Guide on How to Stay Current in Python

This guide is designed to help beginners understand Python and how to stay current with the Python ecosystem!

Table of Contents:

  1. Introduction
  2. Python’s Evolution
  3. Version Management
  4. Python Enhancement Proposals (PEPs)
  5. Popular Python Libraries
  6. Online Resources
  7. Community Engagement
  8. Networking and Conferences
  9. Best Practices and Coding Standards
  10. Continuous Learning

1. Introduction

Python has become one of the leading programming languages in the world, thanks to its readability, versatility, and large community of developers. As Python continues to grow and evolve, it’s crucial for developers to stay current with the latest developments. This guide will provide you with tips and resources to help you stay up-to-date with Python’s ever-changing landscape and stay current in Python.

2. Python’s Evolution

Python is a high-level, general-purpose programming language that has seen significant evolution since its creation in the late 1980s. The language was originally developed by Guido van Rossum, a Dutch programmer, who named it after the British comedy group, Monty Python. Python was designed to be easy to read, write, and understand, with a syntax that emphasizes code readability and simplicity.

Python’s early versions were primarily used for scripting and automation tasks, such as system administration, data processing, and web scraping. As the language matured, it gained popularity in scientific computing, data analysis, and machine learning due to its simplicity, flexibility, and large ecosystem of scientific libraries.

Python’s evolution has been marked by several key milestones. In 2000, Python 2.0 was released, which introduced new features such as list comprehensions and a garbage collector. Python 2.x remained the dominant version for many years, but its development was halted in 2020, and it is no longer supported as of 2021.

In 2008, Python 3.0 was released, which was a major overhaul of the language that introduced many new features and improvements, including improved Unicode support, better handling of exceptions, and a simpler syntax. However, the release of Python 3.0 was not without controversy, as it introduced some backwards-incompatible changes that made it difficult for some users to migrate from Python 2.x.

Since the release of Python 3.0, the language has continued to evolve rapidly, with new features and improvements being added with each release. Some notable additions include type hints, which allow developers to specify the types of variables and function arguments, and async/await syntax, which makes it easier to write asynchronous code.

Python’s popularity has also grown significantly over the years, with the language consistently ranking as one of the most popular programming languages in industry surveys. Today, Python is used for a wide range of applications, from web development and data analysis to scientific computing and machine learning. The language’s simplicity, readability, and versatility continue to make it a popular choice for developers around the world.

3. Version Management

With many Python projects and environments, managing different Python versions can be a challenge. To stay up-to-date, you’ll need a way to switch between different Python versions easily. Here are some tools that can help:

pyenv: A popular version management tool that lets you switch between multiple Python versions with ease. It works by creating a separate environment for each Python version, allowing you to have multiple versions installed on your system without conflicts.

conda: A package and environment manager primarily used for managing Python and R packages. Conda also has built-in support for managing different Python versions, making it a great choice for data science projects.

4. Python Enhancement Proposals (PEPs)

Python Enhancement Proposals (PEPs) are the primary mechanism for proposing new features, collecting community input, and documenting the design decisions made in Python. PEPs can be a great resource for staying current with the language since they often discuss and propose new features and improvements.

Some important PEPs to be familiar with include:

  • PEP 8: Style Guide for Python Code
  • PEP 20: The Zen of Python
  • PEP 484: Type Hints
  • PEP 572: Assignment Expressions (The “walrus operator”)

You can browse through the entire list of PEPs on the official Python website.

5. Popular Python Libraries

Staying current with popular Python libraries can help you write more efficient and effective code. Some popular Python libraries include:

NumPy: A library for numerical computing, providing support for arrays, matrices, and mathematical functions.

Pandas: A data manipulation library that provides data structures like DataFrames and Series for handling large datasets.

Matplotlib: A plotting library for creating static, animated, and interactive visualizations in Python.

Scikit-learn: A machine learning library that provides simple and efficient tools for data mining and data analysis.

TensorFlow: An open-source machine learning framework developed by Google for various machine learning and deep learning tasks.

Flask: A lightweight web framework for building web applications in Python.

Keep an eye on libraries’ updates and new releases, as they often come with essential features, bug fixes, and improvements.

6. Online Resources

There are many online resources available to help you stay current with Python. Some of these resources include:

Python.org: The official Python website is an excellent resource for news, updates, and documentation related to Python.

Python Weekly: A free weekly newsletter featuring the latest Python news, articles, projects, and events.

Real Python: A website offering in-depth tutorials, articles, and videos on a wide range of Python topics.

Talk Python To Me: A podcast on various Python topics, including interviews with notable Python developers and experts.

7. Community Engagement

Community engagement is an essential aspect of staying current in Python. Python has a large and active community of developers who are constantly working on new projects, libraries, and frameworks. Engaging with this community can help you stay up-to-date with the latest trends and best practices in Python development.

One way to engage with the Python community is to attend local meetups and conferences. These events provide a great opportunity to network with other developers and learn about new projects and libraries. Many meetups and conferences also offer talks and workshops on specific topics, such as machine learning, web development, or data analysis.

Another way to engage with the Python community is to participate in online forums and discussion groups. Popular forums include Reddit’s r/Python, Python’s official mailing list, and the Python Discord server. These forums are great places to ask questions, share ideas, and get feedback on your projects. They also provide a platform for developers to contribute to open-source projects and collaborate on new initiatives.

