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.

1. 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

2. Create a one-dimensional array
   In Numpy, a one-dimensional array is a list containing elements of the same data type. We can use the ndarray 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]

3. 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]]

4. 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)

  Output:

  [[0. 0. 0. 0.]
   [0. 0. 0. 0.]
   [0. 0. 0. 0.]]

• Create an array of all 1s

  ones_array = np.ones((2, 3))
  print(ones_array)

  Output:

  [[1. 1. 1.]
   [1. 1. 1.]]

• Create empty array

  empty_array = np.empty((2, 2))
  print(empty_array)

  Output:

  [[4.94e-323 9.88e-323]
   [1.48e-322 1.97e-322]]

##Create sequence array
5. In some cases, we want to create a sequence array, that is, an evenly spaced array. Numpy provides the
   arange function and the linspace function to create such an array.

• Use the

  arange function to create a sequence array

  sequence_array = np.arange(0, 10, 2)
  print(sequence_array)

  Output: [0 2 4 6 8]

• Use the

  linspace function to create a sequence array

  sequence_array = np.linspace(0, 1, 5)
  print(sequence_array)

  Output: [0. 0.25 0.5 0.75 1. ]

Creation of random array
6. 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, randn and randint, etc.

• Create a random array

  random_array = np.random.random((2, 3))
  print(random_array)

  Output:

  [[0.59525333 0.78593695 0.30467253]
   [0.83647996 0.09302248 0.85711096]]

• Create a random array that obeys the normal distribution

  normal_array = np.random.randn(3, 3)
  print(normal_array)

  Output:

  [[-0.96338454 -0.44881001  0.01016194]
   [-0.78893991 -0.32811758  0.11091332]
   [ 0.87585342  0.49660924 -0.52104011]]

• Create an array of random integers

  random_int_array = np.random.randint(1, 10, (2, 4))
  print(random_int_array)

  Output:

  [[3 9 3 3]
   [1 9 7 5]]

  Introduction 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.

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