Python

Quiz: Using Python’s pip to Manage Your Projects’ Dependencies

Interactive Quiz ⋅ 8 QuestionsBy Philipp Acsany Share In this quiz, you’ll test your understanding of Python’s standard package manager, pip. You’ll revisit the concepts behind pip, important commands, and how to install packages. The quiz contains 8 questions and there is no time limit. You’ll get 1 point for each correct answer. At the end of the quiz, you’ll receive a total score. The maximum score is 100%. Good luck! « Browse All Python Quizzes    

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Python

Python 3.13 Preview: Free Threading and a JIT Compiler

Although the final release of Python 3.13 is scheduled for October 2024, you can download and install a preview version today to explore the new features. Notably, the introduction of free threading and a just-in-time (JIT) compiler are among the most exciting enhancements, both designed to give your code a significant performance boost. Check out what’s new in the Python changelog for a complete list of the upcoming features and improvements. This document contains a quick summary of the release […]

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Python

Let’s build and optimize a Rust extension for Python

If your Python code isn’t fast enough, you have many options for compiled languages to write a faster extension. In this article we’ll focus on Rust, which benefits from: Modern tooling, including a package repository called crates.io, and built-in build tool (cargo). Excellent Python integration and tooling. The Rust package (they’re known as “crates”) for Python support is PyO3. For packaging you can use setuptools-rust, for integration with existing setuptools projects, or for standalone extensions you can use Maturin. Memory- […]

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Python

Customizing VS Code Through Color Themes

A well-designed coding environment not only enhances your focus and productivity but also makes coding sessions more enjoyable. In this Code Conversation, your instructor Philipp Acsany will guide you step-by-step through the process of finding, installing, and adjusting color themes in VS Code. You’ll explore the various options available in VS Code and learn how to make fine adjustments to create a setup that suits your personal preferences. In this video course, you’ll: Learn about Themes in VS Code Find […]

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Python

It’s time to stop using Python 3.8

Upgrading to new software versions is work, and work that doesn’t benefit your software’s users. Users care about features and bug fixes, not how up-to-date you are. So it’s perhaps not surprising how many people still use Python 3.8. As of September 2024, about 14% of packages downloaded from PyPI were for Python 3.8. This includes automated downloads as part of CI runs, so it doesn’t mean 3.8 is used in 14% of applications, but that’s still 250 million packages […]

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Python

When should you upgrade to Python 3.13?

Python 3.13 will be out October 1, 2024—but should you switch to it immediately? And if you shouldn’t upgrade just yet, when should you? Immediately after the release, you probably didn’t want to upgrade just yet. But from December 2024 and onwards, upgrading is definitely worth trying, though it may not succeed. To understand why, we need to consider Python packaging, the software development process, and take a look at the history of past releases. The problems with a new […]

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Machine Learning

Comparing Scikit-Learn and TensorFlow for Machine Learning

Comparing Scikit-Learn and TensorFlow for Machine LearningImage by Editor | Ideogram Choosing a machine learning (ML) library to learn and utilize is essential during the journey of mastering this enthralling discipline of AI. Understanding the strengths and limitations of popular libraries like Scikit-learn and TensorFlow is essential to choose the one that adapts to your needs. This article discusses and compares these two popular Python libraries for ML under eight criteria. Scope of Models and Techniques Let’s start by highlighting […]

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Machine Learning

Filling the Gaps: A Comparative Guide to Imputation Techniques in Machine Learning

In our previous exploration of penalized regression models such as Lasso, Ridge, and ElasticNet, we demonstrated how effectively these models manage multicollinearity, allowing us to utilize a broader array of features to enhance model performance. Building on this foundation, we now address another crucial aspect of data preprocessing—handling missing values. Missing data can significantly compromise the accuracy and reliability of models if not appropriately managed. This post explores various imputation strategies to address missing data and embed them into our […]

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Machine Learning

Automating Data Cleaning Processes with Pandas

Automating Data Cleaning Processes with Pandas Few data science projects are exempt from the necessity of cleaning data. Data cleaning encompasses the initial steps of preparing data. Its specific purpose is that only the relevant and useful information underlying the data is retained, be it for its posterior analysis, to use as inputs to an AI or machine learning model, and so on. Unifying or converting data types, dealing with missing values, eliminating noisy values stemming from erroneous measurements, and […]

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