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Build a Simple ETL Pipeline in Python
A simple ETL pipeline that reads JSON Lines, transforms records with filtering and normalization, and writes the result to JSON.
import json
from pathlib import Path
def read_input(file_path: Path) -> list[dict]:
"""Read JSON lines file into list of dicts."""
with file_path.open("r", encoding="utf-8") as f:
return [json.loads(line) for line in f if line.strip()]
def transform(records: list[dict]) -> list[dict]:
"""Transf…How to Get the Breadth-First Traversal Order of a Graph in Python
Performs a breadth-first search on an adjacency list and returns the order nodes are visited, using a deque for efficient queue operations.
from collections import deque
def bfs_order(adjacency, start=0):
"""Return the order nodes are visited in a breadth-first traversal."""
visited = set()
order = []
queue = deque([start])
visited.add(start)
while queue:
node = queue.popleft()
order.append(node)
for neig…Fill PDF Form Fields from a Mock Template in Python
Fills a PDF-style form template dictionary with user data, preserving template fields and formatting output as JSON.
import json
template = {
"first_name": "",
"last_name": "",
"email": "",
"phone": "",
"date_of_birth": "",
"address": "",
"city": "",
"state": "",
"zip_code": "",
"agree_to_terms": False
}
def fill_pdf_form(template: dict, data: dict) -> dict:
for key, value in data.items…ETL in Python: Extract CSV, Transform Dict, Load JSON
Build a simple ETL pipeline in Python that reads a CSV file, transforms each row (stripping whitespace and converting numeric fields), and writes the result to JSON.
import csv
import json
from pathlib import Path
def extract_csv(file_path):
"""Read CSV file and return list of row dictionaries."""
with Path(file_path).open('r', newline='', encoding='utf-8') as f:
reader = csv.DictReader(f)
return list(reader)
def transform_dicts(rows):
"""Transform ro…How to Implement a Data Helper Class in Python for Production Deployments
Build an environment-aware data helper in Python that loads config, extracts, transforms, and reports on JSON data using small, testable functions.
"""Production-style data helper for beginners.
Demonstrates:
- environment-aware config
- central data extraction
- small, testable functions
"""
import os
import json
from pathlib import Path
from typing import List, Dict, Any
def load_config(env: str = os.getenv("APP_ENV", "development")) -> Dict[str, Any]:
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Samples vs tutorials and challenges
Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.