Numpy Cheatsheet.txt

Numpy Cheatsheet
Numpy is a powerful library for scientific computing in Python. It provides a variety of functions and operations for working with arrays and matrices, as well as tools for linear algebra, random number generation, and statistical analysis.

Installing Numpy
To use Numpy, you will first need to install it using pip:


pip install numpy
Importing Numpy
To use Numpy in your code, you will need to import it using the following import statement:


import numpy as np
Creating Arrays
There are several ways to create arrays in Numpy:

From a list or tuple
You can create a Numpy array from a list or tuple using the array() function:


a = np.array([1, 2, 3])
print(a)  # [1 2 3]

b = np.array((4, 5, 6))
print(b)  # [4 5 6]
Using special functions
You can also create arrays using special functions provided by Numpy, such as zeros(), ones(), and empty():


a = np.zeros(3)  # [0. 0. 0.]
b = np.ones(4)  # [1. 1. 1. 1.]
c = np.empty(5)  # [1. 1. 1. 1. 1.]
Using arange()
The arange() function allows you to create an array with a range of values:


a = np.arange(0, 10, 2)  # [0 2 4 6 8]
Array Attributes
You can access various attributes of a Numpy array using the following properties:


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

print(a.shape)  # (3,)
print(a.ndim)  # 1
print(a.dtype)  # dtype('int64')
print(a.size)  # 3
print(a.itemsize)  # 8 (size of each element in bytes)
print(a.nbytes)  # 24 (total size in bytes)
Array Indexing and Slicing
You can access elements of a Numpy array using indexing and slicing, similar to Python lists:


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

print(a[0])  # 1
print(a[-1])  # 6
print(a[1:3])  # [2 3]
print(a[::-1])  # [6 5 4 3 2 1]
Array Reshaping
Reshape array to new shape

np.reshape(array, new_shape)
Flatten array to one-dimensional

array.flatten()
Transpose array

array.T

Array Stacking and Splitting
Stack arrays vertically (row-wise)

np.vstack((array1, array2))
Stack arrays horizontally (column-wise)

np.hstack((array1, array2))
Stack arrays in sequence depth-wise (along third dimension)

np.dstack((array1, array2))
Split array along rows

np.split(array, indices_or_sections, axis=0)
Split array along columns

np.split(array, indices_or_sections, axis=1)
Split array along depth (third dimension)

np.dsplit(array, indices_or_sections)