# Pandas - Apply Different Functions on DataFrame Columns with apply()

## Pandas: Custom Function Exercise-5 with Solution

Write a Pandas program that applies different functions on DataFrame columns using apply().

Apply different functions to different columns of a DataFrame by passing a dictionary to apply().

**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 different custom functions
def add_one(x):
return x + 1
def multiply_by_two(x):
return x * 2
# Apply different functions to columns
df['A'] = df['A'].apply(add_one)
df['B'] = df['B'].apply(multiply_by_two)
# Output the result
print(df)
```

Output:

A B C 0 2 8 7 1 3 10 8 2 4 12 9

**Explanation:**

- Created a DataFrame with columns 'A', 'B', 'C'.
- Defined two functions: add_one() and multiply_by_two().
- Applied add_one() to column 'A' and multiply_by_two() to column 'B'.
- Displayed the modified DataFrame.

**Python-Pandas Code Editor:**

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