10 Useful Python Tools For Python Developers in 2022
Tags: python, programming, beginners, webdev
Published: 2022-06-15 18:14:19

Python is no longer surprising as one of the most popular languages among developers and in the community of data scientists. It is a beginner-friendly language, which you can use as a springboard for learning other languages and programming frameworks. Furthermore, you can use the programming language to build simple games, making it easy to create prototypes quickly.

Numerous factors have contributed to Python's popularity, but there are two main ones. The first one is Syntax, and another is the extensive collection of libraries and tools for scientific computing and [Data Science](https://coursesity.com/best-tutorials-learn/data-science). 

In the right hands, these Python tools can be valuable for many different purposes. Consequently, this article will examine some of the most popular Python tools used by developers, coders, and Data Scientists around the globe. Let's now take a look at the best Python tools for Python developers.

## Scikit-Learn
Scikit-Learn is an open-source tool extensively used by developers, machine learning engineers, and data scientists for data mining and analysis. A remarkable feature of Scikit-Learn is its speed in performing benchmarks on toy datasets. The main features of this Python library are classification, clustering, dimensionality reduction, regression, model selection, and preprocessing.

## Theano
Theano is a Python library designed specifically for expressing multidimensional arrays. It allows you to define, optimize, and evaluate multidimensional arrays comprising mathematical computations. To name a few prominent features, the library integrates tightly with NumPy, makes transparent use of GPUs, produces efficient symbolic differentiation, performs speed and stability optimizations, dynamic C code generation, and performs extensive unit testing.

## Keras
Keras is an open-source high-level neural network library based on Python. It is built around four main principles: user-friendliness, modularity, extensibility, and Python compatibility. It enables you to express neural networks in the most straightforward manner imaginable. Furthermore, it is compatible with popular neural network frameworks such as TensorFlow, CNTK, and Theano. It's ideal for machine learning and deep learning.

## SciPy
SciPy is an open-source library ecosystem based on Python for scientific and technical computing. Most of the time, it is used in the fields like engineering, math, and science. NumPy, IPython, and Pandas are some Python libraries that SciPy incorporates to provide libraries for common math and science programming tasks. It is a great tool to manipulate numbers on a computer and display results visually as well.

## Selenium
Without a doubt, [Selenium](https://coursesity.com/best-tutorials-learn/selenium) is the best Python development tool available. The open-source framework is used to automate web applications. You can write Selenium scripts in many other programming languages like Java, C#, Python, PHP, Perl, Ruby, and .Net.

In addition, you can run the test on any browser (Chrome, Firefox, Safari, Opera, and Internet Explorer) across all three major operating systems - Windows, macOS, and Linux. You can also manage test cases and generate reports using Selenium by integrating it with tools like JUnit and TestNG.

## TestComplete
Just like Selenium, it is also an automation testing software used to automate desktop, mobile, and web applications. Nevertheless, you need a commercial license to utilize this software. Additionally, it allows you to perform keyword-driven testing. It includes a simple record and playback feature.

## Robot Framework
It is an open-source generic test automation framework for acceptance testing and acceptance test-driven development. This framework is keyword driven and uses tabular test data syntax. With Robot Framework, you can integrate a variety of test automation frameworks. You can bring more out of this framework by integrating it with Java or Python libraries. Additionally, you can use this framework for Android and iOS test automation.

## Scrapy
Scrapy is an open-source and collaborative framework based on Python. It's essentially an application framework for constructing web spiders (user-defined classes) that crawl and retrieve data from online pages. Its primary purpose is to extract data from websites. [Learning Scrapy](https://coursesity.com/course-detail/free-scrapy-tutorial-python-scrapy-for-beginners) will be beneficial to every Python Developer.

## Beautiful Soup
It is a Python library for parsing HTML and XML files and extracting data. You can use it in conjunction with your favorite parser to navigate, search, and alter a parse tree using Pythonic idioms. This utility can convert incoming documents to Unicode and exiting documents to UTF-8 automatically, and it's useful for things like screen scraping. It's a fantastic tool that can save you a lot of time.

## LXML
It is highly feature-rich and one of the most easy-to-use Python tools designed for C libraries (libxml2 and libxslt). It provides secure and convenient access to the libxml2 and libxslt libraries via the ElementTree API. It is distinct in that it combines the speed and XML capabilities of these tools with the simplicity of a native Python API. It also adds support for XPath, RelaxNG, XML Schema, XSLT, and C14N to the ElementTree API.

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