Real Python

Spyder: Your IDE for Data Science Development in Python

2026-03-16T14:00:00+00:00

There are many different integrated development environments (IDEs) to choose from for Python development. One popular option for data-focused work is Spyder, an open-source Python IDE geared toward scientists, engineers, and data analysts. Its name comes from Scientific PYthon Development EnviRonment.

Out of the box, it has powerful plotting, what-if, and profiling capabilities. It also integrates well with the data science ecosystem, is extensible with first- or third-party plugins, and has a relatively quick learning curve.

How does Spyder stack up against other Python IDEs? It depends on your use case. It’s not as powerful or customizable as VS Code, nor does it pretend to be. It does, however, excel for data science workflows:

Use Case	Pick Spyder	Pick an Alternative
Optimized for data science workflows	✅	—
Dedicated to Python	✅	—
Full-featured	—	VS Code
Supports interactive notebooks	✅ With a plugin	Jupyter, VS Code

If you’re focused on data science in Python, Spyder is a strong fit. For a more full-featured IDE or heavy notebook use, consider Jupyter or VS Code instead.

You can get a handy Spyder IDE cheat sheet at the link below:

Get Your Free Spyder Cheat Sheet: Click here to download the PDF with installation steps, shortcuts, and interface tips.

Take the Quiz: Test your knowledge with our interactive “Spyder: Your IDE for Data Science Development in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Spyder: Your IDE for Data Science Development in Python

Test your knowledge of the Spyder IDE for Python data science, including its Variable Explorer, Plots pane, and Profiler.

Start Using the Spyder IDE

You can install Spyder in a few ways: as a standalone program, through a prepackaged distribution, or from the command line. You can also try out Spyder online.

To install Spyder as a standalone application, go to the Spyder download page. When you visit the site, it detects your operating system and offers the appropriate download. Once you download your install file, open it and follow the directions.

You can also install a Python distribution tailored to data science, such as Anaconda or WinPython. Both of these choices include Spyder in their base installations.

You’ll likely want to install dependencies and useful data libraries in addition to Spyder. In this case, first create a Python virtual environment, then use this command:

$ conda create -c conda-forge -n spyder-env spyder numpy scipy pandas matplotlib sympy cython

The install process for pip is similar. To install spyder together with common packages, run:

$ python -m pip install spyder numpy scipy pandas matplotlib sympy cython

For more information on installing Spyder, refer to their install guide.

Out of the box, the Spyder interface consists of three panes:

On the left, you see code in the Editor pane. In the bottom right, you’ll find the IPython Console. Here, you can run code and check past commands using the History tab. The top-right area includes tabs such as Help, Debugger, Files, Find, and Code Analysis. You’ll learn about the Variable Explorer, Plots, and Profiler in the upcoming sections.

Explore Data With the Variable Explorer

Read the full article at https://realpython.com/spyder-ide/ »

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Quiz: Speed Up Python With Concurrency

2026-03-16T12:00:00+00:00

In this quiz, you’ll test your understanding of Speed Up Python With Concurrency.

You’ll explore how I/O-bound programs face latency, which concurrency patterns to use, the differences between threading, asyncio, and multiprocessing, and how the Global Interpreter Lock (GIL) affects Python programs.

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Quiz: Splitting, Concatenating, and Joining Python Strings

2026-03-14T12:00:00+00:00

How well do you know Python string manipulation? In this quiz, you’ll revisit concepts like string immutability, splitting and joining with different arguments, and building safe string operations for tasks like CSV handling.

You’ll get hands-on practice with real examples from the Splitting, Concatenating, and Joining Strings in Python course. Ready to test your skills?

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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.

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Quiz: Your Python Coding Environment on Windows: Setup Guide

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

Test your understanding of Python Coding Setup on Windows.

By working through this quiz, you’ll review the key steps for setting up a Python development environment on Windows. You’ll cover system updates, Windows Terminal, package managers, PowerShell profiles, environment variables, and safe use of remote scripts.

You’ll also check practical details like configuring Path, managing Python versions, using version control, and streamlining your workflow.

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Quiz: How to Use Ollama to Run Large Language Models Locally

2026-03-12T12:00:00+00:00

In this quiz, you’ll test your understanding of How to Use Ollama to Run Large Language Models Locally.

By working through this quiz, you’ll revisit how to install Ollama, pull and manage models, chat with local LLMs from your terminal, and connect them to AI coding tools.

Running models locally means your prompts stay private and no API keys or cloud services are needed. See how well you remember the key commands and concepts.

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Quiz: Working With Files in Python

2026-03-12T12:00:00+00:00

In this quiz, you’ll test your understanding of Working With Files in Python.

By working through this quiz, you’ll revisit key techniques for handling files and directories in Python. You’ll practice safely opening files, iterating over directories, and filtering entries to select only files or subdirectories.

You’ll also explore creating directories and managing files and directories, including deleting, copying, and renaming them.

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Pydantic AI: Build Type-Safe LLM Agents in Python

2026-03-11T14:00:00+00:00

Pydantic AI is a Python framework for building LLM agents that return validated, structured outputs using Pydantic models. Instead of parsing raw strings from LLMs, you get type-safe objects with automatic validation.

If you’ve used FastAPI or Pydantic before, then you’ll recognize the familiar pattern of defining schemas with type hints and letting the framework handle the type validation for you.

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

Pydantic AI uses BaseModel classes to define structured outputs that guarantee type safety and automatic validation.
The @agent.tool decorator registers Python functions that LLMs can invoke based on user queries and docstrings.
Dependency injection with deps_type provides type-safe runtime context like database connections without using global state.
Validation retries automatically rerun queries when the LLM returns invalid data, which increases reliability but also API costs.
Google Gemini, OpenAI, and Anthropic models support structured outputs best, while other providers have varying capabilities.

Before you invest time learning Pydantic AI, it helps to understand when it’s the right tool for your project. This decision table highlights common use cases and what to choose in each scenario:

Use Case	Pydantic AI	If not, look into …
You need structured, validated outputs from an LLM	✅	-
You’re building a quick prototype or single-agent app	✅	-
You already use Pydantic or FastAPI	✅	-
You need a large ecosystem of pre-built integrations (vector stores, retrievers, and so on)	-	LangChain or LlamaIndex
You want fine-grained control over prompts with no framework overhead	-	Direct API calls

Pydantic AI emphasizes type safety and minimal boilerplate, making it ideal if you value the FastAPI-style development experience.

