NumPy Arrays

Basic operations with arrays in NumPy

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Numpy is a powerful library for working with arrays in Python. It provides a variety of functions and methods for creating, indexing, slicing, and manipulating arrays. In this manual, we will cover the basics of creating arrays, indexing and slicing arrays, array operations and methods, and broadcasting and reshaping arrays.

Creating Arrays

There are several ways to create arrays in Numpy. The most common way is to use the np.array() function, which takes a list or other iterable as an argument and returns an array. For example:

Numpy is a powerful library for working with arrays in Python. It provides a variety of functions and methods for creating, indexing, slicing, and manipulating arrays. In this manual, we will cover the basics of creating arrays, indexing and slicing arrays, array operations and methods, and broadcasting and reshaping arrays.

Creating Arrays
There are several ways to create arrays in Numpy. The most common way is to use the np.array() function, which takes a list or other iterable as an argument and returns an array. For example:

import numpy as np

# create a 1-dimensional array from a list
a = np.array([1, 2, 3, 4, 5])
print(a) # [1 2 3 4 5]

# create a 2-dimensional array from a list of lists
b = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(b)
# [[1 2 3]
#  [4 5 6]
#  [7 8 9]]

You can also use the np.zeros() function. This function creates an array of a given shape and fills it with zeros. For example:

b = np.zeros((3, 4))
print(b)
#[[0. 0. 0. 0.]
# [0. 0. 0. 0.]
# [0. 0. 0. 0.]]

The function np.ones() creates an array of a given shape and fills it with ones. For example:

c = np.ones((2, 3))
print(c)
# [[1. 1. 1.]
# [1. 1. 1.]]

The function np.arange() creates an array with a range of numbers, similar to the range() function. For example:

d = np.arange(5) # [0, 1, 2, 3, 4]

The function np.linspace()creates an array of evenly spaced numbers over a specified interval. For example:

e = np.linspace(0, 10, 5) # [0, 2.5, 5, 7.5, 10]