﻿ Applying custom function to salary: Pandas DataFrame operation

# Applying custom function to salary: Pandas DataFrame operation

## Python Pandas Numpy: Exercise-29 with Solution

Apply a custom function to each element in a Pandas DataFrame.

Sample Solution:

Python Code:

``````import pandas as pd

# Create a sample DataFrame
data = {'Name': ['Imen', 'Karthika', 'Cosimo', 'Cathrine'],
'Age': [25, 30, 22, 35],
'Salary': [50000, 60000, 45000, 70000]}

df = pd.DataFrame(data)

# Define a custom function
def double_salary(salary):
return salary * 2

# Apply the custom function to the 'Salary' column
df['Double_Salary'] = df['Salary'].apply(double_salary)

# Display the DataFrame with the new column
print(df)
```
```

Output:

```       Name  Age  Salary  Double_Salary
0      Imen   25   50000         100000
1  Karthika   30   60000         120000
2    Cosimo   22   45000          90000
3  Cathrine   35   70000         140000
```

Explanation:

Here's a breakdown of the above code:

• First we create a sample DataFrame (df) with columns 'Name', 'Age', and 'Salary'.
• Next we define a custom function "double_salary" that doubles the input value.
• The df['Salary'].apply(double_salary) line applies the custom function to each element in the 'Salary' column.
• The result is stored in a new column 'Double_Salary', which is added to the DataFrame.
• The DataFrame with the new column is printed.

Flowchart:

Python Code Editor:

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