Replacing missing values using Pandas in Python

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

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:

Output:

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

Output: