Performing element-wise addition in Pandas DataFrame with NumPy array

Last update on December 21 2024 07:43:31 (UTC/GMT +8 hours)

Perform element-wise addition of a NumPy array and 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)

# Create a NumPy array
numpy_array = np.array([1000, 2000, 3000, 4000, 5000])

# Perform element-wise addition using numpy.add()
df['Updated_Salary'] = np.add(df['Salary'], numpy_array)

# Display the updated DataFrame
print(df)

Output:

        Name  Age  Salary  Updated_Salary
0  Teodosija   25   50000           51000
1     Sutton   30   60000           62000
2     Taneli   22   45000           48000
3      David   35   70000           74000
4       Ross   28   55000           60000

Explanation:

In the exerciser above -

• First we create a sample DataFrame (df) with columns 'Name', 'Age', and 'Salary'.
• Next we create a NumPy array numpy_array with values to add element-wise to the 'Salary' column.
• The numpy.add(df['Salary'], numpy_array) function performs element-wise addition, and the result is assigned to a new column 'Updated_Salary'.
• The updated DataFrame is then printed to the console.

Flowchart:

Python Code Editor:

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