pandas4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2719da0f-eee2-4f6b-b60a-2c01ae104638",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "86da99a2-58c4-496d-80a5-466a8b1cbf42",
   "metadata": {},
   "outputs": [],
   "source": [
    "data={'a':[1,2,3,4],\n",
    "     'b':[4,5,6,7],\n",
    "     'c':['anwar','sudh','nadim','aadithya']}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d844b9f-96b8-407a-9132-217a579c52b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e62b9734-c26b-42b8-b0b8-f2dd03e9a7f8",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "0  1  4     anwar\n",
       "1  2  5      sudh\n",
       "2  3  6     nadim\n",
       "3  4  7  aadithya"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4114f23b-8e3b-466a-8c6c-153a6efe0586",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('c',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7535b073-1f6e-4141-ab12-7911b316179b",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>anwar</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sudh</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nadim</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aadithya</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a  b\n",
       "c             \n",
       "anwar     1  4\n",
       "sudh      2  5\n",
       "nadim     3  6\n",
       "aadithya  4  7"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "121bfe74-3765-488e-aa4a-45e90f3b51da",
   "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>c</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>anwar</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sudh</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nadim</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>aadithya</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c  a  b\n",
       "0     anwar  1  4\n",
       "1      sudh  2  5\n",
       "2     nadim  3  6\n",
       "3  aadithya  4  7"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "747f67e7-9df8-4ead-94c1-a01dd8f042ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "data={'a':[1,2,3,4],\n",
    "     'b':[4,5,6,7],\n",
    "     'c':['anwar','sudh','nadim','aadithya']}\n",
    "df=pd.DataFrame(data,index=['a','b','c','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12845fe8-6319-4a7b-8d27-951004a5e908",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "a  1  4     anwar\n",
       "b  2  5      sudh\n",
       "c  3  6     nadim\n",
       "d  4  7  aadithya"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5c42e1ff-93a6-4221-ac97-5d12ed242d6d",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "c  3  6     nadim\n",
       "b  2  5      sudh\n",
       "a  1  4     anwar\n",
       "d  4  7  aadithya"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reindex(['c','b','a','d'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9160e8a4-c07d-4d19-a8c5-cc2acaaa3dfd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', a        1\n",
      "b        4\n",
      "c    anwar\n",
      "Name: a, dtype: object)\n",
      "('b', a       2\n",
      "b       5\n",
      "c    sudh\n",
      "Name: b, dtype: object)\n",
      "('c', a        3\n",
      "b        6\n",
      "c    nadim\n",
      "Name: c, dtype: object)\n",
      "('d', a           4\n",
      "b           7\n",
      "c    aadithya\n",
      "Name: d, dtype: object)\n"
     ]
    }
   ],
   "source": [
    "for i in df.iterrows():  ## print eatch row\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c807231d-8cba-4eca-846b-80bad4d144ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('a', a    1\n",
      "b    2\n",
      "c    3\n",
      "d    4\n",
      "Name: a, dtype: int64)\n",
      "('b', a    4\n",
      "b    5\n",
      "c    6\n",
      "d    7\n",
      "Name: b, dtype: int64)\n",
      "('c', a       anwar\n",
      "b        sudh\n",
      "c       nadim\n",
      "d    aadithya\n",
      "Name: c, dtype: object)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_768/36948316.py:1: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for i in df.iteritems():\n"
     ]
    }
   ],
   "source": [
    "for i in df.iteritems():\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "964f8506-5f5c-4015-8b24-e01cb7316765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a                        10\n",
       "b                        22\n",
       "c    anwarsudhnadimaadithya\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def test(x):\n",
    "    return x.sum()\n",
    "df.apply(test,axis=0)   ## apply for one attitibute(column)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c22ba600-ee3d-4e0e-a685-a3a5809e8fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df12=df[['a','b']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "58ba9a96-cce9-4dfe-894b-ce3f86437872",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b\n",
       "a  1  4\n",
       "b  2  5\n",
       "c  3  6\n",
       "d  4  7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "97a5f03d-0a98-4dfe-8f5e-19e69f89a68b",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>16</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a   b\n",
       "a   1  16\n",
       "b   4  25\n",
       "c   9  36\n",
       "d  16  49"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df12.applymap(lambda x:x**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8999468f-a4a3-4045-b2d2-3c5b5736073f",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "a  1  4     anwar\n",
       "b  2  5      sudh\n",
       "c  3  6     nadim\n",
       "d  4  7  aadithya"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "545087ca-417a-4a01-89a6-7df0207f1b79",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values('c',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bc49fa3-f661-406b-abe3-819c7d006523",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0536208b-2be0-40e3-bce5-edf0c2bf8dcd",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "d  4  7  aadithya\n",
       "a  1  4     anwar\n",
       "c  3  6     nadim\n",
       "b  2  5      sudh"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "557cea2b-dc1b-4751-93f2-f7c0ff7f929f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7d181cf2-fbad-4825-bc09-5d587434d6a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85879ff5-9fe3-48fa-9fe3-6e2012e74787",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c\n",
       "a  1  4     anwar\n",
       "b  2  5      sudh\n",
       "c  3  6     nadim\n",
       "d  4  7  aadithya"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0f51a824-9837-48ed-bb72-195ffa2a9e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth',1000)\n",
    "df3 = pd.DataFrame({\"desc\": [\"Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.\",\"my name is sudh\" , \"i use to take data science mastes class\"]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a6dd9a6d-45a3-48e9-b956-8f41e11a1f95",
   "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>desc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>my name is sudh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i use to take data science mastes class</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            desc\n",
       "0  Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.\n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                my name is sudh\n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        i use to take data science mastes class"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "faff96a5-17c9-4754-bc36-b04e2a220464",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['char_len']=df3['desc'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b5515ca2-72db-4512-97b5-e97b70868b97",
   "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>desc</th>\n",
       "      <th>char_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.</td>\n",
       "      <td>765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>my name is sudh</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i use to take data science mastes class</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            desc  \\\n",
       "0  Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                my name is sudh   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        i use to take data science mastes class   \n",
       "\n",
       "   char_len  \n",
       "0       765  \n",
       "1        15  \n",
       "2        39  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9f66f93f-c3ab-4b62-b03f-829e7d74f89f",
   "metadata": {},
   "outputs": [],
   "source": [
    "t = \"i use to take data science mastes class\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9559131d-5d07-430a-921c-4a5cf2e8305e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(t.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e8a36d23-965e-4f62-8438-ae7c7400d6aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['word_count']=df3['desc'].apply(lambda x:len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c6abb166-ab69-418b-925f-b58c9b06de5f",
   "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>desc</th>\n",
       "      <th>char_len</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.</td>\n",
       "      <td>765</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>my name is sudh</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>i use to take data science mastes class</td>\n",
       "      <td>39</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            desc  \\\n",
       "0  Data Science Masters course is highly curated and uniquely designed according to the latest industry standards. This program instills students the skills essential to knowledge discovery efforts to identify standard, novel, and truly differentiated solutions and decision-making, including skills in managing, querying, analyzing, visualizing, and extracting meaning from extremely large data sets. This trending program provides students with the statistical, mathematical and computational skills needed to meet the large-scale data science challenges of today's professional world. You will learn all the stack required to work in data science industry including cloud infrastructure and real-time industry projects. This course will be taught in Hindi language.   \n",
       "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                my name is sudh   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        i use to take data science mastes class   \n",
       "\n",
       "   char_len  word_count  \n",
       "0       765         104  \n",
       "1        15           4  \n",
       "2        39           8  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "bf0b2832-862b-4e2d-b570-643b17ac5d25",
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>char_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>anwar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>sudh</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>nadim</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>aadithya</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a  b         c  char_len\n",
       "a  1  4     anwar       NaN\n",
       "b  2  5      sudh       NaN\n",
       "c  3  6     nadim       NaN\n",
       "d  4  7  aadithya       NaN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bd98d5d9-81ea-4487-a697-3d0d9a25fede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "403360c6-0528-45c8-a930-71df44c262c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4cbe119-2caf-4f51-88bc-2ed643f8b99b",
   "metadata": {},
   "outputs": [],
   "source": [
    "median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6d38c38c-78d9-4c8e-8d1a-51f584d5c2e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "Name: a, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7dcf4da1-aa85-4936-bee2-61235cf4c8a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2909944487358056"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fde6f69d-3a8a-48d0-a561-77a4efcf9794",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "a4fa7a3c-b08c-4185-b44f-b7d94996d8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "f88842ae-1e7c-44ef-b1dd-6bb493585828",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6666666666666667"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['a'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5cbb7c08-163e-4fe2-a182-b760cf58baa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.DataFrame({'a':[1,2,3,4,5,6,7,8,9]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c1fdd36d-2ab4-4d2b-85ab-59c9619a8265",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a\n",
