NumPy Python Library Cheat sheet

HunerOn Cheatsheet #01

NumPy Cheatsheet Overview

This NumPy cheat sheet is designed to help users quickly recap and refresh their memory on the syntax and methods of the NumPy library. It contains a collection of essential functions for creating and manipulating arrays, performing mathematical operations on them, and calculating aggregate statistics. The cheat sheet also includes functions for reshaping and transposing arrays, concatenating and stacking them, and splitting them into smaller arrays.

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Please be aware that this is just a quick view of the first release. To access the latest version with all updates, we recommend downloading or viewing it on our GitHub repository, which also includes a Jupyter Notebook file that allows you to run each command separately, making it even easier to understand and learn.

# INSTALLING NumPy ----------------------------------------
# pip install numpy


# NumPy CODEBASE ----------------------------------------
# https://github.com/numpy/numpy


# IMPORTING NumPy ----------------------------------------
import numpy as np


# CREATING NumPy ARRAYS ----------------------------------------
# From Python List
np.array([1, 2, 3])

# 1D Array with Zeros
np.zeros(5)

# 2D Array with Ones
np.ones((2, 3))

# Array with Linspace
np.linspace(0, 20, num=10)

# Array with a Range
np.arange(10)

# Array with a Range and a Step
np.arange(5, 51, 5)

# Array with Random Values
np.random.rand(3, 2)

# Array with Custom Values
np.array([[1, 2], [3, 4]])


# ACCESSING ELEMENTS IN ARRAYS ----------------------------------------
# Array
arr1 = np.array([1, 2, 3])

# Access the first element
arr1[0]

# Access elements from index 1 to 3
arr1[1:3]

# Access elements from the start to index 2
arr1[:2]

# Access elements from index 1 to the end
arr1[1:]

# Access the last element
arr1[-1]


# SLICING ----------------------------------------
# Array 
arr2 = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

# Slice rows from index 1 to 2
arr2[1:3, :]

# Slice columns from index 0 to 1
arr2[:, 0:2]


# CONCATENATING ----------------------------------------
# Arrays
a1 = np.array([1, 2, 3])
b1 = np.array([4, 5, 6])

# Concatenate
np.concatenate((a1, b1))

# Vertical
np.vstack((a1, b1))

# Horizontal
np.hstack((a1, b1))


# STACKING ----------------------------------------
# Arrays
a2 = np.array([1, 2, 3])
b2 = np.array([4, 5, 6])

# with axis 0
np.stack((a2, b2), axis=0)

# with axis 1
np.stack((a2, b2), axis=1)


# SPILITING ----------------------------------------
# Arrray
arr3 = np.array([1, 2, 3, 4, 5, 6])

np.split(arr3, 3)
print(np.split(arr3, [2, 4]))


# BROADCASTING ----------------------------------------
a3 = np.array([1, 2, 3])
b3 = 2

# Multiply 
a3 * b3

# Plus
a3 + b3


# UNIVERSAL FUNCTIONS ----------------------------------------
# Array 
arr4 = np.array([1, 2, 3])

# Sine
np.sin(arr4)

# Cosine 
np.cos(arr4)

# Exponential
np.exp(arr4)


# AGGREGATE FUNCTIONS ----------------------------------------
# Array 
arr5 = np.array([1, 2, 3])

# Minimum
arr5.min()

# Maxiumum
arr5.max()

# Varience
arr5.var()

# Sum
arr5.sum()

# Mean
arr5.mean()

# Standard Deviation
arr5.std()

# Any
arr5.any()

# All
arr5.all()

# Product
arr5.prod()


# ARRAY MANIPULATION ----------------------------------------
# Transposing an Array
arr6 = np.array([[1, 2], [3, 4]])

arr6.transpose()

# Reshaping an Array
arr7 = np.array([1, 2, 3, 4])

arr7.reshape((2, 2))

# Flattening an Array
arr8 = np.array([[1, 2], [3, 4]])

arr8.flatten()


# ITERATING ----------------------------------------
arr9 = np.array([[1, 2], [3, 4]])

for x in np.nditer(arr9):
    print(x)


# MASKING ----------------------------------------
arr10 = np.array([1, 2, 3, 4, 5, 6])

arr10[arr10 > 3]


# MESHGRID ----------------------------------------
x = np.array([1, 2, 3])
y = np.array([4, 5, 6])

X, Y = np.meshgrid(x, y)
print(X, Y)