Pandas-HandlingMissingValues.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "38049b9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb1f14a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b8eebc6d",
   "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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>150.50</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50  2012-10-05         3002       5002.0\n",
       "1       NaN     270.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26         NaN         3001       5001.0\n",
       "3   70004.0     110.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50  2012-09-10         3002       5002.0\n",
       "5   70005.0    2400.60  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43  2012-10-10         3004          NaN\n",
       "8   70003.0    2480.40  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q1\n",
    "df = pd.DataFrame({\n",
    "'ord_no':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],\n",
    "'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],\n",
    "'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "74964afb",
   "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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ord_no  purch_amt  ord_date  customer_id  salesman_id\n",
       "0    False      False     False        False        False\n",
       "1     True      False     False        False        False\n",
       "2    False      False      True        False        False\n",
       "3    False      False     False        False         True\n",
       "4     True      False     False        False        False\n",
       "5    False      False     False        False        False\n",
       "6     True      False     False        False        False\n",
       "7    False      False     False        False         True\n",
       "8    False      False     False        False        False\n",
       "9    False      False     False        False        False\n",
       "10    True      False     False        False        False\n",
       "11   False      False     False        False         True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4280700c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ord_no          True\n",
       "purch_amt      False\n",
       "ord_date        True\n",
       "customer_id    False\n",
       "salesman_id     True\n",
       "dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q2\n",
    "df.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "485f46f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ord_no         4\n",
       "purch_amt      0\n",
       "ord_date       1\n",
       "customer_id    0\n",
       "salesman_id    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q3\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "94fe155f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70001</td>\n",
       "      <td>150.5</td>\n",
       "      <td>?</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004</td>\n",
       "      <td>110.5</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>5001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.5</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005</td>\n",
       "      <td>2400.6</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>--</td>\n",
       "      <td>5760</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010</td>\n",
       "      <td>?</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003</td>\n",
       "      <td>12.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>--</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012</td>\n",
       "      <td>2480.4</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013</td>\n",
       "      <td>3045.6</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>--</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ord_no purch_amt    ord_date customer_id salesman_id\n",
       "0   70001     150.5           ?        3002        5002\n",
       "1     NaN    270.65  2012-09-10        3001        5003\n",
       "2   70002     65.26         NaN        3001           ?\n",
       "3   70004     110.5  2012-08-17        3003        5001\n",
       "4     NaN     948.5  2012-09-10        3002         NaN\n",
       "5   70005    2400.6  2012-07-27        3001        5002\n",
       "6      --      5760  2012-09-10        3001        5001\n",
       "7   70010         ?  2012-10-10        3004           ?\n",
       "8   70003     12.43  2012-10-10          --        5003\n",
       "9   70012    2480.4  2012-06-27        3002        5002\n",
       "10    NaN    250.45  2012-08-17        3001        5003\n",
       "11  70013    3045.6  2012-04-25        3001          --"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q4\n",
    "df = pd.DataFrame({\n",
    "'ord_no':[70001,np.nan,70002,70004,np.nan,70005,\"--\",70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,\"?\",12.43,2480.4,250.45, 3045.6],\n",
    "'ord_date': ['?','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,\"--\",3002,3001,3001],\n",
    "'salesman_id':[5002,5003,\"?\",5001,np.nan,5002,5001,\"?\",5003,5002,5003,\"--\"]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0bf6f32",
   "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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>150.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003.0</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>12.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50         NaN       3002.0       5002.0\n",
       "1       NaN     270.65  2012-09-10       3001.0       5003.0\n",
