Day2 Webinar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day 2 of Data Analysis Webinar\n",
    "\n",
    "## Firstly Lets Start by covering up the topics we studied yesterday\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introduction to Jupyter Notebook\n",
    "\n",
    "* Jupyter Notebook has a cell for operations. Each cell has 2 mode of Operation:Command Mode which is represented by blue border and coding mode/edit mode which is represented by green border.\n",
    "\n",
    "* In command mode, we can have various shortcuts like\n",
    "   \n",
    "    * B-> Adds a new Cell below\n",
    "    * A-> Adds a new Cell above\n",
    "    * M-> Makes the Cell as Markdown/Comment \n",
    "    * DD-> Deleting the Cell\n",
    "  \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In edit mode, we write the code and have various shortcuts like\n",
    " *  CTRL+Enter ->  Execute a Cell\n",
    " * ALT + Enter -> Execute a new cell and insert a new cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "print('Hello')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Python Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Things that we have covered:\n",
    "    *  Printing\n",
    "    *  Variables\n",
    "    *  Input using Python\n",
    "    *  Lists\n",
    "    *  Accessing the lists \n",
    "    *  Tuples\n",
    "    *  Dictonary\n",
    "    *  Arithmetic Operations\n",
    "     * Conditional Statements\n",
    "     * Looping\n",
    "     * Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello World\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saphal\n"
     ]
    }
   ],
   "source": [
    "#Define a variable by assigning the value to the name\n",
    "a=\"Saphal\"\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a Number\n",
      "2\n",
      "Enter another number3\n",
      "The sum of numbers is 5\n"
     ]
    }
   ],
   "source": [
    "#Take Input in Python\n",
    "print('Enter a Number')\n",
    "\n",
    "#input() always results in string value\n",
    "a=input()\n",
    "\n",
    "#Another way of defining input\n",
    "b=input('Enter another number')\n",
    "#Typecast\n",
    "a=int(a)\n",
    "b=int(b)\n",
    "print('The sum of numbers is',a+b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Lists in Python are same as array in C but much more powerful and versatile but consumes more memory\n",
    "a=[1,2,3,'Saphal',1.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'Saphal', 1.5]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Accessing the list\n",
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Saphal'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Negative indexing starts the list from last\n",
    "a[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We can define range to access the value from lists. \n",
    "#Please note that 0:3 are index of the list not the value and the last index is excluded.\n",
    "# Index 0,1,2\n",
    "a[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#changing the value of list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 'Saphal', 1.5]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "a[0]=\"Kathford\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Kathford', 2, 3, 'Saphal', 1.5]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Tuples and list are almost same but we cannot modify the value of tuples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=(1,2,3,'Saphal',1.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3, 'Saphal', 1.5)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-30-2aebdf4fa0c6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mb\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"Kathford\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "b[1]=\"Kathford\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 'Saphal')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dictonary is much more powerful datatype in Python which gives us key value vair i.e we can set the index by ourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#{'key':'value'}\n",
    "c={'Saphal':20,'Smiriti':22,'Rojal':24}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Saphal': 20, 'Smiriti': 22, 'Rojal': 24}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#[0:3]\n",
    "c['Saphal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10Saphal\n"
     ]
    }
   ],
   "source": [
    "#Arithmetic Operations\n",
    "a=10\n",
    "b=20\n",
    "c=\"Saphal\"\n",
    "print(str(a)+c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ok\n"
     ]
    }
   ],
   "source": [
    "#Conditional Statements\n",
    "a=6\n",
    "if(a==5):\n",
    "    print('true')\n",
    "elif(a==6):\n",
    "        print('Ok')\n",
    "else:\n",
    "    print('false')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#For Loops  \n",
    "for i in range(1,5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "#while loops \n",
    "j=0\n",
    "while(j<5):\n",
    "    print(j)\n",
    "    #no ++ or -- operators in python\n",
    "    j+=1\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "#functions in python\n",
    "#scope of function\n",
    "def average(num1,num2):\n",
    "    return num1+num2\n",
    "#end scope of function\n",
    "a=average(2,3)\n",
    "print(a)##Fucnction "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Numpy\n",
    "\n",
    "* Create Numpy Array\n",
    "* Access the Array\n",
    "* Different Types of Array\n",
    "* Mathematics\n",
    "* Linear Algebra "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Manipulation \n",
    "28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "conda install numpy/\n",
    "pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Numpy Array\n",
    "# np.array([List])\n",
    "a=np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gives the shape of array\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a 2d numpy array\n",
    "#np.array([[list1][list2]])\n",
    "b=np.array([[1,2,3,4,5],[6,7,8,9,10]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=np.array([1,2,3,4,5],dtype=\"int16\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5], dtype=int16)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5],\n",
       "       [ 6,  7,  8,  9, 10]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accessing a 2d Array\n",
