# Python Dev > Use this skill whenever writing, debugging, or reviewing Python code. Triggers include any Python script creation, code refactoring, debugging Python errors, package management with pip/conda, virtual environment setup, test writing, or when the user mentions .py files or Python-specific tools/libraries. Apply these patterns for code structure, logging, error handling, and architectural preferences. - Skill: `tomevault-io/python-dev` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/python-dev` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/python-dev/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/python-dev --- # Dependencies If you need to install, update, or otherwise modify Python dependencies, use the `python-dependencies` skill. # Python version Assume you are using Python 3.10 or greater; older versions have reached end of life. This means you can safely use the following: - Improved type hinting, with less reliance on `Typing` module - Generics can be used --- such as `list[str]` and `dict[str, int]` --- without needing to use `List`, `Dict`, etc. from the `Typing` module - Can use `|` for union types. For example, use `str | None` instead of `Optional[str]` and `int | float` instead of `Union[int, float]` - The walrus operator (`:=`) that assigns values to variables as part of a larger expression. Don't over use this, but use it where it is clear and idiomatic ```python if (n := len(a)) > 10: # Assign to `n` and return; avoids redundant `len` call. print(f"List is too long ({n} elements, expected <= 10)") # Regular expression match objects discount = 0.0 if (mo := re.search(r'(\d+)% discount', advertisement)): discount = float(mo.group(1)) / 100.0 while (block := f.read(256)) != '': process(block) # List comprehensions with filtering conditions [clean_name.title() for name in names if (clean_name := normalize('NFC', name)) in allowed_names] ``` - Positional-only parameters. Use the function parameter syntax `/` to indicate that some function parameters must be specified positionally and cannot be used as keyword arguments. One useful application is that it allows the parameter name to be changed in the future without risk of breaking client code. ```python # a, b -- positional only # c, d -- positional or keyword # e, f -- required to be keywords def f(a, b, /, c, d, *, e, f): print(a, b, c, d, e, f) # Valid f(10, 20, 30, d=40, e=50, f=60) # Invalid f(10, b=20, c=30, d=40, e=50, f=60) # b cannot be a keyword argument f(10, 20, 30, 40, 50, f=60) # e must be a keyword argument ``` - `=` in f-strings. An f-string such as f'{expr=}' will expand to the text of the expression, an equal sign, then the representation of the evaluated expression ``` >>> user = 'eric_idle' >>> member_since = date(1975, 7, 31) >>> f'{user=} {member_since=}' "user='eric_idle' member_since=datetime.date(1975, 7, 31)" >>> delta = date.today() - member_since >>> f'{user=!s} {delta.days=:,d}' 'user=eric_idle delta.days=16,075' # left side of `=` will always display the whole expression >>> print(f'{theta=} {cos(radians(theta))=:.3f}') "theta=30 cos(radians(theta))=0.866" ``` - `dict` merge (`|`) and update (`|=`) operators - `str.removeprefix(prefix)` and `str.removesuffix(suffix)` - `zoneinfo` standard library module for getting timezone info from IANA time zone database ```python from zoneinfo import ZoneInfo from datetime import datetime, timedelta # Daylight saving time dt = datetime(2020, 10, 31, 12, tzinfo=ZoneInfo("America/Los_Angeles")) print(dt) # 2020-10-31 12:00:00-07:00 dt.tzname() # PDT # Standard time dt += timedelta(days=7) print(dt) # 2020-11-07 12:00:00-08:00 print(dt.tzname()) # PST ``` - `graphlib` standard library module with `graphlib.TopologicalSorter` method - Structural pattern matching with `match <...>` statement. For more examples, see `pattern-matching.md`. ```python def http_error(status): match status: case 400: return "Bad request" case 404: return "Not found" case 418: return "I'm a teapot" # Combine multiple cases with `|` case 401 | 403: return "Not allowed" case _: return "Something's wrong with the internet" ``` # Logging Prefer the `logging` module to `print` statements in production code. Use `print` only in short code snippets, minimal examples (when explaining something to the user), or for debugging. # Reporting Prefer structured reporting via classes and rendering methods. For example: ```python @dataclass class BenchmarkResults: name: str mean_ms: float std_ms: float def render_text_report(list[BenchmarkResults]) -> str: lines = [ "Benchmark Report", "=" * 40, "", ] for r in results: lines.append(f"{r.name:20s}: {r.mean_ms:.3f} ± {r.std_ms:.3f} ms") return "\n".join(lines) report = render_text_report(results) with open("report.txt", "w") as f: f.write(report) ``` Avoid long sequences of multiple `f.write("...")