Would you like to quickly add data to a map with Python? Have you wanted to create beautiful interactive maps and export them as a stand-alone static web page? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We share a recent Real Python tutorial about using Python Folium to create geospatial data visualizations. Folium harnesses the power of the JavaScript library Leaflet. The project shares how to combine this graphical power with Python’s data-wrangling strength.

Christopher shares a recent Python Enhancement Proposal (PEP) about the Global Interpreter Lock (GIL) in CPython. The PEP proposes a change to the build process that implements a flag for optionally building a GIL-less interpreter.

We share several other articles and projects from the Python community, including a news update, a YAML document from hell, a set of logging practices to follow, a discussion about the discourse surrounding the recent Python packaging user survey, a modern Python UI library based on Tkinter, and a lightweight tool kit for bounding boxes.

Course Spotlight: Everyday Project Packaging With pyproject.toml

In this Code Conversation video course, you’ll learn how to package your everyday projects with pyproject.toml. Playing on the same team as the import system means you can call your project from anywhere, ensure consistent imports, and have one file that’ll work for many build systems.

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Have you worked on a project that needed an orchestration tool? How do you define the workflow of an entire data pipeline or a messaging system with Python? This week on the show, Calvin Hendryx-Parker is back to talk about using Apache Airflow and orchestrating Python projects.

Calvin is the co-founder and CTO of Six Feet Up and a Python Web Conference co-organizer. He’s recently been working on a massive project that requires thousands of jobs involving transferring and transforming data. Through his research into orchestration systems, he found Apache Airflow.

Airflow is an open-source tool to define, schedule, and monitor workflows. The platform is pure Python and integrates with a wide variety of services. We discuss how workflows are defined by creating directed acyclic graphs (DAG).

Calvin talks about how a recent project outgrew the system and how his team built a clever solution using Python. We also discuss the upcoming Python Web Conference and what virtual attendees can expect.

Course Spotlight: Python Basics: Object-Oriented Programming

In this video course, you’ll get to know OOP, or object-oriented programming. You’ll learn how to create a class, use classes to create new objects, and instantiate classes with attributes.

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Have you used the Python Read-Eval-Print Loop (REPL) to explore the language and learn about how it operates? Would it help if it provided syntax highlighting, definitions, and code completion and behaved more like an IDE? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss the drop-in REPL replacement bpython. bpython enhances the interactivity of a Python REPL session. It’s also a powerful teaching tool for instructors and students to experiment with and explore Python code.

Christopher shares a recent Python Enhancement Proposal (PEP) about formalizing the grammar for f-strings. The PEP describes a reduction in the underlying parser code complexity and provides for future features like comments in multiline f-strings.

We share several other articles and projects from the Python community, including a news roundup, a collection of surveys to classify Python virtual environment workflows, a course about context managers and Python’s with statement, a discussion about microfeatures that we would like to see adopted in Python, a Python terminal music player, and an infinite array powered by AI.

Course Spotlight: Context Managers and Python’s with Statement

In this video course, you’ll learn what the Python with statement is and how to use it with existing context managers. You’ll also learn how to create your own context managers.

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How can you get more performance from your existing data science infrastructure? What if a DataFrame library could take advantage of your machine’s available cores and provide built-in methods for handling larger-than-RAM datasets? This week on the show, Liam Brannigan is here to discuss Polars.

Liam is an experienced data scientist working in finance, technology, and environmental analysis. He’s recently started contributing to the documentation for Polars and developing a training course for the library.

We talk about the library’s overall speed and lack of additional dependencies. Liam explains the advantages of lazy vs eager mode and which to choose when performing data exploration or attempting to load a dataset larger than your RAM.

We also discuss potential barriers to switching to Polars from a pandas workflow. Across our conversation, we explore several other libraries and technologies, including Apache Arrow, DuckDB, query optimization, and the “rustification” of Python tools.

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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Have you embraced the use of comprehensions in your Python journey? Are you familiar with all the varieties of comprehension constructs? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent article that surveys Python’s comprehensions and generators. This overview includes code snippets and the fundamentals of creating list, set, and dictionary comprehensions. We weigh the advantages of using a comprehension versus the more familiar for loops that they replace.

Christopher shares an article about how there may be infighting between the parallelism in your Python code and the parallelism within the libraries that you’re using. These complex system interactions can cause processing slowdowns and hard-to-trace bottlenecks.

We share several other articles and projects from the Python community, including a news roundup, a Python linter comparison, an overview of multiprocessing race conditions in Python, a discussion covering import statement styles, a project for WASM-powered Jupyter tools running in the browser, and a collection of easter eggs and jokes hidden inside Python itself.

Course Spotlight: Understanding Python List Comprehensions

Python list comprehensions make it easy to create lists while performing sophisticated filtering, mapping, and conditional logic on their members. In this course, you’ll learn when to use list comprehensions in Python and how to create them effectively.

