Real Python

ChatterBot: Build a Chatbot With Python

2026-05-06T14:00:00+00:00

The Python ChatterBot library lets you build a self-learning command-line chatbot with just a few lines of code. You’ll set up a basic bot, clean real WhatsApp conversation data with regular expressions, and train your chatbot on that custom corpus. You’ll also plug in a local LLM through Ollama to augment its replies with contextual knowledge.

By the end of this tutorial, you’ll understand that:

ChatterBot is a Python library that combines text processing, machine learning, and a local database to generate chatbot replies.
A minimal ChatterBot script instantiates ChatBot, collects user input in a loop, and returns matching responses through .get_response().
Training with ListTrainer and default settings stores conversation pairs in a SQLite database that ChatterBot queries with Levenshtein distance to pick each reply.
ChatterBot can call a local LLM through OllamaLogicAdapter, voting against other logic adapters with a confidence score.
ChatterBot was revived in 2025 with spaCy-based NLP, CSV and JSON trainers, and experimental LLM support.

Along the way, you’ll move from a potted plant that can only echo hello to a chatbot that chats knowledgeably about houseplants. You can follow along with your own WhatsApp export or grab the provided sample data below.

Get Your Code: Click here to download the free sample code that you’ll use to build a chatbot with Python’s Chatterbot.

Take the Quiz: Test your knowledge with our interactive “ChatterBot: Build a Chatbot With Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

ChatterBot: Build a Chatbot With Python

Test your understanding of the ChatterBot Python library, from training a basic bot with ListTrainer to wiring in a local LLM through Ollama.

Preview the Chatbot

At the end of this tutorial, you’ll have a command-line chatbot that can respond to your inputs with semi-meaningful replies:

You’ll achieve that by preparing WhatsApp chat data and using it to train the chatbot. Beyond learning from your automated training, the chatbot will improve over time as it gets more exposure to questions and replies from user interactions.

Project Overview

The ChatterBot library combines text processing, machine learning algorithms, and data storage and retrieval to allow you to build flexible chatbots.

You can build an industry-specific chatbot by training it with relevant data. Additionally, the chatbot will remember user responses and continue building its internal graph structure to improve the responses that it can give.

Note: After a long hiatus, ChatterBot was revived in early 2025 with support for modern Python, new training formats for CSV and JSON data, and even experimental LLM integration. Under the hood, ChatterBot now uses spaCy for language processing, which gives it a more robust NLP pipeline than before.

If you want to develop an LLM-first chatbot, Real Python’s LLM Application Development With Python learning path takes you through the concepts and libraries step by step:

Learning Path

LLM Application Development With Python

13 Resources ⋅ Skills: OpenAI, Ollama, OpenRouter, Prompt Engineering, LangChain, LlamaIndex, ChromaDB, MarkItDown, RAG, Embeddings, Pydantic AI, LangGraph, MCP

In this tutorial, you’ll start with an untrained chatbot that’ll showcase how quickly you can create an interactive chatbot using Python’s ChatterBot. You’ll also notice how small the vocabulary of an untrained chatbot is.

Next, you’ll learn how you can train such a chatbot and check on the slightly improved results. The more plentiful and high-quality your training data is, the better your chatbot’s responses will be.

Therefore, you’ll either fetch the conversation history of one of your WhatsApp chats or use the provided chat.txt file that you can download here:

Get Your Code: Click here to download the free sample code that you’ll use to build a chatbot with Python’s Chatterbot.

It’s rare that input data comes exactly in the form you need, so you’ll clean the chat export data to get it into a useful input format. This process will show you some tools you can use for data cleaning, which may help you prepare other input data to feed to your chatbot.

After data cleaning, you’ll retrain your chatbot and give it another spin to experience the improved performance. Finally, you’ll hook a local LLM into your chatbot to augment the variety and contextual relevance of its responses.

When you work through this process from start to finish, you’ll get a good idea of how you can build and train a Python chatbot with the ChatterBot library so that it can provide an interactive experience with relevant replies.

Prerequisites

Before you get started, make sure that you have Python 3.10 or later installed, which is the minimum Python version that ChatterBot supports. If you need help setting up Python, check out Python 3 Installation & Setup Guide.

Read the full article at https://realpython.com/build-a-chatbot-python-chatterbot/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Python & APIs: A Winning Combo for Reading Public Data

2026-05-06T12:00:00+00:00

In this quiz, you’ll test your understanding of Python & APIs: A Winning Combo for Reading Public Data.

By working through this quiz, you’ll revisit how APIs send requests and responses, how the requests library works, what status codes and headers mean, and how to handle authentication, pagination, and rate limits in your own code.

Good luck!

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: How to Use OpenCode for AI-Assisted Python Coding

2026-05-06T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Use OpenCode for AI-Assisted Python Coding.

By working through these questions, you’ll revisit how to install OpenCode, connect it to an AI provider, configure project context with AGENTS.md, and take advantage of features like mid-session model switching and built-in language servers.

If you’d like a broader look at AI-assisted Python development, you can also follow the Python Coding With AI learning path.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Use Codex CLI to Enhance Your Python Projects

2026-05-05T14:00:00+00:00

After watching this video course, you’ll be able to use Codex CLI to add features to a Python project directly from your terminal. Codex CLI is an AI-powered coding assistant that runs inside your terminal. It understands your project structure, reads your files, and proposes multi-file changes using natural language instructions.

Instead of copying code from a browser or relying on an IDE plugin, you’ll use Codex CLI to implement a real feature in a multi-file Python project directly from your terminal.

In the following lessons, you’ll install and configure Codex CLI, use it to implement a deletion feature in a contact book app, and then refine that feature through iterative prompting.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Python Application Layouts: A Reference

2026-05-05T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Application Layouts: A Reference.

By working through this quiz, you’ll revisit how to structure one-off scripts, installable packages, larger applications with internal packages, and web projects built with Django or Flask.

A dependable starting layout helps you avoid coder’s block so you can focus on writing code instead of figuring out where it should live.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

A New Python Packaging Council and Other News for May 2026

2026-05-04T14:00:00+00:00

April gave Python developers a new governing body. PEP 772 was accepted on April 16, creating a dedicated Python Packaging Council that will make binding decisions about packaging standards and tools. After years of informal coordination through the Python Packaging Authority (PyPA), the packaging community now has its own elected five-member council with authority comparable to the Steering Council.

On the release side, Python 3.15.0 alpha 8 dropped with a refreshed JIT delivering 6–7 percent speedups on x86-64 Linux and 12–13 percent on AArch64 macOS. The core team also decided to revert 3.14’s incremental garbage collector after production reports of runaway memory use, with the fix landing in the upcoming 3.14.5 patch release. The next pre-release is the first beta, scheduled for May 5, which marks the feature freeze for Python 3.15.

Elsewhere, Google released the open-weights Gemma 4 family, Starlette 1.0 shipped for FastAPI’s foundation, and the broader Python ecosystem absorbed the news that OpenAI acquired Astral, the company behind uv, Ruff, and ty. Get ready to dig into the biggest Python news from the past month!

Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.

Python Releases and PEP Highlights

April pushed Python 3.15 to its final alpha before the beta freeze, walked back the incremental garbage collector introduced in 3.14, and gave the Steering Council a busy month of PEP decisions. The packaging community even got its own elected governing body for the first time. Plenty to unpack on the language and process side of the ecosystem.

Python 3.15.0 Alpha 8: Final Alpha Before Beta Freeze

Python 3.15.0a8 landed on April 7, released alongside maintenance updates 3.14.4 and 3.13.13. Release manager Hugo van Kemenade confirmed that a8 is the final alpha before the beta phase begins. If you maintain a library, this is the last alpha where you can file an issue against an unreleased feature and reasonably expect it to land before the freeze.

Alpha 8 consolidates a long list of PEPs you’ve been hearing about in earlier alphas:

PEP 810: Explicit lazy imports, which we covered last month
PEP 814: frozendict as a built-in type
PEP 799: Statistical sampling profiler
PEP 798: Unpacking in comprehensions
PEP 686: UTF-8 as the default encoding
PEP 728: TypedDict enhancements

The headline number is the JIT performance jump. On x86-64 Linux, the alpha reports a 6–7 percent geometric mean improvement over the standard interpreter. On AArch64 macOS, the gain is 12–13 percent over the tail-calling interpreter introduced in 3.14. Those aren’t microbenchmark curiosities. They’re cumulative gains across a broad suite of workloads.

Note: If you haven’t tried the alpha yet, installing it in an isolated environment is a good idea. Running uv python install 3.15.0a8 pulls the binary, and pyenv handles alpha builds too. The next pre-release, 3.15.0 beta 1, is scheduled for May 5, which marks the feature freeze. After that, no new PEPs land in 3.15.

Incremental GC Reverted in 3.14.5 and 3.15

On April 16, release manager Hugo van Kemenade proposed reverting the incremental garbage collector that debuted in Python 3.14, and the core team agreed. The revert will ship in Python 3.14.5 and also make it into 3.15 before feature freeze.

The reasoning is practical. Neil Schemenauer’s testing on production workloads showed that the incremental collector cut maximum pause times from 26 ms down to 1.3 ms, which looks great on paper. But peak memory usage climbed to as much as 5x the generational baseline in the worst case, and total runtime went up, not down, because of the extra bookkeeping.

For most Python programs, like web apps, data pipelines, and batch jobs, reducing long pauses isn’t the win that matters. Memory pressure is.

The unusual part is doing this in a patch release. The working assumption during 3.14’s release cycle was that the incremental GC had earned its place. Rolling it back in 3.14.5 is a reminder that “passed the benchmark suite” and “works in production” aren’t the same thing. If you noticed your 3.14 deployments using noticeably more memory than 3.13, this is almost certainly why, and 3.14.5 should give you the old behavior back.

Note: The incremental approach isn’t dead. The core team noted that it could return in Python 3.16 through a proper PEP review process, which the original implementation had skipped. If you were on the fence about the switch, waiting for the formal design round is probably the right call.

PEP 772 Accepted: Python Gets a Packaging Council

On April 16, the Python Software Foundation (PSF) and the Steering Council accepted PEP 772, which creates a five-member Packaging Council with broad authority over packaging standards, tools, and implementations. It’s one of the biggest governance changes the ecosystem has seen since the Steering Council itself was established back in 2019.

Council members will be elected by PSF voting members who opt into the election. The council runs on staggered two-year terms, with two seats and three seats rotating in different cycles to preserve institutional continuity. Decision-making emphasizes consensus over voting, following the same pattern that has worked for the Steering Council.

The practical impact is that a formal, elected body now owns decisions about tools like pip, setuptools, and PyPI, replacing the ambiguous delegation model defined in PEP 609.

If you’ve ever wondered why packaging decisions in Python sometimes feel stuck in committee, PEP 772 is the structural answer to that complaint. It also sets the stage for the council to weigh in on LLM-era packaging concerns, which are popping up faster than the PyPA’s informal coordination could address them.

PEP 803 Accepted: Stable ABI Goes Free-Threaded

PEP 803 was accepted on March 30 and targets Python 3.15. It defines abi3t, a new variant of the stable ABI that works with free-threaded builds. When the Steering Council accepted PEP 779 last year, it promised free-threading would get a proper stable ABI story for 3.15. PEP 803 is that follow-through.

Read the full article at https://realpython.com/python-news-may-2026/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Data Management With Python, SQLite, and SQLAlchemy

2026-05-04T12:00:00+00:00

In this quiz, you’ll test your understanding of the tutorial Data Management With Python, SQLite, and SQLAlchemy.

By working through this quiz, you’ll revisit how Python, SQLite, and SQLAlchemy work together to give your programs reliable data storage.

You’ll also check your grasp of primary and foreign keys, SQL operations, and the SQLAlchemy models that let you work with your data as Python objects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Revisit Python Fundamentals

2026-05-03T12:00:00+00:00

In this quiz, you’ll revisit the core concepts covered in the Revisit Python Fundamentals learning path:

Learning Path

Revisit Python Fundamentals

13 Resources ⋅ Skills: Python, Variables, Data Types, REPL, Operators, Expressions, Exceptions, Comments, Keywords, IDLE, String Conversion

The 15 questions span variables, data types, operators, expressions, keywords, and exceptions, giving you a way to check that you understood the most important ideas.

Take your time and revisit any topics that feel rusty before moving on to the next learning path.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #293: Agentic Data Science Pair Programming With marimo pair

2026-05-01T12:00:00+00:00

How do you add agent skills to your data science workflow? How can a coding agent assist with data wrangling and research? This week on the show, Trevor Manz from marimo joins us to discuss marimo pair.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: The Factory Method Pattern and Its Implementation in Python

2026-05-01T12:00:00+00:00

In this quiz, you’ll test your understanding of The Factory Method Pattern and Its Implementation in Python.

Factory Method is one of the most widely used design patterns, and it’s a powerful tool for separating object creation from object use in your code.

By working through this quiz, you’ll revisit the components of the pattern, recognize opportunities to apply it, and see how you can implement a reusable, general-purpose solution in Python.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Using Python for Data Analysis

2026-04-30T12:00:00+00:00

In this quiz, you’ll test your understanding of Using Python for Data Analysis.

By working through this quiz, you’ll revisit the stages of a data analysis workflow, including cleansing raw data with pandas, spotting outliers and typos, and using regression to find relationships between variables.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

AI Coding Agents Guide: A Map of the Four Workflow Types

2026-04-29T14:00:00+00:00

AI coding agents can read your code, reason about changes, and act on your behalf. To choose the right one, it helps to understand the four common workflow types: integrated development environment (IDE), terminal, pull request (PR), and cloud.

In this tutorial, you’ll:

Identify the four common agent interaction modes
Understand what makes each workflow distinct
Recognize which mode fits common development scenarios
Weigh the risks and tradeoffs of each workflow

Before exploring the four workflow types, it’s worth looking at what makes a coding tool agentic in the first place.

Take the Quiz: Test your knowledge with our interactive “AI Coding Agents Guide: A Map of the Four Workflow Types” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

AI Coding Agents Guide: A Map of the Four Workflow Types

Check your understanding of how AI coding agents fit into your workflow through four interaction modes: IDE, terminal, pull request, and cloud.

Get Your Cheat Sheet: Click here to download your free AI coding agents cheat sheet and keep the four workflow types at your fingertips when choosing the right agent for the job.

Understanding AI Coding Agents

While standard chatbots provide one-off answers, coding agents are designed for autonomy, operating through a continuous execution loop to solve complex tasks. This loop typically follows four distinct steps:

Read: They read relevant files from your codebase to form their context.
Reason: They determine the logical steps needed to achieve your goal.
Act: They execute those steps by editing files, running terminal commands, or using external tools.
Evaluate: They check the results of their actions to see if more work is needed.

