Remove changes to concat()

audformat/core/utils.py

@@ -37,7 +37,6 @@ def concat(
         objs: typing.Sequence[typing.Union[pd.Series, pd.DataFrame]],
         *,
         overwrite: bool = False,
-        rename: bool = False,
 ) -> typing.Union[pd.Series, pd.DataFrame]:
     r"""Concatenate objects.
 
@@ -69,20 +68,14 @@ def concat(
     Args:
         objs: objects
         overwrite: overwrite values where indices overlap
-        rename: rename column
-            instead of raising an error
-            if two columns have the same name,
-            but different content.
-            Add ``'-1'`` at end of second column
 
     Returns:
         concatenated objects
 
     Raises:
         ValueError: if level and dtypes of object indices do not match
         ValueError: if columns with the same name have different dtypes
-        ValueError: if ``rename`` is ``False``
-            and values in the same position do not match
+        ValueError: if values in the same position do not match
 
     Examples:
         >>> concat(
@@ -175,21 +168,6 @@ def concat(
         f3   0 days NaT    2.0      b
 
     """
-
-    def add_column(columns, column):
-        # Adjust dtype and initialize empty column in columns.
-        if pd.api.types.is_integer_dtype(column.dtype):
-            dtype = 'Int64'
-        elif pd.api.types.is_bool_dtype(column.dtype):
-            dtype = 'boolean'
-        else:
-            dtype = column.dtype
-        columns[column.name] = pd.Series(
-            index=index,
-            dtype=dtype,
-        )
-        return columns
-
     if not objs:
         return pd.Series([], index=pd.Index([]), dtype='object')
 
@@ -216,7 +194,6 @@ def add_column(columns, column):
 
     # reindex all columns to the new index
     columns_reindex = {}
-    rename_counter = 0
     for column in columns:
 
         # if we already have a column with that name, we have to merge them
@@ -265,31 +242,32 @@ def add_column(columns, column):
                     combine.dropna(inplace=True)
                     differ = combine['left'] != combine['right']
                     if np.any(differ):
-                        if rename:
-                            # Rename column with different labels
-                            rename_counter += 1
-                            column.name = f'{column.name}-{rename_counter}'
-                            columns_reindex = add_column(
-                                columns_reindex,
-                                column,
-                            )
-                        else:
-                            max_display = 10
-                            overlap = combine[differ]
-                            msg_overlap = str(overlap[:max_display])
-                            msg_tail = '\n...' \
-                                if len(overlap) > max_display \
-                                else ''
-                            raise ValueError(
-                                "Found overlapping data in column "
-                                f"'{column.name}':\n"
-                                f"{msg_overlap}{msg_tail}"
-                            )
+                        max_display = 10
+                        overlap = combine[differ]
+                        msg_overlap = str(overlap[:max_display])
+                        msg_tail = '\n...' \
+                            if len(overlap) > max_display \
+                            else ''
+                        raise ValueError(
+                            "Found overlapping data in column "
+                            f"'{column.name}':\n"
+                            f"{msg_overlap}{msg_tail}"
+                        )
 
             # drop NaN to avoid overwriting values from other column
             column = column.dropna()
         else:
-            columns_reindex = add_column(columns_reindex, column)
+            # Adjust dtype and initialize empty column
+            if pd.api.types.is_integer_dtype(column.dtype):
+                dtype = 'Int64'
+            elif pd.api.types.is_bool_dtype(column.dtype):
+                dtype = 'boolean'
+            else:
+                dtype = column.dtype
+            columns_reindex[column.name] = pd.Series(
+                index=index,
+                dtype=dtype,
+            )
         columns_reindex[column.name][column.index] = column
 
     # Use `None` to force `{}` return the correct index, see