Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
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"
…Create Data Helper Functions in Python for Beginners
Build reusable Python helper functions to load, filter, sort, summarize, and save JSON data — a beginner-friendly starting point for small data pipelines.
import json
from pathlib import Path
from typing import Any, Dict, List
def load_json_file(filepath: str) -> Dict[str, Any]:
"""Load JSON data from a file."""
with Path(filepath).open("r", encoding="utf-8") as file:
return json.load(file)
def filter_by_key(
data: List[Dict[str, Any]], key: str,…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=(",", ":"…Group Python Events into Sessions with a Gap Timeout
Groups timestamped events into sessions, starting a new session when the time gap exceeds a specified timeout.
from itertools import groupby
from datetime import datetime, timedelta
def session_window_group(events, gap_seconds=300):
"""Group events into sessions where gap > gap_seconds starts a new session."""
if not events:
return []
events = sorted(events, key=lambda x: x[0])
sessions = []
c…How to Compress Pipeline Output Gzip Per Partition in Python
Compress each partition of pipeline output into a separate gzip file and verify the compressed data by reading it back.
import gzip
import io
import random
from pathlib import Path
def compress_partition(partition_data: list[str], output_path: Path) -> int:
"""Compress a partition of data to a gzip file, returns bytes written."""
with gzip.open(output_path, 'wt', encoding='utf-8') as f:
f.writelines(partition_data)
…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 Events by Minute with a Tumbling Window in Python
Group timestamps into fixed 60-second tumbling windows and count events per bucket using a dict.
from collections import defaultdict
from datetime import datetime, timedelta
def tumbling_window_count(events, window_seconds=60):
buckets = defaultdict(int)
for event in events:
ts = datetime.fromisoformat(event["timestamp"])
bucket_start = ts - timedelta(seconds=ts.second % window_seconds,
…How to Explode an Array Field into Multiple Rows in Python
This code flattens a list of dictionaries by exploding each array field value into its own row, duplicating the other fields as needed.
from collections import defaultdict
data = [
{"id": 1, "name": "Alice", "tags": ["python", "data", "ai"]},
{"id": 2, "name": "Bob", "tags": ["web", "devops"]},
{"id": 3, "name": "Carol", "tags": []},
]
def explode_array_field(records, array_field):
result = []
for record in records:
for v…How to Group Rows by Key into Nested Arrays in Python
This code groups rows in a list of dictionaries by a specified key and returns a dictionary with each key mapped to a list of values from another key.
from collections import defaultdict
def implode_rows(rows, key, value_key):
grouped = defaultdict(list)
for row in rows:
grouped[row[key]].append(row[value_key])
return dict(grouped)
if __name__ == "__main__":
data = [
{"category": "fruit", "item": "apple"},
{"category": "fr…How to Implement Incremental Load with Watermark by updated_at in Python
Load only new or changed rows into SQLite by comparing an updated_at timestamp against a stored watermark, returning counts and the new watermark.
import sqlite3
from datetime import datetime, timedelta
def watermark_incremental_load(db_path, table_name, last_watermark, source_data):
"""Load only rows with updated_at greater than the last watermark."""
conn = sqlite3.connect(db_path)
cursor = conn.cursor()
# Create table if it doesn't exist
…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 Merge Multiple Data Sources in Python
A beginner-friendly helper that merges lists of dictionaries from multiple sources into one combined list using key filtering.
import json
def merge_pipeline_data(*data_sources, keys=()):
"""Merge multiple data sources (list of dicts) into a single list of merged dicts.
Args:
*data_sources: One or more lists of dictionaries.
keys: Tuple of keys to include from each source (empty means all keys).
