fillna(method = 'ffill')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "2d739755",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04         28.0        9.0   Sunny\n",
       "2017-01-05         28.0        7.0    Snow\n",
       "2017-01-06         32.0        7.0    Rain\n",
       "2017-01-07         32.0        8.0    Rain\n",
       "2017-01-08         34.0        8.0   Sunny\n",
       "2017-01-09         34.0        8.0  Cloudy\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#backward fill \n",
    "#it works from bottom to top \n",
    "new_df = df.fillna(method='bfill')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "d8fd96fd",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>28.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>32.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04         28.0        9.0   Sunny\n",
       "2017-01-05         28.0        7.0    Snow\n",
       "2017-01-06         32.0        7.0    Rain\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-08          NaN        NaN   Sunny\n",
       "2017-01-09         34.0        8.0  Cloudy\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.fillna(method='bfill',limit=1)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "23eda552",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>Rain</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>Sunny</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           temperature windspeed   event\n",
       "day                                     \n",
       "2017-01-01        32.0       6.0    Rain\n",
       "2017-01-04         9.0       9.0   Sunny\n",
       "2017-01-05        28.0      Snow    Snow\n",
       "2017-01-06         7.0       7.0     NaN\n",
       "2017-01-07        32.0      Rain    Rain\n",
       "2017-01-08       Sunny     Sunny   Sunny\n",
       "2017-01-09         NaN       NaN     NaN\n",
       "2017-01-10        34.0       8.0  Cloudy\n",
       "2017-01-11        40.0      12.0   Sunny"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.fillna(method='bfill',axis = 'columns')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e3ad7f84",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.00</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>9.00</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>7.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.25</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>32.666667</td>\n",
       "      <td>7.50</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>33.333333</td>\n",
       "      <td>7.75</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>8.00</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>12.00</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01    32.000000       6.00    Rain\n",
       "2017-01-04    30.000000       9.00   Sunny\n",
       "2017-01-05    28.000000       8.00    Snow\n",
       "2017-01-06    30.000000       7.00     NaN\n",
       "2017-01-07    32.000000       7.25    Rain\n",
       "2017-01-08    32.666667       7.50   Sunny\n",
       "2017-01-09    33.333333       7.75     NaN\n",
       "2017-01-10    34.000000       8.00  Cloudy\n",
       "2017-01-11    40.000000      12.00   Sunny"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#interpolate\n",
    "new_df = df.interpolate()\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "6d95ae43",
   "metadata": {},
   "outputs": [],
   "source": [
    "#axis=0 refers to operations along the rows (vertically).\n",
    "#axis=1 refers to operations along the columns (horizontally)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "7a77ac2c",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropna\n",
    "new_df = df.dropna()\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "20a6babc",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04          NaN        9.0   Sunny\n",
       "2017-01-05         28.0        NaN    Snow\n",
       "2017-01-06          NaN        7.0     NaN\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-08          NaN        NaN   Sunny\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.dropna(how='all')\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7d4f1014",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "day                                       \n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-04          NaN        9.0   Sunny\n",
       "2017-01-05         28.0        NaN    Snow\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#thresh-specify the minimum number of non-null values required for a row or column to be kept.\n",
    "new_df = df.dropna(thresh=2)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "990a2605",
   "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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-05</th>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-07</th>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-08</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-10</th>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-11</th>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            temperature  windspeed   event\n",
       "2017-01-01         32.0        6.0    Rain\n",
       "2017-01-02          NaN        NaN     NaN\n",
       "2017-01-03          NaN        NaN     NaN\n",
       "2017-01-04          NaN        9.0   Sunny\n",
       "2017-01-05         28.0        NaN    Snow\n",
       "2017-01-06          NaN        7.0     NaN\n",
       "2017-01-07         32.0        NaN    Rain\n",
       "2017-01-08          NaN        NaN   Sunny\n",
       "2017-01-09          NaN        NaN     NaN\n",
       "2017-01-10         34.0        8.0  Cloudy\n",
       "2017-01-11         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#inserting missing data \n",
    "\n",
    "dt = pd.date_range(\"01-01-2017\",\"01-11-2017\")\n",
    "idx = pd.DatetimeIndex(dt)\n",
    "df.reindex(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "131e2bf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handle missing data\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7186d996",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2017</td>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed     event\n",
       "0  01-01-2017           32          6      Rain\n",
       "1  01-02-2017       -99999          7     Sunny\n",
       "2  01-03-2017           28     -99999      Snow\n",
       "3  01-04-2017       -99999          7  No event\n",
       "4  01-05-2017           32     -99999      Rain\n",
       "5  01-06-2017           31          2     Sunny\n",
       "6  01-06-2017           34          5  No event"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_data (2).csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "ffcd772c",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed     event\n",
       "0  01-01-2017         32.0        6.0      Rain\n",
       "1  01-02-2017          NaN        7.0     Sunny\n",
       "2  01-03-2017         28.0        NaN      Snow\n",
       "3  01-04-2017          NaN        7.0  No event\n",
       "4  01-05-2017         32.0        NaN      Rain\n",
       "5  01-06-2017         31.0        2.0     Sunny\n",
       "6  01-06-2017         34.0        5.0  No event"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace(-99999, value=np.NaN)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fbf4411b",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed     event\n",
       "0  01-01-2017         32.0          6      Rain\n",
       "1  01-02-2017          NaN          7     Sunny\n",
       "2  01-03-2017         28.0     -99999      Snow\n",
       "3  01-04-2017          NaN          7  No event\n",
       "4  01-05-2017         32.0     -99999      Rain\n",
       "5  01-06-2017         31.0          2     Sunny\n",
       "6  01-06-2017         34.0          5  No event"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace({\n",
    "    'temperature' : -99999,\n",
    "    \"windspeed\": -88888,\n",
    "    \"event\" : '0'\n",
    "},np.NaN)\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "69d55b99",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2017</td>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed     event\n",
       "0  01-01-2017           32          6      Rain\n",
       "1  01-02-2017       -99999          7     Sunny\n",
       "2  01-03-2017           28     -99999      Snow\n",
       "3  01-04-2017       -99999          7  No event\n",
       "4  01-05-2017           32     -99999      Rain\n",
       "5  01-06-2017           31          2     Sunny\n",
       "6  01-06-2017           34          5  No event"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.replace({\n",
    "    -9999 : 'np.NaN',\n",
    "    'no Event' : 'sunny'\n",
    "})\n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6b21f3a3",
   "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>day</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-01-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-02-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-03-2017</td>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-04-2017</td>\n",
       "      <td>-99999</td>\n",
       "      <td>7</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01-05-2017</td>\n",
       "      <td>32</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>01-06-2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>No event</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          day  temperature  windspeed     event\n",
       "0  01-01-2017           32          6      Rain\n",
       "1  01-02-2017       -99999          7     Sunny\n",
       "2  01-03-2017           28     -99999      Snow\n",
       "3  01-04-2017       -99999          7  No event\n",
       "4  01-05-2017           32     -99999      Rain\n",
       "5  01-06-2017           31          2     Sunny\n",
       "6  01-06-2017           34          5  No event"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Regex\n",
    "# when windspeed is 6 mph, 7 mph etc. & temperature is 32 F, 28 F etc.\n",
    "new_df = df.replace({'temperature': '[A-Za-z]', 'windspeed': '[a-z]'},'', regex=True) \n",
    "new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "cd7d523a",
   "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>score</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>exceptional</td>\n",
       "      <td>rob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>average</td>\n",
       "      <td>maya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>good</td>\n",
       "      <td>parthiv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>poor</td>\n",
       "      <td>tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>average</td>\n",
       "      <td>julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>exceptional</td>\n",
       "      <td>erica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         score  student\n",
       "0  exceptional      rob\n",
       "1      average     maya\n",
       "2         good  parthiv\n",
       "3         poor      tom\n",
       "4      average   julian\n",
       "5  exceptional    erica"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({   \n",
    "    'score': ['exceptional','average', 'good', 'poor', 'average', 'exceptional'],\n",
    "    'student': ['rob', 'maya', 'parthiv', 'tom', 'julian', 'erica']\n",
    "})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "810fe004",
   "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>score</th>\n",
       "      <th>student</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>rob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>maya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>parthiv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>tom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>julian</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>erica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   score  student\n",
       "0      4      rob\n",
       "1      2     maya\n",
       "2      3  parthiv\n",
       "3      1      tom\n",
       "4      2   julian\n",
       "5      4    erica"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace(['poor', 'average', 'good', 'exceptional'], [1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "50a6df14",
   "metadata": {},
   "outputs": [],
   "source": [
    "#group by ( split apply combine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0ba14ea0",
   "metadata": {
    "scrolled": true
   },
   "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>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>66</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>82</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>77</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>79</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date      city  temperature  humidity\n",
       "0  05-01-2017  new york           65        56\n",
       "1  05-02-2017  new york           66        58\n",
       "2  05-03-2017  new york           68        60\n",
       "3  05-01-2017    mumbai           75        80\n",
       "4  05-02-2017    mumbai           78        83\n",
       "5  05-03-2017    mumbai           82        85\n",
       "6  05-01-2017   beijing           80        26\n",
       "7  05-02-2017   beijing           77        30\n",
       "8  05-03-2017   beijing           79        35"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\Downloads\\weather_by_cities.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "01249202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000002332EEB8F40>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby ('city')\n",
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "8de16254",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beijing\n",
      "         date     city  temperature  humidity\n",
      "6  05-01-2017  beijing           80        26\n",
      "7  05-02-2017  beijing           77        30\n",
      "8  05-03-2017  beijing           79        35\n",
      "mumbai\n",
      "         date    city  temperature  humidity\n",
      "3  05-01-2017  mumbai           75        80\n",
      "4  05-02-2017  mumbai           78        83\n",
      "5  05-03-2017  mumbai           82        85\n",
      "new york\n",
      "         date      city  temperature  humidity\n",
      "0  05-01-2017  new york           65        56\n",
      "1  05-02-2017  new york           66        58\n",
      "2  05-03-2017  new york           68        60\n"
     ]
    }
   ],
   "source": [
    "for city,city_df in g:\n",
    "    print(city)\n",
    "    print(city_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "08857502",
   "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>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>82</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    city  temperature  humidity\n",
       "3  05-01-2017  mumbai           75        80\n",
       "4  05-02-2017  mumbai           78        83\n",
       "5  05-03-2017  mumbai           82        85"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.get_group('mumbai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a37f9e9d",
   "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>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>80</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>82</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>68</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date  temperature  humidity\n",
       "city                                       \n",
       "beijing   05-03-2017           80        35\n",
       "mumbai    05-03-2017           82        85\n",
       "new york  05-03-2017           68        60"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "979b1a8e",
   "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>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>77</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                date  temperature  humidity\n",
       "city                                       \n",
       "beijing   05-01-2017           77        26\n",
       "mumbai    05-01-2017           75        80\n",
       "new york  05-01-2017           65        56"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "cd11f628",
   "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>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>78.666667</td>\n",
       "      <td>30.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>78.333333</td>\n",
       "      <td>82.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>66.333333</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          temperature   humidity\n",
       "city                            \n",
       "beijing     78.666667  30.333333\n",
       "mumbai      78.333333  82.666667\n",
       "new york    66.333333  58.000000"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "675d0b13",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"8\" halign=\"left\">humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>3.0</td>\n",
       "      <td>78.666667</td>\n",
       "      <td>1.527525</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30.333333</td>\n",
       "      <td>4.509250</td>\n",
       "      <td>26.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>3.0</td>\n",
       "      <td>78.333333</td>\n",
       "      <td>3.511885</td>\n",
       "      <td>75.0</td>\n",
       "      <td>76.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>82.666667</td>\n",
       "      <td>2.516611</td>\n",
       "      <td>80.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>83.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>3.0</td>\n",
       "      <td>66.333333</td>\n",
       "      <td>1.527525</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.5</td>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>56.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         temperature                                                     \\\n",
       "               count       mean       std   min   25%   50%   75%   max   \n",
       "city                                                                      \n",
       "beijing          3.0  78.666667  1.527525  77.0  78.0  79.0  79.5  80.0   \n",
       "mumbai           3.0  78.333333  3.511885  75.0  76.5  78.0  80.0  82.0   \n",
       "new york         3.0  66.333333  1.527525  65.0  65.5  66.0  67.0  68.0   \n",
       "\n",
       "         humidity                                                     \n",
       "            count       mean       std   min   25%   50%   75%   max  \n",
       "city                                                                  \n",
       "beijing       3.0  30.333333  4.509250  26.0  28.0  30.0  32.5  35.0  \n",
       "mumbai        3.0  82.666667  2.516611  80.0  81.5  83.0  84.0  85.0  \n",
       "new york      3.0  58.000000  2.000000  56.0  57.0  58.0  59.0  60.0  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "310c8677",
   "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>date</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  temperature  humidity\n",
       "city                                 \n",
       "beijing      3            3         3\n",
       "mumbai       3            3         3\n",
       "new york     3            3         3"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d19155fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "beijing     3\n",
       "mumbai      3\n",
       "new york    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "40dad22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       "beijing     AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "mumbai      AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "new york    AxesSubplot(0.125,0.11;0.775x0.77)\n",
       "dtype: object"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA45ElEQVR4nO3de3RU5b3/8c8kM5ncA0SSELkFGy+AVCwtF+0KPRXQgx5PtaVKtfDDsmjVSrQaQFABMVx6SrHFhYUq0irVYxWPp7YoVMSjiCCFahFFS7gUiFFJMglJJpd5fn8kM8wkkzCTy85MeL/WmsVkz957nofJZj48+7ufbTPGGAEAAFgkprsbAAAAzi2EDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApezd3YDmPB6PTpw4oZSUFNlstu5uDgAACIExRhUVFcrOzlZMTNtjGxEXPk6cOKEBAwZ0dzMAAEA7HDt2TP37929znYgLHykpKZIaG5+amtrNrQEAAKFwuVwaMGCA73u8LREXPrynWlJTUwkfAABEmVBKJig4BQAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWCit81NfXa8GCBcrJyVFCQoKGDBmixYsXy+Px+NYxxmjhwoXKzs5WQkKCxo8fr/3793d6wwEAQHQKK3wsX75cjz/+uFavXq0DBw5oxYoV+vnPf65f//rXvnVWrFihlStXavXq1dq9e7eysrI0YcIEVVRUdHrjAQBA9AkrfLzzzju6/vrrNXnyZA0ePFjf/e53NXHiRL333nuSGkc9Vq1apfnz5+uGG27Q8OHDtWHDBlVVVWnjxo1d0gEAABBdwrqx3JVXXqnHH39cBw8e1IUXXqi///3veuutt7Rq1SpJUlFRkYqLizVx4kTfNk6nU3l5edqxY4dmzZrVYp9ut1tut9v3s8vlamdXzm7BSx8oJd6h9KQ49Wl6pCc5lZ7c+DzeEdtl7w0AABqFFT7mzJmj8vJyXXzxxYqNjVVDQ4MeeeQR3XzzzZKk4uJiSVJmZmbAdpmZmTpy5EjQfS5dulSLFi1qT9vDUlPXoKd3Hm1znaS4WPVJjlOfJKfO8waU5LimsOJUelKcL6ikJzmVEEdYAQAgXGGFj+eee05PP/20Nm7cqGHDhmnfvn3Kz89Xdna2pk2b5luv+e10jTGt3mJ33rx5uueee3w/u1wuDRgwIJxmhcQY6d6JF+rL07U61fT4orJWp067dep0reoajE7XNuj0qWodO1Ud0j4THLHqkxSn85K9IylOv3DiDSpO30hLYlxsSLcaBgCgJwsrfNx3332aO3eubrrpJknSpZdeqiNHjmjp0qWaNm2asrKyJDWOgPTr18+3XUlJSYvREC+n0ymn09ne9ocsIS5Wd/5bbtDXjDGqcNfrVGWtvjzt1peVjeHEG1S+rHQHhJYvT9eqtt6j6roGHS+r1vGy0MJKvCNG6UlOv1M+LQOKd6QlPdmpJMIKAKAHCit8VFVVKSYmsEY1NjbWd6ltTk6OsrKytGXLFo0cOVKSVFtbq+3bt2v58uWd1OTOZ7PZlBrvUGq8Q4PPSzrr+sYYVbrrzwSUprDyxWm373nz4OKu96imzhNWWImzxwTUp5yXHBhc+jSFlPSm0JLitBNWAAARL6zwcd111+mRRx7RwIEDNWzYMO3du1crV67UjBkzJDV+iefn56uwsFC5ubnKzc1VYWGhEhMTNXXq1C7pQHew2WxKiXcoJd6hQemhhZWq2ga/UOJuOuXjDSiNy770Pa9VdV2Daus9Olleo5PlNSG1Ky42Rr2THAFFtGdGWJyBoSXJqdQEwgoAwHo2Y4wJdeWKigo98MAD2rRpk0pKSpSdna2bb75ZDz74oOLi4iQ1ftEuWrRIv/nNb1RaWqrRo0frscce0/Dhw0N6D5fLpbS0NJWXlys1NbV9veoBqmsbgpwCCgwo3mWnKmt1urYh7PdwxNrUO9E7guJXVNu80LbpeWq8QzExhBUAiAbe//hWuutVUVPf9GedKmvqVdvg0fWXnd+p7xfO93dY4cMKhI/2qalr8J0C+rKpiPbLSr+A0hRWvMGl0l0f9nvExjSGlTOnfM4EFF+til+Q6ZVAWAGAcHk8RqdrG8NCZU29Kpr+9P7sqqnzPa90t3y9oqZOFe56nXbXy9PKN3yCI1YHHr66U9sdzvd3WKddELniHbE6v1eCzu+VENL6NXUNKq0KDChfVga/EujL07WqqKlXg8foi0q3vqh0n/0NJMXY1BhW/C5PPlO/0hRa/C5f7p0Yp1jCCoAo1eANDX6jDBV+oaDFCITfz/7hobK2Xp05LBBjk1LiHUp22pUSb1ey067keLs8HtNt/0EkfJyj4h2x6peWoH5poYUVd32DSk/X+UZVzoysuP3qVrzBxS1XTWPi/rIpvITC1hRW2roayL9+pXeiQ/ZY7o0IoGPqGzw67W5QhbuuxWhDYzioazkC4a6Xq6ZelX6jEO05/d2W2BibLyykxDuU0hQavOEhxen3PEi48K6f4Ii8KycJHwiJ0x6rrLRYZaXFh7R+XYNHpf5X/TRd+RP0CqHTtSqrqpMx8gWYUNhsUlqCo3EkxTuqkhzkSqCm0NI7KU4OwgrQY9Q1eHS6afTAN4LgDj7a4AsRQcJFdV3nhgZHrM0XBvyDQEq8Nzw4zoSEgNcdvnCREm+X0x4TcaGhsxA+0CUcsTHKSI1XRmpoYaW+waPSqroW86r4nxLyn2ultKpWxkhlVXUqq6rToc9Ph/Q+qfH2wEuW/U4JBV4h1LhOnJ2wAnS22nqPXwio8xth8B9dODPaUFETOOLgDRI1dZ6zv1kY4uwxSvUbOWgMBw6/0YfAEYVkZ+Bog/d1p53Zr8+G8IGIYI+NUd8Up/qmOCWlnHX9Bo9RaVVts1M+zS5h9jslVFpVK4+RXDWNQ6WHvggtrKTE2/3mWnH6zWbb7AqhpuX8o4OerKauIWj9gjcouILWN/jVNjSFidr6zg0N8Y4YX0gINqLgCwrx9jPhwvd64/IkZyzHr4UIH4hKsTE2nZfs1HnJTin45LkBGjxG5dV1AaMoZ07/tLyEubSqVg0e4xuuPfxlVUjtSnbam10JFPy+QN7TQ9zMEF3NGCN3vafFpZbN6xe8QcE/JPi/7r08szMlxsUGCQnB6xe8P/ufzkiJtyvJaed0ahQifOCcEBtj841YfCXj7Ot7PEaumjq/kRS336XMwa8QqvcY3z/UR0+FFlb8b2YYWFTbbNr9phluuZnhucMYo+q6hhZBoKLmzJUSrV1q6bsUs+nn+taut2wn/5GDgJDgV/wYtDjSL1wkxcVSMH4OI3wAQcTE2NQrMU69EuNCWt8YI1d1/ZlTPc1mru3smxm2XqsSWGjLzQytFzixU2DxY2CICH6pZYXfaENDJ4YGm01KjmsWGLwhocXowpnTFM1HIJLi7FwSjw4jfACdwGazKS3RobREh4b0Pfv6Vt3M0Nl0f6Dm0+v3SQ5+hVDyOXx/oNYmdgo4HdFstKGi2aWWZ5vYqT1ibAo4FdEyJJwJCilthIhERyyT/iFiED6AbtCemxmerm04E0oqW7mE2W+uFXe9R+56j06U1+hEqPcHanYzw6D3BfKeEoqQmxk2+J3uqmwlKLhqzrzWfLTBf6ShM/nP0RA4etAYIppfVdF8tCE1PnLnaAA6ivABRAGbzeb7Eoukmxk6Ym2tFNWemcH2PL9TRP43M2w+sVPLQse6ICMQ3p+7bmInuzc0+F0J0drkTt55GVq+7lC8o+fO0QB0FOED6IFsNpuSnI1XAgzokxjSNu25mWFdg9FnLrc+c4U25b49xqbUBIeqaxs6fWKnOHtMYAhofnVEkOLIxrtTB45A9OSJnYBIQfgAIElKiItV/7hE9e8dWlhpz80M6z2mxQy2/nM0BJufwf90RPDiSAdzNABRhvABoF3aezNDV3W9b36HJKedWWSBcxDhA4AlztzMsLtbAqC78V8OAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYKK3wMHjxYNputxeOOO+6QJBljtHDhQmVnZyshIUHjx4/X/v37u6ThAAAgOoUVPnbv3q2TJ0/6Hlu2bJEkfe9735MkrVixQitXrtTq1au1e/duZWVlacKECaqoqOj8lgMAgKgUVvjo27evsrKyfI8//elPuuCCC5SXlydjjFatWqX58+frhhtu0PDhw7VhwwZVVVVp48aNXdV+AAAQZdpd81FbW6unn35aM2bMkM1mU1FRkYqLizVx4kTfOk6nU3l5edqxY0er+3G73XK5XAEPAADQc7U7fLz00ksqKyvT9OnTJUnFxcWSpMzMzID1MjMzfa8Fs3TpUqWlpfkeAwYMaG+TAABAFGh3+HjiiSd0zTXXKDs7O2C5zWYL+NkY02KZv3nz5qm8vNz3OHbsWHubBAAAooC9PRsdOXJEW7du1YsvvuhblpWVJalxBKRfv36+5SUlJS1GQ/w5nU45nc72NAMAAEShdo18rF+/XhkZGZo8ebJvWU5OjrKysnxXwEiNdSHbt2/XuHHjOt5SAADQI4Q98uHxeLR+/XpNmzZNdvuZzW02m/Lz81VYWKjc3Fzl5uaqsLBQiYmJmjp1aqc2GgAARK+ww8fWrVt19OhRzZgxo8VrBQUFqq6u1u23367S0lKNHj1ar732mlJSUjqlsQAAIPrZjDGmuxvhz+VyKS0tTeXl5UpNTe3u5gAAgBCE8/3NvV0AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGCpsMPH8ePHdcsttyg9PV2JiYm67LLLtGfPHt/rxhgtXLhQ2dnZSkhI0Pjx47V///5ObTQAAIheYYWP0tJSXXHFFXI4HPrLX/6iDz/8UL/4xS/Uq1cv3zorVqzQypUrtXr1au3evVtZWVmaMGGCKioqOrvtAAAgCtmMMSbUlefOnau3335b//d//xf0dWOMsrOzlZ+frzlz5kiS3G63MjMztXz5cs2aNeus7+FyuZSWlqby8nKlpqaG2jQAANCNwvn+Dmvk4+WXX9aoUaP0ve99TxkZGRo5cqTWrVvne72oqEjFxcWaOHGib5nT6VReXp527NgRdJ9ut1sulyvgAQAAeq6wwsehQ4e0Zs0a5ebm6tVXX9WPf/xj3XXXXfrd734nSSouLpYkZWZmBmyXmZnpe625pUuXKi0tzfcYMGBAe/oBAACiRFjhw+Px6PLLL1dhYaFGjhypWbNmaebMmVqzZk3AejabLeBnY0yLZV7z5s1TeXm573Hs2LEwuwAAAKJJWOGjX79+Gjp0aMCySy65REePHpUkZWVlSVKLUY6SkpIWoyFeTqdTqampAQ8AANBzhRU+rrjiCn388ccByw4ePKhBgwZJknJycpSVlaUtW7b4Xq+trdX27ds1bty4TmguAACIdvZwVr777rs1btw4FRYWasqUKdq1a5fWrl2rtWvXSmo83ZKfn6/CwkLl5uYqNzdXhYWFSkxM1NSpU7ukAwAAILqEFT6+/vWva9OmTZo3b54WL16snJwcrVq1Sj/4wQ986xQUFKi6ulq33367SktLNXr0aL322mtKSUnp9MYDAIDoE9Y8H1Zgng8AAKJPl83zAQAA0FGEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsFRY4WPhwoWy2WwBj6ysLN/rxhgtXLhQ2dnZSkhI0Pjx47V///5ObzQAAIheYY98DBs2TCdPnvQ9PvjgA99rK1as0MqVK7V69Wrt3r1bWVlZmjBhgioqKjq10QAAIHqFHT7sdruysrJ8j759+0pqHPVYtWqV5s+frxtuuEHDhw/Xhg0bVFVVpY0bN3Z6wwEAQHQKO3x88sknys7OVk5Ojm666SYdOnRIklRUVKTi4mJNnDjRt67T6VReXp527NjR6v7cbrdcLlfAAwAA9FxhhY/Ro0frd7/7nV599VWtW7dOxcXFGjdunL788ksVFxdLkjIzMwO2yczM9L0WzNKlS5WWluZ7DBgwoB3dAAAA0SKs8HHNNdfoxhtv1KWXXqqrrrpKr7zyiiRpw4YNvnVsNlvANsaYFsv8zZs3T+Xl5b7HsWPHwmkSAACIMh261DYpKUmXXnqpPvnkE99VL81HOUpKSlqMhvhzOp1KTU0NeAAAgJ6rQ+HD7XbrwIED6tevn3JycpSVlaUtW7b4Xq+trdX27ds1bty4DjcUAAD0DPZwVr733nt13XXXaeDAgSopKdGSJUvkcrk0bdo02Ww25efnq7CwULm5ucrNzVVhYaESExM1derUrmo/AACIMmGFj3/961+6+eab9cUXX6hv374aM2aMdu7cqUGDBkmSCgoKVF1drdtvv12lpaUaPXq0XnvtNaWkpHRJ4wEAQPSxGWNMdzfCn8vlUlpamsrLy6n/AAAgSoTz/c29XQAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJYifAAAAEsRPgAAgKUIHwAAwFKEDwAAYKkOhY+lS5fKZrMpPz/ft8wYo4ULFyo7O1sJCQkaP3689u/f39F2AgCAHqLd4WP37t1au3atRowYEbB8xYoVWrlypVavXq3du3crKytLEyZMUEVFRYcbCwAAol+7wkdlZaV+8IMfaN26derdu7dvuTFGq1at0vz583XDDTdo+PDh2rBhg6qqqrRx48ZOazQAAIhe7Qofd9xxhyZPnqyrrroqYHlRUZGKi4s1ceJE3zKn06m8vDzt2LEj6L7cbrdcLlfAAwAA9Fz2cDd49tlntWfPHr333nstXisuLpYkZWZmBizPzMzUkSNHgu5v6dKlWrRoUbjNAAAAUSqskY9jx45p9uzZeuaZZxQfH9/qejabLeBnY0yLZV7z5s1TeXm573Hs2LFwmgQAAKJMWCMfe/bsUUlJib72ta/5ljU0NOjNN9/U6tWr9fHHH0tqHAHp16+fb52SkpIWoyFeTqdTTqezPW0HAABRKKyRj29/+9v64IMPtG/fPt9j1KhR+sEPfqB9+/ZpyJAhysrK0pYtW3zb1NbWavv27Ro3blynNx4AAESfsEY+UlJSNHz48IBlSUlJSk9P9y3Pz89XYWGhcnNzlZubq8LCQiUmJmrq1Kmd12oAABC1wi44PZuCggJVV1fr9ttvV2lpqUaPHq3XXntNKSkpnf1WAAAgCtmMMaa7G+HP5XIpLS1N5eXlSk1N7e7mAACAEITz/c29XQAAgKUIHwAAwFKEDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABYivABAAAsRfgAAACWInwAAABLET4AAIClCB8AAMBShA8AAGApwgcAALAU4QMAAFiK8AEAACxF+AAAAJayd3cDAABAFzJGqiiWTh2SSoukU0VSXZV09dJuaxLhAwCAaNdQJ5UfawwWpw5JpYcDn9dXB64fGydNXCLFxHZHawkfAABEhdqqxiBR2hQqThWdeV52TDINrW9ri5V6DZB650h9hkh9choDC+EDAIBzXHVps2BRdOZ5xcm2t7XHN4WLnDN/ep/3GijFOqzpQwgIHwAAWMVbfxFs9OJUkVRT1vb28WmBoxf+z5OzpJjouI6E8AEAQGdqqJfKjwYfvThV1LL+ornkrJbBwjuSkdjHmj50McIHAADhqqtuKuoMMnpRfkzy1Le+bUD9RVPA8J0uGSzFJVnVi25D+AAAIJjqUr8rRoqkU4fPhIyQ6i8GBwaLCK2/6A6EDwDAuSmg/qIocB6MU4fOXn/hTDsTKgJCxpCoqr/oDoQPAEDP1VDfNP/FoZb1F6WHGyfbaktyZstg4X2e0Fuy2SzpRk9D+AAARDdf/UWQ0Yuz1l/ESGkDWhZ29hlyztRfdAfCBwAg8nnrL4JdPVJxou1tvfUXwa4eof6iWxA+AADdzxip8rMgE2w1jWRUl7a9vX/9RfOQkdKP+osIQ/gAAFjDW38RMMHW4TP3Hwml/qK1Cbaov4gqhA8AQOfxr79oPotn2dEQ6y+CjF70Hiw5k63qBboY4QMAEJ7qsiDTg4dYfxHrDFLY2fQ8bYBkj7OkC+hehA8AQCBf/UWQ0YtTh0Ksvxgc/BJV6i8gwgcAnJsC6i+KztRdeENGyPUXQSbYov4CZ0H4AICeqq5aKj3Scu6LkOsv+gcfvaD+Ah1E+ACAaOarv2h2D5JTh0Krv+g9OPjoBfUX6EKEDwCIZMZIlSXBRy9OFUnVp9re3pnaeoFnSnar9RcNDQ2qq6npgg4hmjkcDsXGxnZ4P4QPAOhuDfWS619Bpgf33n/kdNvbJ2W0Mj14jpTYJ6z6C2OMiouLVVZW1qEuoefq1auXsrKyZOtAXQ/hAwCsUFfTGCSCjV6UHQmt/iLYBFudXH/hDR4ZGRlKTEzs0BcMehZjjKqqqlRSUiJJ6tevX7v3RfgAgM7iX3/hu0T1cONz1wlJpvVt/esvmoeMXgMtqb9oaGjwBY/09PQufz9En4SEBElSSUmJMjIy2n0KhvABAKHy1l8Em/si1PqL3oODTw/eRv2FVerq6iRJiYmJ3doORDbv70ddXR3hAwA6hX/9RYt7kBSFWH8RZPSiz5Cw6y+6C6da0JbO+P0gfAA499TVNNZZBBu9KDsqeepa3zag/qJ5yBgsOVMs6wYQrQgfAHqmmvKWhZ3eO6ietf4i7szpkeZXj1hUfwH0ZGGFjzVr1mjNmjU6fPiwJGnYsGF68MEHdc0110hqrIRdtGiR1q5dq9LSUo0ePVqPPfaYhg0b1ukNB3COC6i/aH6J6qEw6i+CTLAVAfUXCM/48eN12WWXadWqVd3dlC61cOFCvfTSS9q3b193N6VDwgof/fv317Jly/SVr3xFkrRhwwZdf/312rt3r4YNG6YVK1Zo5cqVeuqpp3ThhRdqyZIlmjBhgj7++GOlpDAUCSBMngap/F9BJtg6HGL9Rd/goxd9cqTE9Kiov8C5oba2VnFx1o2oGWPU0NAgu72bToCYDurdu7f57W9/azwej8nKyjLLli3zvVZTU2PS0tLM448/HvL+ysvLjSRTXl7e0aYBiAa11caUfGTMR38xZsdjxrxyrzG/v9GYR0casyjdmIdSW38s7GXMyuHGPHWtMS/fZcz//dKY/S8Zc/J9Y2pc3d2zqFNdXW0+/PBDU11d3d1NCcu0adOMGs+j+R5FRUVm//795pprrjFJSUkmIyPD3HLLLebzzz/3bZeXl2fuvPNOM3v2bNOrVy+TkZFhfvOb35jKykozffp0k5ycbIYMGWL+/Oc/+7bZtm2bkWT+9Kc/mREjRhin02m+8Y1vmPfffz+gTW+//bb55je/aeLj403//v3NT3/6U1NZWel7fdCgQebhhx8206ZNM6mpqeaHP/yhMcaYgoICk5ubaxISEkxOTo5ZsGCBqa2tNcYYs379+hb9XL9+vSkqKjKSzN69e337Ly0tNZLMtm3bAtq9efNm87Wvfc04HA7z+uuvG4/HY5YvX25ycnJMfHy8GTFihHn++efb/Ptu7fcknO/vdkeehoYGPf/88zp9+rTGjh2roqIiFRcXa+LEib51nE6n8vLytGPHDs2aNau9bwUgmtW7G2ssXMcb/yw/Fnj1iOu4Qqq/CHb1CPUXXc4Yo+q6BsvfN8ERG/JVFY8++qgOHjyo4cOHa/HixZIav6Py8vI0c+ZMrVy5UtXV1ZozZ46mTJmi119/3bfthg0bVFBQoF27dum5557TT37yE7300kv6zne+o/vvv1+//OUvdeutt+ro0aMBlyDfd999evTRR5WVlaX7779f//Ef/6GDBw/K4XDogw8+0KRJk/Twww/riSee0Oeff64777xTd955p9avX+/bx89//nM98MADWrBggW9ZSkqKnnrqKWVnZ+uDDz7QzJkzlZKSooKCAn3/+9/XP/7xD23evFlbt26VJKWlpemzzz4L+e+1oKBA//Vf/6UhQ4aoV69eWrBggV588UWtWbNGubm5evPNN3XLLbeob9++ysvLC3m/4Qo7fHzwwQcaO3asampqlJycrE2bNmno0KHasWOHJCkzMzNg/czMTB05cqTV/bndbrndbt/PLpcr3CYB6C511U3Bwvv415nn5U3Pq744+37iUppOiwS5RDU1W4rp+L0k0D7VdQ0a+uCrlr/vh4snKTEutK+otLQ0xcXFKTExUVlZWZKkBx98UJdffrkKCwt96z355JMaMGCADh48qAsvvFCS9NWvftX35T9v3jwtW7ZM5513nmbOnOnbz5o1a/T+++9rzJgxvn099NBDmjBhgqTGANO/f39t2rRJU6ZM0c9//nNNnTpV+fn5kqTc3Fz96le/Ul5entasWaP4+HhJ0r/927/p3nvvDeiLfxAZPHiwfvazn+m5555TQUGBEhISlJycLLvd7utnuBYvXuxr9+nTp7Vy5Uq9/vrrGjt2rCRpyJAheuutt/Sb3/wmssLHRRddpH379qmsrEwvvPCCpk2bpu3bt/teb55UjTFtptelS5dq0aJF4TYDQFerrQocsXAdb/b8hFT1ZWj7ssdLqec3Bom0/i1HMqi/QCfbs2ePtm3bpuTkllPP//Of//SFjxEjRviWx8bGKj09XZdeeqlvmfc/1N4pxb28X9aS1KdPH1100UU6cOCA770//fRTPfPMM751jDHyeDwqKirSJZdcIkkaNWpUi7b98Y9/1KpVq/Tpp5+qsrJS9fX1Sk1NDbv/rfF/zw8//FA1NTW+MOJVW1urkSNHdtp7BhN2+IiLi/MVnI4aNUq7d+/Wo48+qjlz5khqvC+A/3zvJSUlLUZD/M2bN0/33HOP72eXy6UBAwaE2ywA4ag9fSZElLcSLqpLQ9uXI/FMsPAFjPMDlyX0JlxEqQRHrD5cPKlb3rcjPB6PrrvuOi1fvrzFa/7fUQ6HI+A1m80WsMz7n2ePx3PW9/Rfd9asWbrrrrtarDNw4EDf86SkpIDXdu7cqZtuukmLFi3SpEmTlJaWpmeffVa/+MUv2nzfmKYrs4w5c/rSO1ttc/7v6e3TK6+8ovPPPz9gPafT2eZ7dlSHy1yNMXK73crJyVFWVpa2bNniS0y1tbXavn170A/fy+l0dnkngXOKu6JZjYV/qGg6NVJTHtq+HElNQSJbSu3f9GfT6IX3eXwvgkUPZrPZQj790Z3i4uLU0HCmNuXyyy/XCy+8oMGDB3fJFR07d+70BYnS0lIdPHhQF198se+99+/f7/uPeqjefvttDRo0SPPnz/cta1620LyfktS3b19J0smTJ33fv6Fcijt06FA5nU4dPXq0S0+xBBPWJ3L//ffrmmuu0YABA1RRUaFnn31Wb7zxhjZv3iybzab8/HwVFhYqNzdXubm5KiwsVGJioqZOndpV7QfOLTWu1msrvA93iMHCmXomQASEC7/RC2cqwQJRYfDgwXr33Xd1+PBhJScn64477tC6det0880367777tN5552nTz/9VM8++6zWrVvX7nuSeC1evFjp6enKzMzU/Pnzdd555+k///M/JUlz5szRmDFjdMcdd2jmzJlKSkrSgQMHtGXLFv36179udZ9f+cpXdPToUT377LP6+te/rldeeUWbNm1q0c+ioiLt27dP/fv3V0pKihISEjRmzBgtW7ZMgwcP1hdffBFQO9KalJQU3Xvvvbr77rvl8Xh05ZVXyuVyaceOHUpOTta0adM69HfUlrDCx2effaZbb71VJ0+eVFpamkaMGKHNmzf7zhcVFBSourpat99+u2+Ssddee405PoCzMaZxNKJF4ab/6MUJqbYitP050/xGLLyBotmpkfjOO48MdLd7771X06ZN09ChQ1VdXa2ioiK9/fbbmjNnjiZNmiS3261Bgwbp6quv9p2m6Ihly5Zp9uzZ+uSTT/TVr35VL7/8sm+ejhEjRmj79u2aP3++vvnNb8oYowsuuEDf//7329zn9ddfr7vvvlt33nmn3G63Jk+erAceeEALFy70rXPjjTfqxRdf1Le+9S2VlZVp/fr1mj59up588knNmDFDo0aN0kUXXaQVK1YEXH3amocfflgZGRlaunSpDh06pF69eunyyy/X/fff36G/n7OxGf+TRBHA5XIpLS1N5eXlnVpkA3QbY6SastZrK7zh4mwTZnnF92qltsI7etGP+4ugXWpqalRUVKScnBzfFRkI9MYbb+hb3/qWSktL1atXr+5uTrdo7fcknO/vyD+RB0QyYxoLM32Fm8eDjF6ckOqqQttfQu9mtRVBRiziks6+HwCIYIQPoDXGNF5K6hud8K+t8Asa9TWh7S8xvWXhZur5ZwJGSj8pLvHs+wGAKEf4wLnJ42kKFs0CRXmz0yEN7rPvS2q8h4j/6IR/jUVaU7BwJHRtnwB0ufHjxyvCqhWiEuEDPY/HI53+PPjkWN5TIxUnpYba0PaXlNFKbUXT85R+koPz4wAQKsIHoounofE26gEzbzY/HXJS8gSfYCeQTUrObKW24vwzwYJ7hwBApyJ8IHJ4GqTKz5rVVzQr4qw4KXnqz74vW4yUnBWktiI7MFjEOs6+LwBApyJ8wBoN9VJlceu1Fa7jUkWxZEK4e6YtpjE4tJi/wm/mzeRMggUARCjCBzquoa4xOASrrfCOWFQWS+bs90aQLbbZrJvNToeknd9YgxHLry4ARCv+BUfb6msbT3UEmxzLO3pR+ZmkEKq/Y+xSSnazmTebTemdnMHt0wGghyN8nMvq3YH3BAl22enpkrPvR5JiHK3XVnhHL5L6Sp0wrTEANDd+/HhddtllWrVqVcS97+DBg5Wfn6/8/HxJjTfr27Rpk+9eMOciwkdPVVcjVZwIXlvhfX7689D2FetsFiaa3dU0tX/jBFoECwDnmBdffFEOR3j1ZSdPnlTv3r0lSYcPH1ZOTo727t2ryy67rAtaGJkIH9GotqrpVEgbU3pXfRnavuzxbRdupp7fGCy4sykAtNCnT5+wt8nKyuqClkQXwkekqT3dcqSi/Hjg6ZDqU6Hty57Q7BRIs8LN1PMb7yVCsADQA3g8HhUUFOi3v/2t4uLi9OMf/1gLFy4MOrpQVlam3r17a9u2bRo/frzvhnGbN2/W3Llz9dFHH2ns2LF69tlntWfPHt1zzz06fvy4Jk+erCeeeEKJiY23Qmh+2qWkpES33Xabtm7dqqysLC1ZsqRFO/1Pu+Tk5EiSRo4cKUnKy8vT4sWL9e1vf1vHjh0LCCo/+9nPtHv3br355ptd+LdoDcKHldyVrddWeJ/XlIW2L0dS67UV3tELggWAjjIm9BsjdiZHYtj/fm3YsEH33HOP3n33Xb3zzjuaPn26rrjiCuXm5oa8j4ULF2r16tVKTEzUlClTNGXKFDmdTm3cuFGVlZX6zne+o1//+teaM2dO0O2nT5+uY8eO6fXXX1dcXJzuuusulZS0Xju3a9cufeMb39DWrVs1bNgwxcXFqU+fPhoyZIh+//vf67777pMk1dfX6+mnn9ayZcvC+juJVISPzlLjar22wnvpqbs8tH3FJbdyy3S/Oov4NIIFgK5XVyUVZlv/vvefCPsOziNGjNBDDz0kScrNzdXq1av117/+NazwsWTJEl1xxRWSpNtuu03z5s3TP//5Tw0ZMkSS9N3vflfbtm0LGj4OHjyov/zlL9q5c6dGjx4tSXriiSd0ySWXtPp+ffv2lSSlp6cHjHLcdtttWr9+vS98vPLKK6qqqtKUKVNC7kskI3ycjTGS2xVk7gr/0YsTjeuEwpnaRuFmU9CIT+3aPgFADzRixIiAn/v169fmqMPZ9pGZmanExERf8PAu27VrV9BtDxw4ILvdrlGjRvmWXXzxxerVq1dYbZAaR1AWLFignTt3asyYMXryySc1ZcoUJSWFF8gi1bkdPoxpPM3hG51o5ZbptZWh7S8+rWVthf/oRUo/ggWA6OJIbByF6I73DXeTZled2Gw2eTwexTRdied/N9q6uuD3f/Lfh81ma3WfwXj3b+uEUemMjAxdd911Wr9+vYYMGaI///nPeuONNzq830hx7oQPd4X01qpmp0ZOSHWnQ9s+oXfrtRVp/RuDhTO5S7sAAJaz2cI+/RFpvKc2Tp486Svs3LdvX6e/zyWXXKL6+nq99957+sY3viFJ+vjjj1VWVtbqNnFxjTeubGhoeWuJH/3oR7rpppvUv39/XXDBBb7TQT3BuRM+YuzS//1X8NcS+rRxy/SmZXHhp3AAQPdLSEjQmDFjtGzZMg0ePFhffPGFFixY0Onvc9FFF+nqq6/WzJkztXbtWtntduXn5yshIaHVbTIyMpSQkKDNmzerf//+io+PV1pamiRp0qRJSktL05IlS7R48eJOb293OnfChyNBGnunlNin5ciFo/VfDABA9HvyySc1Y8YMjRo1ShdddJFWrFihiRMndvr7rF+/Xj/60Y+Ul5enzMxMLVmyRA888ECr69vtdv3qV7/S4sWL9eCDD+qb3/ym7/RKTEyMpk+frsLCQv3whz/s9LZ2J5vxPwkWAVwul9LS0lReXq7UVOojAMAqNTU1KioqUk5OjuLj47u7OZA0c+ZMffbZZ3r55Ze7uyk+rf2ehPP9fe6MfAAAECXKy8u1e/duPfPMM/qf//mf7m5OpyN8AAAQYa6//nrt2rVLs2bN0oQJE7q7OZ2O8AEAQITpSZfVBsNtSAEAgKUIHwAAwFKEDwBAgNZm8ASkzvn9oOYDACCpcbbNmJgYnThxQn379lVcXFynTBWOnsEYo9raWn3++eeKiYnxzc7aHoQPAICkxkmtcnJydPLkSZ040Q33c0FUSExM1MCBA333zGkPwgcAwCcuLk4DBw5UfX190PuN4NwWGxsru93e4RExwgcAIID3bq7N7+gKdBYKTgEAgKUIHwAAwFKEDwAAYKmIq/nw3mTX5XJ1c0sAAECovN/b3u/xtkRc+KioqJAkDRgwoJtbAgAAwlVRUaG0tLQ217GZUCKKhTwej06cOKGUlJROn9zG5XJpwIABOnbsmFJTUzt135Ggp/dP6vl9pH/Rr6f3saf3T+r5feyq/hljVFFRoezs7LPOARJxIx8xMTHq379/l75Hampqj/yF8urp/ZN6fh/pX/Tr6X3s6f2Ten4fu6J/Zxvx8KLgFAAAWIrwAQAALHVOhQ+n06mHHnpITqezu5vSJXp6/6Se30f6F/16eh97ev+knt/HSOhfxBWcAgCAnu2cGvkAAADdj/ABAAAsRfgAAACWInwAAABLRXX4OH78uG655Ralp6crMTFRl112mfbs2dPmNtu3b9fXvvY1xcfHa8iQIXr88cdbrPPCCy9o6NChcjqdGjp0qDZt2tRVXWhTuP178cUXNWHCBPXt21epqakaO3asXn311YB1nnrqKdlsthaPmpqaru5OC+H274033gja9o8++ihgvUj5/KTw+zh9+vSgfRw2bJhvnUj5DAcPHhy0HXfccUer20TT8Rdu/6Lt+JPC72O0HYPh9i+ajj9Jqq+v14IFC5STk6OEhAQNGTJEixcvlsfjaXO7iDgOTZQ6deqUGTRokJk+fbp59913TVFRkdm6dav59NNPW93m0KFDJjEx0cyePdt8+OGHZt26dcbhcJg//vGPvnV27NhhYmNjTWFhoTlw4IApLCw0drvd7Ny504pu+bSnf7NnzzbLly83u3btMgcPHjTz5s0zDofD/O1vf/Ots379epOammpOnjwZ8LBae/q3bds2I8l8/PHHAW2vr6/3rRMpn58x7etjWVlZQN+OHTtm+vTpYx566CHfOpHyGZaUlAS8/5YtW4wks23btqDrR9PxZ0z4/Yum488r3D5G2zEYbv+i6fgzxpglS5aY9PR086c//ckUFRWZ559/3iQnJ5tVq1a1uk2kHIdRGz7mzJljrrzyyrC2KSgoMBdffHHAslmzZpkxY8b4fp4yZYq5+uqrA9aZNGmSuemmm9rf2HZoT/+CGTp0qFm0aJHv5/Xr15u0tLQO77ej2tM/7z98paWlra4TKZ+fMZ3zGW7atMnYbDZz+PBh37JI+Qybmz17trnggguMx+MJ+no0HX/BnK1/wUTq8deas/Ux2o7B5sL9DCP9+Js8ebKZMWNGwLIbbrjB3HLLLa1uEynHYdSednn55Zc1atQofe9731NGRoZGjhypdevWtbnNO++8o4kTJwYsmzRpkt577z3V1dW1uc6OHTs6twNn0Z7+NefxeFRRUaE+ffoELK+srNSgQYPUv39/XXvttdq7d29nNj0kHenfyJEj1a9fP33729/Wtm3bAl6LlM9P6pzP8IknntBVV12lQYMGBSyPhM/QX21trZ5++mnNmDGj1RtCRtPx11wo/Wsuko+/YMLpY7Qcg/7a8xlG+vF35ZVX6q9//asOHjwoSfr73/+ut956S//+7//e6jYRcxx2WoyxmNPpNE6n08ybN8/87W9/M48//riJj483GzZsaHWb3Nxc88gjjwQse/vtt40kc+LECWOMMQ6HwzzzzDMB6zzzzDMmLi6u8zvRhvb0r7kVK1aYPn36mM8++8y37J133jG///3vzb59+8ybb75pbrzxRpOQkGAOHjzYFd1oVXv699FHH5m1a9eaPXv2mB07dpif/OQnxmazme3bt/vWiZTPz5iOf4YnTpwwsbGx5rnnngtYHimfob/nnnvOxMbGmuPHj7e6TjQdf82F0r/mIvn4CyaUPkbbMegv3M8wGo4/j8dj5s6da2w2m7Hb7cZms5nCwsI2t4mU4zBqw4fD4TBjx44NWPbTn/40YOioudzc3BYfzFtvvWUk+c7ZORwOs3HjxoB1nn76aeN0Ojup5aFpT//8bdy40SQmJpotW7a0uV5DQ4P56le/an7605+2u63t0dH+eV177bXmuuuuC9hvJHx+3rZ0pI+FhYUmPT3duN3uNtfrrs/Q38SJE821117b5jrRdPw1F0r//EX68RdMuH30iuRj0F+4/YuG4+8Pf/iD6d+/v/nDH/5g3n//ffO73/3O9OnTxzz11FOtbhMpx2HUnnbp16+fhg4dGrDskksu0dGjR1vdJisrS8XFxQHLSkpKZLfblZ6e3uY6mZmZndTy0LSnf17PPfecbrvtNv33f/+3rrrqqjbXjYmJ0de//nV98sknHWpvuDrSP39jxowJaHukfH5Sx/pojNGTTz6pW2+9VXFxcW2u212fodeRI0e0detW/ehHP2pzvWg6/vyF2j+vaDj+mgu3j/4i+Rj0Crd/0XL83XfffZo7d65uuukmXXrppbr11lt19913a+nSpa1uEynHYdSGjyuuuEIff/xxwLKDBw+2ODfnb+zYsdqyZUvAstdee02jRo2Sw+Foc51x48Z1UstD057+SdIf/vAHTZ8+XRs3btTkyZPP+j7GGO3bt0/9+vXrUHvD1d7+Nbd3796AtkfK5yd1rI/bt2/Xp59+qttuu+2s63bXZ+i1fv16ZWRknPX3LZqOP3+h9k+KnuOvuXD62FwkH4Ne4fYvWo6/qqoqxcQEfo3Hxsa2ealtxByHnTaGYrFdu3YZu91uHnnkEfPJJ5+YZ555xiQmJpqnn37at87cuXPNrbfe6vvZe4nR3XffbT788EPzxBNPtLjE6O233zaxsbFm2bJl5sCBA2bZsmXdcplYe/q3ceNGY7fbzWOPPRZwCVhZWZlvnYULF5rNmzebf/7zn2bv3r3m//2//2fsdrt59913I75/v/zlL82mTZvMwYMHzT/+8Q8zd+5cI8m88MILvnUi5fMzpn199LrlllvM6NGjg+43Uj5DYxqHnAcOHGjmzJnT4rVoPv68wulfNB1//sLpY7Qdg8aE1z+vaDn+pk2bZs4//3zfpbYvvviiOe+880xBQYFvnUg9DqM2fBhjzP/+7/+a4cOHG6fTaS6++GKzdu3agNenTZtm8vLyApa98cYbZuTIkSYuLs4MHjzYrFmzpsV+n3/+eXPRRRcZh8NhLr744oADy0rh9i8vL89IavGYNm2ab538/HwzcOBAExcXZ/r27WsmTpxoduzYYVGPAoXbv+XLl5sLLrjAxMfHm969e5srr7zSvPLKKy32GymfnzHt+x0tKyszCQkJLdb1iqTP8NVXX/XN+9BctB9/xoTXv2g7/rzC6WM0HoPh/o5G0/HncrnM7NmzzcCBA018fLwZMmSImT9/fkCdSqQehzZjjOm8cRQAAIC2RW3NBwAAiE6EDwAAYCnCBwAAsBThAwAAWIrwAQAALEX4AAAAliJ8AAAASxE+AACApQgfAADAUoQPAABgKcIHAACwFOEDAABY6v8Dci1GHUgkm1EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAABW3klEQVR4nO3dd3hUVf7H8fdk0kMSSICQkJACoYUqIMUgKCIWyu66ixVByro/dRXdta6IsCiwxdXVXVFBcEXUXRVFUayA9A7SAyQhlEBo6X3m/v64kBCkJJDkziSf1/Pkeci5d2a+l8nNfHLPuefYDMMwEBEREaklHlYXICIiIvWLwoeIiIjUKoUPERERqVUKHyIiIlKrFD5ERESkVil8iIiISK1S+BAREZFapfAhIiIitcrT6gLO5XQ6OXz4MIGBgdhsNqvLERERkUowDIOcnBwiIiLw8Lj4tQ2XCx+HDx8mKirK6jJERETkMhw4cIDIyMiL7uNy4SMwMBAwiw8KCrK4GhEREamM7OxsoqKiyj7HL8blwseZrpagoCCFDxERETdTmSETGnAqIiIitUrhQ0RERGqVwoeIiIjUKpcb81EZhmFQWlqKw+GwuhRxMXa7HU9PT92mLSLiwtwufBQXF5Oenk5+fr7VpYiL8vf3Jzw8HG9vb6tLERGR83Cr8OF0OklJScFutxMREYG3t7f+wpUyhmFQXFzMsWPHSElJIT4+/pIT3YiISO1zq/BRXFyM0+kkKioKf39/q8sRF+Tn54eXlxf79++nuLgYX19fq0sSEZFzuOWfhfprVi5GPx8iIq5Nv6VFRESkVil8iIiISK1S+BAREZFapfBRS/r378/48eOtLqPGPf/883Tp0sXqMkRExIW51d0uYp3i4uJanTfDMAwcDgeenvoRFRGpFoYBR7fB7kVQcApuetGyUtz+yodhGOQXl1ryZRhGpWocNWoUS5cu5ZVXXsFms2Gz2UhNTWXHjh3ccsstNGjQgLCwMEaMGMHx48fLHte/f39+//vfM378eBo1akRYWBhvvvkmeXl53HfffQQGBtKyZUu++uqrsscsWbIEm83GwoUL6dy5M76+vvTs2ZOtW7dWqGnlypVce+21+Pn5ERUVxcMPP0xeXl7Z9piYGKZMmcKoUaMIDg5m3LhxADz55JO0bt0af39/4uLimDBhAiUlJQDMmTOHSZMmsWXLlrLjnDNnDqmpqdhsNjZv3lz2/JmZmdhsNpYsWVKh7q+//pru3bvj4+PDsmXLMAyDv/zlL8TFxeHn50fnzp356KOPqvQzIiJSb5UUQNLX8MWj8I8EmJEIi6fAuregKMeystz+z8qCEgftn/vaktfeMXkQ/t6X/i985ZVXSEpKokOHDkyePBkAh8NBv379GDduHC+99BIFBQU8+eSTDB8+nB9++KHsse+88w5PPPEEa9eu5cMPP+T//u//+PTTT/nlL3/JM888wz/+8Q9GjBhBWlpahblPHn/8cV555RWaNWvGM888w9ChQ0lKSsLLy4utW7cyaNAg/vznPzNr1iyOHTvGQw89xEMPPcTs2bPLnuOvf/0rEyZM4Nlnny1rCwwMZM6cOURERLB161bGjRtHYGAgTzzxBLfffjvbtm1j0aJFfPfddwAEBwdz9OjRSv+fPvHEE/ztb38jLi6Ohg0b8uyzz/LJJ5/w+uuvEx8fz48//sg999xDkyZN6NevX6WfV0Sk3shOh6RFZuhIXgKlBeXbPP2g5XXQehDYrLv+4Pbhwx0EBwfj7e2Nv78/zZo1A+C5557jqquu4sUXyy97vf3220RFRZGUlETr1q0B6Ny5c9mH/9NPP820adNo3Lhx2ZWI5557jtdff52ffvqJXr16lT3XxIkTGThwIGAGmMjISObPn8/w4cP561//yl133VU2BiU+Pp5//vOf9OvXj9dff71sYq7rr7+eP/7xjxWO5ewgEhMTwx/+8Ac+/PBDnnjiCfz8/GjQoAGenp5lx1lVkydPLqs7Ly+Pl156iR9++IHevXsDEBcXx/Lly3njjTcUPkREAJxOSN9sho2kryB9S8XtQZFm2Gh9E8T2BS8/S8o8m9uHDz8vOzsmD7LstS/Xhg0bWLx4MQ0aNPjZtn379pWFj06dOpW12+12QkND6dixY1lbWFgYABkZGRWe48yHNUBISAht2rRh586dZa+9d+9e3nvvvbJ9DMMom76+Xbt2AHTv3v1ntX300Ue8/PLL7N27l9zcXEpLSwkKCqry8V/I2a+5Y8cOCgsLy8LIGcXFxXTt2rXaXlNExO0U55lXNXZ/BXu+gdyzrzDbILL76cBxM4QlgIstReL24cNms1Wq68PVOJ1OhgwZwvTp03+2LTw8vOzfXl5eFbbZbLYKbWfWtnE6nZd8zbP3vf/++3n44Yd/tk+LFi3K/h0QEFBh2+rVq7njjjuYNGkSgwYNIjg4mA8++IC///3vF33dMzOOnj1G5sw4kXOd/ZpnjmnhwoU0b968wn4+Pj4XfU0RkTonM+301Y1FkLIMHEXl27wbQMvrzasb8TdCgybW1VkJ7vep7aa8vb1xOBxl31911VV8/PHHxMTE1MgdHatXry4LEqdOnSIpKYm2bduWvfb27dtp1apVlZ5zxYoVREdH86c//amsbf/+/RX2Ofc4AZo0MU+C9PT0sisWZw8+vZD27dvj4+NDWlqaulhEpP5xOuDQBvPqRtLXkLG94vaG0dDmZjNwRPcBT/f5o0zho5bExMSwZs0aUlNTadCgAQ8++CBvvfUWd955J48//jiNGzdm7969fPDBB7z11lvY7ZffpQPm2InQ0FDCwsL405/+ROPGjfnFL34BmHes9OrViwcffJBx48YREBDAzp07+fbbb3n11Vcv+JytWrUiLS2NDz74gB49erBw4ULmz5//s+NMSUlh8+bNREZGEhgYiJ+fH7169WLatGnExMRw/PjxCmNHLiQwMJA//vGPPProozidThITE8nOzmblypU0aNCAkSNHXtH/kYiIyynMhn0/mFc39nwD+SfKt9k8IKqX2Z3S5mZo3NrlulMqy+1vtXUXf/zjH7Hb7bRv354mTZpQXFzMihUrcDgcDBo0iA4dOvDII48QHBxcLQujTZs2jUceeYRu3bqRnp7OggULyubp6NSpE0uXLmXPnj307duXrl27MmHChArdPeczbNgwHn30UR566CG6dOnCypUrmTBhQoV9brvtNm666Sauu+46mjRpwvvvvw+Yg2lLSkro3r07jzzyCFOmTKnUcfz5z3/mueeeY+rUqbRr145Bgwbx+eefExsbexn/KyIiLuhkMqz6N7wzFP4SB/8bCVveN4OHTzB0uA1+9RY8vg9GfwWJ46FJG7cNHgA2o7KTVdSS7OxsgoODycrK+tlAxsLCQlJSUoiNjdVS6RewZMkSrrvuOk6dOkXDhg2tLscS+jkREZfmKIUDa8w7U5K+huNJFbeHxpffndKiF9i9zv88LuZin9/nUreLiIhITSs4BXu/N8dv7P0WCrPKt3l4mmM2Wt9kfoW2tK7OWqLwISIiUt0MA47vKb+6kbYajLMG4/uFmHeltB4ErQaAb7B1tVpA4aOO6d+/f6WnfRcRkWpUWgxpK821U5IWwamUitubti/vTonsAR5XdmOBO1P4EBERuVx5x2HPt+YVjr0/QPFZ66XYvSGm7+nulBuhUYxlZboahQ8REZHKMgzI2GFe2di9CA6uA8662hzQ1AwarW+CuOvA5+ezWIvCh4iIyMWVFELq8vLF2rLSKm5v1ql8sGhEV6iG6RLqOoUPERGRc+UcMSf52r0IkhdDSX75Nk9fiOtvjt+IHwTBzS/4NHJ+Ch8iIiKGYa4Ge2Zl2MObKm4PjDhrZdhrwdvfmjrrCIWPWtK/f3+6dOnCyy+/7HKvGxMTw/jx4xk/fjxgLkA3f/78sunYRUTqpOJ8SFlavjJsTnrF7c27ne5OGWR2rbjxjKKuRuGjjvvkk09+tjLupaSnp9OoUSMAUlNTiY2NZdOmTXTp0qUGKhQRqUVZB89aGfZHKC0s3+YVAC2vK18ZNjDMujrrOIWPOi4kJKTKj2nWrFkNVCIiYgGnEw5vLL875ejWituDW0Cb01c3ohPBS0sy1AYNya1FTqeTJ554gpCQEJo1a8bzzz8PmFcXbDZbhWXmMzMzsdlsLFmyBDDXbLHZbHz99dd07doVPz8/rr/+ejIyMvjqq69o164dQUFB3HnnneTnlw+M6t+/f1l3CkBGRgZDhgzBz8+P2NhY3nvvvZ/VabPZ+PTTTwHKFnDr2rUrNpuN/v378+OPP+Ll5cWRI0cqPO4Pf/gD11577ZX/R4mIXImiHNixAD59AP7eGmYOgB//agaPMyvDDpgI/7cKxv8Et/wVWt2g4FGL3P/Kh2FUHIVcm7z8q9QH+M477/DYY4+xZs0aVq1axahRo7jmmmuIj4+v9HM8//zzvPbaa/j7+zN8+HCGDx+Oj48P8+bNIzc3l1/+8pe8+uqrPPnkk+d9/KhRozhw4AA//PAD3t7ePPzww2RkZFzw9dauXcvVV1/Nd999R0JCAt7e3oSEhBAXF8e7777L448/DkBpaSlz585l2rRplT4WEZFqcyq1vDsldTk4isu3+QSZU5i3vglaDYSAUMvKFJP7h4+SfHgxwprXfuYweAdUevdOnToxceJEAOLj43nttdf4/vvvqxQ+pkyZwjXXXAPAmDFjePrpp9m3bx9xcXEA/PrXv2bx4sXnDR9JSUl89dVXrF69mp49ewIwa9Ys2rVrd8HXa9KkCQChoaEVumPGjBnD7Nmzy8LHwoULyc/PZ/jw4ZU+FhGRy+YoNSf4Sjo9lfmxXRW3h8RB65vN7pQWvcHT25o65bzcP3y4kU6dOlX4Pjw8/KJXHS71HGFhYfj7+5cFjzNta9euPe9jd+7ciaenJ927dy9ra9u2LQ0bNqxSDWBeQXn22WdZvXo1vXr14u2332b48OEEBFQ+jImIVElBJuz73rzCsecbc6XYM2z20yvDnr4dtnHl/6iT2uf+4cPL37wCYdVrV2X3c+46sdlsOJ1OPE7Phnf2gnAlJSWXfA6bzXbB5zyfM89vq4bbxZo2bcqQIUOYPXs2cXFxfPnll2XjU0REqs3xveVXN9JWgbO0fJtvw4orw/o1sqxMqRr3Dx82W5W6PlzRma6N9PR0unbtClBh8Gl1adeuHaWlpaxfv56rr74agN27d5OZmXnBx3h7m5cqHQ7Hz7aNHTuWO+64g8jISFq2bFnWHSQictkcJWbISPranH/j5L6K2xu3OX13yk0QeTXY3f9jrD7Su+YC/Pz86NWrF9OmTSMmJobjx4/z7LPPVvvrtGnThptuuolx48bx5ptv4unpyfjx4/Hz87vgY5o2bYqfnx+LFi0iMjISX19fgoODARg0aBDBwcFMmTKFyZMnV3u9IlJP5J88vTLsItj7PRRllW/z8IKYa06P37jRHMshbk+32rqIt99+m5KSErp3784jjzzClClTauR1Zs+eTVRUFP369eNXv/oVv/3tb2natOkF9/f09OSf//wnb7zxBhEREQwbNqxsm4eHB6NGjcLhcHDvvffWSL0iUgcZBmTshOX/gFmD4K8tYf5vYfsnZvDwbwxd7obh/4EnkuHez6DX7xQ86hCbcfZAAxeQnZ1NcHAwWVlZBAUFVdhWWFhISkoKsbGx+PrqfmxXMG7cOI4ePcqCBQusLqWMfk5EXFBp0emVYU/fDpu5v+L2sA7lK8M2vwo87NbUKZftYp/f51K3i1yWrKws1q1bx3vvvcdnn31mdTki4opyM8y7UpIWwb7FUJxbvs3uA3H9yleGbRhlXZ1S66rU7VJaWsqzzz5LbGwsfn5+xMXFMXny5AveXXH//fdjs9lqfTE1qXnDhg1j6NCh3H///QwcONDqckTEFRgGpP8ES/8Kb10Pf4uHzx6EnZ+bwaNBM7hqJNzxPjyZAnf/D3qMVfCoh6p05WP69OnMmDGDd955h4SEBNavX899991HcHAwjzzySIV9P/30U9asWUNEhEUTgEmN0m21IgJASYG5QFvSIrNLJftQxe0RXc9aGbYzeGiooVQxfKxatYphw4Zx6623AuZS7O+//z7r16+vsN+hQ4d46KGH+Prrr8v2FRGROiL78OmxG19D8hIoLSjf5uUPcded7k65EYLCLStTXFeVwkdiYiIzZswgKSmJ1q1bs2XLFpYvX16hW8XpdDJixAgef/xxEhISLvmcRUVFFBUVlX2fnZ1dlZJERKSmOZ2Qvql8sGj6lorbgyLL596ISQSvC9++LwJVDB9PPvkkWVlZtG3bFrvdjsPh4IUXXuDOO+8s22f69Ol4enry8MMPV+o5p06dyqRJk6pUtIvdoCMuRj8fItWgKNe8qpG0yBw0mnv0rI02iOxefndKWEKVFtkUqVL4+PDDD5k7dy7z5s0jISGBzZs3M378eCIiIhg5ciQbNmzglVdeYePGjZWewvvpp5/mscceK/s+OzubqKjzDz46M5V4fn7+RSfGkvotP99c5fjcqedF5BIy08qvbqQsA0f5VWm8G0DL66HNzebKsA2aWFenuL0qzfMRFRXFU089xYMPPljWNmXKFObOncuuXbt4+eWXeeyxx8rWKgFzWm4PDw+ioqJITU295Gtc6j7h9PR0MjMzadq0Kf7+/tWyTonUDYZhkJ+fT0ZGBg0bNiQ8XH3NIhfldMDB9eWDRTO2V9zeKKZ8Zdjoa7QyrFxUjc3zkZ+fXyFYANjt9rJbbUeMGMENN9xQYfugQYMYMWIE9913X1Ve6oLOLOte1dVgpf5o2LBh2c+JiJyjMAv2/VC+Mmz+ifJtNg+I6lU+fqNxa3WnSI2oUvgYMmQIL7zwAi1atCAhIYFNmzbx0ksvMXr0aABCQ0MJDQ2t8BgvLy+aNWtGmzZtqqVgm81GeHg4TZs2veDKr1J/eXl5YbdrZkSRCk7sK+9O2b/inJVhg6HVDeYVjlYDwD/Eujql3qhS+Hj11VeZMGECDzzwABkZGURERHD//ffz3HPP1VR9F2S32/UhIyJyPo5SOLDaDBu7F8GJPRW3h8aXX92I6gl2jY+S2uVWa7uIiMgF5J80V4RNWgR7vzW7V87w8