Have you wondered how Python manages memory? How are your variables stored in memory, and when do they get deleted? This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article on Python memory management, we also talk about another article about creating even and non-even spaced arrays in Python with np.linspace.

We share an article titled “The Unholy Way of Using Virtual Environments”. This leads to a discussion on how to structure the directories around a virtual environment.

We also cover several other articles and projects from the Python community including, storing a list in an int, why you should use an ORM (Object Relational Manager), unraveling not in Python, an open-source Python fuzzer, and Python static website generators.

Course Spotlight: How Python Manages Memory

Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

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np.linspace(): Create Evenly or Non-Evenly Spaced Arrays – In this tutorial, you’ll learn how to use NumPy’s np.linspace() effectively to create an evenly or non-evenly spaced range of numbers. You’ll explore several practical examples of the function’s many uses in numerical applications.

The Unholy Way of Using Virtual Environments – If you’ve used virtual environments before, you may have created a venv/ folder inside the root directory of your project. This is standard, but has some downsides. Have you every thought about reversing this and putting your project inside your venv/ folder?

Storing a list in an int – For a fun exercise, learn how you can leverage Python’s unlimited integer precision to encode and store lists of any size as a single integer. Because, why not?

Why Should You Use an ORM (Object Relational Mapper)? – Budding web developers learning Model-View-Controller frameworks are taught that they should use an Object Relational Mapper (ORM) to interface with their databases. But the “why” is often brushed aside or omitted entirely, leaving a fledgling programmer with burning questions like “What are ORMs, anyway?” and “What problems do they solve?”

Unravelling not in Python – In the next blog post in his series about Python’s syntactic sugar, Brett Cannon tackles what would seem to be a very simple bit of syntax, but which actually requires diving into multiple layers to fully implement: not.

How Python Manages Memory – Get ready for a deep dive into the internals of Python to understand how it handles memory management. By the end of this course, you’ll know more about low-level computing, understand how Python abstracts lower-level operations, and find out about Python’s internal memory management algorithms.

Announcing the Atheris Python Fuzzer – Get started with fuzzing, a technique for automatically finding bugs in Python code by repeatedly trying various inputs to your program, using Google’s newly open-sourced Python fuzzer called Atheris.

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Have you started to use generators in Python? Are you unsure why you would even use one over a regular function? How do you use the special “send” method and the “yield from” syntax? This week on the show, we have Reuven Lerner to talk about his PyCon Africa 2020 talk titled “Generators, coroutines, and nanoservices.”

Reuven helps developers around the world become more fluent in Python. We talk about some of his teaching techniques and also how he continues to learn. Reuven is a believer in the continued practice of Python through exercises. We discuss his book “Python Workout” and his Weekly Python Exercise courses.

Course Spotlight: Python Generators 101

In this step-by-step course, you’ll learn about generators and yielding in Python. You’ll create generator functions and generator expressions using multiple Python yield statements. You’ll also learn how to build data pipelines that take advantage of these Pythonic tools.

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If you’re coming to Python from a different language, you may not know about a useful tool for working with loops, Python’s built-in enumerate function. This week on the show, David Amos is here, and he has brought another batch of PyCoder’s Weekly articles and projects.

Along with the Real Python article covering the details of the enumerate function, we also talk about another article about constructing Python graphical user interface elements in PyQt.

David shares a couple of resources for data scientists, including an article about skills not taught in data science boot camps, and a project for creating synthetic data.

We also cover several other articles and projects from the Python community including, an update about youtubedl, hunting for malicious packages on PyPI, using Python’s bisect module, 73 examples to help you master f-strings, and game programming in Jupyter notebooks.

Course Spotlight: Formatting Python Strings

In this course, you’ll see two items to add to your Python string formatting toolkit. You’ll learn about Python’s string format method and the formatted string literal, or f-string. You’ll learn about these formatting techniques in detail and add them to your Python string formatting toolkit.

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The youtube-dl Repository Has Been Restored on GitHub With Help From the Electronic Frontier Foundation

Python enumerate(): Simplify Looping With Counters – Once you learn about for loops in Python, you know that using an index to access items in a sequence isn’t very Pythonic. So what do you do when you need that index value? In this tutorial, you’ll learn all about Python’s built-in enumerate(), where it’s used, and how you can emulate its behavior.

Hunting for Malicious Packages on PyPI – Jordan Wright installed every package on PyPI to look for malicious content. And he didn’t just inspect code, he actually ran the packages. Brave soul! Learn how he set-up this project and what he learned on his adventure.

Using Python’s bisect module – Python’s bisect module has tools for searching and inserting values into sorted lists. It’s one of his “batteries-included” features that often gets overlooked, but can be a great tool for optimizing certain kinds of code.

73 Examples to Help You Master Python’s f-Strings – f-Strings might be one of the most beloved features in Python 3.6+. Here are 73 examples of how to use f-strings to improve your Python code.

10 Python Skills They Don’t Teach in Bootcamp – Here are ten practical and little-known pandas tips to help you take your skills to the next level.

