Remove all prints

sdv/data_processing/data_processor.py

@@ -841,11 +841,7 @@ def reverse_transform(self, data, reset_keys=False):
             for column in self._hyper_transformer._output_columns
             if column in data.columns
         ]
-        print(f'All columns: {self._hyper_transformer._output_columns}')
-        print(f'columns in data: {data.columns}')
-        print(f'data into reverse_transformer: {data}')
         reversed_data = data
-        print(f'reversible_columns before : {reversible_columns}')
         try:
             if not data.empty:
                 reversed_data = self._hyper_transformer.reverse_transform_subset(
@@ -854,8 +850,6 @@ def reverse_transform(self, data, reset_keys=False):
         except rdt.errors.NotFittedError:
             LOGGER.info(f'HyperTransformer has not been fitted for table {self.table_name}')
 
-        print(f'reversed_data.columns : {reversed_data.columns}')
-
         for transformer in self.grouped_columns_to_transformers.values():
             if not transformer.output_columns:
                 reversed_data = transformer.reverse_transform(reversed_data)
@@ -867,8 +861,6 @@ def reverse_transform(self, data, reset_keys=False):
             for column in self.metadata.columns.keys() - set(sampled_columns + self._keys)
             if self._hyper_transformer.field_transformers.get(column)
         ]
-        for col in missing_columns:
-            print(f'Col: {col} : {self._hyper_transformer.field_transformers.get(col)}')
         if missing_columns and num_rows:
             anonymized_data = self._hyper_transformer.create_anonymized_columns(
                 num_rows=num_rows,

sdv/single_table/base.py

@@ -606,12 +606,10 @@ def _sample_rows(self, num_rows, conditions=None, transformed_conditions=None,
             self._set_random_state(FIXED_RNG_SEED)
 
         need_sample = self._data_processor.get_sdtypes(primary_keys=False) or keep_extra_columns
-        print(f'need_sample: {need_sample}')
         if self._model and need_sample:
 
             if conditions is None:
                 raw_sampled = self._sample(num_rows)
-                print(f'sampled rows: {raw_sampled.columns}')
             else:
                 try:
                     raw_sampled = self._sample(num_rows, transformed_conditions)

sdv/single_table/utils.py

@@ -303,7 +303,7 @@ def unflatten_dict(flat):
 
             else:
                 subdict = unflattened.setdefault(key, {})
-                if subkey.isdigit():
+                if subkey.isdigit() and key != 'univariates':
                     subkey = int(subkey)
 
                 inner = subdict.setdefault(subkey, {})