Condition for Replacing NaN with Zero in a Dataset
Description:
In data preprocessing, missing values represented by NaN (Not a Number) can be problematic
for analyses and machine learning models.
Replacing NaN values with zero is a common approach to handle missing data.
The Pandas library provides the fillna() method to replace NaN values efficiently.
Step-by-Step Process
Import Libraries: Import the necessary libraries, such as Pandas, for data manipulation.
Load the Dataset: Load your dataset into a Pandas DataFrame.
Identify Missing Values: Use isna() or isnull() to check for NaN values.
Replace NaN with Zero: Use the fillna(0) method to replace all NaN values in the dataset with zero.
Save or Use the Updated DataFrame: Save the modified dataset or proceed with further analysis.