Problem / Solution Reference

Quick lookup for common Python tasks mapped to idiomatic, production-ready solutions for Python 3.14.

Recipe

Ctrl+F your problem. Copy the solution. Adapt names to your project.

When to reach for this:

"How do I do X in Python?" moments during implementation
Code review standardization
Onboarding quick reference

Problem / Solution Table

Problem	Idiomatic Solution
Read a file	`Path("f.txt").read_text(encoding="utf-8")`
Write a file	`Path("f.txt").write_text(data, encoding="utf-8")`
List files recursively	`Path("dir").rglob("*.py")`
HTTP GET request	`httpx.get(url, timeout=30.0)`
Async HTTP	`async with httpx.AsyncClient() as c: await c.get(url)`
Parse JSON	`json.loads(text)` or `response.json()`
Validate config	Pydantic `BaseSettings` or `BaseModel`
CLI tool	Typer or Click with `[project.scripts]`
Run tests	`uv run pytest`
Format code	`uv run ruff format .`
Lint code	`uv run ruff check --fix .`
Type check	`uv run mypy src/`
Cache function results	`@lru_cache(maxsize=128)`
Parallel I/O	`asyncio.gather(*tasks)`
Parallel CPU	`ProcessPoolExecutor`
Safe temp file	`tempfile.NamedTemporaryFile(delete=False)`
Env var with default	`os.environ.get("KEY", "default")`
Log structured	`logging.getLogger(__name__).info("msg", extra={...})`
Retry on failure	`tenacity @retry` or manual with backoff
Parse datetime	`datetime.fromisoformat(s)` or `dateutil.parser`
Unique items preserving order	`list(dict.fromkeys(items))`
Flatten nested list	`[x for sub in nested for x in sub]`
Count occurrences	`collections.Counter(items)`
Group by key	`itertools.groupby(sorted(items, key=k), key=k)` or `defaultdict`
Merge dicts	`{d1, d2}` or `d1 \| d2` (3.9+)
Dataclass quickly	`@dataclass(frozen=True, slots=True)`
Enum constants	`class Color(Enum): RED = "red"`
Context manager	`@contextmanager` decorator
Subprocess safely	`subprocess.run(cmd, check=True, capture_output=True)`
Load .env	`pydantic-settings` with `env_file=".env"`
Test HTTP API	`TestClient(app)` (FastAPI)
Mock external call	`@patch("mymodule.httpx.get")`
Serialize dataclass	`dataclasses.asdict(obj)`
Deep copy	`copy.deepcopy(obj)`
Check type at runtime	`isinstance(x, MyClass)` not `type(x) == MyClass`
Sort dict by value	`sorted(d.items(), key=lambda kv: kv[1])`
Read CSV	`csv.DictReader(open("f.csv"))` or `polars.read_csv("f.csv")`
Read parquet	`polars.read_parquet("f.parquet")` or `pd.read_parquet`
Generate UUID	`uuid.uuid4()`
Hash password	`argon2.PasswordHasher().hash(password)`
Secure token	`secrets.token_urlsafe(32)`
Parse URL	`urllib.parse.urlparse(url)`
Time a function	`time.perf_counter()` before/after
Profile CPU	`cProfile.run("main()")`
Install package	`uv add package`
Lock dependencies	`uv lock && uv sync --frozen`

Working Example

"""Common patterns combined in a realistic script."""
from __future__ import annotations
 
import logging
from functools import lru_cache
from pathlib import Path
 
import httpx
from pydantic import BaseModel
 
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
 
 
class Config(BaseModel):
    api_url: str = "https://api.example.com"
    cache_size: int = 128
 
 
@lru_cache(maxsize=128)
def fetch_status(base_url: str, service: str) -> dict:
    with httpx.Client(base_url=base_url, timeout=10.0) as client:
        r = client.get(f"/status/{service}")
        r.raise_for_status()
        return r.json()
 
 
def main() -> None:
    config = Config()
    for svc in ("billing", "auth", "notifications"):
        data = fetch_status(config.api_url, svc)
        log.info("service ok", extra={"service": svc, "status": data.get("status")})
    out = Path("report.json")
    out.write_text(str(fetch_status.cache_info()), encoding="utf-8")
 
 
if __name__ == "__main__":
    main()