       "2   70002.0      65.26         NaN       3001.0          NaN\n",
       "3   70004.0     110.50  2012-08-17       3003.0       5001.0\n",
       "4       NaN     948.50  2012-09-10       3002.0          NaN\n",
       "5   70005.0    2400.60  2012-07-27       3001.0       5002.0\n",
       "6       NaN    5760.00  2012-09-10       3001.0       5001.0\n",
       "7   70010.0        NaN  2012-10-10       3004.0          NaN\n",
       "8   70003.0      12.43  2012-10-10          NaN       5003.0\n",
       "9   70012.0    2480.40  2012-06-27       3002.0       5002.0\n",
       "10      NaN     250.45  2012-08-17       3001.0       5003.0\n",
       "11  70013.0    3045.60  2012-04-25       3001.0          NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace({\"?\": np.nan, \"--\": np.nan})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "aff56cfa",
   "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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>150.50</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50  2012-10-05         3002       5002.0\n",
       "1       NaN     270.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26         NaN         3001       5001.0\n",
       "3   70004.0     110.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50  2012-09-10         3002       5002.0\n",
       "5   70005.0    2400.60  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43  2012-10-10         3004          NaN\n",
       "8   70003.0    2480.40  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q5\n",
    "df = pd.DataFrame({\n",
    "'ord_no':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[150.5,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],\n",
    "'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],\n",
    "'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3a488e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
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       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ord_no  purch_amt    ord_date  customer_id  salesman_id\n",
       "0  70001.0     150.50  2012-10-05         3002       5002.0\n",
       "5  70005.0    2400.60  2012-07-27         3001       5001.0\n",
       "8  70003.0    2480.40  2012-10-10         3003       5003.0\n",
       "9  70012.0     250.45  2012-06-27         3002       5002.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4505a5fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>3002</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>270.65</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>65.26</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110.50</td>\n",
       "      <td>3003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>948.50</td>\n",
       "      <td>3002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2400.60</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5760.00</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1983.43</td>\n",
       "      <td>3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2480.40</td>\n",
       "      <td>3003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>250.45</td>\n",
       "      <td>3002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>75.29</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3045.60</td>\n",
       "      <td>3001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    purch_amt  customer_id\n",
       "0      150.50         3002\n",
       "1      270.65         3001\n",
       "2       65.26         3001\n",
       "3      110.50         3003\n",
       "4      948.50         3002\n",
       "5     2400.60         3001\n",
       "6     5760.00         3001\n",
       "7     1983.43         3004\n",
       "8     2480.40         3003\n",
       "9      250.45         3002\n",
       "10      75.29         3001\n",
       "11    3045.60         3001"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q6\n",
    "df.dropna(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8d46e6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <td>3003.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id\n",
       "0       NaN        NaN         NaN          NaN\n",
       "1       NaN     270.65  2012-09-10       3001.0\n",
       "2   70002.0      65.26         NaN       3001.0\n",
       "3   70004.0     110.50  2012-08-17       3003.0\n",
       "4       NaN     948.50  2012-09-10       3002.0\n",
       "5   70005.0    2400.60  2012-07-27       3001.0\n",
       "6       NaN    5760.00  2012-09-10       3001.0\n",
       "7   70010.0    1983.43  2012-10-10       3004.0\n",
       "8   70003.0    2480.40  2012-10-10       3003.0\n",
       "9   70012.0     250.45  2012-06-27       3002.0\n",
       "10      NaN      75.29  2012-08-17       3001.0\n",
       "11  70013.0    3045.60  2012-04-25       3001.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q7\n",
    "df = pd.DataFrame({\n",
    "'ord_no':[np.nan,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[np.nan,270.65,65.26,110.5,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,3045.6],\n",
    "'ord_date': [np.nan,'2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[np.nan,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001]})\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b67bc5fb",
   "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",
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       "    }\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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id\n",
       "1       NaN     270.65  2012-09-10       3001.0\n",
       "2   70002.0      65.26         NaN       3001.0\n",
