Real Python

Quiz: Your Guide to the CPython Source Code

2026-08-20T12:00:00+00:00

In this quiz, you’ll test your understanding of Your Guide to the CPython Source Code.

You’ll revisit how the source tree is laid out, why the interpreter is written in C, and what really happens when you run a program: bytecode, memory management, and the frames that let a generator remember where it left off.

Note: The tutorial follows CPython 3.8, so some file paths and C function names have moved since. The concepts still hold.

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How to Use Claude Code to Write and Debug Python

2026-08-19T14:00:00+00:00

In this tutorial, you’ll learn how to use Claude Code to build and debug Python projects using natural-language commands directly from your terminal. Unlike browser-based AI assistants, where you copy code back and forth, Claude Code operates inside your project directory. It reads your files, runs shell commands, proposes edits as diffs, and waits for your approval before making any changes.

You’ll start by installing and configuring Claude Code, then use it to build a command-line application from scratch. After that, you’ll return to the project as if picking it up after time away, and ask Claude Code to find and fix bugs. Along the way, you’ll develop a repeatable workflow: plan first, review every change, commit often, and clear context between sessions.

Get Your Code: Click here to download the free sample code you’ll use to build and debug mini-contacts, the command-line contact manager you create with Claude Code in this tutorial.

Take the Quiz: Test your knowledge with our interactive “How to Use Claude Code to Write and Debug Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use Claude Code to Write and Debug Python

Test your understanding of Claude Code by working through installation, plan mode, diff review, and debugging a Python project.

Prerequisites

Claude Code is a standalone application that doesn’t depend on any specific Python version. Before you start, make sure you have the following tools and accounts ready:

A paid Anthropic account: To use all Claude Code features, you need either a Claude subscription at the Pro, Max, or Team tier, or an Anthropic Console account with API billing enabled.
Git: You’ll use Git as a safety net throughout this tutorial, committing between tasks so you can always revert if Claude Code makes an unwanted change. If you’re new to Git, work through Introduction to Git and GitHub and configure your name and email before continuing.

You don’t need prior experience with AI coding tools. This tutorial assumes you’re trying Claude Code for the first time and walks through every step from installation to building a working project.

Step 1: Install and Configure Claude Code

Claude Code ships as a self-contained native binary, with no Node.js, global npm packages, or version manager required. In this step, you’ll install it, sign in to your Anthropic account, and take a quick tour of the built-in commands before writing any code.

Install and Authenticate

Claude Code installs from a single script. Pick the command for your operating system. On Windows, you can install natively in PowerShell or use Windows Subsystem for Linux (WSL), which runs the Linux command:

PS> irm https://claude.ai/install.ps1 | iex

$ curl -fsSL https://claude.ai/install.sh | bash

The installer drops a claude binary into your path and handles auto-updates going forward. Verify the install by checking the version:

$ claude --version

If you see a version number, you’re ready to authenticate. Start Claude Code to trigger the login flow:

$ claude

The first launch shows Claude Code’s welcome screen in your terminal, which looks something like this:

On the first run, Claude Code opens a browser tab for login. Sign in with your Claude Pro, Max, or Team account, or choose the Anthropic Console option if you’re billing through the API. Once authenticated, Claude Code stores a login credential locally and won’t prompt you again unless it expires or you run the /logout command.

Note: Because Claude Code can edit and delete files, only run it inside project directories you’re actively working on. Starting it from your home directory gives the agent access to far more than it needs and will trigger a warning.

With Claude Code installed and authenticated, you’re ready to look at what it can do.

Explore the CLI Interface

Read the full article at https://realpython.com/how-to-use-claude-code/ »

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Quiz: How to Review AI-Generated Python Code Efficiently

2026-08-19T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Review AI-Generated Python Code Efficiently.

By working through this quiz, you’ll revisit the five-step review workflow, the automated checks that clear the mechanical noise, and the issues that coding agents get wrong most often, like silently wrong logic, swallowed errors, hallucinated APIs, and blocked event loops.

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Quiz: How to Use Claude Code to Write and Debug Python

2026-08-19T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Use Claude Code to Write and Debug Python.

By working through this quiz, you’ll revisit how to install and authenticate Claude Code, how its permission modes differ, and how a plan-first workflow keeps you in control of every change that lands in your project.

You’ll also cover returning to a codebase after time away, triaging the bugs you find there, and sizing your changes so each diff stays reviewable.

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Working With Python's deque

2026-08-18T14:00:00+00:00

You can use Python’s deque for efficient appends and pops at both ends of a sequence-like data type. These capabilities are critical when you need to implement queue and stack data structures that operate efficiently even under heavy workloads.

In this video course, you’ll learn how deque works, when to use it over a list, and how to apply it in real code.

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

deque internally uses a doubly linked list, so end operations are O(1) while random indexing is O(n).
You can build a FIFO queue with .append() and .popleft(), and a LIFO stack with .append() and .pop().
deque supports indexing but doesn’t support slicing.
Passing a value to maxlen creates a bounded deque that drops items from the opposite end when full.
In CPython, .append(), .appendleft(), .pop(), .popleft(), and len() are thread-safe for multithreaded use.

Up next, you’ll get started with deque, benchmark it against list, and explore how it shines in real-world use cases, such as queues, stacks, history buffers, and thread-safe producer-consumer setups.

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Quiz: Arduino With Python: How to Get Started

2026-08-18T12:00:00+00:00

In this quiz, you’ll test your understanding of Arduino With Python: How to Get Started.

By working through this quiz, you’ll revisit how to set up circuits, upload the Firmata sketch to your board, and control digital and analog inputs and outputs from Python.

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Quiz: Working With Python's deque

2026-08-18T12:00:00+00:00

In this quiz, you’ll test your understanding of Working With Python’s deque.

By working through this quiz, you’ll revisit how a deque stores its items in a doubly linked list, why appends and pops at both ends are fast, and when a list is still the better choice.

You’ll also check your grasp of building queues with .append() and .popleft(), and of using maxlen to keep a bounded history of recent items.

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How to Debug Python Code With an AI Agent

2026-08-17T14:00:00+00:00

AI debugging is fixing broken code by pairing with an AI coding agent instead of hunting down bugs on your own. In this tutorial, you’ll debug Python code with an AI agent by reproducing a bug with a failing test, giving your agent the context it needs, and verifying the fix that gets your program running again.

Most developers seek what’s called a flow state when building programs or fixing code. This is a state of high focus. However, it can occasionally be interrupted by bugs. While debugging has for decades been largely carried out by humans aided by tools like pdb, AI coding agents have now become instrumental to the process, helping to make it faster.

The Python project that you’ll debug in this guide places you in the position of an alien pet owner using a tool to keep your pet happy. This tool has checks for when your pet is well fed and well rested. However, you’ve learned that your pet is never happy no matter how often it eats or rests. You suspect a bug, and your AI agent has come to the rescue.

By applying the steps in this tutorial, you’ll get this broken pet project back into a working state:

You’ll use the newly released Antigravity CLI by Google to see this workflow play out in real time. But if that’s not your preferred coding agent, that’s fine too, and you can still follow along with the steps.

Get Your Code: Click here to download the free sample code you’ll use to debug the broken alien pet project with an AI coding agent.

Take the Quiz: Test your knowledge with our interactive “How to Debug Python Code With an AI Agent” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Debug Python Code With an AI Agent

Test your understanding of debugging Python code with an AI agent, from reproducing a bug with a failing test to verifying the fix.