` statements. # Type Safety and Data Structures ## Dataclasses for structured data When defining data structures that hold multiple related fields, prefer `dataclasses` over dictionaries or plain classes: ```python from dataclasses import dataclass @dataclass class Config: model_name: str learning_rate: float batch_size: int = 32 # with sensible defaults ``` **When to use dataclasses:** - Representing configuration objects - Return values with multiple related fields - Domain objects with clear schemas - Data that gets passed between functions **When dictionaries are fine:** - Simple key-value lookups with dynamic keys - JSON-like data being serialized/deserialized immediately - Truly heterogeneous collections - One-off data structures in small scripts ## Type Annotations Use precise type annotations for function signatures and class attributes, especially in: - Public APIs and library code - Functions with non-obvious parameter types - Code that will be maintained by multiple people ```python from pathlib import Path def load_config(path: Path, validate: bool = True) -> Config: """Load configuration from file.""" ... def process_results( data: list[dict[str, float]], threshold: float | None = None ) -> dict[str, list[float]]: """Process and group results by category.""" ... ``` **Don't over-annotate:** - Skip annotations where types are immediately obvious from context - Use `# type: ignore` sparingly for legitimate edge cases - Avoid overly complex generic types that reduce readability ## When to skip this pattern - Quick exploratory scripts or notebooks - Prototyping where the data structure is still evolving - Simple scripts under ~100 lines with obvious data flow - Performance-critical code where overhead matters (though dataclasses are quite fast) # Early Returns Prefer early returns over nested if/else structures. Early returns reduce nesting depth, make error conditions explicit upfront, and keep the main logic (the "happy path") unindented and easier to follow. This pattern helps agents reason about control flow more clearly. ## Simple validation ```python # ❌ Nested condition obscures main logic def load_config(path: Path) -> dict[str, Any]: if path.exists(): with open(path) as f: return json.load(f) else: return {} # ✅ Guard clause makes intent clear; main logic follows naturally def load_config(path: Path) -> dict[str, Any]: if not path.exists(): return {} with open(path) as f: return json.load(f) ``` ## Multiple guard clauses ```python # ❌ Deep nesting makes code hard to follow def process_data(data: list[float], threshold: float | None) -> list[float]: if len(data) > 0: if not any(math.isnan(x) for x in data): if threshold is not None: return [x for x in data if x > threshold] else: return data else: raise ValueError("Data contains NaN values") else: return [] # ✅ Stack guard clauses at the top; happy path is clear and unindented def process_data(data: list[float], threshold: float | None) -> list[float]: if len(data) == 0: return [] if any(math.isnan(x) for x in data): raise ValueError("Data contains NaN values") if threshold is None: return data return [x for x in data if x > threshold] # Main logic is clear ``` ## Raising exceptions as early exits ```python # ❌ Nested validation with else blocks def calculate_metric(values: list[float]) -> float: if isinstance(values, list): if all(isinstance(x, (int, float)) for x in values): if len(values) >= 3: return statistics.mean(values) / statistics.stdev(values) else: raise ValueError("Need at least 3 values") else: raise TypeError("All values must be numeric") else: raise TypeError("values must be a list") # ✅ Validation guards upfront; calculation stands alone def calculate_metric(values: list[float]) -> float: if not isinstance(values, list): raise TypeError("values must be a list") if not all(isinstance(x, (int, float)) for x in values): raise TypeError("All values must be numeric") if len(values) < 3: raise ValueError("Need at least 3 values") return statistics.mean(values) / statistics.stdev(values) ``` ## Dataclass and dict validation ```python # ❌ Unnecessary else after conditional processing @dataclass class Config: model_name: str learning_rate: float batch_size: int = 32 def load_experiment(config_dict: dict[str, Any]) -> Config | None: if "model_name" not in config_dict: logging.warning("Missing model_name in config") return None else: if "learning_rate" not in config_dict: logging.warning("Missing learning_rate in config") return None else: return Config( model_name=config_dict["model_name"], learning_rate=config_dict["learning_rate"], batch_size=config_dict.get("batch_size", 32), ) # ✅ Handle validation early; construction has no unnecessary else blocks def load_experiment(config_dict: dict[str, Any]) -> Config | None: if "model_name" not in config_dict: logging.warning("Missing model_name in config") return None if "learning_rate" not in config_dict: logging.warning("Missing learning_rate in config") return None return Config( model_name=config_dict["model_name"], learning_rate=config_dict["learning_rate"], batch_size=config_dict.get("batch_size", 32), ) ``` ## Paths Use `pathlib.Path` objects and their methods when representing and manipulating paths. Avoid using strings for paths. When writing functions that work with paths, prefer the strict `argument: Path` annotation. Be aware that some Python functions may expect strings and be unable to handle `Path` objects. In these cases, cast the `Path` to a `str` when calling the function: ```python from pathlib import Path myfile = Path("~").expanduser() / "data" / "myfile" def some_function(filename: str): # NOTE: Expects `str`, not `Path` # ... some_function(str(myfile)) # Convert path to string only for the function call ``` --- > Converted and distributed by [TomeVault](https://tomevault.io/claim/ashiklom) — claim your Tome and manage your conversions.