def _df_append_right(self, df_or_s):
misc.start(
'appending to the right. note, this is a destructive operation')
if (type(df_or_s) is scipy.sparse.coo.coo_matrix):
self_sparse = None
for c in self.columns:
misc.debug('\tappending column: ' + c)
c_coo = scipy.sparse.coo_matrix(self[[c]])
self.drop([c], 1, inplace=True)
gc.collect()
if self_sparse == None: self_sparse = c_coo
else: self_sparse = scipy.sparse.hstack((self_sparse, c_coo))
self_sparse = scipy.sparse.hstack((self_sparse, df_or_s))
misc.stop('done appending to the right')
return self_sparse
elif utils.is_sparse(df_or_s) and not utils.is_sparse(self):
misc.debug('converting data frame to a sparse frame')
self = self.to_sparse(fill_value=0)
if type(df_or_s) is pd.Series: self[df_or_s.name] = df_or_s.values
else:
if type(df_or_s) is pd.DataFrame:
columns = df_or_s.columns.tolist()
right = df_or_s.values
else:
columns = [ ` i ` + '_2' for i in range(df_or_s.shape[1])]
right = df_or_s
def _df_append_fit_transformer(self, fit_transformer, method='transform'):
misc.start('append_fit_transformer')
if 'fit' not in method: fit_transformer.fit(self)
new_X = getattr(fit_transformer, method)(self)
if utils.is_sparse(new_X): new_X = new_X.todense()
columns = map(lambda i: 'n_new_col_' + ` i `, range(new_X.shape[1]))
new_df = pd.DataFrame(new_X, columns=columns)
X = self.copy().append_right(new_df)
misc.start('done append_fit_transformer')
return X