       "3   70004.0     110.50  2012-08-17       3003.0\n",
       "4       NaN     948.50  2012-09-10       3002.0\n",
       "5   70005.0    2400.60  2012-07-27       3001.0\n",
       "6       NaN    5760.00  2012-09-10       3001.0\n",
       "7   70010.0    1983.43  2012-10-10       3004.0\n",
       "8   70003.0    2480.40  2012-10-10       3003.0\n",
       "9   70012.0     250.45  2012-06-27       3002.0\n",
       "10      NaN      75.29  2012-08-17       3001.0\n",
       "11  70013.0    3045.60  2012-04-25       3001.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "014d0cfc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Orders DataFrame:\n",
      "     ord_no  purch_amt    ord_date  customer_id\n",
      "0       NaN        NaN         NaN          NaN\n",
      "1       NaN     270.65  2012-09-10       3001.0\n",
      "2   70002.0      65.26         NaN       3001.0\n",
      "3       NaN        NaN         NaN          NaN\n",
      "4       NaN     948.50  2012-09-10       3002.0\n",
      "5   70005.0    2400.60  2012-07-27       3001.0\n",
      "6       NaN    5760.00  2012-09-10       3001.0\n",
      "7   70010.0    1983.43  2012-10-10       3004.0\n",
      "8   70003.0    2480.40  2012-10-10       3003.0\n",
      "9   70012.0     250.45  2012-06-27       3002.0\n",
      "10      NaN      75.29  2012-08-17       3001.0\n",
      "11      NaN        NaN         NaN          NaN\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>270.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id\n",
       "1       NaN     270.65  2012-09-10       3001.0\n",
       "2   70002.0      65.26         NaN       3001.0\n",
       "4       NaN     948.50  2012-09-10       3002.0\n",
       "5   70005.0    2400.60  2012-07-27       3001.0\n",
       "6       NaN    5760.00  2012-09-10       3001.0\n",
       "7   70010.0    1983.43  2012-10-10       3004.0\n",
       "8   70003.0    2480.40  2012-10-10       3003.0\n",
       "9   70012.0     250.45  2012-06-27       3002.0\n",
       "10      NaN      75.29  2012-08-17       3001.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q8\n",
    "df = pd.DataFrame({\n",
    "'ord_no':[np.nan,np.nan,70002,np.nan,np.nan,70005,np.nan,70010,70003,70012,np.nan,np.nan],\n",
    "'purch_amt':[np.nan,270.65,65.26,np.nan,948.5,2400.6,5760,1983.43,2480.4,250.45, 75.29,np.nan],\n",
    "'ord_date': [np.nan,'2012-09-10',np.nan,np.nan,'2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17',np.nan],\n",
    "'customer_id':[np.nan,3001,3001,np.nan,3002,3001,3001,3004,3003,3002,3001,np.nan]})\n",
    "print(\"Original Orders DataFrame:\")\n",
    "print(df)\n",
    "df.dropna(thresh=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "321a573d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ord_no  purch_amt    ord_date  customer_id\n",
       "2  70002.0      65.26         NaN       3001.0\n",
       "5  70005.0    2400.60  2012-07-27       3001.0\n",
       "7  70010.0    1983.43  2012-10-10       3004.0\n",
       "8  70003.0    2480.40  2012-10-10       3003.0\n",
       "9  70012.0     250.45  2012-06-27       3002.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Q9\n",
    "# Write a Pandas program to drop those rows from a given DataFrame in which \n",
    "# specific columns have missing values.\n",
    "df.dropna(subset=['ord_no', 'customer_id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cf37d831",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>2400.60</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>2480.40</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ord_no  purch_amt    ord_date  customer_id\n",
       "5  70005.0    2400.60  2012-07-27       3001.0\n",
       "7  70010.0    1983.43  2012-10-10       3004.0\n",
       "8  70003.0    2480.40  2012-10-10       3003.0\n",
       "9  70012.0     250.45  2012-06-27       3002.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q10\n",
    "df.dropna(inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e5ac0712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q11\n",
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1a117208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         0.0\n",
       "1         0.0\n",
       "2     70002.0\n",
       "3         0.0\n",
       "4         0.0\n",
       "5     70005.0\n",
       "6         0.0\n",
       "7     70010.0\n",
       "8     70003.0\n",
       "9     70012.0\n",
       "10        0.0\n",
       "11        0.0\n",
       "Name: ord_no, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q12\n",
    "df['ord_no'].fillna(0, inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "29de58fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ord_no</th>\n",
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       "      <th>2</th>\n",
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       "      <td>3002.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
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       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
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       "      <td>2012-06-27</td>\n",
       "      <td>3002.0</td>\n",
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       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>3001.0</td>\n",
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       "      <th>11</th>\n",
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       "      <td>75.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt    ord_date  customer_id\n",
       "0       NaN        NaN         NaN          NaN\n",
       "1       NaN     270.65  2012-09-10       3001.0\n",