This loop repeats until the task is completed or the agent hands control back to you. Unlike simple predictive text or one-off prompts, agents bridge the gap between suggestion and execution by autonomously navigating the development workflow.

The core agent loop will generally stay the same, but where an agent runs will shape how you interact with it:

In an editor, it works alongside you.
In a terminal, you guide it step by step.
In pull requests, it reviews changes asynchronously.
In the cloud, it works in a managed environment and reports back later.

These environments define four primary agent types, each enabling a distinct workflow: IDE agents, terminal agents, PR agents, and cloud agents.

Exploring the Four Workflow Types

The four workflow types describe interaction modes and don’t always map cleanly to product categories. The same tool often spans multiple workflows. For example, Claude Code runs in your terminal, in your editor, and in the cloud with Claude Code on the web. It can also review pull requests with Code Review.

The goal is to match the workflow to the task. The diagram below summarizes the four types at a glance:

Read the full article at https://realpython.com/ai-coding-agents-guide/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Python 3.13: A Modern REPL

2026-04-29T12:00:00+00:00

Test your knowledge of the redesigned interactive interpreter introduced in Python 3.13: A Modern REPL, including the help system, multiline statement editing, code pasting improvements, and the history browser.

Good luck!

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: ChatterBot: Build a Chatbot With Python

2026-04-29T12:00:00+00:00

In this quiz, you’ll test your understanding of ChatterBot: Build a Chatbot With Python.

You’ll revisit how ChatterBot learns from conversation data, how it picks replies based on similarity to what it’s already seen, and how it can pull in a local LLM to round out its responses.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Testing Your Code With Python's unittest

2026-04-28T14:00:00+00:00

The Python standard library ships with a testing framework named unittest, which you can use to write automated tests for your code. The unittest package has an object-oriented approach where test cases derive from a base class, which has several useful methods.

The framework supports many features that will help you write consistent unit tests for your code. These features include test cases, fixtures, test suites, and test discovery capabilities.

In this video course, you’ll learn how to:

Write unittest tests with the TestCase class
Explore the assert methods that TestCase provides
Use unittest from the command line
Group test cases using the TestSuite class
Create fixtures to handle setup and teardown logic

To get the most out of this video course, you should be familiar with some important Python concepts, such as object-oriented programming, inheritance, and assertions. Having a good understanding of code testing is a plus.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Quiz: Use Codex CLI to Enhance Your Python Projects

2026-04-28T12:00:00+00:00

In this quiz, you’ll test your understanding of Use Codex CLI to Enhance Your Python Projects.

By working through this quiz, you’ll revisit how to install and configure Codex CLI, use Plan mode to review changes before they land, and refine features through iterative prompting in your terminal.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

How to Conceptualize Python Fundamentals for Greater Mastery

2026-04-27T14:00:00+00:00

Struggling to conceptualize Python fundamentals is a common problem learners face. If you’re unable to put a fundamental concept into perspective and form a clear mental picture of what it’s about, it’ll be difficult to understand and apply it.

In this guide, you’ll walk through a framework of steps to help you better conceptualize Python fundamentals. This process is helpful for Python developers and learners at any experience level, but especially for beginners. If you are just starting out, this guide will help you build a solid understanding of the basics.

You might want to set aside twenty minutes or so to read through the tutorial, and another thirty minutes to practice on a few key concepts. You should also gather a list of difficult topics, your preferred learning resources, and a note-taking app or pen and paper.

Click the link below to download a free cheat sheet that covers the framework steps you’ll walk through in this guide:

Get Your Cheat Sheet: Click here to download a free PDF that outlines the framework of steps for conceptualizing Python fundamentals.

Take the Quiz: Test your knowledge with our interactive “How to Conceptualize Python Fundamentals for Greater Mastery” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Conceptualize Python Fundamentals for Greater Mastery

Check your understanding of a framework for conceptualizing Python fundamentals, from defining concepts to comparing similar ideas.

Step 1: Define the Concept in Your Own Words

Begin by briefly describing the concept in your own words. You can write your definition in the downloadable worksheet provided with this tutorial. Note that writing is a powerful tool for reinforcing learning, as educator and former Rutgers University professor Janet Emig asserted in her paper, Writing as a Mode of Learning.

Answer Key Questions for Defining a Concept

As a framework for your definition, consider these key questions:

What: What is a short description of the concept?
Why: Why is the concept important in the broader Python context?
How: How is the concept used in a Python program?

These questions will help you establish a core understanding of the concept you’re learning.

You might feel intimidated when you’re trying to define a Python concept. If you need help, there are many resources that can assist you. Real Python’s Reference section has concise definitions of Python keywords, built-in types, standard library modules, and more to help you build your own descriptions.

If you’re a visual learner, using an illustration can be a powerful way to enhance your understanding. In addition to a written definition, you can draw a picture or diagram to illustrate the concept. For example, the Variables in Python: Usage and Best Practices tutorial shows some example images of how you might picture variables. If you look at the Lists vs Tuples in Python tutorial, you can see a diagram of a Python list.

While pictures can be helpful, being able to conceptualize doesn’t necessarily mean you have to think visually. There are different thinking styles. Some researchers suggest that people can be visual or verbal thinkers. Pattern-based thinking is another style. Several of the tips in this tutorial encourage you to explore different aspects of these styles, depending on which works best for you.

View Examples of Concept Definitions

You might find a couple of examples helpful in understanding how to define difficult concepts. Suppose you’re studying variables. Here are possible responses to the key questions:

What: A variable is a name that points to an object stored in the program’s memory.
Why: Variables are key for data processing.
How: Assigning a value to a variable using the assignment operator (=) allows you to access your program’s data in a user-friendly way. You can then access and change the value by name throughout the program as needed.

This description provides a concise summary of what a variable is, why it matters, and how to use one. You can also include an example of variable usage as an addendum to your definition:

>>> age = 25

Here, you created a variable called age and assigned it a value of 25. From now on, you can use the variable name age to access, modify, or use the variable’s value.

Or, you might be learning about lists. Your definitions could look like this:

What: A list is a sequence of values or objects.
Why: Working with sequences of items is a common, foundational task in programming. Python lists make this important work easier.
How: You can create a list by writing a pair of square brackets, with a comma-separated sequence of items inside them. Assign the list to a variable to use it throughout your program.

Here’s a short Python list that demonstrates the points in the definitions above:

Read the full article at https://realpython.com/conceptualize-python-fundamentals/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #292: Becoming a Better Python Developer Through Learning Rust

2026-04-24T12:00:00+00:00

How can learning Rust help make you a better Python Developer? How do techniques required by a compiled language translate to improving your Python code? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Altair: Declarative Charts With Python

2026-04-22T14:00:00+00:00

There’s a moment many data analysts know well: you have a new dataset and a clear question, and you open a notebook only to find yourself writing boilerplate axis and figure setup before you’ve even looked at the data. Matplotlib gives you fine-grained control, but that control comes with a cost. Altair takes a completely different approach to data visualization in Python.

Instead of scripting every visual detail, you describe what your data means. This includes specifying which column goes on which axis, what should be colored, and what should be interactive. Altair then generates the visualization.

If you’re wondering whether it’s worth adding another visualization library to your toolkit, here’s how Altair and Matplotlib compare:

Use Case	Pick Altair	Pick Matplotlib
Interactive exploratory charts in notebooks	✅	—
Pixel-precise publication figures or 3D plots	—	✅

Altair generates web-native charts. The output is HTML and JavaScript, which means charts render right in your notebook and can be saved as standalone HTML files or embedded in web pages. It’s not a replacement for Matplotlib, and it doesn’t try to be. Think of them as tools you reach for in different situations.