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It’s been another year of changes at Real Python! The Real Python team has written, edited, curated, illustrated, and produced a mountain of Python material this year. We added some new members to the team, updated the site’s features, and created new styles of tutorials and video courses.

Three members of the Real Python team join us this week, Kate Finegan, Geir Arne Hjelle, and Leodanis Pozo Ramos. We wanted to share a year-end wrap-up with tutorials, step-by-step projects, and video courses that showcase what our team created this year.

Kate and Geir Arne help to shepherd articles through the multi-stage editing process. Along with the rest of the team, they make sure these resources impart crucial Python knowledge and provide a thorough didactic experience. Leodanis’ name has been featured many times on this podcast, and it was great to talk to him about writing tutorials and diving deep into the Pythonic details.

We hope you enjoy this review! Programming note, there won’t be an episode next week, but we’ll be back in January and look forward to bringing you a year full of great guests, articles, and topics.

Course Spotlight: Building Python Project Documentation With MkDocs

In this video course, you’ll learn how to build professional documentation for a Python package using MkDocs and mkdocstrings. These tools allow you to generate nice-looking and modern documentation from Markdown files and, more importantly, from your code’s docstrings.

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What advantages can a build system provide for a Python developer? What new skills are required when working with a team of developers? This week on the show, Benjy Weinberger from Toolchain is here to discuss the Pants build system and getting started with continuous integration (CI).

Benjy is one of the core developers of the Pants build system. He talks about the software tools and processes that a build system simplifies. We discuss how an individual developer can take advantage of continuous integration. We also cover some of the expectations when moving into professional software development.

Have you learned about or started to use tools like linters, code formatters, import sorters, type checkers, and packaging systems? A build system is designed to combine all of those tools into a simplified, one-step process to share your best code.

Benjy explains concepts like implementing fine-grained invalidation, moving to a monorepo, and using a build system for data science projects. He also shares his tips for getting started with Pants and finding help within the community.

Course Spotlight: Testing Your Code With pytest

In this video course, you’ll learn how to take your testing to the next level with pytest. You’ll cover intermediate and advanced pytest features such as fixtures, marks, parameters, and plugins. With pytest, you can make your test suites fast, effective, and less painful to maintain.

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How do you start packaging your code with pyproject.toml? Would you like to join a conversation that gently walks you through setting up your Python projects to share? This week on the show, Christopher Trudeau is here, bringing another batch of PyCoder’s Weekly articles and projects.

We discuss a recent code conversation featuring Real Python team members Ian Currie and Geir Arne Hjelle. The video dives into the officially sanctioned way to configure your project using a pyproject.toml file. We cover how this relatively new approach will help you package your code for use on your system or for sharing with others.

Christopher shares a Real Python tutorial about using pathlib to get a list of all the files within a directory. We’re both fans of pathlib and how it simplifies working with file paths. The tutorial digs into methods to recursively list all directory contents or create a conditional listing.

We share several other articles and projects from the Python community, including an explanation of Python bytecode, an argument for always using [closed, open) intervals, a discussion about building the monolith before microservices, a way to parse natural language time and date expressions, and a project for posting on Mastodon.

Course Spotlight: Using Python’s pathlib Module

In this video course, you’ll learn how to effectively work with file system paths in Python 3 using the pathlib module in the standard library.

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How do you prepare a dataset for machine learning (ML)? How do you go beyond cleaning the data and move toward measuring how the model performs? This week on the show, Jodie Burchell, developer advocate for data science at JetBrains, returns to talk about strategies for better ML model performance.

Jodie starts by defining some terms for the conversation. We talk about targets, features, and supervised learning.

We discuss three common ways that data can alter model performance and which Python tools can help spot and avoid them. Jodie shares personal experiences of working through these pitfalls. We also share a healthy collection of resources to explore and learn more.

Course Spotlight: Combining Data in pandas With concat() and merge()

In this video course, you’ll learn two techniques for combining data in pandas: merge() and concat(). Combining Series and DataFrame objects in pandas is a powerful way to gain new insights into your data.

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How do you build a REST API using the Flask web framework? How can you quickly add endpoints while automatically generating documentation? This week on the show, Real Python author Philipp Acsany is here to discuss his tutorial series “Python REST APIs With Flask, Connexion, and SQLAlchemy.” Christopher Trudeau is also here with another batch of PyCoder’s Weekly articles and projects.

Philipp talks about updating a set of tutorials to use current libraries and best practices. The series takes you through building the base Flask project, defining endpoints, creating documentation, adding a persistent database, and implementing models with SQLAlchemy.

Christopher shares an article about contributing to an existing internal or open-source project by properly preparing pull requests. The article is titled “Ten Tasty Ingredients for a Delicious Pull Request”.

We share several other articles and projects from the Python community, including more suspicious PyPI packages using new tactics, method chaining in pandas, tools to find syntax errors without stopping, a library for searching text in videos using optical character recognition (OCR), a project for visualizing CPython’s specializing adaptive interpreter, and a library for building CLI applications based on type hints.

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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