DataPrep V0.4.4 is out now! Click here for more.
Low-Code
Data Preparation
Collect, clean, and visualize your data in python with a few lines of code
- from dataprep.datasets import load_dataset
- from dataprep.eda import create_report
- df = load_dataset("titanic")
- create_report(df).show()
- from dataprep.connector import connect
- dc = connect("twitter", _auth={"client_id":client_id, "client_secret":client_secret})
- df = await dc.query("twitter", q="covid-19", _count=1000)
- from dataprep.datasets import load_dataset
- from dataprep.clean import clean_address
- df = load_dataset("waste_hauler")
- clean_address(df, "LOCAL ADDRESS")
Think different.
"We were disappointed, if not surprised, to see that data wrangling still takes the lion’s share of time in a typical data professional’s day. Data preparation and cleansing takes valuable time away from real data science work and has a negative impact on overall job satisfaction."
2020 State of Data Science: Moving From Hype Toward Maturity, Anaconda
Designed for Notebook Users
DataPrep is designed for computational notebooks, the most popular environment among data scientists.
Integrate Seamlessly with the Python Ecosystem
DataPrep is built using Pandas/Dask DataFrame and can be seamlessly integrated with other Python libraries.
Embrace Open Source
DataPrep is free, open-source software released under the MIT license. Anyone can reuse DataPrep code for any purpose.
DataPrep.EDA
DataPrep.EDA is the fastest and the easiest EDA tool in Python. It allows data scientists to understand a Pandas/Dask DataFrame with a few lines of code in seconds.
DataPrep.Clean
DataPrep.Clean aims to provide a large number of functions with a unified interface for cleaning and standardizing data of various semantic types in a Pandas or Dask DataFrame.
DataPrep.Connector
DataPrep.Connector provides an intuitive, open-source API wrapper that speeds up development by standardizing calls to multiple APIs as a simple workflow. Streamline calls to multiple APIs through one intuitive library.
DataPrep.Connector also support loading data from databases through SQL queries. With one line of code, you can speed up pandas.read_sql by 10X with 3X less memory usage!
DataPrep Components
DataPrep
DataPrep.Connector
Available to use
DataPrep.EDA
Available to use
DataPrep.Clean
Available to use
DataPrep.Feature
Planning
DataPrep.Integrate
Planning
... and more
News
Fetching data...
Contribution
There are many ways to contribute to DataPrep:
- Submit bugs and verify fixes.
- Review the source code and changes.
- Engage with other DataPrep users and developers on StackOverflow.
- Help each other in the DataPrep Community Discord and Mail list & Forum.
- Provide use cases and write down your user experience.
- Please take a look at our wiki for development documentations!
Getting Started
Learning DataPrep is easy whether you are a data scientist or a beginner in Python:
- Install the latest release.
- Read the user guide.
- Watch the tutorials.
- Checkout the examples.
© 2022 SFU Database System Lab. MIT Licensed.