Replacing missing values using Pandas in Python (original) (raw)

Last Updated : 23 Jul, 2025

Dataset is a collection of attributes and rows. Data set can have missing data that are represented by NA in Python and in this article, we are going to replace missing values in this article

We consider this data set: Dataset

data set

In our data contains missing values in quantity, price, bought, forenoon and afternoon columns,

So, We can replace missing values in the quantity column with mean, price column with a median, Bought column with standard deviation. Forenoon column with the minimum value in that column. Afternoon column with maximum value in that column.

Approach:

• Import the module
• Load data set
• Fill in the missing values
• Verify data set

Syntax:

Mean: data=data.fillna(data.mean())

Median: data=data.fillna(data.median())

Standard Deviation: data=data.fillna(data.std())

Min: data=data.fillna(data.min())

Max: data=data.fillna(data.max())

Below is the Implementation:

Python3 `

importing pandas module

import pandas as pd

loading data set

data = pd.read_csv('item.csv')

display the data

print(data)

`

Output:

Then after we will proceed with Replacing missing values with mean, median, mode, standard deviation, min & max

Python3 `

replacing missing values in quantity

column with mean of that column

data['quantity'] = data['quantity'].fillna(data['quantity'].mean())

replacing missing values in price column

with median of that column

data['price'] = data['price'].fillna(data['price'].median())

replacing missing values in bought column with

standard deviation of that column

data['bought'] = data['bought'].fillna(data['bought'].std())

replacing missing values in forenoon column with

minimum number of that column

data['forenoon'] = data['forenoon'].fillna(data['forenoon'].min())

replacing missing values in afternoon column with

maximum number of that column

data['afternoon'] = data['afternoon'].fillna(data['afternoon'].max())

print(Data)

`

Output: