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


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:

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

Python Code Editor:

Previous: Loading a CSV file into a Pandas DataFrame with Python.
Next: Selecting rows based on multiple conditions in Pandas DataFrame.

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