Get Your Code: Click here to download the free sample code you’ll use to build interactive Python charts the declarative way with Altair.

Take the Quiz: Test your knowledge with our interactive “Altair: Declarative Charts With Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Altair: Declarative Charts With Python

Test your knowledge of Altair, the declarative data visualization library for Python that turns DataFrames into interactive charts.

Start Using Altair in Python

It’s a good idea to install Altair in a dedicated virtual environment. It pulls in several dependencies like pandas and the Vega-Lite renderer, and a virtual environment keeps them from interfering with your other projects. Create one and install Altair with pip:

$ python -m venv altair-venv
$ source altair-venv/bin/activate
(altair-venv) $ python -m pip install altair

This tutorial uses Python 3.14 and Altair 6.0. All the code runs inside a Jupyter notebook, which is the most common environment for interactive data exploration with Altair. If you prefer a different JavaScript-capable environment like VS Code, Google Colab, or JupyterLab, feel free to use that instead. To launch a Jupyter notebook, run the following:

(altair-venv) $ python -m pip install notebook
(altair-venv) $ jupyter notebook

The second command launches the Jupyter Notebook server in your browser. Create a new notebook and enter the following code, which builds a bar chart from a small DataFrame containing daily step counts for one week:

import altair as alt
import pandas as pd

steps = pd.DataFrame({
    "Day": ["1-Mon", "2-Tue", "3-Wed", "4-Thu", "5-Fri", "6-Sat", "7-Sun"],
    "Steps": [6200, 8400, 7100, 9800, 5500, 9870, 3769],
})

weekly_steps = alt.Chart(steps).mark_bar().encode(
    x="Day",
    y="Steps",
)
weekly_steps

You should see a bar chart displaying daily step counts:

The dataset is intentionally minimal because data isn’t the main focus: it has seven rows for seven days, and two columns for the day name and step count. Notice how the weekly_steps chart is constructed. Every Altair chart follows this same pattern. It’s built from these three building blocks:

Data: A pandas DataFrame handed to alt.Chart().
Mark: The visual shape you want, chosen via .mark_*(). Here, .mark_bar() draws bars. Other options include .mark_point(), .mark_line(), and .mark_arc().
Encode: The mapping from data columns to visual properties, declared inside .encode(). Here, Day goes to the x-axis and Steps to the y-axis.

This is Altair’s core grammar in action: Data → Mark → Encode. You’ll use it every time.

Read the full article at https://realpython.com/altair-python/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Leverage OpenAI's API in Your Python Projects

2026-04-21T14:00:00+00:00

Python’s openai library provides the tools you need to integrate the ChatGPT API into your Python applications. With it, you can send text prompts to the API and receive AI-generated responses. You can also guide the AI’s behavior with developer role messages and handle both simple text generation and more complex code creation tasks.

After watching this video course, you’ll understand how examples like this work under the hood. You’ll learn the fundamentals of using the ChatGPT API from Python and have code examples you can adapt for your own projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Gemini CLI vs Claude Code: Which to Choose for Python Tasks

2026-04-20T14:00:00+00:00

When comparing Gemini CLI vs Claude Code, the answer to “which one is better?” is usually it depends. Both tools boost productivity for Python developers, but they have different strengths. Choosing the right one depends on your budget, workflow, and what you value most in generated code.

Gemini CLI, for instance, is known for its generous free tier, while Claude Code is a paid tool known for its production-ready output.

In this tutorial, you’ll explore features such as user experience, performance, code quality, and usage cost to help make that decision easier. The AI coding assistance these tools provide right in your terminal generally makes writing Python code much more seamless, helping you save time and be more productive.

This table highlights the key differences at a glance:

Use Case	Gemini CLI	Claude Code
You need free generous usage limits	✅	—
You need Google Cloud integration	✅	—
You need faster task completion	—	✅
You need code close to production quality	—	✅

You can see that Gemini CLI is a promising choice if you’re looking for free usage limits and prefer Google Cloud integration. However, if you want to complete tasks faster, Claude Code has an edge. Both tools produce code of good quality, but Claude Code generates code that is closer to production quality. If you’d like a more thorough comparison, then read on.

Get Your Code: Click here to download the free sample code for the to-do app projects built with Gemini CLI and Claude Code in this tutorial.

Take the Quiz: Test your knowledge with our interactive “Gemini CLI vs Claude Code: Which to Choose for Python Tasks” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Gemini CLI vs Claude Code: Which to Choose for Python Tasks

Compare Gemini CLI and Claude Code across user experience, performance, code quality, and cost to find the right AI coding tool for you.

Metrics Comparison: Gemini CLI vs Claude Code

To ground the comparisons in hands-on data, both tools are tested using the same prompt throughout this tutorial:

Prompt

Build a CLI-based mini to-do application in Python. It should allow users to create tasks, mark tasks as completed, list tasks with filtering for completed and pending tasks, delete tasks, include error handling, persist tasks to a local JSON file, and include basic unit tests.

For a fair comparison, Gemini CLI is tested on its free tier using Gemini 3 Flash Preview, which is the default model the free tier provides access to. Claude Code is tested on the Pro plan using Claude Sonnet 4.6, which is the model Claude Code primarily uses for everyday interactions on that plan.

Each tool will run this prompt three times. Completion time, token usage, and the quality of the generated code are recorded from the runs and are referenced in the Performance, Code Quality, and Usage Cost sections of this tutorial.

Note: If you want to learn more about these tools so you can compare them yourself, Real Python has you covered. The How to Use Google’s Gemini CLI for AI Code Assistance tutorial covers installation, authentication, and hands-on usage, while the Getting Started With Claude Code video course walks you through setup and core features.

You should also be comfortable using your terminal, since both Gemini CLI and Claude Code are command-line tools.

The table below provides more detailed metrics to help with each comparison:

Metric	Gemini CLI	Claude Code
User Experience	Intuitive, browser-based auth, terminal-native	Minimal setup, terminal-native, strong project awareness
Performance	Good performance, however slower generation speed	Good performance, code is generated generally faster
Code Quality	Solid, better for exploratory tasks	Strong, better for production-grade work
Usage Cost	Free tier available; paid plans for heavier use	Requires a paid subscription to get started

The following sections explore each metric in detail, so you can decide which tool fits your workflow best.

User Experience

When writing Python programs, it helps to be able to comfortably use your tools without dealing with unintuitive interfaces. Both Gemini CLI and Claude Code prioritize a smooth terminal experience, but user experience goes beyond the interface itself—installation, setup, available models, and features offered are also part of it.

Installation and Setup

A few differences exist between Gemini CLI and Claude Code during installation. Gemini CLI requires a Google account for authentication. Claude Code doesn’t need a Google account. Instead, it requires an Anthropic subscription or API key.

Gemini CLI is first installed using npm:

$ npm install -g @google/gemini-cli

You can also install Gemini CLI with Anaconda, MacPorts, or Homebrew, which you can find in the Gemini CLI documentation.

