High Performance Python: Master Speed, Scale, and Efficiency

Learn the pythonic style that favors explicit, simple code to maximize readability; slice sequences, iterate over lists and data structures, and use block statements and the width statement.

Explore Python slice semantics: start inclusive, end exclusive, with lists, tuples, strings, and bytes, including negative indices, slice assignments, and the creation of new lists.

Master Python slice semantics by using stride to select even indexes, reverse sequences, avoid risks from negative strides, and prefer positive stride with no start or end when possible.

Observe how to use range for looping, generate random 64-bit strings with bit operations, and leverage enumerate to pair indices with items for cleaner iteration of lists.

Explore processing iterators in Python by pairing lists with zip and zip_longest, compare lazy generators in Python 3 versus Python 2, and use izip for efficient parallel iteration.

Learn common Python loop and exception handling mistakes, clarify for-else behavior, and replace else-after-loops with clear returns or helper functions for readability and performance.

Explore try, except, else, and finally in Python to manage file I/O and JSON data, showing how to open, process, and safely close resources with precise exception handling.

Explore context managers and the with statement in Python, using contextlib to create reusable setup and teardown with yield, decorators, and temporary logging level changes via try/finally.

Explore Python's sequence syntax, from comprehensions to generator expressions and generator functions, and avoid common iterator pitfalls while learning when to prefer generator expressions over comprehensions.

Explore using list comprehensions to map and filter data efficiently, compare them with map and filter approaches, and illustrate dictionary and set comprehensions.

Master list comprehensions for multi-level looping, flattening matrices, and preserving structure, while applying the two-expression rule to keep code readable and maintainable.

Use generator expressions to avoid materializing large lists and enable memory-efficient, lazy processing of huge inputs, composing generators for on-demand calculations like line lengths and square roots in Python.

Convert list-based word indexing to generators to yield results on demand, reducing memory usage. Learn streaming file input with a generator that yields word indexes line by line.

Be defensive by recognizing that iterators can be exhausted; implement a container that follows the iterator protocol to enable safe, multiple passes for the normalization function on large iterables.

Explore how Python functions break large programs into smaller pieces, boosting readability and reusability, and learn closures, statefulness, and keyword arguments, optional arguments, and keyword-only arguments to reduce bugs.

Explore how closures interact with variable scope to control sort order via a key function, and learn patterns for nonlocal usage, stateful closures, and Python 2 workarounds.

Explore how Python uses first-class functions as hooks to customize behavior, leveraging key functions, lambdas, and callable objects with default dicts and stateful closures.

Use star args to make the last positional parameter optional, reducing visual noise, but they convert inputs to a tuple and can exhaust memory; for future extension, prefer keyword-only arguments.

Keyword arguments improve readability, enable defaults, and allow backwards-compatible extensions in Python functions. Always pass optional parameters by keyword, never as positional, to avoid ambiguity.

Learn how keyword-only arguments improve clarity and safety in Python, using safe division with ignore overflow and ignore zero division, plus star args and kwargs for Python 2 compatibility.

Show why dynamic defaults misbehave when they hold non static values like the current time, and teach using None as a default to create fresh mutable defaults with clear documentation.

Learn how Python's classes express program behavior with objects, and how to grow functionality over time, while avoiding common object oriented pitfalls and writing flexible classes for changing requirements.

Refactor complex bookkeeping from dictionaries into dedicated classes, replacing named tuples with clear abstractions for by-subject grade books that support weighted scores.

Show how Python favors plain attributes over getters and setters in a resistor example, then use properties for safe, validated behavior without surprising side effects.

Explains public, private, and protected attributes in Python, shows how name mangling affects access, and recommends using protected attributes to preserve extensibility.

Discover how class method polymorphism enables generic object construction for inputs and workers. Reduce glue code by using config-driven class methods to connect components.

Discover how Python enables concurrent and parallel programming using system calls and subprocesses. Explore built-in facilities like threads, locks, and queues, and why true parallelism remains challenging.

Learn to manage child processes with the subprocess module, pipe input and output, run parallel tasks, and use communicate for results.

Demonstrate how Python's global interpreter lock limits cpu-bound threads and why threads enable concurrency for blocking io. Show how releasing the global interpreter lock during io calls enables io-bound parallelism.

Learn how the Python global interpreter lock can still allow data races when threads increment a shared counter; use threading.Lock with a with block to ensure atomic updates.

Explore high-performance Python concurrency by building a pipelined, thread-based workflow using closable queues, backpressure with buffer sizes, and task tracking to process download, resize, and upload stages in parallel.

Learn how to use concurrent.futures to compare thread-based and process-based parallelism, explain the Python GIL limits, and identify when multiprocessing accelerates isolated, high-leverage tasks.

Move your Python programs from development to production and harden them for reliability across scenarios, using built-in modules for testing, debugging, and optimization.

Use virtual environments to isolate Python dependencies and reproduce exact setups with pip freeze and a requirements.txt for collaboration.

Understand how to gain confidence in Python programs by writing tests with the unittest module. Learn unit tests, test cases, setup and teardown, and the value of integration tests.

Explore interactive debugging with pdb to diagnose a bubble sort bug, using breakpoints, set trace, and stepping to inspect locals and stack frames and identify an off-by-one error.

Profile before optimizing by identifying slow hotspots with Python’s built-in profilers, distinguish total versus cumulative time with stats, and apply bisect left to speed up insertion sort.

Learn to use tracemalloc to pinpoint memory leaks by taking memory snapshots, comparing allocations, and revealing exact stack traces of the largest memory users in Python 3.

Review Python practices for statements and expressions, and highlight the power of comprehensions and generators. Explore object oriented design with classes, functions and arguments, and concurrent, robust programs.