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How to Log Errors with Structured Fields in Python
Logs error details as structured dictionary fields using Python's logging module with extra parameters.
import logging
import sys
def log_structured_error(operation: str, user_id: int, status_code: int, error_msg: str):
"""Log an error with structured fields using a dictionary."""
logger = logging.getLogger("structured_logger")
logger.setLevel(logging.ERROR)
# Create console handler if not already …Generate a Beautiful Folder Tree Visualization in Python
A Python utility that creates a visual tree of a directory structure, excluding common files, with configurable depth.
import os
from pathlib import Path
class FolderTree:
def __init__(self, root_path=".", ignore_list=None, max_depth=3):
self.root = Path(root_path)
self.ignore = set(ignore_list or [".git", "__pycache__", ".DS_Store"])
self.max_depth = max_depth
def generate(self):
tree…How to Automatically Merge Hundreds of Excel Files Without Losing Formatting in Python
Merge all .xlsx files in a folder into a single Excel workbook, preserving individual sheet structures with sheet name prefixes.
import pandas as pd
from pathlib import Path
def merge_excel_files(folder_path: str, output_path: str) -> None:
"""
Merge all .xlsx files in a folder into a single Excel file,
preserving individual sheet structures.
"""
folder = Path(folder_path)
excel_files = list(folder.glob("*.xlsx"))
…How to Parse Apache Log Files in Python
Parse Apache common log format lines into structured dictionaries using Python's standard library.
import re
from pathlib import Path
def parse_apache_line(line):
pattern = r'^(\S+) (\S+) (\S+) \[([^\]]+)\] "(\S+) (\S+) (\S+)" (\d{3}) (\S+)'
match = re.match(pattern, line)
if not match:
return None
ip, ident, user, timestamp, method, path, protocol, status, size = match.groups()
return …Build a Case-Insensitive Dict with a Wrapper Class in Python
Create a custom dict subclass that treats keys as case-insensitive by normalizing them to lowercase, with a full set of common dict methods.
class CaseInsensitiveDict:
def __init__(self, data=None):
self._data = {}
if data:
self.update(data)
def __setitem__(self, key, value):
self._data[str(key).lower()] = value
def __getitem__(self, key):
return self._data[str(key).lower()]
def __delitem__(sel…How to Build a Two-Way Dictionary in Python
Implement a BiDict class that supports both forward key-to-value and reverse value-to-key lookups with a simple add, delete, and update API.
class BiDict:
def __init__(self, data=None):
self.forward = {}
self.backward = {}
if data:
self.update(data)
def update(self, data):
for key, value in data.items():
self[key] = value
def __setitem__(self, key, value):
self.forward[key] = val…How to Deep Merge Nested Dicts Recursively in Python
Recursively merge two Python dictionaries, with overlay values taking precedence while preserving nested structures.
def deep_merge(base, overlay):
"""
Recursively merge two dictionaries.
Values in 'overlay' take precedence over 'base'.
"""
result = base.copy()
for key, value in overlay.items():
if key in result and isinstance(result[key], dict) and isinstance(value, dict):
result[key…How to Implement Disjoint Set Union Find in Python
Implement a Disjoint Set Union-Find data structure using a Python dictionary for parent tracking, with path compression and connectivity checks.
class DisjointSet:
def __init__(self):
self.parent = {}
def find(self, x):
# Path compression
if self.parent[x] != x:
self.parent[x] = self.find(self.parent[x])
return self.parent[x]
def union(self, x, y):
# Initialize if not present
if x not in…LRU Cache with OrderedDict in Python
Implement an LRU cache using collections.OrderedDict to track insertion order and evict the least-recently-used item when capacity is exceeded.
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity):
self.capacity = capacity
self.cache = OrderedDict()
def get(self, key):
if key not in self.cache:
return -1
self.cache.move_to_end(key)
return self.cache[key]
def put(sel…Unflatten Dot Keys to Nested Dict in Python
Convert a flat dictionary with dot-separated keys into a nested dictionary structure using recursive setdefault loops.
def unflatten_dot_keys(flat_dict):
result = {}
for flat_key, value in flat_dict.items():
parts = flat_key.split(".")
current = result
for part in parts[:-1]:
current = current.setdefault(part, {})
current[parts[-1]] = value
return result
if __name__ == "__main_…How to Build a Linked List Node Class in Python
Create a Node class and a LinkedList class with insert, remove, and display methods to manage a singly linked list.
