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.
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Iterators and generators in Python explained: yield, pipelines that stream big files in flat memory, itertools, and why a generator silently runs only once.
Lists vs tuples in Python beyond 'one is mutable': the real differences in hashability, memory, safety and intent, plus a decision rule that works in practice.
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.
NumPy indexing and slicing explained: why editing a slice changes your original array, when masks and fancy indexing copy instead, and how to avoid both bugs.
NumPy broadcasting explained: the two shape rules, why a (3,1) and a (4,) array give (3,4), and the silent bug that turns a vector sum into a matrix.
How to use Python tuples well: creating them, unpacking, using them as dict keys, named tuples, and the one-element and += gotchas that catch everybody.
The 5 Ps of data science (Problem, People, Process, Platform, Portfolio): the question each forces, the failure it exposes, and why the order matters.
The future of programming languages, using TIOBE, Stack Overflow and GitHub data: why Python and TypeScript lead, types are winning, and what to learn.
Python basics with runnable examples: names and objects, lists, dicts and sets, control flow and comprehensions, plus the traps behind most beginner bugs.
How to install Anaconda on Windows step by step: which PATH checkbox to leave unticked, licence terms, a Jupyter kernel, and fixes for common conda errors.
Data analyst vs data scientist: how daily work, skills, pay and entry routes differ, with one dataset analysed both ways and a rule for which to target first.