Day-1/PythonNumpyPandas/Pandas_Intro/Pandas_Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Pandas and Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Series([], dtype: float64)\n"
     ]
    }
   ],
   "source": [
    "#Creating an empty Series\n",
    "\n",
    "ser = pd.Series() \n",
    "  \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([1,2,3,'kathmandu','pokhara'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3', 'kathmandu', 'pokhara'], dtype='<U21')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     Kathmandu\n",
      "1       Pokhara\n",
      "2       Chitwan\n",
      "3    Biratnagar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Create a series by passing a list\n",
    "\n",
    "cities = pd.Series(['Kathmandu', 'Pokhara', 'Chitwan','Biratnagar'])\n",
    "print(cities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     Kathmandu\n",
      "1       Pokhara\n",
      "2       Chitwan\n",
      "3    Biratnagar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Creating series from a list\n",
    "\n",
    "List = ['Kathmandu', 'Pokhara', 'Chitwan','Biratnagar']\n",
    "  \n",
    "ser = pd.Series(List) \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     Kathmandu\n",
      "1       Pokhara\n",
      "2       Chitwan\n",
      "3    Biratnagar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Creating series from an array\n",
    "  \n",
    "\n",
    "data = np.array(['Kathmandu', 'Pokhara', 'Chitwan','Biratnagar']) \n",
    "  \n",
    "ser = pd.Series(data) \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a     Kathmandu\n",
      "b       Pokhara\n",
      "c       Chitwan\n",
      "d    Biratnagar\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#Creating a series from array with index\n",
    "\n",
    "data = np.array(['Kathmandu', 'Pokhara', 'Chitwan','Biratnagar']) \n",
    "  \n",
    "# providing an index \n",
    "ser = pd.Series(data, index =['a', 'b', 'c', 'd']) \n",
    "print(ser) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Does it remind you of something in Python DataTypes?\n",
    "Dictionaries??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas DataFrames\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'Country': 'Nepal', 'Capital': 'Kathmandu', 'Population': 28095714}, {'Country': 'India', 'Capital': 'New Delhi', 'Population': 1352642280}, {'Country': 'Bangladesh', 'Capital': 'Dhaka', 'Population': 161376708}, {'Country': 'Maldives', 'Population': 11111111}]\n"
     ]
    }
   ],
   "source": [
    "# Countries_data\n",
    "\n",
    "data = [{'Country': 'Nepal', 'Capital': 'Kathmandu', 'Population': 28095714 },\n",
    "         {'Country': 'India', 'Capital': 'New Delhi', 'Population': 1352642280 },\n",
    "         {'Country': 'Bangladesh', 'Capital': 'Dhaka', 'Population': 161376708 },\n",