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def insert(self, data):
new_node = Node(data)
if not self.head:
self.head = new_node
else:
current = self.…Implement Insert Delete GetRandom O(1) in Python
Build a RandomizedSet class that supports insert, delete, and get_random in average O(1) time using a list and a dictionary mapping values to indices.
import random
class RandomizedSet:
def __init__(self):
self.values = []
self.index_map = {}
def insert(self, val):
if val in self.index_map:
return False
self.index_map[val] = len(self.values)
self.values.append(val)
return True
def delete(self…Parse ReAct Logs into Thought Action Observation Steps in Python
Parse a ReAct agent's textual log into structured steps with thought, action, and observation using regex and named tuples.
import re
from collections import namedtuple
ReActStep = namedtuple("ReActStep", ["thought", "action", "observation"])
def parse_react_log(log: str) -> list[ReActStep]:
"""Parse a ReAct log into structured thought/action/observation steps."""
pattern = re.compile(
r"Thought:\s*(?P<thought>.+?)\s*"
…Build a Complete Website Sitemap Generator Without External Services
Crawl a website recursively using only Python's standard library to generate a structured sitemap of internal links.
import json
from urllib.parse import urlparse, urljoin
from collections import deque
import urllib.request
import urllib.error
import re
from html.parser import HTMLParser
class SitemapParser(HTMLParser):
def __init__(self, base_url):
super().__init__()
self.base_url = base_url
self.links …Extract Schema.org Structured Data from Any Website in Python
A Python tool that fetches a webpage and extracts all JSON-LD structured data (Schema.org) embedded in <script> tags with type="application/ld+json".
import requests
from bs4 import BeautifulSoup
import json
def extract_schema_org(url):
"""Extract structured data (Schema.org) from a website."""
try:
response = requests.get(url, timeout=10)
response.raise_for_status()
except requests.exceptions.RequestException as e:
return {"err…Generate Release Notes Markdown from PR Titles in Python
Generate structured Markdown release notes from a list of pull request titles using conventional commit types.
import json
from datetime import datetime, timezone
PRS = [
{"title": "feat: add user login", "number": 12, "merged_at": "2025-01-10"},
{"title": "fix: resolve payment timeout", "number": 13, "merged_at": "2025-01-11"},
{"title": "chore: bump dependencies", "number": 14, "merged_at": "2025-01-12"},
{"…How to generate and parse an interactive rebase TODO list in Python
Generate a Git interactive rebase TODO list from commit data and parse it back into structured records.
import re
from collections import namedtuple
Commit = namedtuple("Commit", ["hash", "subject"])
def generate_rebase_todo(commits, action="pick"):
todo_lines = []
for i, commit in enumerate(commits):
if i == 0 and action == "reword":
todo_lines.append(f"reword {commit.hash} {commit.subject…Mock Google Pub/Sub publish and pull in Python
A lightweight in-memory mock of Google Pub/Sub with publisher/subscriber classes to test topic-based fan-out and message pulling without real infrastructure.
import json
import time
from collections import deque
from dataclasses import dataclass, field
from typing import Any, Callable
@dataclass
class Message:
data: str
attributes: dict[str, str] = field(default_factory=dict)
message_id: str | None = None
ack_id: str | None = None
class MockPublisher:
…Mock Route53 change_resource_record_sets in Python
This code demonstrates how to mock AWS Route53 change_resource_record_sets API calls using the botocore Stubber, allowing you to test DNS update logic without touching real infrastructure.
import boto3
from botocore.exceptions import ClientError
def mock_change_resource_record_sets():
"""Demonstrates Route53 change_resource_record_sets with a mock client."""
# Create a mock Route53 client
route53 = boto3.client('route53', region_name='us-east-1',
aws_access_key_id…How to Bind and Mock structlog Context in Python
Shows how to bind persistent key-value context to a structlog logger, unbind keys, and mock the logger in tests to verify context is passed correctly.
import structlog
from unittest.mock import patch
logger = structlog.get_logger()
def demo():
logger = structlog.get_logger()
logger = logger.bind(user_id=42, request_id="abc123")
logger.info("user logged in", action="login")
# Unbind a key
logger = logger.unbind("user_id")
logger.info("r…Mocking loguru for Structured Logging in Python
Simulate loguru's structured logging with a custom mock that captures JSON-formatted log entries with bound context.
import json
import sys
from io import StringIO
from unittest.mock import patch
def mock_loguru():
# Simulate a structured logger with context binding
class StructuredLogger:
def __init__(self):
self.context = {}
def bind(self, **kwargs):
logger = StructuredLogger()
…How to Use TypedDict for Data Validation in Python
Define a TypedDict schema and validate raw dictionary input with type hints for safer, more readable data handling.
from typing import Any, Dict, List, Optional, Union, TypedDict, Literal
class Product(TypedDict):
product_id: int
name: str
price: Union[int, float]
in_stock: bool
tags: Optional[List[str]]
def validate_product(data: Dict[str, Any]) -> Product:
product_id: int = int(data["product_id"])
na…How to Implement CQRS with Separate Read and Write Models in Python
Implements Command Query Responsibility Segregation (CQRS) by splitting data into separate write and read models with dedicated repositories, using dataclasses for structure.
from dataclasses import dataclass, field
from typing import List, Dict, Optional
@dataclass
class OrderWriteModel:
order_id: int
customer: str
items: List[str] = field(default_factory=list)
def add_item(self, item: str) -> None:
self.items.append(item)
@dataclass
class OrderReadModel:
…How to Implement the Flyweight Pattern in Python
Implements the Flyweight design pattern to share immutable intrinsic state (character + font) across many document objects, reducing memory usage.
class Character:
"""Flyweight - stores only intrinsic state (shared)."""
def __init__(self, char: str, font: str):
self.char = char
self.font = font
def render(self, size: int) -> str:
return f"{self.char}_{self.font}_{size}"
class CharacterFactory:
"""Flyweight factory - ma…Browse by section
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