Fix tests

audformat/core/table.py

@@ -881,6 +881,8 @@ def _load_csv(self, path: str):
         than the method applied here.
         We first load the CSV file as a :class:`pyarrow.Table`
         and convert it to a dataframe afterwards.
+        If this fails,
+        we fall back to :func:`pandas.read_csv()`.
 
         Args:
             path: path to table, including file extension
@@ -905,20 +907,32 @@ def _load_csv(self, path: str):
             # If pyarrow fails to parse the CSV file
             # https://github.com/audeering/audformat/issues/449
 
-            # Replace dtype with converter for dates or timestamps
+            # Collect csv file columns and data types.
+            # index
+            columns_and_dtypes = self._levels_and_dtypes
+            # columns
+            for column_id, column in self.columns.items():
+                if column.scheme_id is not None:
+                    columns_and_dtypes[column_id] = self.db.schemes[
+                        column.scheme_id
+                    ].dtype
+                else:
+                    columns_and_dtypes[column_id] = define.DataType.OBJECT
+
+            # Replace data type with converter for dates or timestamps
             converters = {}
             dtypes_wo_converters = {}
-            for level, dtype in self._levels_and_dtypes.items():
-                if dtype == "date":
-                    converters[level] = lambda x: pd.to_datetime(x)
-                elif dtype == "time":
-                    converters[level] = lambda x: pd.to_timedelta(x)
+            for column, dtype in columns_and_dtypes.items():
+                if dtype == define.DataType.DATE:
+                    converters[column] = lambda x: pd.to_datetime(x)
+                elif dtype == define.DataType.TIME:
+                    converters[column] = lambda x: pd.to_timedelta(x)
                 else:
-                    dtypes_wo_converters[level] = to_pandas_dtype(dtype)
+                    dtypes_wo_converters[column] = to_pandas_dtype(dtype)
 
             df = pd.read_csv(
                 path,
-                usecols=levels + columns,
+                usecols=list(columns_and_dtypes.keys()),
                 dtype=dtypes_wo_converters,
                 index_col=levels,
                 converters=converters,

tests/test_table.py

@@ -1144,9 +1144,9 @@ def test_load(tmpdir):
 
 
 def test_load_broken_csv(tmpdir):
-    r"""Test loading of malformed CSV files.
+    r"""Test loading of malformed csv files.
 
-    If csv files contain a lot of special character,
+    If csv files contain a lot of special characters,
     or a different number of columns,
     than specified in the database header,
     loading of them should not fail.
@@ -1160,18 +1160,39 @@ def test_load_broken_csv(tmpdir):
     build_dir = audeer.mkdir(tmpdir, "build")
 
     # Create database with single table and column
+    #
+    # Ensure:
+    #
+    # * it contains an empty table
+    # * the columns use schemes with time and date data types
+    # * at least one column has no scheme
+    #
+    # as those cases needed special care with csv files
+    #
     db = audformat.Database("mydb")
+    db.schemes["date"] = audformat.Scheme("date")
+    db.schemes["time"] = audformat.Scheme("time")
     db["table"] = audformat.Table(audformat.filewise_index("file.wav"))
-    db["table"]["column"] = audformat.Column()
-    db["table"]["column"].set(["label"])
-
-    # Add another column to dataframe,
-    # without adding a column to the header
-    db["table"].df["hidden-column"] = ["hidden-label"]
+    db["table"]["date"] = audformat.Column(scheme_id="date")
+    db["table"]["date"].set([pd.to_datetime("2018-10-26")])
+    db["table"]["time"] = audformat.Column(scheme_id="time")
+    db["table"]["time"].set([pd.Timedelta(1)])
+    db["table"]["no-scheme"] = audformat.Column()
+    db["table"]["no-scheme"].set(["label"])
+    db["empty-table"] = audformat.Table(audformat.filewise_index())
+    db["empty-table"]["column"] = audformat.Column()
+
+    # Add a hidden column to the table dataframes,
+    # without adding it to the table header
+    db["table"].df["hidden"] = ["hidden"]
+    db["empty-table"].df["hidden"] = []
 
     db.save(build_dir, storage_format="csv")
     db_loaded = audformat.Database.load(build_dir, load_data=True)
-    assert "hidden-column" not in db_loaded["table"].df
+    assert "table" in db_loaded
+    assert "empty-table" in db_loaded
+    assert "hidden" not in db_loaded["table"].df
+    assert "hidden-column" not in db_loaded["empty-table"].df
 
 
 def test_load_old_pickle(tmpdir):