watex.view.QuickPlot.naiveviz#

QuickPlot.naiveviz(x=None, y=None, kind='scatter', s_col='lwi', leg_kws={}, **pd_kws)[source]#

Creates a plot to visualize the samples distributions according to the geographical coordinates x and y.

Parameters

  • x (str ,) – Column name to hold the x-axis values

  • y (str,) – column na me to hold the y-axis values

  • s_col (column for scatter points. ‘Default is fs time the features) – column lwi.

  • pd_kws (dict, optional,) – Pandas plot keywords arguments

  • leg_kws (dict, kws) – Matplotlib legend keywords arguments

  • 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.transformers import StratifiedWithCategoryAdder
>>> from watex.view.plot import QuickPlot
>>> from watex.datasets import load_bagoue
>>> df = load_bagoue ().frame
>>> stratifiedNumObj= StratifiedWithCategoryAdder('flow')
>>> strat_train_set , *_=         ...    stratifiedNumObj.fit_transform(X=df)
>>> pd_kws ={'alpha': 0.4,
...         'label': 'flow m3/h',
...         'c':'flow',
...         'cmap':plt.get_cmap('jet'),
...         'colorbar':True}
>>> qkObj=QuickPlot(fs=25.)
>>> qkObj.fit(strat_train_set)
>>> qkObj.naiveviz( x= 'east', y='north', **pd_kws)