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How to Mock isort Output to Test Import Sorting in Python
Uses isort with check mode and a unittest mock to verify whether a Python source string has correctly sorted imports.
import isort
from unittest.mock import patch
code = """
import os
import sys
import json
import pathlib
"""
def check_imports_sorted(code_str):
with patch("isort.api.output") as mock_output:
isort.code(code_str, check=True, show_diff=True)
return mock_output.called
if __name__ == "__main__":
…How to Convert Strings to Types in Python Using TypeVar
A beginner-friendly helper that converts a string to int, float, bool, or str with type hints and graceful failure handling.
from typing import TypeVar, Optional
T = TypeVar("T")
def convert_data(value: str, target_type: type[T]) -> Optional[T]:
"""Convert string value to target type; return None on failure."""
try:
if target_type is int:
return int(value)
elif target_type is float:
return f…How to Parse Data with Type Hints in Python
A beginner-friendly helper that parses simple dictionary- or list-like strings into typed Python structures using modern typing annotations.
from typing import Any, Dict, List, Union
def parse_data(raw: str) -> Union[Dict[str, Any], List[Any], str]:
"""Parse a simple string into structured data using type hints."""
cleaned = raw.strip()
if not cleaned:
return {}
if cleaned.startswith("{") and cleaned.endswith("}"):
…How to Sort Data in Python
Sort sequences with type-safe helpers that handle mixed data with a string fallback.
from typing import Any, TypeVar, Protocol, Sequence, Iterable
T = TypeVar("T")
Comparable = TypeVar("Comparable", bound="Comparable")
class Sortable(Protocol):
def __lt__(self, other: Any) -> bool: ...
S = TypeVar("S", bound=Sortable)
def sort_data(data: Sequence[S], *, reverse: bool = False) -> list[S]:
"…How to Use Literal Type Hints in Python
Use typing.Literal to restrict a function parameter to specific allowed string values and get static type checking.
from typing import Literal
def get_status_message(status: Literal["active", "inactive", "pending"]) -> str:
"""Return a message based on the status value."""
if status == "active":
return "Account is active"
elif status == "inactive":
return "Account is inactive"
else:
return "…How to use Optional type hint in Python
Use the Optional type hint to indicate a parameter can be a string or None, with an example function that handles both cases.
from typing import Optional
def greet(name: Optional[str]) -> str:
if name is None:
return "Hello, anonymous!"
else:
return f"Hello, {name}!"
if __name__ == "__main__":
print(greet("Alice"))
print(greet(None))How to Implement a Factory Method by Type String in Python
A factory method maps a type string to a class, creating and returning the appropriate object instance while handling unknown types gracefully.
class Animal:
def speak(self):
raise NotImplementedError
class Dog(Animal):
def speak(self):
return "Woof!"
class Cat(Animal):
def speak(self):
return "Meow!"
class AnimalFactory:
@staticmethod
def create(animal_type: str) -> Animal:
animal_types = {
…Convert Protobuf to JSON and Dict in Python
Provides static helper methods to convert between protobuf messages, JSON strings, and Python dictionaries using the google.protobuf library.
from google.protobuf.json_format import MessageToJson, Parse
import json
class DataConverter:
"""Helper class to convert between protobuf messages and common formats."""
@staticmethod
def to_json(message, indent=2):
"""Convert a protobuf message to JSON string."""
return MessageToJson(me…How to Decode Basic Auth Credentials in Python
Decode username and password from a Basic Auth header string using base64 and standard string operations.
import base64
def decode_basic_auth(header_value):
"""
Decode credentials from a Basic Auth header value.
Expected format: "Basic base64encoded(username:password)"
Returns a tuple (username, password).
"""
if not header_value.startswith("Basic "):
raise ValueError("Invalid Basic A…How to Serialize and Deserialize JSON Event Payloads in Python
Define an EventPayload class with custom to_json and from_json methods to convert event objects to JSON strings and back, using datetime parsing.
import json
from datetime import datetime
class EventPayload:
def __init__(self, event_id, event_type, timestamp, data):
self.event_id = event_id
self.event_type = event_type
self.timestamp = timestamp
self.data = data
def to_json(self):
return json.dumps({
…Cache Data in Redis with Python
A beginner-friendly Redis cache helper that stores JSON strings with a TTL and retrieves them with the redis-py client.
import redis
class DataCache:
def __init__(self, host="localhost", port=6379, db=0):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
def cache_data(self, key, value, ttl=60):
self.client.setex(key, ttl, value)
def get_cached_data(self, key):
return …How to Create a Deployment Environment Tag in Python
Generate a standardized deployment tag string by combining service and environment names with an f-string.
def mock_env_tag(service, environment):
return f"{service}-{environment}"
if __name__ == "__main__":
service = "api-gateway"
environment = "production"
tag = mock_env_tag(service, environment)
print(f"Deployment tag: {tag}")Partition Data by Hash Key Mod N in Python
Returns a partition index for a string key by hashing it with MD5 and taking modulo N, then groups sample keys into partitions.
import hashlib
def partition_key(key: str, num_partitions: int) -> int:
"""Return partition index for key using MD5 hash mod N."""
digest = hashlib.md5(key.encode()).hexdigest()
return int(digest, 16) % num_partitions
if __name__ == "__main__":
keys = ["alice", "bob", "carol", "dave", "eve"]
nu…How to Load CSV Training Data in Python Without Pandas
Load CSV training data using Python's standard library and mock it with io.StringIO for testing, returning headers and rows as dictionaries.
import csv
from pathlib import Path
def load_csv_training_data(file_path: str | Path) -> tuple[list[str], list[dict[str, str]]]:
"""Load CSV training data and return headers plus rows as dictionaries."""
with open(file_path, mode="r", newline="", encoding="utf-8") as csv_file:
reader = csv.DictReader…One Hot Encode Categories in Python
Convert a list of categorical strings into one-hot encoded numeric vectors using pure Python and NumPy.
import numpy as np
categories = ["red", "green", "blue", "red", "blue", "green", "red"]
unique = sorted(set(categories))
lookup = {cat: i for i, cat in enumerate(unique)}
one_hot = []
for cat in categories:
row = [0] * len(unique)
row[lookup[cat]] = 1
one_hot.append(row)
print("Categories:", categories…How to Mock Date Sharding by Range in Python
Split a date interval into fixed-size contiguous shards, returning each window as an ISO date string pair.
from datetime import date, timedelta
def shard_ranges(start_date, end_date, shard_days=7):
if start_date > end_date:
raise ValueError("start_date cannot be after end_date")
shards = []
current = start_date
while current <= end_date:
shard_end = min(current + timedelta(days=shard_days …Generate a docker-compose.yml with mock services in Python
Build a docker-compose.yml string from a Python dict of service names and images, then write it to a file.
import yaml
from pathlib import Path
def generate_mock_compose(services: dict) -> str:
compose = {
"version": "3.9",
"services": {}
}
for name, image in services.items():
compose["services"][name] = {
"image": image,
"container_name": f"mock-{name}",
…How to Build a Data Helper for Production Deployment in Python
Build a reusable DataHelper class that loads configs, validates required keys, normalizes string values, and logs schema details — a production-ready data processing pattern.
import json
from pathlib import Path
from typing import Any, Dict
class DataHelper:
"""Common data processing patterns for production deployment."""
def __init__(self, config_path: str | Path):
self.config_path = Path(config_path)
self.config = self._load_config()
def _load_confi…Browse by section
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