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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 Filter a List of Dictionaries by Category in Python
Filter a list of dictionaries to include only records whose category is in an allowed set.
def filter_data(records, categories):
"""Return only records whose category is in the allowed set."""
allowed = set(categories)
filtered = []
for record in records:
if record["category"] in allowed:
filtered.append(record)
return filtered
if __name__ == "__main__":
data = …How to Index a List of Records by Unique ID in Python
Build a dictionary that maps each record's unique id to the record itself from a list of dictionaries.
from typing import List, Dict, Any
def index_by_id(records: List[Dict[str, Any]], id_field: str = "id") -> Dict[Any, Dict[str, Any]]:
"""Build a dictionary mapping each record's unique id to the record itself."""
return {record[id_field]: record for record in records}
if __name__ == "__main__":
sample_re…How to Transform a List of Dictionaries with Sets in Python
Normalize a list of dict records — cleaning names, extracting unique tags with sets, and building a standardized result.
def transform_data(raw_records):
"""Transform a list of dict records into normalized data with sets for unique values."""
normalized = []
unique_names = set()
all_tags = set()
for record in raw_records:
# Normalize name to lowercase and strip whitespace
name = record.get("name"…How to Build a Data Helper Class in Python with OOP
Create a beginner-friendly Python class that loads CSV data, filters records by field, and counts entries using object-oriented programming.
class DataHelper:
"""A beginner-friendly OOP helper for handling simple datasets."""
def __init__(self, filename):
self.filename = filename
self.data = self._load_data()
def _load_data(self):
"""Load data from a CSV file into a list of dictionaries."""
import csv
…How to Create a Data Formatter Class in Python
A beginner-friendly helper class to format lists, dictionaries, and stored records into readable strings.
class DataFormatter:
"""Helper class for beginners to format common data types."""
def __init__(self, name="data"):
self.name = name
self.records = []
def add_record(self, key, value):
"""Add a key-value record to the formatter."""
self.records.append({"key": key, …How to Use NamedTuples for Lightweight Records in Python
Create lightweight, immutable data records with namedtuple that behave like tuples but have named fields for improved readability and access.
from collections import namedtuple
Point = namedtuple("Point", ["x", "y"])
p = Point(3, 4)
print(p)
print(p.x, p.y)
print(p[0], p[1])
x, y = p
print(x, y)
print(p._asdict())
p2 = p._replace(x=10)
print(p2)
if __name__ == "__main__":
print("NamedTuple demo complete")How to merge dictionaries by a key in Python with a class
This code defines a DataMerger class that collects dictionary records and merges them by a specified key, combining fields from multiple records with the same key.
class DataMerger:
def __init__(self):
self.records = []
def add_record(self, record):
if isinstance(record, dict):
self.records.append(record)
else:
raise TypeError("Record must be a dictionary")
def merge_by_key(self, key):
merged = {}
for …How to Create a Simple Data Helper in Python for LLM Projects
Create a beginner-friendly Python class that stores, filters, and serializes data records for AI/LLM workflows.
import json
from typing import Any, Dict, List, Optional
class DataHelper:
"""Simple helper for beginners to manage data in AI/LLM projects."""
def __init__(self, data: Optional[List[Dict[str, Any]]] = None) -> None:
self.data: List[Dict[str, Any]] = data or []
def add_item(self, item: Dict[str…Prepare LLM prompt data with a Python helper class
A beginner-friendly Python class that collects records, converts them to JSON, and produces a quick summary for building LLM prompt context.
import json
from typing import Any, Dict, List
class DataHelper:
"""Simple helper to prepare data for LLM prompts."""
def __init__(self):
self.data = []
def add(self, item: Dict[str, Any]) -> "DataHelper":
self.data.append(item)
return self
def to_json(self) -> s…How to Import Users from CSV into LDAP-like Dicts in Python
Reads a CSV of user records and converts each row into an LDAP-style dictionary with standard attributes using Python's csv module.
import csv
import io
from pathlib import Path
def mock_ldap_import(csv_path):
"""
Reads a CSV file with user data and returns a list of LDAP-like user dicts.
