clarite.plot.distributions

clarite.plot.distributions(data, filename:str, continuous_kind:str='count', nrows:int=4, ncols:int=3, quality:str='medium', variables:Union[List[str], NoneType]=None, sort:bool=True)

Create a pdf containing histograms for each binary or categorical variable, and one of several types of plots for each continuous variable.

Parameters
data: pd.DataFrame

The DataFrame containing data to be plotted

filename: string

Name of the saved pdf file. The extension will be added automatically if it was not included.

continuous_kind: string

What kind of plots to use for continuous data. Binary and Categorical variables will always be shown with histograms. One of {‘count’, ‘box’, ‘violin’, ‘qq’}

nrows: int (default=4)

Number of rows per page

ncols: int (default=3)

Number of columns per page

quality: ‘low’, ‘medium’, or ‘high’

Adjusts the DPI of the plots (150, 300, or 1200)

variables: List[str] or None

Which variables to plot. If None, all variables are plotted.

sort: Boolean (default=True)

Whether or not to sort variable names

Returns
None

Examples

>>> import clarite
>>> clarite.plot.distributions(df[['female', 'occupation', 'LBX074']], filename="test")
../../_images/distributions_count.png
>>> clarite.plot.distributions(df[['female', 'occupation', 'LBX074']], filename="test", continuous_kind='box')
../../_images/distributions_box.png
>>> clarite.plot.distributions(df[['female', 'occupation', 'LBX074']], filename="test", continuous_kind='violin')
../../_images/distributions_violin.png
>>> clarite.plot.distributions(df[['female', 'occupation', 'LBX074']], filename="test", continuous_kind='qq')
../../_images/distributions_qq.png