Array From Existing Data - NumPy Basics

NumPy is a Python library used for numerical computations. It provides a powerful ndarray object that can be used to represent arrays and matrices. There are several ways to create an array in NumPy, and one of the simplest is to create an array from existing data.

Syntax

The syntax for creating an array from existing data in NumPy is as follows:

numpy.array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)

Here, object can be a list, tuple, or any sequence-like object, and dtype specifies the data type of the resulting array.

Example

Consider the following example, where we create an array from an existing list:

import numpy as np

my_list = [1, 2, 3, 4, 5]
my_array = np.array(my_list)

print(my_array)

Output:

[1 2 3 4 5]

Here, we first import the NumPy library using the alias np. We then create a list called my_list with the values [1, 2, 3, 4, 5]. Finally, we create a NumPy array called my_array using the np.array() method and pass in my_list as the object.

Explanation

Creating an array from existing data in NumPy involves passing an existing sequence-like object, such as a list or tuple, to the np.array() method. NumPy automatically creates an array with the same data types and values as the original object.

Use

Creating an array from existing data in NumPy is useful when you have a pre-existing sequence of data that you want to convert to a NumPy array for faster and easier computation.

Important Points

• In NumPy, all elements of an array must be of the same data type.
• NumPy can handle large amounts of data much more efficiently than traditional Python lists.
• NumPy arrays are mutable, which means that they can be modified after creation.

Summary

Creating an array from existing data in NumPy is a simple process that involves passing an existing sequence-like object to the np.array() method. This is useful when you have pre-existing data that you want to convert to a NumPy array for faster and easier computation. NumPy arrays are efficient and mutable, making them an important tool for numerical computations in Python.