Prerequisites

To work through this tutorial comfortably, you should have the following at your fingertips:

Git commands: You should be comfortable setting up Git in your project’s root folder and working with a few basic commands.
Python 3.11 or newer: You’ll need Python to run the sample project.
A working AI coding agent: You’ll be using one to debug, so being familiar with an agent that can read and write files is important. This tutorial uses Antigravity CLI, and Step 1 walks through the setup.
Python testing with pytest: You should be comfortable reading and writing tests with pytest. You’ll write a failing test to reproduce the bug, then rerun it to confirm your agent’s fix works.

With these prerequisites in place, you’re ready to set up the alien pet project and start tracking down the bug that’s keeping your pet unhappy.

Step 1: Set Up Your Workspace for AI Debugging

If you already have a preferred agent set up, or you’ve already installed Antigravity CLI, skip the setup below and scroll to the directory tree to add the required files to your workspace.

To set up Antigravity CLI for this tutorial, you’ll need to have a Google account. If you’re familiar with the just-retired Gemini CLI, you’ll find that the setup processes are quite similar. First, go to the Antigravity CLI installation guide for instructions on installing it.

Choose the appropriate installation command for your operating system and run it in your terminal:

PS> irm https://antigravity.google/cli/install.ps1 | iex

$ curl -fsSL https://antigravity.google/cli/install.sh | bash

Once that’s done, your new AI coding agent is successfully installed. Next, create your project folder, initialize a virtual environment to safely isolate your dependencies, and activate it:

$ mkdir alien-pet-care
$ cd alien-pet-care
$ python -m venv venv
$ source venv/bin/activate

Activating the virtual environment ensures that any packages you install moving forward remain self-contained within this project directory. Depending on your operating system, your terminal prompt should now show the (venv) prefix.

Note: If you’re a Windows user running PowerShell, activate your environment using venv\Scripts\activate. If your system blocks the script, you can temporarily permit execution for your current session by running Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope Process.

Now, launch the CLI configuration inside your project directory by typing the following command:

(venv) $ agy

This triggers a first-launch setup, where you’ll be prompted to log in with your Google account details. You’ll be redirected to a page or instructed to paste an authorization code in an input field:

Read the full article at https://realpython.com/ai-debugging/ »

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Quiz: How to Debug Python Code With an AI Agent

2026-08-17T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Debug Python Code With an AI Agent.

By working through this quiz, you’ll revisit how to set up a workspace for AI debugging, reproduce a bug with a failing test, hand your agent the context it needs, and verify a fix before you trust it.

Along the way, you’ll also think about what makes a bug report specific enough to act on and how to spot a fix that only looks right.

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The Real Python Podcast – Episode #307: Improving NumPy Performance on Free-Threaded Python

2026-08-14T12:00:00+00:00

What bottlenecks were preventing NumPy from scaling on free-threaded Python? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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Quiz: How to Publish an Open-Source Python Package to PyPI

2026-08-14T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Publish an Open-Source Python Package to PyPI.

By working through this quiz, you’ll revisit how to name and configure a package with pyproject.toml, specify dependencies, version your releases, build source archives and wheels, and upload the result to PyPI.

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How to Integrate OpenTelemetry With a FastAPI App

2026-08-12T14:00:00+00:00

Adding observability to your applications is crucial for diagnosing issues in production. In this tutorial, you’ll learn how to integrate OpenTelemetry—an open-source observability framework—into a FastAPI application. After following along, you’ll be able to instrument FastAPI with OpenTelemetry to export traces and correlate them with your application logs, allowing you to explore your telemetry in a dedicated dashboard:

The image above previews the Jaeger UI that you’ll build toward over the course of this tutorial.

As your application grows, tracking down bugs across different services can become challenging. By adding observability, you gain clear insights into how your application performs and exactly where errors happen.

To achieve this, you’ll use OpenTelemetry, often called OTel. This vendor-neutral framework provides a standard way to collect telemetry data from your applications. It supports multiple programming languages, including Python, and exports data to various backends like Jaeger, Prometheus, and Grafana.

For this tutorial, you’ll use Jaeger, a popular open-source backend, to visualize your data. Together, OpenTelemetry and Jaeger let you analyze request paths, troubleshoot errors, and understand system bottlenecks.

Prerequisites

To follow along with this tutorial, you’ll need a couple of tools on your system:

Python 3.10 or higher
Docker to run the Jaeger backend container

Even if you’re new to Docker, you’ll only run a single command to start a ready-made container, and this tutorial walks you through it. If you’d like to go deeper, then you can explore the Docker tutorials later.

You should also be comfortable with a basic FastAPI app. If you need a refresher on the framework, then check out the introductory guide to get started with FastAPI. If you prefer learning by video, Real Python’s Start Building With FastAPI course covers the same essentials.

You can download the complete source code for the examples in this tutorial to use as a reference:

Get Your Code: Click here to download the free sample code you’ll use to instrument a FastAPI app with OpenTelemetry, exporting traces to a Jaeger backend, adding custom spans, correlating logs with traces, and tuning sampling.

Take the Quiz: Test your knowledge with our interactive “How to Integrate OpenTelemetry With a FastAPI App” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Integrate OpenTelemetry With a FastAPI App

Instrument a FastAPI app with OpenTelemetry to export traces, add custom spans, and correlate your application logs with traces.

Step 1: Build Your FastAPI OpenTelemetry Pipeline

To begin, you need a backend to receive and display your traces. Jaeger’s all-in-one Docker image bundles everything into a single container for local development.

Open your terminal and start the Jaeger container using Docker:

$ docker run -d --name jaeger \
    -p 16686:16686 \
    -p 4317:4317 \
    -p 4318:4318 \
    jaegertracing/all-in-one:latest

This command launches Jaeger in the background. The port mappings each serve a distinct purpose. Port 16686 exposes the web UI that you’ll use to explore your traces, while ports 4317 and 4318 accept incoming telemetry via the OpenTelemetry Protocol (OTLP) over gRPC and HTTP, respectively. Keeping these apart matters later because your application will send data to an OTLP port, and you’ll view the results on the UI port.

Next, you’ll install the FastAPI framework along with the OpenTelemetry libraries. Before you do, create and activate a virtual environment so that the packages don’t end up in your system Python:

PS> python -m venv venv
PS> .\venv\Scripts\Activate.ps1

$ python -m venv venv
$ source venv/bin/activate

With your virtual environment active, install the packages. You’ll need the core OpenTelemetry distribution, the specific FastAPI instrumentation package, and the OTLP exporter to send your data to Jaeger:

(venv) $ python -m pip install fastapi uvicorn \
         opentelemetry-distro \
         opentelemetry-instrumentation-fastapi \
         opentelemetry-exporter-otlp

Now that the tools are installed, you can build your application. Create a file named main.py and add the following code:

Read the full article at https://realpython.com/fastapi-opentelemetry/ »

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Quiz: How to Integrate OpenTelemetry With a FastAPI App

2026-08-12T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Integrate OpenTelemetry With a FastAPI App.

By working through this quiz, you’ll revisit how to instrument a FastAPI application with OpenTelemetry, create custom spans for your business logic, correlate your logs with traces, and configure sampling to manage overhead.

Adding observability to your applications helps you diagnose issues in production and understand exactly where errors happen.

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Coding With OpenCode: AI-Assisted Python

2026-08-11T14:00:00+00:00

OpenCode is an open-source AI coding agent that runs in your terminal and lets you analyze and refactor a Python project through conversational commands. In this video course, you’ll install it on your system, set it up with a free Google Gemini API key, and learn the basics of how to use it in your daily programming work.

