Numpy version iteration guide

From old version to new version: numpy version update guide

1. Introduction
Numpy is one of the most commonly used mathematics libraries in Python and is widely used in scientific computing. , data analysis and machine learning fields. Numpy makes processing large-scale data sets more efficient and easier by providing efficient array operations and mathematical functions.

Although Numpy had many powerful features when it was initially released, as time went by and received feedback from developers and users, Numpy continued to undergo version updates and feature improvements. Each new version brings some new features and improvements, and may also introduce some backward-incompatible changes.

This article will provide a version update guide for users using Numpy from the old version to the new version. We will introduce important updates in historical versions of Numpy in turn, and give specific code examples to help readers better understand and adapt to the new version of Numpy.

2. Version update guide

1. Numpy 1.14 update guide:
   Numpy 1.14 version introduces some new functions and optimizations. The most significant change is the introduction of new Array filling method-fill method. This method can be used to fill an array with specified values, which is very convenient.

Code example:

import numpy as np

arr = np.zeros((3, 3))
arr.fill(5)

print(arr)

Output:

[[5. 5. 5.]
 [5. 5. 5.]
 [5. 5. 5.]]

2. Numpy 1.15 update guide:
   Numpy 1.15 version mainly improves some aspects of multi-dimensional arrays operate. One of the important changes is the introduction of the einsum function, which can be used to perform operations such as tensor calculations and matrix multiplication. Additionally, a numpy.core._exceptions.VisibleDeprecationWarning warning has been introduced, which will be the default behavior in the next few releases.

Code Example:

import numpy as np

arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

result = np.einsum('ij,jk->ik', arr1, arr2)

print(result)

Output:

[[19 22]
 [43 50]]

3. Numpy 1.16 Update Guide:
   Numpy 1.16 version introduces some new functions and methods , such as stack, hstack, and vstack, are used to stack multiple arrays in different dimensions. In addition, the dtype parameter is also introduced to specify the data type of the array.

Code Example:

import numpy as np

arr1 = np.array([1, 2, 3])
arr2 = np.array([4, 5, 6])

result = np.vstack((arr1, arr2))

print(result)

Output:

[[1 2 3]
 [4 5 6]]

4. Numpy 1.17 Update Guide:
   Numpy 1.17 version introduces some new functions and optimizations , the most important of which is the introduction of the isnat function, which is used to check whether a date is an invalid date (NaT). In addition, support for random number generators has been enhanced, including more distribution functions and efficient random number generation.

Code example:

import numpy as np

arr = np.array(['2000-01-01', '2000-01-02', '2000-01-03'], dtype='datetime64')

result = np.isnat(arr)

print(result)

Output:

[False False False]

3. Summary
This article introduces the version update of Numpy, focusing on some important Features and improvements. By reading this article, readers can learn about the important changes in each version of Numpy, and quickly get started and adapt to the new version of Numpy through specific code examples.

If you are upgrading your application or project to the latest version of Numpy, it is recommended that you carefully read the corresponding version update guide and documentation before upgrading to ensure that your code is compatible with the new version, and It works fine.

I wish you better results in using Numpy!

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