Dataprep: Data Preparation in Python
Project description
Dataprep
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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
Installation
pip install dataprep
Examples & Usages
Detailed examples can be found in the examples folder.
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
andplot_missing
a column name.
df = ...
plot_missing(df, x="some_column_name")
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!
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")
Contribution
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!
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