OpenCode works as a conversational assistant you explicitly direct. Ask it to analyze functions, refactor code, or explain issues. Press Enter to send your query, and you’ll get a response with full awareness of your project context. It supports more than seventy-five AI providers, including Anthropic, OpenAI, and Google Gemini.

If you’re a Python developer who prefers working in the terminal, OpenCode offers deliberate, context-aware assistance and a customizable AGENTS.md configuration file.

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Quiz: Coding With OpenCode: AI-Assisted Python

2026-08-11T12:00:00+00:00

In this quiz, you’ll test your understanding of the concepts covered in Coding With OpenCode: AI-Assisted Python.

By working through this quiz, you’ll revisit how to install OpenCode, connect it to a free Google Gemini API key, and use Plan and Build modes to analyze and refactor a Python project without leaving your terminal.

You’ll also check what you know about OpenCode sessions, the command palette, and switching between AI models on the fly.

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Qt Designer and Python: Build Your GUI Applications Faster

2026-08-10T14:00:00+00:00

Qt Designer is a tool that lets you build the graphical user interface (GUI) of a PyQt application visually, by dragging and dropping widgets onto a form instead of writing layout code by hand. In Python, you save those designs as .ui files and load them into your apps, which can speed up your GUI development considerably.

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

Qt Designer produces XML-based .ui files that don’t depend on a particular Qt binding, so you build your GUI visually and then use it from PyQt6 code.
You install the PyQt6 bindings with pip install pyqt6 and get the Qt Designer app from PySide6, your system package manager, or the official Qt installer.
The pyuic6 command turns a .ui file into a Python module, while uic.loadUi() reads the same file at runtime.
A single Qt Designer form can hold a complete main window with menus, toolbars, and a status bar, or a standalone dialog.
You connect a widget’s signals to slots visually in Qt Designer, without hand-writing the connection code.

You’ll also see when it makes sense to reach for Qt Designer instead of hand coding your GUIs in plain Python.

For a better understanding of the topics in this tutorial, you can check out the following resources:

You’ll put all this knowledge together by using the GUIs that you’ll build with Qt Designer in a sample text editor application. You can get the code and all the required resources to build this application by clicking the link below:

Get the Source Code: Click here to get the source code you’ll use to learn about creating Python GUI applications with Qt Designer in this tutorial.

Take the Quiz: Test your knowledge with our interactive “Qt Designer and Python: Build Your GUI Applications Faster” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Qt Designer and Python: Build Your GUI Applications Faster

Test your knowledge of Qt Designer for Python. Build PyQt GUIs visually with drag-and-drop, signals and slots, and .ui files.

Getting Started With Qt Designer

Qt Designer is a Qt tool that provides you with a what-you-see-is-what-you-get (WYSIWYG) user interface to create GUIs for your PyQt applications productively and efficiently. With this tool, you create GUIs by dragging and dropping QWidget objects on an empty form. After that, you can arrange them into a coherent GUI using different layout managers.

Qt Designer also allows you to preview your GUIs using different styles and resolutions, connect signals and slots, create menus and toolbars, and more.

Qt Designer is platform and programming language independent. It doesn’t produce code in any particular programming language, but it creates .ui files. These files are XML files with detailed descriptions of how to generate Qt-based GUIs.

You can translate the content of .ui files into Python code with pyuic6, which is a command-line tool that comes with PyQt. Then you can use this Python code in your GUI applications. You can also read .ui files directly and load their content to generate the associated GUI.

Installing and Running Qt Designer

There are several ways to install Qt Designer, and the right one depends on your platform. Qt Designer is a separate application, so you’ll set up two separate pieces: the PyQt6 bindings that your code will use, and the Qt Designer application itself.

First, create and activate a Python virtual environment and install PyQt6 with pip:

$ python3 -m venv ./venv
$ source venv/bin/activate
(venv) $ python -m pip install pyqt6

This installs PyQt6 along with a private copy of the required Qt libraries. It also gives you pyuic6, a command-line tool that you’ll use later to turn your .ui files into Python code.

Note: In PyQt5, you installed Qt Designer through the pyqt5-tools package. In PyQt6, pyqt6-tools is no longer maintained and won’t install on current Python versions, so you’ll install Qt Designer on its own instead.

Next, install the Qt Designer application. Because Qt Designer saves its work in binding-agnostic .ui files, you can get the tool in whatever way is most convenient for your platform.

Even though your application code uses PyQt6, the quickest cross-platform way to get Qt Designer is to install PySide6, which ships with a ready-to-run copy of the tool:

(venv) $ python -m pip install pyside6
(venv) $ pyside6-designer

The pyside6-designer command launches the same Qt Designer that you’d get directly from Qt. You design your GUI here and then load the resulting .ui file from PyQt6.

You can also install Qt Designer in a few platform-specific ways:

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

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The Real Python Podcast – Episode #306: Programmatically Developing LLM Prompts With DSPy

2026-08-07T12:00:00+00:00

How can you move from manually writing prompts for an LLM application toward defining them programmatically? This week on the show, Brett Kennedy returns to discuss his new book "Building LLM Applications with DSPy."

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Quiz: How to Use Type Hints for Multiple Return Types in Python

2026-08-07T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Use Type Hints for Multiple Return Types in Python.

By working through this quiz, you’ll revisit how to annotate a function that returns one value of alternative types, and how to describe several pieces of data at once with a tuple.

You’ll also practice annotating callbacks, factory functions, and generators, defining type aliases to keep long annotations readable, and checking your work with a static type checker.

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Validating Data With Pointblank in Python

2026-08-05T14:00:00+00:00

At 3 a.m., a scheduled data pipeline fails with an AssertionError, and the traceback can’t tell you whether the cause is one stray row or a systematic bleed that should halt the entire run. Pointblank, a Python data-validation library, sharpens that decision: it checks a Polars or pandas DataFrame against rules you declare, and each rule accumulates failures until it crosses a threshold you set.

When a rule trips, you learn its name, the offending column, and the fraction of rows that failed, and Pointblank’s pb command-line tool wires that signal into a continuous integration step.

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

A validation plan chains col_vals_*() checks onto pb.Validate and runs when you call .interrogate().
Thresholds turn a pass/fail check into warning, error, and critical tiers based on the fraction of failing rows.
Actions trigger messages or callbacks when a step crosses a threshold, letting a policy decide the outcome.
get_sundered_data() routes the passing or failing rows out of an interrogated plan, depending on its type argument.
A plan stored as YAML runs through the pb command-line tool, where --fail-on critical returns a nonzero exit code for CI.

Pointblank can also validate SQL tables in DuckDB or PostgreSQL directly, so it complements parse-time schema tools such as Pydantic rather than replacing them. In this tutorial, you’ll see where Pointblank overlaps with Pandera and Great Expectations, two other Python data-validation libraries, and when to choose each one.

To get the most out of this tutorial, you should feel comfortable reading basic Polars or pandas DataFrame code, running Python scripts from the command line, and working in a terminal with a package manager like uv or pip. A virtual environment helps keep those installs isolated.

To follow along, you’ll validate a small built-in dataset, add thresholds and actions, sunder a messier table of atom data, and finally move the whole plan into YAML.

Get Your Code: Click here to download the free sample code you’ll use to validate DataFrames with Pointblank, from severity thresholds and row sundering to YAML-driven checks.

