Numpy

TL;DR

Provides fast, memory-efficient arrays for numerical data.
Supports element-wise arithmetic and a wide range of mathematical functions.
Includes linear algebra and aggregation routines commonly used in scientific and data analysis workflows.

Definition

Numpy is a powerful and versatile Python library that allows for efficient manipulation and analysis of large arrays and matrices of numerical data. It provides a variety of mathematical functions and operations that can be applied to these arrays.

Explanation

Numpy enables element-wise operations on arrays, letting you perform arithmetic across corresponding elements without explicit loops. It also supports linear algebra routines (for example, matrix multiplication, matrix inverse, determinants, and solving linear systems). In addition, Numpy offers array manipulation and aggregation functions such as sum, mean, min, max, and sorting, which are useful for data analysis and visualization. These capabilities make Numpy useful for scientific and technical computing and for working with both large arrays of data and simpler numerical tasks.

Examples

Element-wise addition

import numpy as np

array_1 = np.array([1, 2, 3, 4])

array_2 = np.array([5, 6, 7, 8])

array_3 = array_1 + array_2

print(array_3) # Output: [6 8 10 12]

Matrix multiplication (linear algebra)

import numpy as np

matrix_1 = np.array([[1, 2], [3, 4]])

matrix_2 = np.array([[5, 6], [7, 8]])

result = np.dot(matrix_1, matrix_2)

print(result) # Output: [[19 22], [43 50]]

Aggregation functions (sum, mean)

import numpy as np

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

result = np.sum(array)

print(result) # Output: 10

import numpy as np

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

result = np.mean(array)

print(result) # Output: 2.5

Use cases

Scientific and technical computing.
Data analysis and visualization.
Working with large arrays of numerical data or performing simple numerical operations.

Arrays
Matrices
Linear algebra