Differences Between Python’s Mutable and Immutable Types

As a Python developer, you’ll have to deal with mutable and immutable objects sooner or later. Mutable objects are those that allow you to change their value or data in place without affecting the object’s identity. In contrast, immutable objects don’t allow this kind of operation. You’ll just have the option of creating new objects of the same type with different values. In Python, mutability is a characteristic that may profoundly influence your decision when choosing which data type to […]

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Data Formulator: Exploring how AI can help analysts create rich data visualizations 

Transforming raw data into meaningful visuals, such as charts, is key to uncovering hidden trends and valuable insights, but even with advances in AI-powered tools, this process remains complex. Integrating AI into the iterative nature of the data visualization process is particularly challenging, as data analysts often struggle to describe complicated tasks in a single text prompt while lacking the direct control of traditional tools. This highlights the need for smarter, more intuitive solutions that combine AI’s  

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Python 3.13: Cool New Features for You to Try

Python 3.13 will be published on October 1, 2024. This new version is a major step forward for the language, although several of the biggest changes are happening under the hood and won’t be immediately visible to you. In a sense, Python 3.13 is laying the groundwork for some future improvements, especially to the language’s performance. As you read on, you’ll learn more about the background for this and dive into some new features that are fully available now. In […]

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Abstracts: September 30, 2024

MARTIN GRAYSON: Pleasure, thank you.  DANIELA MASSICETI: Thanks very much, Amber. Nice to be here.  TINGLE: So, Daniela, let’s start with a Find My Things overview. What is it, how does it work, and who’s it for?  MASSICETI: I think the best way I can describe Find My Things is a personalizable object recognizer. So when we think about object recognizers in the past, they’ve, kind of, been what I would call generic object recognizers. So they can only really […]

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Navigating Missing Data Challenges with XGBoost

XGBoost has gained widespread recognition for its impressive performance in numerous Kaggle competitions, making it a favored choice for tackling complex machine learning challenges. Known for its efficiency in handling large datasets, this powerful algorithm stands out for its practicality and effectiveness. In this post, we will apply XGBoost to the Ames Housing dataset to demonstrate its unique capabilities. Building on our prior discussion of the Gradient Boosting Regressor (GBR), we will explore key features that differentiate XGBoost from GBR, […]

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Quiz: Python 3.13: Free Threading and a JIT Compiler

Interactive Quiz ⋅ 16 QuestionsBy Bartosz Zaczyński Share In this quiz, you’ll test your understanding of the new features in Python 3.13. By working through this quiz, you’ll revisit how to compile a custom Python build, disable the Global Interpreter Lock (GIL), enable the Just-In-Time (JIT) compiler, determine the availability of new features at runtime, assess the performance improvements in Python 3.13, and make a C extension module targeting Python’s new ABI. The quiz contains 16 questions and there is […]

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Quiz: Python 3.13: Cool New Features for You to Try

Interactive Quiz ⋅ 9 QuestionsBy Geir Arne Hjelle Share In this quiz, you’ll test your understanding of Python 3.13: Cool New Features for You to Try. By working through this quiz, you’ll review the key updates and improvements in this version of Python. The quiz contains 9 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! […]

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Quiz: Syntactic Sugar: Why Python Is Sweet and Pythonic

Interactive Quiz ⋅ 10 QuestionsBy Leodanis Pozo Ramos Share Test your understanding of Python’s most common pieces of syntactic sugar and how they make your code more Pythonic and readable. Take this quiz after reading our Syntactic Sugar: Why Python is Sweet and Pythonic tutorial. The quiz contains 10 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%. […]

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Boosting Over Bagging: Enhancing Predictive Accuracy with Gradient Boosting Regressors

Ensemble learning techniques primarily fall into two categories: bagging and boosting. Bagging improves stability and accuracy by aggregating independent predictions, whereas boosting sequentially corrects the errors of prior models, improving their performance with each iteration. This post begins our deep dive into boosting, starting with the Gradient Boosting Regressor. Through its application on the Ames Housing Dataset, we will demonstrate how boosting uniquely enhances models, setting the stage for exploring various boosting techniques in upcoming posts. Let’s get started. Boosting […]

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