NumPy: Python’s Numerical Powerhouse

Introduction NumPy (Numerical Python) is a fast and powerful library used for numerical computing in Python. It provides support for multi-dimensional arrays, mathematical functions, and efficient data handling, making it a must-have for data science, machine learning, and scientific computing. Installing Numpy To install Numpy, simply run: pip install numpy Creating a NumPy Array import numpy as np arr = np.array([1, 2, 3, 4, 5]) print(arr) Basic Operations with NumPy a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print(a + b) print(a * b) print(np.dot(a, b)) Reshaping and Slicing Arrays matrix = np.array([[1, 2, 3], [4, 5, 6]]) print(matrix.shape) print(matrix[0, :]) print(matrix[:, 1]) Why Use NumPy? ✅ Faster than Python lists ✅ Built-in mathematical functions ✅ Efficient memory management NumPy is essential for anyone working with data in Python! Try it out and unlock the full potential of numerical computing.

Apr 3, 2025 - 15:38

Introduction

NumPy (Numerical Python) is a fast and powerful library used for numerical computing in Python. It provides support for multi-dimensional arrays, mathematical functions, and efficient data handling, making it a must-have for data science, machine learning, and scientific computing.

Installing Numpy

To install Numpy, simply run:

pip install numpy

Creating a NumPy Array

import numpy as np

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

Basic Operations with NumPy

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

print(a + b)
print(a * b)
print(np.dot(a, b))

Reshaping and Slicing Arrays

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

print(matrix[0, :])
print(matrix[:, 1])

Why Use NumPy?

✅ Faster than Python lists
✅ Built-in mathematical functions
✅ Efficient memory management

NumPy is essential for anyone working with data in Python! Try it out and unlock the full potential of numerical computing.