feat(python): Add "drop_empty_cols" parameter for read_excel and `r…

…ead_ods`

py-polars/polars/_utils/various.py

@@ -253,7 +253,7 @@ def deduplicate_names(names: Iterable[str]) -> list[str]:
     seen: MutableMapping[str, int] = Counter()
     deduped = []
     for nm in names:
-        deduped.append(f"{nm}{seen[nm]}" if nm in seen else nm)
+        deduped.append(f"{nm}{seen[nm] - 1}" if nm in seen else nm)
         seen[nm] += 1
     return deduped
 

py-polars/polars/io/spreadsheet/functions.py

@@ -66,7 +66,7 @@ def _sources(
     return sources, read_multiple_workbooks
 
 
-def _standardize_duplicate_names(s: str) -> str:
+def _standardize_duplicates(s: str) -> str:
     """Standardize columns with '_duplicated_n' names."""
     return re.sub(r"_duplicated_(\d+)", repl=r"\1", string=s)
 
@@ -277,7 +277,8 @@ def read_excel(
         Indicate whether to omit empty rows when reading data into the DataFrame.
     drop_empty_cols
         Indicate whether to omit empty columns (with no headers) when reading data into
-        the DataFrame.
+        the DataFrame (note that empty column identification may vary depending on the
+        underlying engine being used).
     raise_if_empty
         When there is no data in the sheet,`NoDataError` is raised. If this parameter
         is set to False, an empty DataFrame (with no columns) is returned instead.
@@ -505,7 +506,8 @@ def read_ods(
         Indicate whether to omit empty rows when reading data into the DataFrame.
     drop_empty_cols
         Indicate whether to omit empty columns (with no headers) when reading data into
-        the DataFrame.
+        the DataFrame (note that empty column identification may vary depending on the
+        underlying engine being used).
     raise_if_empty
         When there is no data in the sheet,`NoDataError` is raised. If this parameter
         is set to False, an empty DataFrame (with no columns) is returned instead.
@@ -1129,5 +1131,5 @@ def _read_spreadsheet_xlsx2csv(
     if cast_to_boolean:
         df = df.with_columns(*cast_to_boolean)
 
-    df = df.rename(_standardize_duplicate_names)
+    df = df.rename(_standardize_duplicates)
     return _reorder_columns(df, columns)