IToPuXjN0JbWlen1Fla20VEpK4zDDieVD5YNG01GI7y7X4hZtBoPci8S8U32LpaRc6h8CEi4i5Ki80xG2fGb5xKqbi9afvyuTciu2tlWHFZCh8iIq4s73j5yrB7f4DinPJtdm+I6WvOvRF/IzSKtq5OkSpQ+BARcSWGAUe3l3enHFwHnDU0L6AptL7RHL8R1x98GlhVqchlU/gQEbFaSSGkLisPHFkHKm5v1snsSmlzE4R31cqw4vYUPkRErJBz5KyVYRdDSX75Nk9f86pG65vM7pTg5paVKVITFD5ERGqDYUD65vLBooc3VdweGGHemdLmZnMch7e/JWWK1AaFDxGRmlKcB8lLy1eGzUmvuL15t/K7U5p11FTmUm8ofIiIVKesg+VjN1J+hNLC8m1eAdDyuvKVYQPDrKtTxEIKHyIiV8LphMMbYfdXZuA4urXi9oYtyq9uxCSCp481dYq4EIUPEZGqKsqpuDJs3rHybTYPiLy6fO2UJm3VnSJyDoUPEZHKOJlSPlg0dTk4z1pV2yfIXBG29c3mCrEBoRd+HhFR+BAROS9HKRxcWz5+49iuittD4soXaovuo5VhRapA4UNE5IyCTNj7nRk29n4LBafKt9nsp1eGHWSGjsatLCtTxN0pfIhI/XZ8LySdHiy6f2XFlWF9G5qTfLUeZHan+DW0qkqROkXhQ0TqF0eJGTLOjN84ua/i9iZty69uRPYAu35NilQ3nVUiUvflnTC7UZIWwd7voSi7fJuHl3kLbOubzNAREmtdnSL1hMKHiNQ9hmEOED0z98bBtWA4y7f7Nz59deMmc9Ivn0DrahWphxQ+RKRuKC0yb4FNWmR+ZaZV3B7WsXztlIirtDKsiIUUPkTEfeVmlI/d2LcYSvLKt9l9IK5f+RWO4Ejr6hSRChQ+RMR9GAYc2Vp+dePQhorbGzQrv7oRey14B1hTp4hclMKHiLi2kgJzgbYz4zdyDlfcHtG1fO2U8M6aylzEDSh8iIjryT5cPrNo8lIoLSjf5uUPcded7k4ZBIHNrKtTRC6LwoeIWM/phPRNsPt0d8qRnypuD4osX6gtpi94+VpTp4hUC4UPEbFGUS4kLz59heMbyMs4a6PNnODrzGDRsAR1p4jUIQofIlJ78k/Cto/N8Rupy8BRXL7NOxBaXW+GjfgbIaCxdXWKSI1S+BCRmnd8L6z+N2yeV3H8RqOYs1aGvQY8vS0rUURqj8KHiNQMw4D9K2DVv8wrHRhme7OO0PE35hWOxq3VnSJSDyl8iEj1cpTA9k9h1WuQvrm8vfVN0Pshcx0VBQ6Rek3hQ0SqR0EmbHwH1rwB2YfMNk9f6Hwn9H4QGsdbWp6IuA6FDxG5MqdSYfUM2PQuFOeabQFN4OrfQvcxEBBqaXki4noUPkTk8hxYa3at7Py8fMXYJu3Mqxwdf6O5OETkghQ+RKTynA4zbKz6l7lM/RktrzdDR8sBGs8hIpek8CEil1aUA5vmwurXIXO/2Wb3ho7DofcD5iRgIiKVpPAhIheWdQjWzIAN70BRltnmFwI9xkCPcRAYZm19IuKWFD5E5OcObzbHc2yfD85Ssy20FfR6wLx7xdvf0vJExL0pfIiIyemEPV/Dytdg//Ly9pi+5niO+EHg4WFdfSJSZyh8iNR3xfmw5X1z+vMTe802D09I+JU5niOiq7X1iUido/AhUl/lHIW1b8L6WVBwymzzCYbuo+Dq+yG4uaXliUjdpfAhUt8c3W7eKrv1f+WryjaMNsdzdL0HfBpYW5+I1HkKHyL1gWHAvu/N8RzJi8vbo3qa4znaDgYPu3X1iUi9ovAhUpeVFJpXOFb9C47tNNtsHtBuqLnIW1QPa+sTkXpJ4UOkLso7DuvfNsd05B0z27wbwFX3Qs/7oVGMpeWJSP2m8CFSlxxLgtX/gi0fQGmh2RbUHHr+zgwefg0tLU9EBBQ+RNyfYUDqMrNrJWlReXt4F+jze2g/DOxelpUnInIuhQ8Rd1VabM5Auuo1OPLT6UYbtLnFHEQa3UeLvImIS1L4EHE3BadgwxxY8wbkpJttnn7Q9W7o+X/QuJWl5YmIXIrCh4i7OJkMq2eYq8uW5JltDcLg6t9C99HgH2JtfSIilaTwIeLKDAMOrIGVr8KuhYBhtjdNgD4PQYfbwNPH0hJFRKpK4UPEFTlKYecCcxDpofXl7a0GmuM54vprPIeIuC2FDxFXUpgNm941u1ey0sw2uw90vt2c/rxpO2vrExGpBgofIq4g8wCsmQEb/wNF2Wabfyj0GAc9xkCDptbWJyJSjRQ+RKx0aIPZtbL9UzAcZlvj1mbXSqfbwcvP0vJERGqCwodIbXM6YPdX5vwcaavK22Ovhd6/h1Y3gIeHdfWJiNQwhQ+R2lKcB5vnwep/m7fNAnh4Qcdfm+M5wjtZW5+ISC1R+BCpadnp5gJv69+GwkyzzTfYnJvj6t9CUISl5YmI1LYqXdstLS3l2WefJTY2Fj8/P+Li4pg8eTJOpxOAkpISnnzySTp27EhAQAARERHce++9HD58uEaKF3FpR7bC/N/Byx1h+Utm8GgUC7f8DR7dATc8r+AhIvVSla58TJ8+nRkzZvDOO++QkJDA+vXrue+++wgODuaRRx4hPz+fjRs3MmHCBDp37sypU6cYP348Q4cOZf369Zd+ARF353TC3u/M8RwpS8vbW/QxB5G2uRk87NbVJyLiAmyGYRiV3Xnw4MGEhYUxa9assrbbbrsNf39/3n333fM+Zt26dVx99dXs37+fFi1aXPI1srOzCQ4OJisri6CgoMqWJmKtkgL46UNY9W84vttss9nNFWV7PwSR3aytT0TkLEWlDnw8q/cPoap8flfpykdiYiIzZswgKSmJ1q1bs2XLFpYvX87LL798wcdkZWVhs9lo2LDhebcXFRVRVFRUoXgRt5F7DNbNNL/yj5tt3oHQbST0vB8aXjpwi4jUlgMn8/nzFzvwsNmYMcK6P4qqFD6efPJJsrKyaNu2LXa7HYfDwQsvvMCdd9553v0LCwt56qmnuOuuuy6YgqZOncqkSZOqXrmIlTJ2wep/wZYPwXE6PAdHQa//g64jwFdX7UTEdRSWOHhjaTL/XrKXolIndg8bKcfziG0cYEk9VQofH374IXPnzmXevHkkJCSwefNmxo8fT0REBCNHjqywb0lJCXfccQdOp5N///vfF3zOp59+mscee6zs++zsbKKioqp4GCK1wDAgeYk5Kdjeb8vbm3czu1baDQW7biATEdfy/c6jTPp8B2kn8wHoFRfC5GEdLAseUMXw8fjjj/PUU09xxx13ANCxY0f279/P1KlTK4SPkpIShg8fTkpKCj/88MNF+358fHzw8dGqnOLCSoth20dm6Di67XSjDdreCn1+D1E9tcibiLic/SfymPT5Dn7YlQFAsyBf/nRrOwZ3Csdm8e+sKoWP/Px8PM6ZedFut5fdagvlwWPPnj0sXryY0NDQ6qlUpLblnzTn5lj7FuQeMdu8/KHrPWb3SkictfWJiJxHQbGD15fsZcaPyRSXOvGy2xidGMvD18cT4OMaV2erVMWQIUN44YUXaNGiBQkJCWzatImXXnqJ0aNHA+Y8IL/+9a/ZuHEjX3zxBQ6HgyNHzF/aISEheHt7V/8RiFS3E/vMWUg3z4MS8zIlgeHmANJuo8CvkaXliYicj2EYfL39KH/+YgeHMgsA6BvfmIlDEmjVtIHF1VVUpVttc3JymDBhAvPnzycjI4OIiAjuvPNOnnvuOby9vUlNTSU2Nva8j128eDH9+/e/5GvoVluxhGHA/pVm18ruL4HTp0WzjuZ6Kwm/BE+FZxFxTcnHcpm4YDvL9ph33TVv6MeEwe0YlNCs1rpYqvL5XaXwURsUPqRWOUpgx2fmpGCHN5W3xw+CPg9BTF+N5xARl5VXVMpri/cyc1kyJQ4Db7sHv702jgeva4Wfd+1OaFhj83yI1BmFWbDhHVjzBmQfNNs8faHzHdDrQWjS2tr6REQuwjAMFm5N54WFO0nPKgTgujZNmDgkgRgL72KpLIUPqV9O7Yc1M2Djf6A412wLaAI9xkGPMRDQ2Nr6REQuYc/RHCYu2M7KfScAiArxY+LgBAa0a2r5XSyVpfAh9cPB9bDyVdi5AIzTd2c1aWuut9JxOHj5WlufiMgl5BSW8Mp3e5izMpVSp4GPpwf/178lv+vXEl8v91ozSuFD6i6nA3YtNMdzHFhT3h53nTkpWKsBGs8hIi7PMAw+23yYF7/cSUaOOaPywPZhPDe4PVEh/hZXd3kUPqTuKcqFTXPN22Uz95ttHl7Qabh5pSMswdr6REQqaWd6NhM/287a1JMAxIT6M3FoAte1aWpxZVdG4UPqjqxDsPYNWD8HirLMNr9G0H0MXD0OAptZWp6ISGVlFZTwj2+TeHf1fhxOA18vD35/fTxj+8ZW+2q0VlD4EPd3eLM5P8f2T8BZaraFtITeD0Dnu8DbPS9Likj943QafLzxINMX7eJ4bjEAt3Rsxp9ubU/zhn4WV1d9FD7EPTmdsOcbczxH6rLy9uhEs2ul9U1wzlIAIiKubNuhLJ77bBsb0zIBaNkkgElDO5AYX/fuwlP4EPdSnA8/fQCr/g0n9phtNjt0+BX0egCaX2VtfSIiVZSZX8zfvtnNe2vSMAzw97bzyIB47rsmFm/PuvlHlMKHuIeco7DuLVg3CwrMgVf4BEO3keaaK8GR1tYnIlJFTqfBh+sP8JdFuziVXwLA0M4RPHNLO5oF1+3b/xU+xLUd3WGO59j6X3CY/Z80bGFe5eh6D/gEWlufiMhl2Hwgk4mfbWPLQXNwfOuwBkwa2oHeLevHSvAKH+J6DAP2/WCO59j3Q3l75NXmeI62g8GuH10RcT8ncov469e7+XD9AQwDAn08GT+wNff2jsbLXje7WM5Hv8HFdZQWwdb/mVc6MnaYbTYPaDfEnBQs6mpr6xMRuUwOp8G8Nfv52zdJZBWYXSy/uqo5T93clqaBdbuL5XwUPsR6eSdg/duw9k3IyzDbvBtA1xHmeI6QWGvrExG5Ahv2n2TCp9vZkZ4NQPvwICYPS6B7TIjFlVlH4UOsc3yPeZVjy/tQaq7KSFBzM3BcNRL8GlpanojIlTiWU8S0r3bx8UZz5ewgX0/+OKgNd/eMxu5Rv5d2UPiQ2mUYkLrcHM+RtKi8Pbwz9P49JPwC7F6WlScicqVKHU7+s2o///g2iZwic+LD27tH8cRNbQht4GNxda5B4UNqh6MEts83Q0f6ltONNmhzszmINPoaLfImIm5vTfIJnvtsO7uP5gDQKTKYycM60CWqobWFuRiFD6lZBadgwzuw5g3IOWy2efpBl7vM22Ubt7K2PhGRanA0u5AXv9zJZ5vN33MN/b14YlBbbu8RVe+7WM5H4UNqxskUWDMDNr4LJXlmW4Mwc4G3bqMhoH7cyy4idVuJw8nsFSm88t0e8ood2Gxw19Ut+OONbWgU4G11eS5L4UOqV9oaWPUq7FoIhtNsa5pgdq10/DV4qr9TROqGFXuPM3HBdvZm5ALQJaohfx7WgY6RwRZX5voUPuTKOUph1+fmnSsH15W3t7rBDB1x12k8h4jUGYczC3hh4U4Wbk0HIDTAmydvbsuvr4rEQ10slaLwIZevKMfsVlnzOmSmmW12b+h0uxk6mraztj4RkWpUVOpg5rIUXvthLwUlDjxscG/vGB4d2JpgP92lVxUKH1J1mQdg7RvmQNIic9Ic/EOhx1jzq0FTa+sTEalmS3ZnMOnzHaQcN8ew9YhpxKShHWgfEWRxZe5J4UMq79BGs2tl+3wwHGZbaLx5laPzHeDlZ219IiLV7MDJfP78xQ6+2XEUgCaBPjxzS1t+0aU5NnUnXzaFD7k4p8OcDGzla5C2srw9pi/0+T20Ggge9WcxJBGpHwpLHLyxNJl/L9lLUakTu4eN+/rE8MgN8QT6qovlSil8yPkV58HmebD633Ay2Wzz8IQOv4beD5gzkoqI1EHf7TjK5C92kHYyH4DecaFMGpZA67BAiyurOxQ+pKKcI+YCb+vfNicIA/ANhu6j4erfQlCEtfWJiNSQ1ON5TP5iBz/sMhe4bBbky59ubcfgTuHqYqlmCh9iOrIVVv3bXNLeaS73TKMY6PWgORupTwNLyxMRqSkFxQ7+vWQvbyxNptjhxMtuY0xiHL+/vhUBPvqYrAn6X63PDAP2fmeut5K8pLw9qhf0eQja3AIedsvKExGpSYZh8PX2I/z5i50cyiwAoG98Y54fmkDLJvqDqyYpfNRHJYXw04fmeI5ju8w2mwe0Hwa9H4LI7tbWJyJSw/Ydy+X5BdtZtuc4AM0b+jFhcDsGJTRTF0stUPioT3KPwfpZsPYtyDdPOLwD4ap7oef90Cja2vpERGpYXlEpr/6wl1nLkylxGHjbPbi/XxwP9G+Fn7eu9NYWhY/64Nhuc36OLR+Ao8hsC4qEXr8zg4ev1iEQkbrNMAy++CmdFxbu5Eh2IQDXtWnCxCEJxDQOsLi6+kfho64yDEhZaoaOPd+Ut0dcZY7naDcM7Hr7RaTuSzqaw8TPtrMq+QQAUSF+TBycwA3twyyurP7Sp09dU1oM2z42Q8fRracbbdD2VnM8R4teWuRNROqFnMISXvluD3NWplLqNPDx9OCB/q24v18cvl7qYrGSwkddkX8SNsyGNW9C7hGzzcsfutwNvf4PQltaW5+ISC0xDINPNx/ixS93cSzH7Gq+sX0YEwa3JyrE3+LqBBQ+3N+JfbD6ddj8HpSYs/HRoBn0/C10uw/8Q6ytT0SkFu04nM3EBdtYl2pOkhgT6s/zQxPo30YLXroShQ93ZBiQtsrsWtm1EDDM9rCO5iJvHW4DT29LSxQRqU1ZBSX849sk/rMqFacBfl52Hrq+FWP7xuLjqS4WV6Pw4U4cpbDjUzN0HN5Y3h5/ozmeI/ZajecQkXrF6TT4aONBpn+1ixN5xQDc0rEZf7q1Pc0baqVtV6Xw4Q4Ks2Djf2DNG5B1wGyz+5jL2Pd+EJq0sbY+ERELbDuUxYTPtrEpLROAlk0CmDS0A4nxja0tTC5J4cOVZabB6hlm8CjOMdv8G8PV46D7GGjQxNr6REQskJlfzF+/3s28tWkYBgR423nkhnhG9YnF29PD6vKkEhQ+XNHBDbDqVdjxGRhOs61xG/MqR6fbwcvX2vpERCzgcBp8uO4Af/16F6fyzQUwh3WJ4Omb29EsWL8X3YnCh6twOszBo6v+BQdWl7fH9TfHc7QcAB5K9CJSP20+kMlzn23jp4NZALQJC2TSsAR6xYVaXJlcDoUPqxXlmrfJrv43nEo12zy8oONvzCsdzTpYWp6IiJVO5Bbxl0W7+XC9Od4t0MeTRwe2ZkTvaLzs+oPMXSl8WCX7sDmAdMNsc0ApgG9D6DEGrv4tBDaztDwRESs5nAbvrdnP377eTXZhKQC3XRXJkze3oWmguljcncJHbUvfYnatbPsYnOYJRUgc9HoAutwF3lrgSETqt/WpJ3nus+3sSM8GoH14EJOHJdA9RpMm1hUKH7XB6TQXd1v1GqQuK2+PvsbsWml9s8ZziEi9dyyniKlf7eSTjYcACPL15PFBbbirZzR2D81hVJcofNSkkgLY8j6s+jec2GO22eyQ8EszdDS/ytr6RERcQKnDyX9W7ecf3yaRU2ReEb69exRP3NSG0AY+FlcnNUHhoybkZsDat2D9LMg3l3DGJwi6jYSev4PgSGvrExFxEauTTzDxs+3sPmrOZdQpMpjJwzrQJaqhtYVJjVL4qE4ZO82ulZ/+Cw5zml+CW5iryl41AnwCra1PRMRFHM0u5IWFO1mw5TAAjfy9eOKmtgzvHqUulnpA4eNKGQYkL4aVr8G+78vbm3eHPg9B2yFg13+ziAhAcamT2StS+Of3e8grdmCzwd09W/CHgW1oFKAFMesLfSpertIi2Po/886VjB1mm80D2g42JwVr0dPa+kREXMzyPceZuGAb+47lAdC1RUP+PKwDHZoHW1yZ1DaFj6rKPwnrZsHaNyEvw2zzCjC7VXr+DkJira1PRMTFHM4sYMrCHXy59QgAoQHePHlzW359VSQe6mKplxQ+Kuv4Xlj9L9j8PpQWmG2BEdDzfug2CvwaWlmdiIjLKSp1MHNZCq/9sJeCEgceNri3dwyPDmxNsJ+X1eWJhRQ+LsYwYP8KczxH0iLAMNubdYI+vzdvmbXrBBIROdeS3RlM+nwHKcfNLpYeMY2YPKwD7cKDLK5MXIHCx/k4SmD7p+bKsulbyttb32zOzxGTCDZdKhQROdeBk/lM/mIH3+44CkCTQB/+dEs7hnWJwKbfm3KawsfZCjJh4zvmmivZ5gx7ePqa0573egAax1tanoiIqyoscTBj6T5eX7KPolIndg8b9/WJ4ZEb4gn01RViqahKc3qXlpby7LPPEhsbi5+fH3FxcUyePBmn01m2j2EYPP/880RERODn50f//v3Zvn17tRderU6lwldPwkvt4dvnzOAR0BSuexYe3QGD/6HgISJyAd/tOMrAfyzl5e/2UFTqpHdcKF890pdnB7dX8JDzqtKVj+nTpzNjxgzeeecdEhISWL9+Pffddx/BwcE88sgjAPzlL3/hpZdeYs6cObRu3ZopU6YwcOBAdu/eTWCgi02ydWAtrHwVdn0BxukA1bS92bXS4dfgpZUTRUQuJPV4HpO/2MEPu8w7/5oF+fLs4Hbc2jFcXSxyUTbDMIzK7jx48GDCwsKYNWtWWdttt92Gv78/7777LoZhEBERwfjx43nyyScBKCoqIiwsjOnTp3P//fdf8jWys7MJDg4mKyuLoKAaGJjkKDXDxqrX4OC68vaWA8zQ0fJ6jecQEbmIgmIH/1q8lzd/TKbY4cTLbmNMYhy/v74VAT7qza+vqvL5XaWfksTERGbMmEFSUhKtW7dmy5YtLF++nJdffhmAlJQUjhw5wo033lj2GB8fH/r168fKlSvPGz6KioooKiqqUHyNKMqFTe/C6n9DZprZZveGTsOh14MQ1r5mXldEpI4wDIOvtx/hz1/s5FCmOeVA3/jGPD80gZZNGlhcnbiTKoWPJ598kqysLNq2bYvdbsfhcPDCCy9w5513AnDkiDmBTFhYWIXHhYWFsX///vM+59SpU5k0adLl1F41ecfg62fM7hW/EOgx1vwKDLv0Y0VE6rl9x3J5fsF2lu05DkDzhn5MGNyOQQnN1MUiVVal8PHhhx8yd+5c5s2bR0JCAps3b2b8+PFEREQwcuTIsv3O/UE0DOOCP5xPP/00jz32WNn32dnZREVFVaWsygmJNbtVQuKg0x3g7V/9ryEiUsfkFZXy6g97mbU8mRKHgbfdg/v7xfFA/1b4edutLk/cVJXCx+OPP85TTz3FHXfcAUDHjh3Zv38/U6dOZeTIkTRr1gwwr4CEh4eXPS4jI+NnV0PO8PHxwcfH53Lrr5obp9TO64iIuDnDMPjip3ReWLiTI9mFAFzftinPDW5PTOMAi6sTd1el8JGfn4+HR8W7c+12e9mttrGxsTRr1oxvv/2Wrl27AlBcXMzSpUuZPn16NZUsIiI1KeloDhM/286q5BMARIX4MXFwAje0Vze1VI8qhY8hQ4bwwgsv0KJFCxISEti0aRMvvfQSo0ePBszulvHjx/Piiy8SHx9PfHw8L774Iv7+/tx11101cgAiIlI9cgpLePm7PcxZmYrDaeDj6cED/Vtxf784fL3UxSLVp0rh49VXX2XChAk88MADZGRkEBERwf33389zzz1Xts8TTzxBQUEBDzzwAKdOnaJnz5588803rjfHh4iIAGYXy6ebD/Hil7s4lmPefXhj+zAmDG5PVIjGx0n1q9I8H7Whxuf5EBGRMjsOZzNxwTbWpZ4CILZxABOHtKd/m6YWVybupsbm+RARkbohq6CEf3ybxH9WpeI0wM/LzkPXt2Js31h8PNXFIjVL4UNEpB5xOg0+2niQ6V/t4kReMQC3dgznmVvb0byhn8XVSX2h8CEiUk9sPZjFcwu2sSktE4CWTQKYNLQDifGNrS1M6h2FDxGROu5UXjF/+2Y389amYRgQ4G3nkRviGdUnFm/PKi1uLlItFD5EROooh9Pgw3UH+MvXu8jMLwFgWJcInrmlHWFBWrVbrKPwISJSB21KO8XEBdv56WAWAG3CApk0LIFecaEWVyai8CEiUqecyC1i+qJd/Hf9QQACfTx5dGBr7u0djaddXSziGhQ+RETqgFKHk3lr0/jb17vJLiwF4LarInnq5rY0Cayl9bNEKknhQ0TEza1PPcmEz7azMz0bgPbhQfz5Fwl0iw6xuDKR81P4EBFxUxk5hUz7ahefbDwEQJCvJ48PasNdPaOxe9gsrk7kwhQ+RETcTInDyX9W7eflb5PIKSrFZoPbu0fx+KA2hDZQF4u4PoUPERE3sjr5BBM/287uozkAdIoMZvKwDnSJamhtYSJVoPAhIuIGjmQV8uKXO1mw5TAAjfy9eOKmtgzvHqUuFnE7Ch8iIi6suNTJ7BUp/PP7PeQVO7DZ4O6eLfjjjW1o6O9tdXkil0XhQ0TERS3fc5yJC7ax71geAF1bNOTPwzrQoXmwxZWJXBmFDxERF3Mos4ApX+zgq21HAAgN8Oapm9ty21WReKiLReoAhQ8RERdRVOpg5rIUXvthLwUlDjxscG/vGB4d2JpgPy+ryxOpNgofIiIuYPHuDCYt2E7qiXwAro4JYdKwBNqFB1lcmUj1U/gQEbHQgZP5TP5iB9/uOApAk0Af/nRLO4Z1icBmUxeL1E0KHyIiFigscTBj6T5eX7KPolInnh427rsmhocHxBPoqy4WqdsUPkREapFhGHy3M4PJX2znwMkCAHrHhTJ5WALxYYEWVydSOxQ+RERqSerxPCZ9vp3Fu48B0CzIl2cHt+PWjuHqYpF6ReFDRKSGFRQ7+Nfivbz5YzLFDidedhtj+8bx0HWtCPDRr2Gpf/RTLyJSQwzDYNG2I0xZuJNDmWYXS9/4xjw/NIGWTRpYXJ2IdRQ+RERqwN6MXCZ9vp1le44D0LyhHxMGt2dQQpi6WKTeU/gQEalGuUWlvPrDHt5enkKJw8Db7sH9/eJ4oH8r/LztVpcn4hIUPkREqoFhGHz+UzovLNzB0ewiAK5v25TnBrcnpnGAxdWJuBaFDxGRK5R0NIeJn21nVfIJAFqE+DNxSHsGtAuzuDIR16TwISJymXIKS3j5uz3MWZmKw2ng4+nBg9e14rfXxuHrpS4WkQtR+BARqSLDMJi/6RAvfrmL47lmF8uN7cOYMLg9USH+Flcn4voUPkREqmDH4WwmLtjGutRTAMQ2DmDikPb0b9PU4spE3IfCh4hIJWQVlPDSN