Applying NumPy function to DataFrame column in Python

Python Pandas Numpy: Exercise-10 with Solution

Apply a NumPy function to a Pandas DataFrame column.

Sample Solution:

Python Code:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'Name': ['Teodosija', 'Sutton', 'Taneli', 'David', 'Ross'],
        'Age': [25, 30, 22, 35, 28],
        'Salary': [50000, 60000, 45000, 70000, 55000]}

df = pd.DataFrame(data)

# Define a NumPy function (e.g., np.sqrt) to apply to the 'Salary' column
df['Sqrt_Salary'] = np.sqrt(df['Salary'])

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

Output:

        Name  Age  Salary  Sqrt_Salary
0  Teodosija   25   50000   223.606798
1     Sutton   30   60000   244.948974
2     Taneli   22   45000   212.132034
3      David   35   70000   264.575131
4       Ross   28   55000   234.520788

Explanation:

In the exerciser above -

• First we create a sample DataFrame (df) with columns 'Name', 'Age', and 'Salary'.
• Next we use the NumPy function np.sqrt() to calculate the square root of each element in the 'Salary' column.
• The result is assigned to a new column 'Sqrt_Salary'.
• The updated DataFrame is then printed to the console.

Flowchart:

Python Code Editor:

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