NumPy Python Library Cheatsheet

NumPy, short for Numerical Python, is a powerful library in the Python ecosystem that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these elements. Whether you’re a data scientist, machine learning engineer, or a scientific researcher, mastering NumPy is essential for efficient numerical computations. In this cheatsheet, we’ll cover the fundamental operations and features that will empower you to wield NumPy with confidence.

Installation

Before diving into NumPy, ensure that you have it installed. You can install it using:

pip install numpy

Importing NumPy

import numpy as np

Creating Arrays

1D Array

array_1d = np.array([1, 2, 3, 4, 5])

2D Array

array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

Common Initialization

zeros_array = np.zeros((3, 3))  # 3x3 array of zeros
ones_array = np.ones((2, 4))    # 2x4 array of ones
identity_matrix = np.eye(3)     # 3x3 identity matrix

Array Operations

Array Shape and Size

shape = array_2d.shape      # Shape of the array
size = array_2d.size        # Total number of elements in the array

Reshape Array

reshaped_array = array_1d.reshape(5, 1)

Transpose Array

transposed_array = array_2d.T

Indexing and Slicing

element = array_1d[2]           # Accessing a single element
subset = array_2d[1:3, 0:2]      # Slicing a 2D array

Element-wise Operations

sum_result = array_1d + 2       # Add 2 to each element
product_result = array_2d * 3    # Multiply each element by 3

Universal Functions

sqrt_result = np.sqrt(array_1d)  # Square root of each element
exp_result = np.exp(array_1d)    # Exponential of each element

Mathematical Operations

Basic Statistics

mean_value = np.mean(array_2d)
median_value = np.median(array_2d)
std_deviation = np.std(array_2d)

Linear Algebra

dot_product = np.dot(array_2d, array_2d.T)  # Dot product of two arrays
matrix_inverse = np.linalg.inv(array_2d)    # Inverse of a matrix

Random Number Generation

random_array = np.random.rand(3, 3)        # 3x3 array of random values between 0 and 1

Broadcasting

NumPy allows operations between arrays of different shapes and sizes, making your code concise and efficient.

broadcasted_result = array_2d + np.array([10, 20, 30])

Saving and Loading Arrays

np.save('saved_array.npy', array_2d)          # Save array to a file
loaded_array = np.load('saved_array.npy')     # Load array from a file

This NumPy cheatsheet covers the basics, but the library is vast and offers many more features and functions. Regular practice and exploration of the official NumPy documentation will deepen your understanding and proficiency. Armed with this cheatsheet, you are well-equipped to handle a wide range of numerical computations efficiently.

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