pandas_part5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Missing data part-2\n",
    "------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>No_of_pkg</th>\n",
       "      <th>Package</th>\n",
       "      <th>travel_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20yrs</td>\n",
       "      <td>Bikash Kumar</td>\n",
       "      <td>1 packages</td>\n",
       "      <td>$100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21yrs</td>\n",
       "      <td>Ashish Shaw</td>\n",
       "      <td>5 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23years</td>\n",
       "      <td>Dipak Kumar</td>\n",
       "      <td>2pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 Years</td>\n",
       "      <td>John Doe</td>\n",
       "      <td>3 pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000</td>\n",
       "      <td>Elisha</td>\n",
       "      <td>5000</td>\n",
       "      <td>$400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5000</td>\n",
       "      <td>Md Shahid</td>\n",
       "      <td>10 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21 yrs</td>\n",
       "      <td>Adrika Roy</td>\n",
       "      <td>7pkgs</td>\n",
       "      <td>$300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24 yrs</td>\n",
       "      <td>Shashi Kumar</td>\n",
       "      <td>2000</td>\n",
       "      <td>$500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age          Name    No_of_pkg Package  travel_id\n",
       "0     20yrs  Bikash Kumar   1 packages   $100           1\n",
       "1     21yrs   Ashish Shaw   5 packages   $200           2\n",
       "2   23years   Dipak Kumar        2pkgs   $100           3\n",
       "3  20 Years      John Doe       3 pkgs   $100           4\n",
       "4      2000        Elisha         5000   $400           5\n",
       "5      5000     Md Shahid  10 packages   $200           6\n",
       "6    21 yrs    Adrika Roy        7pkgs   $300           7\n",
       "7    24 yrs  Shashi Kumar         2000   $500           8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"datasets/travel.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing specific value some another value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "       Suppose your dataframe contains some invalid values and you want to replace it with some other values like 0 or NaN.\n",
    "    In this case special values are 5000 and 2000.You can see the following result where 2000 and 5000 is replaced by NaN\n",
    "    value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>No_of_pkg</th>\n",
       "      <th>Package</th>\n",
       "      <th>travel_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20yrs</td>\n",
       "      <td>Bikash Kumar</td>\n",
       "      <td>1 packages</td>\n",
       "      <td>$100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21yrs</td>\n",
       "      <td>Ashish Shaw</td>\n",
       "      <td>5 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23years</td>\n",
       "      <td>Dipak Kumar</td>\n",
       "      <td>2pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 Years</td>\n",
       "      <td>John Doe</td>\n",
       "      <td>3 pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Elisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Md Shahid</td>\n",
       "      <td>10 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21 yrs</td>\n",
       "      <td>Adrika Roy</td>\n",
       "      <td>7pkgs</td>\n",
       "      <td>$300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24 yrs</td>\n",
       "      <td>Shashi Kumar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age          Name    No_of_pkg Package  travel_id\n",
       "0     20yrs  Bikash Kumar   1 packages   $100           1\n",
       "1     21yrs   Ashish Shaw   5 packages   $200           2\n",
       "2   23years   Dipak Kumar        2pkgs   $100           3\n",
       "3  20 Years      John Doe       3 pkgs   $100           4\n",
       "4       NaN        Elisha          NaN   $400           5\n",
       "5       NaN     Md Shahid  10 packages   $200           6\n",
       "6    21 yrs    Adrika Roy        7pkgs   $300           7\n",
       "7    24 yrs  Shashi Kumar          NaN   $500           8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.replace([\"5000\",\"2000\"],np.NaN)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    There is one problem with this approach is it will replace all the values which you have passed in the list with\n",
    "    you your value but in many other cases you dont want it like if you have 50000 in price column it is valid but if\n",
    "    50000 is in name column it is not valid in this case.So you only want to replace 50000 of name column with NaN but\n",
    "    not of price column.\n",
    "    In that case you need to pass the disctionary in the replace column.This disctionary will contain name of the column\n",
    "    and the value you want to replace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>No_of_pkg</th>\n",
       "      <th>Package</th>\n",
