How do I search SQLite text for a pattern with LIKE?
Use SQLite LIKE with % and _ to filter text, then check the matching rows and understand its default case behavior.
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How can I add type hints to a small function's parameters and return value?
See where parameter and return annotations go, how to choose ones that match a function’s behavior, and what they do not enforce.
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How can I use unittest cleanup to prevent one test affecting another?
Capture the original state, register its restoration with addCleanup, and use a checklist to keep temporary test changes from leaking.
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What should a context manager do when the managed block raises an exception?
Learn how to run cleanup on normal and exceptional exits, while allowing errors to propagate unless suppression is intentional.
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When should I use a context manager in a small Python function?
Learn when a small function benefits from scoped setup and cleanup, with a before-and-after example and a practical checklist.
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When should I use a relative import in a small Python package?
Learn when package-relative imports are useful, how they differ from absolute imports, and why launch method matters.
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How can I validate a dataclass's fields without hiding construction errors?
Use __post_init__ for a clear construction-time invariant check, then test both valid construction and the specific error for invalid fields.
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When should I make a dataclass immutable?
Choose `frozen=True` for value-like records whose fields should not be reassigned; use a mutable dataclass when field updates are part of the design.
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Why is a mutable dataclass default rejected, and what pattern should I use?
Learn why direct mutable dataclass defaults can be shared or rejected, and use a factory to create a fresh value when needed.
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How can I organize a small program into modules without circular imports?
Learn to spot an import cycle, move genuinely shared code to a lower-level module, and check the resulting dependency direction.
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How can I test a function that depends on the current time?
Separate the clock from the time-based calculation, then test the calculation with a fixed time and clear boundary cases.
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Why does a unittest pass alone but fail with the whole test suite?
Trace order-dependent and shared-state failures, then reset test data so each unittest behaves independently.
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How do I run one unittest test while debugging a function?
Run the test for the function you’re investigating, then use unittest discovery to return to the broader suite.
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When should I use assert in a small Python program?
Learn when `assert` is useful for internal assumptions, why it should not validate required inputs, and how to choose an explicit exception instead.
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When should a function raise an exception instead of returning an error value?
Choose a signaling style by asking whether the outcome is expected, what the caller should do, and whether the result is unambiguous.
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How can I test several cases for the same function without duplicating test logic?
Put input-and-expected-result pairs in a list, then use one loop and a named subtest for each case so failures are easier to identify.
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How can I avoid a function with too many unrelated responsibilities?
Learn to spot distinct jobs inside a function, split only where it improves clarity, and compare a worked before-and-after example.
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How can I design a function that validates input and reports a useful error?
See a C# validation function that checks its input contract and reports which argument is invalid and why.
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Why does code after return never run?
Learn how `return` exits a function, spot unreachable statements, and compare a minimal C# example with a corrected version.
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How do I decide what a function should return?
Choose a return style that makes the function’s result or action clear, with small C# examples and a reusable decision checklist.
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When should I use positional-only, keyword-only, and ordinary parameters?
Compare Python parameter styles with a single signature, allowed and rejected calls, and a practical rule for choosing among them.
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How can I check for duplicate keys while parsing a JSON object?
Use Python’s JSON object-pairs hook to reject repeated keys before they disappear into a dictionary, while keeping valid input as a usable Python value.
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How can I convert a small JSON array of objects to CSV?
Choose CSV columns explicitly, check every JSON object for missing or extra keys, and write compatible records to a separate file with a clear issue report.
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How can I convert a small CSV file to JSON without changing the source?
Convert a small CSV into a separate JSON file, keep CSV values as text, and check the saved output against the records read from the source.
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