How can I Fill Missing Values in One Column with Data From Another Column in Pandas?

Utilizing the Power of fillna() to Impute Missing Values with an Entire Column

In the realm of data manipulation, it is often necessary to impute missing values to ensure data integrity. Pandas, a versatile data analysis library, provides the fillna() method to efficiently handle this task. However, extending its functionality to fill missing values with an entire column requires a specific approach.

Previous attempts to fill missing values in one column with corresponding values from another column often involved inefficient row-by-row looping. To optimize performance and adhere to best practices, an alternative method leveraging fillna() is essential.

Here's how to effectively pass an entire column as an argument to fillna():

import pandas as pd

# Create a DataFrame with missing values
df = pd.DataFrame({'Day': [1, 2, 3, 4],
                   'Cat1': ['cat', 'dog', 'cat', np.nan],
                   'Cat2': ['mouse', 'elephant', 'giraf', 'ant']})

# Fill missing values in Cat1 using values from Cat2
df['Cat1'].fillna(df['Cat2'], inplace=True)

# Display the imputed DataFrame
print(df)

This code successfully fills the missing value in 'Cat1' on the fourth row with 'ant,' extracted from the corresponding row in 'Cat2.' The resulting DataFrame exhibits complete data, ensuring its validity for subsequent analysis.

By leveraging fillna()'s ability to accept column arguments, you can efficiently impute missing values with data from another column in a single operation. This approach not only enhances data quality but also optimizes computational efficiency, making it an indispensable tool in your data wrangling toolbox.

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