Python
Documentation lies. Not because developers intend to mislead, but because code evolves and docstrings do not. A function gets refactored, its return format changes, and somewhere in the codebase an outdated >>> example claims it returns a list when it now returns a generator. The user copies that
Python
Testing async Python code requires an event loop to run coroutines. pytest-asyncio and anyio provide that infrastructure and make async tests look nearly identical to synchronous ones. The Problem with Async Tests You can't call await outside a coroutine, so this doesn't work: # Won'
Temporal
Temporal activities are where your workflow touches the outside world — HTTP calls, database writes, email sends. Testing workflows in isolation means replacing activities with mocks that return predictable values without making real external calls. Why Mock Activities Activities in Temporal are the "impure" part of the system — they
Fuzz Testing
Hypothesis is the most accessible entry point to property-based and fuzz testing for Python developers. Unlike AFL++ or libFuzzer which require C/C++ code and complex toolchains, Hypothesis works with any Python function, integrates with pytest, and produces minimal counterexamples automatically. It's been finding real bugs in
Testing
Testing pandas code requires different patterns than testing application code. DataFrames are complex objects—you rarely want to assert on the entire DataFrame at once. Instead, assert on specific columns, row counts, data types, and statistical properties. Use pytest fixtures for reusable test data, pandas.testing for DataFrame comparisons, and
Testing
Great Expectations (GX) is a Python library for defining and running data quality assertions against DataFrames, files, and databases. You define Expectations (e.g., "this column should be non-null"), group them into Expectation Suites, and run them via Checkpoints. Failed Expectations produce HTML Data Docs reports showing
Machine Learning
scikit-learn pipelines combine preprocessing steps and models into a single object. They're elegant for training and inference, but they introduce testing challenges: how do you test a transformer in isolation? How do you verify that the pipeline doesn't leak information? How do you catch regressions
Embedded Systems
Embedded systems testing has a reputation for being painful — manual probe sessions, proprietary test frameworks, and tests that only run on physical hardware. Python and pytest change the equation. You can write expressive, maintainable tests that run against real hardware or simulators, integrate with CI, and give embedded teams the
Testing
Hardcoded test data is one of the most persistent sources of flaky tests and maintenance burden in software projects. When you write name = "John Doe" in a test fixture, you've coupled your test to a specific string that means nothing. When you write email = "test@
Testing
Behave is Python's most established BDD framework. It reads Gherkin .feature files, maps each step to a Python function, and executes them against your application. If you work in Python and want to practise behaviour-driven development, Behave is the natural starting point. This tutorial takes you from
Integration Testing
Mocking database calls in Python tests creates a false sense of security. testcontainers-python spins up real PostgreSQL, Redis, and MongoDB containers via Docker during your pytest run, giving you genuine integration coverage without managing external services. Key Takeaways testcontainers-python integrates with pytest through fixtures. Define containers in conftest.
Testing
Unit testing is the practice of verifying that individual functions and classes behave correctly in isolation. For Python projects, it is the first line of defense against regressions — every time you change code, your unit tests tell you immediately whether something broke. This guide covers the two dominant frameworks, unittest