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How to replace blank values (white space) with NaN in Pandas?

Given a Pandas DataFrame, we have to replace blank values (white space) with NaN. By Pranit Sharma Last updated : September 22, 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 the 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.

Problem statement

Given a Pandas DataFrame, we have to replace blank values (white space) with NaN.

Replacing blank values with NaN in Pandas

For this purpose, we are going to use DataFrame.replace() method. This method is used to replace a string, regex, list, dictionary, series, number, etc. The syntax of this method is:

DataFrame.replace(
    to_replace=None, 
    value=_NoDefault.no_default, 
    *, 
    inplace=False, 
    limit=None, 
    regex=False, 
    method=_NoDefault.no_default
)

import pandas as pd

Let us understand with the help of an example,

Python program to replace blank values with NaN in Pandas

# Importing pandas package
import pandas as pd

# Imorting numpy package
import numpy as np

# Creating dictionary
d = {
    'Fruits':['Apple','Orange',' '],
    'Price':[50,40,30],
    'Vitamin':['C','D',' ']
}

# Creating DataFrame
df = pd.DataFrame(d)

# Display original DataFrame
print("Orginial DataFrame:\n",df,"\n")

# Replacing blank values with NaN values
df = df.replace(' ', np.nan, regex=True)

# Display modified DataFrame
print("Modified DataFrame:\n",df)

Output

The output of the above program is:

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