       "      <th>travel_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20yrs</td>\n",
       "      <td>Bikash Kumar</td>\n",
       "      <td>1 packages</td>\n",
       "      <td>$100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21yrs</td>\n",
       "      <td>Ashish Shaw</td>\n",
       "      <td>5 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23years</td>\n",
       "      <td>Dipak Kumar</td>\n",
       "      <td>2pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 Years</td>\n",
       "      <td>John Doe</td>\n",
       "      <td>3 pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Elisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Md Shahid</td>\n",
       "      <td>10 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21 yrs</td>\n",
       "      <td>Adrika Roy</td>\n",
       "      <td>7pkgs</td>\n",
       "      <td>$300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24 yrs</td>\n",
       "      <td>Shashi Kumar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$500</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age          Name    No_of_pkg Package  travel_id\n",
       "0     20yrs  Bikash Kumar   1 packages   $100           1\n",
       "1     21yrs   Ashish Shaw   5 packages   $200           2\n",
       "2   23years   Dipak Kumar        2pkgs   $100           3\n",
       "3  20 Years      John Doe       3 pkgs   $100           4\n",
       "4       NaN        Elisha          NaN   $400           5\n",
       "5       NaN     Md Shahid  10 packages   $200           6\n",
       "6    21 yrs    Adrika Roy        7pkgs   $300           7\n",
       "7    24 yrs  Shashi Kumar          NaN   $500           8"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.replace({\n",
    "    'Age':[\"2000\",\"5000\"],\n",
    "    'No_of_pkg':[\"2000\",\"5000\"],\n",
    "    \"travel_id\":[0]\n",
    "},np.NaN)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    If you want to replace some specific value like 5000 with any other value and so on.In that case you need \n",
    "    to pass the disctionary with all keys which you want to replace and values which you want to replace with.\n",
    "    Here 5000,2000,8 are values to replaced are the keys and np.NaN & 10 are values to be replaced with are value\n",
    "    of the disctionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>No_of_pkg</th>\n",
       "      <th>Package</th>\n",
       "      <th>travel_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20yrs</td>\n",
       "      <td>Bikash Kumar</td>\n",
       "      <td>1 packages</td>\n",
       "      <td>$100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21yrs</td>\n",
       "      <td>Ashish Shaw</td>\n",
       "      <td>5 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23years</td>\n",
       "      <td>Dipak Kumar</td>\n",
       "      <td>2pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20 Years</td>\n",
       "      <td>John Doe</td>\n",
       "      <td>3 pkgs</td>\n",
       "      <td>$100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Elisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Md Shahid</td>\n",
       "      <td>10 packages</td>\n",
       "      <td>$200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21 yrs</td>\n",
       "      <td>Adrika Roy</td>\n",
       "      <td>7pkgs</td>\n",
       "      <td>$300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24 yrs</td>\n",
       "      <td>Shashi Kumar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$500</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age          Name    No_of_pkg Package  travel_id\n",
       "0     20yrs  Bikash Kumar   1 packages   $100           1\n",
       "1     21yrs   Ashish Shaw   5 packages   $200           2\n",
       "2   23years   Dipak Kumar        2pkgs   $100           3\n",
       "3  20 Years      John Doe       3 pkgs   $100           4\n",
       "4       NaN        Elisha          NaN   $400           5\n",
       "5       NaN     Md Shahid  10 packages   $200           6\n",
       "6    21 yrs    Adrika Roy        7pkgs   $300           7\n",
       "7    24 yrs  Shashi Kumar          NaN   $500          10"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.replace({\n",
    "    \"5000\":np.NaN,\n",
    "    \"2000\":np.NaN,\n",
    "       8:10\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** All the values in the dataframe that belongs to keys of disctionary will be replaced no matter what column it is."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing unnecessary character from columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Suppose your dataframe contains unnecessary characters with your data values.Here years/yrs/Yrs/Years\n",
    "    in Age column, same in the No_of_pkg & Package columns are unnecessary charactors which you dont want \n",
    "    and these charactor will prevent you from applying any kind of operation in data analysis.So you want\n",
    "    to get rid of it.In that case you have to pass regex as a value and column name as a key of the \n",
