# Numpy Array Operations in Python

In this Numpy tutorial we want to learn about Numpy Array Operations in Python, Numpy is a powerful library in Python for numerical computations, and it provides support for multi-dimensional arrays and different mathematical operations. it has the ability to efficiently manipulate arrays, also it is an essential tool for scientific computing, data analysis, and machine learning tasks. in this article we want to talk about some common numpy array operations.

First of all you need to install Numpy, for that you can use pip.

## How to Create Numpy Arrays

Before we dive into array operations, let’s start by creating numpy arrays. Numpy provides different methods to create arrays, including:

• Using the np.array() function: This function takes a Python list or tuple and converts it into a numpy array.
• Using functions like np.zeros(), np.ones() and np.arange(): These functions create arrays of zeros, ones, or a range of values.

This is an example

Run the complete code and this will be the result

## Numpy Array Manipulation

Numpy provides several functions to manipulate arrays, such as reshaping, slicing and concatenating.

• Reshaping Arrays: The np.reshape() function allows us to change the shape of an array, while maintaining the same elements.

This is an example

This will be the result

• Concatenating Arrays: Numpy provides functions like np.concatenate() and np.vstack() to concatenate arrays vertically or horizontally.

This will be the result

## Numpy Mathematical Operations

Numpy simplifies mathematical computations by allowing element wise operations on arrays, as well as matrix operations.

• Element-wise Operations: Numpy supports arithmetic operations like addition, subtraction, multiplication and division on arrays.

Example:

• Matrix Operations: Numpy provides functions for matrix operations like dot product (np.dot()), matrix multiplication (np.matmul()) and transpose (np.transpose()).

Example: