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Data pipelines & processing

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

3 matches

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.

etl csv json

Python

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…

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Data pipelines & processing easy

How to Filter Data in Python

Filter a list of dictionaries by exact key-value matches or numerical ranges using concise list comprehensions.

filtering list-comprehension dictionaries

Python

from typing import List, Dict, Any


def filter_data(
    data: List[Dict[str, Any]], key: str, value: Any
) -> List[Dict[str, Any]]:
    """Return records where data[key] equals value."""
    return [record for record in data if record.get(key) == value]


def filter_by_range(
    data: List[Dict[str, Any]], key: str…

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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…

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