    "         {'Country':'Maldives','Population':11111111}]\n",
    "\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Country    Capital  Population\n",
      "0       Nepal  Kathmandu    28095714\n",
      "1       India  New Delhi  1352642280\n",
      "2  Bangladesh      Dhaka   161376708\n",
      "3    Maldives        NaN    11111111\n"
     ]
    }
   ],
   "source": [
    "# Create dataframe from Python data structures\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>Kathmandu</td>\n",
       "      <td>28095714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>New Delhi</td>\n",
       "      <td>1352642280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>Dhaka</td>\n",
       "      <td>161376708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11111111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Country    Capital  Population\n",
       "0       Nepal  Kathmandu    28095714\n",
       "1       India  New Delhi  1352642280\n",
       "2  Bangladesh      Dhaka   161376708\n",
       "3    Maldives        NaN    11111111"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df  #typing the name of the dateframe gives you a nicely formatted table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataFrames from Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let us create three series for cities, population and elevation\n",
    "\n",
    "cities = ['Kathmandu', 'Pokhara', 'Chitwan', 'Biratnagar']\n",
    "population = [975453, 426759, 280502, 242548]\n",
    "elevation = [1400, 1400, 208, 80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty DataFrame, and add new columns to it from the series we created earlier\n",
    "\n",
    "cities_df = pd.DataFrame()\n",
    "cities_df['name'] = cities\n",
    "cities_df['population'] = population\n",
    "cities_df['elevation'] = elevation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>population</th>\n",
       "      <th>elevation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kathmandu</td>\n",
       "      <td>975453</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pokhara</td>\n",
       "      <td>426759</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chitwan</td>\n",
       "      <td>280502</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Biratnagar</td>\n",
       "      <td>242548</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name  population  elevation\n",
       "0   Kathmandu      975453       1400\n",
       "1     Pokhara      426759       1400\n",
       "2     Chitwan      280502        208\n",
       "3  Biratnagar      242548         80"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name          object\n",
      "population     int64\n",
      "elevation      int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# The columns have different dtypes\n",
    "\n",
    "print(cities_df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "saarc = pd.read_csv('../data/saarc.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>GDPperCapita</th>\n",
       "      <th>GrowthRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>37171921</td>\n",
       "      <td>19585</td>\n",
       "      <td>2017</td>\n",
       "      <td>2.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>161376708</td>\n",
       "      <td>314656</td>\n",
       "      <td>4992</td>\n",
       "      <td>7.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>754388</td>\n",
       "      <td>2627</td>\n",
       "      <td>9540</td>\n",
       "      <td>6.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>1352642280</td>\n",
       "      <td>2716746</td>\n",
       "      <td>7874</td>\n",
       "      <td>7%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>515696</td>\n",
       "      <td>5302</td>\n",
       "      <td>21760</td>\n",
       "      <td>6.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>28095714</td>\n",
       "      <td>28812</td>\n",
       "      <td>2905</td>\n",
       "      <td>6.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>212228286</td>\n",
       "      <td>278019</td>\n",
       "      <td>5839</td>\n",
       "      <td>3.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>21228763</td>\n",
       "      <td>88223</td>\n",
       "      <td>13397</td>\n",
       "      <td>3%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country  Population      GDP  GDPperCapita GrowthRate\n",
       "0  Afghanistan    37171921    19585          2017      2.30%\n",
       "1   Bangladesh   161376708   314656          4992      7.70%\n",
       "2       Bhutan      754388     2627          9540      6.40%\n",
       "3        India  1352642280  2716746          7874         7%\n",
       "4     Maldives      515696     5302         21760      6.90%\n",
       "5        Nepal    28095714    28812          2905      6.20%\n",
       "6     Pakistan   212228286   278019          5839      3.30%\n",
       "7    Sri Lanka    21228763    88223         13397         3%"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saarc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 5 columns):\n",
      "Country         8 non-null object\n",
      "Population      8 non-null int64\n",
      "GDP             8 non-null int64\n",
      "GDPperCapita    8 non-null int64\n",
      "GrowthRate      8 non-null object\n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "saarc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Afghanistan\n",
       "1     Bangladesh\n",
       "2         Bhutan\n",
       "3          India\n",
