watex.view.QuickPlot.numfeatures#

QuickPlot.numfeatures(features=None, coerce=False, map_lower_kws=None, **sns_kws)[source]#

Plots qualitative features distribution using correlative aspect. Be sure to provide numerical features as data arguments.

Parameters:

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

  • coerce (bool,) – Constraint the data to read all features and keep only the numerical values. An error occurs if False and the data contains some non-numericalfeatures. default is False.

  • map_lower_kws (dict, Optional) – a way to customize plot. Is a dictionnary of sns.pairplot map_lower kwargs arguments. If the diagram kind is kde, plot is customized with the provided map_lower_kws arguments. if None, will check whether the diag_kind argument on sns_kws is kde before triggering the plotting map.

  • sns_kws (dict,) – Keywords word arguments of seabon pairplots. Refer to http://seaborn.pydata.org/generated/seaborn.pairplot.html for further details.

  • 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(mapflow =False, tname='flow'
                          ).fit(data)
>>> qkObj.sns_style ='darkgrid',
>>> qkObj.fig_title='Quantitative features correlation'
>>> sns_pkws={'aspect':2 ,
...          "height": 2,
# ...          'markers':['o', 'x', 'D', 'H', 's',
#                         '^', '+', 'S'],
...          'diag_kind':'kde',
...          'corner':False,
...          }
>>> marklow = {'level':4,
...          'color':".2"}
>>> qkObj.numfeatures(coerce=True, map_lower_kws=marklow, **sns_pkws)
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