Returns:
…How to Partition Output Files by Date Key in Python
Group output files into a dictionary partitioned by a YYYYMMDD date key extracted from the filename prefix.
from pathlib import Path
from collections import defaultdict
def partition_files_by_date(directory: str) -> dict:
"""Partition output files by date key extracted from filename (YYYYMMDD prefix)."""
path = Path(directory)
partitions = defaultdict(list)
for file in path.iterdir():
if file.i…How to Reduce Aggregate Counts from Mapped Chunks in Python
Combine a list of mapped chunk dictionaries into a single aggregated count dictionary using functools.reduce.
from functools import reduce
from collections import defaultdict
def aggregate_chunks(mapped_chunks):
"""Combine mapped chunk counts into a single aggregate dict."""
return reduce(
lambda acc, chunk: {
**acc,
**{k: acc.get(k, 0) + v for k, v in chunk.items()}
},
…How to Safely Coerce Strings to Numbers in Python
A safe conversion function that turns strings into integers or floats, returning a fallback value when conversion fails.
import math
def to_number(value, fallback=None):
"""Safely coerce a string to int or float, returning fallback on failure."""
if isinstance(value, (int, float)):
return value
try:
# Try int first for clean whole numbers
return int(value)
except (ValueError, TypeError):
…How to Stream a Large JSONL File Line by Line in Python
Process a large JSON-lines file incrementally using streaming techniques to avoid loading the entire file into memory.
import json
def process_large_file(filepath, chunk_size=8192):
"""
Stream a large JSON-lines file line by line, processing each record
without loading the entire file into memory.
"""
total_count = 0
total_sum = 0
with open(filepath, 'r') as f:
while True:
chunk = …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 …How to Unpivot Wide to Long with pandas melt in Python
This code demonstrates how to use pandas.melt to unpivot a wide DataFrame into a tidy long format, converting subject columns into rows.
import pandas as pd
# Sample wide-format data
df_wide = pd.DataFrame({
'id': [1, 2, 3],
'name': ['Alice', 'Bob', 'Charlie'],
'math': [90, 85, 95],
'science': [80, 92, 88]
})
print("Original wide DataFrame:")
print(df_wide)
# Melt: unpivot subject columns into rows
df_long = pd.melt(
df_wide,
…How to Validate Data in a Python Pipeline
A helper module to validate common record types — email, positive integer, and non-empty string list — before processing data in a pipeline.
from typing import Any, Iterable
def is_valid_email(email: str) -> bool:
"""Basic email check: one '@', no spaces, dot after '@'."""
if "@" not in email or " " in email:
return False
local, _, domain = email.partition("@")
return bool(local) and "." in domain
def is_positive_int(value: Any)…How to route late-arriving data to a side output in Python
Separate late-arriving events from a streaming data batch into a dead-letter side output list using a timestamp threshold.
from collections import defaultdict
def late_arriving_side_output(events, late_threshold_ts):
"""
Mock a streaming pipeline that separates late-arriving data events
into a side output list (e.g., for dead-letter analysis).
events: list of (timestamp, data) tuples, timestamps as ints.
late_thresho…Pipeline stage compose functions left to right in Python
Compose multiple functions into a left-to-right pipeline so each stage receives the output of the previous one.
def compose(*funcs):
"""Compose functions left to right: compose(f, g, h)(x) == h(g(f(x)))"""
def composed(arg):
result = arg
for func in funcs:
result = func(result)
return result
return composed
if __name__ == "__main__":
def add_one(x):
return x + 1
…Python Exponential Backoff Retry Example
Retry a flaky function with exponential backoff and jitter-free delays, printing each attempt and finally returning the successful result.
import random
import time
def flaky_function():
if random.random() < 0.6:
raise ConnectionError("Temporary network error")
return "success"
def retry_with_exponential_backoff(func, max_retries=5, base_delay=1.0):
for attempt in range(max_retries + 1):
try:
return func()
…Rollback dataset to previous snapshot pointer in Python
A SnapshotManager class stores timestamped data snapshots and rolls back to the most recent snapshot at or before a target time.
from datetime import datetime, timedelta
class SnapshotManager:
def __init__(self):
self.snapshots = {} # timestamp -> data
self.current_pointer = None
def create_snapshot(self, data):
timestamp = datetime.now()
self.snapshots[timestamp] = data
self.current_pointer =…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
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Data pipelines & processing — Python code examples
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