When installing Claude Code, you run the following commands:

Read the full article at https://realpython.com/gemini-cli-vs-claude-code/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #291: Reassessing the LLM Landscape & Summoning Ghosts

2026-04-17T12:00:00+00:00

What are the current techniques being employed to improve the performance of LLM-based systems? How is the industry shifting from post-training towards context engineering and multi-agent orchestration? This week on the show, Jodie Burchell, data scientist and Python Advocacy Team Lead at JetBrains, returns to discuss the current AI coding landscape.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Python Game Development

2026-04-16T12:00:00+00:00

Build Python games from command-line projects to 2D graphical games with turtle, Tkinter, Pygame, and Arcade.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Variables in Python: Usage and Best Practices

2026-04-15T14:00:00+00:00

In Python, variables are symbolic names that refer to objects or values stored in your computer’s memory. They allow you to assign descriptive names to data, making it easier to manipulate and reuse values throughout your code. You create a Python variable by assigning a value using the syntax variable_name = value.

By the end of this tutorial, you’ll understand that:

Variables in Python are symbolic names pointing to objects or values in memory.
You define variables by assigning them a value using the assignment operator.
Python variables are dynamically typed, allowing type changes through reassignment.
Python variable names can include letters, digits, and underscores but can’t start with a digit. You should use snake case for multi-word names to improve readability.
Variables exist in different scopes (global, local, non-local, or built-in), which affects how you can access them.
You can have an unlimited number of variables in Python, limited only by computer memory.

To get the most out of this tutorial, you should be familiar with Python’s basic data types and have a general understanding of programming concepts like loops and functions.

Don’t worry if you don’t have all this knowledge yet and you’re just getting started. You won’t need this knowledge to benefit from working through the early sections of this tutorial.

Get Your Code: Click here to download the free sample code that shows you how to use variables in Python.

Take the Quiz: Test your knowledge with our interactive “Variables in Python: Usage and Best Practices” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Variables in Python: Usage and Best Practices

Test your understanding of Python variables, from creation and naming conventions to dynamic typing, scopes, and type hints.

Getting to Know Variables in Python

In Python, variables are names associated with concrete objects or values stored in your computer’s memory. By associating a variable with a value, you can refer to the value using a descriptive name and reuse it as many times as needed in your code.

Variables behave as if they were the value they refer to. To use variables in your code, you first need to learn how to create them, which is pretty straightforward in Python.

Creating Variables With Assignments

The primary way to create a variable in Python is to assign it a value using the assignment operator and the following syntax:

variable_name = value

In this syntax, you have the variable’s name on the left, then the assignment (=) operator, followed by the value you want to assign to the variable at hand. The value in this construct can be any Python object, including strings, numbers, lists, dictionaries, or even custom objects.

Note: To learn more about assignments, check out Python’s Assignment Operator: Write Robust Assignments.

Here are a few examples of variables:

>>> word = "Python"

>>> number = 42

>>> coefficient = 2.87

>>> fruits = ["apple", "mango", "grape"]

>>> ordinals = {1: "first", 2: "second", 3: "third"}

>>> class SomeCustomClass: pass
>>> instance = SomeCustomClass()

In this code, you’ve defined several variables by assigning values to names. The first five examples include variables that refer to different built-in types. The last example shows that variables can also refer to custom objects like an instance of your SomeCustomClass class.

Setting and Changing a Variable’s Data Type

Apart from a variable’s value, it’s also important to consider the data type of the value. When you think about a variable’s type, you’re considering whether the variable refers to a string, integer, floating-point number, list, tuple, dictionary, custom object, or another data type.

Python is a dynamically typed language, which means that variable types are determined and checked at runtime rather than during compilation. Because of this, you don’t need to specify a variable’s type when you’re creating the variable. Python will infer a variable’s type from the assigned object.

Note: In Python, variables themselves don’t have data types. Instead, the objects that variables reference have types.

For example, consider the following variables:

>>> name = "Jane Doe"
>>> age = 19
>>> subjects = ["Math", "English", "Physics", "Chemistry"]

>>> type(name)
<class 'str'>
>>> type(age)
<class 'int'>
>>> type(subjects)
<class 'list'>

In this example, name refers to the "Jane Doe" value, so the type of name is str. Similarly, age refers to the integer number 19, so its type is int. Finally, subjects refers to a list, so its type is list. Note that you don’t have to explicitly tell Python which type each variable is. Python determines and sets the type by checking the type of the assigned value.

Read the full article at https://realpython.com/python-variables/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Vector Databases and Embeddings With ChromaDB

2026-04-14T14:00:00+00:00

The era of large language models (LLMs) is here, bringing with it rapidly evolving libraries like ChromaDB that help augment LLM applications. You’ve most likely heard of chatbots like OpenAI’s ChatGPT, and perhaps you’ve even experienced their remarkable ability to reason about natural language processing (NLP) problems.

Modern LLMs, while imperfect, can accurately solve a wide range of problems and provide correct answers to many questions. However, due to the limits of their training and the number of text tokens they can process, LLMs aren’t a silver bullet for all tasks.

You wouldn’t expect an LLM to deliver relevant responses about topics that don’t appear in its training data. For example, if you asked ChatGPT to summarize information in confidential company documents, you’d be out of luck. You could show some of these documents to ChatGPT, but there’s a limit to how many documents you can upload before you exceed ChatGPT’s maximum token count. How would you select which documents to show ChatGPT?

To address these limitations and scale your LLM applications, a great option is to use a vector database like ChromaDB. A vector database allows you to store encoded unstructured objects, like text, as lists of numbers that can be compared to one another. For instance, you can find a collection of documents relevant to a question you’d like an LLM to answer.

In this video course, you’ll learn about:

Representing unstructured objects with vectors
Using word and text embeddings in Python
Harnessing the power of vector databases
Encoding and querying over documents with ChromaDB
Providing context to LLMs like ChatGPT with ChromaDB

After watching, you’ll have the foundational knowledge to use ChromaDB in your NLP or LLM applications. Before watching, you should be comfortable with the basics of Python and high school math.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

How to Add Features to a Python Project With Codex CLI

2026-04-13T14:00:00+00:00

After reading this guide, you’ll be able to use Codex CLI to add features to a Python project directly from your terminal. Codex CLI is an AI-powered coding assistant that runs inside your terminal. It understands your project structure, reads your files, and proposes multi-file changes using natural language instructions.

Instead of copying code from a browser or relying on an IDE plugin, you’ll use Codex CLI to implement a real feature in a multi-file Python project directly from your terminal:

Example of Using Codex CLI to Implement a Project Feature

In the following steps, you’ll install and configure Codex CLI, use it to implement a deletion feature in a contact book app, and then refine that feature through iterative prompting.

Take the Quiz: Test your knowledge with our interactive “How to Add Features to a Python Project With Codex CLI” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Add Features to a Python Project With Codex CLI

Test your knowledge of Codex CLI, the AI-powered terminal tool for adding features to Python projects with natural language.

Prerequisites

To follow this guide, you should be familiar with the Python language. You’ll also need an OpenAI account with either a paid ChatGPT subscription or a valid API key, which you’ll connect to Codex CLI once you install it. Additionally, you’ll need to have Node.js installed, since Codex CLI is distributed as an npm package.

To make it easier for you to experiment with Codex CLI, download the RP Contacts project by clicking the link below:

Get Your Code: Click here to download the free source code for the RP Contacts sample project used in this tutorial.

The project RP Contacts is a text-based interface (TUI) that allows you to manage contacts directly in the terminal through a Textual app. It’s an adapted version of the project from Real Python’s tutorial Build a Contact Book App With Python, Textual, and SQLite. It differs from the original in that it uses uv to manage the project, and the TUI buttons Delete and Clear All haven’t been implemented—that’s what you’ll use Codex CLI for.