Contributing to open-source projects is another great way to engage with the Python community. By contributing to a project, you can gain hands-on experience with the latest tools and techniques in Python development. It also provides an opportunity to network with other developers and potentially land a job in the industry.

8. Networking and Conferences

Attending a Python conference is one of the best ways to network, learn new skills, share knowledge, stay up to date with changes, and experience the Python community firsthand. The largest Python conference is PyCon US, organized by the Python Software Foundation, which hosts 3-4 days of talks, tutorials, poster sessions, and coding sprints. EuroPython is the largest European Python conference, held annually in Europe, and features many of the same opportunities. For Python developers in Asia, PyCon India is a popular conference choice.

At a Python conference, you’ll find presentations on diverse topics from working with Python in science and data to building web applications. Well-known members of the Python community, as well as newcomers, propose 30 or 45 minute talks on their areas of expertise. You’ll also find opportunities for mentorship and longer, intensive training through tutorial sessions and coding sprints. In-person conferences also facilitate networking through social events, lunch breaks, and meeting other attendees with similar interests. Some conferences release talks and tutorials on their YouTube channels after the events for those unable to attend in person. Overall, attending a major Python conference, even just once, can be a highly valuable experience for any developer looking to become more engaged with the Python community.

9. Best Practices and Coding Standards

To write clean, consistent, and readable Python code, it’s important to follow a set of best practices and style standards. The most well-known is PEP 8, the Style Guide for Python Code. PEP 8 covers recommendations for indentation, line length, naming conventions, whitespace, and more. Following PEP 8 helps ensure Python code across different projects and developers embraces a similar style and aesthetic.

Other useful resources for writing good Python code include:

•PEP 20 – The Zen of Python: A set of 19 aphorisms describing ideal Python design and usage.
•Google Python Style Guide: Google’s internal style guide for Python, with some small differences from PEP 8.
•Code linting tools: Tools like Pylint, Flake8, and Black can analyze your code and detect style issues or syntax errors. They are useful to catch problems early on.
•Code complexity tools: Tools like Radon can measure the complexity of your Python code and detect methods or files that could be simplified. Keeping code complexity low improves readability and maintainability.
•Code formatters: Black and YAPF are tools that can automatically format Python code to conform to PEP 8 standards.

10. Continuous Learning

As with any programming language, continuous learning is vital to improve your Python skills over time. Here are some recommendations to strengthen your Python chops:

•Work on side projects: The best way to really learn Python is to build your own projects. Some ideas include building a web app, creating a data visualization, training a machine learning model, or automating a tedious task.

•Read books on Python: Some highly-rated books include “Fluent Python” by Luciano Ramalho, “Effective Python” by Brett Slatkin, and “Python Tricks” by Dan Bader.

•Participate in code challenges: Websites like LeetCode, HackerRank, and Codewars offer short coding challenges to keep your skills sharp. Try solving a few Python problems each week.

•Contribute to open source: A great way to improve your skills is by contributing to an open source Python project. You can report and fix bugs, implement features, update documentation, and more. Most projects are very welcoming to new contributors.

•Stay active in the community: Continue engaging with the Python community by attending meetups, participating in discussions on Python forums and Reddit, advocating for Python, and spreading your knowledge. Constant interaction with other developers will motivate you to keep learning.

Keeping your Python knowledge up to date requires continuous effort, but will make you a stronger developer and allow you to build more interesting projects over time. With a wealth of resources and an active, supportive community, continuous learning Python can be highly rewarding. Staying passionate and consistent in your learning will keep you at the top of your Python game.

Want to learn more about Python? Consider taking our Introduction to Python course or download our guide on Becoming a Data Scientist below:


How to Become a Data Scientist PDF

Your FREE Guide to Become a Data Scientist

Discover the path to becoming a data scientist with our comprehensive FREE guide! Unlock your potential in this in-demand field and access valuable resources to kickstart your journey.

Don’t wait, download now and transform your career!


Pierian Training
Pierian Training
Pierian Training is a leading provider of high-quality technology training, with a focus on data science and cloud computing. Pierian Training offers live instructor-led training, self-paced online video courses, and private group and cohort training programs to support enterprises looking to upskill their employees.

You May Also Like

Python Basics, Tutorials

Plotting Time Series in Python: A Complete Guide

Introduction Time series data is a type of data that is collected over time at regular intervals. It can be used to analyze trends, patterns, and behaviors over time. In order to effectively analyze time series data, it is important to visualize it in a way that is easy to understand. This is where plotting […]

Python Basics, Tutorials

A Beginner’s Guide to Scipy.ndimage

Introduction Scipy.ndimage is a package in the Scipy library that is used to perform image processing tasks. It provides functions to perform operations like filtering, interpolation, and morphological operations on images. In this guide, we will cover the basics of Scipy.ndimage and how to use it to manipulate images. What is Scipy.ndimage? Scipy.ndimage is a […]

Python Basics, Tutorials

Adding Subtitles to Plots in Python: A Complete Guide

Introduction Adding subtitles to plots is an essential part of data visualization. Subtitles provide context to the plot and help the viewer understand the purpose of the visualization. In Python, adding subtitles to plots is a straightforward process that can be achieved using Matplotlib – a popular data visualization library. Matplotlib provides the `title()` function […]