Modern tooling
uv, ruff, pyproject.toml, packaging, and current Python project workflows.
Data Conversion Helper Functions in Python
A set of beginner-friendly helper functions to convert between JSON strings and Python data, parse dates, and read/write files using pathlib.
from datetime import datetime
from pathlib import Path
import json
def to_json(data, indent=2):
"""Convert Python data to pretty-printed JSON string."""
return json.dumps(data, indent=indent, default=str)
def from_json(json_string):
"""Parse JSON string back into Python data."""
return json.loads(jso…
How to Build a Chainable Filter Helper in Python
A beginner-friendly dataclass helper that chains filters, uniqueness, and slicing on any sequence, returning a plain list at the end.
from dataclasses import dataclass
from typing import Callable, Iterator, Sequence, TypeVar
T = TypeVar("T")
@dataclass
class FilterAssistant:
"""Beginner-friendly helper to filter any collection."""
data: Sequence[T]
def where(self, predicate: Callable[[T], bool]) -> "FilterAssistant":
return …
How to Build a Wheel with Hatchling in Python
Build a Python wheel using the hatchling build backend and the build package, handling missing project metadata automatically.
import subprocess
import sys
import tempfile
from pathlib import Path
def build_wheel_with_hatchling(project_dir: str) -> str:
"""Build a wheel using hatchling and return the wheel file path."""
project_path = Path(project_dir)
# Simulate a minimal project structure if missing
if not (project_path /…
How to Format Data with Python's datetime and JSON Helpers
A beginner-friendly set of helper functions to format dates and safely read/write JSON files in Python.
from datetime import datetime
from pathlib import Path
import json
def format_today(pattern: str = "%Y-%m-%d") -> str:
"""Return today's date formatted with the given pattern."""
return datetime.now().strftime(pattern)
def load_json(file_path: str) -> dict:
"""Read and parse a JSON file safely."""
…
How to Generate a Mock Rollbar Error Report in Python
Create a realistic fake Rollbar error report with random timestamps, levels, messages, and counts for testing and demos.
import json
import random
import time
from datetime import datetime, timedelta
def mock_rollbar_report(n_errors=5):
messages = [
"TypeError: unsupported operand type(s) for +: 'int' and 'str'",
"KeyError: 'user_id'",
"ValueError: invalid literal for int() with base 10: 'abc'",
"At…
How to Load and Inspect CSV Data with a Dataclass Helper in Python
This code defines a DataHelper dataclass that reads a CSV file into a list of dictionaries and prints basic dataset information.
from pathlib import Path
from dataclasses import dataclass
from typing import Any
@dataclass
class DataHelper:
"""Simple helper for loading and inspecting CSV data."""
filepath: Path
def load_csv(self, *, delimiter: str = ",") -> list[dict[str, Any]]:
"""Read CSV into a list of dictionaries."""
…
How to Load and Inspect Data Files in Python
A beginner-friendly DataLoader dataclass that loads JSON or text files and provides methods to preview and inspect the data.
from dataclasses import dataclass, field
from pathlib import Path
import json
from typing import Any, Dict, List
@dataclass
class DataLoader:
"""Simple helper to load and inspect data files for beginners."""
path: Path
data: Any = field(init=False, default=None)
def __post_init__(self) -> None:
…
How to Load and Save CSV and JSON Files in Python
A beginner-friendly data helper that loads or saves CSV and JSON files using only the Python standard library, with automatic format detection from the file extension.
from pathlib import Path
import json
import csv
def load_data(file_path):
"""Load CSV or JSON data from disk based on file extension."""
path = Path(file_path)
if path.suffix == ".json":
with path.open() as f:
return json.load(f)
elif path.suffix == ".csv":
with path.open(…
How to Parametrize Tests in Python with pytest
This code demonstrates how to use pytest's @pytest.mark.parametrize decorator to run a single test function against multiple input sets, ensuring comprehensive coverage with minimal code duplication.
import pytest
def multiply(a, b):
return a * b
@pytest.mark.parametrize("x, y, expected", [
(2, 3, 6),
(4, 5, 20),
(0, 10, 0),
(7, 1, 7),
])
def test_multiply(x, y, expected):
result = multiply(x, y)
assert result == expected, f"multiply({x}, {y}) = {result}, expected {expected}"
if _…
How to Parse and Extract Nested Data in Python
Load JSON files with Path and recursively extract values by key from nested Python structures using modern typing and standard library.
import json
from pathlib import Path
from typing import Any, Dict, List, Union
def load_data(filepath: Union[str, Path]) -> Union[Dict[str, Any], List[Any]]:
"""Load JSON data from a file with modern Path handling."""
path = Path(filepath)
if not path.exists():
raise FileNotFoundError(f"File not f…
How to Save and Load JSON Files in Python
Create a simple data helper to save Python dictionaries as pretty-printed JSON files and load them back reliably using pathlib and the stdlib json module.
import json
from pathlib import Path
from typing import Any
def save_json(data: Any, filename: str) -> None:
"""Save data as pretty-printed JSON to the current directory."""
path = Path(filename)
with path.open("w", encoding="utf-8") as f:
json.dump(data, f, indent=2, ensure_ascii=False)
def lo…
How to Use prompt_toolkit Autocomplete in Python
Demonstrates an interactive command-line prompt with autocomplete using prompt_toolkit's WordCompleter and a mock dataset.
from prompt_toolkit import prompt
from prompt_toolkit.completion import WordCompleter
def main():
"""Demo of prompt_toolkit autocomplete with a mock dataset."""
# A simple mock "database" of programming languages
languages = [
"Python", "Java", "JavaScript", "TypeScript", "C++", "C#",
"Go"…
How to Validate Data with a Simple Dict-Based Rules Helper in Python
Validates a dictionary against a set of callable rules, printing pass/fail per field and returning an overall boolean.
import json
from pathlib import Path
from typing import Any, Callable
def validate_data(
data: dict[str, Any],
rules: dict[str, Callable[[Any], bool]],
path: Path | None = None,
) -> bool:
"""Validate a dict against a set of simple rules."""
all_valid = True
for field, validator in rules.item…
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