       "2   70002.0      65.26         NaN       3001.0\n",
       "3       NaN      65.26         NaN          NaN\n",
       "4       NaN     948.50  2012-09-10       3002.0\n",
       "5   70005.0    2400.60  2012-07-27       3001.0\n",
       "6       NaN    5760.00  2012-09-10       3001.0\n",
       "7   70010.0    1983.43  2012-10-10       3004.0\n",
       "8   70003.0    2480.40  2012-10-10       3003.0\n",
       "9   70012.0     250.45  2012-06-27       3002.0\n",
       "10      NaN      75.29  2012-08-17       3001.0\n",
       "11      NaN      75.29         NaN          NaN"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q13\n",
    "df['purch_amt'].fillna(method='pad', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9e2dbbb8",
   "metadata": {},
   "outputs": [
    {
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       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
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       "      <th>6</th>\n",
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       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
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       "      <th>7</th>\n",
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       "      <td>1983.43</td>\n",
       "      <td>19.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
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       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
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       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>35.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50     10.50  2012-10-05         3002       5002.0\n",
       "1       NaN        NaN     20.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26       NaN         NaN         3001       5001.0\n",
       "3   70004.0     110.50     11.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50     98.50  2012-09-10         3002       5002.0\n",
       "5   70005.0        NaN       NaN  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00     57.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43     19.43  2012-10-10         3004          NaN\n",
       "8   70003.0        NaN       NaN  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45     25.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29     75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60     35.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "'ord_no':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[150.5,np.nan,65.26,110.5,948.5,np.nan,5760,1983.43,np.nan,250.45, 75.29,3045.6],\n",
    "'sale_amt':[10.5,20.65,np.nan,11.5,98.5,np.nan,57,19.43,np.nan,25.45, 75.29,35.6],\n",
    "'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],\n",
    "'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "19769127",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
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       "      <td>57.00</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>3004</td>\n",
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       "      <td>70003.0</td>\n",
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       "      <td>25.45</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>25.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>35.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50     10.50  2012-10-05         3002       5002.0\n",
       "1       NaN        NaN     20.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26     11.50         NaN         3001       5001.0\n",
       "3   70004.0     110.50     11.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50     98.50  2012-09-10         3002       5002.0\n",
       "5   70005.0        NaN     57.00  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00     57.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43     19.43  2012-10-10         3004          NaN\n",
       "8   70003.0        NaN     25.45  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45     25.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29     75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60     35.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sale_amt'].fillna(method='bfill', inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c8f8842",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
       "      <td>11.50</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>57.00</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>19.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>25.45</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>25.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>35.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50     10.50  2012-10-05         3002       5002.0\n",
       "1       NaN     250.45     20.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26     11.50         NaN         3001       5001.0\n",
       "3   70004.0     110.50     11.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50     98.50  2012-09-10         3002       5002.0\n",
       "5   70005.0     250.45     57.00  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00     57.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43     19.43  2012-10-10         3004          NaN\n",
       "8   70003.0     250.45     25.45  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45     25.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29     75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60     35.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q14\n",