Once you’ve downloaded the project, you want to check that you can run it. As mentioned, the project uses uv for dependency management—you can tell by the uv.lock file in the project root—so make sure you have uv installed. If you don’t have uv yet, follow the official installation instructions.

Once you have uv installed and you’re at the root directory of the project, you can run the project:

$ uv run rpcontacts

When you run the command rpcontacts through uv for the first time, uv will create a virtual environment, install the dependencies of your project, and start the RP Contacts TUI. If all goes well, you should see a TUI in your terminal with a couple of buttons and an empty contact listing:

Once the TUI opens, create some test contacts by using the Add button and filling in the form that pops up. After creating a couple of fake contacts, quit the application by pressing Q.

Finally, you’ll want to initialize a Git repository at the root of your project and commit all your files:

$ git init .
$ git add .
$ git commit -m "First commit."

Codex CLI will modify your code, and you can never tell whether the changes will be good or not. Versioning your code makes it straightforward to roll back any changes made by LLMs if you don’t like them.

If you want to explore other AI-powered coding tools alongside Codex CLI, Real Python’s Python Coding With AI learning path brings together tutorials and video courses on AI-assisted coding, prompt engineering, and LLM development.

Step 1: Install and Configure Codex CLI

With all the accessory setup out of the way, it’s now time to install Codex CLI. For that, you’ll want to check the official OpenAI documentation to see the most up-to-date installation instructions. As of now, OpenAI recommends using npm:

Read the full article at https://realpython.com/codex-cli/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #290: Advice on Managing Projects & Making Python Classes Friendly

2026-04-10T12:00:00+00:00

What goes into managing a major project? What techniques can you employ for a project that's in crisis? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Dictionaries in Python

2026-04-08T14:00:00+00:00

Python dictionaries are a powerful built-in data type that allows you to store key-value pairs for efficient data retrieval and manipulation. Learning about them is essential for developers who want to process data efficiently. In this tutorial, you’ll explore how to create dictionaries using literals and the dict() constructor, as well as how to use Python’s operators and built-in functions to manipulate them.

By learning about Python dictionaries, you’ll be able to access values through key lookups and modify dictionary content using various methods. This knowledge will help you in data processing, configuration management, and dealing with JSON and CSV data.

By the end of this tutorial, you’ll understand that:

A dictionary in Python is a mutable collection of key-value pairs that allows for efficient data retrieval using unique keys.
Both dict() and {} can create dictionaries in Python. Use {} for concise syntax and dict() for dynamic creation from iterable objects.
dict() is a class used to create dictionaries. However, it’s commonly called a built-in function in Python.
.__dict__ is a special attribute in Python that holds an object’s writable attributes in a dictionary.
Python dict is implemented as a hashmap, which allows for fast key lookups.

To get the most out of this tutorial, you should be familiar with basic Python syntax and concepts such as variables, loops, and built-in functions. Some experience with basic Python data types will also be helpful.

Get Your Code: Click here to download the free sample code that you’ll use to learn about dictionaries in Python.

Take the Quiz: Test your knowledge with our interactive “Dictionaries in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Dictionaries in Python

Test your knowledge of Python's dict data type: how to create, access, and modify key-value pairs using built-in methods and operators.

Getting Started With Python Dictionaries

Dictionaries are one of Python’s most important and useful built-in data types. They provide a mutable collection of key-value pairs that lets you efficiently access and mutate values through their corresponding keys:

>>> config = {
...     "color": "green",
...     "width": 42,
...     "height": 100,
...     "font": "Courier",
... }

>>> # Access a value through its key
>>> config["color"]
'green'

>>> # Update a value
>>> config["font"] = "Helvetica"
>>> config
{
    'color': 'green',
    'width': 42,
    'height': 100,
    'font': 'Helvetica'
}

A Python dictionary consists of a collection of key-value pairs, where each key corresponds to its associated value. In this example, "color" is a key, and "green" is the associated value.

Dictionaries are a fundamental part of Python. You’ll find them behind core concepts like scopes and namespaces as seen with the built-in functions globals() and locals():

>>> globals()
{
    '__name__': '__main__',
    '__doc__': None,
    '__package__': None,
    ...
}

The globals() function returns a dictionary containing key-value pairs that map names to objects that live in your current global scope.

Python also uses dictionaries to support the internal implementation of classes. Consider the following demo class:

>>> class Number:
...     def __init__(self, value):
...         self.value = value
...

>>> Number(42).__dict__
{'value': 42}

The .__dict__ special attribute is a dictionary that maps attribute names to their corresponding values in Python classes and objects. This implementation makes attribute and method lookup fast and efficient in object-oriented code.

You can use dictionaries to approach many programming tasks in your Python code. They come in handy when processing CSV and JSON files, working with databases, loading configuration files, and more.

Python’s dictionaries have the following characteristics:

Mutable: The dictionary values can be updated in place.
Dynamic: Dictionaries can grow and shrink as needed.
Efficient: They’re implemented as hash tables, which allows for fast key lookup.
Ordered: Starting with Python 3.7, dictionaries keep their items in the same order they were inserted.

The keys of a dictionary have a couple of restrictions. They need to be:

Hashable: This means that you can’t use unhashable objects like lists as dictionary keys.
Unique: This means that your dictionaries won’t have duplicate keys.

In contrast, the values in a dictionary aren’t restricted. They can be of any Python type, including other dictionaries, which makes it possible to have nested dictionaries.

Dictionaries are collections of pairs. So, you can’t insert a key without its corresponding value or vice versa. Since they come as a pair, you always have to insert a key with its corresponding value.

Note: In some situations, you may want to add keys to a dictionary without deciding what the associated value should be. In those cases, you can use the .setdefault() method to create keys with a default or placeholder value.

Read the full article at https://realpython.com/python-dicts/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Using Loguru to Simplify Python Logging

2026-04-07T14:00:00+00:00

Logging is a vital programming practice that helps you track, understand, and debug your application’s behavior. Loguru is a Python library that provides simpler, more intuitive logging compared to Python’s built-in logging module.

Good logging gives you insights into your program’s execution, helps you diagnose issues, and provides valuable information about your application’s health in production. Without proper logging, you risk missing critical errors, spending countless hours debugging blind spots, and potentially undermining your project’s overall stability.

By the end of this video course, you’ll understand that:

Logging in Python can be simple and intuitive with the right tools.
Using Loguru lets you start logging immediately without complex configuration.
You can customize log formats and send logs to multiple destinations like files, the standard error stream, or external services.
Loguru provides powerful debugging capabilities that make troubleshooting easier.
Loguru supports structured logging with JSON formatting for modern applications.

After watching this course, you’ll be able to quickly implement better logging in your Python applications. You’ll spend less time wrestling with logging configuration and more time using logs effectively to debug issues. This will help you build production-ready applications that are easier to troubleshoot when problems occur.

To get the most from this course, you should be familiar with Python concepts like functions, decorators, and context managers. You might also find it helpful to have some experience with Python’s built-in logging module, though this isn’t required.

Don’t worry if you’re new to logging in Python. This course will guide you through everything you need to know to get started with Loguru and implement effective logging in your applications.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

D-Strings Could End Your textwrap.dedent() Days and Other Python News for April 2026

2026-04-06T14:00:00+00:00

If you’ve ever wrapped a multiline string in textwrap.dedent() and wondered why Python can’t just handle that for you, then your PEP has arrived. PEP 822 proposes d-strings, a new d"""...""" prefix that automatically strips leading indentation. It’s one of those small quality-of-life ideas that make you wonder why it didn’t exist already. The PEP is currently a draft proposal.

