Generating a Pandas DataFrame from a NumPy array with custom column names in Python

Python Pandas Numpy: Exercise-2 with Solution

Create a DataFrame from a NumPy array with custom column names.

Sample Solution:

Python Code:

import pandas as pd
import numpy as np

# Create a NumPy array
numpy_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Define custom column names
column_names = ['Column1', 'Column2', 'Column3']

# Create a DataFrame with custom column names
df = pd.DataFrame(data=numpy_array, columns=column_names)

# Display the DataFrame
print(df)

Output:

   Column1  Column2  Column3
0        1        2        3
1        4        5        6
2        7        8        9

Explanation:

• Importing Libraries:
  import pandas as pd
  import numpy as np
  Imports the Pandas and NumPy libraries with the aliases "pd" and "np".
• Creating a NumPy Array:
  numpy_array = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) Creates a 2D NumPy array named "numpy_array".
• Defining Custom Column Names:
  column_names = ['Column1', 'Column2', 'Column3']
  Defines custom column names in a list named "column_names".
• Creating a DataFrame with Custom Column Names:
  df = pd.DataFrame(data=numpy_array, columns=column_names)
  Uses the pd.DataFrame constructor to create a DataFrame (df) from the NumPy array with specified column names.
• Displaying the DataFrame:
  print(df)

Flowchart:

Python Code Editor:

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