Top Python Tools Every Python Developer Should Know About [2021]

Introduction

In this write up we discuss some of the most useful tools that Python developers and their strong community has developed making the life of Python developers a lot easier. 

Python as the world knows it today is one of the easiest programming languages to pick up and thus the most popular of all. Python has huge community support which helps bring out a number of useful tools that can be used within the Python environment to achieve tasks that would take hundreds of lines of code if not more. We shall split this python tools list into 3 categories, Data Science in Python, Automation Testing, and Web scraping tools. These tools also called packages and also what makes Python one of the most favored programming languages. There are a number of packages in the Data Science field that Python and its community boasts of.

Let’s look briefly at some of the popular and useful python tools made by Python developers. While most of them are useful in the field of Data Science, others can be used to extract information from sources like random text or websites. The list is divided into various categories of python development tools, in order to group and compare tools with similar objectives. 

1. Data Science/ Machine Learning Python Tools
2. Automation Testing Python Tools
3. Web scraping Python Tools

1. Data Science/ Machine Learning Python Tools

There are a number of Data Science tools in Python built by the Python community contributors, and a few by proprietary holdings. Some of the noteworthy tools are listed below.

• Scikit-Learn

Scikit-Learn is a free and open-source Machine Learning and Data Science specific package or library for use within the Python programming environment. Scikit-Learn is also used by Data Scientists, Data Analysts, Machine Learning engineers, and in some cases developers to perform tasks like classification, clustering, regression, dimensionality reduction, and model selection and pre-processing. It is built on NumPy, SciPy, and matplotlib all of which are community contributions.

• Keras

A Python tool, not talked about a lot but packs a punch, Keras is again an open-source tool used in the field of Data Science, particularly in the field of neural networks. Keras is actually a Python interface for the TensorFlow library and is used for deep learning based on neural networks. With its consistent and simple APIs, it minimizes user intervention in common use cases. Keras is built to scale up to large clusters of graphics processing units (GPUs) or even an entire TPU. You can easily export models to be run on a browser using JavaScript, making it easy to deploy. You can even deploy Keras models over the web as an API.

• Theano

Theano and open-source Python library is targeted at evaluating mathematical expressions and more so the matrix-valued ones. Theano optimizes compiling for mathematical expressions and uses NumPy like syntax. Theano is optimized to run efficiently on either CPU or GPU based architectures.

• SciPy

Sci-Py is again an open-source Python library or package that is designed to handle scientific and technical computing. One of the libraries that is extensively used in the field of Science, Maths and Engineering Maths, SciPy is again based on NumPy and pandas. An excellent tool for manipulating numbers along with a visual representation of mathematical computation.

2. Automation Testing Python Tools

Automation testing tools are used to test developed software before deployment. Testing involves a lot of repetitive tasks that can be automated with the help of automation testing tools. We shall explore such Python testing tools that are compatible with Python and thus can be programmed to do a certain task or number of tasks in a programmatic manner.

• Selenium

Selenium is a web driver tool designed to perform black-box testing for web applications. Selenium supports Python natively by providing libraries for Python developers. Using these libraries in a Python development environment you can create Selenium test scripts for any web application, attempting to test the functionality, in other words, sending the input and checking the output generated.

Selenium is a widely used tool for black-box testing of web-based applications, and having a Python library for creating automated test scripts makes for thorough functional testing of any web application.

• Robot Framework

Another generic open-source test automation framework, Robot Framework is designed for Acceptance testing and ATTD or Acceptance Test-Driven Development. Robot Framework is used for mobile app testing, supporting both Android and iOS. Python comes into the picture with the Python test libraries made available by Robot Framework developers to extend the testing capabilities. 

• TestComplete

TestComplete is a GUI-based test automation tool, with an easy-to-use interface for testing web, mobile, and desktop applications. TestComplete is also AI-powered and is able to recognize objects. TestComplete is also capable of keyword-driven testing. With TestComplete you can also record and playback activities much like Excel macro recording. TestComplete offers a Python library allowing testers to develop their own script in Python for carrying out tests on web and desktop applications.

3. Web scraping Python Tools

• Beautiful Soup

Beautiful Soup is a Python library useful for extracting information from web pages. It primarily works by parsing for HTML or XML tags. It can automatically convert documents to Unicode or UTF-8. Beautiful Soup is not only capable of isolating data from HTML tags, but also capable of altering the HTML content.

• LXML

LXML is a Python library used to parse XML and HTML code within the Python development environment. Since it interfaces with two C libraries namely libxml1 and libxslt, the execution time is faster. It also provides support for XPath, XML Schema, RelaxNG, XSLT, and C14N.

• Scrapy

Scrapy is a Python library written in Python and used for crawling websites to extract data. Scrapy can be easily extended to add new functionality without having to mess with the core. Since it is written in Python, it is portable across Windows, FreeBSD, and Linux.

Conclusion

This brings us to the end of discussing the various Python tools available. Python is an easy to use, versatile programming language. With Python the possibilities are endless. If you are new to Python, you can log on to Jigsaw Academy and get access to an array of Python courses that will ensure you get a sound foundation in Python. If you are familiar with Python, it is time to scale up your skills with additional packages that extend the functionality of Python easily. Log onto Jigsaw Academy today to get access to hundreds of courses on Python and Python extended tools and libraries.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

ALSO READ

• Best Continuous Integration Tools You Can’t Ignore in 2021