# sfu-db/dataprep

Open-source low code data preparation library in python. Collect, clean and visualization your data in python with a few lines of code.

Repository: https://github.com/sfu-db/dataprep
Canonical: https://ross.abutalabs.com/products/dataprep
Homepage: http://dataprep.ai
Language: Python
License: MIT
License Family: permissive
Topics: dataprep, data-science, datapreparation, dataconnector, eda, exploratory-data-analysis, data-exploration, connector, cleaning, datacleaning, apiwrapper, webconnector, apis
Last push: 2024-06-27T16:57:45+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2670, "days_push": 797, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2248, forks 226 (observed 2026-08-28T04:06:30.233675+00:00)

## What it is
DataPrep is a Python library for low-code data preparation, offering modules to collect data from common APIs (connector), run fast exploratory data analysis with interactive profile reports (eda), and clean/standardize messy data (clean). It works with Pandas and Dask DataFrames and is designed for notebook users.

## Use cases
- generate an eda profile report from a pandas dataframe
- clean and standardize addresses or phone numbers in a dataset
- collect data from twitter or other apis into a dataframe
- explore a dataset with a few lines of code
- prepare messy csv data for machine learning
- visualize column distributions in a notebook

## When to avoid
- you need a full ETL pipeline orchestration tool
- your data sources are unsupported by the connector
- you need a highly maintained library with frequent updates
- you prefer writing explicit custom cleaning logic

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, data-visualization, web-scraping, etl, analytics
- domain: data-science, data-visualization, analytics
- platform: python, cross-platform
- tags: eda, data-cleaning, data-connector, low-code, pandas, dask, profile-report, exploratory-data-analysis, data-engineering

## Member repositories
- sfu-db/dataprep (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:30.233675+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:43:57.341577+00:00, confidence not recorded.
  - readme: https://github.com/sfu-db/dataprep (fetched 2026-08-28T04:06:30.233675+00:00, sha 73ade4d2d404)
  - homepage: http://dataprep.ai (fetched 2026-08-29T10:24:17.542592+00:00, sha ab5e31194204)
  - registry_pypi: https://pypi.org/pypi/dataprep/json (fetched 2026-08-29T10:24:17.551599+00:00, sha 4a58141c68ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.