       "0  1\n",
       "1  2\n",
       "2  3\n",
       "3  4\n",
       "4  5\n",
       "5  6\n",
       "6  7\n",
       "7  8\n",
       "8  9"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ed214ba1-fc00-4fad-8340-5b0a6c023651",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a\n",
       "0  1.0\n",
       "1  2.0\n",
       "2  3.0\n",
       "3  4.0\n",
       "4  5.0\n",
       "5  6.0\n",
       "6  7.0\n",
       "7  8.0\n",
       "8  9.0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rolling(window=1).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9ed60ffb-47ae-48e8-b2c3-81bf08e7e015",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a\n",
       "0  NaN\n",
       "1  1.5\n",
       "2  2.5\n",
       "3  3.5\n",
       "4  4.5\n",
       "5  5.5\n",
       "6  6.5\n",
       "7  7.5\n",
       "8  8.5"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rolling(window=2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1e17bf13-e474-4d93-9df2-97ac31506ace",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a\n",
       "0  NaN\n",
       "1  NaN\n",
       "2  2.0\n",
       "3  3.0\n",
       "4  4.0\n",
       "5  5.0\n",
       "6  6.0\n",
       "7  7.0\n",
       "8  8.0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rolling(window=3).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "f133d09c-45ef-4bcd-be16-d2cfa7d76eaa",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      a\n",
       "0   NaN\n",
       "1   3.0\n",
       "2   5.0\n",
       "3   7.0\n",
       "4   9.0\n",
       "5  11.0\n",
       "6  13.0\n",
       "7  15.0\n",
       "8  17.0"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.rolling(window=2).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4e6747c4-6df5-4f69-9b87-a126f42774e9",
   "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>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    a\n",
       "0   1\n",
       "1   3\n",
       "2   6\n",
       "3  10\n",
       "4  15\n",
       "5  21\n",
       "6  28\n",
       "7  36\n",
       "8  45"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "38cf41f5-2c70-4b1e-b7ee-958465ed653b",
   "metadata": {},
   "outputs": [],
   "source": [
    "##python pandas Date Functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "6d9dcb59-2b8d-46cf-96f4-8a2e610bf3a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.date_range(start='2022-03-22' ,end='2022-06-22')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "fe9edcb9-9826-4c73-945b-63a4312e1a9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2022-03-22', '2022-03-23', '2022-03-24', '2022-03-25',\n",
       "               '2022-03-26', '2022-03-27', '2022-03-28', '2022-03-29',\n",
       "               '2022-03-30', '2022-03-31', '2022-04-01', '2022-04-02',\n",
       "               '2022-04-03', '2022-04-04', '2022-04-05', '2022-04-06',\n",
       "               '2022-04-07', '2022-04-08', '2022-04-09', '2022-04-10',\n",
       "               '2022-04-11', '2022-04-12', '2022-04-13', '2022-04-14',\n",
       "               '2022-04-15', '2022-04-16', '2022-04-17', '2022-04-18',\n",
       "               '2022-04-19', '2022-04-20', '2022-04-21', '2022-04-22',\n",
       "               '2022-04-23', '2022-04-24', '2022-04-25', '2022-04-26',\n",
       "               '2022-04-27', '2022-04-28', '2022-04-29', '2022-04-30',\n",
       "               '2022-05-01', '2022-05-02', '2022-05-03', '2022-05-04',\n",
       "               '2022-05-05', '2022-05-06', '2022-05-07', '2022-05-08',\n",
       "               '2022-05-09', '2022-05-10', '2022-05-11', '2022-05-12',\n",
       "               '2022-05-13', '2022-05-14', '2022-05-15', '2022-05-16',\n",
       "               '2022-05-17', '2022-05-18', '2022-05-19', '2022-05-20',\n",
       "               '2022-05-21', '2022-05-22', '2022-05-23', '2022-05-24',\n",
       "               '2022-05-25', '2022-05-26', '2022-05-27', '2022-05-28',\n",
       "               '2022-05-29', '2022-05-30', '2022-05-31', '2022-06-01',\n",
       "               '2022-06-02', '2022-06-03', '2022-06-04', '2022-06-05',\n",
       "               '2022-06-06', '2022-06-07', '2022-06-08', '2022-06-09',\n",
       "               '2022-06-10', '2022-06-11', '2022-06-12', '2022-06-13',\n",
       "               '2022-06-14', '2022-06-15', '2022-06-16', '2022-06-17',\n",
       "               '2022-06-18', '2022-06-19', '2022-06-20', '2022-06-21',\n",
       "               '2022-06-22'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "baae6ff7-126a-44e7-b495-b13dc7b80f86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "8e540220-3392-4402-ace0-dd5d18c2c8ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_date=pd.DataFrame({'date':data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6a39b619-d3a4-4774-b14b-ec214a1201af",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-03-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-03-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2022-06-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2022-06-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2022-06-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2022-06-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2022-06-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>93 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date\n",
       "0  2022-03-22\n",
       "1  2022-03-23\n",
       "2  2022-03-24\n",
       "3  2022-03-25\n",
       "4  2022-03-26\n",
       "..        ...\n",
       "88 2022-06-18\n",
       "89 2022-06-19\n",
       "90 2022-06-20\n",
       "91 2022-06-21\n",
       "92 2022-06-22\n",
       "\n",
