How to combine a NumPy array and a Pandas DataFrame into one DataFrame?

NumPy: Interoperability Exercise-19 with Solution

Write a NumPy program to combine a NumPy array and a Pandas DataFrame into a single DataFrame and print it.

Sample Solution:

Python Code:

import numpy as np
import pandas as pd

# Create a NumPy array
array = np.array([[10, 20], [30, 40], [50, 60]])

# Create a Pandas DataFrame
data = {
    'A': [1, 2, 3],
    'B': ['a', 'b', 'c']
}
df = pd.DataFrame(data)

# Combine the NumPy array and Pandas DataFrame into a single DataFrame
# Adding columns to match the DataFrame structure
array_df = pd.DataFrame(array, columns=['C', 'D'])

# Concatenate both DataFrames
combined_df = pd.concat([df, array_df], axis=1)

# Print the combined DataFrame
print(combined_df)

Output:

   A  B   C   D
0  1  a  10  20
1  2  b  30  40
2  3  c  50  60

Explanation:

• Import NumPy and Pandas Libraries: Import the NumPy and Pandas libraries to handle arrays and DataFrames.
• Create NumPy Array: Define a NumPy array with some example data.
• Create Pandas DataFrame: Define a Pandas DataFrame with some example data, ensuring it has the same number of rows as the NumPy array.
• Convert Array to DataFrame: Convert the NumPy array to a DataFrame, specifying column names to match the structure of the original DataFrame.
• Combine DataFrames: Use pd.concat() to concatenate the original DataFrame and the new DataFrame created from the NumPy array along the columns (axis=1).
• Print Combined DataFrame: Output the resulting combined DataFrame to verify the merge.

Python-Numpy Code Editor:

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