Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
Count Records Processed per Category in Python
Use a Counter dictionary to track how many records of each type (ok, error, retry) were processed in a data pipeline.
from collections import Counter
import random
processed_counter = Counter()
def process_records(records):
for record in records:
processed_counter[record] += 1
return len(records)
if __name__ == "__main__":
sample_records = [random.choice(["ok", "error", "retry"]) for _ in range(10)]
print(f…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…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…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…How to Count JSON Records in Python
Read a JSON file and count the number of top-level records, handling both list and dictionary structures.
import json
from pathlib import Path
def count_records(json_file):
"""Count top-level records in a JSON file."""
with open(json_file, "r") as f:
data = json.load(f)
# Handle both list of records and dict of records
if isinstance(data, list):
return len(data)
elif isinstance(da…How to Group Data by Key in Python
Group a list of dictionaries by a specified key using a defaultdict and compute per-group averages.
from collections import defaultdict
def group_by_key(data, key):
grouped = defaultdict(list)
for item in data:
grouped[item[key]].append(item)
return dict(grouped)
if __name__ == "__main__":
records = [
{"name": "Alice", "dept": "Engineering", "score": 85},
{"name": "Bob", "de…How to List Failed Records in a Dead Letter Queue Mock in Python
A mock Dead Letter Queue stores failed processing records with error details and timestamps, lists them, and exports to JSON.
import json
from datetime import datetime, timedelta
import random
class DeadLetterQueue:
def __init__(self):
self.failed_records = []
def add_failed_record(self, record_id, payload, error_message):
self.failed_records.append({
"record_id": record_id,
"payload": paylo…How to Merge Incremental Snapshot Upsert Dict in Python
Merge a snapshot dict into a base dict, recursively updating nested dictionaries while preferring snapshot values on conflicts.
def merge_upsert(base: dict, snapshot: dict) -> dict:
"""
Merge a snapshot dict into a base dict, preferring snapshot values
on key conflicts (upsert semantics). Nested dicts are merged recursively.
"""
result = dict(base)
for key, value in snapshot.items():
if key in result and i…How to Sort a List of Dictionaries by Key in Python
A reusable helper function that sorts a list of dictionaries by a specified key, with optional descending order support.
from typing import List
def sort_records(records: List[dict], key: str, descending: bool = False) -> List[dict]:
"""Sort a list of dictionaries by a specified key."""
return sorted(records, key=lambda record: record[key], reverse=descending)
def demonstrate_sorting() -> None:
users = [
{"name": …Test a Python Pipeline with Fixture Sample Rows
Test pipeline functions with sample rows provided by a pytest fixture, verifying required keys and value constraints.
import pytest
def get_value(data: dict, key: str):
return data.get(key)
def sample_rows():
return [
{"name": "Alice", "age": 30, "city": "London"},
{"name": "Bob", "age": 25, "city": "Paris"},
{"name": "Charlie", "age": 35, "city": "Berlin"},
]
@pytest.fixture
def sample_data(…Browse by section
Each section groups closely related Python snippets.
Data pipelines & processing — Python code examples
What you will find here
This page collects data pipelines & processing snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.
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.