3_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e86b3e37",
   "metadata": {},
   "source": [
    "# Python Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3fc5b42",
   "metadata": {},
   "source": [
    "Pandas stands for panel data and is the core library for data manipulation and data analysis\n",
    "It consists of single and multiple dimensional data structures for data manipulation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c840ef0a",
   "metadata": {},
   "source": [
    "### Python data structures\n",
    "Single dimensional, Multi dimensional "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eb43aad",
   "metadata": {},
   "source": [
    "### Pandas Series Object\n",
    "Series object is one dimensional labeled array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b952ac0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "s1 = pd.Series([1,2,3,4,5])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b7c0105a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(s1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "00173bcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1\n",
       "b    2\n",
       "c    3\n",
       "d    4\n",
       "e    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "s1 = pd.Series([1,2,3,4,5],index=['a','b','c','d','e'])\n",
    "s1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5a0d3ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Series Object from Dictionary\n",
    "\n",
    "import pandas as pd\n",
    "pd.Series({'a':10,'b':20,'c':30})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "93ede561",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    20.0\n",
       "c    30.0\n",
       "d     NaN\n",
       "a    10.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.Series({'a':10,'b':20,'c':30},index=['b','c','d','a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3acc5a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting individual values\n",
    "\n",
    "import pandas as pd\n",
    "s1 = pd.Series([1,2,3,4,5,6,7,8,9])\n",
    "s1[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc6a363d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "7    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3f8e7f62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "4    5\n",
       "5    6\n",
       "6    7\n",
       "7    8\n",
       "8    9\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1[-8:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d060efe4",
   "metadata": {},
   "source": [
    "## Basic Math operations on Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0febab3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     6\n",
       "1     7\n",
       "2     8\n",
       "3     9\n",
       "4    10\n",
       "5    11\n",
       "6    12\n",
       "7    13\n",
       "8    14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1+5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c1ae5acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11\n",
       "1    22\n",
       "2    33\n",
       "3    44\n",
       "4    55\n",
       "5    66\n",
       "6    77\n",
       "7    88\n",
       "8    99\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1 = pd.Series([1,2,3,4,5,6,7,8,9])\n",
    "s2 = pd.Series([10,20,30,40,50,60,70,80,90])\n",
    "s1+s2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a19a7266",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pandas dataframe\n",
    "\n",
    "#  Dataframe is a two dimensional labelled data structure\n",
    "# A dataframe comprises of rows and column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca478bd3",
   "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>Marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Md</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ahoj</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AKD</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Jack</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Dan</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Name  Marks\n",
       "0    Md     87\n",
       "1  Ahoj     88\n",
       "2   AKD     91\n",
       "3  Jack     86\n",
       "4   Dan     85"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating a dataframe\n",
    "\n",
    "import pandas as pd\n",
    "pd.DataFrame({\"Name\":[\"Md\",\"Ahoj\",\"AKD\",\"Jack\",\"Dan\"],\"Marks\":[87,88,91,86,85]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f814c791",
   "metadata": {},
   "source": [
    "## Dataframe in-Built function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49f9b4b9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "iris = pd.read_csv(\"Documents\\iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "66debf75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Client</th>\n",
       "      <th>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-04-2020</td>\n",
       "      <td>Kotak Bank</td>\n",
       "      <td>38 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-04-2020</td>\n",
       "      <td>HSBC</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-04-2020</td>\n",
       "      <td>SBI</td>\n",
       "      <td>81 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-04-2020</td>\n",
       "      <td>HDFC</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-04-2020</td>\n",
       "      <td>ICICI</td>\n",
       "      <td>88 M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Client Sales in Million\n",
       "0  01-04-2020  Kotak Bank            38 M \n",
       "1  02-04-2020        HSBC            30 M \n",
       "2  03-04-2020         SBI            81 M \n",
       "3  04-04-2020        HDFC            30 M \n",
       "4  05-04-2020       ICICI            88 M "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12425fde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Client</th>\n",
       "      <th>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06-04-2020</td>\n",
       "      <td>Bank of India</td>\n",
       "      <td>18 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07-04-2020</td>\n",
       "      <td>Punjab Bank</td>\n",
       "      <td>24 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08-04-2020</td>\n",
       "      <td>Mahindra Bank</td>\n",
       "      <td>57 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09-04-2020</td>\n",
       "      <td>YES Bank</td>\n",
       "      <td>27 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10-04-2020</td>\n",