Python and PyQt: Creating Menus, Toolbars, and Status Bars – In this step-by-step tutorial, you’ll learn how to create, customize, and use Python menus, toolbars, and status bars for creating GUI applications using PyQt.

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One of the best ways to learn something well is to teach it. This week on the show, we have Kelly Schuster-Paredes and Sean Tibor from the Teaching Python podcast.

Sean and Kelly teach middle school students Python and share their art and science of teaching Python on their podcast. They wanted to come on the show to talk about the Real Python articles, quizzes, and other resources they use when teaching their students.

We also talk about teaching students how to research topics and use things like advanced search with Google. We discuss using cloud-based tools like collaborative notebooks and some of the core Python concepts students need for a solid foundation.

Kelly and Sean also talk about how the changes to teaching over the past year have had some unexpected benefits. They also talk about a few recent guests and topics covered on their podcast.

Course Spotlight: Basic Data Types in Python

In this course, you’ll learn the basic data types that are built into Python, like numbers, strings, and Booleans. You’ll also get an overview of Python’s built-in functions.

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Are you interested in learning more about Natural Language Processing? Have you heard of sentiment analysis? This week on the show, Kyle Stratis returns to talk about his new article titled, Use Sentiment Analysis With Python to Classify Movie Reviews. David Amos is also here, and all of us cover another batch of PyCoder’s Weekly articles and projects.

Kyle discusses an article about distance metrics for machine learning. David shares a Real Python article about Python signal processing and Fourier transforms with scipy.fft. We also cover several other articles and projects from the Python community including, simulating real-world processes in Python with SimPy, working with Microsoft Excel using Python and OpenPyXL, why running code during import is a bad idea, what I wish I knew as a junior dev, the Raspberry Pi 400 personal computer, dynamic sky replacement and harmonization in videos with SkyAR.

Course Spotlight: Simulating Real-World Processes in Python With SimPy

In this step-by-step course, you’ll see how you can use the SimPy package to model real-world processes with a high potential for congestion. You’ll create an algorithm to approximate a complex system, and then you’ll design and run a simulation of that system in Python.

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Use Sentiment Analysis With Python to Classify Movie Reviews – In this tutorial, you’ll learn about sentiment analysis and how it works in Python. You’ll then build your own sentiment analysis classifier with spaCy that can predict whether a movie review is positive or negative.

OpenPyXL: Working with Microsoft Excel Using Python – Ah, Excel. Everyone loves to hate it. But let’s face it. Excel is one of the most popular pieces of software ever written. But you love Python, not Excel, which is why you might want to learn OpenPyXL.

An Illustration of Why Running Code During Import Is a Bad Idea (And How It Happens Anyway) – Code that runs when a module is imported is usually a code smell. But sometimes there’s no way around it.

Distance Metrics for Machine Learning – Many machine learning algorithms can be summarized as transforming data to n-dimensional vectors and computing similarity between points by means of some distance metric. This article explores four of these metrics—the Euclidean, Manhattan, Minkowski, and Hamming distances—and how to compute them with Python.

What I Wish I Knew as a Junior Dev – Some of these are things even senior devs need to be reminded of sometimes!

Fourier Transforms With scipy.fft: Python Signal Processing – In this tutorial, you’ll learn how to use the Fourier transform, a powerful tool for analyzing signals with applications ranging from audio processing to image compression. You’ll explore several different transforms provided by Python’s scipy.fft module.

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So you built a web application in Python. Now how are you going to authorize users? Security goes beyond authentication. Who gets to do what, where, and when? This week on the show, we have Sam Scott, chief technology officer from Oso. Oso is an open-source policy engine for authorization that you embed in your application.

Sam talks about the typical security and authorization challenges developers face. He discusses building an engine on top of your existing Flask or Django app. We cover the concept of policies, business logic, and some common paradigms.

Course Spotlight: Exploring HTTPS and Cryptography in Python

In this course, you’ll gain a working knowledge of the various factors that combine to keep communications over the Internet safe. You’ll see concrete examples of how to keep information secure and use cryptography to build your own Python HTTPS application.

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Are you ready to move beyond flat files for your data in Python? Maybe you’re not sure where to start with databases and SQL. This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We cover a Real Python article about managing data with SQLite and SQLAlchemy.

David explores the intricacies of using the modulo operator (%). We also cover several other articles and projects from the Python community including, how to shoot yourself in the foot with python, exploring fractals on a cloud computer, the DMCA takedown request for youtube-dl, python for feature film, an online multiplayer text-based game framework, and a sorting algorithms visualizer.

Course Spotlight: Playing and Recording Sound in Python

In this course, you’ll learn about libraries that can be used for playing and recording sound in Python, such as PyAudio and python-sounddevice. You’ll also see code snippets for playing and recording sound files and arrays, as well as for converting between different sound file formats.

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How to Shoot Yourself in the Foot With Python, Part 1 – If you’re new to Python, you might find yourself confused by some of the situations described in this article. Learn about five mistakes you could make, why they happen, and how to fix them.

