/python-pipeline

name: python-pipeline
description: Python data pipelines with modular architecture. Use for content workflows, batch jobs, or Google Sheets/Drive integration.

Python data pipeline development

Patterns for building production-quality data processing pipelines with Python.

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**Targeted at Python 3.11+** for `asyncio.TaskGroup` and exception groups; Python 3.12+ for the lighter `type X = ...` syntax. Pin a 3.13+ runtime if you want the JIT or experimental free-threading; the patterns here don't depend on either.

Choosing a DataFrame engine: pandas vs polars vs DuckDB

For a long time pandas was the default for any tabular work in Python. As of 2026 the default has shifted: **polars** is the right pick for multi-GB pipelines on a single machine, **DuckDB** is the right pick when SQL or larger-than-RAM scans are involved, and **pandas** stays useful for small data and the ML/notebook ecosystem (scikit-learn, statsmodels, plotnine all speak it natively).

| Tool | When | Why | |---|---|---| | pandas | < ~1 GB data, ML interop, single-threaded familiarity | Mature, ubiquitous, eager DataFrame model. Slowest in benchmarks but most ecosystem support. | | polars | 1 GB - tens of GB on one box, performance-critical pipelines | Multithreaded by default, lazy query engine, Arrow-native. ~5x speedup over pandas on filter / aggregate at 100M rows. | | DuckDB | SQL workflows, larger-than-RAM, parquet/CSV scanning, joins across many files | Vectorized + pipelined execution, cost-based optimizer, streaming scans. Works great as a thin wrapper over a directory of parquet files. |

All three speak Apache Arrow, so zero-copy interop between them is the pragmatic answer most of the time:

import polars as pl
import duckdb

# Polars: read a directory of CSVs, filter, group
df = (
    pl.scan_csv('data/articles_*.csv')
      .filter(pl.col('published_at') >= '2026-01-01')
      .group_by('source')
      .agg(pl.len().alias('count'), pl.col('word_count').mean())
      .collect()
)

# DuckDB: same shape with SQL, no intermediate copy
con = duckdb.connect()
df = con.execute("""
    SELECT source, COUNT(*) AS count, AVG(word_count) AS avg_wc
    FROM 'data/articles_*.csv'
    WHERE published_at >= '2026-01-01'
    GROUP BY source
""").pl()  # returns a Polars DataFrame; use .df() for pandas

# Hand off to pandas only at the boundary that needs it (e.g. scikit-learn)
import pandas as pd
pdf = df.to_pandas()

If your pipeline already uses pandas everywhere, don't pre-emptively rewrite. Migrate the bottleneck stages first, typically the CSV-load + filter step.

Architecture patterns

Modular processor architecture

src/
├── workflow.py              # Main orchestrator
├── dispatcher.py            # Content-type router
├── processors/
│   ├── __init__.py
│   ├── base.py             # Abstract base class
│   ├── article_processor.py
│   ├── video_processor.py
│   └── audio_processor.py
├── services/
│   ├── sheets_service.py   # Google Sheets integration
│   ├── drive_service.py    # Google Drive integration
│   └── ai_service.py       # Gemini API wrapper
├── utils/
│   ├── logger.py
│   └── rate_limiter.py
└── config.py               # Environment configuration

Dispatcher pattern

from typing import Protocol
from urllib.parse import urlparse

class Processor(Protocol):
    def can_process(self, url: str) -> bool: ...
    def process(self, url: str, metadata: dict) -> dict: ...

class Dispatcher:
    def __init__(self):
        self.processors: list[Processor] = [
            ArticleProcessor(),
            VideoProcessor(),
            AudioProcessor(),
            SocialProcessor(),
        ]

    def dispatch(self, url: str, metadata: dict) -> dict:
        for processor in self.processors:
            if processor.can_process(url):
                return processor.process(url, metadata)
        raise ValueError(f"No processor found for URL: {url}")

# Pattern-based routing
class ArticleProcessor:
    DOMAINS = ['nytimes.com', 'washingtonpost.com', 'medium.com']

    def can_process(self, url: str) -> bool:
        domain = urlparse(url).netloc.replace('www.', '')
        return any(d in domain for d in self.DOMAINS)

CSV-based pipeline workflow

import csv
from pathlib import Path
from dataclasses import dataclass, asdict
from typing import Iterator

@dataclass
class Record:
    id: str
    url: str
    title: str | None = None
    content: str | None = None
    status: str = 'pending'

def read_input(path: Path) -> Iterator[Record]:
    with open(path, 'r', encoding='utf-8') as f:
        reader = csv.DictReader(f)
        for row in reader:
            yield Record(**{k: v for k, v in row.items() if k in Record.__annotations__})

def write_output(records: list[Record], path: Path):
    with open(path, 'w', encoding=