Pivot: High-Performance Python Pipeline Tool
Change your code. Pivot knows what to run.
Pivot is a Python pipeline tool with automatic code change detection. Stages are plain
Python functions; you wire them together by calling them inside a Pipeline block, and
Pivot works out what needs to re-run.
- Automatic code change detection using Python introspection
- No paths in pipeline code: Pivot chooses where every output is stored
- Per-stage lock files for fast parallel writes
- Warm worker pools with preloaded imports
- Content-addressable caching with S3 or Google Cloud Storage remotes
Python: 3.13+ | Platform: Unix (Linux/macOS)
Quick Start
pip install pivot
mkdir my_project && cd my_project
git init && pivot init
Put a raw input under data/raw/:
mkdir -p data/raw
printf 'name,value\nalice,100\nbob,200\ncarol,\n' > data/raw/records.csv
Define stages and the pipeline in pipeline.py:
# pipeline.py
import pandas as pd
from pivot import Pipeline, StageParams, stage
class CleanParams(StageParams):
min_value: int = 0
@stage
def clean(records: pd.DataFrame, params: CleanParams) -> pd.DataFrame:
kept = records.dropna()
return kept[kept["value"] >= params.min_value]
@stage
def summarize(rows: pd.DataFrame) -> dict:
return {"count": len(rows), "total": int(rows["value"].sum())}
with Pipeline("demo") as pipeline:
records = pipeline.input("records.csv")
cleaned = clean(records, params=CleanParams(min_value=50))
summarize(cleaned)
Then run it; Pivot resolves the DAG from the handles passed between stages:
pivot repro # runs clean, then summarize
pivot repro # nothing changed: both stages are cached
pivot repro --watch # re-run automatically on file changes
Pivot writes outputs to data/demo/clean.jsonl and data/demo/summarize.yaml.
Learn More
- Documentation
- Source code
- Interactive TUI:
pip install pivot[tui], thenpivot repro --tui
1 maintainer
Metadata
1 maintainer
Release files for pivot 0.5.4
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