Numpy Tutorial: Learn Array Creation from Scratch
Numpy Tutorial: Learn to create arrays from scratch, specific code examples are required
Overview:
Numpy is an open source mathematics library for Python that provides a large number of Mathematical functions and data structures, especially arrays (Arrays). Arrays are a very common and important data structure in machine learning and data analysis, so learning how to create and manipulate arrays is critical. This tutorial aims to introduce the creation of arrays in Numpy from scratch to help readers get started quickly.
- Import Numpy library
Before we begin, we first need to import the Numpy library. Usually, we use the import statement to import the Numpy library into our Python code.
import numpy as np
- Create a one-dimensional array
In Numpy, a one-dimensional array is a list containing elements of the same data type. We can use thendarray
function provided by Numpy to create a one-dimensional array.
array_1d = np.array([1, 2, 3, 4, 5]) print(array_1d)
Output: [1 2 3 4 5]
- Create a two-dimensional array
A two-dimensional array is a table structure containing multiple rows and columns. We can create a two-dimensional array using a variety of methods, the most common of which is from a list of lists.
array_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) print(array_2d)
Output:
[[1 2 3] [4 5 6] [7 8 9]]
- Create an array of a specific type
In some cases, we need to create an array of a specific type, such as an array of all 0s, An array of all ones or an empty array. Numpy provides some functions to create these special types of arrays.
Create an array of all 0s
zeros_array = np.zeros((3, 4)) print(zeros_array)
Copy after loginOutput:
[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]]
Copy after loginCreate an array of all 1s
ones_array = np.ones((2, 3)) print(ones_array)
Copy after loginOutput:
[[1. 1. 1.] [1. 1. 1.]]
Copy after loginCreate empty array
empty_array = np.empty((2, 2)) print(empty_array)
Copy after loginOutput:
[[4.94e-323 9.88e-323] [1.48e-322 1.97e-322]]
Copy after login
- ##Create sequence array
- In some cases, we want to create a sequence array, that is, an evenly spaced array. Numpy provides the
arangefunction and the
linspacefunction to create such an array.
- Use the
arange
function to create a sequence array
Output: [0 2 4 6 8]sequence_array = np.arange(0, 10, 2) print(sequence_array)
Copy after login - Use the
linspace
function to create a sequence array
Output: [0. 0.25 0.5 0.75 1. ]sequence_array = np.linspace(0, 1, 5) print(sequence_array)
Copy after login
- Creation of random array
- In addition to the above methods, we can also use the random function provided by Numpy to create a random array. Commonly used random functions include
random,
rand,
randnand
randint, etc.
- Create a random arrayOutput:
random_array = np.random.random((2, 3)) print(random_array)
Copy after login[[0.59525333 0.78593695 0.30467253] [0.83647996 0.09302248 0.85711096]]
Copy after login - Create a random array that obeys the normal distributionOutput:
normal_array = np.random.randn(3, 3) print(normal_array)
Copy after login[[-0.96338454 -0.44881001 0.01016194] [-0.78893991 -0.32811758 0.11091332] [ 0.87585342 0.49660924 -0.52104011]]
Copy after login - Create an array of random integersOutput:
random_int_array = np.random.randint(1, 10, (2, 4)) print(random_int_array)
Copy after loginIntroduction to this article It explains the creation of arrays in the Numpy library, including the creation of one-dimensional arrays and two-dimensional arrays, as well as the creation methods of specific types of arrays, sequence arrays and random arrays, and provides specific code examples. I hope this tutorial can help readers understand and master the creation of arrays in Numpy.[[3 9 3 3] [1 9 7 5]]
Copy after loginThe above is the detailed content of Numpy Tutorial: Learn Array Creation from Scratch. For more information, please follow other related articles on the PHP Chinese website!

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