Python for backend, automation, and data roles, including the runtime behaviour that surprises candidates who only know the syntax.
12 published across 7 topics.
Python fundamentals60 short answers on one page, for revising rather than studying.Data model, mutability, scoping, comprehensions, and the truthiness and copying gotchas.
A closure captures the variable, not the value it held at the time. All three lambdas share one cell for the loop name, the loop leaves that cell holding its final value, and every call reads it then. Bind per iteration with a default argument or a factory to fix it.
Default expressions are evaluated once while the def statement runs and the resulting objects are stored on the function object in __defaults__, so every call that omits the argument shares the same list or dict and sees every prior mutation.
Dunder methods, MRO and multiple inheritance, descriptors, dataclasses, and properties.
Implement the dunders that opt your type into a protocol, treating repr, eq and hash as one decision, and let a dataclass generate the boilerplate when the fields are the identity. super() follows the C3 linearisation of type(self), so it calls the next class in the MRO rather than your own base.
A descriptor is a class attribute defining __get__, and optionally __set__ or __delete__, that intercepts attribute access on instances. property is one. Whether it defines __set__ decides whether it beats the instance dictionary, which is the whole of attribute-lookup precedence.
Closures, laziness, context managers, and writing composable higher-order utilities.
A generator produces items on demand and keeps only the current item plus its suspended frame, so memory stays flat as the input grows; it is the wrong tool when you need a length, indexing, or more than one pass, and when per-item resume overhead outweighs the space saved.
Threads versus processes versus asyncio, what the GIL actually blocks, and choosing per workload.
The GIL lets only one thread execute Python bytecode at a time, but it is released around blocking I/O and inside many C extensions, so threads still scale I/O-bound work; use processes for pure-Python CPU work and asyncio for very high connection counts.
With gather they keep running unsupervised and their failures may surface as an unretrieved-exception warning. A TaskGroup, added in Python 3.11, cancels the siblings, waits for them to finish unwinding, and raises an ExceptionGroup you handle with except*.
Reference counting, garbage collection, profiling, and where interpreter overhead dominates.
CPython frees an object the instant its reference count reaches zero, so steady growth is usually something still holding a reference rather than a leak. The cycle collector reclaims only reference cycles. Measure with tracemalloc first, then use __slots__ or generators to cut per-object and peak cost.
ORM behaviour and N+1, middleware, dependency injection, async endpoints, and validation.
An N+1 happens when the ORM follows a relation lazily once per row; select_related fixes forward foreign keys with a SQL join, prefetch_related issues one extra query per relation and joins in Python. An async endpoint gets slower when it contains a blocking call, because that stalls the whole event loop.
The job was enqueued inside an open transaction, so the row was invisible to the worker's connection until commit, and a fast worker won the race. Register the side effect with transaction.on_commit so it fires after the outermost atomic block commits, and never if it rolls back.
pytest fixtures and parametrisation, mocking and patching boundaries, and coverage honestly read.
A patch must replace the name in the module under test, because a from-import binds its own reference that the original module never consults again. Fixture scope is a shared lifetime, parametrisation belongs on the inputs rather than the assertions, and coverage records execution, not verification.
Make it reproducible before you make it pass. Pin the ordering seed, then work through the sources of non-determinism the test is exposed to - wall-clock time, global random state, hash-dependent iteration, real network calls and shared resources - and inject each one so the test controls it.