Python Strings: F-Strings, Methods and the strip() Trap
Python strings in practice: f-strings, slicing, the methods worth memorising, why += loops slow down, and the encoding default that breaks scripts on Windows.
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Regular Expressions in Python, Part 1: Syntax, Functions, Flags
Regular expressions in Python from the ground up: raw strings, core syntax, match vs search vs fullmatch, flags, and five mistakes that silently break patterns.
NumPy Tutorial for Beginners: NumPy Arrays in Python (Part 1)
NumPy tutorial for beginners: what an array is, why it ran about 20 times faster than a list here, how one stray string turns numbers into text, and more.
Advanced Python Regex: Groups, Lookarounds and Substitution (Part 2)
Advanced Python regex with tested output: named groups, lookarounds, re.sub with a function, and why a 24-character input made (a+)+$ run for 10 seconds.
Python Numeric Data Types: int, float, Decimal and When to Use Each
Python numeric data types explained: int, float, Decimal, Fraction and complex, why 0.1 + 0.2 is not 0.3, and how to handle money without rounding errors.
Inheritance and Composition in Python: OOP in Python, Part 2
Inheritance and composition in Python with tested code: when to use each, how super() really works, duck typing, and why deep class hierarchies backfire.
Python Operators: What Each One Returns, and Where It Bites
Python operators explained with outputs for every example, plus why is works for 256 but fails for 257 and why and/or rarely return booleans.
Python Basics: Names, Collections, Control Flow and Comprehensions
Python basics with runnable examples: names and objects, lists, dicts and sets, control flow and comprehensions, plus the traps behind most beginner bugs.
Exception Handling in Python: Catch Narrowly, Log Everything
Exception handling in Python done properly: try, except, else and finally, custom exceptions, chaining and logging, and why a bare except can break Ctrl-C.
Python Functions: Writing User-Defined Functions Worth Reusing
How to write Python functions: arguments, defaults, *args and **kwargs, scope, type hints and docstrings, plus the mutable default bug everyone hits once.
Programming for Data Science: How Much Code Do You Really Need?
Programming for data science: how much SQL and Python you need, what reproducible code looks like, the notebook traps, and where AI assistants help or hurt.
Advanced Plotly Charts: Subplots, Animation and 3D (Plotly Part 2)
Advanced Plotly charts in Python: subplots, dual axes, animation, 3D and heatmaps, plus the axis-range mistake that silently drops points from animations.
Iterators and Generators in Python: Process Files Larger Than RAM
Iterators and generators in Python explained: yield, pipelines that stream big files in flat memory, itertools, and why a generator silently runs only once.