numpy is an open source numerical computing library based on Python. It is widely used and favored by many researchers and developers in the fields of scientific computing, data analysis and machine learning. The numpy library provides tools for efficient numerical calculations and data processing through multi-dimensional array objects and a set of functions for manipulating these arrays.

In recent years, the numpy library has been continuously updated, and each version brings new features and improvements, allowing users to use it more efficiently to carry out various data calculation tasks. This article will introduce the latest version of the numpy library, focus on some of its new features and improvements, and give specific code examples to make it easier for readers to understand and use.

1. New features and improvements of numpy version 1.18:

numpy 1.18 version is the latest version of the numpy library, which mainly introduces the following new features and improvements:

(1) Performance optimization: Numpy version 1.18 has made a series of performance optimizations to improve the speed of array operations and calculations. For example, the new ufunc implementation improves the performance of arithmetic operations and speeds up the calculation of general functions.

(2) New functions and methods: numpy version 1.18 introduces some new functions and methods, extending the functionality of the numpy library. These include the nanquantile function for calculating a certain quantile among many elements, and the replace function for replacing a specific value in an array with a specified value, etc.

(3) Broadcast rule changes: Broadcasting is an important feature in the numpy library, and some changes and corrections have been made in version 1.18. The new broadcast rules are more concise and clear, making it easier for users to perform array operations.

A code example is given below to demonstrate the use of the nanquantile function:

import numpy as np

Create an array containing nan values

arr = np.array([[1, 2, 3], [4, np.nan, 6], [7, 8, np.nan]])

Calculate the 50th percentile of the array

q = np.nanquantile(arr, 0.5)

print(q) # Output result: 4.0

2. New features and improvements in numpy version 1.19:

Numpy version 1.19 is the next planned version of the numpy library. Although it has not been officially released, some new features and improvements have been proposed and are under development.

(1) New array methods: Version 1.19 plans to introduce some new array methods to make it easier for users to process and operate arrays. These include the count_nonzero method for calculating the number of non-zero elements in the array, and the partition method for partitioning the array.

(2) New data types: Version 1.19 will also introduce some new data types, expanding the support range of the numpy library. For example, the new datetime64 data type will provide more convenient time and date calculation and processing functions.

The following is a code example to demonstrate the use of the partition method:

import numpy as np

Create an array

arr = np.array( [6, 2, 1, 8, 10])

Use the partition method to divide the array into two parts

p = np.partition(arr, 2)

print (p) # Output result: [1 2 6 8 10]

Through the above examples, readers can clearly understand the new features and improvements of the numpy library in the latest version, and learn how to use these functions to develop data Computational and processing tasks. In addition to the features introduced above, the numpy library has many other useful functions and methods. Readers can refer to the official numpy documentation to further explore its potential. In short, understanding the latest numpy version features and improvements will help developers and researchers use the numpy library more efficiently to solve practical problems.

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