how to become a Python developer (2022 Guide)

Python Software

Python is one of the world’s most popular programming languages, featuring multiple applications across a variety of disciplines including data science, data analytics, web development, and scientific and technical computing. Read on to learn about the top Python software and tools.

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Python software and tools are increasingly crucial to help Python Developers, Data Scientists, Machine Learning Engineers, and many other professionals use the Python programming language as efficiently and conveniently as possible to accomplish a range of different job tasks.

8 Best Python Software Tools

Python is a powerful and accessible all-purpose programming language that has become a worldwide standard in data and software development because of its versatility, intuitive nature, and lack of barriers to access.

Python has become the most popular programming language because it has such a diverse range of uses, as there is a Python library or a Python package for virtually any recurring task you might need to tackle. The key for experienced Python programmers is simply deciding which tools and software to use in each scenario.

With the full spectrum of Python applications in mind, here are the 8 best Python software tools.

  1. PyCharm
  2. Jupyter Notebook
  3. Scikit-Learn
  4. Keras
  5. Scrapy
  6. Selenium
  7. Robot Framework
  8. Sphinx

1. PyCharm

PyCharm is a popular integrated development environment (IDE) for any Python Developer because it has built-in developer tools for debugging (smoothly integrated with Visual Studio Code).

Pricing

  • Free basic plan
  • Paid subscription

Platforms

  • Windows
  • MacOS
  • Linux

PyCharm offers a number of useful features, including testing assistance, code quality enhancement, intelligent automated code refactoring, code inspections, virtual environment configuration, and an autocomplete feature that will suggest keywords while you write programs and your Python code.

Created by JetBrains — a top publisher of Python development tools — PyCharm also supports modern web development frameworks including Django, web2py, Google App Engine, Pyramid, and Flask, while also supporting Anaconda and integrating with the NumPy and Matplotlib Python libraries to further its usefulness for scientific computing.

Companies That Use PyCharm: Twitter, Symantec, Groupon, Yelp, HP, Dice

Best IDE for Python beginners

PyCharm Features

  • PyCharm’s smart assistance allows for convenient project navigation, live error checking, and intelligent code completion
  • In addition to its other features, PyCharm also offers cross-technology interoperability, remote development, and integrated testing facilities

2. Jupyter Notebook

Jupyter Notebook is intuitive open-source software that is one of the top Python tools for code execution, interactive development, documentation, and to write Python programs.

Unlike some other IDEs, Jupyter only needs to execute only one cell to test code rather than having to run the whole program. Using Jupyter’s browser-based web application component, you can add notes and edit your code with automatic Python syntax highlighting and in-browser inspections.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

With Jupyter, your notebooks can be easily shared with others using Dropbox, email, GitHub, or the Jupyter Notebook Viewer, and Jupyter supports more than 40 programming languages in addition to Python, including R, Julia, and Scala.

Companies That Use Jupyter: Google, NASA, Bloomberg, Soundcloud

Best for showcasing and sharing code

Jupyter Notebook Features

  • Built-in feedback enables an experiment-driven approach by testing your code immediately and viewing messages to identify errors
  • Allows you to produce interactive outputs including HTML, images, videos, and custom MIME types
  • Integrates with big data tools like Apache Spark

3. Scikit-Learn

Scikit-Learn is an open-source machine learning library used extensively for data analysis, predictive analytics, data mining, and more. Built on the SciPy, NumPy, and Matplotlib libraries, Scikit features a variety of classification, regression, and clustering algorithms and helpful ML applications including model selection and dimensionality reduction.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

With a clean API and thorough online documentation, Scikit is an accessible library for beginners. Part of the reason it is so intuitive is that no matter what algorithm or model you’re using, the code structure for model training remains the same.

Companies That Use Scikit-Learn: Spotify, JPMorgan, Evernote, Booking.com

Best library for predictions and neuroimaging

Scikit-Learn Features

  • Allows you to more efficiently perform object classification, help with continuous-valued attribute prediction and regression, gradient boosting, and more
  • Easy and free to use, and beginners can consult the Scikit-Learn Python tutorial
  • Enables you to experiment with a variety of models, which is especially helpful early in a Python project

4. Keras

Marketed as “deep learning for humans,” Keras is an open-source neural network API library that simplifies and expedites the creation of deep neural networks. Offering straightforward APIs, Keras reduces the number of user actions needed for typical use cases and provides actionable error messages that will allow you to thoroughly troubleshoot your code.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

Built on the principles of user friendliness and modularity, Keras can enable you to expand your AI neural network programming ecosystem by running on top of back-ends like TensorFlow, Theano, Microsoft Cognitive Toolkit, and more.

Companies That Use Keras: Netflix, Yelp, Uber, Instacart

Best library for developing deep learning models

Keras Features

  • Offers extensive documentation and developer guides
  • Can be deployed anywhere, allowing you to export Keras models to JavaScript to run in the web browser, or to TF Lite to run on Android, iOS, and embedded devices

5. Scrapy

Scrapy is an open-source collaborative framework written in Python that allows you to develop spiders to crawl the Internet and “scrape” information — or in other words, extract data — from websites much more efficiently than you could do on your own.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

Scrapy offers intuitive packages and methods to extract data using CSS selectors or HTML tags, as well as an interactive shell console for experimenting with CSS and XPath expressions to scrape data.

There are other robust applications as well, including data mining, automation testing, as well as generating feed exports (in formats including XML, CSV, and JSON) and storing them in multiple backends.

Companies That Use Scrapy: Zyte, Arbisoft, Datahut, Lambert Labs

Best web-crawling and scraping framework

Scrapy Features

  • Written in Python and runs on Windows, Mac, Linux and BSD
  • Strong community support on GitHub and StackOverflow
  • Designed to be easily extensible, enabling you to plug in your own functionality using signals and an API

6. Selenium

Selenium is a key testing tool or framework for writing automated test cases and conducting cross-platform testing to ensure your Python program runs on multiple platforms. With Selenium, you can conduct a variety of types of tests (manual, automated, and cross-browser) for Python-based web applications.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

Although the open-source automation testing tool is written in Java, it has a Python package that offers API access to most of Selenium’s functions. In addition to writing test Python script, you can test scripts in many other programming languages including PHP, Perl, C#, Ruby, and Java.

Companies That Use Selenium: Netflix, Google, HubSpot, Fitbit

Best tool for simplified test management and reporting

Selenium Features

  • Can be integrated with tools like TestNG and JUnit to create reports or manage test cases
  • Highly customizable, so it can even be used with tailored software builds
  • Allows you to write your own purpose-driven test cases for any software

7. Robot Framework

Robot Framework is an extensible and generic open-source framework that can be used for test automation, acceptance testing and acceptance test-driven development, and software development more generally.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

Supported by a broader foundation and community, Robot Framework is free to use without any licensing costs. It can integrate with almost any other tool — including libraries implemented with Python, Java, or many other programming languages — to generate powerful automation solutions. In addition to web application testing, Robot Framework is also useful for iOS and Android test automation.

Companies That Use Robot Framework: Nokia, Cisco, Conde Nast, Finnair

Best for acceptance level testing

Robot Framework Features

  • Features easy, intuitive syntax with keywords that humans can read
  • Has a rich ecosystem with tools and libraries developed for separate projects

8. Sphinx

Sphinx is a powerful documentation generator Python tool that is loaded with features for writing strong technical documentation. Essentially, it allows a writer to focus on content knowing that Sphinx can create beautiful Python documentation.

Pricing

  • Free

Platforms

  • Windows
  • MacOS
  • Linux

With Sphinx, you can take a set of plain text source files and generate many different output formats using that same source material, including HTML, PDF, documents for e-readers (ePub), and many more. Originally created for Python, Sphinx can now be applied to software projects in a variety of languages. Although Sphinx’s primary application is for documentation, it can also be used to generate homepages, blogs, and even books.

Companies That Use Sphinx: Spyder, MongoDB, Django, Dataverse

Best for intelligent Python documentation

Sphinx Features

  • Strong ecosystem of first-party and third-party extensions
  • Provides an extensive system of documentation and cross-referencing code