    "    disctionary which you have passed in the replace() function as well as you also have to set regex=True\n",
    "    and a pass an empty string\n",
    "    \n",
    "* ** <code> [A-Za-z] </code> ** : This is the regex of all the character from A to Z and a to z.\n",
    "* <b><code> \\$ </code></b> : This is the regex for **<code>$</code>** sign.\n",
    "* For futher information about regex go to this link https://medium.com/factory-mind/regex-tutorial-a-simple-cheatsheet-by-examples-649dc1c3f285\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Name</th>\n",
       "      <th>No_of_pkg</th>\n",
       "      <th>Package</th>\n",
       "      <th>travel_id</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20</td>\n",
       "      <td>Bikash Kumar</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21</td>\n",
       "      <td>Ashish Shaw</td>\n",
       "      <td>5</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23</td>\n",
       "      <td>Dipak Kumar</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>John Doe</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Elisha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Md Shahid</td>\n",
       "      <td>10</td>\n",
       "      <td>200</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>21</td>\n",
       "      <td>Adrika Roy</td>\n",
       "      <td>7</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>24</td>\n",
       "      <td>Shashi Kumar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age          Name No_of_pkg Package  travel_id\n",
       "0   20  Bikash Kumar        1     100           1\n",
       "1   21   Ashish Shaw        5     200           2\n",
       "2   23   Dipak Kumar         2    100           3\n",
       "3  20       John Doe        3     100           4\n",
       "4  NaN        Elisha       NaN    400           5\n",
       "5  NaN     Md Shahid       10     200           6\n",
       "6  21     Adrika Roy         7    300           7\n",
       "7  24   Shashi Kumar       NaN    500          10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=df2.replace({\n",
    "    'Age':'[A-Za-z]',\n",
    "    'No_of_pkg':'[A-Za-z]',\n",
    "    'Package':'\\$'\n",
    "},\"\",regex=True)\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mapping from one list to another list "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     If your dataset contains data which is repeating more than once or you want to change some set of string in to\n",
    "     number then you have apply list mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>grades</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>poor</td>\n",
       "      <td>Shahid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>excellent</td>\n",
       "      <td>Adrika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>very good</td>\n",
       "      <td>Bikash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>average</td>\n",
       "      <td>Ashish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>good</td>\n",
       "      <td>Ganesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>very good</td>\n",
       "      <td>Zahid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>outstanding</td>\n",
       "      <td>Mohan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>poor</td>\n",
       "      <td>Sohan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        grades    name\n",
       "0         poor  Shahid\n",
       "1    excellent  Adrika\n",
       "2    very good  Bikash\n",
       "3      average  Ashish\n",
       "4         good  Ganesh\n",
       "5    very good   Zahid\n",
       "6  outstanding   Mohan\n",
       "7         poor   Sohan"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydis={\n",
    "    \"name\":[\"Shahid\",\"Adrika\",\"Bikash\",\"Ashish\",\"Ganesh\",\"Zahid\",\"Mohan\",\"Sohan\"],\n",
    "    \"grades\":[\"poor\",\"excellent\",\"very good\",\"average\",\"good\",\"very good\",\"outstanding\",\"poor\"]\n",
    "      }\n",
    "df=pd.DataFrame(mydis)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>grades</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>Shahid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>Adrika</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>Bikash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>Ashish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Ganesh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>Zahid</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>Mohan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5</td>\n",
       "      <td>Sohan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   grades    name\n",
       "0       5  Shahid\n",
       "1       9  Adrika\n",
       "2       8  Bikash\n",
       "3       6  Ashish\n",
       "4       7  Ganesh\n",
       "5       8   Zahid\n",
       "6      10   Mohan\n",
       "7       5   Sohan"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=df.replace([\"poor\",\"average\",\"good\",\"very good\",\"excellent\",\"outstanding\"],[5,6,7,8,9,10])\n",
    "df2"
   ]
  }
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