7t5d/V+nAb4edn5/YBWjEmMxcdTXSwiVaHwISJyEU6nwUcbDjJ90S5O5BUDcGuncP50SzsiGvpZXJ2Ie1L4EBG5gK0Hs5jw2TY2H8gEoFXTBkwamsA1rRpbW5iIm1P4EBE5x6m8Yv76zW7eX5uGYUCAt51HbohnVJ9YvD09rC5PxO0pfIiInOZwGnywLo2/fr2bzPwSAIZ1ieCZW9oRFuRrcXUidYfCh4gIsDHtFBM/287WQ1kAtG0WyKShCfSMC7W4MpG6R+FDROq1E7lFTF+0i/+uPwhAoI8nj93YmhG9ovG0q4tFpCYofIhIvVTqcPLemjT+/s1usgtLAbjtqkieurktTQJ9LK5OpG5T+BCRemd96kkmfLadnenZACREBDF5WALdokMsrkykflD4EJF6IyOnkGlf7uKTTYcACPL15PFBbbirZzR2D5vF1YnUHwofIlLnlTicvLMylZe/20NuUSk2G9zRI4o/3tiG0AbqYhGpbQofIlKnrdp3gokLtpF0NBeAzpHBTBrWgS5RDa0tTKQeU/gQkTrpSFYhL3y5k8+3HAagkb8XT9zUltu7R+GhLhYRSyl8iEidUlzq5O0VKfzz+z3kFzuw2eDuni34441taOjvbXV5IoLCh4jUIcv2HGPigu0kH8sD4KoWDZk8rAMdmgdbXJmInE3hQ0Tc3qHMAqZ8sYOvth0BoHEDb568qS23XRWpLhYRF6TwISJuq6jUwVs/JvPa4r0UljjxsMG9vWN4dGBrgv28rC5PRC5A4UNE3NLi3RlMWrCd1BP5AFwdE8KkYQm0Cw+yuDIRuRSFDxFxKwdO5jPp8x18t/MoAE0CffjTLe0Y1iUCm01dLCLuQOFDRNxCYYmD15fsY8bSfRSVOvH0sHHfNTE8PCCeQF91sYi4E4UPEXFphmHw3c4MJn+xnQMnCwDo0zKUSUMTiA8LtLg6EbkcCh8i4rJSjucx6fPtLNl9DIBmQb48O7gdt3YMVxeLiBtT+BARl5NfXMq/Fu/lrR9TKHY48bLbGNs3joeua0WAj35tibg7ncUi4jIMw2DRtiP8+YsdHM4qBKBvfGOeH5pAyyYNLK5ORKqLwoeIuIS9Gbk8v2A7y/ceB6B5Qz8mDG7PoIQwdbGI1DEKHyJiqdyiUl79fg+zlqdQ6jTw9vTgd9fG8X/9W+Hnbbe6PBGpAR5V2TkmJgabzfazrwcffBCA3NxcHnroISIjI/Hz86Ndu3a8/vrrNVK4iLg3wzD4bPMhBvx9CW/8mEyp02BA26Z8++i1PHZjGwUPkTqsSlc+1q1bh8PhKPt+27ZtDBw4kN/85jcAPProoyxevJi5c+cSExPDN998wwMPPEBERATDhg2r3spFxG3tPpLDxAXbWJ18EoAWIf5MHNKeAe3CLK5MRGpDlcJHkyZNKnw/bdo0WrZsSb9+/QBYtWoVI0eOpH///gD89re/5Y033mD9+vUKHyLCqbxiXlu8lzkrU3E4DXw8PXjwulb89to4fL10pUOkvrjsMR/FxcXMnTuXxx57rGwwWGJiIgsWLGD06NFERESwZMkSkpKSeOWVVy74PEVFRRQVFZV9n52dfbkliYiLSj6Wy9srUvhow0EKS5wADEoI49lb2xMV4m9xdSJS2y47fHz66adkZmYyatSosrZ//vOfjBs3jsjISDw9PfHw8GDmzJkkJiZe8HmmTp3KpEmTLrcMEXFRhmGwJuUkM5el8P2uoxiG2d4+PIgnbmpD/zZNrS1QRCxjM4wzvxKqZtCgQXh7e/P555+Xtf3tb3/jrbfe4m9/+xvR0dH8+OOPPP3008yfP58bbrjhvM9zvisfUVFRZGVlERSk1SlF3E2Jw8mXW9OZuSyFrYeyytoHtG3K2L5x9IoL0a2zInVQdnY2wcHBlfr8vqzwsX//fuLi4vjkk0/KxnIUFBQQHBzM/PnzufXWW8v2HTt2LAcPHmTRokXVXryIuI6sghI+WJvGnJWppJ+eIMzH04PbukUyJjFWk4SJ1HFV+fy+rG6X2bNn07Rp0woho6SkhJKSEjw8Kt69a7fbcTqdl/MyIuIGDpzM5+0VKfx33QHyis274Ro38Obe3jHc0yuakABviysUEVdT5fDhdDqZPXs2I0eOxNOz/OFBQUH069ePxx9/HD8/P6Kjo1m6dCn/+c9/eOmll6q1aBGx3ob9p5i1PJlF247gPH39tHVYA8YmxjG0S4TuXhGRC6py+Pjuu+9IS0tj9OjRP9v2wQcf8PTTT3P33Xdz8uRJoqOjeeGFF/jd735XLcWKiLUcToOvtx9h5rJkNqZllrX3jW/M2L5xXBvfWOM5ROSSLnvAaU3RmA8R15NbVMp/1x1g9soUDpwsAMDb7sGwLhGM6RtL22Y6V0Xquxof8yEi9UN6VgFzVqQyb20aOYWlADTy9+KeXtGM6B1N00BfiysUEXek8CEiP7PtUBZvLUtm4U/plJ4e0BHXOIDRibHcdlWk1l0RkSui8CEiADidBt/vymDmsmTWpJwsa+8VF8LYxDiub9sUDw+N5xCRK6fwIVLPFRQ7+GjjQd5enkLK8TwAPD1sDO4UzpjEODpGBltcoYjUNQofIvVURk4h/1m5n7lr9pOZXwJAoK8nd/Vswag+MYQH+1lcoYjUVQofIvXMriPZzFyWwoLNhyl2mBMARoX4MfqaWH7TPYoGPvq1ICI1S79lROoBwzD4cc9xZi5LZtme42XtV7VoyLi+cdyY0Ay7xnOISC1R+BCpwwpLHHy2+RCzlqeQdDQXAA8b3NwhnNGJsXSLbmRxhSJSHyl8iNRBJ3KLmLs6jXdXp3I8txiAAG87t/dowX3XxBAV4m9xhSJSnyl8iNQhezNymbU8hU82HqSo1BzPER7sy33XxHB7jxYE+3lZXKGIiMKHiNszDINVySeYuSyFH3ZllLV3bB7M2L6x3NIxHC+7x0WeQUSkdil8iLip4lInC7ceZuayFLYfzgbAZoMBbcMY1zeWq2NDtMibiLgkhQ8RN5OVX8J7a/fzzspUjmYXAeDr5cFvukVx3zUxxDVpYHGFIiIXp/Ah4ib2n8jj7eUp/Hf9QQpKHAA0CfRhVJ8Y7rq6BY0CvC2uUESkchQ+RFyYYRhs2H+Kt5Yl882OoxjmGm+0bRbI2L5xDOkcjo+nFnkTEfei8CHigkodThZtP8Jby1LYciCzrL1/myaMTYzjmlahGs8hIm5L4UPEheQUlvDhugPMXpHKocwCALw9Pfhll+aM6RtL67BAiysUEblyCh8iLuBQZgFzVqTwwdoD5BSVAhAS4M2IXtHc0yuaJoE+FlcoIlJ9FD5ELLTlQCYzl6fw5dZ0HE5zQEfLJgGM7RvHL7s2x9dL4zlEpO5R+BCpZQ6nwXc7jzJzWTLrUk+VtfdpGcq4vnH0a90EDy3yJiJ1mMKHSC3JLy7low0HeXt5Cqkn8gHwstsY0jmCMYmxJEQEW1yhiEjtUPgQqWFHswt5Z2Uq761JI6ugBIAgX0/u7hXNyN4xNAv2tbhCEZHapfAhUkN2HM5m5vJkPt9ymBKHOZ4jOtSfMYmx3HZVJAE+Ov1EpH7Sbz+RauR0GixNOsbM5cms2HuirL1HTCPG9o3jhnZh2DWeQ0TqOYUPkWpQWOJg/qZDzFqewt6MXADsHjZu7tCMsX3j6BLV0NoCRURciMKHyBU4nlvEu6v2M3f1fk7kFQPQwMeTO3pEMeqaGCIb+VtcoYiI61H4ELkMe47mMGt5Cp9sOkRxqROA5g39uO+aGG7vEUWgr5fFFYqIuC6FD5FKMgyDFXtPMHN5Mkt2Hytr7xzVkHF9Y7kpoRmedg8LKxQRcQ8KHyKXUFTq4PMt6cxclsyuIzkA2GxwY/swxvWNo1t0Iy3yJiJSBQofIheQmV/Me2vSeGdlKhk5RQD4edkZ3j2S0YmxRIcGWFyhiIh7UvgQOUfK8TzeXp7CRxsOUlDiACAsyIeRfWK4++pogv01nkNE5EoofIhgjudYm3KSmctT+G7nUQxzTjDahwcxtm8sgztF4O2p8RwiItVB4UPqtRKHky+3pjNreQo/Hcwqa7++bVPGJsbSu2WoxnOIiFQzhQ+pl7ILS/hgbRpzVqRyOKsQAB9PD351VSRjEmNo1TTQ4gpFROouhQ+pVw6czGf2ilQ+XJdGXrE5nqNxA29G9Irhnl4tCG3gY3GFIiJ1n8KH1Asb004xa1kKX21Lx3l6PEd80waM7RvLsC7N8fWyW1ugiEg9ovAhdZbDafDN9iPMXJ7Chv2nytr7xjdmTGIs/Vo30XgOERELKHxInZNXVMp/1x/g7RUpHDhZAICX3cawLs0ZkxhLu/AgiysUEanfFD6kzkjPKmDOylTmrUkjp7AUgIb+XtzTM5p7e0fTNMjX4gpFRAQUPqQO2HYoi5nLkvnip3RKTw/oiG0cwOjEWH59VSR+3hrPISLiShQ+xC05nQY/7Mpg5vJkViefLGvvGRvC2L5xDGjbFA8PjecQEXFFCh/iVgqKHXy88SBvL08h+XgeAHYPG4M7hTMmMZZOkQ2tLVBERC5J4UPcQkZOIe+u2s/c1fs5lV8CQKCvJ3dd3YKRfWKIaOhncYUiIlJZCh/i0nYfyWHmsmQ+23yYYocTgMhGfoy+JpbhPaJo4KMfYRERd6Pf3OJyDMNg2Z7jvLUsmWV7jpe1d23RkHF947ixfRiedi3yJiLirhQ+xGUUlTr4bPNhZi1LYffRHAA8bHBTh2aMSYyjW3QjiysUEZHqoPAhljuZV8x7q/fzzqr9HM8tAiDA287wHlHc1yeWFqH+FlcoIiLVSeFDLLPvWC6zlqfw8YaDFJWa4znCg30Z1SeGO65uQbCfl8UViohITVD4kFplGAark08yc1ky3+/KKGvv0DyIcX3juKVjOF4azyEiUqcpfEitKHE4WfhTOjOXJ7PtUDYANhsMaBvG2L6x9IwN0SJvIiL1hMKH1Kis/BLeX5fGnBWpHMkuBMDXy4Nfd4tk9DWxxDVpYHGFIiJS2xQ+pEakncjn7RUp/Hf9AfKLHQA0buDDqD7R3NUzmpAAb4srFBERqyh8SLXasP8kb/2Ywjc7jnB6jTfaNgtkTGIsQ7tE4OOpRd5EROo7hQ+5YqUOJ19vP8rM5clsSsssa+/Xuglj+8aS2KqxxnOIiEgZhQ+5bDmFJfx3/UFmr0jh4KkCALztHvyya3PG9I2ldVigxRWKiIgrUviQKjuUWcCcFSl8sPYAOUWlAIQEeHNPr2hG9IqmSaCPxRWKiIgrq9KECjExMdhstp99Pfjgg2X77Ny5k6FDhxIcHExgYCC9evUiLS2t2guX2vfTwUwefn8T1/5lMW8tSyGnqJS4JgG8+MuOrHzqeh4b2FrBQ0RELqlKVz7WrVuHw+Eo+37btm0MHDiQ3/zmNwDs27ePxMRExowZw6RJkwgODmbnzp34+vpWb9VSaxxOg+93HmXmshTWpp4sa+8dF8q4a2Pp37opHh4azyEiIpVnMwzDuNwHjx8/ni+++II9e/Zgs9m444478PLy4t13373sgrKzswkODiYrK4ugoKDLfh65MvnFpXy84SCzlqeQeiIfAE8PG0M6RzAmMZYOzYMtrlBERFxJVT6/L3vMR3FxMXPnzuWxxx7DZrPhdDpZuHAhTzzxBIMGDWLTpk3Exsby9NNP84tf/OKCz1NUVERRUVGF4sU6GdmFvLMqlffWpJGZXwJAkK8nd/WMZlSfGJoF6yqWiIhcmcsOH59++imZmZmMGjUKgIyMDHJzc5k2bRpTpkxh+vTpLFq0iF/96lcsXryYfv36nfd5pk6dyqRJky63DKkmOw5nM2t5Cgu2HKLEYV4MaxHiz5jEWH7dLZIAH41NFhGR6nHZ3S6DBg3C29ubzz//HIDDhw/TvHlz7rzzTubNm1e239ChQwkICOD9998/7/Oc78pHVFSUul1qgdNpsHTPMWYtS2H53uNl7d2jGzG2bxwD24dh13gOERGphBrvdtm/fz/fffcdn3zySVlb48aN8fT0pH379hX2bdeuHcuXL7/gc/n4+ODjozskalNhiYNPNx1i5vIU9mbkAuBhg5s7hjM2MZauLRpZXKGIiNRllxU+Zs+eTdOmTbn11lvL2ry9venRowe7d++usG9SUhLR0dFXVqVUi+O5RcxdvZ93V+3nRF4xAA18PLm9RxSj+sQQFeJvcYUiIlIfVDl8OJ1OZs+ezciRI/H0rPjwxx9/nNtvv51rr72W6667jkWLFvH555+zZMmS6qpXLsPejBxmLU/h442HKC51AhAR7Mt918Ry+9VRBPl6WVyhiIjUJ1UOH9999x1paWmMHj36Z9t++ctfMmPGDKZOncrDDz9MmzZt+Pjjj0lMTKyWYqXyDMNg5b4TzFyWzOLdx8raO0cGM7ZvHDd3aIanvUpzzImIiFSLK5rnoyZono8rU1zq5PMth5m5PIWd6eZtyzYbDGwXxrhr4+ge3UiLvImISLWrlXk+xLVk5hfz3po03lmZSkaOefeQn5ed33SPZPQ1scQ0DrC4QhEREZPCh5tLPZ7H2ytS+N/6gxSUmFPfNw30YWSfGO7u2YKG/t4WVygiIlKRwocbMgyDdamnmLksmW93HuVMx1m78CDGJsYypHME3p4azyEiIq5J4cONlDqcfLntCLOWJbPlYFZZ+3VtmjCubxy9W4ZqPIeIiLg8hQ83kF1YwodrDzBnZSqHMgsA8Pb04LarmjMmMZZWTQMtrlBERKTyFD5c2MFT+cxekcqH6w6QW1QKQGiANyN6R3NPr2gaN9DMsCIi4n4UPlzQ5gOZvLUsma+2puM8PZ6jVdMGjE2M5Rddm+PrZbe2QBERkSug8OEiHE6Db3ccYeayFNbvP1XWntiqMWP6xtIvvgkeWuRNRETqAIUPi+UVlfK/9Qd4e0UqaSfzAfCy2xjauTlj+8bSLlwTrYmISN2i8GGRI1mFzFmZyrw1+8kuNMdzBPt5cU+vFozsHUPTIF+LKxQREakZCh+1bNuhLGYtT+HzLYcpPT2gIybUnzGJsdzWLRJ/b70lIiJSt+mTrhY4nQaLd2cwc1kKq5JPlLVfHRvC2MRYBrQLw67xHCIiUk8ofNSgwhIHH288yKzlKSQfywPA7mHj1o7hjO0bS6fIhtYWKCIiYgGFjxpwLKeId1elMndNGifzigEI9PHkzp4tGNUnhoiGfhZXKCIiYh2Fj2qUdDSHmcuS+XTTYYodTgCaN/RjdGIst/eIooGP/rtFRET0aXiFDMNg+d7jvLUshR+TjpW1d4lqyLi+cQxKCMPTrkXeREREzlD4uExFpQ4+23yYWctS2H00BwAPGwxKaMbYvrF0iw6xuEIRERHXpPBRRafyipm7ej/vrNrP8dwiAPy97QzvHsXoa2JpEepvcYUiIiKuTeGjkpKP5TJreQofbzxIYYk5nqNZkC+jronhzh4tCPb3srhCERER96DwcRGGYbAm5SQzlyXz/a4MjNOLvCVEBDGubxy3dgrHS+M5REREqkTh4zxKHE6+3JrOW8uS2XYou6z9hnZNGZMYR6+4EGw2TQomIiJyORQ+zpJVUMIHa9OYszKV9KxCAHw8Pfh1t0hGJ8bSskkDiysUERFxfwofwIGT+cxansJ/1x8gv9gBQOMGPozsHc3dvaIJCfC2uEIREZG6o16Hjw37TzFzWTJfbz/C6TXeaBMWyJi+sQztHIGvl93aAkVEROqgehc+Sh1OvtlxlLeWJbMpLbOs/drWTRibGEvf+MYazyEiIlKD6k34yCsq5cN1B3h7RQoHTxUA4G334BddIxiTGEebZoEWVygiIlI/1JvwcSK3mCkLd+A0oJG/FyN6RXNP72iaBvpaXZqIiEi9Um/CR4tQf8b2jSM61J9fdY3Ez1vjOURERKxQb8IHwDO3tLO6BBERkXpP03OKiIhIrVL4EBERkVql8CEiIiK1SuFDREREapXCh4iIiNQqhQ8RERGpVQofIiIiUqsUPkRERKRWKXyIiIhIrVL4EBERkVql8CEiIiK1SuFDREREapXCh4iIiNQql1vV1jAMALKzsy2uRERERCrrzOf2mc/xi3G58JGTkwNAVFSUxZWIiIhIVeXk5BAcHHzRfWxGZSJKLXI6nRw+fJjAwEBsNlu1Pnd2djZRUVEcOHCAoKCgan1uV1DXjw/q/jHq+NxfXT/Gun58UPePsaaOzzAMcnJyiIiIwMPj4qM6XO7Kh4eHB5GRkTX6GkFBQXXyB+qMun58UPePUcfn/ur6Mdb144O6f4w1cXyXuuJxhgacioiISK1S+BAREZFaVa/Ch4+PDxMnTsTHx8fqUmpEXT8+qPvHqONzf3X9GOv68UHdP0ZXOD6XG3AqIiIidVu9uvIhIiIi1lP4EBERkVql8CEiIiK1SuFDREREapXbho/XX3+dTp06lU2S0rt3b7766quLPmbp0qV069YNX19f4uLimDFjxs/2+fjjj2nfvj0+Pj60b9+e+fPn19QhXFRVj++TTz5h4MCBNGnSpGz/r7/+usI+c+bMwWaz/eyrsLCwpg/nZ6p6fEuWLDlv7bt27aqwn6u8f1D1Yxw1atR5jzEhIaFsH1d6D881depUbDYb48ePv+h+7nQenq0yx+du5+HZKnN87ngenq0yx+hO5+Hzzz//sxqaNWt20ce4yvnntuEjMjKSadOmsX79etavX8/111/PsGHD2L59+3n3T0lJ4ZZbbqFv375s2rSJZ555hocffpiPP/64bJ9Vq1Zx++23M2LECLZs2cKIESMYPnw4a9asqa3DKlPV4/vxxx8ZOHAgX375JRs2bOC6665jyJAhbNq0qcJ+QUFBpKenV/jy9fWtjUOqoKrHd8bu3bsr1B4fH1+2zZXeP6j6Mb7yyisVju3AgQOEhITwm9/8psJ+rvIenm3dunW8+eabdOrU6aL7udt5eEZlj8/dzsMzKnt8Z7jTeXhGZY/R3c7DhISECjVs3br1gvu61Pln1CGNGjUyZs6ced5tTzzxhNG2bdsKbffff7/Rq1evsu+HDx9u3HTTTRX2GTRokHHHHXdUf7GX4WLHdz7t27c3Jk2aVPb97NmzjeDg4BqorHpc7PgWL15sAMapU6cu+HhXf/8Mo2rv4fz58w2bzWakpqaWtbnie5iTk2PEx8cb3377rdGvXz/jkUceueC+7ngeVuX4zsfVz8OqHJ+7nodX8h668nk4ceJEo3PnzpXe35XOP7e98nE2h8PBBx98QF5eHr179z7vPqtWreLGG2+s0DZo0CDWr19PSUnJRfdZuXJlzRReSZU5vnM5nU5ycnIICQmp0J6bm0t0dDSRkZEMHjz4Z3+RWaEqx9e1a1fCw8MZMGAAixcvrrDNVd8/uLz3cNasWdxwww1ER0dXaHe19/DBBx/k1ltv5YYbbrjkvu54Hlbl+M7lDufh5Ryfu52HV/Ieuvp5uGfPHiIiIoiNjeWOO+4gOTn5gvu60vnncgvLVcXWrVvp3bs3hYWFNGjQgPnz59O+ffvz7nvkyBHCwsIqtIWFhVFaWsrx48cJDw+/4D5HjhypsWO4mKoc37n+/ve/k5eXx/Dhw8va2rZty5w5c+jYsSPZ2dm88sorXHPNNWzZsqXCZdPaUpXjCw8P580336Rbt24UFRXx7rvvMmDAAJYsWcK1114LXPg9tur9g8t/D9PT0/nqq6+YN29ehXZXew8/+OADNmzYwPr16yu1v7udh1U9vnO5+nlY1eNzx/PwSt5DVz8Pe/bsyX/+8x9at27N0aNHmTJlCn369GH79u2Ehob+bH+XOv+q9TpKLSsqKjL27NljrFu3znjqqaeMxo0bG9u3bz/vvvHx8caLL75YoW358uUGYKSnpxuGYRheXl7GvHnzKuwzd+5cw8fHp2YO4BKqcnxnmzdvnuHv7298++23F93P4XAYnTt3Nn7/+99XV8lVcrnHd8bgwYONIUOGlH3vau+fYVz+Mb744otGaGioUVRUdNH9rHwP09LSjKZNmxqbN28ua7vUJW13Og8v5/jO5urn4ZUe3xmufB5e6TG6w3l4ttzcXCMsLMz4+9//ft7trnT+uXW3i7e3N61ataJ79+5MnTqVzp0788orr5x332bNmv0suWVkZODp6VmWEC+0z7kpsLZU5fjO+PDDDxkzZgz//e9/L3mJ0cPDgx49erBnz57qLLvSLuf4ztarV68Ktbva+weXd4yGYfD2228zYsQIvL29L7qvle/hhg0byMjIoFu3bnh6euLp6cnSpUv55z//iaenJw6H42ePcafz8HKO7wx3OA+v5PjO5srn4ZUco7uch2cLCAigY8eOF6zDlc4/tw4f5zIMg6KiovNu6927N99++22Ftm+++Ybu3bvj5eV10X369OlTMwVX0cWOD+D9999n1KhRzJs3j1tvvbVSz7d582bCw8Ors8zLdqnjO9emTZsq1O7q7x9U7hiXLl3K3r17GTNmTKWez6r3cMCAAWzdupXNmzeXfXXv3p27776bzZs3Y7fbf/YYdzoPL+f4wH3Ow8s9vnO58nl4JcfoLufh2YqKiti5c+cF63Cp869ar6PUoqefftr48ccfjZSUFOOnn34ynnnmGcPDw8P45ptvDMMwjKeeesoYMWJE2f7JycmGv7+/8eijjxo7duwwZs2aZXh5eRkfffRR2T4rVqww7Ha7MW3aNGPnzp3GtGnTDE9PT2P16tUuf3zz5s0zPD09jX/9619Genp62VdmZmbZPs8//7yxaNEiY9++fcamTZuM++67z/D09DTWrFnj8sf3j3/8w5g/f76RlJRkbNu2zXjqqacMwPj444/L9nGl988wqn6MZ9xzzz1Gz549z/ucrvQens+5l7Td/Tw816WOz93Ow3Nd6vjc8Tw816WO8Qx3OA//8Ic/GEuWLDGSk5ON1atXG4MHDzYCAwPL7sxx5fPPbcPH6NGjjejoaMPb29to0qSJMWDAgLJf6oZhGCNHjjT69etX4TFLliwxunbtanh7exsxMTHG66+//rPn/d///me0adPG8PLyMtq2bVvhpKpNVT2+fv36GcDPvkaOHFm2z/jx440WLVqUPeeNN95orFy5shaPqlxVj2/69OlGy5YtDV9fX6NRo0ZGYmKisXDhwp89r6u8f4ZxeT+jmZmZhp+fn/Hmm2+e9zld6T08n3N/sbv7eXiuSx2fu52H57rU8bnjeXiuyvyMust5ePvttxvh4eGGl5eXERERYfzqV7+qMKbMlc8/m2EYRvVeSxERERG5sDo15kNERERcn8KHiIiI1CqFDxEREalVCh8iIiJSqxQ+REREpFYpfIiIiEitUvgQERGRWqXwISIiIrVK4UNERERqlcKHiIiI1CqFDxEREalVCh8iIiJSq/4fDbcty42tkZUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "g.plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ae75d0f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#concat dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "b54c2709",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "india_weather = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80, 60, 78]\n",