    "# ar_name[row,column]\n",
    "b[0,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [6, 7]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[0:2,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#different types of Matrices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#matrix with all zeros\n",
    "np.zeros((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#matrix with all ones\n",
    "np.ones((2,3 ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 12, 12],\n",
       "       [12, 12, 12],\n",
       "       [12, 12, 12],\n",
       "       [12, 12, 12]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#matrix with your own number\n",
    "np.full((4,3),12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3729738 , 0.1777665 ],\n",
       "       [0.78143314, 0.68584191]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Matrix with random number\n",
    "np.random.rand(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identity Matrix\n",
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mathematics in Numpy\n",
    "a=np.array([2,4,6,8,10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,  16,  36,  64, 100], dtype=int32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Arithmetic Operations with Numpy\n",
    "a**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.array([4,5,6,1,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 20, 36,  8, 30])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Operations with 2 numpy array\n",
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.41614684, -0.65364362,  0.96017029, -0.14550003, -0.83907153])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cos(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear Algebra"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.ones((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=np.full((3,2),5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 5],\n",
       "       [5, 5],\n",
       "       [5, 5]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15., 15.],\n",
       "       [15., 15.]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for the matrix multiplication\n",
    "np.matmul(a,b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.det(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "a=np.array([[2,3,4],[5,6,7]])\n",
    "#Finding minimum \n",
    "np.min(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding Maximum \n",
    "np.max(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5, 6, 7, 5])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Appending the data in numpy array\n",
    "np.append(a,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "#boolean masking\n",
    "a=np.array([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False,  True,  True])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#gives the boolean result\n",
    "a>3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a>3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find more about Numpy on its docs: https://numpy.org/doc/1.18/user/quickstart.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Series in Pandas\n",
    "* Accessing the Series\n",
    "* Modifying the Series\n",
    "* DataFrame in Pandas\n",
    "* Accessing the DataFrame\n",
    "* Operations with DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pip install pandas/conda install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a series\n",
    "s1=pd.Series([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#give name to series \n",
    "s1.name=\"Roll No of 5 Students\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.index={'John','Abishek','Mamta','Nisha','Om'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         3\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note:  A series looks more like a list or dictonary but it is backed by numpy array and its computation is very fast."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "s2=pd.Series({\n",
    "    'Rojal':20,\n",
    "    'Saphal':21\n",
    "},name=\"Age of 2 Students\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rojal     20\n",
       "Saphal    21\n",
       "Name: Age of 2 Students, dtype: int64"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accessing the Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         3\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accesing through index number\n",
    "s1[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#accesing through index name\n",
    "s1['John']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#searching through index same as s1[0]\n",
    "s1.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John     1\n",
       "Nisha    2\n",
       "Om       3\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the only difference is last value is included here\n",
    "#[0:3] 0,1,2\n",
    "s1['John':'Om']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John     1\n",
       "Nisha    2\n",
       "Om       3\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.iloc[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         3\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       False\n",
       "Nisha      False\n",
       "Om         False\n",
       "Mamta       True\n",
       "Abishek     True\n",
       "Name: Roll No of 5 Students, dtype: bool"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1>3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[s1>3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         3\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       False\n",
       "Nisha      False\n",
       "Om          True\n",
       "Mamta       True\n",
       "Abishek    False\n",
       "Name: Roll No of 5 Students, dtype: bool"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#use And, OR or NOt Operator && || !=\n",
    "(s1>=3) & (s1<5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Om       3\n",
       "Mamta    4\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[(s1>=3) & (s1<5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         3\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John       1\n",
       "Nisha      2\n",
       "Om         4\n",
       "Mamta      4\n",
