def summary(self, ncp=None, nobs=None): if ncp is None: ncp = self.p if nobs is None: nobs = self.n print('n : {}'.format(nobs)) print('p : {}'.format(ncp)) print('Cumulative explained variance by eigenvalue') util.progress_bar([round(val, 2) for val in self.cum_var_exp], bar_length=40, ncp=ncp) print('\nCA analysis performed with Prince')
def summary(self, ncp=None, nobs=None): if ncp is None: ncp = self.p if nobs is None: nobs = self.n print('n : {}'.format(nobs)) print('p : {}'.format(ncp)) if self.reduced is True: print('Reduced dataframe') if len(self.categorical.columns) != 0: print('The following variables are not quantitative: {}'.format( self.categorical.columns)) if self.na_values: print('Missing values are imputed by the mean of the considered variable') print('Cumulative explained variance by eigenvalue') util.progress_bar([round(val, 2) for val in self.cum_var_exp], bar_length=40, ncp=ncp) print('\nPCA analysis performed in Python')