What this demonstrates:

Pydantic for config, httpx for HTTP, lru_cache for deduplication
pathlib for file I/O, logging with extras
Patterns from the reference table composed together

Gotchas

Using outdated idioms - os.path over pathlib, % formatting over f-strings. Fix: prefer right column solutions.
Copy-paste without context - async sync mismatch. Fix: match solution to your sync/async context.
No error handling - examples are minimal. Fix: add try/except and timeouts in production.
lru_cache on unhashable args - TypeError. Fix: hashable arguments only or custom cache key.
requests library for new code - httpx preferred for sync and async parity. Fix: standardize on httpx.

Alternatives

Alternative	Use When	Don't Use When
Stack Overflow search	Novel problem	Common task listed here
stdlib docs	Edge case behavior	Quick idiom lookup
AI code gen	Boilerplate scaffolding	Security-critical patterns

FAQs

pathlib or os.path?

pathlib for all new code. os.path only for legacy compatibility.

requests or httpx?

httpx for new projects. Supports sync and async with same API.

dataclass or Pydantic?

dataclass for internal data. Pydantic for validation boundaries.

How do I contribute new entries?

Propose additions in team style guide when a pattern repeats in review.

Are these Python 3.14 specific?

Most work on 3.13+. Match patterns use 3.9+ dict merge syntax.

Sync vs async column?

Use async column in FastAPI/asyncio apps. Sync for scripts and blocking CLIs.

pandas or Polars for CSV?

Polars for performance. pandas for ecosystem compatibility.

How do I handle retries?

tenacity library with exponential backoff and jitter.

Testing these patterns?

Each pattern is unit-testable. See linked pytest articles.

Where is the full stdlib?

docs.python.org/3/library/ - this page is the curated shortcut.

50 Python Rules - principles behind solutions
PEP 8 & Style Reference - style conventions
Python Fundamentals - language basics
Standard Library Overview - stdlib map

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

Recipe

Ctrl+F your problem. Copy the solution. Adapt names to your project.

When to reach for this:

"How do I do X in Python?" moments during implementation
Code review standardization
Onboarding quick reference

Problem / Solution Table

Problem	Idiomatic Solution
Read a file	`Path("f.txt").read_text(encoding="utf-8")`
Write a file	`Path("f.txt").write_text(data, encoding="utf-8")`
List files recursively	`Path("dir").rglob("*.py")`
HTTP GET request	`httpx.get(url, timeout=30.0)`
Async HTTP	`async with httpx.AsyncClient() as c: await c.get(url)`
Parse JSON	`json.loads(text)` or `response.json()`
Validate config	Pydantic `BaseSettings` or `BaseModel`
CLI tool	Typer or Click with `[project.scripts]`
Run tests	`uv run pytest`
Format code	`uv run ruff format .`
Lint code	`uv run ruff check --fix .`
Type check	`uv run mypy src/`
Cache function results	`@lru_cache(maxsize=128)`
Parallel I/O	`asyncio.gather(*tasks)`
Parallel CPU	`ProcessPoolExecutor`
Safe temp file	`tempfile.NamedTemporaryFile(delete=False)`
Env var with default	`os.environ.get("KEY", "default")`
Log structured	`logging.getLogger(__name__).info("msg", extra={...})`
Retry on failure	`tenacity @retry` or manual with backoff
Parse datetime	`datetime.fromisoformat(s)` or `dateutil.parser`
Unique items preserving order	`list(dict.fromkeys(items))`
Flatten nested list	`[x for sub in nested for x in sub]`
Count occurrences	`collections.Counter(items)`
Group by key	`itertools.groupby(sorted(items, key=k), key=k)` or `defaultdict`
Merge dicts	`{d1, d2}` or `d1 \| d2` (3.9+)
Dataclass quickly	`@dataclass(frozen=True, slots=True)`
Enum constants	`class Color(Enum): RED = "red"`
Context manager	`@contextmanager` decorator
Subprocess safely	`subprocess.run(cmd, check=True, capture_output=True)`
Load .env	`pydantic-settings` with `env_file=".env"`
Test HTTP API	`TestClient(app)` (FastAPI)
Mock external call	`@patch("mymodule.httpx.get")`
Serialize dataclass	`dataclasses.asdict(obj)`
Deep copy	`copy.deepcopy(obj)`
Check type at runtime	`isinstance(x, MyClass)` not `type(x) == MyClass`
Sort dict by value	`sorted(d.items(), key=lambda kv: kv[1])`
Read CSV	`csv.DictReader(open("f.csv"))` or `polars.read_csv("f.csv")`
Read parquet	`polars.read_parquet("f.parquet")` or `pd.read_parquet`
Generate UUID	`uuid.uuid4()`
Hash password	`argon2.PasswordHasher().hash(password)`
Secure token	`secrets.token_urlsafe(32)`
Parse URL	`urllib.parse.urlparse(url)`
Time a function	`time.perf_counter()` before/after
Profile CPU	`cProfile.run("main()")`
Install package	`uv add package`
Lock dependencies	`uv lock && uv sync --frozen`

Working Example

"""Common patterns combined in a realistic script."""
from __future__ import annotations
 
import logging
from functools import lru_cache
from pathlib import Path
 
import httpx
from pydantic import BaseModel
 
logging.basicConfig(level=logging.INFO)
log = logging.getLogger(__name__)
 
 
class Config(BaseModel):
    api_url: str = "https://api.example.com"
    cache_size: int = 128
 
 
@lru_cache(maxsize=128)
def fetch_status(base_url: str, service: str) -> dict:
    with httpx.Client(base_url=base_url, timeout=10.0) as client:
        r = client.get(f"/status/{service}")
        r.raise_for_status()
        return r.json()
 
 
def main() -> None:
    config = Config()
    for svc in ("billing", "auth", "notifications"):
        data = fetch_status(config.api_url, svc)
        log.info("service ok", extra={"service": svc, "status": data.get("status")})
    out = Path("report.json")
    out.write_text(str(fetch_status.cache_info()), encoding="utf-8")
 
 
if __name__ == "__main__":
    main()

What this demonstrates:

Pydantic for config, httpx for HTTP, lru_cache for deduplication
pathlib for file I/O, logging with extras
Patterns from the reference table composed together

Gotchas

Using outdated idioms - os.path over pathlib, % formatting over f-strings. Fix: prefer right column solutions.
Copy-paste without context - async sync mismatch. Fix: match solution to your sync/async context.
No error handling - examples are minimal. Fix: add try/except and timeouts in production.
lru_cache on unhashable args - TypeError. Fix: hashable arguments only or custom cache key.
requests library for new code - httpx preferred for sync and async parity. Fix: standardize on httpx.

Alternatives

Alternative	Use When	Don't Use When
Stack Overflow search	Novel problem	Common task listed here
stdlib docs	Edge case behavior	Quick idiom lookup
AI code gen	Boilerplate scaffolding	Security-critical patterns

FAQs

pathlib or os.path?

pathlib for all new code. os.path only for legacy compatibility.

requests or httpx?

httpx for new projects. Supports sync and async with same API.

dataclass or Pydantic?

dataclass for internal data. Pydantic for validation boundaries.

How do I contribute new entries?

Propose additions in team style guide when a pattern repeats in review.

Are these Python 3.14 specific?

Most work on 3.13+. Match patterns use 3.9+ dict merge syntax.

Sync vs async column?

Use async column in FastAPI/asyncio apps. Sync for scripts and blocking CLIs.

pandas or Polars for CSV?

Polars for performance. pandas for ecosystem compatibility.

How do I handle retries?

tenacity library with exponential backoff and jitter.

Testing these patterns?

Each pattern is unit-testable. See linked pytest articles.

Where is the full stdlib?

docs.python.org/3/library/ - this page is the curated shortcut.

50 Python Rules - principles behind solutions
PEP 8 & Style Reference - style conventions
Python Fundamentals - language basics
Standard Library Overview - stdlib map

Stack versions: This page was written for Python 3.14.0, FastAPI 0.115+, Django 5.2, Flask 3.1, Pydantic 2, PyTorch 2.6+, pandas 2.2+, Polars 1.x, ruff 0.9+, and uv 0.6+.

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