dataprep 0.2.8

Dataprep: Data Preparation in Python

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Dataprep lets 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

Documentation | Mail List & Forum

Installation

pip install dataprep

Examples & Usages

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

EDA

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!

Data Connector

You can download Yelp business search result into a pandas DataFrame, using two lines of code, without taking deep looking into the Yelp documentation! Moreover, Data Connector will automatically do the pagination for you so that you can specify the desire count of the returned results without even considering the count-per-request restriction from the API.

The code requests 120 records even though Yelp restricts you can only fetch 50 per request.

Contribute

There are many ways to contribute to Dataprep.

• Submit bugs and help us verify fixes as they are checked in.
• Review the source code changes.
• Engage with other Dataprep users and developers on StackOverflow.
• Help each other in the Dataprep Community Discord and Mail list & Forum.
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• Contribute bug fixes.
• Providing use cases and writing down your user experience.

Please take a look at our wiki for development documentations!