NumPy Array in Python (original) (raw)

Last Updated : 24 Jan, 2025

**NumPy (Numerical Python) is a powerful library for numerical computations in Python. It is commonly referred to multidimensional container that holds the same data type. It is the core data structure of the NumPy library and is optimized for numerical and scientific computation in Python.

Table of Content

• Create NumPy Arrays
• Key Attributes of NumPy Arrays
• Operations on NumPy Arrays
• Dimensions in NumPy Arrays
• NumPy Arrays vs Python Lists
• Data Allocation in Numpy Array

In this article, we will explore **NumPy Array in Python.

Create NumPy Arrays

To start using NumPy, import it as follows:

import numpy as np

NumPy array’s objects allow us to work with arrays in Python. The array object is called ndarray. NumPy arrays are created using the array() function

**Example:

Python `

import numpy as np

Creating a 1D array

x = np.array([1, 2, 3])

Creating a 2D array

y = np.array([[1, 2], [3, 4]])

Creating a 3D array

z = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])

print(x) print(y) print(z)

`

Output

[1 2 3] [[1 2] [3 4]] [[[1 2] [3 4]]

[[5 6] [7 8]]]

Key Attributes of NumPy Arrays

NumPy arrays have attributes that provide information about the array:

• shape : Returns the dimensions of the array.
• dtype: Returns the data type of the elements.
• ndim: Returns the number of dimensions.

**Example:

Python `

import numpy as np arr = np.array([[1, 2, 3], [4, 5, 6]])

print(arr.shape)
print(arr.dtype)
print(arr.ndim)

`

Operations on NumPy Arrays

NumPy supports element-wise and matrix operations, including addition, subtraction, multiplication, and division:

**Example:

Python `

import numpy as np

Element-wise addition

x = np.array([1, 2, 3]) y = np.array([4, 5, 6]) print(x + y) # Output: [5 7 9]

Matrix multiplication

a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6], [7, 8]]) print(np.dot(a, b))

`

Output

[5 7 9] [[19 22] [43 50]]

Dimensions in NumPy Arrays

NumPy arrays can have multiple dimensions, allowing users to store data in multilayered structures.

**NumPy Arrays vs Python Lists

• **Fixed Size: Arrays have a fixed size, while lists can dynamically grow.
• **Homogeneous Data: Arrays require uniform data types; lists can store mixed types.
• **Performance: Arrays are faster due to their optimized implementation.
• **Memory Efficiency: Arrays use contiguous memory blocks, unlike lists.