Pandas - Fill missing values in a DataFrame using a custom function

Last update on May 06 2025 13:18:54 (UTC/GMT +8 hours)

16. Fill Missing Values with Custom Function Using apply()

Write a Pandas program to apply a custom function to fill missing values in a DataFrame.

This exercise shows how to apply a custom function to fill missing values in a DataFrame based on column-specific logic.

Sample Solution :

Code :

import pandas as pd
import numpy as np

# Create a sample DataFrame with missing values
df = pd.DataFrame({
    'A': [1, 2, np.nan],
    'B': [np.nan, 5, 6],
    'C': [7, 8, np.nan]
})

# Define a custom function to fill missing values based on column logic
def fill_missing(x):
    if pd.isna(x):
        return 0  # Fill NaN with 0
    return x

# Apply the function element-wise using applymap()
df_filled = df.applymap(fill_missing)

# Output the result
print(df_filled)

Output:

     A    B    C
0  1.0  0.0  7.0
1  2.0  5.0  8.0
2  0.0  6.0  0.0

Explanation:

Created a DataFrame with missing values (NaN).
Defined a function fill_missing() that replaces missing values with 0.
Applied the function element-wise to the DataFrame using applymap().
Returned a DataFrame where all missing values have been filled with 0.

For more Practice: Solve these Related Problems:

Write a Pandas program to fill missing values in a DataFrame column by applying a custom function with apply() that returns the column mean.
Write a Pandas program to use apply() to impute missing values in each column based on a custom rule (e.g., forward fill or interpolation).
Write a Pandas program to apply a function via apply() that replaces NaN values with a computed statistic for that column.
Write a Pandas program to create a custom imputation function and apply it to a DataFrame to fill missing data conditionally.

Go to:

Previous: Use map() with Lambda for Custom Function.
Next: Normalize Data in a DataFrame with a Custom Function.

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