Code Wrangling at its Best
In this comprehensive tutorial, we guide you through creating a simple modal line editor in Python. Starting from basic functionality, we progressively add features like inserting lines before or after a specified line and implementing a yank command. Perfect for those looking to enhance their Python skills with practical text editing capabilities.
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Implementing Regular Expressions (RegEx) from scratch in Python provides a deeper understanding of how RegEx works. This tutorial guides you step-by-step through creating a RegEx engine using finite automata, covering essential RegEx operations, converting RegEx to NFA using Thompson’s Algorithm, and building a full-featured RegEx engine.
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Versioning tools are essential for managing software versions efficiently. Popular integrated development environments (IDEs) like Visual Studio Code, IntelliJ IDEA, and PyCharm offer plugins and built-in tools to handle versioning. These tools help automate version updates, tag releases, and integrate with version control systems like Git. They simplify the process and ensure consistency, reducing the chances of human error.
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Enhance your Python programming skills with the definitive guide to Python 3 decorators. Discover their definition, usage, and implementation, along with insightful examples to propel your code to new heights of functionality and elegance.
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In many programming scenarios, you may encounter situations where you need to dynamically add properties to classes. This can be especially useful in game development, where different game objects may require unique properties based on their specific functionality or state. In this tutorial, we will explore five methods of dynamically adding properties to classes: the crude method, monkey patching, and using decorators, Meta-Classes, and the setattr method. We will illustrate each method using a scenario of an adventure game where we have a Room class and an Item class. This allows our code to be more general.
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Introduction Last time we discussed our mission, built a lexer and tree printer to be used throughout our experiments, and introduced the Recursive decent parser. Parsing mathematical expressions involves interpreting their structure, which can be complex due to the presence of operators with different precedence levels and associativity rules. In this article series, we delve into
In the context of Abstract Syntax Trees (ASTs) and parse trees, the terms “higher” and “lower” precedence, as well as tree “depth,” take on a different meaning due to the way parsers traverse the tree structure. In this context, “higher” precedence refers to nodes that are deeper in the tree, further away from the root. When parsers descend into the tree to evaluate expressions or execute algorithms, they typically start at the root and move downwards towards the leaves. Therefore, nodes that are deeper in the tree, or have a higher depth, are processed first, followed by nodes closer to the root. Conversely, nodes closer to the root have lower depth and are processed later in the parsing or evaluation process. This understanding is crucial for parsers and algorithms that rely on tree traversal to correctly interpret and evaluate expressions or perform other operations on tree structures. I have heard these terms used in reverse and for trees in general, this may be correct. I mention this because it can cause confusion, it is worth clarifying these terms when conversing with others.
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In the world of software development, understanding the differences between Class-Based OOP and Prototype-Based OOP is essential. While Class-Based OOP provides structure and clarity, Prototype-Based OOP offers flexibility and dynamic behavior. Let’s delve deeper into these two programming paradigms and explore their characteristics, examples, and implications in real-world scenarios.
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Unlock the potential of structs in Python and C through our detailed tutorial. Delve into the world of composite data types, understand their applications, and master the art of struct usage in both Python and C programming. Elevate your coding expertise with this insightful guide.
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Discover the world of IEEE 754 Double Precision Floating Point in Python with our in-depth exploration. Dive into the Python struct module, understand the nuances of floating-point representation, and grasp the essentials of numerical computing. Gain hands-on experience with the provided Python code for IEEE 754 implementation, unraveling the complexities of sign bits, exponents, and fractions.
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