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Data pipelines & processing

ETL-style flows, batch transforms, validation, and moving data between formats.

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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.

json pipeline helpers

Python

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,…

14 0 Open

Data pipelines & processing easy

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.

json data cleaning data pipelines

Python

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."""…

14 0 Open

Data pipelines & processing easy

How to Parse Data in Python: A Beginner's Helper

This helper parses a JSON payload, extracts user names, emails, and signup dates, then summarizes the results.

json parsing data-processing

Python

import json
from datetime import datetime
from typing import Dict, List


def parse_data(payload: str) -> Dict[str, List]:
    """Parse a JSON payload and extract useful fields."""
    raw = json.loads(payload)
    users = raw.get("users", [])

    parsed = {
        "names": [],
        "emails": [],
        "signup_…

15 0 Open

Data pipelines & processing easy

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.

csv data-processing pathlib

Python

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…

13 0 Open

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