       "Abishek    5\n",
       "Name: Roll No of 5 Students, dtype: int64"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#assigning the value\n",
    "s1['Om']=4\n",
    "s1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://docs.google.com/spreadsheets/d/19fF4LlDe8EUZIobORBKeMK1PXuHBpEm90hh6URh5BF8/edit#gid=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Data Frame \n",
    "df=pd.DataFrame({\n",
    "    'Name':['Saphal','Smiriti','Rojal'],\n",
    "    'Roll No':[1,2,3]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "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>Name</th>\n",
       "      <th>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal        1\n",
       "1  Smiriti        2\n",
       "2    Rojal        3"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding Index to Dataframe\n",
    "df.index=['Kathford','Texas','Himalaya']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#shape of dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3 entries, Kathford to Himalaya\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   Name     3 non-null      object\n",
      " 1   Roll No  3 non-null      int64 \n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 72.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "#information about dataframe object=string\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Roll No\n",
       "count      3.0\n",
       "mean       2.0\n",
       "std        1.0\n",
       "min        1.0\n",
       "25%        1.5\n",
       "50%        2.0\n",
       "75%        2.5\n",
       "max        3.0"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe the data\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Indexing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kathford     Saphal\n",
       "Texas       Smiriti\n",
       "Himalaya      Rojal\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name       Rojal\n",
       "Roll No        3\n",
       "Name: Himalaya, dtype: object"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting from index number\n",
    "df.iloc[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name       Saphal\n",
       "Roll No         1\n",
       "Name: Kathford, dtype: object"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selecting from index name\n",
    "df.loc['Kathford']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#also includes the last value here\n",
    "df.loc['Kathford':'Himalaya']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Kathford    False\n",
       "Texas       False\n",
       "Himalaya     True\n",
       "Name: Roll No, dtype: bool"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#selection using condition\n",
    "df['Roll No']>2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name  Roll No\n",
       "Himalaya  Rojal        3"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Roll No']>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping stuffs and this is immutable operation i.e it doesn't change the existing dataframe.\n",
    "df.drop(['Kathford'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets talk about Mutable and Immutable Operations in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>Roll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KF</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll\n",
       "KF         Saphal     1\n",
       "Texas     Smiriti     2\n",
       "Himalaya    Rojal     3"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Renaming fields\n",
    "df.rename(\n",
    "    columns={\n",
    "        'Roll No':'Roll'\n",
    "    },\n",
    "    index={\n",
    "        'Kathford':'KF'\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No\n",
       "Kathford   Saphal        1\n",
       "Texas     Smiriti        2\n",
       "Himalaya    Rojal        3"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding new Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Percentage']=[20,30,40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>Roll No</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kathford</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Himalaya</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Name  Roll No  Percentage\n",
       "Kathford   Saphal        1          20\n",
       "Texas     Smiriti        2          30\n",
       "Himalaya    Rojal        3          40"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning in Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data cleaning involves various  process. We will be discussing:\n",
    " * Finding Missing Data\n",
    " * Invalid Values\n",
    " * Duplication of Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#represnets a null value NaN=Not a Number\n",
    "np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "series=pd.Series([1,np.nan,3,np.nan,\"saphal\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1       NaN\n",
       "2         3\n",
       "3       NaN\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3     True\n",
       "4    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checks is null\n",
    "pd.isnull(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1    False\n",
       "2     True\n",
       "3    False\n",
       "4     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks is not null\n",
    "pd.notnull(series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "2         3\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping the null values\n",
    "series.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1       NaN\n",
       "2         3\n",
       "3       NaN\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         0\n",
       "2         3\n",
       "3         0\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filling  the null values\n",
    "series.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         1\n",
       "2         3\n",
       "3         3\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 types of fill is possible- Forward Fill and Backward Fill\n",
    "series.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         1\n",
       "1         3\n",
       "2         3\n",
       "3    saphal\n",
       "4    saphal\n",
