An open-source Python library for Data Scientists & Data Analysts designed to simplify the exploratory data analysis process. Using Edvart, you can explore data sets and generate reports with minimal coding.
This simplifies the code base and does not limit the usability.
The users might need a bit more code to use the omit_columns semantic
but it's IMHO not radical and maybe even more transparent.
Currently, users might be a bit confused whether use_columns or
omit_columns take precendence in cases like:
.add_bivariate_analysis(use_columns=['a', 'b'], omit_columns=['b', 'c'].
Should the resulting columns be ['a', 'b'] or just ['a']?
Using just one argument makes it 100% clear.
BREAKING_CHANGE: use_columns and omit_columns parameters in report
classes are replaced by columns which has the same semantic as
use_columns.
E.g. call .add_bivariate_analysis(omit_columns=['a']) can be replaced
by .add_bivariate_analysis(columns=set(df.columns) - {'a'})
This simplifies the code base and does not limit the usability. The users might need a bit more code to use the
omit_columnssemantic but it's IMHO not radical and maybe even more transparent.Currently, users might be a bit confused whether
use_columnsoromit_columnstake precendence in cases like:.add_bivariate_analysis(use_columns=['a', 'b'], omit_columns=['b', 'c']. Should the resulting columns be['a', 'b']or just['a']? Using just one argument makes it 100% clear.BREAKING_CHANGE:
use_columnsandomit_columnsparameters in report classes are replaced bycolumnswhich has the same semantic asuse_columns. E.g. call.add_bivariate_analysis(omit_columns=['a'])can be replaced by.add_bivariate_analysis(columns=set(df.columns) - {'a'})Closes #121