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Dataprep let you prepare your data using a single library with a few lines of code.

Currently, you can use dataprep to:

• Collect data from common data sources (through dataprep.data_connector)
• Do your exploratory data analysis (through dataprep.eda)
• ...more modules are coming

pip install dataprep

The following examples can give you an impression of what dataprep can do:

• Documentation: Data Connector
• Documentation: EDA
• EDA Case Study: Titanic
• EDA Case Study: House Price

There are common tasks during the exploratory data analysis stage, like a quick look at the columnar distribution, or understanding the correlations between columns.

The EDA module categorizes these EDA tasks into functions helping you finish EDA tasks with a single function call.

• Want to understand the distributions for each DataFrame column? Use plot.

• Want to understand the correlation between columns? Use plot_correlation.

• Or, if you want to understand the impact of the missing values for each column, use plot_missing.

• You can drill down to get more information by given plot, plot_correlation and plot_missing a column name. E.g. for plot_missing:

Don't forget to checkout the examples folder for detailed demonstration!

You can download Yelp business search result into a pandas DataFrame, using two lines of code, without taking deep looking into the Yelp documentation!

from dataprep.data_connector import Connector

dc = Connector("yelp", auth_params={"access_token":"<Your yelp access token>"})
df = dc.query("businesses", term="korean", location="seattle")

Dataprep is in its early stage. Any contribution including:

• Filing an issue
• Providing use cases
• Writing down your user experience
• Submitting a PR
• ...

are greatly appreciated!

Please take a look at our wiki for development documentations!