Adds standard LDAP attributes that would come from directory schema.
"""
with open(csv_path, newline="", encoding="utf-8") as csvfile:
…How to Perform a DNS Lookup for A Records in Python
Resolve a hostname to IPv4 A records using Python's built-in socket.getaddrinfo and return a sorted list of addresses.
import socket
def get_a_records(hostname):
"""Fetch A records (IPv4 addresses) for a given hostname."""
try:
# getaddrinfo with family AF_INET restricts to IPv4 (A records)
infos = socket.getaddrinfo(hostname, None, socket.AF_INET)
# Each info tuple: (family, type, proto, canonname, so…Attach Source File Metadata to Records in Python
Add a source filename field to each record in a list by merging a new key into every dictionary using a dict unpacking comprehension.
from pathlib import Path
import json
def attach_source_metadata(records, source_file):
"""Attach source filename metadata to each record."""
return [
{**record, "source": Path(source_file).name}
for record in records
]
if __name__ == "__main__":
source = "/data/raw/customers.csv"
…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…Fan Out Records to Multiple Sinks in Python
Distribute the same records across multiple target sinks (database, API, queue, etc.) using a defaultdict-based fan-out pattern.
import json
from collections import defaultdict
SINKS = ["database", "api", "message_queue", "data_lake", "monitoring"]
def fan_out(records, *sinks):
dist = defaultdict(list)
for record in records:
for sink in sinks:
dist[sink].append(record)
return dict(dist)
if __name__ == "__main_…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…Generate a Deterministic Hash for Deduplication in Python
Create a stable SHA-256 fingerprint from nested data and file contents to deduplicate records in a data pipeline.
import hashlib
import json
from pathlib import Path
def natural_key_hash(data, salt=""):
"""
Generate a deterministic fingerprint from raw data (dict/list/str).
Uses JSON canonical-ish serialization with sorted keys and SHA-256.
"""
canonical = json.dumps(data, sort_keys=True, separators=(",", ":"…How to Build a Simple Data Pipeline in Python
A beginner-friendly data pipeline that loads JSON, filters records by a field value, and aggregates counts per category.
import json
from pathlib import Path
def load_json(filepath: str | Path) -> list[dict]:
"""Load a JSON file containing a list of records."""
with Path(filepath).open("r", encoding="utf-8") as f:
return json.load(f)
def filter_records(records: list[dict], field: str, value) -> list[dict]:
"""Kee…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 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 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 Track Checkpoint Offset After Batch Commit in Python
A batch processor that tracks the last successfully committed offset after processing records in batches, advancing the checkpoint only when each batch commits successfully.
import json
from typing import Any
class BatchProcessor:
"""Tracks checkpoint offset after committing batches."""
def __init__(self, batch_size: int = 3):
self.batch_size = batch_size
self.offset = 0 # last successfully committed offset (exclusive)
self.total_committed = 0
def …Idempotent Pipeline Dedupe by Record ID Set in Python
Filters records against a persistent set of seen IDs, returning only new ones and the updated set for idempotent pipeline processing.
def dedupe_records(records, seen_ids=None):
"""Return records whose id has not been seen before."""
if seen_ids is None:
seen_ids = set()
unique = []
for record in records:
record_id = record.get("id")
if record_id not in seen_ids:
seen_ids.add(record_id)
…Implement Exactly-Once Transaction Log in Python
A mock transaction log that deduplicates transaction IDs so each is recorded only once, with a dataclass for records and simple in-memory storage.
from dataclasses import dataclass
from typing import Dict, Optional
@dataclass
class TxnRecord:
txn_id: str
status: str
class ExactlyOnceTxnLog:
def __init__(self) -> None:
self._log: Dict[str, TxnRecord] = {}
self._processed_ids: set = set()
def record(self, txn_id: str, status: s…Browse by section
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