DataPrep
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
About DataPrep
DataPrep is an open-source Python library for data preparation with modules to collect data, perform exploratory data analysis, and clean and standardize data. It is installed with pip install -U dataprep and works with Pandas and Dask DataFrames. Its EDA module can create profile reports with create_report and generate plots for tasks including correlation and missing values, with automatic detection of patterns such as outliers. Its Clean module provides functions for cleaning and validating columns using a consistent syntax, a GUI in Jupyter Notebook, and a report summarizing data alterations. Its Connector module loads data from web APIs and databases.
Key features
- Create profile reports from Pandas/Dask DataFrames with create_report
- Generate plots for EDA tasks including correlation and missing values
- Automatically highlight data insights such as outliers in columns
- Clean and standardize columns using unified clean_type functions
- Validate column values with validate_type functions
- Load data from web APIs and databases via connector
Best for: Python users working with Pandas or Dask DataFrames who need to collect, explore, and clean data with little code.
Good for
- Accepts both Pandas and Dask DataFrames, including data in a Dask cluster
- Provides a Jupyter Notebook GUI for cleaning a DataFrame without coding
- Generates a report summarizing alterations made during cleaning
Watch out for
- Additional modules are described as still coming
DataPrep at a glance
- Website
- github.com/sfu-db/dataprep
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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DataPrep FAQ
How do I install DataPrep?
Install it with pip install -U dataprep.
What input does create_report accept?
A Pandas or Dask DataFrame.
What syntax do the cleaning functions use?
Each function follows clean_{type}(df, 'column name'), for example clean_country(df, 'country').
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