       "dtype: object"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Same for Data Frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Data Frame \n",
    "df=pd.DataFrame({\n",
    "    'Name':['Saphal','Smiriti','Rojal'],\n",
    "    'Roll No':[1,np.nan,3]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal      1.0\n",
       "1  Smiriti      NaN\n",
       "2    Rojal      3.0"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Name  Roll No\n",
       "0  False    False\n",
       "1  False     True\n",
       "2  False    False"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checks for Null Value\n",
    "pd.isnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Roll No\n",
       "0  True     True\n",
       "1  True    False\n",
       "2  True     True"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks for not null value\n",
    "pd.notnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name       0\n",
       "Roll No    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal      1.0\n",
       "1  Smiriti      NaN\n",
       "2    Rojal      3.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#How=all & how=any\n",
    "df.dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal      1.0\n",
       "1  Smiriti      0.0\n",
       "2    Rojal      3.0"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fill the data \n",
    "df\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal      1.0\n",
       "1  Smiriti      1.0\n",
       "2    Rojal      3.0"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 ways for filling the data- Forward Fill and Backward Fill\n",
    "df.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "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>Roll No</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Roll No\n",
       "0   Saphal      1.0\n",
       "1  Smiriti      3.0\n",
       "2    Rojal      3.0"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Invalid Datas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Data Frame \n",
    "df=pd.DataFrame({\n",
    "    'Name':['Saphal','Smiriti','Rojal'],\n",
    "    'Age':[22,200,10],\n",
    "    'Gender':['M','F','C']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>200</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>10</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age Gender\n",
       "0   Saphal   22      M\n",
       "1  Smiriti  200      F\n",
       "2    Rojal   10      C"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'F', 'C'], dtype=object)"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checks for unique value in column\n",
    "df['Gender'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Rojal</td>\n",
       "      <td>10</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age Gender\n",
       "0   Saphal   22      M\n",
       "1  Smiriti   20      F\n",
       "2    Rojal   10      M"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace({\n",
    "    'Gender':{\n",
    "    'C':'M'\n",
    "    }\n",
    ",'Age':{\n",
    "    200:20\n",
    "}}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Duplicated Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Data Frame \n",
    "df=pd.DataFrame({\n",
    "    'Name':['Saphal','Smiriti','Smiriti'],\n",
    "    'Age':[22,20,20],\n",
    "    'Gender':['M','F','F']\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Saphal</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age Gender\n",
       "0   Saphal   22      M\n",
       "1  Smiriti   20      F\n",
       "2  Smiriti   20      F"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can pass keep method with the duplicated\n",
    "df.duplicated(keep=\"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1    False\n",
       "2     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated(keep=\"first\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "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>Age</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Smiriti</td>\n",
       "      <td>20</td>\n",
       "      <td>F</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name  Age Gender\n",
       "1  Smiriti   20      F"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated(keep=\"last\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The same applies for series too\n",
    "\n",
    "https://pandas.pydata.org/docs/getting_started/index.html#getting-started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lets jump into data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import json "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading data from CSV\n",
    "#read_xml\n",
    "#read_json\n",
    "corona_data=pd.read_csv('data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>date</th>\n",
       "      <th>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>115</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>116</td>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>118</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>119</td>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        date  totalCases  newCases  totalRecoveries  \\\n",
       "0             0  2020-01-30           0         0                0   \n",
       "1             1  2020-01-31           0         0                1   \n",
       "2             2  2020-02-01           0         0                1   \n",
       "3             3  2020-02-02           0         0                1   \n",
       "4             4  2020-02-03           0         0                1   \n",
       "..          ...         ...         ...       ...              ...   \n",
       "115         115  2020-05-24         602        19               88   \n",
       "116         116  2020-05-25         681        79              113   \n",
       "117         117  2020-05-26         771        90              155   \n",
       "118         118  2020-05-27         771         0              155   \n",
       "119         119  2020-05-28         771         0              155   \n",
       "\n",
       "     newRecoveries  totalDeaths  newDeaths  \n",
       "0                0            0          0  \n",
       "1                1            0          0  \n",
       "2                0            0          0  \n",
       "3                0            0          0  \n",
