NumPy Aggregation, Sorting and Linear Algebra in Python (Part 4)
NumPy aggregation along axes, NaN-safe statistics, argmax, sorting and linalg.solve, plus why NumPy and pandas return different standard deviations.
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Python Lambda Function: Where It Helps and When to Use def
A Python lambda function is a one-expression anonymous function. Learn where it helps (sort keys, callbacks), when def wins, and the loop bug it causes.
Lists vs Tuples in Python: Differences and When to Use Each
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.
Python Tuples: Unpacking, Dict Keys and the Trailing-Comma Trap
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.
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.
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.
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.
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.