Get Your Code: Click here to download the free sample code you’ll use to work with Pydantic AI and build type-safe LLM agents in Python.

Take the Quiz: Test your knowledge with our interactive “Pydantic AI: Build Type-Safe LLM Agents in Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

Pydantic AI: Build Type-Safe LLM Agents in Python

Learn the trade-offs of using Pydantic AI in production, including validation retries, structured outputs, tool usage, and token costs.

Start Using Pydantic AI to Create Agents

Before you dive into building agents with Pydantic AI and Python, you’ll need to install it and set up an API key for your chosen language model provider. For this tutorial, you’ll use Google Gemini, which offers a free tier perfect for experimentation.

Note: Pydantic AI is LLM-agnostic and supports multiple AI providers. Check the Model Providers documentation page for more details on other providers.

You can install Pydantic AI from the Python Package Index (PyPI) using a package manager like pip. Before running the command below, you should create and activate a virtual environment:

(venv) $ python -m pip install pydantic-ai

This command installs all supported model providers, including Google, Anthropic, and OpenAI. From this point on, you just need to set up your favorite provider’s API key to use their models with Pydantic AI. Note that in most cases, you’d need a paid subscription to get a working API key.

Note: You can also power your Pydantic AI apps with local language models. To do this, you can use Ollama with your favorite local models. In this scenario, you won’t need to set up an API key.

If you prefer a minimal installation with only Google Gemini support, you can install the slim package instead:

(venv) $ python -m pip install "pydantic-ai-slim[google]"

You need a personal Google account to use the Gemini free tier. You’ll also need a Google API key to run the examples in this tutorial, so head over to ai.google.dev to get a free API key.

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

(venv) PS> $ENV:GOOGLE_API_KEY = "your-api-key-here"

(venv) $ export GOOGLE_API_KEY="your-api-key-here"

With the installation complete and your API key configured, you’re ready to create your first agent. The Python professionals on Real Python’s team have technically reviewed and tested all the code examples in this tutorial, so you can work through them knowing they run as shown.

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

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Quiz: Create and Modify PDF Files in Python

2026-03-11T12:00:00+00:00

In this quiz, you’ll test your understanding of Creating and Modifying PDF Files in Python.

By working through this quiz, you’ll practice reading, extracting, and modifying PDFs using the pypdf library. You’ll also review how to write new PDFs, concatenate and merge files, crop pages, and encrypt or decrypt documents.

These skills help you automate PDF workflows and handle documents programmatically in Python.

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Quiz: Threading in Python

2026-03-11T12:00:00+00:00

In this quiz, you’ll test your understanding of Threading in Python.

By working through this quiz, you’ll revisit how to create and manage threads, use ThreadPoolExecutor, prevent race conditions with locks, and build producer-consumer pipelines with the queue module.

You can also review the written tutorial An Intro to Threading in Python for additional details and code examples.

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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.

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Quiz: Python Descriptors: An Introduction

2026-03-10T12:00:00+00:00

In this quiz, you’ll test your understanding of Python Descriptors.

By working through this quiz, you’ll revisit the descriptor protocol, how .__get__() and .__set__() control attribute access, and how to implement read only descriptors. You’ll also explore data vs. non-data descriptors, attribute lookup order, and the .__set_name__() method.

These exercises help you reason about real descriptor behavior and see when and why to use them in your code.

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Python Gains frozendict and Other Python News for March 2026

2026-03-09T14:00:00+00:00

After years of community requests, Python is finally getting frozendict. The Steering Council accepted PEP 814 in February, bringing an immutable, hashable dictionary as a built-in type in Python 3.15. It’s one of those additions that feels overdue, and the frozenset-to-set analogy makes it immediately intuitive. This is just one piece of a busy month of Python news.

Beyond that, February brought security patches, AI SDK updates, and some satisfying infrastructure improvements under Python’s hood. Time to dive into the biggest Python news from the past month!

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Python Releases and PEP Highlights

The Steering Council was active in February, with several PEP decisions landing. On the release side, both the 3.14 and 3.13 branches got maintenance updates.

Python 3.15.0 Alpha 6: Comprehension Unpacking and More

Python 3.15.0a6 shipped on February 11, continuing the alpha series toward the May 5 beta freeze. This release includes several accepted PEPs that are now testable:

PEP 798: Unpacking in comprehensions using * and **
PEP 799: A high-frequency, low-overhead statistical sampling profiler
PEP 686: UTF-8 as the default text encoding
PEP 728: TypedDict with typed extra items

PEP 798 is the kind of quality-of-life improvement that makes you smile. It lets you flatten or merge collections directly in a comprehension:

>>> lists = [[1, 2], [3, 4], [5]]
>>> [*it for it in lists]
[1, 2, 3, 4, 5]

>>> dicts = [{"a": 1}, {"b": 2}]
>>> {**d for d in dicts}
{'a': 1, 'b': 2}

No more writing explicit loops just to concatenate a list of lists.

The JIT compiler continues to show gains: 3-4% on x86-64 Linux over the standard interpreter and 7-8% on AArch64 macOS over the tail-calling interpreter, matching the numbers from alpha 5.

Note: Alpha 7 is scheduled for March 10, 2026, with the beta phase starting May 5. If you maintain packages, now is a great time to test against early builds.

Python 3.14.3 and 3.13.12: Maintenance Releases

On February 3, the team shipped Python 3.14.3 with around 300 bug fixes and Python 3.13.12 with about 240 fixes. No new features here, but if you’re running either version in production, it’s worth grabbing these patches to stay current.

PEP 814 Accepted: `frozendict` Joins the Built-Ins

This one has been on many Python developers’ wishlists for over a decade. PEP 814, authored by Victor Stinner and Donghee Na, adds frozendict as a built-in immutable dictionary type in Python 3.15.

The concept is straightforward. Just as frozenset gives you an immutable version of set, frozendict gives you an immutable version of dict:

>>> config = frozendict(host="localhost", port=8080)
>>> config["host"]
'localhost'

>>> config["host"] = "0.0.0.0"
Traceback (most recent call last):
    ...
TypeError: 'frozendict' object does not support item assignment

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

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Quiz: Introduction to Python SQL Libraries

2026-03-09T12:00:00+00:00

In this quiz, you’ll test your understanding of Python SQL Libraries.

Work through this quiz to connect to databases with Python, interact with SQLite, MySQL, and PostgreSQL, run SQL queries, and write scripts that work across databases.

Reinforce your practical skills for building Python apps backed by databases.

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Quiz: Pydantic AI: Build Type-Safe LLM Agents in Python

2026-03-09T12:00:00+00:00

In this quiz, you’ll test your understanding of Pydantic AI: Build Type-Safe LLM Agents in Python.

You’ll revisit what Pydantic AI is, how to specify a model provider, how to return structured outputs using Pydantic models, how to register and select tools, how dependency injection works, and what trade-offs to expect in production.

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How to Use the OpenRouter API to Access Multiple AI Models via Python

2026-03-04T14:00:00+00:00

One of the quickest ways to call multiple AI models from a single Python script is to use OpenRouter’s API, which acts as a unified routing layer between your code and multiple AI providers. By the end of this guide, you’ll access models from several providers through one unified API, as shown in the image below:

This convenience matters because the AI ecosystem is highly fragmented: each provider exposes its own API, authentication scheme, rate limits, and model lineup. Working with multiple providers often requires additional setup and integration effort, especially when you want to experiment with different models, compare outputs, or evaluate trade-offs for a specific task.

OpenRouter gives you access to thousands of models from leading providers such as OpenAI, Anthropic, Mistral, Google, and Meta. You switch between them without changing your application code.

Get Your Code: Click here to download the free sample code that shows you how to use the OpenRouter API to access multiple AI models via Python.

Take the Quiz: Test your knowledge with our interactive “How to Use the OpenRouter API to Access Multiple AI Models via Python” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Use the OpenRouter API to Access Multiple AI Models via Python

Test your Python skills with OpenRouter: learn unified API access, model switching, provider routing, and fallback strategies.

Prerequisites

Before diving into OpenRouter, you should be comfortable with Python fundamentals like importing modules, working with dictionaries, handling exceptions, and using environment variables. If you’re familiar with these basics, the first step is authenticating with OpenRouter’s API.

Step 1: Connect to OpenRouter’s API

Before using OpenRouter, you need to create an account and generate an API key. Some models require prepaid credits for access, but you can start with free access to test the API and confirm that everything is working.

To generate an API key:

Create an account at OpenRouter.ai or sign in if you already have an account.
Select Keys from the dropdown menu and create an API key.
Fill in the name, something like OpenRouter Testing.
Leave the remaining defaults and click Create.

Copy the generated key and keep it secure. In a moment, you’ll store it as an environment variable rather than embedding it directly in your code.

To call multiple AI models from a single Python script, you’ll use OpenRouter’s API. You’ll use the requests library to make HTTP calls, which gives you full control over the API interactions without requiring a specific SDK. This approach works with any HTTP client and keeps your code simple and transparent.

First, create a new directory for your project and set up a virtual environment. This isolates your project dependencies from your system Python installation:

$ mkdir openrouter-project/
$ cd openrouter-project/
$ python -m venv venv/

Now, you can activate the virtual environment:

PS> venv\Scripts\activate

$ source venv/bin/activate

You should see (venv) in your terminal prompt when it’s active. Now you’re ready to install the requests package for conveniently making HTTP calls:

(venv) $ python -m pip install requests

Read the full article at https://realpython.com/openrouter-api/ »

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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.

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Automate Python Data Analysis With YData Profiling

2026-03-02T14:00:00+00:00

The YData Profiling package generates an exploratory data analysis (EDA) report with a few lines of code. The report provides dataset and column-level analysis, including plots and summary statistics to help you quickly understand your dataset. These reports can be exported to HTML or JSON so you can share them with other stakeholders.

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

YData Profiling generates interactive reports containing EDA results, including summary statistics, visualizations, correlation matrices, and data quality warnings from DataFrames.
ProfileReport creates a profile you can save with .to_file() for HTML or JSON export, or display inline with .to_notebook_iframe().
Setting tsmode=True and specifying a date column with sortby enables time series analysis, including stationarity tests and seasonality detection.
The .compare() method generates side-by-side reports highlighting distribution shifts and statistical differences between datasets.

To get the most out of this tutorial, you’ll benefit from having knowledge of pandas.

Note: The examples in this tutorial were tested using Python 3.13. Additionally, you may need to install setuptools<81 for backward compatibility.

You can install this package using pip:

$ python -m pip install ydata-profiling

Once installed, you’re ready to transform any pandas DataFrame into an interactive report. To follow along, download the example dataset you’ll work with by clicking the link below:

Get Your Code: Click here to download the free sample code and start automating Python data analysis with YData Profiling.

The following example generates a profiling report from the 2024 flight delay dataset and saves it to disk:

import pandas as pd
from ydata_profiling import ProfileReport

df = pd.read_csv("flight_data_2024_sample.csv")

profile = ProfileReport(df)
profile.to_file("flight_report.html")

This code generates an HTML file containing interactive visualizations, statistical summaries, and data quality warnings:

You can open the file in any browser to explore your data’s characteristics without writing additional analysis code.

There are a number of tools available for high-level dataset exploration, but not all are built for the same purpose. The following table highlights a few common options and when each one is a good fit:

Use case	Pick	Best for
You want to quickly generate an exploratory report	`ydata-profiling`	Generating exploratory data analysis reports with visualizations
You want an overview of a large dataset	`skimpy` or `df.describe()`	Providing fast, lightweight summaries in the console
You want to enforce data quality	`pandera`	Validating schemas and catching errors in data pipelines

Overall, YData Profiling is best used as an exploratory report creation tool. If you’re looking to generate an overview for a large dataset, using SkimPy or a built-in DataFrame library method may be more efficient. Other tools, like Pandera, are more appropriate for data validation.

If YData Profiling looks like the right choice for your use case, then keep reading to learn about its most important features.

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

Interactive Quiz

Automate Python Data Analysis With YData Profiling

Test your knowledge of YData Profiling, including report creation, customization, performance optimization, time series analysis, and comparisons.

Building a Report With YData Profiling

Read the full article at https://realpython.com/ydata-profiling-eda/ »

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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.

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How to Run Your Python Scripts and Code

2026-02-25T14:00:00+00:00

Running Python scripts is essential for executing your code. You can run Python scripts from the command line using python script.py, directly by making files executable with shebangs on Unix systems, or through IDEs and code editors. Python also supports interactive execution through the standard REPL for testing code snippets.

This tutorial covers the most common practical approaches for running Python scripts across Windows, Linux, and macOS.

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

The python command followed by a script filename executes the code from the command line on all operating systems.
Script mode runs code from files sequentially, while interactive mode uses the REPL for execution and testing with immediate feedback.
Unix systems require executable permissions and a shebang line like #!/usr/bin/env python3 to run scripts directly as programs.
The python command’s -m option runs Python modules by searching sys.path rather than requiring file paths.
IDEs like PyCharm and code editors like Visual Studio Code provide built-in options to run scripts from the environment interface.

To get the most out of this tutorial, you should know the basics of working with your operating system’s terminal and file manager. It’d also be beneficial to be familiar with a Python-friendly IDE or code editor and with the standard Python REPL (Read-Eval-Print Loop).

Free Download: Get a sample chapter from Python Tricks: The Book that shows you Python’s best practices with simple examples you can apply instantly to write more beautiful + Pythonic code.

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

Interactive Quiz

How to Run Your Python Scripts

One of the most important skills you need to build as a Python developer is to be able to run Python scripts and code. Test your understanding on how good you are with running your code.

What Scripts and Modules Are

In computing, the term script refers to a text file containing a logical sequence of orders that you can run to accomplish a specific task. These orders are typically expressed in a scripting language, which is a programming language that allows you to manipulate, customize, and automate tasks.

Scripting languages are usually interpreted at runtime rather than compiled. So, scripts are typically run by an interpreter, which is responsible for executing each order in a sequence.

Python is an interpreted language. Because of that, Python programs are commonly called scripts. However, this terminology isn’t completely accurate because Python programs can be way more complex than a simple, sequential script.

In general, a file containing executable Python code is called a script—or an entry-point script in more complex applications—which is a common term for a top-level program. On the other hand, a file containing Python code that’s designed to be imported and used from another Python file is called a module.

So, the main difference between a module and a script is that modules store importable code while scripts hold executable code.

Note: Importable code is code that defines something but doesn’t perform a specific action. Some examples include function and class definitions. In contrast, executable code is code that performs specific actions. Some examples include function calls, loops, and conditionals.

In the following sections, you’ll learn how to run Python scripts, programs, and code in general. To kick things off, you’ll start by learning how to run them from your operating system’s command line or terminal.

How to Run Python Scripts From the Command Line

In Python programming, you’ll write programs in plain text files. By convention, files containing Python code use the .py extension, and there’s no distinction between scripts or executable programs and modules. All of them will use the same extension.

Note: On Windows systems, the extension can also be .pyw for those applications that should use the pythonw.exe launcher.

To create a Python script, you can use any Python-friendly code editor or IDE (integrated development environment). To keep moving forward in this tutorial, you’ll need to create a basic script, so fire up your favorite text editor and create a new hello.py file containing the following code:

print("Hello, World!")

This is the classic "Hello, World!" program in Python. The executable code consists of a call to the built-in print() function that displays the "Hello, World!" message on your screen.

With this small program in place, you’re ready to learn different ways to run it. You’ll start by running the program from your command line, which is arguably the most commonly used approach to running scripts.

Using the `python` Command

To run Python scripts with the python command, you need to open a command-line window and type in the word python followed by the path to your target script:

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

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Start Building With FastAPI

2026-02-24T14:00:00+00:00

FastAPI is a web framework for building APIs with Python. It leverages standard Python type hints to provide automatic validation, serialization, and interactive documentation. When you’re deciding between Python web frameworks, FastAPI stands out for its speed, developer experience, and built-in features that reduce boilerplate code for API development:

Use Case	Pick FastAPI	Pick Flask or Django
You want to build an API-driven web app	✅	—
You need a full-stack web framework	—	✅
You value automatic API documentation	✅	—

Whether you’re building a minimal REST API or a complex backend service, understanding core features of FastAPI will help you make an informed decision about adopting it for your projects. To get the most from this video course, you’ll benefit from having basic knowledge of Python functions, HTTP concepts, and JSON handling.

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Python for Loops: The Pythonic Way

2026-02-23T14:00:00+00:00

Python’s for loop allows you to iterate over the items in a collection, such as lists, tuples, strings, and dictionaries. The for loop syntax declares a loop variable that takes each item from the collection in each iteration. This loop is ideal for repeatedly executing a block of code on each item in the collection. You can also tweak for loops further with features like break, continue, and else.

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

Python’s for loop iterates over items in a data collection, allowing you to execute code for each item.
To iterate from 0 to 10, you use the for index in range(11): construct.
To repeat code a number of times without processing the data of an iterable, use the for _ in range(times): construct.
To do index-based iteration, you can use for index, value in enumerate(iterable): to access both index and item.

In this tutorial, you’ll gain practical knowledge of using for loops to traverse various collections and learn Pythonic looping techniques. You’ll also learn how to handle exceptions and use asynchronous iterations to make your Python code more robust and efficient.

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

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

Interactive Quiz

Python for Loops: The Pythonic Way

In this quiz, you'll test your understanding of Python's for loop. You'll revisit how to iterate over items in a data collection, how to use range() for a predefined number of iterations, and how to use enumerate() for index-based iteration.

Getting Started With the Python `for` Loop

In programming, loops are control flow statements that allow you to repeat a given set of operations a number of times. In practice, you’ll find two main types of loops:

for loops are mostly used to iterate a known number of times, which is common when you’re processing data collections with a specific number of data items.
while loops are commonly used to iterate an unknown number of times, which is useful when the number of iterations depends on a given condition.

Python has both of these loops and in this tutorial, you’ll learn about for loops. In Python, you’ll generally use for loops when you need to iterate over the items in a data collection. This type of loop lets you traverse different data collections and run a specific group of statements on or with each item in the input collection.

In Python, for loops are compound statements with a header and a code block that runs a predefined number of times. The basic syntax of a for loop is shown below:

for variable in iterable:
    <body>

In this syntax, variable is the loop variable. In each iteration, this variable takes the value of the current item in iterable, which represents the data collection you need to iterate over. The loop body can consist of one or more statements that must be indented properly.

Here’s a more detailed breakdown of this syntax:

for is the keyword that initiates the loop header.
variable is a variable that holds the current item in the input iterable.
in is a keyword that connects the loop variable with the iterable.
iterable is a data collection that can be iterated over.
<body> consists of one or more statements to execute in each iteration.

Here’s a quick example of how you can use a for loop to iterate over a list:

>>> colors = ["red", "green", "blue", "yellow"]

>>> for color in colors:
...     print(color)
...
red
green
blue
yellow

In this example, color is the loop variable, while the colors list is the target collection. Each time through the loop, color takes on a successive item from colors. In this loop, the body consists of a call to print() that displays the value on the screen. This loop runs once for each item in the target iterable. The way the code above is written is the Pythonic way to write it.

However, what’s an iterable anyway? In Python, an iterable is an object—often a data collection—that can be iterated over. Common examples of iterables in Python include lists, tuples, strings, dictionaries, and sets, which are all built-in data types. You can also have custom classes that support iteration.

Note: Python has both iterables and iterators. Iterables support the iterable protocol consisting of the .__iter__() special method. Similarly, iterators support the iterator protocol that’s based on the .__iter__() and .__next__() special methods.

Both iterables and iterators can be iterated over. All iterators are iterables, but not all iterables are iterators. Python iterators play a fundamental role in for loops because they drive the iteration process.

A deeper discussion on iterables and iterators is beyond the scope of this tutorial. However, to learn more about them, check out the Iterators and Iterables in Python: Run Efficient Iterations tutorial.

You can also have a loop with multiple loop variables:

>>> points = [(1, 4), (3, 6), (7, 3)]

>>> for x, y in points:
...     print(f"{x = } and {y = }")
...
x = 1 and y = 4
x = 3 and y = 6
x = 7 and y = 3

In this loop, you have two loop variables, x and y. Note that to use this syntax, you just need to provide a tuple of loop variables. Also, you can have as many loop variables as you need as long as you have the correct number of items to unpack into them. You’ll also find this pattern useful when iterating over dictionary items or when you need to do parallel iteration.

Sometimes, the input iterable may be empty. In that case, the loop will run its header once but won’t execute its body:

>>> for item in []:
...     print(item)
...

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

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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.

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How to Install Python on Your System: A Guide

2026-02-18T14:00:00+00:00

To learn how to install Python on your system, you can follow a few straightforward steps. First, check if Python is already installed by opening a command-line interface and typing python --version or python3 --version.

You can install Python on Windows using the official installer from Python.org or through the Microsoft Store. On macOS, you can use the official installer or Homebrew. For Linux, use your package manager or build Python from source.

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

Check if Python is installed by running python --version or python3 --version in a command-line interface.
Upgrade Python by downloading and installing the latest version from Python.org.
Install and manage multiple Python versions with pyenv to keep them separate.

This tutorial covers installing the latest Python on the most important platforms or operating systems, such as Windows, macOS, Linux, iOS, and Android. However, it doesn’t cover all the existing Linux distributions, as that would be a massive task. Nevertheless, you’ll find instructions for the most popular distributions available today.

To get the most out of this tutorial, you should be comfortable using your operating system’s terminal or command line.

Free Bonus: Click here to get a Python Cheat Sheet and learn the basics of Python 3, like working with data types, dictionaries, lists, and Python functions.

Take the Quiz: Test your knowledge with our interactive “How to Install Python on Your System: A Guide” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

How to Install Python on Your System: A Guide

In this quiz, you'll test your understanding of how to install or update Python on your computer. With this knowledge, you'll be able to set up Python on various operating systems, including Windows, macOS, and Linux.

Windows: How to Check or Get Python

In this section, you’ll learn to check whether Python is installed on your Windows operating system (OS) and which version you have. You’ll also explore three installation options that you can use on Windows.

Note: In this tutorial, you’ll focus on installing the latest version of Python in your current operating system (OS) rather than on installing multiple versions of Python. If you want to install several versions of Python in your OS, then check out the Managing Multiple Python Versions With pyenv tutorial. Note that on Windows machines, you’d have to use pyenv-win instead of pyenv.

For a more comprehensive guide on setting up a Windows machine for Python programming, check out Your Python Coding Environment on Windows: Setup Guide.

Checking the Python Version on Windows

To check whether you already have Python on your Windows machine, open a command-line application like PowerShell or the Windows Terminal.

Follow the steps below to open PowerShell on Windows:

Press the Win key.
Type PowerShell.
Press Enter.

Alternatively, you can right-click the Start button and select Windows PowerShell or Windows PowerShell (Admin). In some versions of Windows, you’ll find Terminal or Terminal (admin).

Note: To learn more about your options for the Windows terminal, check out Your Python Coding Environment on Windows: Setup Guide.

With the command line open, type in the following command and press the Enter key:

PS> python --version
Python 3.x.z

Using the --version switch will show you the installed version. Note that the 3.x.z part is a placeholder here. In your machine, x and z will be numbers corresponding to the specific version you have installed.

Alternatively, you can use the -V switch:

PS> python -V
Python 3.x.z

You can also use the py launcher, which is the Python launcher for Windows and is especially helpful if you plan to work with multiple Python versions:

PS> py --version
Python 3.x.z

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

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Write Python Docstrings Effectively

2026-02-17T14:00:00+00:00

Writing clear, consistent docstrings in Python helps others understand your code’s purpose, parameters, and outputs. In this video course, you’ll learn about best practices, standard formats, and common pitfalls to avoid, ensuring your documentation is accessible to users and tools alike.

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

Docstrings are strings used to document your Python code and can be accessed at runtime.
Python comments and docstrings have important differences.
One-line and multiline docstrings are classifications of docstrings.
Common docstring formats include reStructuredText, Google-style, NumPy-style, and doctest-style.
Antipatterns such as inconsistent formatting should be avoided when writing docstrings.

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TinyDB: A Lightweight JSON Database for Small Projects

2026-02-16T14:00:00+00:00

TinyDB is a Python implementation of a NoSQL, document-oriented database. Unlike a traditional relational database, which stores records across multiple linked tables, a document-oriented database stores its information as separate documents in a key-value structure. The keys are similar to the field headings, or attributes, in a relational database table, while the values are similar to the table’s attribute values.

TinyDB uses the familiar Python dictionary for its document structure and stores its documents in a JSON file.

TinyDB is written in Python, making it easily extensible and customizable, with no external dependencies or server setup needed. Despite its small footprint, it still fully supports the familiar database CRUD features of creating, reading, updating, and deleting documents using an API that’s logical to use.

The table below will help you decide whether TinyDB is a good fit for your use case:

Use Case	TinyDB	Possible Alternatives
Local, small dataset, single-process use (scripts, CLIs, prototypes)	✅	simpleJDB, Python’s `json` module, SQLite
Local use that requires SQL, constraints, joins, or stronger durability	—	SQLite, PostgreSQL
Multi-user, multi-process, distributed, or production-scale systems	—	PostgreSQL, MySQL, MongoDB

Whether you’re looking to use a small NoSQL database in one of your projects or you’re just curious how a lightweight database like TinyDB works, this tutorial is for you. By the end, you’ll have a clear sense of when TinyDB shines, and when it’s better to reach for something else.

Get Your Code: Click here to download the free sample code you’ll use in this tutorial to explore TinyDB.

Take the Quiz: Test your knowledge with our interactive “TinyDB: A Lightweight JSON Database for Small Projects” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

TinyDB: A Lightweight JSON Database for Small Projects

If you're looking for a JSON document-oriented database that requires no configuration for your Python project, TinyDB could be what you need.

Get Ready to Explore TinyDB

TinyDB is a standalone library, meaning it doesn’t rely on any other libraries to work. You’ll need to install it, though.

You’ll also use the pprint module to format dictionary documents for easier reading, and Python’s csv module to work with CSV files. You don’t need to install either of these because they’re included in Python’s standard library.

So to follow along, you only need to install the TinyDB library in your environment. First, create and activate a virtual environment, then install the library using pip:

(venv) $ python -m pip install tinydb

Alternatively, you could set up a small pyproject.toml file and manage your dependencies using uv.

When you add documents to your database, you often do so manually by creating Python dictionaries. In this tutorial, you’ll do this, and also learn how to work with documents already stored in a JSON file. You’ll even learn how to add documents from data stored in a CSV file.

These files will be highlighted as needed and are available in this tutorial’s downloads. You might want to download them to your program folder before you start to keep them handy:

Get Your Code: Click here to download the free sample code you’ll use in this tutorial to explore TinyDB.

Regardless of the files you use or the documents you create manually, they all rely on the same world population data. Each document will contain up to six fields, which become the dictionary keys used when the associated values are added to your database:

Field	Description
`continent`	The continent the country belongs to
`location`	Country
`date`	Date population count made
`% of world`	Percentage of the world’s population
`population`	Population
`source`	Source of population

As mentioned earlier, the four primary database operations are Create, Read, Update, and Delete—collectively known as the CRUD operations. In the next section, you’ll learn how you can perform each of them.

To begin with, you’ll explore the C in CRUD. It’s time to get creative.

Create Your Database and Documents

The first thing you’ll do is create a new database and add some documents to it. To do this, you create a TinyDB() object that includes the name of a JSON file to store your data. Any documents you add to the database are then saved in that file.

Documents in TinyDB are stored in tables. Although it’s not necessary to create a table manually, doing so can help you organize your documents, especially when working with multiple tables.

To start, you create a script named create_db.py that initializes your first database and adds documents in several different ways. The first part of your script looks like this:

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

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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.

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What Exactly Is the Zen of Python?

2026-02-11T14:00:00+00:00

The Zen of Python is a collection of 19 aphorisms that capture the guiding principles behind Python’s design. You can display them anytime by running import this in a Python REPL. Tim Peters wrote them in 1999 as a joke, but they became an iconic part of Python culture that was even formalized as PEP 20.

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

The Zen of Python is a humorous poem of 19 aphorisms describing Python’s design philosophy
Running import this in a Python interpreter displays the complete text of the Zen of Python
Tim Peters wrote the Zen of Python in 1999 as a tongue-in-cheek comment on a mailing list
The aphorisms are guidelines, not strict rules, and some intentionally contradict each other
The principles promote readability, simplicity, and explicitness while acknowledging that practicality matters

Experienced Pythonistas often refer to the Zen of Python as a source of wisdom and guidance, especially when they want to settle an argument about certain design decisions in a piece of code. In this tutorial, you’ll explore the origins of the Zen of Python, learn how to interpret its mysterious aphorisms, and discover the Easter eggs hidden within it.

You don’t need to be a Python master to understand the Zen of Python! But you do need to answer an important question: What exactly is the Zen of Python?

Free Bonus: Click here to download your Easter egg hunt to discover what’s hidden inside Python!

Take the Quiz: Test your knowledge with our interactive “What Exactly Is the Zen of Python?” quiz. You’ll receive a score upon completion to help you track your learning progress:

Interactive Quiz

What Exactly Is the Zen of Python?

Learn and test the Zen of Python, its guiding aphorisms, and tips for writing clearer, more readable, and maintainable code.

In Short: It’s a Humorous Poem Listing Python Philosophies

According to the Python glossary, which contains definitions of popular terms related to this programming language, the Zen of Python is a:

Listing of Python design principles and philosophies that are helpful in understanding and using the language. The listing can be found by typing “import this” at the interactive prompt. (Source)

Indeed, when you type the indicated import statement into an interactive Python REPL, then you’ll be presented with the nineteen aphorisms that make up the Zen of Python:

>>> import this
The Zen of Python, by Tim Peters

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!

The byline reveals the poem’s author, Tim Peters, who’s a renowned software engineer and a long-standing CPython core developer best known for inventing the Timsort sorting algorithm. He also authored the doctest and timeit modules in the Python standard library, along with making many other contributions.

Take your time to read through the Zen of Python and contemplate its wisdom. But don’t take the aphorisms literally, as they’re more of a guiding set of principles rather than strict instructions. You’ll learn about their humorous origins in the next section.

How Did the Zen of Python Originate?

The idea of formulating a single document that would encapsulate Python’s fundamental philosophies emerged among the core developers in June 1999. As more and more people began coming to Python from other programming languages, they’d often bring their preconceived notions of software design that weren’t necessarily Pythonic. To help them follow the spirit of the language, a set of recommendations for writing idiomatic Python was needed.

The initial discussion about creating such a document took place on the Python mailing list under the subject The Python Way. Today, you can find this conversation in the official Python-list archive. If you look closely at the first message from Tim Peters in that thread, then you’ll notice that he clearly outlined the Zen of Python as a joke. That original form has stuck around until this day:

Clearly a job for Guido alone – although I doubt it’s one he’ll take on (fwiw, I wish he would too!). Here’s the outline he would start from, though <wink>:

Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren’t special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one– and preferably only one –obvious way to do it. Although that way may not be obvious at first unless you’re Dutch. Now is better than never. Although never is often better than right now. If the implementation is hard to explain, it’s a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea – let’s do more of those!

There you go: 20 Pythonic Fec^H^H^HTheses on the nose, counting the one I’m leaving for Guido to fill in. If the answer to any Python design issue isn’t obvious after reading those – well, I just give up <wink>. (Source)

The wink and the playful way of self-censoring some toilet humor are clear giveaways that Tim Peters didn’t want anyone to take his comment too seriously.

Note: In case you didn’t get the joke, he started to write something like Feces but then used ^H—which represents a Backspace in older text editors like Vim—to delete the last three letters and make the word Theses. Therefore, the intended phrase is 20 Pythonic Theses.

Eventually, these nearly twenty theses got a proper name and were formally codified in a Python Enhancement Proposal document. Each PEP document receives a number. For example, you might have stumbled on PEP 8, which is the style guide for writing readable Python code. Perhaps as an inside joke, the Zen of Python received the number PEP 20 to signify the incomplete number of aphorisms in it.

To win your next argument about what makes good Python code, you can back up your claims with the Zen of Python. If you’d like to refer to a specific aphorism instead of the entire poem, then consider visiting pep20.org, which provides convenient clickable links to each principle.

And, in case you want to learn the poem by heart while having some fun, you can now listen to a song with the Zen of Python as its lyrics. Barry Warsaw, another core developer involved with Python since its early days, composed and performed this musical rendition. The song became the closing track on a special vinyl record entitled The Zen Side of the Moon, which was auctioned at PyCon US 2023.

Okay. Now that you have a rough idea of what the Zen of Python is and how it came about, you might be asking yourself whether you should really follow it.

Should You Obey the Zen of Python?

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

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Improving Your Tests With the Python Mock Object Library

2026-02-10T14:00:00+00:00

When you’re writing robust code, tests are essential for verifying that your application logic is correct, reliable, and efficient. However, the value of your tests depends on how well they demonstrate these qualities. Obstacles such as complex logic and unpredictable dependencies can make writing valuable tests challenging. The Python mock object library, unittest.mock, can help you overcome these obstacles.

By the end of this course, you’ll be able to:

Create Python mock objects using Mock
Assert that you’re using objects as you intended
Inspect usage data stored on your Python mocks
Configure certain aspects of your Python mock objects
Substitute your mocks for real objects using patch()
Avoid common problems inherent in Python mocking

You’ll begin by learning what mocking is and how it will improve your tests!

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The Real Python Podcast – Episode #283: Improving Your GitHub Developer Experience

2026-02-06T12:00:00+00:00

What are ways to improve how you're using GitHub? How can you collaborate more effectively and improve your technical writing? This week on the show, Adam Johnson is back to talk about his new book, "Boost Your GitHub DX: Tame the Octocat and Elevate Your Productivity".

[ 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 ]

Getting Started With Google Gemini CLI

2026-02-03T14:00:00+00:00

This video course will teach you how to use Gemini CLI to bring Google’s AI-powered coding assistance directly into your terminal. After you authenticate with your Google account, this tool will be ready to help you analyze code, identify bugs, and suggest fixes—all without leaving your familiar development environment.

Imagine debugging code without switching between your console and browser, or picture getting instant explanations for unfamiliar projects. Like other command-line AI assistants, Google’s Gemini CLI brings AI-powered coding assistance directly into your command line, allowing you to stay focused in your development workflow.

Whether you’re troubleshooting a stubborn bug, understanding legacy code, or generating documentation, this tool acts as an intelligent pair-programming partner that understands your codebase’s context.

You’re about to install Gemini CLI, authenticate with Google’s free tier, and put it to work on an actual Python project. You’ll discover how natural language queries can help you understand code faster and catch bugs that might slip past manual review.

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The Real Python Podcast – Episode #282: Testing Python Code for Scalability & What's New in pandas 3.0

2026-01-30T12:00:00+00:00

How do you create automated tests to check your code for degraded performance as data sizes increase? What are the new features in pandas 3.0? Christopher Trudeau is back on the show this week with another batch of PyCoder's Weekly articles and projects.

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Create Callable Instances With Python's .call()

2026-01-27T14:00:00+00:00

In Python, a callable is any object that you can call using a pair of parentheses and, optionally, a series of arguments. Functions, classes, and methods are all common examples of callables in Python. Besides these, you can also create custom classes that produce callable instances. To do this, you can add the .__call__() special method to your class.

Instances of a class with a .__call__() method behave like functions, providing a flexible and handy way to add functionality to your objects. Understanding how to create and use callable instances is a valuable skill for any Python developer.

In this video course, you’ll:

Understand the concept of callable objects in Python
Create callable instances by adding a .__call__() method to your classes
Compare .__init__() and .__call__() and understand their distinct roles
Build practical examples that use callable instances to solve real-world problems

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The Real Python Podcast – Episode #281: Continuing to Improve the Learning Experience at Real Python

2026-01-23T12:00:00+00:00

If you haven't visited the Real Python website lately, then it's time to check out a great batch of updates on realpython.com! Dan Bader returns to the show this week to discuss improvements to the site and more ways to learn Python.

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uv vs pip: Python Packaging and Dependency Management

2026-01-20T14:00:00+00:00

When it comes to Python package managers, the choice often comes down to uv vs pip. You may choose pip for out-of-the-box availability, broad compatibility, and reliable ecosystem support. In contrast, uv is worth considering if you prioritize fast installs, reproducible environments, and clean uninstall behavior, or if you want to streamline workflows for new projects.

In this video course, you’ll compare both tools. To keep this comparison meaningful, you’ll focus on the overlapping features, primarily package installation and dependency management.

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The Real Python Podcast – Episode #280: Considering Fast and Slow in Python Programming

2026-01-16T12:00:00+00:00

How often have you heard about the speed of Python? What's actually being measured, where are the bottlenecks---development time or run time---and which matters more for productivity? Christopher Trudeau is back on the show this week, bringing 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 ]

Intro to Object-Oriented Programming (OOP) in Python

2026-01-13T14:00:00+00:00

Object-oriented programming (OOP) is one of the most significant and essential topics in programming. This course will give you a foundational conceptual understanding of object-oriented programming to help you elevate your Python skills.

You’ll learn how to define custom types using classes and how to instantiate those classes into Python objects that can be used throughout your program.

Finally, you’ll discover how classes can inherit from one another, with a brief introduction to inheritance, enabling you to write maintainable and less redundant Python 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 #279: Coding Python With Confidence: Beginners Live Course Participants

2026-01-09T12:00:00+00:00

Are you looking for that solid foundation to begin your Python journey? Would the accountability of scheduled group classes help you get through the basics and start building something? This week, two members of the Python for Beginners live course discuss their experiences.

[ 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 ]

Tips for Using the AI Coding Editor Cursor

2026-01-06T14:00:00+00:00

Cursor is an AI-powered integrated development environment (IDE) based on the Visual Studio Code codebase. It comes with a multi-agent interface and the Composer model for fast, agentic coding while keeping a familiar editor workflow with project-aware chat, code completion, and inline edits.

In this course, you will:

Understand why Cursor might work for you
Learn how to use different modes & models
See how to run multiple agents at a time
Resolve a tiny merge conflict
Run a project and fix a bug
Learn commands and practice with the 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 ]

The Real Python Podcast – Episode #278: PyCoder's Weekly 2025 Top Articles & Hidden Gems

2026-01-02T12:00:00+00:00

PyCoder's Weekly included over 1,500 links to articles, blog posts, tutorials, and projects in 2025. Christopher Trudeau is back on the show this week to help wrap up everything by sharing some highlights and uncovering a few hidden gems from the pile.

[ 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

Spyder: Your IDE for Data Science Development in Python

Start Using the Spyder IDE

Explore Data With the Variable Explorer

Quiz: Speed Up Python With Concurrency

Quiz: Splitting, Concatenating, and Joining Python Strings

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

Quiz: Your Python Coding Environment on Windows: Setup Guide

Quiz: How to Use Ollama to Run Large Language Models Locally

Quiz: Working With Files in Python

Pydantic AI: Build Type-Safe LLM Agents in Python

Start Using Pydantic AI to Create Agents

Quiz: Create and Modify PDF Files in Python

Quiz: Threading in Python

Working With APIs in Python: Reading Public Data

Quiz: Python Descriptors: An Introduction

Python Gains frozendict and Other Python News for March 2026

Python Releases and PEP Highlights

Python 3.15.0 Alpha 6: Comprehension Unpacking and More

Python 3.14.3 and 3.13.12: Maintenance Releases

PEP 814 Accepted: frozendict Joins the Built-Ins

Quiz: Introduction to Python SQL Libraries

Quiz: Pydantic AI: Build Type-Safe LLM Agents in Python

How to Use the OpenRouter API to Access Multiple AI Models via Python

Prerequisites

Step 1: Connect to OpenRouter’s API

What Does Python's __init__.py Do?

Automate Python Data Analysis With YData Profiling

Building a Report With YData Profiling

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

How to Run Your Python Scripts and Code

What Scripts and Modules Are

How to Run Python Scripts From the Command Line

Using the python Command

Start Building With FastAPI

Python for Loops: The Pythonic Way

Getting Started With the Python for Loop

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

How to Install Python on Your System: A Guide

Windows: How to Check or Get Python

Checking the Python Version on Windows

Write Python Docstrings Effectively

TinyDB: A Lightweight JSON Database for Small Projects

Get Ready to Explore TinyDB

Create Your Database and Documents

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

What Exactly Is the Zen of Python?

In Short: It’s a Humorous Poem Listing Python Philosophies

How Did the Zen of Python Originate?

Should You Obey the Zen of Python?

Improving Your Tests With the Python Mock Object Library

The Real Python Podcast – Episode #283: Improving Your GitHub Developer Experience

Getting Started With Google Gemini CLI

The Real Python Podcast – Episode #282: Testing Python Code for Scalability & What's New in pandas 3.0

Create Callable Instances With Python's .__call__()

The Real Python Podcast – Episode #281: Continuing to Improve the Learning Experience at Real Python

uv vs pip: Python Packaging and Dependency Management

The Real Python Podcast – Episode #280: Considering Fast and Slow in Python Programming

Intro to Object-Oriented Programming (OOP) in Python

The Real Python Podcast – Episode #279: Coding Python With Confidence: Beginners Live Course Participants

Tips for Using the AI Coding Editor Cursor

The Real Python Podcast – Episode #278: PyCoder's Weekly 2025 Top Articles & Hidden Gems

PEP 814 Accepted: `frozendict` Joins the Built-Ins

What Does Python's init.py Do?

Using the `python` Command

Getting Started With the Python `for` Loop

Create Callable Instances With Python's .call()