    "df['purch_amt'].fillna(df['purch_amt'].median(), inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c99ee94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>sale_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
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       "      <th>0</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>150.50</td>\n",
       "      <td>10.50</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>20.65</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
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       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.50</td>\n",
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       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.50</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.43</td>\n",
       "      <td>19.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.45</td>\n",
       "      <td>25.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.29</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.60</td>\n",
       "      <td>35.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no  purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0     150.50     10.50  2012-10-05         3002       5002.0\n",
       "1       NaN        NaN     20.65  2012-09-10         3001       5003.0\n",
       "2   70002.0      65.26       NaN         NaN         3001       5001.0\n",
       "3   70004.0     110.50     11.50  2012-08-17         3003          NaN\n",
       "4       NaN     948.50     98.50  2012-09-10         3002       5002.0\n",
       "5   70005.0        NaN       NaN  2012-07-27         3001       5001.0\n",
       "6       NaN    5760.00     57.00  2012-09-10         3001       5001.0\n",
       "7   70010.0    1983.43     19.43  2012-10-10         3004          NaN\n",
       "8   70003.0        NaN       NaN  2012-10-10         3003       5003.0\n",
       "9   70012.0     250.45     25.45  2012-06-27         3002       5002.0\n",
       "10      NaN      75.29     75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0    3045.60     35.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "'ord_no':[70001,np.nan,70002,70004,np.nan,70005,np.nan,70010,70003,70012,np.nan,70013],\n",
    "'purch_amt':[150.5,np.nan,65.26,110.5,948.5,np.nan,5760,1983.43,np.nan,250.45, 75.29,3045.6],\n",
    "'sale_amt':[10.5,20.65,np.nan,11.5,98.5,np.nan,57,19.43,np.nan,25.45, 75.29,35.6],\n",
    "'ord_date': ['2012-10-05','2012-09-10',np.nan,'2012-08-17','2012-09-10','2012-07-27','2012-09-10','2012-10-10','2012-10-10','2012-06-27','2012-08-17','2012-04-25'],\n",
    "'customer_id':[3002,3001,3001,3003,3002,3001,3001,3004,3003,3002,3001,3001],\n",
    "'salesman_id':[5002,5003,5001,np.nan,5002,5001,5001,np.nan,5003,5002,5003,np.nan]})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "23277b9e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.500000</td>\n",
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       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.500000</td>\n",
       "      <td>98.50</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.000000</td>\n",
       "      <td>57.00</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.430000</td>\n",
       "      <td>19.43</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.450000</td>\n",
       "      <td>25.45</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.290000</td>\n",
       "      <td>75.29</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.600000</td>\n",
       "      <td>35.60</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no    purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0   150.500000     10.50  2012-10-05         3002       5002.0\n",
       "1       NaN  1376.614444     20.65  2012-09-10         3001       5003.0\n",
       "2   70002.0    65.260000       NaN         NaN         3001       5001.0\n",
       "3   70004.0   110.500000     11.50  2012-08-17         3003          NaN\n",
       "4       NaN   948.500000     98.50  2012-09-10         3002       5002.0\n",
       "5   70005.0  1376.614444       NaN  2012-07-27         3001       5001.0\n",
       "6       NaN  5760.000000     57.00  2012-09-10         3001       5001.0\n",
       "7   70010.0  1983.430000     19.43  2012-10-10         3004          NaN\n",
       "8   70003.0  1376.614444       NaN  2012-10-10         3003       5003.0\n",
       "9   70012.0   250.450000     25.45  2012-06-27         3002       5002.0\n",
       "10      NaN    75.290000     75.29  2012-08-17         3001       5003.0\n",
       "11  70013.0  3045.600000     35.60  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['purch_amt'].fillna(df['purch_amt'].mean(), inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3a3eadcb",
   "metadata": {},
   "outputs": [
    {
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       "      <td>11.500</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>948.500000</td>\n",
       "      <td>98.500</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>77.750</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5760.000000</td>\n",
       "      <td>57.000</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.430000</td>\n",
       "      <td>19.430</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>22.440</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.450000</td>\n",
       "      <td>25.450</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>75.290000</td>\n",
       "      <td>75.290</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.600000</td>\n",
       "      <td>35.600</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no    purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0   150.500000    10.500  2012-10-05         3002       5002.0\n",
