NumPy and Pandas are two powerful libraries in the Python ecosystem for data manipulation and analysis. Converting a DataFrame column to a NumPy array is a common operation when you need to perform array-based operations on the data. In this section, we will explore various methods to achieve this task.

Prerequisites

1. NumPy Arrays: NumPy arrays are the core data structure in the NumPy library. They provide a way to store and manipulate numerical data efficiently. Converting a DataFrame column to a NumPy array allows you to leverage the array’s functionality for various mathematical operations.
2. DataFrame Column Selection : In Pandas, accessing a single column of a DataFrame results in a Pandas Series. Converting this Series to a NumPy array is a straightforward process.

Step 1: Creating a sample dataset for demonstration:

Output:

Original DataFrame:
   Numeric_Column
0              51
1              92
2              14
3              71
4              60

Step 2: Using methods and examples

a. Using the values Attribute:

The values attribute in Pandas returns the underlying data as a NumPy array. This is a simple and direct way to convert a DataFrame column to a NumPy array.

Output: The output numpy_array_values is a NumPy array containing the values from the ‘Numeric_Column’ of the DataFrame df.

[51 92 14 71 60]

b. to_numpy() method:

The to_numpy() method in Pandas converts the DataFrame or Series to a NumPy array. It provides flexibility and options for handling different data types.

Output:

The output shows numpy_array_to_numpy now holds the NumPy array representation of the ‘Numeric_Column’.

[51 92 14 71 60]

c. asarray() method:

The asarray() function in NumPy converts the input to an array. It can be applied to a Pandas Series to convert it into a NumPy array.

Output: numpy_array_asarray shows the NumPy array representation of the ‘Numeric_Column’ obtained through the asarray() function

NumPy Array using asarray() function:
[51 92 14 71 60]