       "      <td>PAYTM</td>\n",
       "      <td>14 M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         Client Sales in Million\n",
       "5  06-04-2020  Bank of India            18 M \n",
       "6  07-04-2020    Punjab Bank            24 M \n",
       "7  08-04-2020  Mahindra Bank            57 M \n",
       "8  09-04-2020       YES Bank            27 M \n",
       "9  10-04-2020          PAYTM            14 M "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ed609b6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85218974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Date</th>\n",
       "      <th>Client</th>\n",
       "      <th>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>01-04-2020</td>\n",
       "      <td>Kotak Bank</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Date      Client Sales in Million\n",
       "count           10          10               10\n",
       "unique          10          10                9\n",
       "top     01-04-2020  Kotak Bank            30 M \n",
       "freq             1           1                2"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fc53410c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Client</th>\n",
       "      <th>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ICICI</td>\n",
       "      <td>88 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bank of India</td>\n",
       "      <td>18 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Punjab Bank</td>\n",
       "      <td>24 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mahindra Bank</td>\n",
       "      <td>57 M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Client Sales in Million\n",
       "4          ICICI            88 M \n",
       "5  Bank of India            18 M \n",
       "6    Punjab Bank            24 M \n",
       "7  Mahindra Bank            57 M "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.iloc[4:8,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a581c64c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Client</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-04-2020</td>\n",
       "      <td>SBI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-04-2020</td>\n",
       "      <td>HDFC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-04-2020</td>\n",
       "      <td>ICICI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06-04-2020</td>\n",
       "      <td>Bank of India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07-04-2020</td>\n",
       "      <td>Punjab Bank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08-04-2020</td>\n",
       "      <td>Mahindra Bank</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         Client\n",
       "2  03-04-2020            SBI\n",
       "3  04-04-2020           HDFC\n",
       "4  05-04-2020          ICICI\n",
       "5  06-04-2020  Bank of India\n",
       "6  07-04-2020    Punjab Bank\n",
       "7  08-04-2020  Mahindra Bank"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.loc[2:7,(\"Date\",\"Client\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e39d6d79",
   "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>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-04-2020</td>\n",
       "      <td>38 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02-04-2020</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03-04-2020</td>\n",
       "      <td>81 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-04-2020</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>05-04-2020</td>\n",
       "      <td>88 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06-04-2020</td>\n",
       "      <td>18 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07-04-2020</td>\n",
       "      <td>24 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08-04-2020</td>\n",
       "      <td>57 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09-04-2020</td>\n",
       "      <td>27 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10-04-2020</td>\n",
       "      <td>14 M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date Sales in Million\n",
       "0  01-04-2020            38 M \n",
       "1  02-04-2020            30 M \n",
       "2  03-04-2020            81 M \n",
       "3  04-04-2020            30 M \n",
       "4  05-04-2020            88 M \n",
       "5  06-04-2020            18 M \n",
       "6  07-04-2020            24 M \n",
       "7  08-04-2020            57 M \n",
       "8  09-04-2020            27 M \n",
       "9  10-04-2020            14 M "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.drop(\"Client\",axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cf06e302",
   "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>Client</th>\n",
       "      <th>Sales in Million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-04-2020</td>\n",
       "      <td>Kotak Bank</td>\n",
       "      <td>38 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04-04-2020</td>\n",
       "      <td>HDFC</td>\n",
       "      <td>30 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>06-04-2020</td>\n",
       "      <td>Bank of India</td>\n",
       "      <td>18 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>07-04-2020</td>\n",
       "      <td>Punjab Bank</td>\n",
       "      <td>24 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>08-04-2020</td>\n",
       "      <td>Mahindra Bank</td>\n",
       "      <td>57 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>09-04-2020</td>\n",
       "      <td>YES Bank</td>\n",
       "      <td>27 M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10-04-2020</td>\n",
       "      <td>PAYTM</td>\n",
       "      <td>14 M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         Client Sales in Million\n",
       "0  01-04-2020     Kotak Bank            38 M \n",
       "3  04-04-2020           HDFC            30 M \n",
       "5  06-04-2020  Bank of India            18 M \n",
       "6  07-04-2020    Punjab Bank            24 M \n",
       "7  08-04-2020  Mahindra Bank            57 M \n",
       "8  09-04-2020       YES Bank            27 M \n",
       "9  10-04-2020          PAYTM            14 M "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris = pd.read_csv(\"Documents\\iris.csv\")\n",
    "iris.drop([1,2,4],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d4cd4b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales      50.142857\n",
       "Profit     61.857143\n",
