Merge pull request #334 from jona-sassenhagen/jona/fix_pandas_future_warning_for_dropping_invariants

Fix pandas future warning for dropping invariants

Author: PaulWestenthanner

category_encoders/backward_difference.py

@@ -214,8 +214,7 @@ def transform(self, X, override_return_df=False):
         X = self.backward_difference_coding(X, mapping=self.mapping)
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/basen.py

@@ -253,8 +253,7 @@ def transform(self, X, override_return_df=False):
         X_out = self.basen_encode(X_out, cols=self.cols)
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X_out.drop(col, 1, inplace=True)
+            X_out = X_out.drop(columns=self.drop_cols)
 
         # impute missing values only in the generated columns
         # generated_cols = util.get_generated_cols(X, X_out, self.cols)

category_encoders/cat_boost.py

@@ -208,8 +208,7 @@ def transform(self, X, y=None, override_return_df=False):
         )
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/count.py

@@ -229,8 +229,7 @@ def transform(self, X, y=None, override_return_df=False):
         X, _ = self._transform_count_encode(X, y)
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/glmm.py

@@ -226,8 +226,7 @@ def transform(self, X, y=None, override_return_df=False):
         # Postprocessing
         # Note: We should not even convert these columns.
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/hashing.py

@@ -202,8 +202,7 @@ def require_data(self, data_lock, new_start, done_index, hashing_parts, cols, pr
                 # Always get df and check it after merge all data parts
                 data_part = self.hashing_trick(X_in=data_part, hashing_method=self.hash_method, N=self.n_components, cols=self.cols)
                 if self.drop_invariant:
-                    for col in self.drop_cols:
-                        data_part.drop(col, 1, inplace=True)
+                    data_part = data_part.drop(columns=self.drop_cols)
                 part_index = int(math.ceil(end_index / self.max_sample))
                 hashing_parts.put({part_index: data_part})
                 if self.verbose == 5:
@@ -308,8 +307,7 @@ def _transform(self, X, override_return_df=False):
         X = self.hashing_trick(X, hashing_method=self.hash_method, N=self.n_components, cols=self.cols)
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/helmert.py

@@ -211,8 +211,7 @@ def transform(self, X, override_return_df=False):
         X = self.helmert_coding(X, mapping=self.mapping)
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/james_stein.py

@@ -289,8 +289,7 @@ def transform(self, X, y=None, override_return_df=False):
         # Postprocessing
         # Note: We should not even convert these columns.
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/leave_one_out.py

@@ -195,8 +195,7 @@ def transform(self, X, y=None, override_return_df=False):
         )
 
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X

category_encoders/m_estimate.py

@@ -221,8 +221,7 @@ def transform(self, X, y=None, override_return_df=False):
         # Postprocessing
         # Note: We should not even convert these columns.
         if self.drop_invariant:
-            for col in self.drop_cols:
-                X.drop(col, 1, inplace=True)
+            X = X.drop(columns=self.drop_cols)
 
         if self.return_df or override_return_df:
             return X