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')