       "Price     542.642857\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris = pd.read_csv(\"Documents\\iris.csv\")\n",
    "iris.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8f82c74c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales      47.0\n",
       "Profit     63.5\n",
       "Price     585.5\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "00b1cd64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales      85\n",
       "Profit     97\n",
       "Price     925\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65228789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sales      15\n",
       "Profit     15\n",
       "Price     116\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0d97c706",
   "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>Sales</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.5</td>\n",
       "      <td>60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.5</td>\n",
       "      <td>298.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>42.5</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.0</td>\n",
       "      <td>462.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.5</td>\n",
       "      <td>157.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.0</td>\n",
       "      <td>421.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.0</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>408.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15.0</td>\n",
       "      <td>287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>37.0</td>\n",
       "      <td>449.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>36.5</td>\n",
       "      <td>445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.5</td>\n",
       "      <td>338.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>25.5</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales  Price\n",
       "0     7.5   60.5\n",
       "1    20.5  298.5\n",
       "2    42.5  249.0\n",
       "3    37.0  462.5\n",
       "4    27.5  157.5\n",
       "5    32.0  421.0\n",
       "6    13.0   58.0\n",
       "7    14.0  408.5\n",
       "8    15.0  287.0\n",
       "9    37.0  449.0\n",
       "10   36.5  445.0\n",
       "11   21.5  338.0\n",
       "12   21.5   94.0\n",
       "13   25.5   70.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def half(s):\n",
    "    return s*0.5\n",
    "\n",
    "iris[['Sales','Price']].apply(half)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a970748",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "January      2\n",
       "February     2\n",
       "March        1\n",
       "April        1\n",
       "May          1\n",
       "June         1\n",
       "July         1\n",
       "August       1\n",
       "September    1\n",
       "October      1\n",
       "November     1\n",
       "December     1\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "iris = pd.read_csv(\"Documents\\iris.csv\")\n",
    "iris['Month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a8339879",
   "metadata": {
    "scrolled": false
   },
   "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>Sales</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Price</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>74</td>\n",
       "      <td>76</td>\n",
       "      <td>925</td>\n",
       "      <td>April</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>817</td>\n",
       "      <td>August</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>43</td>\n",
       "      <td>50</td>\n",
       "      <td>676</td>\n",
       "      <td>December</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41</td>\n",
       "      <td>33</td>\n",
       "      <td>597</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>51</td>\n",
       "      <td>97</td>\n",
       "      <td>140</td>\n",
       "      <td>February</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>121</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>43</td>\n",
       "      <td>89</td>\n",
       "      <td>188</td>\n",
       "      <td>January</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>26</td>\n",
       "      <td>58</td>\n",
       "      <td>116</td>\n",
       "      <td>July</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>64</td>\n",
       "      <td>34</td>\n",
       "      <td>842</td>\n",
       "      <td>June</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85</td>\n",
       "      <td>68</td>\n",
       "      <td>498</td>\n",
       "      <td>March</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "      <td>315</td>\n",
       "      <td>May</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>73</td>\n",
       "      <td>94</td>\n",
       "      <td>890</td>\n",
       "      <td>November</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>74</td>\n",
       "      <td>15</td>\n",
       "      <td>898</td>\n",
       "      <td>October</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>30</td>\n",
       "      <td>86</td>\n",
       "      <td>574</td>\n",
       "      <td>September</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sales  Profit  Price      Month\n",
       "3      74      76    925      April\n",
       "7      28      35    817     August\n",
       "11     43      50    676   December\n",
       "1      41      33    597   February\n",
       "13     51      97    140   February\n",
       "0      15      72    121    January\n",
       "12     43      89    188    January\n",
       "6      26      58    116       July\n",
       "5      64      34    842       June\n",
       "2      85      68    498      March\n",
       "4      55      59    315        May\n",
       "10     73      94    890   November\n",
       "9      74      15    898    October\n",
       "8      30      86    574  September"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.sort_values(by='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e50fdea",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c8f2a388",
   "metadata": {},
   "source": [
    "# Analyzed by\n",
    "\n",
    "\n",
    "𝐌𝐝 𝐑𝐚𝐢𝐞𝐬𝐡,\n",
    "Enrollment number : 𝟏𝟗𝐔𝐌𝐄𝟏𝟏𝟔,\n",
    "Registration number : 𝟏𝟗𝟏𝟏𝟑𝟒𝟓,\n",
    "B Tech,7𝐭𝐡 semester,Section : 𝐀,\n",
    "Mechanical Engineering Department,\n",
    "National Institute of Technology Agartala, Tripura 799046,\n",
    "\n"
   ]
  }
 ],
 "metadata": {
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