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# NumPy Cheatsheet

NumPy is a popular Python library for numerical computing. It provides a wide range of tools for working with arrays, matrices, and numerical operations. This cheatsheet provides a quick reference for some of NumPy's unique features, including code blocks for creating arrays, indexing, slicing, broadcasting, and more. Additionally, it includes a list of resources for further learning.

## Creating Arrays

import numpy as np

Create a 1D array

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

Create a 2D array

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

Create an array of zeros

np.zeros((3, 3))

Create an array of ones

np.ones((2, 2))

Create an array with a range of values

np.arange(0, 10, 2)

Create an array with random values

np.random.rand(3, 3)

Create an array with normally distributed random values

np.random.randn(3, 3)


## Indexing and Slicing

Index a 1D array

x[0]

Index a 2D array

y[0, 1]

Slice a 1D array

x[1:3]

Slice a 2D array

y[:, 1]

Boolean indexing

x[x > 2]


## Broadcasting

Add a scalar to an array

x + 1

Add two arrays

x + y

Multiply two arrays

x * y

Multiply an array by a scalar

x * 2


## Other Useful Features

Compute the dot product of two arrays

np.dot(x, y)

Transpose an array

y.T

Reshape an array

x.reshape((3, 1))

Compute the sum of an array

x.sum()

Compute the mean of an array

x.mean()

Compute the standard deviation of an array

x.std()


## Resources

- [NumPy documentation](https://numpy.org/doc/stable/)
- [NumPy quickstart tutorial](https://numpy.org/doc/stable/user/quickstart.html)
- [Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/index.html)