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

Interactive Quiz

Validating Data With Pointblank in Python

Validate data in Python with Pointblank. Declare quality checks, split clean rows from failing ones, and rerun validation plans from YAML.

Get Started With Pointblank in Python

Start by running one validation plan against a built-in dataset. The examples use uv because it runs self-contained scripts that declare and manage their own dependencies through inline script metadata.

If you don’t already have uv, you can install it using the standalone installer by running the command below:

PS> powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

$ curl -LsSf https://astral.sh/uv/install.sh | sh

If you don’t have curl installed on your system, then you can use wget as shown below:

$ wget -qO- https://astral.sh/uv/install.sh | sh

Now that you have uv, the script below declares pointblank[pl] as a dependency. The [pl] suffix is a pip extra that pulls in Polars alongside Pointblank, so uv run pointblank_quickstart.py builds an isolated environment and runs the file in one step.

Note: If you’d rather not use uv, install the dependencies with python -m pip install 'pointblank[pl]' in a virtual environment and run each script with python instead.

Pointblank ships a small example table named small_table with thirteen rows and eight columns. Its columns carry deliberately generic names, a through f alongside two date columns, which keeps the focus on the validation mechanics rather than any one domain.

The first script validates three of them, one for each common kind of check. Since small_table has only thirteen rows, you can print the whole thing from the Python REPL and see every value before you run anything.

The scripts in this tutorial declare their own dependencies, but a bare REPL doesn’t, so launch one with Pointblank and Polars already available:

$ uv run --with 'pointblank[pl]' python

Then load the dataset and print the three columns you’ll validate:

>>> import pointblank as pb
>>> import polars as pl
>>> small_table = pb.load_dataset("small_table", tbl_type="polars")
>>> with pl.Config(tbl_rows=13):
...     print(small_table.select(["c", "d", "f"]))
shape: (13, 3)
┌──────┬─────────┬──────┐
│ c    ┆ d       ┆ f    │
│ ---  ┆ ---     ┆ ---  │
│ i64  ┆ f64     ┆ str  │
╞══════╪═════════╪══════╡
│ 3    ┆ 3423.29 ┆ high │
│ 8    ┆ 9999.99 ┆ low  │
│ 3    ┆ 2343.23 ┆ high │
│ null ┆ 3892.4  ┆ mid  │
│ 7    ┆ 283.94  ┆ low  │
│ 4    ┆ 3291.03 ┆ mid  │
│ 3    ┆ 843.34  ┆ high │
│ 2    ┆ 1035.64 ┆ low  │
│ 9    ┆ 837.93  ┆ high │
│ 9    ┆ 837.93  ┆ high │
│ 7    ┆ 833.98  ┆ low  │
│ 8    ┆ 108.34  ┆ low  │
│ null ┆ 2230.09 ┆ high │
└──────┴─────────┴──────┘

You’ll write one rule for each of these three columns, listed in the same order as the table, with bounds chosen to illustrate the check rather than to describe real measurements:

c is an integer column that the plan requires to be non-null, and two of its rows are null.
d is a numeric column that the plan requires to fall between 0 and 5000, and one row, 9999.99, falls outside that range.
f is a categorical column that the plan limits to the labels low, mid, and high, and every row already matches.

With the whole table in view, the failure counts you’ll see in a moment are no surprise: two null c values, one out-of-range d value, and a clean f column. When you validate your own data, you’d swap these for your real columns and the thresholds your pipeline expects.

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

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Sending Emails Using Python

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

You probably found this video course because you want to send emails with Python to automate confirmation messages, password resets, or scheduled notifications. Python’s standard library covers the whole pipeline, from making a server connection to building the message and sending it to one or many recipients. This course walks through every step in working code.

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

A safe testing setup uses a throwaway Gmail account with an app password, a local aiosmtpd debug server, or a privacy-focused provider like Posteo or Proton Mail.
A secure SMTP session uses .SMTP_SSL() with ssl.create_default_context(), which validates the server certificate and encrypts your credentials and message content.
The EmailMessage class from the email package assembles plain text, HTML alternatives, file attachments, and personalized fields through .set_content(), .add_alternative(), and .add_attachment().
Setting msg["reply-to"] or any other RFC 5322 header on an EmailMessage routes replies to a different mailbox than the sender address.
For high-volume sending, transactional email services like SendGrid, Mailgun, and Brevo provide deliverability, statistics, and API libraries that go beyond what smtplib alone offers.

Before you jump into the code, you’ll set up a throwaway email account or a local debug server so you can experiment freely without spamming real inboxes.

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MCP Gets Its Biggest Rewrite and Other Python News for August 2026

2026-08-03T14:00:00+00:00

Some months, the AI news is just a trickle of point releases. But this past month was like a firehose. The protocol that connects agents to your tools got its biggest rewrite since inception, and Anthropic, OpenAI, Google, Meta, Moonshot, and a brand-new lab founded by a former OpenAI CTO all shipped new models in a single month.

Besides the models, the rest of the Python ecosystem was equally active. The major scientific libraries dropped support for 3.13t and settled on 3.14t as their free-threaded target. Python 3.15 shipped its final beta, the JIT team responded to the Steering Council’s ultimatum, the PSF opened nominations for its board and the first-ever Packaging Council, and Astral shipped breaking minors of uv and Ruff. The MCP rewrite comes first.

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

MCP Drops Sessions and Goes Stateless

Two things landed on July 28, and together they make this the most disruptive month MCP has had since it launched. The 2026-07-28 specification was finalized with a revision that changes how servers are deployed, and the Python SDK shipped a 2.0 the same day, renaming the class most Python servers are built on.

What the New Spec Changes

The main change is that the protocol is now stateless. A client used to open with a handshake and then carry a session ID through the rest of the conversation. Both are gone. Protocol version and client capabilities now ride along on every request, and a new server/discover call fetches what a server can do.

Under the new specification, any request can land on any instance, and a plain round-robin load balancer is enough. If you only call MCP servers, then the stateless switch is what affects you the most.

If you ship an MCP server, then your migration list looks like this:

Remove the session machinery: The handshake and the session header are both gone, so anything you built to track a client between requests goes with them.
Validate the Mcp-Method and Mcp-Name headers: Clients now send them so gateways can route without cracking open the request body, and your server has to check that they match the request.
Change your missing-resource error: Use the standard JSON-RPC invalid-params code, -32602, instead of MCP’s own -32002.
Rework your server-initiated requests: Roots, Sampling, and Elicitation no longer call back to the client. Instead, your server returns a result that the client uses to make a follow-up request, which is the biggest rewrite on this list.
Move off the experimental Tasks API: Tasks have graduated from the core spec to an extension and have gained a new lifecycle along the way.

Roots, Sampling, and Logging all entered formal deprecation, and all three keep working for now. The spec also adopted a deprecation policy promising at least a year’s notice before anything is removed, with a 90-day floor reserved for active security risks. That’s more warning than this ecosystem has offered before.

Authorization also hardened, picking up issuer validation and issuer-bound credentials on top of its OAuth 2.0 foundation.

Extensions, which existed before without any process around them, finally got one: namespaced identities and independent release cycles. That’s what let Tasks move out of the core without dragging the whole protocol along, and MCP Apps now arrives as an official extension under that process. A server can send interactive HTML that the host renders in a sandboxed iframe, so a tool call returns a real interface instead of a wall of text.

