Data pipelines & processing
ETL-style flows, batch transforms, validation, and moving data between formats.
Build a Python Utility That Detects Duplicate Records Across Multiple Excel Sheets
A Python utility that uses pandas to find overlapping records across different Excel sheets based on specified key columns.
import pandas as pd
from pathlib import Path
def find_duplicate_records_across_sheets(file_path: str, key_columns: list, sheet_names: list) -> dict:
"""
Detect duplicate records across multiple Excel sheets based on specified key columns.
Args:
file_path: Path to the Excel file
key_co…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,…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 Build Data Processing Functions in Python
Create reusable helper functions to load, filter, transform, and aggregate CSV data in Python.
import csv
from pathlib import Path
def load_data(filepath):
"""Load CSV data into a list of dicts."""
with open(filepath, "r", newline="", encoding="utf-8") as f:
return list(csv.DictReader(f))
def filter_rows(rows, column, value):
"""Keep rows where column equals value."""
return [row for…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 Implement Slowly Changing Dimension Type 2 History in Python
Build a type-2 slowly changing dimension pipeline that closes old records and opens new ones when customer data changes.
from datetime import datetime, timedelta
def apply_scd_type2(records, current_date):
"""Returns active records after inserting new records with type-2 history."""
history = []
active = {}
for record in records:
key = record["customer_id"]
if key in active:
active[key]["end…How to Process CSV Data in Python with a Data Helper
Build a beginner-friendly data helper in Python that loads a CSV file, filters rows by a condition, and summarizes numeric fields.
import csv
from pathlib import Path
DATA = [
{"name": "Alice", "score": 88, "passed": True},
{"name": "Bob", "score": 42, "passed": False},
{"name": "Carol", "score": 95, "passed": True},
]
def load_csv(file_path: Path) -> list[dict]:
with file_path.open(newline="", encoding="utf-8") as f:
r…How to perform a star schema join in Python
Denormalize mock fact and dimension tables by building lookup dicts and enriching each sales fact with customer, product, and date attributes.
from datetime import date
# Mock dimension tables
customers = [
{"customer_id": 1, "name": "Alice", "city": "New York"},
{"customer_id": 2, "name": "Bob", "city": "Los Angeles"},
{"customer_id": 3, "name": "Carol", "city": "Chicago"},
]
products = [
{"product_id": 101, "name": "Laptop", "category": "…Browse by section
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Data pipelines & processing — Python code examples
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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.
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