watex.view.QuickPlot.joint2features#

QuickPlot.joint2features(features, *, join_kws=None, marginals_kws=None, **sns_kws)[source]#

Joint method allows to visualize correlation of two features.

Draw a plot of two features with bivariate and univariate graphs.

Parameters

  • features (list) – List of numerical features to plot for correlating analyses. will raise an error if features does not exist in the data

  • join_kws (dict, optional) – Additional keyword arguments are passed to the function used to draw the plot on the joint Axes, superseding items in the joint_kws dictionary.

  • marginals_kws (dict, optional) – Additional keyword arguments are passed to the function used to draw the plot on the marginals Axes.

  • sns_kwargs (dict, optional) – keywords arguments of seaborn joinplot methods. Refer to <http://seaborn.pydata.org/generated/seaborn.jointplot.html> for more details about usefull kwargs to customize plots.

  • data (str or pd.core.DataFrame) – Path -like object or Dataframe. Long-form (tidy) dataset for plotting. Each column should correspond to a variable, and each row should correspond to an observation. If data is given as path-like object,`QuickPlot` reads and sanitizes data before plotting. Be aware in this case to provide the target name and possible the classes for data inspection. Both str or dataframe need to provide the name of target.

Returns

Returns self for easy method chaining.

Return type

QuickPlot instance

Notes

The argument for data must be passed to fit method. data parameter is not allowed in other QuickPlot method. The description of the parameter data is to give a synopsis of the kind of data the plot expected. An error will raise if force to pass data argument as a keyword arguments.

Examples

>>> from watex.view.plot import QuickPlot
>>> from watex.datasets import load_bagoue
>>> data = load_bagoue ().frame
>>> qkObj = QuickPlot( lc='b', sns_style ='darkgrid',
...             fig_title='Quantitative features correlation'
...             ).fit(data)
>>> sns_pkws={
...            'kind':'reg' , #'kde', 'hex'
...            # "hue": 'flow',
...               }
>>> joinpl_kws={"color": "r",
                'zorder':0, 'levels':6}
>>> plmarg_kws={'color':"r", 'height':-.15, 'clip_on':False}
>>> qkObj.joint2features(features=['ohmS', 'lwi'],
...            join_kws=joinpl_kws, marginals_kws=plmarg_kws,
...            **sns_pkws,
...            )