Treat third-party MCP servers with the same scrutiny you’d give any dependency. The official registry verifies who published a server through GitHub or DNS ownership, but there’s still no code-signing requirement and no review of what a server actually does, and a server you install gets to describe its own tools to your agent.

The Python SDK Renames FastMCP

The spec is only part of the story if you write Python. The MCP Python SDK reached 2.0.0 alongside it, and pip install mcp now provides the 2.x line.

The first change you’ll notice is a rename. FastMCP is now MCPServer, with no alias and no deprecation shim:

# Before, on mcp 1.x
from mcp.server.fastmcp import FastMCP

mcp = FastMCP("Demo")

# After, on mcp 2.x
from mcp.server.mcpserver import MCPServer

mcp = MCPServer("Demo")

The rest of 2.0 is broader than the rename suggests:

Wire types are snake_case now: result.isError is now result.is_error, and tool.inputSchema is now tool.input_schema. These types have also moved into a separate mcp-types package, although mcp.types stays as a permanent alias.
One Client replaces three layers: The old arrangement of a transport, a ClientSession, and an initialize() call now collapses into a single object that connects to a URL, a stdio subprocess, or an in-memory server object for tests.
httpx is now httpx2: The SDK moved to the next-generation HTTP client, which is worth knowing if you pin httpx elsewhere in your dependency tree.
Synchronous handlers run on worker threads: They no longer block the event loop. Your handler code doesn’t change, but asyncio.get_running_loop() now raises inside them.
McpError is now MCPError: It’s the same acronym capitalization as MCPServer, so any except McpError clause needs the new spelling.

One MCPServer serves both protocol eras, so 2025-era clients keep working against a migrated server with nothing to configure. The 1.x line moves to maintenance mode with security fixes only, so if you’re not ready this month, then pin mcp>=1.28,<2 and come back to it.

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

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The Real Python Podcast – Episode #305: Should You Understand Your Entire Python Codebase?

2026-07-31T12:00:00+00:00

Should you understand the entirety of your codebase? How familiar are you with Python's built-in functions? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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CrewAI in Python: Coordinating Teams of AI Agents

2026-07-29T14:00:00+00:00

Have you ever asked ChatGPT to research something, analyze the findings, and then write a polished report, all in one prompt? You probably got something back, but it wasn’t great. That’s because you’re asking a single model to wear way too many hats at once. CrewAI is a Python framework that solves this by letting you build a crew of specialized AI agents, each focusing on one part of the job.

For example, instead of cramming research, validation, and writing into a single prompt, you create a team. One agent researches, another validates, and a third writes. Each focuses on what it does best, working together to complete the task.

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

CrewAI coordinates teams where each agent has a specific role, goal, and backstory that influence its behavior.
Sequential workflows automatically pass outputs from one agent to the next, making coordinated pipelines straightforward.
The context parameter gives you fine-grained control over which task outputs feed into other tasks.
You can equip agents with tools like web scraping to expand what they can do beyond text generation.
API costs multiply quickly since each agent makes separate LLM calls, and verbose logging helps you debug agent behavior.

Before you invest time learning CrewAI, you should understand when it’s the right tool for your project. This comparison highlights the key trade-offs:

Use Case	Pick CrewAI	Pick LangGraph	Pick AG2 / AutoGen
You need structured, role-based workflows	✅	-	-
You want minimal boilerplate to go from prototype to production	✅	-	-
You need complex state machines with conditional branching	-	✅	-
Your agents need conversational, chat-driven coordination	-	-	✅

CrewAI’s sweet spot is workflows where agents have clear responsibilities and work in a predictable sequence. If you need fine-grained state management with branching logic, LangGraph gives you more control. If your agents need conversational back-and-forth, AG2—the community fork of AutoGen—supports chat-driven coordination patterns and may be a better fit.

Microsoft AutoGen is now in maintenance mode, so new projects typically choose AG2 or Microsoft Agent Framework.

Get Your Code: Click here to download the free sample code you’ll use to build and coordinate teams of AI agents with CrewAI in Python.

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

Interactive Quiz

CrewAI in Python: Coordinating Teams of AI Agents

Check your understanding of CrewAI in Python. Review how to define agent roles, assign tasks, add tools, and coordinate multi-agent workflows.

Get Started With CrewAI in Python

Before you dive into building multi-agent systems with CrewAI, you’ll need to install it and set up an API key for your chosen language model provider. This tutorial uses Google’s Gemini model, which you can access for free through Google AI Studio.

Note: CrewAI is model-agnostic, so you’re not locked into Gemini. It also works with OpenAI, Anthropic’s Claude, Mistral, Groq, and Ollama for local models, among others. Check the CrewAI documentation for the full list of supported providers.

You can install CrewAI from PyPI using pip. Before running the command below, you should create and activate a virtual environment. CrewAI requires Python 3.10 to 3.13, so run python --version first—installing on Python 3.14 or later will silently resolve to a much older CrewAI release and then fail to build:

$ python -m pip install 'crewai[tools,google-genai]'

This installs CrewAI’s core framework, the native Google Gemini provider, and prebuilt tools like web scraping that agents can use during their work.

You’ll need a Gemini API key to run the examples in this tutorial. If you don’t have one yet, then head over to Google AI Studio to sign in with your Google account and generate an API key for free.

Once you have your API key, set it as an environment variable:

PS> $ENV:GEMINI_API_KEY = "your-gemini-api-key-here"

$ export GEMINI_API_KEY="your-gemini-api-key-here"

With your environment variable set, CrewAI is ready to connect to Gemini in your projects.

To get started with CrewAI, you can create a single agent that acts as a travel advisor:

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

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Managing Imports With Python's all

2026-07-28T14:00:00+00:00

Python has something called wildcard imports, which look like from module import *. This type of import allows you to quickly get all the objects from a module into your namespace. However, using this import on a package can be confusing because it’s not clear what you want to import: subpackages, modules, objects? Python has the __all__ variable to work around this issue.

The __all__ variable is a list of strings where each string represents the name of a variable, function, class, or module that you want to expose to wildcard imports.

In this video course, you’ll:

Understand wildcard imports in Python
Use __all__ to control the modules that you expose to wildcard imports
Control the names that you expose in modules and packages
Explore other use cases of the __all__ variable
Learn some benefits and best practices of using __all__

To get the most out of this course, you should be familiar with a few Python concepts, including modules and packages, and the import system.

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How to Use Google's Antigravity CLI for AI Code Assistance

2026-07-27T14:00:00+00:00

Antigravity CLI is a terminal-based AI coding tool that brings Google’s agent assistance into your workflow. After installing it and signing in with your Google account, you can ask questions about your code, add new features, track down bugs, and even dispatch multiple subagents to work on separate tasks in parallel, all without leaving your shell:

Antigravity CLI

Antigravity CLI replaced Gemini CLI, which Google retired in June 2026. If you’ve used Gemini CLI, then a lot of this will feel familiar. Your skills, hooks, subagents, and plugins carry over, and the tool still reads its configuration from ~/.gemini/.

You don’t need any of that history to follow along, though. This is a hands-on guide to using Antigravity CLI, not a migration manual. But if you’re switching, then the Coming From Gemini CLI? section rounds up what changed and what to watch out for.

You’re about to install Antigravity CLI, sign in with your Google account, and put it to work on a Python project. It’ll read an unfamiliar module, review it against your conventions, and refactor the code while the agent verifies its own work.

Take the Quiz: Test your knowledge with our interactive “How to Use Google's Antigravity CLI for AI Code Assistance” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use Google's Antigravity CLI for AI Code Assistance

Test your knowledge of Google's Antigravity CLI, a terminal-based AI coding agent, and how to use it to read, review, and refactor Python code.

