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How to Process Lines of Text in Python
Strip whitespace, split a multi-line string, count words per line, and print structured summaries using basic string methods and loops.
text = """ Python is great!
Coding is fun.
Python skills help you grow. """
lines = text.strip().splitlines()
line_count = len(lines)
processed = []
for line in lines:
stripped = line.strip()
word_count = len(stripped.split())
processed.append({
"original": line,
"stripped": stripp…How to Build an Error Code Enum in Python
Define an API error code enum with descriptions and build structured error payloads for HTTP responses.
from enum import Enum
class APIErrorCode(Enum):
SUCCESS = 0
BAD_REQUEST = 400
UNAUTHORIZED = 401
FORBIDDEN = 403
NOT_FOUND = 404
CONFLICT = 409
INTERNAL_ERROR = 500
def describe_error(code):
descriptions = {
APIErrorCode.SUCCESS: "Request completed successfully",
APIE…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 …Convert File Data to a Dictionary in Python
This function scans a directory and converts each file's metadata (name, size, extension) into a structured dictionary for easy access.
from pathlib import Path
def convert_files_data(directory: str) -> dict:
data = {}
base = Path(directory)
if not base.exists():
return data
for file in base.iterdir():
if file.is_file():
data[file.name] = {
"size": file.stat().st_size,
"exten…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 …How to Write Simple XML Documents with ElementTree in Python
Create well-structured XML documents in memory using Python's built-in ElementTree module, complete with nested elements, attributes, and text content.
import xml.etree.ElementTree as ET
def create_xml_document():
# Create root element
root = ET.Element("catalog")
# Create a book element with attributes and children
book1 = ET.SubElement(root, "book", id="bk101")
ET.SubElement(book1, "author").text = "Gambardella, Matthew"
ET.SubElement(…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*"
…Serialize and Format Data for LLM Prompts in Python
Use dataclasses and the json module to convert Python objects to JSON strings, parse them back, and format structured data into prompt-friendly text for LLM calls.
import json
from dataclasses import dataclass, asdict
@dataclass
class Recipe:
"""Simple data model to represent a recipe."""
name: str
cuisine: str
prep_minutes: int
def to_json(recipe: Recipe) -> str:
"""Serialize a Recipe to a JSON string."""
return json.dumps(asdict(recipe), indent=2)
…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 …How to Mock a Whisper API Transcription Stub in Python
Simulate an OpenAI Whisper-style transcription response with a dataclass request model and a mock function that returns structured audio transcription output.
import json
from dataclasses import dataclass
from typing import Optional
@dataclass
class AudioRequest:
file_path: str
language: Optional[str] = None
def to_api_payload(self) -> dict:
return {"file": self.file_path, "language": self.language}
def mock_whisper_transcribe(payload: dict) -> dict:
…How to Parse Terraform Plan Output in Python
Parse mock Terraform plan output text into structured add, change, and destroy lists using Python.
import json
from typing import Dict, List
def parse_terraform_plan_output(plan_output_text: str) -> Dict[str, List[str]]:
"""
Parses a mock Terraform plan output text into a structured dictionary.
"""
parsed: Dict[str, List[str]] = {"add": [], "change": [], "destroy": []}
for line in plan_output_…How to Split PDF Pages into Ranges in Python
Simulates splitting a PDF into page ranges by validating and returning structured range splits for automation workflows.
import os
def split_pdf_ranges(pdf_name, num_pages, ranges):
"""
Simulates splitting a PDF by returning the page ranges that would be split.
Args:
pdf_name (str): Name of the PDF file.
num_pages (int): Total number of pages in the PDF.
ranges (list of tuple): List of (start, end) …ETL in Python: Extract CSV, Transform Dicts, Load JSON
Build a simple ETL pipeline that reads a CSV, normalizes keys and converts price to float, then writes structured JSON.
import csv
import json
from pathlib import Path
def etl_csv_to_json(csv_path: str, json_path: str) -> None:
"""Extract CSV, transform rows to dicts, load to JSON."""
with open(csv_path, mode='r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
records = list(reader)
# Trans…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…How to Convert Data Types in a Python Data Pipeline
Demonstrates a simple Python data pipeline that converts string values to proper types (bool, int, float, datetime) and outputs structured JSON.
import json
from datetime import datetime
def convert_value(value):
"""Convert string values to appropriate Python types."""
if value.lower() == "true":
return True
if value.lower() == "false":
return False
if value.isdigit():
return int(value)
try:
return float(val…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 Build a Git Helper Class in Python
A beginner-friendly GitHelper class that wraps common git commands (status, log, branch) into reusable Python methods with structured output.
import subprocess
import json
from pathlib import Path
class GitHelper:
def __init__(self, repo_path="."):
self.repo = Path(repo_path)
def run(self, *args):
result = subprocess.run(
["git", *args],
cwd=self.repo,
capture_output=True,
text=True,…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…Upload Assets to GitHub Release with Python Mock
Simulates uploading binary and text assets to a GitHub release using a mock server, returning structured metadata for each upload.
import json
import os
import tempfile
from datetime import datetime
class ReleaseUploader:
"""Simulates uploading assets to a release with a mock server."""
def __init__(self, owner: str, repo: str, tag: str):
self.owner = owner
self.repo = repo
self.tag = tag
self.uploade…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 Structured Dict Typing in Python
Define and use TypedDict to add type hints to dictionaries, improving code clarity and enabling static type checking in your Python projects.
from typing import TypedDict
class User(TypedDict):
name: str
age: int
email: str
def greet(user: User) -> str:
return f"Hello {user['name']}, age {user['age']}, contact {user['email']}"
if __name__ == "__main__":
alice: User = {"name": "Alice", "age": 30, "email": "alice@example.com"}
pr…How to Add a Correlation ID Tracing Header in Python
A mock middleware generates or preserves a correlation ID header and logs structured JSON messages with it for API request tracing.
import uuid
import json
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class Request:
headers: dict = field(default_factory=dict)
def get(self, key, default=None):
return self.headers.get(key, default)
class CorrelationIdMiddleware:
def __init__(self, header_name…How to Create an RFC 7807 Error JSON in Python
Construct a structured error response using the RFC 7807 Problem Details format with a reusable function.
import json
from typing import Dict
def create_rfc7807_error(
type_: str,
title: str,
status: int,
detail: str,
instance: str,
extra_fields: Dict[str, object] | None = None,
) -> str:
"""
Build a JSON string following RFC 7807 Problem Details format.
"""
problem = {
"t…Generate Synthetic CPU Utilization Metrics in Python
Creates realistic time-series CPU utilization samples with timestamps, noise, and output as structured JSON for observability demos and testing.
from datetime import datetime, timedelta
import random
import json
def generate_metric_samples(base_value, noise, count=60, interval_minutes=1):
"""Generate realistic CPU utilization samples for a given time window."""
timestamps = []
values = []
now = datetime.utcnow()
start_time = now - timede…Browse by section
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- Open a sample, read How it works, and copy the code block
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