    "})\n",
    "india_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8e1e9796",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_weather = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "    \"temperature\": [21,14,35],\n",
    "    \"humidity\": [68, 65, 75]\n",
    "})\n",
    "us_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "bd44b3ce",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([india_weather,us_weather])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d6e3965f",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "3  new york           21        68\n",
       "4   chicago           14        65\n",
       "5   orlando           35        75"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([india_weather,us_weather],ignore_index = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d2b1992d",
   "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></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">india</th>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Us</th>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             city  temperature  humidity\n",
       "india 0    mumbai           32        80\n",
       "      1     delhi           45        60\n",
       "      2  banglore           30        78\n",
       "Us    0  new york           21        68\n",
       "      1   chicago           14        65\n",
       "      2   orlando           35        75"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([india_weather,us_weather],keys=[\"india\",\"Us\"])\n",
    "df\n",
    "                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "078a2124",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['india']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "7a2e0910",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['Us']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "1622072b",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature\n",
       "0    mumbai           32\n",
       "1     delhi           45\n",
       "2  banglore           30"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Concatenation Using Index\n",
    "\n",
    "temperature_df = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "}, index=[0,1,2])\n",
    "temperature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "26955f0a",
   "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>city</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city  windspeed\n",
       "1   delhi          7\n",
       "0  mumbai         12"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "windspeed_df = pd.DataFrame({\n",
    "    \"city\": [\"delhi\",\"mumbai\"],\n",
    "    \"windspeed\": [7,12],\n",
    "}, index=[1,0])\n",
    "windspeed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "58697302",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>city</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>delhi</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature    city  windspeed\n",
       "0    mumbai           32  mumbai       12.0\n",
       "1     delhi           45   delhi        7.0\n",
       "2  banglore           30     NaN        NaN"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([temperature_df,windspeed_df],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "b611d5ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Humid\n",
       "1      Dry\n",
       "2     Rain\n",
       "Name: event, dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([\"Humid\",\"Dry\",\"Rain\"], name=\"event\")\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8614e6a9",
   "metadata": {
    "scrolled": true
   },
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>Humid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>Dry</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  event\n",
       "0    mumbai           32  Humid\n",
       "1     delhi           45    Dry\n",
       "2  banglore           30   Rain"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([temperature_df,s],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "52316a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "0263610b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "8716b65f",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baltimore</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature\n",
       "0   new york           21\n",
       "1    chicago           14\n",
       "2    orlando           35\n",
       "3  baltimore           32"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'city' : ['new york','chicago','orlando','baltimore'],\n",
    "    'temperature' : [21,14,35,32]\n",
    "})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "78006536",
   "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>city</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>san francisco</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  humidity\n",
       "0        chicago        65\n",
       "1       new york        68\n",
       "2  san francisco        71"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\n",
    "    'city': ['chicago','new york','san francisco'],\n",
    "    'humidity' : [65,68,71]\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "b9125d1a",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df1,df2,on='city')\n",
    "df\n",
    "\n",
    "#retrieve matching rows from table 1 and table 2 , kinda inner join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2ca81368",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baltimore</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>san francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  temperature  humidity      _merge\n",
       "0       new york         21.0      68.0        both\n",
       "1        chicago         14.0      65.0        both\n",
       "2        orlando         35.0       NaN   left_only\n",
       "3      baltimore         32.0       NaN   left_only\n",
       "4  san francisco          NaN      71.0  right_only"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df1,df2,on='city',how='outer',indicator = True)\n",
    "df\n",
    "\n",
    "#retrieve all data from table , kinda outer join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7fa5b5a8",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baltimore</td>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0   new york           21      68.0\n",
       "1    chicago           14      65.0\n",
       "2    orlando           35       NaN\n",
       "3  baltimore           32       NaN"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df1,df2,on='city',how = 'left')\n",
    "df\n",
    "\n",
    "#retrieve all data from left table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "41c902f1",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14.0</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>21.0</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>san francisco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            city  temperature  humidity\n",
       "0        chicago         14.0        65\n",
       "1       new york         21.0        68\n",
       "2  san francisco          NaN        71"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(df1,df2,on='city',how = 'right')\n",
    "df\n",
    "\n",
    "#retrieve all data from right table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "6bcb83b2",
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlandp</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>batlimore</td>\n",
       "      <td>38</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0   new york           21        65\n",
       "1    chicago           14        68\n",
       "2    orlandp           35        71\n",
       "3  batlimore           38        75"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#suffixes\n",
    "df1 = pd.DataFrame({\n",
    "    'city': ['new york','chicago','orlandp','batlimore'],\n",
    "    'temperature' : [21,14,35,38],\n",
    "    'humidity' : [65,68,71,75]\n",
    "})\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "43c82149",
   "metadata": {
    "scrolled": true
   },
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chicago</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>san diego</td>\n",
       "      <td>35</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0    chicago           21        65\n",
       "1   new york           14        68\n",
       "2  san diego           35        71"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#suffixes\n",
    "df2 = pd.DataFrame({\n",
    "    'city': ['chicago','new york','san diego'],\n",
    "    'temperature' : [21,14,35],\n",
    "    'humidity' : [65,68,71]\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "97dc3b3d",
   "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>city</th>\n",
       "      <th>temperature_x</th>\n",
       "      <th>humidity_x</th>\n",
       "      <th>temperature_y</th>\n",
       "      <th>humidity_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature_x  humidity_x  temperature_y  humidity_y\n",
       "0  new york             21          65             14          68\n",
       "1   chicago             14          68             21          65"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1,df2,on='city')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "6e808e74",
   "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>city</th>\n",
       "      <th>temperature_left</th>\n",
       "      <th>humidity_left</th>\n",
       "      <th>temperature_right</th>\n",
       "      <th>humidity_right</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature_left  humidity_left  temperature_right  \\\n",
       "0  new york                21             65                 14   \n",
       "1   chicago                14             68                 21   \n",
       "\n",
       "   humidity_right  \n",
       "0              68  \n",
       "1              65  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1,df2,on='city',suffixes = ('_left','_right'))\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d0c606f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot table - pivot allows you to transform or reshape data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "7330d842",
   "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>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>65.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>70.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>72.0</td>\n",
       "      <td>62.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>78.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>82.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>80.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        date      city  temperature  humidity\n",
