Stay ConnectedA weekly Python podcast hosted by Christopher Bailey with interviews, coding tips, and conversation with guests from the Python community.
The show covers a wide range of topics including Python programming best practices, career tips, and related software development topics. Join us every Friday morning to hear what’s new in the world of Python programming and become a more effective Pythonista.
Copyright: © 2020 Real Python
Have you thought about contributing to an open-source Python project? What are possible entry points for intermediate developers? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss a recent article by Stefanie Molin called “5 Ways to Get Started in Open Source.” Christopher shares his experience with suggesting features and potential bug fixes. We talk about common entry points for beginners and provide additional resources.
We cover a recent Real Python tutorial about creating QR codes with Python. The tutorial introduces the library Segno and tours the features. By working through the examples, you’ll be ready to build a QR code project yourself.
We also cover several other articles and projects from the Python community, including a couple of release announcements, an introduction to Python’s functools module, Hatch as an alternative for packaging, options for when NumPy is too slow, a simple diceware generator project, and a collection of machine learning recipes.
Course Spotlight: Caching in Python With lru_cache
Caching is an essential optimization technique. In this video course, you’ll learn how to use Python’s @lru_cache decorator to cache the results of your functions using the LRU cache strategy. This is a powerful technique you can use to leverage the power of caching in your implementations.
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Our TOPPODCAST Picks functools module with real-world examples to help show you how and when to use each feature.Discussion:
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functools — Higher-order functions and operations on callable objects — Python documentationLevel up your Python skills with our expert-led courses:
When choosing a tool for profiling Python code performance, should it focus on the CPU, GPU, memory, or individual lines of code? What if it looked at all those factors and didn’t alter code performance while measuring it? This week on the show, we talk about Scalene with Emery Berger, Professor of Computer Science at the University of Massachusetts Amherst.
Emery talks about his background in memory management and his collaboration on Hoard, a scalable memory manager system used in Mac OS X. We discuss the need for improving code performance on modern computer architecture. He highlights this idea by contrasting the familiar limitations of Moore’s law with the lesser-known rule of Dennard scaling.
Working with his students in the university lab, they developed Scalene. Scalene is a high-performance CPU, GPU, and memory profiler. It can look at code from the individual function or line-by-line level and compare time spent in Python vs C code. Emery talks about the recent Scalene feature of AI-powered optimization proposals and covers a couple of examples. He also shares a collection of additional Python code-assistant tools from their lab.
Course Spotlight: What Does if name == “main” Mean in Python?
In this video course, you’ll learn all about Python’s name-main idiom. You’ll learn what it does in Python, how it works, when to use it, when to avoid it, and how to refer to it.
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Are you writing efficient Python with as few lines of code as possible? Are you familiar with the many built-in language features that will simplify your code and make it more Pythonic? Christopher Trudeau is back on the show this week, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss a recent post from Bob Belderbos titled “Make Each Line Count, Keeping Things Simple in Python.” We provide many of our favorite Pythonic examples and the language mistakes that we’ve learned from. We also share multiple resources to add to your learning path.
Microsoft has announced a limited beta program for Python in Excel. We dig into the current details, requirements, and potential use cases.
We cover several other articles and projects from the Python community, including a group of announcements from the Python Software Foundation, a showcase of the Polars DataFrame library, immortal objects in Python, a code image generator Python project, an MS Paint clone in the terminal, and a Django ORM cheatsheet.
Course Spotlight: Process Images Using the Pillow Library and Python
In this video course, you’ll learn how to use the Python Pillow library to deal with images and perform image processing. You’ll also explore using NumPy for further processing, including to create animations.
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enumerate(): Simplify Loops That Need Counters – Real Pythonall(): Check Your Iterables for Truthiness – Real Pythonany() in Python – Real PythonProjects:
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What should you consider when picking a font for coding in Python? What characters and their respective glyphs should you check before making your decision? This week on the show, we talk with Real Python author and core team member Philipp Acsany about his recent article, Choosing the Best Coding Font for Programming.
Philipp shares some of his background as a font engineer and graphic designer. We talk about how font design tools were his introduction to programming in Python.
We discuss how the frequent use of underscores, at signs, parentheses, and asterisks in Python’s syntax should affect your decision. Philipp’s tutorial provides several resources to help you find a monospace font that fits your coding requirements.
Course Spotlight: Create a Python Wordle Clone With Rich
In this step-by-step project, you’ll build your own Wordle clone with Python. Your game will run in the terminal, and you’ll use Rich to ensure your word-guessing app looks good. Learn how to build a command-line application from scratch and then challenge your friends to a wordly competition!
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How can you improve a classification model while avoiding overfitting? Once you have a model, what tools can you use to explain it to others? This week on the show, we talk with author and Python trainer Matt Harrison about his new book Effective XGBoost: Tuning, Understanding, and Deploying Classification Models.
Matt talks about the process of developing the book and how he wanted it to be an interactive experience for the reader. He explains the concept of gradient boosting and provides metaphors for developing a model. He shares his appreciation for exploratory data analysis as a crucial step in understanding your data.
He also shares additional libraries to help you explain your model. We discuss how difficult it is to develop the story of how the model works to share it with stakeholders.
He illustrates why covering the complete process is essential, from exploring data and building a model to finally deploying it. He shares many of the tools he found along the way.