       "4                0            0          0  \n",
       "..             ...          ...        ...  \n",
       "115             17            4          0  \n",
       "116             25            4          0  \n",
       "117             42            4          0  \n",
       "118              0            4          0  \n",
       "119              0            4          0  \n",
       "\n",
       "[120 rows x 8 columns]"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corona_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>date</th>\n",
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       "      <th>totalRecoveries</th>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2020-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        date  totalCases  newCases  totalRecoveries  \\\n",
       "0           0  2020-01-30           0         0                0   \n",
       "1           1  2020-01-31           0         0                1   \n",
       "2           2  2020-02-01           0         0                1   \n",
       "3           3  2020-02-02           0         0                1   \n",
       "4           4  2020-02-03           0         0                1   \n",
       "\n",
       "   newRecoveries  totalDeaths  newDeaths  \n",
       "0              0            0          0  \n",
       "1              1            0          0  \n",
       "2              0            0          0  \n",
       "3              0            0          0  \n",
       "4              0            0          0  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#First 4 data\n",
    "corona_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>date</th>\n",
       "      <th>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>115</td>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>116</td>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>117</td>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>118</td>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>119</td>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0        date  totalCases  newCases  totalRecoveries  \\\n",
       "115         115  2020-05-24         602        19               88   \n",
       "116         116  2020-05-25         681        79              113   \n",
       "117         117  2020-05-26         771        90              155   \n",
       "118         118  2020-05-27         771         0              155   \n",
       "119         119  2020-05-28         771         0              155   \n",
       "\n",
       "     newRecoveries  totalDeaths  newDeaths  \n",
       "115             17            4          0  \n",
       "116             25            4          0  \n",
       "117             42            4          0  \n",
       "118              0            4          0  \n",
       "119              0            4          0  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last 4 data\n",
    "corona_data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading data from API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=requests.get('https://data.nepalcorona.info/api/v1/covid/timeline')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'date': '2020-01-30',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 0,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-01-31',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 1,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-01',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-02',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-03',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-04',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-05',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-06',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-07',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-08',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-09',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-10',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-11',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-12',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-13',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-14',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-15',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-16',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-17',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-18',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-19',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 1,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-20',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 1,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-21',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-22',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-23',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-24',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-25',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-26',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-27',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-28',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-02-29',\n",
       "  'totalCases': 0,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 2,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 0,\n",
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       "  'newRecoveries': 1,\n",
       "  'totalDeaths': 3,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-20',\n",
       "  'totalCases': 426,\n",
       "  'newCases': 25,\n",
       "  'totalRecoveries': 46,\n",
       "  'newRecoveries': 8,\n",
       "  'totalDeaths': 3,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-21',\n",
       "  'totalCases': 456,\n",
       "  'newCases': 30,\n",
       "  'totalRecoveries': 50,\n",
       "  'newRecoveries': 4,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 1},\n",
       " {'date': '2020-05-22',\n",
       "  'totalCases': 515,\n",
       "  'newCases': 59,\n",
       "  'totalRecoveries': 71,\n",
       "  'newRecoveries': 21,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-23',\n",
       "  'totalCases': 583,\n",
       "  'newCases': 68,\n",
