Pandas filling NaNs in categorical data

Learn, how can we fill the NaN values in categorical data? By Pranit Sharma Last updated : September 30, 2023

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and data.

While creating a DataFrame or importing a CSV file, there could be some NaN values in the cells. NaN values mean "Not a Number" which generally means that there are some missing values in the cell.

Categorical data

Categorical data is a type of data that has some certain category or characteristic, the value of categorical data is not a single value, rather it consists of classified values, for example, an email can be considered spam or not spam, if we consider 1 as spam and 0 as not spam, we have a classified data in the form of 0 or 1, this is called categorical data.

Filling NaN values in categorical data

For this purpose, we will use the .add_categories("E").fillna("E"), where "E" is the value to be filled instead of NaN in the categorial data.

Let us understand how to fill NaN values in categorical Data.

Python program for filling NaNs in categorical data

# Importing pandas package
import pandas as pd

# Importing numpy package
import numpy as np

# Creating a list
s = ['A','B','C','D',np.nan]

# Creating a Series
se = pd.Series(s,dtype='category')

# Display Series
print("Created Series:\n",se)

# Filling NaN values in this series
result ="E").fillna("E")

# Display result


The output of the above program is:

Example: Pandas filling NaNs in categorical data

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