This week’s episode is brought to you by Scout APM.
Course Spotlight: Starting With Linear Regression in Python
In this video course, you’ll get started with linear regression in Python. Linear regression is one of the fundamental statistical and machine learning techniques, and Python is a popular choice for machine learning.
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Have you ever encountered strange behavior when trying something new in Python? What are common quirks hiding within the language? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
We discuss a recent blog post that lists a collection of quirky Python behaviors. We share a few examples with explanations but leave several as puzzles to dig into.
Christopher transitions our discussion into Python features that can be difficult to explain to a new programmer. We also share some of our own stumbling blocks while learning the language.
We cover several other articles and projects from the Python community, including a news update, previewing Python 3.12’s more intuitive and consistent f-strings, finding performance bottlenecks with profiling, emulating the 6502 processor in Python, using Rich to inspect Python objects, and plotting statistical data with Lets-Plot.
This week’s episode is brought to you by Porkbun.
Course Spotlight: Graph Your Data With Python and ggplot
In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way.
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inspect feature, which gives you loads of information on an object.Additional Links:
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What are the new ways to describe your data in pandas 2.0? Will the addition of Apache Arrow to the data back end foster the growth of data interoperability? This week on the show, we talk with pandas core developer Marc Garcia about the release of pandas 2.0.
Marc shares his background and work on pandas. We discuss the history of data representation in pandas and the need to move beyond NumPy. We also talk about how Apache Arrow only solves some of the issues.
We dig into the potential of an Apache Arrow back end and how it could offer interoperability between data platforms. We also cover the moderate adoption and backward-compatibility concerns. Marc also shares his thoughts on making pandas more extensible.
Course Spotlight: The pandas DataFrame: Working With Data Efficiently
In this course, you’ll get started with pandas DataFrames, which are powerful and widely used two-dimensional data structures. You’ll learn how to perform basic operations with data, handle missing values, work with time-series data, and visualize data from a pandas DataFrame.
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What are all the different versions of Python? You may have heard of Cython, Brython, PyPy, or others and wondered where they fit into the Python landscape. This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
Christopher shares an article from the Bite Code blog about all the different forms that Python can take. CPython is the reference implementation of the language, which is what we usually discuss. He lists several alternative projects and the use cases.
We also discuss a recent Real Python tutorial about IPython. IPython is an interactive Python shell from the team that developed Jupyter Notebooks. It includes a set of IDE-like features and unique magic commands. The tutorial digs into using the tool to learn more about Python and explore your code.
We cover several other articles and projects from the Python community, including several news updates, the state of WASI support for CPython, how Python uses garbage collection, a discussion about the current AI echo chamber, an async Python web microframework, a stand-alone CSV editor, and a project for identifying unused dependencies to avoid a bloated virtual environment.
This week’s episode is brought to you by Scout APM.
Course Spotlight: Mazes in Python Part 1: Building and Visualizing
In this two-part video course project, you’ll build a maze solver in Python using graph algorithms from the NetworkX library. Along the way, you’ll design a binary file format for the maze, represent it in an object-oriented way, and visualize the solution using scalable vector graphics (SVG).
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Are you getting the most out of your Postgres database? What features could you leverage to improve your Python project? This week on the show, Craig Kerstiens from Crunchy Data is here to discuss getting the most out of Postgres.
Craig shares his years of PostgreSQL expertise with advice on getting more from the platform. We talk about rich data types for describing ranges, geospatial data, and JSON.
Craig shares tools for accessing performance statistics from the command line and strategies for optimizing your terminal settings for SQL searches. He discusses Postgres extensions for customizing the database to your needs. Craig also provides multiple resources for learning more and an online tool for practicing within a playground environment.
Course Spotlight: Creating Web Maps From Your Data With Python Folium
You’ll learn how to create web maps from data using Folium. The package combines Python’s data-wrangling strengths with the data-visualization power of the JavaScript library Leaflet. In this video course, you’ll create and style a choropleth world map showing the ecological footprint per country.
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What principles should you consider when designing a Python library? How do you construct a library API that’s understandable and easy to use? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.
We share an article about building library APIs. The piece provides advice for package structure, naming, error handling, and more. The author guides you toward Pythonic principles by comparing clunky vs elegant design examples.
Christopher discusses his recent video course on Jinja templating. The course covers creating text files with programmatic content and employing rich templates to structure the front end of Python web applications.
We cover several other articles and projects from the Python community, including several news updates, reasons why membership tests are fast for the range() function, CLI tools hidden in the Python standard library, a thread about the right way to install Python, recipes for using the Polars library, and a project for feature flags within Django.
This week’s episode is brought to you by Snyk.
Course Spotlight: Jinja Templating
With Jinja, you can build rich templates that power the front end of your web applications. But you can use Jinja without a web framework running in the background. Anytime you want to create text files with programmatic content, Jinja can help you out.
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bdist_egg Uploads to PyPIrange() in Python?News:
bdist_egg Uploads to PyPI – PEP 715 has been accepted and as of August 1, 2023, the .egg format will no longer be accepted as an upload. Existing eggs on PyPI will remain in place.Show Links:
range() in Python? – In Python, range() is most commonly used in for loops. However, ranges have some other use cases too, as they share many properties with lists. In this tutorial, you’ll explore why it’s so fast to perform a membership test on a Python range.Discussion:
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