Prerequisites

To follow along with this tutorial, you’ll need the following:

Google Account: A Google account lets you sign in to Antigravity CLI. A free account is enough to finish this tutorial, although the free tier’s usage limits are tight and have shifted since launch, so expect to bump into them on longer sessions.
Python 3.12 or higher: You’ll work with a small Python command-line application. If you haven’t already, install Python, making sure the minimum version is Python 3.12.

Because Antigravity CLI is a command-line tool, you should feel comfortable navigating your terminal and running basic shell commands. The tool ships as a single binary for macOS, Linux, and Windows, so any of these platforms will work. Unlike Gemini CLI, it doesn’t depend on Node.js, which means there’s no separate runtime to install first.

Go ahead and download the supporting materials for the Python project you’ll work with throughout this tutorial:

Get Your Code: Click here to download the free sample code you’ll use to follow along as Antigravity CLI reads, reviews, and refactors a small Python command-line tool.

Once you’ve extracted the files, you’ll find an expense-report/ directory with a tiny command-line tool that reads a CSV of transactions and prints a per-category spending summary. To get the most out of this tutorial, initialize a new Git repository in that directory and make an initial commit so you have a clean baseline to diff against later:

$ cd expense-report/
$ git init
$ git add .
$ git commit -m "Initial commit"

This project is the kind of small, undocumented script you might inherit from a teammate, which makes it a good testing ground for Antigravity CLI’s code-reading and refactoring features.

Step 1: Install and Set Up Antigravity CLI

Before you can use Antigravity CLI, you need to install it and sign in with your Google account. In this step, you’ll run the official installer, verify the freshly installed agy command, authenticate, and pick a capable model.

Install the Binary

Antigravity CLI is distributed as a self-contained binary file that ships with the entire Go runtime, so you can download and install it using a single command. Here’s how you can run the official installer script for your platform:

PS> irm https://antigravity.google/cli/install.ps1 | iex

$ curl -fsSL https://antigravity.google/cli/install.sh | bash

The installer places the agy executable in a local bin/ directory, which is ~/.local/bin/ on macOS and Linux. As part of the same run, the installer’s setup step also tries to wire up your PATH and shell profile for you.

Open a new terminal session, or reload your shell profile, and then confirm that the installation worked by checking the version:

$ agy --version
1.0.12

If you see a version number, then you’re ready to authenticate. Because it’s a single Go binary, Antigravity CLI updates itself in place whenever a newer release is available. But you can trigger that manually at any time with agy update.

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

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The Real Python Podcast – Episode #304: Configuring a Versatile LLM Harness & Scraping the Web With Scrapy

2026-07-24T12:00:00+00:00

Which is more important, the model or the "harness" around an LLM? What are ways to assemble an efficient agentic developer workflow? This week on the show, Ayan Pahwa joins us to discuss harnessing, web scraping, and self-hosting Python applications.

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Tool-Agnostic Python Lock Files With PEP 751 and pylock.toml

2026-07-22T14:00:00+00:00

The Python lock file, pylock.toml, records exact dependencies your project needs so installs come out the same every time. It isn’t the first of its kind, though—most modern package tools already define their own lock formats. This has led to a fragmented landscape where reproducibility is often limited to a single tool’s workflow, with each ecosystem effectively speaking its own lock file dialect.

From this fragmentation comes a shared standard: pylock.toml. Defined in PEP 751, pylock.toml standardizes reproducible dependency management across different tools, eliminating vendor lock-in and fragmented requirements.txt workflows. Its purpose is to capture a fully resolved set of dependencies in a format that every tool can understand.

But doesn’t requirements.txt already do this? Not exactly. requirements.txt isn’t so much a standardized specification as an implementation detail of pip. The Requirements File Format specifies that “requirements files serve as a list of items to be installed by pip, when using pip install.”

Still, both files exist to enumerate dependencies that should end up in an environment. The difference lies in how that information is represented, either as tool-specific instructions or as a structured, tool-agnostic record of a resolved state:

If you…	`pylock.toml`	`requirements.txt`
Want to share a single lock file with teammates who use different tools (`uv`, `pip`, `pdm`)	✅	—
Need reproducible, hash-verified installs in CI	✅	—
Want to install without resolving dependencies on the machine	✅	—
Need to pass `pip` flags like `--index-url` or `--no-deps` from the file	—	✅
Want to jot down a couple of dependencies for a quick script by hand	—	✅

This means pylock.toml is the better choice when you care about reproducibility across tools, the integrity of installed artifacts, or a single shared source of truth that can be consumed in different workflows. It’s especially useful in team settings or continuous integration (CI) pipelines where consistency matters more than manual control.

In contrast, requirements.txt remains a good fit for lightweight workflows, quick experiments, or cases where direct control over pip behavior and flags is more important than cross-tool interoperability or strict locking guarantees.

Get Your Code: Click here to download the free sample code you’ll use to generate, inspect, and reuse pylock.toml lock files across pip, uv, and pdm.

Take the Quiz: Test your knowledge with our interactive “Tool-Agnostic Python Lock Files With PEP 751 and pylock.toml” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Tool-Agnostic Python Lock Files With PEP 751 and pylock.toml

Test your understanding of Python lock files and PEP 751, from generating a pylock.toml with pip to installing it across uv and pdm.

Generating a Python Lock File With `pip`

With pip version 25.1 or newer, you can generate a pylock.toml file from a requirements.txt file using the experimental pip lock command. At its core, the pylock.toml will serve as a snapshot of every dependency pip would install.

Before you can use pip lock, you’ll need to define a small set of explicit dependencies in a standard requirements file:

pandas
scipy

This file expresses only the top-level packages to be installed in your environment. It doesn’t describe how pip resolves those packages or what additional dependencies they need to work.

From here, you can have pip produce a fully resolved lock file:

$ python -m pip lock --requirement requirements.txt
WARNING: pip lock is currently an experimental command. It may be
⮑ removed/changed in a future release without prior warning.
Collecting pandas (from -r requirements.txt)
  Downloading pandas-3.0.2-cp314-cp314-manylinux_2_24_x86_64...
Collecting scipy (from -r requirements.txt)
  Downloading scipy-1.17.1-cp314-cp314-manylinux_2_27_x86_64...
Collecting numpy>=2.3.3 (from pandas->-r requirements.txt)
  Downloading numpy-2.4.4-cp314-cp314-manylinux_2_27_x86_64...
Collecting python-dateutil>=2.8.2 (from pandas->-r requirements.txt)
  Downloading python_dateutil-2.9.0.post0-py2.py3-none-any...
Collecting six>=1.5 (from python-dateutil>=2.8.2->pandas->-r requirements.txt)
  Downloading six-1.17.0-py2.py3-none-any.whl.metadata (1.7 kB)

This triggers the resolution process. pip builds a complete dependency graph starting from the explicit packages (pandas, scipy), resolves all required transitive dependencies, and downloads compatible versions for the current Python environment and platform.

This means the result contains both:

Explicit dependencies: What you specified directly, like pandas and scipy
Transitive dependencies: What those packages require to function, like numpy

You can explore that split for yourself in the interactive graph below. Start with only pandas declared to see the full set it resolves to, then toggle scipy on and watch your two requested packages expand into a larger set of pinned dependencies:

Pay attention to numpy as you toggle scipy on and off. The install log above attributes numpy to pandas, but scipy depends on it too, so the resolver doesn’t lock it twice—it records a single, shared numpy entry that satisfies both. That’s exactly what a lock file does for you: it turns the short list of packages you asked for into the complete, deduplicated set that will actually be installed.

By capturing the complete dependency graph, pylock.toml enables reproducible and verifiable installations across environments.

Read the full article at https://realpython.com/python-lock-file-pylock-toml/ »

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Learning Path: FastAPI: Python API Development With Light Speed

2026-07-22T12:00:00+00:00

Learn FastAPI from the ground up. Build REST APIs, serve web pages with Jinja2 templates, and create a complete URL shortener project in Python.

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Exploring Python's Built-in Functions

2026-07-21T14:00:00+00:00

Python’s built-in functions are predefined functions you can use anywhere in your code without any imports. They handle common tasks across math, data type creation, iterable processing, and input and output. Knowing which ones to reach for makes your code shorter and more Pythonic.

In this video course, you’ll:

Recognize Python’s built-in functions and the built-in scope they live in
Use the right built-in for tasks involving math, data types, iterables, and I/O
Distinguish real functions from classes that only look like them
Apply built-ins to solve practical problems with less code you have to write yourself

To get the most out of this video course, you’ll need to be familiar with Python programming, including topics like working with built-in data types, functions, classes, decorators, scopes, and the import system.

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Using NumPy reshape() to Change the Shape of an Array

2026-07-20T14:00:00+00:00

The main data structure you’ll use in NumPy, Python’s core library for numerical computing, is the N-dimensional array. Sometimes you need to reorganize the values in a NumPy array into a different layout. That’s what NumPy’s reshape() is for. It changes an array’s shape without changing its data, so you can fit the same values into whatever configuration your program needs.

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

You reshape a NumPy array by passing a compatible shape to .reshape(), which rearranges the data without changing its values.
A new shape is compatible only when its total number of elements matches the original array.
reshape() can add or remove dimensions, so the new array doesn’t need the same number of dimensions as the original.
The order parameter sets whether the data is read in row-major ("C") or column-major ("F") order.
Passing -1 for one dimension lets reshape() infer that dimension’s length automatically.

The shape of an array describes the number of dimensions in the array and the length of each dimension. In this tutorial, you’ll learn how to reshape a NumPy array to place all its data in a different configuration. When you complete this tutorial, you’ll be able to alter the shape of any array to suit your application’s needs.

For this tutorial, you should be familiar with the basics of NumPy and N-dimensional arrays. You can read NumPy Tutorial: Your First Steps Into Data Science in Python to learn more about NumPy before diving in.

Supplemental Material: Click here to download the supporting materials that you’ll use with NumPy reshape().

Take the Quiz: Test your knowledge with our interactive “Using NumPy reshape() to Change the Shape of an Array” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Using NumPy reshape() to Change the Shape of an Array

Practice NumPy reshape() by changing an array's shape, adding or removing dimensions, and controlling how the data gets rearranged.

Install NumPy

You’ll need to install NumPy to your environment to run the code in this tutorial and explore reshape(). You can install the package using pip within a virtual environment. Select either the Windows or Linux + macOS tab below to see instructions for your operating system:

PS> python -m venv venv
PS> .\venv\Scripts\activate
(venv) PS> python -m pip install numpy

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

It’s a convention to use the alias np when you import NumPy. To get started, you can import NumPy in the Python REPL:

>>> import numpy as np

Now that you’ve installed NumPy and imported the package in a REPL environment, you’re ready to start working with NumPy arrays.

Understand the Shape of NumPy Arrays

You’ll use NumPy’s ndarray in this tutorial. In this section, you’ll review the key features of this data structure, including an array’s overall shape and number of dimensions.

You can create an array from a list of lists:

>>> import numpy as np
>>> numbers = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
>>> numbers
array([[1, 2, 3, 4],
       [5, 6, 7, 8]])

The function np.array() returns an object of type np.ndarray. This data structure is the main data type in NumPy.

You can describe the shape of an array using the length of each dimension of the array. NumPy represents this as a tuple of integers. The array numbers has two rows and four columns. Therefore, this array has a (2, 4) shape:

>>> numbers.shape
(2, 4)

You can represent the same data using a different shape:

Both of these arrays contain the same data. The array with the shape (2, 4) has two rows and four columns, and the array with the shape (4, 2) has four rows and two columns. You can check the number of dimensions of an array using .ndim:

>>> numbers.ndim
2

Read the full article at https://realpython.com/numpy-reshape/ »

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The Real Python Podcast – Episode #303: Free-Threaded Python's History & uv in Production

2026-07-17T12:00:00+00:00

How many attempts have been made to remove Python's Global Interpreter Lock (GIL)? How do they compare to the current approach? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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Understanding Mixin Classes in Python

2026-07-14T14:00:00+00:00

Mixins offer a powerful way to reuse code across multiple Python classes without forcing them into a rigid inheritance hierarchy. Instead of building deep and brittle class trees, you can use mixins to share common behaviors in a modular and flexible way. Learn when and how to implement mixin classes effectively in your Python projects.

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

Mixins allow you to add isolated, reusable functionalities to classes without enforcing a strict type hierarchy.
Python has no dedicated syntax for declaring mixins. You create mixins by defining classes with specific behaviors that other classes can inherit without forming an is-a relationship.
Mixins rely on multiple inheritance to combine features from different classes, enhancing flexibility and code reuse.
Stateful mixins require careful design to manage instance attributes and avoid conflicts with other classes.
Python’s method resolution order (MRO) determines the order in which classes are inherited, affecting how mixins are applied.

To get the most out of this video course, you should have a good understanding of object-oriented programming (OOP), SOLID principles, inheritance, and Python classes.

So, what is a Python mixin, and when should you use one?

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The Real Python Podcast – Episode #302: Constructing and Judging Modern Agentic Workflows

2026-07-10T12:00:00+00:00

How can you improve your LLM agent systems through specification enrichment? What are the advantages of having an LLM act as a judge within an agent system? This week on the show, Senior IEEE Member and Quality Engineer Suneet Malhotra joins us to discuss building and evaluating agentic architecture.

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Testing MCP Servers With a Python MCP Client

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

Building an MCP client in Python is a great way to test MCP servers directly from your terminal. In this video course, you’ll build a minimal MCP client for the command line. It can connect to an MCP server through the standard input/output (stdio) transport, list the server’s capabilities, and interact with the server’s tools, prompts, and resources directly and deterministically.

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

You can build an MCP client app for the command line using the MCP Python SDK.
You can connect a client to an MCP server through the stdio transport and point it at any server from the command line.
You can list a server’s capabilities by calling .list_tools(), .list_prompts(), and .list_resources() on a ClientSession instance.
You can call tools, get prompts, and fetch resources from an MCP server through dedicated CLI subcommands.

Next, you’ll move through setup, client structure, server connection, and capability discovery to test MCP servers from your terminal.

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The Real Python Podcast – Episode #301: Running Python Locally in a Sandbox

2026-07-03T12:00:00+00:00

How do you avoid the risk of running a Python application locally that could be malicious, break your code, or leak private data? How can you create a sandboxed local environment using WASM and MicroPython? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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Managing and Measuring Python Code Quality

2026-06-30T14:00:00+00:00

Producing high-quality Python code involves using appropriate tools and consistently applying best practices. High-quality code is functional, readable, maintainable, efficient, and secure. It adheres to established standards and has excellent documentation.

You can achieve these qualities by following best practices such as descriptive naming, consistent coding style, modular design, and robust error handling. To help you with all this, you can use tools such as linters, formatters, and profilers.

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

Checking the quality of Python code involves using tools like linters and static type checkers to ensure adherence to coding standards and detect potential errors.
Writing quality code in Python requires following best practices, such as clear naming conventions, modular design, and comprehensive testing.
Good Python code is characterized by readability, maintainability, efficiency, and adherence to standards like PEP 8.
Making Python code look good involves using formatters to ensure consistent styling and readability, aligning with established coding styles.
Making Python code readable means using descriptive names for variables, functions, classes, modules, and packages.

Enjoy working through this course as you learn more about the strategies, tools, and best practices that will help you write high-quality Python code.

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The Real Python Podcast – Episode #300: Maintaining Your Python Developer Instincts While Using LLM Tools

2026-06-26T12:00:00+00:00

Do you feel like your Python skills are atrophying after using LLM coding tools? How do you add the right kind of friction into your coding routine to keep your developer instincts sharp? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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Using LlamaIndex for RAG in Python

2026-06-23T14:00:00+00:00

Discover how to use LlamaIndex with practical examples. This framework helps you build retrieval-augmented generation (RAG) apps using Python. LlamaIndex lets you load your data and documents, create and persist searchable indexes, and query an LLM using your data as context.

In this course, you’ll learn the basics of installing the package, setting AI providers, spinning up a query engine, and running queries against remote or local models.

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

You use LlamaIndex to connect your data to LLMs, allowing you to build AI agents, workflows, query engines, and chat engines.
You can perform RAG with LlamaIndex to retrieve relevant context at query time, helping the LLM generate grounded answers and minimize hallucinations.

You’ll start by preparing your environment and installing LlamaIndex. From there, you’ll learn how to load your own files, build and save an index, choose different AI providers, and run targeted queries over your data through a query engine.

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Building Python Skills for the Job Market

2026-06-16T14:00:00+00:00

When you’re learning Python, the sheer volume of topics to explore can feel overwhelming because there’s so much you could focus on. Should you dive into web frameworks before exploring data science? Is test-driven development something you need right away? And which skills actually matter to employers in the age of AI-assisted software development?

By the end of this course, you’ll have:

A clear understanding of which Python skills employers consistently look for
A personalized Python developer roadmap showing where you are and where you need to go
A weekly practice plan that makes consistent progress feel achievable

Python itself is relatively beginner-friendly, but its versatility makes it easy to wander without direction. Without a clear plan, you can spend months studying topics that won’t help you land your first developer job.

This course will show you how to build a focused learning strategy that aligns with real job market demands. You’ll learn how to research what employers value, assess your current strengths and gaps, and structure a practice routine that turns scattered study sessions into steady progress.

Instead of guessing what to learn next, you’ll have a concrete document that shows you exactly where to focus:

Work through this course to identify the skills you need and set yourself up for success.

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The Real Python Podcast – Episode #299: EuroPython 2026: Celebrating 25 Years

2026-06-12T12:00:00+00:00

What's happening at EuroPython 2026? The conference celebrates its 25th anniversary this year in Kraków, Poland. This week on the show, organizers Mia Bajić and Daria Linhart Grudzien join me to discuss this year's conference.

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The Real Python Podcast – Episode #298: Reducing the Size of Python Docker Containers

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

How can you easily reduce the size of a Python Docker container? What are the exceptions you should catch in your code? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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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.

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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.

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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. Choose an editor-based assistant like Cursor or a terminal-based one like Claude Code, then make it part of your daily workflow.

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Learning Path: Perfect Your Python Development Setup

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

Set up a Python development environment with VS Code, PyCharm, virtual environments, Git, pyenv, Docker, and AI coding tools like Claude Code and Cursor.

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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.

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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.

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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.

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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.

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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.

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Real Python

Quiz: Your Guide to the CPython Source Code

How to Use Claude Code to Write and Debug Python

Prerequisites

Step 1: Install and Configure Claude Code

Install and Authenticate

Explore the CLI Interface

Quiz: How to Review AI-Generated Python Code Efficiently

Quiz: How to Use Claude Code to Write and Debug Python

Working With Python's deque

Quiz: Arduino With Python: How to Get Started

Quiz: Working With Python's deque

How to Debug Python Code With an AI Agent

Prerequisites

Step 1: Set Up Your Workspace for AI Debugging

Quiz: How to Debug Python Code With an AI Agent

The Real Python Podcast – Episode #307: Improving NumPy Performance on Free-Threaded Python

Quiz: How to Publish an Open-Source Python Package to PyPI

How to Integrate OpenTelemetry With a FastAPI App

Prerequisites

Step 1: Build Your FastAPI OpenTelemetry Pipeline

Quiz: How to Integrate OpenTelemetry With a FastAPI App

Coding With OpenCode: AI-Assisted Python

Quiz: Coding With OpenCode: AI-Assisted Python

Qt Designer and Python: Build Your GUI Applications Faster

Getting Started With Qt Designer

Installing and Running Qt Designer

The Real Python Podcast – Episode #306: Programmatically Developing LLM Prompts With DSPy

Quiz: How to Use Type Hints for Multiple Return Types in Python

Validating Data With Pointblank in Python

Get Started With Pointblank in Python

Sending Emails Using Python

MCP Gets Its Biggest Rewrite and Other Python News for August 2026

MCP Drops Sessions and Goes Stateless

What the New Spec Changes

The Python SDK Renames FastMCP

The Real Python Podcast – Episode #305: Should You Understand Your Entire Python Codebase?

CrewAI in Python: Coordinating Teams of AI Agents

Get Started With CrewAI in Python

Managing Imports With Python's __all__

How to Use Google's Antigravity CLI for AI Code Assistance

Prerequisites

Step 1: Install and Set Up Antigravity CLI

Install the Binary

The Real Python Podcast – Episode #304: Configuring a Versatile LLM Harness & Scraping the Web With Scrapy

Tool-Agnostic Python Lock Files With PEP 751 and pylock.toml

Generating a Python Lock File With pip

Learning Path: FastAPI: Python API Development With Light Speed

Exploring Python's Built-in Functions

Using NumPy reshape() to Change the Shape of an Array

Install NumPy

Understand the Shape of NumPy Arrays

The Real Python Podcast – Episode #303: Free-Threaded Python's History & uv in Production

Understanding Mixin Classes in Python

The Real Python Podcast – Episode #302: Constructing and Judging Modern Agentic Workflows

Testing MCP Servers With a Python MCP Client

The Real Python Podcast – Episode #301: Running Python Locally in a Sandbox

Managing and Measuring Python Code Quality

The Real Python Podcast – Episode #300: Maintaining Your Python Developer Instincts While Using LLM Tools

Using LlamaIndex for RAG in Python

Building Python Skills for the Job Market

The Real Python Podcast – Episode #299: EuroPython 2026: Celebrating 25 Years

The Real Python Podcast – Episode #298: Reducing the Size of Python Docker Containers

Learning Path: Python Game Development

Learning Path: LLM Application Development With Python

Learning Path: Python Coding With AI

Learning Path: Perfect Your Python Development Setup

Learning Path: Important Standard Library Modules

Learning Path: Functions and Scopes

Learning Path: Create Graphical User Interfaces (GUI)

Learning Path: Network Programming and Security

Learning Path: Python Control Flow and Loops

Managing Imports With Python's all

Generating a Python Lock File With `pip`