March also delivered Python 3.15.0 alpha 7 with lazy imports you can finally test and security patches across three older branches. On the ecosystem side, GPT-5.4 landed with a tool search feature that changes agentic workflows. Meanwhile, the Python Insider blog migration moved 307 posts to a new home without breaking a single URL. It’s time to get into the biggest Python news from the past month.

Join Now: Click here to join the Real Python Newsletter and you’ll never miss another Python tutorial, course, or news update.

Python Releases and PEP Highlights

March brought the penultimate alpha of Python 3.15 with a long-awaited feature that finally lets Python developers defer imports cleanly. On top of that, security patches landed for three older branches, and a fresh PEP proposal showed up that could clean up your multiline strings for good.

Python 3.15.0 Alpha 7: Lazy Imports Land

Python 3.15.0a7 dropped on March 10, the second-to-last alpha before the beta freeze on May 5. The headline feature you can finally test is PEP 810, explicit lazy imports. The Steering Council accepted PEP 810 back in November, but this is the first alpha where the implementation is available to try.

The idea is straightforward: prefix any import statement with lazy, and the module won’t actually load until you first access an attribute on it:

lazy import json
lazy from datetime import timedelta

# The json module isn't loaded yet, so no startup cost

# Later, when you actually use it:
data = json.loads(payload)  # Now it loads

The PEP authors note that 17 percent of standard library imports are already placed inside functions to defer loading. Tools like Django’s management commands, Click-based CLIs, and codebases heavy on type checking often spend hundreds of milliseconds on imports they might never use. Lazy imports make that optimization explicit and clean, without scattering imports deep inside function bodies.

Note: Alpha 7 also continues to ship the JIT compiler improvements from earlier alphas, with 3–4 percent geometric mean gains on x86-64 Linux and 7–8 percent on AArch64 macOS. Alpha 8 is scheduled for April 7, with the beta phase starting May 5.

Security Releases: Python 3.12.13, 3.11.15, and 3.10.20

On March 3, Thomas Wouters released security-only patches across three older Python branches. The updates fix several CVEs, including two XML parsing vulnerabilities (CVE-2026-24515 and CVE-2026-25210), patched by upgrading the bundled libexpat to 2.7.4. Additional fixes cover an XML memory amplification bug and the rejection of control characters in HTTP headers and URL parsing.

If you’re still running Python 3.12 or older in production, applying these patches is highly recommended. Python 3.12 is now in security-fixes-only mode, so no binary installers are provided. You’ll need to build from source.

PEP 822: Dedented Multiline Strings (D-Strings)

PEP 822, authored by Inada Naoki, proposes a new d"""...""" string prefix that automatically strips leading indentation from multiline strings, using the same algorithm as textwrap.dedent().

Anyone who’s written a multiline SQL query or help text inside a function and battled with indentation knows the pain:

import textwrap

# Before: awkward indentation or textwrap.dedent() wrapper
def get_query():
    return textwrap.dedent("""\
        SELECT name, email
        FROM users
        WHERE active = true
    """)

# With d-strings: clean and readable
def get_query():
    return d"""
        SELECT name, email
        FROM users
        WHERE active = true
    """

The d prefix combines with f, r, b, and even the upcoming t (template strings) prefixes. PEP 822 was submitted to the Steering Council on March 9 and targets Python 3.15, though a decision hasn’t landed yet. If you’ve ever wished Python strings would just handle indentation for you, this one’s worth keeping an eye on.

Other PEPs in Progress

Read the full article at https://realpython.com/python-news-april-2026/ »

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Adding Python to PATH

2026-03-31T14:00:00+00:00

You may need to add Python to PATH if you’ve installed Python, but typing python on the command line doesn’t seem to work. You might see a message saying that python isn’t recognized, or you might end up running the wrong version of Python.

A common fix for these problems is adding Python to the PATH environment variable. In this video course, you’ll learn how to add Python to PATH. You’ll also learn what PATH is and why it’s essential for tools like the command line to be able to find your Python installation.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #289: Limitations in Human and Automated Code Review

2026-03-27T12:00:00+00:00

With the mountains of Python code that it's possible to generate now, how's your code review going? What are the limitations of human review, and where does machine review excel? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Understanding CRUD Operations in SQL

2026-03-24T14:00:00+00:00

CRUD operations are at the heart of nearly every application you interact with. As a developer, you usually want to create data, read or retrieve data, update data, and delete data. Whether you access a database or interact with a REST API, only when all four operations are present are you able to make a complete data roundtrip in your app.

Creating, reading, updating, and deleting are so vital in software development that these methods are widely referred to as CRUD. Understanding CRUD will give you an actionable blueprint when you build applications and help you understand how the applications you use work behind the scenes. So, what exactly does CRUD mean?

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #288: Automate Exploratory Data Analysis & Invent Python Comprehensions

2026-03-20T12:00:00+00:00

How do you quickly get an understanding of what's inside a new set of data? How can you share an exploratory data analysis with your team? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: LLM Application Development With Python

2026-03-19T12:00:00+00:00

Build LLM-powered applications in Python. Call model APIs, craft prompts, add retrieval-augmented generation, create AI agents, and connect via MCP.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Downloading Files From URLs With Python

2026-03-17T14:00:00+00:00

Python makes it straightforward to download files from a URL with its robust set of libraries. For quick tasks, you can use the built-in urllib module or the requests library to fetch and save files. When working with large files, streaming data in chunks can help save memory and improve performance.

You can also perform parallel file downloads using ThreadPoolExecutor for multithreading or the aiohttp library for asynchronous tasks. These approaches allow you to handle multiple downloads concurrently, significantly reducing the total download time if you’re handling many files.

By the end of this video course, you’ll understand that:

You can use Python to download files with libraries like urllib and requests.
To download a file using a URL in Python, you can use urlretrieve() or requests.get().
To extract data from a URL in Python, you use the response object from requests.
To download a CSV file from a URL in Python, you may need to specify the format in the URL or query parameters.

In this video course, you’ll be downloading a range of economic data from the World Bank Open Data platform.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #287: Crafting and Editing In-Depth Tutorials at Real Python

2026-03-13T12:00:00+00:00

What goes into creating the tutorials you read at Real Python? What are the steps in the editorial process, and who are the people behind the scenes? This week on the show, Real Python team members Martin Breuss, Brenda Weleschuk, and Philipp Acsany join us to discuss topic curation, review stages, and quality assurance.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Working With APIs in Python: Reading Public Data

2026-03-10T14:00:00+00:00

Python is an excellent choice for working with Application Programming Interfaces (APIs), allowing you to efficiently consume and interact with them. By using the Requests library, you can easily fetch data from APIs that communicate using HTTP, such as REST, SOAP, or GraphQL APIs. This video course covers the essentials of consuming REST APIs with Python, including authentication and handling responses.

By the end of this video course, you’ll understand that:

An API is an interface that allows different systems to communicate, typically through requests and responses.
Python is a versatile language for consuming APIs, offering libraries like Requests to simplify the process.
REST and GraphQL are two common types of APIs, with REST being more widely used for public APIs.
To handle API authentication in Python, you can use API keys or more complex methods like OAuth to access protected resources.

Knowing how to consume an API is one of those magical skills that, once mastered, will crack open a whole new world of possibilities, and consuming APIs using Python is a great way to learn such a skill.

