# 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'].

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