       "1       NaN  1376.614444    20.650  2012-09-10         3001       5003.0\n",
       "2   70002.0    65.260000    16.075         NaN         3001       5001.0\n",
       "3   70004.0   110.500000    11.500  2012-08-17         3003          NaN\n",
       "4       NaN   948.500000    98.500  2012-09-10         3002       5002.0\n",
       "5   70005.0  1376.614444    77.750  2012-07-27         3001       5001.0\n",
       "6       NaN  5760.000000    57.000  2012-09-10         3001       5001.0\n",
       "7   70010.0  1983.430000    19.430  2012-10-10         3004          NaN\n",
       "8   70003.0  1376.614444    22.440  2012-10-10         3003       5003.0\n",
       "9   70012.0   250.450000    25.450  2012-06-27         3002       5002.0\n",
       "10      NaN    75.290000    75.290  2012-08-17         3001       5003.0\n",
       "11  70013.0  3045.600000    35.600  2012-04-25         3001          NaN"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q15\n",
    "df['sale_amt'].interpolate(method='linear', direction = 'forward', inplace=True) \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2f267378",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q16\n",
    "df['salesman_id'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7d19e54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['salesman_id'].value_counts(dropna=False).loc[np.nan]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "d8f59af1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q17\n",
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e0928eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 1,  4,  6, 10], dtype=int64),)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q18\n",
    "df['ord_no'].isnull().to_numpy().nonzero()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2b3d5a01",
   "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>ord_no</th>\n",
       "      <th>purch_amt</th>\n",
       "      <th>sale_amt</th>\n",
       "      <th>ord_date</th>\n",
       "      <th>customer_id</th>\n",
       "      <th>salesman_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>10.500</td>\n",
       "      <td>2012-10-05</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>20.650</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70002.0</td>\n",
       "      <td>65.260000</td>\n",
       "      <td>16.075</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70004.0</td>\n",
       "      <td>110.500000</td>\n",
       "      <td>11.500</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3003</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>948.500000</td>\n",
       "      <td>98.500</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>70005.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>77.750</td>\n",
       "      <td>2012-07-27</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>5760.000000</td>\n",
       "      <td>57.000</td>\n",
       "      <td>2012-09-10</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>70010.0</td>\n",
       "      <td>1983.430000</td>\n",
       "      <td>19.430</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3004</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>70003.0</td>\n",
       "      <td>1376.614444</td>\n",
       "      <td>22.440</td>\n",
       "      <td>2012-10-10</td>\n",
       "      <td>3003</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>70012.0</td>\n",
       "      <td>250.450000</td>\n",
       "      <td>25.450</td>\n",
       "      <td>2012-06-27</td>\n",
       "      <td>3002</td>\n",
       "      <td>5002.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>70001.0</td>\n",
       "      <td>75.290000</td>\n",
       "      <td>75.290</td>\n",
       "      <td>2012-08-17</td>\n",
       "      <td>3001</td>\n",
       "      <td>5003.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>70013.0</td>\n",
       "      <td>3045.600000</td>\n",
       "      <td>35.600</td>\n",
       "      <td>2012-04-25</td>\n",
       "      <td>3001</td>\n",
       "      <td>5001.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ord_no    purch_amt  sale_amt    ord_date  customer_id  salesman_id\n",
       "0   70001.0   150.500000    10.500  2012-10-05         3002       5002.0\n",
       "1   70001.0  1376.614444    20.650  2012-09-10         3001       5003.0\n",
       "2   70002.0    65.260000    16.075  2012-09-10         3001       5001.0\n",
       "3   70004.0   110.500000    11.500  2012-08-17         3003       5001.0\n",
       "4   70001.0   948.500000    98.500  2012-09-10         3002       5002.0\n",
       "5   70005.0  1376.614444    77.750  2012-07-27         3001       5001.0\n",
       "6   70001.0  5760.000000    57.000  2012-09-10         3001       5001.0\n",
       "7   70010.0  1983.430000    19.430  2012-10-10         3004       5001.0\n",
       "8   70003.0  1376.614444    22.440  2012-10-10         3003       5003.0\n",
       "9   70012.0   250.450000    25.450  2012-06-27         3002       5002.0\n",
       "10  70001.0    75.290000    75.290  2012-08-17         3001       5003.0\n",
       "11  70013.0  3045.600000    35.600  2012-04-25         3001       5001.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Q19\n",
    "df.fillna(df.mode().iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5133d6d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Q20\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d3221b93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.isnull(), cbar=False, cmap=\"YlGnBu\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}