       "[93 rows x 1 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "7651acc7-a025-4446-a696-c2034b625bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_date.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "80e1679f-f398-4b8f-ac94-c655e16410d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7=pd.DataFrame({'date':['2022-06-22','2022-06-20','2022-06-19']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7d02f496-a090-4680-b5b9-68ab0790ee25",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date\n",
       "0  2022-06-22\n",
       "1  2022-06-20\n",
       "2  2022-06-19"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "5bb2bd78-eb1f-445b-a60f-e01ba7f7e208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "837aeb21-0d7a-400c-aeb5-4c6b23fd970e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['Updated_date']=pd.to_datetime(df7['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "85bef76f-b58f-4757-94e6-e59cbf26e251",
   "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>Updated_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022-06-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022-06-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022-06-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date Updated_date\n",
       "0  2022-06-22   2022-06-22\n",
       "1  2022-06-20   2022-06-20\n",
       "2  2022-06-19   2022-06-19"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2aa4c048-80df-4e6d-a2cd-b36c2c0376e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                    object\n",
       "Updated_date    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7.dtypes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "1de71456-ad2e-4d34-9d06-91aea3775bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['year']=df7['Updated_date'].dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8811443f-020d-4620-85a3-87d304d7719f",
   "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>Updated_date</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date Updated_date  year\n",
       "0  2022-06-22   2022-06-22  2022\n",
       "1  2022-06-20   2022-06-20  2022\n",
       "2  2022-06-19   2022-06-19  2022"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "5ed882b7-b1ea-4f96-8465-5a8f728de81e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['month']=df7['Updated_date'].dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "74259efd-12d8-45c7-a47f-a699a6a73ef3",
   "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>Updated_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date Updated_date  year  month\n",
       "0  2022-06-22   2022-06-22  2022      6\n",
       "1  2022-06-20   2022-06-20  2022      6\n",
       "2  2022-06-19   2022-06-19  2022      6"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d07a8d5e-043c-4a22-99c8-5292de6a2246",
   "metadata": {},
   "outputs": [],
   "source": [
    "df7['day']=df7['Updated_date'].dt.day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "e6b314bf-ba61-458a-8d56-e06fb1b4ee53",
   "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>Updated_date</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022-06-22</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022-06-20</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022-06-19</td>\n",
       "      <td>2022</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date Updated_date  year  month  day\n",
       "0  2022-06-22   2022-06-22  2022      6   22\n",
       "1  2022-06-20   2022-06-20  2022      6   20\n",
       "2  2022-06-19   2022-06-19  2022      6   19"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "b7b55087-3a00-4a66-baa1-3f1bfd5676a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Pandas Time Delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4bf209c0-8fcb-48a2-ad3d-6fae7db80c94",
   "metadata": {},
   "outputs": [],
   "source": [
    "time=pd.Timedelta(days=1,hours=5,minutes=354)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a6cd35e0-5879-41f9-868a-0c3911f5a5c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt=pd.to_datetime('2022-06-20')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3f1863af-1b16-4420-8279-db90e40c75c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2022-06-21 10:54:00')"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt+time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "9d3be031-d82f-466f-b9de-1a4b14457bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Python Pandas - Categorical Data\n",
    "\n",
    "data = [\"sudh\" , \"krish\" , \"hitesh\" , \"navin\",\"sudh\" ,\"sudh\" ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "ca10ac3c-3cef-48bf-84e3-5a3a3b732d13",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat=pd.Categorical(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "59a23bb1-6d15-4e0d-ad68-42c921b8f6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['sudh', 'krish', 'hitesh', 'navin', 'sudh', 'sudh']\n",
       "Categories (4, object): ['hitesh', 'krish', 'navin', 'sudh']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "5292456e-e9c9-46b4-9d1e-c0cd7d7f18c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hitesh    1\n",
       "krish     1\n",
       "navin     1\n",
       "sudh      3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "219e3463-de04-4ed9-8aa7-4417a4fc8824",
   "metadata": {},
   "outputs": [],
   "source": [
    "##Pandas visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "78d9f6de-5f88-4ede-b1e4-50a05f9794f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "d=pd.Series([1,2,3,4,5,6,7,8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "a0b94da5-620a-4b63-b85b-5c734ff9f3b6",
   "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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "db2c7e6d-684f-4090-a6fc-d554bd3b6296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "18b7d0ec-5492-42ac-be85-ad6e6190be50",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.Series([1,2,5,4,3,6,2,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7080f22d-b048-4e84-826f-0383355cf901",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05ecc7ad-d27e-436e-b392-dc9e4e53cd69",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}