Top 10 Python Tools Every Python Developer Should Know About [2022]

It's no longer surprising to learn that Python is one of the most popular programming languages among programmers and data scientists. Python's success can be attributed to several factors, but two, in particular, stand out.

Python has a relatively simple grammar that is almost identical to mathematical terminology, making it straightforward to understand and master.

Second, it provides a comprehensive treatment of scientific computing and data science (libraries, tools, and so on).

Python is useful for data science for a variety of reasons. Today, we'll go through some of the most popular Python tools used by developers, coders, and data scientists all over the world. If you're new to data science and want to learn more, check out our data science certification from renowned universities.

If you know how to use Python tools correctly, they can be useful for a variety of tasks. So, without further ado, let's have a look at some of the best Python tools available!

Data Science Python tools

1) Scikit-Learn

Scikit-Learn is a data science and machine learning toolbox that is open source. It is used for data mining and data analysis by developers, ML engineers, and data scientists. One of Scikit-best Learn's distinguishing features is its lightning-fast performance while performing various benchmarks on toy datasets.

The key features of this tool include classification, regression, clustering, dimensionality reduction, model selection, and preprocessing. It has a grid, random searches, and a consistent and user-friendly API.

2) Keras

Keras is an open-source high-level neural network library written in Python. It's perfect for deep learning and machine learning. Keras is based on four key principles: usability, modularity, extensibility, and Python compatibility. It allows you to represent neural networks in the most basic way possible. Because Keras is built in Python, it can operate on top of popular neural network frameworks such as TensorFlow, CNTK, and Theano.

3) Theano

Theano is a Python module specialised to representing multidimensional arrays. It allows you to create, optimise, and test multidimensional array-based mathematical processes. Its strong relationship with NumPy, transparent use of GPU, efficient symbolic differentiation, efficiency and stability improvements, dynamic C code generation, and extensive unit testing are only a few of its distinguishing features.

4) SciPy

SciPy is an open-source Python-based library ecosystem for scientific and technical computing. It is commonly used in subjects like as mathematics, physics, and engineering. SciPy creates libraries for common math and scientific programming tasks by using existing Python packages such as NumPy, IPython, and Pandas. It's an excellent software for manipulating numbers on a computer and viewing the results.

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Automation Testing Python tools

5) Selenium

Selenium is without a doubt one of the most powerful Python development tools available. It's a web application automation framework that's open-source. You can use Selenium to create test scripts in a variety of languages, including Java, C#, Python, PHP, Perl, Ruby, and.Net.

You may also run tests on any browser (Chrome, Firefox, Safari, Opera, and Internet Explorer) on any of the three major operating systems (Windows, macOS, and Linux). Selenium may also be used to manage test cases and create reports with tools like JUnit and TestNG.

6) Robot Framework

This is the another open-source generic test automation framework for acceptance testing and acceptance test-driven development (ATTD). This framework is keyword-driven and employs tabular test data format. Robot Framework combines a number of frameworks to satisfy a range of test automation requirements.

You may enhance the framework's capabilities by connecting it to Python or Java libraries. Robot Framework may be used to automate Android and iOS tests, as well as web app testing.

7) TestComplete

TestComplete is automated testing software that can be used to automate web, mobile, and desktop tests. However, to utilize it, you must first obtain a commercial license. TestComplete, like Robot Framework, allows you to perform keyword-driven testing. It includes a simple record and playback feature.

Python, VBScript, and C++ script are among the scripting languages supported. Software testers can use keyword-driven testing in the same way that Robot Framework does. This Python tool's GUI object recognition capabilities can both detect and update UI elements, which is a noteworthy feature. This cuts down on the time it takes to maintain test scripts.

Web Scraping Python tools

8) Beautiful Soup

Python is a tool that parses and extracts data from HTML and XML files. You may use it in conjunction with your preferred parser to traverse, search, and modify a tree structure using Pythonic idioms. The application can automatically change receiving documents to Unicode and outgoing documents to UTF-8, and it's handy for tasks like screen scraping. It's an excellent tool that can save you a significant amount of time.

9) LXML

LXML is a Python-based tool for the libxml2 and libxslt C libraries. It is one of the most feature-rich and user-friendly libraries for processing XML and HTML in Python. Using the ElementTree API provides safe and convenient access to the libxml2 and libxslt libraries.

What makes it special is that it combines the speed and XML capabilities of these tools with the ease of use of a native Python API. Furthermore, it adds support for XPath, RelaxNG, XML Schema, XSLT, and C14N to the ElementTree API.

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10) Scrapy

Scrapy is a Python-based open-source and collaborative framework. It's essentially an application framework for creating web spiders (user-defined classes) that crawl online pages and retrieve data from them. It is primarily used to extract information from websites.

Scrapy is a high-level web crawling and scraping framework that may also be used for data mining, automated testing, and other purposes. It runs smoothly on all three major operating systems: Windows, Mac OS X, and Linux.