By the end of this video course, you’ll be able to use Python to consume most of the APIs that you come across. If you’re a developer, then knowing how to consume APIs with Python will empower you to integrate data from various online sources into your applications.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

What Does Python's init.py Do?

2026-03-03T14:00:00+00:00

Python’s special __init__.py file marks a directory as a regular Python package and allows you to import its modules. This file runs automatically the first time you import its containing package. You can use it to initialize package-level variables, define functions or classes, and structure the package’s namespace clearly for users.

By the end of this video course, you’ll understand that:

A directory without an __init__.py file becomes a namespace package, which behaves differently from a regular package and may cause slower imports.
You can use __init__.py to explicitly define a package’s public API by importing specific modules or functions into the package namespace.
The Python convention of using leading underscores helps indicate to users which objects are intended as non-public, although this convention can still be bypassed.
Code inside __init__.py runs only once during the first import, even if you run the import statement multiple times.

Understanding how to effectively use __init__.py helps you structure your Python packages in a clear, maintainable way, improving usability and namespace management.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #286: Overcoming Testing Obstacles With Python's Mock Object Library

2026-02-27T12:00:00+00:00

Do you have complex logic and unpredictable dependencies that make it hard to write reliable tests? How can you use Python's mock object library to improve your tests? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #285: Exploring MCP Apps & Adding Interactive UIs to Clients

2026-02-20T12:00:00+00:00

How can you move your MCP tools beyond plain text? How do you add interactive UI components directly inside chat conversations? This week on the show, Den Delimarsky from Anthropic joins us to discuss MCP Apps and interactive UIs in MCP.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

The Real Python Podcast – Episode #284: Running Local LLMs With Ollama and Connecting With Python

2026-02-13T12:00:00+00:00

Would you like to learn how to work with LLMs locally on your own computer? How do you integrate your Python projects with a local model? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Python Coding With AI

2025-12-07T12:00:00+00:00

Use AI coding assistants to write, review, and debug Python code faster. Pick from Claude Code, Cursor, or Gemini CLI and start coding.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Python Control Flow and Loops

2024-11-14T12:00:00+00:00

Learn Python control flow and loops. Use conditional statements, Boolean operators, for and while loops, and keywords like break and continue.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Modules and Packages

2024-11-14T12:00:00+00:00

Learn Python's import system, organize code into modules and packages, manage dependencies with pip, and publish to PyPI.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Functions and Scopes

2024-11-14T12:00:00+00:00

Learn to define Python functions, use parameters and return values, create inner functions, and understand namespaces and scope.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Create Graphical User Interfaces (GUI)

2024-11-14T12:00:00+00:00

Build Python GUI applications with Tkinter, PyQt, wxPython, and Kivy. Learn layouts, event handling, threading, and database integration.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Important Standard Library Modules

2024-11-14T12:00:00+00:00

Explore Python standard library modules including math, datetime, JSON, CSV, regex, subprocess, and argparse through hands-on tutorials and courses.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Network Programming and Security

2024-11-14T12:00:00+00:00

Learn network programming and security in Python. Work with CRUD operations, REST APIs, HTTPS, and socket programming to build networked apps.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Learning Path: Functional Programming With Python

2024-11-14T12:00:00+00:00

Explore functional programming in Python. Learn lambda functions, map(), filter(), reduce(), closures, decorators, and recursion.

[ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

Real Python

ChatterBot: Build a Chatbot With Python

Preview the Chatbot

Project Overview

LLM Application Development With Python

Prerequisites

Quiz: Python & APIs: A Winning Combo for Reading Public Data

Quiz: How to Use OpenCode for AI-Assisted Python Coding

Use Codex CLI to Enhance Your Python Projects

Quiz: Python Application Layouts: A Reference

A New Python Packaging Council and Other News for May 2026

Python Releases and PEP Highlights

Python 3.15.0 Alpha 8: Final Alpha Before Beta Freeze

Incremental GC Reverted in 3.14.5 and 3.15

PEP 772 Accepted: Python Gets a Packaging Council

PEP 803 Accepted: Stable ABI Goes Free-Threaded

Quiz: Data Management With Python, SQLite, and SQLAlchemy

Quiz: Revisit Python Fundamentals

Revisit Python Fundamentals

The Real Python Podcast – Episode #293: Agentic Data Science Pair Programming With marimo pair

Quiz: The Factory Method Pattern and Its Implementation in Python

Quiz: Using Python for Data Analysis

AI Coding Agents Guide: A Map of the Four Workflow Types

Understanding AI Coding Agents

Exploring the Four Workflow Types

Quiz: Python 3.13: A Modern REPL

Quiz: ChatterBot: Build a Chatbot With Python

Testing Your Code With Python's unittest

Quiz: Use Codex CLI to Enhance Your Python Projects

How to Conceptualize Python Fundamentals for Greater Mastery

Step 1: Define the Concept in Your Own Words

Answer Key Questions for Defining a Concept

View Examples of Concept Definitions

The Real Python Podcast – Episode #292: Becoming a Better Python Developer Through Learning Rust

Altair: Declarative Charts With Python

Start Using Altair in Python

Leverage OpenAI's API in Your Python Projects

Gemini CLI vs Claude Code: Which to Choose for Python Tasks

Metrics Comparison: Gemini CLI vs Claude Code

User Experience

Installation and Setup

The Real Python Podcast – Episode #291: Reassessing the LLM Landscape & Summoning Ghosts

Learning Path: Python Game Development

Variables in Python: Usage and Best Practices

Getting to Know Variables in Python

Creating Variables With Assignments

Setting and Changing a Variable’s Data Type

Vector Databases and Embeddings With ChromaDB

How to Add Features to a Python Project With Codex CLI

Prerequisites

Step 1: Install and Configure Codex CLI

The Real Python Podcast – Episode #290: Advice on Managing Projects & Making Python Classes Friendly

Dictionaries in Python

Getting Started With Python Dictionaries

Using Loguru to Simplify Python Logging

D-Strings Could End Your textwrap.dedent() Days and Other Python News for April 2026

Python Releases and PEP Highlights

Python 3.15.0 Alpha 7: Lazy Imports Land

Security Releases: Python 3.12.13, 3.11.15, and 3.10.20

PEP 822: Dedented Multiline Strings (D-Strings)

Other PEPs in Progress

Adding Python to PATH

The Real Python Podcast – Episode #289: Limitations in Human and Automated Code Review

Understanding CRUD Operations in SQL

The Real Python Podcast – Episode #288: Automate Exploratory Data Analysis & Invent Python Comprehensions

Learning Path: LLM Application Development With Python

Downloading Files From URLs With Python

The Real Python Podcast – Episode #287: Crafting and Editing In-Depth Tutorials at Real Python

Working With APIs in Python: Reading Public Data

What Does Python's __init__.py Do?

The Real Python Podcast – Episode #286: Overcoming Testing Obstacles With Python's Mock Object Library

The Real Python Podcast – Episode #285: Exploring MCP Apps & Adding Interactive UIs to Clients

The Real Python Podcast – Episode #284: Running Local LLMs With Ollama and Connecting With Python

Learning Path: Python Coding With AI

Learning Path: Python Control Flow and Loops

Learning Path: Modules and Packages

Learning Path: Functions and Scopes

Learning Path: Create Graphical User Interfaces (GUI)

Learning Path: Important Standard Library Modules

Learning Path: Network Programming and Security

What Does Python's init.py Do?