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How to drop infinite values from DataFrames in Pandas?

Given a DataFrame, we have to drop infinite values from it. By Pranit Sharma Last updated : September 20, 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. DataFrame can be created with the help of python dictionaries or lists but in the real world, CSV files are imported and then converted into DataFrames. Inside DataFrame, certain values can not be analyzed or operated to analyze the data efficiently.

Problem statement

Given a DataFrame, we have to drop infinite values from it.

Dropping infinite values from DataFrames in Pandas

For this purpose, first, we will replace all the infinite values with "NaN" using the df.replace() method, and then we will drop all the "NaN" values using the df.drop() method with the parameter inplace=True.

pandas.DataFrame.replace() Method

The pandas.DataFrame.replace() method in Pandas is a simple method that takes two parameters, first is the value of the string, list, dictionary, etc which has to be replaced. Secondly, it takes the value with which our data has to be replaced.

pandas.DataFrame.drop() Method

This method is used to remove a specified row or column from the pandas DataFrame. Since rows and columns are based on index and axis values respectively, by passing index or axis value inside pandas.DataFrame.drop() method we can delete that particular row or column.

import pandas as pd

Let us understand with the help of an example.

Python program to drop infinite values from DataFrames in Pandas

# Importing pandas package
import pandas as pd

# Importing numpy package
import numpy as np

# Create a dictionary for the dataframe
dict = {
    'Name': ['Sudhir', 'Pranit', 'Ritesh','Sanskriti', 'Rani'],
    'Age': [16, 27, np.inf, -np.inf, 29],
    'Marks': [40, 24, 50, 48, 33]
}

# Converting Dictionary to Pandas Dataframe
df = pd.DataFrame(dict)

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

# Replacing infinite with nan
df.replace([np.inf, -np.inf], np.nan, inplace=True)

# Display df with NaN values
print("DataFrame with NaN values:\n",df,"\n")

# Dropping the rows with nan values
df.dropna(inplace=True)

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

Output

The output of the above program is:

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