       "  'totalRecoveries': 71,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-24',\n",
       "  'totalCases': 602,\n",
       "  'newCases': 19,\n",
       "  'totalRecoveries': 88,\n",
       "  'newRecoveries': 17,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-25',\n",
       "  'totalCases': 681,\n",
       "  'newCases': 79,\n",
       "  'totalRecoveries': 113,\n",
       "  'newRecoveries': 25,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-26',\n",
       "  'totalCases': 771,\n",
       "  'newCases': 90,\n",
       "  'totalRecoveries': 155,\n",
       "  'newRecoveries': 42,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-27',\n",
       "  'totalCases': 771,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 155,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0},\n",
       " {'date': '2020-05-28',\n",
       "  'totalCases': 771,\n",
       "  'newCases': 0,\n",
       "  'totalRecoveries': 155,\n",
       "  'newRecoveries': 0,\n",
       "  'totalDeaths': 4,\n",
       "  'newDeaths': 0}]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(r.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting JSON into dataframes\n",
    "#read_json ([])\n",
    "df = pd.read_json(json.dumps(r.json()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  totalCases  newCases  totalRecoveries  newRecoveries  \\\n",
       "0   2020-01-30           0         0                0              0   \n",
       "1   2020-01-31           0         0                1              1   \n",
       "2   2020-02-01           0         0                1              0   \n",
       "3   2020-02-02           0         0                1              0   \n",
       "4   2020-02-03           0         0                1              0   \n",
       "..         ...         ...       ...              ...            ...   \n",
       "115 2020-05-24         602        19               88             17   \n",
       "116 2020-05-25         681        79              113             25   \n",
       "117 2020-05-26         771        90              155             42   \n",
       "118 2020-05-27         771         0              155              0   \n",
       "119 2020-05-28         771         0              155              0   \n",
       "\n",
       "     totalDeaths  newDeaths  \n",
       "0              0          0  \n",
       "1              0          0  \n",
       "2              0          0  \n",
       "3              0          0  \n",
       "4              0          0  \n",
       "..           ...        ...  \n",
       "115            4          0  \n",
       "116            4          0  \n",
       "117            4          0  \n",
       "118            4          0  \n",
       "119            4          0  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date               0\n",
       "totalCases         0\n",
       "newCases           0\n",
       "totalRecoveries    0\n",
       "newRecoveries      0\n",
       "totalDeaths        0\n",
       "newDeaths          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"CovidData.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  totalCases  newCases  totalRecoveries  newRecoveries  \\\n",
       "0   2020-01-30           0         0                0              0   \n",
       "1   2020-01-31           0         0                1              1   \n",
       "2   2020-02-01           0         0                1              0   \n",
       "3   2020-02-02           0         0                1              0   \n",
       "4   2020-02-03           0         0                1              0   \n",
       "..         ...         ...       ...              ...            ...   \n",
       "115 2020-05-24         602        19               88             17   \n",
       "116 2020-05-25         681        79              113             25   \n",
       "117 2020-05-26         771        90              155             42   \n",
       "118 2020-05-27         771         0              155              0   \n",
       "119 2020-05-28         771         0              155              0   \n",
       "\n",
       "     totalDeaths  newDeaths  \n",
       "0              0          0  \n",
       "1              0          0  \n",
       "2              0          0  \n",
       "3              0          0  \n",
       "4              0          0  \n",
       "..           ...        ...  \n",
       "115            4          0  \n",
       "116            4          0  \n",
       "117            4          0  \n",
       "118            4          0  \n",
       "119            4          0  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'totalCases', 'newCases', 'totalRecoveries', 'newRecoveries',\n",
       "       'totalDeaths', 'newDeaths'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the columns of DataFrame\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>totalRecoveries</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  totalCases  newCases  totalRecoveries  newRecoveries  \\\n",
       "0 2020-01-30           0         0                0              0   \n",
       "1 2020-01-31           0         0                1              1   \n",
       "2 2020-02-01           0         0                1              0   \n",
       "3 2020-02-02           0         0                1              0   \n",
       "4 2020-02-03           0         0                1              0   \n",
       "\n",
       "   totalDeaths  newDeaths  \n",
       "0            0          0  \n",
       "1            0          0  \n",
       "2            0          0  \n",
       "3            0          0  \n",
       "4            0          0  "
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d02d2da748>"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Relational Plot \n",
    "sns.relplot(x=\"totalCases\",y=\"totalRecoveries\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d02d36a748>"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 452.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"totalCases\",y=\"newDeaths\",data=df,hue=\"newDeaths\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x2d02d3d0708>"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Pairplot\n",
