dataprep 0.2.0

Dataprep: Data Preparation in Python

DataPrep

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Dataprep is a collection of functions that helps you accomplish tasks before you build a predictive model.

Implementation Status

Currently, you can use dataprep to:

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

Installation

pip install dataprep

dataprep is in its alpha stage now, so please manually specific the version number.

Examples & Usages

More detailed examples can be found at the examples folder.

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!

from dataprep.data_connector import Connector

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

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.

from dataprep.eda import plot

df = ...

plot(df)

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

from dataprep.eda import plot_correlation

df = ...

plot_correlation(df)

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

from dataprep.eda import plot_missing

df = ...

plot_missing(df)

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

df = ...

plot_missing(df, x="some_column_name")

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

Contribution

Contribution is always welcome. If you want to contribute to dataprep, be sure to read the contribution guidelines.