       "4       Maldives\n",
       "5          Nepal\n",
       "6       Pakistan\n",
       "7      Sri Lanka\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select single column only\n",
    "saarc['Country']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Country</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>19585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>314656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>2627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>India</td>\n",
       "      <td>2716746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Maldives</td>\n",
       "      <td>5302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nepal</td>\n",
       "      <td>28812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>278019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>88223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Country      GDP\n",
       "0  Afghanistan    19585\n",
       "1   Bangladesh   314656\n",
       "2       Bhutan     2627\n",
       "3        India  2716746\n",
       "4     Maldives     5302\n",
       "5        Nepal    28812\n",
       "6     Pakistan   278019\n",
       "7    Sri Lanka    88223"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "saarc[['Country','GDP']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2627 2716746 431746.25 931573.569652944\n"
     ]
    }
   ],
   "source": [
    "print(saarc['GDP'].min(),\n",
    "      saarc['GDP'].max(), \n",
    "      saarc['GDP'].mean(),\n",
    "      saarc['GDP'].std())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed8c1ae358>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaEAAAEGCAYAAADMsSqUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8li6FKAAAZI0lEQVR4nO3dfbRddX3n8fdHIEqrCC0pdQESamnH9AnlDjK2jlgVA7Pa4JRxEVuJrizRKkzto7TTDlbsGp1pdZZVcbBhETotqKg102IZiljtAw83gjxWzShIKJVUENpKRfA7f+xf4ORyHxPO+SXc92uts84+3/07e39z17353P07++6dqkKSpB6e1LsBSdLyZQhJkroxhCRJ3RhCkqRuDCFJUjf79m5gT3fwwQfXqlWrerchSXuVLVu2/GNVrVxonCG0gFWrVjE9Pd27DUnaqyS5fTHjnI6TJHVjCEmSujGEJEndGEKSpG4MIUlSN4aQJOkxtm+Ha68dnsfJEJIk7eSii+CII+ClLx2eL7pofPsyhCRJj9i+HTZsgAcegPvuG543bBjfEZEhJEl6xG23wYoVO9f222+oj4MhJEl6xKpV8OCDO9e+9a2hPg6GkCTpEStXwsaNsP/+cMABw/PGjUN9HLx2nCRpJ+vWwUteMkzBrVo1vgACQ0iSNIuVK8cbPjs4HSdJ6sYQkiR1YwhJkroxhCRJ3RhCkqRuDCFJUjeGkCSpG0NIktTN2EIoyeFJrkxyS5Kbk/xCq78lyZ1Jrm+Pk0be8+tJtib5fJKXjdTXtNrWJGeN1I9McnWrfzDJilZ/cnu9ta1ftdA+JEmTN84joYeAX66q1cBxwBuTrG7r3lVVR7fHpQBt3anADwFrgPcl2SfJPsB7gROB1cC6ke28o23r+4F7gQ2tvgG4t9Xf1cbNuY/xfQkkSfMZWwhV1V1V9dm2/E/ArcCh87xlLXBxVX2zqr4MbAWObY+tVfWlqnoQuBhYmyTATwKXtPdvAk4e2damtnwJ8OI2fq59SJI6mMhnQm067DnA1a10RpIbkpyf5KBWOxS4Y+Rt21ptrvp3A1+vqodm1HfaVlt/Xxs/17Zm9nt6kukk09vHfW9bSVrGxh5CSZ4KfAR4U1XdD5wLPAs4GrgL+L1x97BUVXVeVU1V1dTKSVzBT5KWqbGGUJL9GALoj6rqowBV9dWqeriqvg18gEenw+4EDh95+2