Numpy

Welcome to the Numpy repository! This repo is dedicated to providing helpful resources, tutorials, and examples for using the Numpy library in Python.

Table of Contents

• Numpy
  • Table of Contents
  • Roadmap
  • Introduction
  • Installation
  • Usage
    • Creating an Array
    • Basic Operations
    • Advanced Operations
  • Features

Roadmap

Introduction

Numpy is a powerful and flexible open-source library for numerical computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.

This repository aims to help users of all skill levels to better understand and utilize the Numpy library through comprehensive guides, code snippets, and example projects.

Installation

To install Numpy, you can use pip, the Python package installer. Ensure you have Python installed, then run:

pip install numpy

For more detailed installation instructions, please refer to the official Numpy installation guide.

Usage

Here are some basic examples to get you started with Numpy:

Creating an Array

import numpy as np

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

Basic Operations

import numpy as np

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

# Element-wise addition
print(array + 5)

# Element-wise multiplication
print(array * 2)

Advanced Operations

import numpy as np

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

# Transpose
print(array.T)

# Dot product
print(np.dot(array, array.T))

For more examples and detailed tutorials, please refer to the official Numpy documentation.

Features

• Support for multi-dimensional arrays and matrices
• A collection of mathematical functions to operate on arrays
• Tools for integrating C/C++ and Fortran code
• Useful linear algebra, random number, and Fourier transform capabilities

⚖ ➤ License

This project is licensed under the MIT License. See LICENSE for details.

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