Data Management With Python, SQLite, and SQLAlchemy – In this tutorial, you’ll learn how to store and retrieve data using Python, SQLite, and SQLAlchemy as well as with flat files. Using SQLite with Python brings with it the additional benefit of accessing data with SQL. By adding SQLAlchemy, you can work with data in terms of objects and methods.

Exploring Fractals on a Cloud Computer – Fractals might be some of the most interesting mathematical structures to study and to visualize. Learn what fractals are and how to create beautiful fractal animations with Python.

The youtube-dl GitHub Repo Has Received a DMCA Takedown Request From the RIAA

Python Modulo in Practice: How to Use the % Operator – In this tutorial, you’ll learn about the Python modulo operator (%). You’ll look at the mathematical concepts behind the modulo operation and how the modulo operator is used with Python’s numeric types. You’ll also see ways to use the modulo operator in your own code.

Python For Feature Film – A look into how Python is used to bring your favorite movies to the big screen.

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You probably have heard of the bestselling Python book, “Automate the Boring Stuff with Python.” What are the next steps after starting to dabble in the Python basics? Maybe you’ve completed some tutorials, created a few scripts, and automated repetitive tasks in your life. This week on the show, we have author Al Sweigart to talk about his new book, “Beyond the Basic Stuff with Python: Best Practices for Writing Clean Code.”

We discuss several topics covered in his new book, including using the command line, setting environment variables, formatting code, naming, and starting with version control. We talk about learning Python by creating games and highlight a couple of Python myths. I also ask Al about his earlier books, and about his idea of creating a curriculum around conference talks.

Course Spotlight: Unicode in Python: Working With Character Encodings

In this course, you’ll get a Python-centric introduction to character encodings and Unicode. Handling character encodings and numbering systems can at times seem painful and complicated, but this guide is here to help with easy-to-follow Python examples.

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Do you have gaps in your Python learning path? If you’re like me, you may have followed a completely random route to learn Python. This week on the show, David Amos is here to talk about the release of the Real Python book, “Python Basics: A Practical Introduction to Python 3”. The book is designed not only to get beginners up to speed but also to help fill in the gaps many intermediate learners may still have.

David has been working on the book for the last two years, and we dive into all the resources that come with it. These include code challenges, quizzes, and multiple projects that are designed to help you cement your learning. We also discuss the people and processes involved in creating, reviewing, and updating the book.

Spotlight: Python Basics: A Practical Introduction to Python 3

Go from beginner to intermediate in Python with this complete curriculum, up-to-date for Python 3.9. Python Basics includes exercises, interactive quizzes, and sample projects, so you’ll always know what to focus on next in order to build a strong Python foundation.

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The Python return statement is such a fundamental part of writing functions. Is it possible you missed some best practices when writing your own return statements? This week on the show, David Amos returns with another batch of PyCoder’s Weekly articles and projects. We also talk functional programming again with an article on the Python map function and processing iterables without a loop.

We cover several other articles and projects from the Python community including, interactive data visualization with Pygal, everything you need to know about namedtuples, PEP 638 syntactic macros, python for kids, the new Nvidia Jetson board, and a reinforcement learning project named football.

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Interactive Data Visualization in Python With Pygal – Pygal is an overlooked library for creating interactive plots that can be turned into SVGs with an optimal resolution for printing or displaying on webpages. Learn how it works in this introductory tutorial.

Python’s map(): Processing Iterables Without a Loop – In this step-by-step tutorial, you’ll learn how Python’s map() works and how to use it effectively in your programs. You’ll also learn how to use list comprehension and generator expressions to replace map() in a Pythonic and efficient way.

Everything You Need to Know About Python’s NamedTuples – Are you using NamedTuple in your code? If you aren’t, learn what they are and why you should consider using them in this comprehensive tutorial.

PEP 638: Syntactic Macros – This brand new PEP, which is still in draft mode, proposes adding support for syntactic macros to Python. Syntactic macros are compile-time functions that extend the language’s syntax without adding any new complexity to the language as a whole.

The Python return Statement: Usage and Best Practices – In this step-by-step tutorial, you’ll learn how to use the Python return statement when writing functions. Additionally, you’ll cover some good programming practices related to the use of return. With this knowledge, you’ll be able to write readable, robust, and maintainable functions in Python.

Python for Kids – In this ten part series, senior software engineer Kevin Thomas presents a kid-friendly comprehensive Python development tutorial utilizing a micro:bit development board. The GitHub repo contains all of the sample code as well as links to each tutorial in the series on LinkedIn. The first seven parts are published and the last three are coming soon!

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Build a Face Recognition System With the Nvidia Jetson Nano 2GB and Python – The Nvidia Jetson Nano is a single board computer similar to a Raspberry Pi. The Jetson Nano really packs a punch, however, thanks to its onboard Nvidia Maxwell GPU.

football: Reinforcement Learning Environment Where Agents Learn to Play Football

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