       "0         0.0  05-01-2017  new york         65.0      56.0\n",
       "1         1.0  05-01-2017  new york         61.0      54.0\n",
       "2         2.0  05-02-2017  new york         70.0      60.0\n",
       "3         3.0  05-02-2017    mumbai         72.0      62.0\n",
       "4         4.0  05-01-2017    mumbai         75.0      80.0\n",
       "5         5.0  05-01-2017    mumbai         78.0      83.0\n",
       "6         6.0  05-01-2017   beijing         82.0      85.0\n",
       "7         7.0  05-02-2017   beijing         80.0      26.0\n",
       "8         NaN         NaN       NaN          NaN       NaN"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\OneDrive\\Desktop\\pivotable_weather.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "8003a1cb",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Unnamed: 0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">humidity</th>\n",
       "      <th colspan=\"3\" halign=\"left\">temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th>05-01-2017</th>\n",
       "      <th>05-02-2017</th>\n",
       "      <th>05-03-2017</th>\n",
       "      <th>05-01-2017</th>\n",
       "      <th>05-02-2017</th>\n",
       "      <th>05-03-2017</th>\n",
       "      <th>05-01-2017</th>\n",
       "      <th>05-02-2017</th>\n",
       "      <th>05-03-2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>beijing</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>26</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>80</td>\n",
       "      <td>77</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mumbai</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "      <td>85</td>\n",
       "      <td>75</td>\n",
       "      <td>78</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new york</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>56</td>\n",
       "      <td>58</td>\n",
       "      <td>60</td>\n",
       "      <td>65</td>\n",
       "      <td>66</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                         humidity                        \\\n",
       "date     05-01-2017 05-02-2017 05-03-2017 05-01-2017 05-02-2017 05-03-2017   \n",
       "city                                                                         \n",
       "beijing           6          7          8         26         30         35   \n",
       "mumbai            3          4          5         80         83         85   \n",
       "new york          0          1          2         56         58         60   \n",
       "\n",
       "         temperature                        \n",
       "date      05-01-2017 05-02-2017 05-03-2017  \n",
       "city                                        \n",
       "beijing           80         77         79  \n",
       "mumbai            75         78         82  \n",
       "new york          65         66         68  "
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='city',columns='date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "216b6de7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot table - it allows to transform or reshape data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "529b43a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(r\"C:\\Users\\thiru\\OneDrive\\Desktop\\pivotable_weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "4f1499f9",
   "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>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>65</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>66</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>75</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>78</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>82</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>80</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>77</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>beijing</td>\n",
       "      <td>79</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        date      city  temperature  humidity\n",
       "0           0  05-01-2017  new york           65        56\n",
       "1           1  05-02-2017  new york           66        58\n",
       "2           2  05-03-2017  new york           68        60\n",
       "3           3  05-01-2017    mumbai           75        80\n",
       "4           4  05-02-2017    mumbai           78        83\n",
       "5           5  05-03-2017    mumbai           82        85\n",
       "6           6  05-01-2017   beijing           80        26\n",
       "7           7  05-02-2017   beijing           77        30\n",
       "8           8  05-03-2017   beijing           79        35"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "7ff6b5af",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Unnamed: 0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"3\" halign=\"left\">humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-01-2017</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-2017</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-2017</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>82</td>\n",
       "      <td>68</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 0                 temperature                 humidity  \\\n",
       "city          beijing mumbai new york     beijing mumbai new york  beijing   \n",
       "date                                                                         \n",
       "05-01-2017          6      3        0          80     75       65       26   \n",
       "05-02-2017          7      4        1          77     78       66       30   \n",
       "05-03-2017          8      5        2          79     82       68       35   \n",
       "\n",
       "                            \n",
       "city       mumbai new york  \n",
       "date                        \n",
       "05-01-2017     80       56  \n",
       "05-02-2017     83       58  \n",
       "05-03-2017     85       60  "
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='date',columns = 'city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "5e34ea05",
   "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>city</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-01-2017</th>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-2017</th>\n",
       "      <td>30</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-2017</th>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "city        beijing  mumbai  new york\n",
       "date                                 \n",
       "05-01-2017       26      80        56\n",
       "05-02-2017       30      83        58\n",
       "05-03-2017       35      85        60"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='date',columns = 'city',values = 'humidity')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "a55db227",
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot table is used to summarize and aggregate data inside datafame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "4f84e818",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Unnamed: 0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">temperature</th>\n",
       "      <th colspan=\"3\" halign=\"left\">humidity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>05-01-2017</th>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>75</td>\n",
       "      <td>65</td>\n",
       "      <td>26</td>\n",
       "      <td>80</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-2017</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>77</td>\n",
       "      <td>78</td>\n",
       "      <td>66</td>\n",
       "      <td>30</td>\n",
       "      <td>83</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-03-2017</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>79</td>\n",
       "      <td>82</td>\n",
       "      <td>68</td>\n",
       "      <td>35</td>\n",
       "      <td>85</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Unnamed: 0                 temperature                 humidity  \\\n",
       "city          beijing mumbai new york     beijing mumbai new york  beijing   \n",
       "date                                                                         \n",
       "05-01-2017          6      3        0          80     75       65       26   \n",
       "05-02-2017          7      4        1          77     78       66       30   \n",
       "05-03-2017          8      5        2          79     82       68       35   \n",
       "\n",
       "                            \n",
       "city       mumbai new york  \n",
       "date                        \n",
       "05-01-2017     80       56  \n",
       "05-02-2017     83       58  \n",
       "05-03-2017     85       60  "
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='date',columns='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "d341836d",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Unnamed: 0</th>\n",
       "      <th colspan=\"3\" halign=\"left\">date</th>\n",
       "      <th colspan=\"3\" halign=\"left\">temperature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>city</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "      <th>beijing</th>\n",
       "      <th>mumbai</th>\n",
       "      <th>new york</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-01-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-02-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>05-03-2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0                        date                          \\\n",
       "city        beijing mumbai new york     beijing      mumbai    new york   \n",
       "humidity                                                                  \n",
       "26              6.0    NaN      NaN  05-01-2017         NaN         NaN   \n",
       "30              7.0    NaN      NaN  05-02-2017         NaN         NaN   \n",
       "35              8.0    NaN      NaN  05-03-2017         NaN         NaN   \n",
       "56              NaN    NaN      0.0         NaN         NaN  05-01-2017   \n",
       "58              NaN    NaN      1.0         NaN         NaN  05-02-2017   \n",
       "60              NaN    NaN      2.0         NaN         NaN  05-03-2017   \n",
       "80              NaN    3.0      NaN         NaN  05-01-2017         NaN   \n",
       "83              NaN    4.0      NaN         NaN  05-02-2017         NaN   \n",
       "85              NaN    5.0      NaN         NaN  05-03-2017         NaN   \n",
       "\n",
       "         temperature                  \n",
       "city         beijing mumbai new york  \n",
       "humidity                              \n",
       "26              80.0    NaN      NaN  \n",
       "30              77.0    NaN      NaN  \n",
       "35              79.0    NaN      NaN  \n",
       "56               NaN    NaN     65.0  \n",
       "58               NaN    NaN     66.0  \n",
       "60               NaN    NaN     68.0  \n",
       "80               NaN   75.0      NaN  \n",
       "83               NaN   78.0      NaN  \n",
       "85               NaN   82.0      NaN  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='humidity',columns='city')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43253528",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}