GtNlf9a8CBSfadUd9pW23909v4ubYlSepgnGfHBdgI3FpV7xypP2Nk2MuBm9ryZuDUdmbbkcBRwDXAtcBR7Uy4FQwnFmyuqgKuBE5p718PfHxkW+vb8inAJ9v4ufYhSepgnPcT+nHgVcCNSa5vtd9gOLvtaKCA24DXAVTVzUk+BNzCcGbdG6vqYYAkZwCXAfsA51fVzW17bwYuTvI24DqG0KM9/2GSrcA9DME17z4kSZOX4QBBc5mamqrp6enebUjSXiXJlqqaWmicV0yQJHVjCEmSujGEJEndGEKSpG4MIUlSN4aQJKkbQ0iS1I0hJEnqxhCSJHVjCEmSujGEJEndGEKSpG4MIUlSN4aQJKkbQ0iS1I0hJEnqxhCSJHVjCEmSujGEJEndGEKSpG4MIUlSN4aQJKkbQ0iS1I0hJEnqxhCSJHVjCEmSujGEJEndjC2Ekhye5MoktyS5OckvtPp3Jbk8yRfb80GtniTvTrI1yQ1JnjuyrfVt/BeTrB+pH5PkxvaedyfJru5DkjR54zwSegj45apaDRwHvDHJauAs4IqqOgq4or0GOBE4qj1OB86FIVCAs4HnAccCZ+8IlTbmtSPvW9PqS9qHJKmPsYVQVd1VVZ9ty/8E3AocCqwFNrVhm4CT2/Ja4MIaXAUcmOQZwMuAy6vqnqq6F7gcWNPWHVBVV1VVARfO2NZS9iFJ6mAinwklWQU8B7gaOKSq7mqr/gE4pC0fCtwx8rZtrTZffdssdXZhHzP7PT3JdJLp7du3L+4fKUlasrGHUJKnAh8B3lRV94+ua0cwNc7978o+quq8qpqqqqmVK1eOqTNJ0lhDKMl+DAH0R1X10Vb+6o4psPZ8d6vfCRw+8vbDWm2++mGz1HdlH5KkDsZ5dlyAjcCtVfXOkVWbgR1nuK0HPj5SP62dwXYccF+bUrsMOCHJQe2EhBOAy9q6+5Mc1/Z12oxtLWUfkqQO9h3jtn8ceBVwY5LrW+03gLcDH0qyAbgdeEVbdylwErAV+AbwGoCquifJOcC1bdxbq+qetvwG4AJgf+AT7cFS9yFJ6iPDRyaay9TUVE1PT/duQ5L2Kkm2VNXUQuO8YoIkqRtDSJLUjSEkSerGEJIkdWMISZK6MYQkSd0YQpKkbgwhSVI3hpAkqRtDSJLUjSEkSerGEJIkdWMISZK6MYQkSd0YQpKkbgwhSVI3hpAkqRtDSJLUjSEkSerGEJIkdWMISZK6MYQkSd0YQpKkbgwhSVI3hpAkqRtDSJLUjSEkSerGEJIkdTO2EEpyfpK7k9w0UntLkjuTXN8eJ42s+/UkW5N8PsnLRuprWm1rkrNG6kcmubrVP5hkRas/ub3e2tavWmgfkqQ+xnkkdAGwZpb6u6rq6Pa4FCDJauBU4Ifae96XZJ8k+wDvBU4EVgPr2liAd7RtfT9wL7Ch1TcA97b6u9q4OffxOP+bJUlLMLYQqqpPA/cscvha4OKq+mZVfRnYChzbHlur6ktV9SBwMbA2SYCfBC5p798EnDyyrU1t+RLgxW38XPuQJHXS4zOhM5Lc0KbrDmq1Q4E7RsZsa7W56t8NfL2qHppR32lbbf19bfxc23qMJKcnmU4yvX379l37V0qSFjTpEDoXeBZwNHAX8HsT3v+iVNV5VTVVVVMrV67s3Y4kPWEtKoSSHPx47KyqvlpVD1fVt4EP8Oh02J3A4SNDD2u1uepfAw5Msu+M+k7bauuf3sbPtS1JUifzhlCSn0qyHbgxybYkz9+dnSV5xsjLlwM7zpzbDJzazmw7EjgKuAa4FjiqnQm3guHEgs1VVcCVwCnt/euBj49sa31bPgX4ZBs/1z4kSZ3su8D63wFeUFV/l+R5wH8HXriYDSe5CDgeODjJNuBs4PgkRwMF3Aa8DqCqbk7yIeAW4CHgjVX1cNvOGcBlwD7A+VV1c9vFm4GLk7wNuA7Y2OobgT9MspXhxIhTF9qHJKmPDAcJc6xMPltVz53r9XIwNTVV09PTvduQpL1Kki1VNbXQuIWOhL4nyS/N9bqq3rmrDUqStFAIfQB42jyvJUnaZfOGUFX99qQakSQtPwueop3kRUk+kuTm9rgkyfET6E2S9AS30Cna/wE4H/hT4JXAzwKXAuePXnxUkqRdsdBnQr8KnFxVnxupXZ9kGvh9hkCSJGmXLDQd970zAgiAqroBOGQ8LUmSlouFQuhfdnGdJEkLWmg67llJNs9SD/B9Y+hHkrSMLBRCa2ep7bjEwu8+zr1IkpaZhULoQOCwqnovQJJrgJUMQfTmMfcmSXqCW+gzoV9juPr0DiuAKYYLk75+TD1JkpaJhY6EVlTV6N1I/6qqvgZ8Lcl3jrEvSdIysNCR0EGjL6rqjJGX3nJUkrRbFgqhq5O8dmYxyevwhnCSpN200HTcLwJ/kuSVwGdb7RjgycDJ42xMkvTEt9BVtO8Gnp/kJ4EfauU/q6pPjr0zSdIT3kJHQgC00DF4JEmPqwVv5SBJ0rgYQpKkbgwhSVI3hpAkqRtDSJLUjSEkSerGEJIkdWMISZK6MYQkSd2MLYSSnJ/k7iQ3jdS+K8nlSb7Yng9q9SR5d5KtSW5I8tyR96xv47+YZP1I/ZgkN7b3vDtJdnUfkqQ+xnkkdAGwZkbtLOCKqjoKuKK9BjgROKo9TgfOhSFQgLOB5wHHAmfvCJU25rUj71uzK/uQJPUzthCqqk8D98worwU2teVNPHol7rXAhTW4CjgwyTOAlwGXV9U9VXUvcDmwpq07oKquqqoCLpyxraXsQ5LUyaQ/Ezqkqu5qy/8AHNKWDwVG7+C6rdXmq2+bpb4r+3iMJKcnmU4yvX379kX+0yRJS9XtxIR2BFN74j6q6ryqmqqqqZUrvYGsJI3LpEPoqzumwNrz3a1+J3D4yLjDWm2++mGz1HdlH5KkTiYdQpuBHWe4rQc+PlI/rZ3BdhxwX5tSuww4IclB7YSEE4DL2rr7kxzXzoo7bca2lrIPSVIni7qp3a5IchFwPHBwkm0MZ7m9HfhQkg3A7cAr2vBLgZOArcA3gNcAVNU9Sc4Brm3j3lpVO052eAPDGXj7A59oD5a6D0lSPxk+NtFcpqamanp6uncbkrRXSbKlqqYWGucVEyRJ3RhCkqRuDCFJUjeGkCSpG0NIktSNISRJ6sYQkiR1YwhJkroxhCRJ3RhCkqRuDCFJUjeGkCSpG0NIktSNISRJ6sYQkiR1YwhJkroxhCRJ3RhCkqRuDCFJUjeGkCSpG0NIktSNISRJ6sYQkiR1YwhJkroxhCRJ3RhCkqRuDCFJUjeGkCSpmy4hlOS2JDcmuT7JdKt9V5LLk3yxPR/U6kny7iRbk9yQ5Lkj21nfxn8xyfqR+jFt+1vbezPfPiRJffQ8EnpRVR1dVVPt9VnAFVV1FHBFew1wInBUe5wOnAtDoABnA88DjgXOHgmVc4HXjrxvzQL7kCR1sCdNx60FNrXlTcDJI/ULa3AVcGCSZwAvAy6vqnuq6l7gcmBNW3dAVV1VVQVcOGNbs+1DktRBrxAq4P8m2ZLk9FY7pKruasv/ABzSlg8F7hh577ZWm6++bZb6fPvYSZLTk0wnmd6+ffuS/3GSpMXZt9N+f6Kq7kzyPcDlSf5udGVVVZIaZwPz7aOqzgPOA5iamhprH5K0nHU5EqqqO9vz3cDHGD7T+WqbSqM9392G3wkcPvL2w1ptvvphs9SZZx+SpA4mHkJJvjPJ03YsAycANwGbgR1nuK0HPt6WNwOntbPkjgPua1NqlwEnJDmonZBwAnBZW3d/kuPaWXGnzdjWbPuQJHXQYzruEOBj7azpfYE/rqo/T3It8KEkG4DbgVe08ZcCJwFbgW8ArwGoqnuSnANc28a9taruactvAC4A9gc+0R4Ab59jH5KkDjKcQKa5TE1N1fT0dO82JGmvkmTLyJ/gzGlPOkVbkrTMGEKSpG4MIUlSN4aQJKkbQ2gZ2b4drr12eJakPYEhtExcdBEccQS89KXD80UX9e5IkgyhZWH7dtiwAR54AO67b3jesMEjIkn9GULLwG23wYoVO9f222+oz8WpO0mTYAgtA6tWwYMP7lz71reG+mycupM0KYbQMrByJWzcCPvvDwccMDxv3DjUZ3LqTtIk9bqVgyZs3Tp4yUuGKbhVq2YPIHh06u6BBx6t7Zi6m+s9krSrDKFlZOXKhYNkqVN3krQ7nI7TTpYydSdJu8sjIT3GYqfuJGl3GUKa1WKm7iRpdzkdJ0nqxhCSJHVjCEmSujGEJEndGEKSpG4MIUlSN4bQBHllaknamSE0IV6ZWpIeyxCaAK9MLUmzM4QmYFduKidJy4EhNAGzXZn6m9+Epz61SzuStMcwhCZg9MrU++8/1J70JDjmGHjb25yWk7R8LcsQSrImyeeTbE1y1jj2ceutsGnT8AzDlam3bIFvf3t4/cADw+O3fgue+UxPVJC0PC27EEqyD/Be4ERgNbAuyerHcx9nngmrV8OrXz08n3nmUP/nf4anPOWx4//1Xz1RQdLytOxCCDgW2FpVX6qqB4GLgbWP18ZvvRXe856da+95z1Cf7bOhHTxRQdJytBxD6FDgjpHX21rtEUlOTzKdZHr7Eg9Prrlm7vroZ0MzeQttScvRcgyhBVXVeVU1VVVTK5d4Z7djj52/vm4d3H47nHPOMDXnLbQlLWfLMYTuBA4feX1Yqz0unv1sOOOMnWtnnDHUd1i5En7zN+ErX4G/+IshlNate7w6kKS9R6qqdw8TlWRf4AvAixnC51rglVV182zjp6amanp6esn7ufXWYQru2GN3DiBJWg6SbKmqqYXG7TuJZvYkVfVQkjOAy4B9gPPnCqDd8exnGz6StJBlF0IAVXUpcGnvPiRpuVuOnwlJkvYQhpAkqRtDSJLUjSEkSepm2Z2ivVRJtgO37+LbDwb+8XFsZxLseTLseTLseTJm6/mIqlrwT/ANoTFKMr2Y8+T3JPY8GfY8GfY8GbvTs9NxkqRuDCFJUjeG0Hid17uBXWDPk2HPk2HPk7HLPfuZkCSpG4+EJEndGEKSpG4MocdBkjVJPp9ka5KzZln/5CQfbOuvTrJq8l0+pqeFev6lJLckuSHJFUmO6NHnjJ7m7Xlk3M8kqSTdT3NdTM9JXtG+1jcn+eNJ9zhLPwt9bzwzyZVJrmvfHyf16HNGT+cnuTvJTXOsT5J3t3/TDUmeO+keZ+lpoZ5/tvV6Y5K/SfJjk+5xlp7m7Xlk3L9N8lCSUxbcaFX52I0Hw+0g/h/wfcAK4HPA6hlj3gC8vy2fCnxwL+j5RcB3tOWf3xt6buOeBnwauAqY2tN7Bo4CrgMOaq+/Zy/o+Tzg59vyauC2nj23Pv498FzgpjnWnwR8AghwHHD1XtDz80e+L07cG3oe+R76JMOdCk5ZaJseCe2+Y4GtVfWlqnoQuBhYO2PMWmBTW74EeHGSTLDHmRbsuaqurKpvtJdXMdyBtqfFfJ0BzgHeAfzrJJubw2J6fi3w3qq6F6Cq7p5wjzMtpucCDmjLTwf+foL9zaqqPg3cM8+QtcCFNbgKODDJMybT3ewW6rmq/mbH9wV7xs/gYr7OAGcCHwEW9b1sCO2+Q4E7Rl5va7VZx1TVQ8B9wHdPpLvZLabnURsYfovsacGe2xTL4VX1Z5NsbB6L+Tr/APADSf46yVVJ1kysu9ktpue3AD+XZBvDb7tnTqa13bLU7/k9zZ7wM7igJIcCLwfOXex7luVN7bR4SX4OmAJe2LuX+SR5EvBO4NWdW1mqfRmm5I5n+E3300l+pKq+3rWr+a0DLqiq30vy74A/TPLDVfXt3o09ESV5EUMI/UTvXhbhfwJvrqpvL3ayxxDafXcCh4+8PqzVZhuzLcm+DFMYX5tMe7NaTM8keQnwX4AXVtU3J9TbXBbq+WnADwOfat/83wtsTvLTVTU9sS53tpiv8zaGuf5vAV9O8gWGULp2Mi0+xmJ63gCsAaiqv03yFIYLWPaeSpzPor7n9zRJfhT4A+DEqur5f8ZiTQEXt5/Bg4GTkjxUVX8y1xucjtt91wJHJTkyyQqGEw82zxizGVjflk8BPlntE7xOFuw5yXOA/wX89B7wOQUs0HNV3VdVB1fVqqpaxTCH3jOAYHHfG3/CcBREkoMZpue+NMkmZ1hMz18BXgyQ5NnAU4DtE+1y6TYDp7Wz5I4D7ququ3o3NZ8kzwQ+Cryqqr7Qu5/FqKojR34GLwHeMF8AgUdCu62qHkpyBnAZw1kh51fVzUneCkxX1WZgI8OUxVaGD/VO7dfxonv+H8BTgQ+332q+UlU/vYf3vEdZZM+XASckuQV4GPjVnr/xLrLnXwY+kOQXGU5SeHXnX6pIchFDmB/cPqs6G9gPoKrez/DZ1UnAVuAbwGv6dPqoRfT8Xxk+O35f+xl8qDpfXXsRPS99m52/dyRJy5jTcZKkbgwhSVI3hpAkqRtDSJLUjSEkSXrEYi9S2sYe0S5wfEOSTyVZ8qWFDCFpDJI8nOT6JDcl+XCS73ict//qJO9ZYMzxSZ4/8vr1SU57PPvQE9IFtD9GXoTfZbgm348CbwX+21J3ZghJ4/FAVR1dVT8MPAi8vkMPxzNciRkY/o6jqi7s0If2IrNdpDTJs5L8eZItST6T5N+0VasZrpgNcCWzX1R4XoaQNH6fAb4fHrlP003t8aZWW5Xk75L8UZJbk1yy48gpyW3tSgokmUryqZkbT/JTGe5TdV2Sv0hySIZ7Vr0e+MV2RPaCJG9J8ivtPUe3C6bekORjSQ5q9U8leUeSa5J8IckLxv/l0V7gPODMqjoG+BXgfa3+OeA/tuWXA09LsqSLMxtC0hi1awWeCNyY5BiGv9R/HsM9bV7bLo8E8IPA+6rq2cD9DPegWqy/Ao6rqucw3Hrh16rqNuD9wLvaEdlnZrznQoYLTf4ocCPDX77vsG9VHQu8aUZdy1CSpzIcUX84yfUMl/PacRuMXwFemOQ6hosc38lw5Y9F87I90njs335gYTgS2shwc8CPVdW/ACT5KPAChuua3VFVf93G/2/gPzPMty/GYcAHM9wfZwXw5fkGJ3k6cGBV/WUrbQI+PDLko+15C7BqkT3oietJwNer6uiZK6rq72lHQi2sfmapV4D3SEgajx2fCR1dVWe2G8TNZ+b1s3a8fohHf06fMsd7fx94T1X9CPC6ecYt1o4rpj+Mv6gue1V1P8MV3v8TPHKr9B9rywdnuI0KwK8D5y91+4aQNDmfAU5O8h1JvpNhDn3HNNkzM9ybB+CVDFNsALcBx7Tln5lju0/n0dsSrB+p/xPDLS52UlX3AfeOfN7zKuAvZ47T8tQuUvq3wA8m2ZZkA/CzwIYknwNu5tETEI4HPp/hFiSHAL+z1P35W440IVX12SQXANe00h9U1XXtJILPA29Mcj5wC4/emfK3gY1JzgE+Ncem38IwX38vw5lKR7b6/wEuSbKWx979dD3w/nYCxJfYA64qrT1DVa2bY9VjTtuuqksYbtmwy7yKttRZC6E/badzS8uK03GSpG48EpIkdeORkCSpG0NIktSNISRJ6sYQkiR1YwhJkrr5/yZ/shYy3tQnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a scatter plot comparing Population and GDP\n",
    "# plt.figure(figsize=(100,10))\n",
    "saarc.plot.scatter(x=\"Population\", y=\"GDP\", c=\"Blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fed89796550>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a scatter plot comparing GDP and GDP_per_Capita\n",
    "\n",
    "saarc.plot.scatter(x=\"GDP\", y=\"GDPperCapita\", c=\"Blue\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise\n",
    "Print mean and standard deviation of GDPperCapita"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean : 8540.5\n",
      "Standard Deviation : 6480.617937908611\n"
     ]
    }
   ],
   "source": [
    "print('Mean : {}\\nStandard Deviation : {}'.format(saarc['GDPperCapita'].mean(), saarc['GDPperCapita'].std()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}