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Filter Records by Required Fields in Python
Filter a list of dictionaries, keeping only records where every required field is present and not None.
def filter_records(records, required_fields):
"""Return only records that have all required fields non-null."""
return [
record for record in records
if all(record.get(field) is not None for field in required_fields)
]
if __name__ == "__main__":
sample_records = [
{"name": "Al…How to Clean and Format Data in Python
This code loads JSON data, cleans records by removing empty fields and normalizing text, then summarizes the results with counts and unique keys.
import json
from pathlib import Path
def load_data(filepath: str) -> dict:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def clean_records(records: list[dict]) -> list[dict]:
"""Remove empty fields and normalize text to lowercase."""…How to Find Missing Values in Large Datasets in Python
Analyze missing values across multiple large pandas DataFrames with counts and percentages.
import pandas as pd
import numpy as np
def find_missing_values_summary(datasets):
"""Analyze missing values across multiple datasets (dict of name: DataFrame)."""
summary = {}
for name, df in datasets.items():
missing_count = df.isnull().sum()
total_rows = len(df)
missing_pct = (mi…How to Safely Coerce Strings to Numbers in Python
A safe conversion function that turns strings into integers or floats, returning a fallback value when conversion fails.
import math
def to_number(value, fallback=None):
"""Safely coerce a string to int or float, returning fallback on failure."""
if isinstance(value, (int, float)):
return value
try:
# Try int first for clean whole numbers
return int(value)
except (ValueError, TypeError):
…Pivot long to wide transformation dict
Transform a list of dictionaries from long format to wide format by pivoting on a key column and aggregating values, using pure Python.
def pivot_long_to_wide(rows, key_col, value_col, id_cols=None):
"""
Convert long-format data (list of dicts) to wide format.
Args:
rows: List of dicts in long format
key_col: Column name to pivot on (becomes new column headers)
value_col: Column name whose values become the cel…Get Git Status Info in Python
Run git commands from Python to gather branch name, number of changes, total commits, and clean status, returning them as a dict.
import subprocess
import json
from pathlib import Path
def get_git_status(repo_path="."):
"""Return basic git info about a repository as a dict."""
try:
branch = subprocess.check_output(
["git", "branch", "--show-current"],
cwd=repo_path,
stderr=subprocess.DEVNULL,…How to Filter Git History to Remove Secret File Entries in Python
A pure-Python mock that filters a repository's history to drop any commit that touched a secret file, so you can plan a cleanup before rewriting Git history.
from pathlib import Path
import json
def filter_history(history, secret_path):
"""Remove entries that touch the secret file."""
return [entry for entry in history if secret_path not in entry["files"]]
if __name__ == "__main__":
repo_history = [
{"commit": "a1b2c3", "message": "Add app", "files": …How to Mock Git Clean Dry Run in Python
Simulate the output of `git clean -n` in Python to preview which untracked files would be removed without actually deleting them.
import subprocess
import sys
def mock_git_clean_dry_run(untracked_files):
"""Simulate `git clean -n` for a given list of untracked files."""
if not untracked_files:
print("No untracked files to remove.")
return
print("Would remove:")
for file in untracked_files:
print(f" {fil…How to Parse Cloud JSON Data in Python
A helper function that safely parses JSON payloads from cloud services into a clean dict with defaults and error handling.
import json
from typing import Dict, Any
def parse_cloud_data(payload: str) -> Dict[str, Any]:
"""Parse a JSON payload from a cloud service into a clean dict."""
try:
data = json.loads(payload)
return {
"status": data.get("status", "unknown"),
"region": data.get("region…How to Use pytest Fixtures and conftest.py for Shared Setup in Python
Learn how to define reusable pytest fixtures for shared setup and use them to keep tests clean and maintainable.
import pytest
class Calculator:
def add(self, a, b):
return a + b
def multiply(self, a, b):
return a * b
@pytest.fixture
def calc():
return Calculator()
@pytest.fixture
def sample_numbers():
return (3, 5)
def test_add(calc, sample_numbers):
a, b = sample_numbers
assert c…How to Cancel an asyncio Task with Graceful Cleanup in Python
Cancel a running asyncio task, handle the cancellation signal inside a worker coroutine to perform cleanup, then re-raise so the cancellation propagates correctly.
import asyncio
async def worker(name: str, sleep: float) -> None:
try:
print(f"{name}: starting")
await asyncio.sleep(sleep)
print(f"{name}: completed")
except asyncio.CancelledError:
print(f"{name}: cancelled, cleaning up...")
await asyncio.sleep(0.2) # Simulate clea…How to Use the pytest tmp_path Fixture for Temporary Directories
Use pytest's built-in tmp_path fixture to create a unique temporary directory per test for clean file I/O testing.
import pytest
def test_write_and_read_file(tmp_path):
# tmp_path is a pytest fixture that provides a temporary directory
# unique to each test invocation
data_file = tmp_path / "data.txt"
data_file.write_text("hello world")
assert data_file.read_text() == "hello world"
def test_multiple_tmp_pat…How to Apply the Clean Architecture Dependency Rule in Python
Demonstrates the dependency rule with a Protocol repository, a use case, and a presenter wired together at a composition root.
from dataclasses import dataclass
from typing import List, Protocol
class Repository(Protocol):
def get_items(self) -> List[str]:
...
@dataclass
class InMemoryRepository:
items: List[str]
def get_items(self) -> List[str]:
return self.items
class UseCase:
"""Application layer depends…How to Build an Anti-Corruption Layer in Python
Wrap a legacy system with a translation layer that converts awkward legacy data into a clean, modern DTO (Data Transfer Object) for use by new code.
class LegacyOrderSystem:
"""Legacy system with awkward, unstructured data."""
def get_order(self):
return {
"order_id": "ORD-123",
"cust": "Acme Corp",
"items": [{"sku": "A1", "qty": 2, "price_each": 10.0}],
"ship_to": "123 Main St, Springfield"
}…Format data in Python using dataclasses like gRPC messages
Convert Python dataclasses to and from dicts and format them gRPC-style for clean data handling.
from dataclasses import dataclass
from typing import Any, Dict, List, Optional
@dataclass
class ProductInfo:
"""Data class representing a gRPC-style product message."""
name: str
price: float
tags: List[str]
description: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
"""C…How to Implement a Redis-Like Cache Dictionary in Python
Build a RedisMockDict class that mimics basic Redis key-value operations with TTL support, expiry cleanup, and standard dict-like methods.
from collections import OrderedDict
import time
class RedisMockDict:
def __init__(self, ttl=None):
self._data = OrderedDict()
self._ttl = ttl # default TTL in seconds, None = no expiry
self._expiry = {}
def set(self, key, value, ttl=None):
"""Set a key-value pair with optiona…How to Build a Microservice Helper in Python
A beginner-friendly Python helper that validates input, normalizes service responses, and simulates user management—showing clean patterns for microservice development.
import json
from typing import Any, Dict, List
class DataValidator:
"""Simple validator for common data patterns."""
@staticmethod
def is_valid_email(value: str) -> bool:
"""Check if value looks like an email."""
return "@" in value and "." in value.split("@")[-1]
@staticmethod
…How to Build an Anti-Corruption Layer in Python
Translate messy legacy system data into a clean domain model using an anti-corruption layer in Python.
class MockLegacySystem:
"""Simulates a legacy system with messy data formats."""
def get_user_data(self):
# Legacy format: fields are abbreviated and types are inconsistent
return {
"usr_id": "USR-123",
"usr_nm": "john_doe",
"email_addrs": "John.Doe@example.c…How to Impute Missing Values with Mean in Python
Replace None values in a list with the mean of the existing values using Python's statistics module.
import statistics
from statistics import mean
def impute_mean(values):
"""Replace None with the mean of the non-None values."""
# Filter out None to compute the mean of existing values
valid = [v for v in values if v is not None]
if not valid:
return values # nothing to impute if all are Non…How to Create a Data Helper Class in Python for JSON Files
Build a beginner-friendly Python helper class to read, write, filter, and summarize JSON data files with clean, reusable methods.
import json
from pathlib import Path
class DataHelper:
"""Simple beginner-friendly helper for reading and writing JSON data files."""
@staticmethod
def read_json(filename):
file_path = Path(filename)
if file_path.exists():
with file_path.open("r", encoding="utf-8") as f:
…How to Drain a Connection Pool Before Exit in Python
Gracefully close all pooled sockets using a thread-safe ConnectionPool that drains connections before program exit.
import socket
import threading
import time
import random
class ConnectionPool:
def __init__(self, size=5):
self.pool = []
self.lock = threading.Lock()
self.closed = False
for _ in range(size):
self.pool.append(self.create_connection())
def create_connection(sel…Browse by section
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