    "sns.pairplot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d02fc73b88>"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"totalCases\",y=\"totalRecoveries\",kind=\"line\",data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2d02e721a88>"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Categorical Plot\n",
    "sns.catplot(x=\"newCases\",y=\"totalCases\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://seaborn.pydata.org/tutorial/relational.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  totalCases  newCases  totalRecoveries  newRecoveries  \\\n",
       "0   2020-01-30           0         0                0              0   \n",
       "1   2020-01-31           0         0                1              1   \n",
       "2   2020-02-01           0         0                1              0   \n",
       "3   2020-02-02           0         0                1              0   \n",
       "4   2020-02-03           0         0                1              0   \n",
       "..         ...         ...       ...              ...            ...   \n",
       "115 2020-05-24         602        19               88             17   \n",
       "116 2020-05-25         681        79              113             25   \n",
       "117 2020-05-26         771        90              155             42   \n",
       "118 2020-05-27         771         0              155              0   \n",
       "119 2020-05-28         771         0              155              0   \n",
       "\n",
       "     totalDeaths  newDeaths  \n",
       "0              0          0  \n",
       "1              0          0  \n",
       "2              0          0  \n",
       "3              0          0  \n",
       "4              0          0  \n",
       "..           ...        ...  \n",
       "115            4          0  \n",
       "116            4          0  \n",
       "117            4          0  \n",
       "118            4          0  \n",
       "119            4          0  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 243,
     "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": [
    "#Line plot\n",
    "plt.plot(df['totalCases'],df['totalRecoveries'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 244,
     "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": [
    "#Adding Label and Title\n",
    "plt.plot(df['totalCases'],df['date'])\n",
    "plt.title(\"Analysis of Corona Data\")\n",
    "plt.xlabel('Total Cases')\n",
    "plt.ylabel('date')\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "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>totalCases</th>\n",
       "      <th>newCases</th>\n",
       "      <th>totalRecoveries</th>\n",
       "      <th>newRecoveries</th>\n",
       "      <th>totalDeaths</th>\n",
       "      <th>newDeaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>2020-05-24</td>\n",
       "      <td>602</td>\n",
       "      <td>19</td>\n",
       "      <td>88</td>\n",
       "      <td>17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>2020-05-25</td>\n",
       "      <td>681</td>\n",
       "      <td>79</td>\n",
       "      <td>113</td>\n",
       "      <td>25</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2020-05-26</td>\n",
       "      <td>771</td>\n",
       "      <td>90</td>\n",
       "      <td>155</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>2020-05-27</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>771</td>\n",
       "      <td>0</td>\n",
       "      <td>155</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>120 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date  totalCases  newCases  totalRecoveries  newRecoveries  \\\n",
       "0   2020-01-30           0         0                0              0   \n",
       "1   2020-01-31           0         0                1              1   \n",
       "2   2020-02-01           0         0                1              0   \n",
       "3   2020-02-02           0         0                1              0   \n",
       "4   2020-02-03           0         0                1              0   \n",
       "..         ...         ...       ...              ...            ...   \n",
       "115 2020-05-24         602        19               88             17   \n",
       "116 2020-05-25         681        79              113             25   \n",
       "117 2020-05-26         771        90              155             42   \n",
       "118 2020-05-27         771         0              155              0   \n",
       "119 2020-05-28         771         0              155              0   \n",
       "\n",
       "     totalDeaths  newDeaths  \n",
       "0              0          0  \n",
       "1              0          0  \n",
       "2              0          0  \n",
       "3              0          0  \n",
       "4              0          0  \n",
       "..           ...        ...  \n",
       "115            4          0  \n",
       "116            4          0  \n",
       "117            4          0  \n",
       "118            4          0  \n",
       "119            4          0  \n",
       "\n",
       "[120 rows x 7 columns]"
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 246,
     "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": [
    "#Adding Label and Title\n",
    "# x,y,z\n",
    "plt.plot(df['totalCases'],df['totalDeaths'])\n",
    "plt.plot(df['totalCases'],df['totalRecoveries'])\n",
    "plt.title(\"Analysis of Corona Data\")\n",
    "plt.xlabel('Total Cases')\n",
    "plt.ylabel('Total Deaths')\n",
    "\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 247,
     "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": [
    "#Adding Legends\n",
    "plt.plot(df['totalCases'],df['totalDeaths'])\n",
    "plt.plot(df['totalCases'],df['totalRecoveries'])\n",
    "plt.title(\"Analysis of Corona Data\")\n",
    "plt.xlabel('Total Cases')\n",
    "plt.ylabel('Total Deaths')\n",
    "plt.legend(['Total Deaths','Total Recoveries'])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## https://matplotlib.org/3.2.1/contents.html"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Regression or Support Vector Machine"
   ]
  }
 ],
 "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}