Pandas - Applying a Custom Function to Columns using apply()

Pandas: Custom Function Exercise-3 with Solution

Write a Pandas program that apply a custom function to each column using apply() function.

In this exercise, we have applied a custom function to calculate the mean of each column using apply() column-wise.

Sample Solution:

Code :

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

# Define a custom function to calculate the mean of a column
def column_mean(column):
    return column.mean()

# Apply the custom function column-wise to calculate the mean of each column
means = df.apply(column_mean, axis=0)

# Add the means as a new row to the DataFrame
df.loc['Mean'] = means

# Output the result
print(df) 

Output:

        A    B    C
0     1.0  4.0  7.0
1     2.0  5.0  8.0
2     3.0  6.0  9.0
Mean  2.0  5.0  8.0                                    

Explanation:

• Create a DataFrame: The sample DataFrame is created with columns 'A', 'B', and 'C', each containing 3 numerical values.
• Define Custom Function: The column_mean function is defined to calculate the mean of a column using column.mean().
• Apply Function to Columns: The apply() function is used with axis=0 to apply column_mean to each column of the DataFrame, calculating the mean of each column.
• Add Means as a Row: The calculated column means are added as a new row labeled 'Mean' using df.loc['Mean'].

Python-Pandas Code Editor:

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