Creating a new column with NumPy operation in Pandas DataFrame

Last update on December 21 2024 07:43:29 (UTC/GMT +8 hours)

Create a new column in a Pandas DataFrame based on the result of a NumPy operation.

Sample Solution:

Python Code:

import pandas as pd
import numpy as np

# Create a sample DataFrame
data = {'Name': ['Teodosija', 'Sutton', 'Taneli', 'Ravshan', 'Ross'],
        'Age': [26, 32, 25, 31, 28],
        'Salary': [50000, 60000, 45000, 70000, 55000]}
df = pd.DataFrame(data)

# Perform a NumPy operation (e.g., multiply Age by 2) and create a new column
df['Double_Age'] = np.multiply(df['Age'], 2)
print("New Dataframe:")
# Display the DataFrame with the new column
print(df)

Output:

New Dataframe:
        Name  Age  Salary  Double_Age
0  Teodosija   26   50000          52
1     Sutton   32   60000          64
2     Taneli   25   45000          50
3    Ravshan   31   70000          62
4       Ross   28   55000          56

Explanation:

In the exerciser above -

• First create a sample DataFrame (df) with columns 'Name', 'Age', and 'Salary'.
• The NumPy operation np.multiply(df['Age'], 2) multiplies each element in the 'Age' column by 2.
• Create a new column 'Double_Age' in the DataFrame and assign the result of the NumPy operation to it.
• The DataFrame is then printed to the console, showing the newly added 'Double_Age' column.

Flowchart:

Python Code Editor:

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