Load csv tables with pandas if pyarrow fails

audformat/core/table.py

@@ -18,6 +18,7 @@
 from audformat.core.column import Column
 from audformat.core.common import HeaderBase
 from audformat.core.common import HeaderDict
+from audformat.core.common import to_pandas_dtype
 from audformat.core.errors import BadIdError
 from audformat.core.index import filewise_index
 from audformat.core.index import index_type
@@ -880,25 +881,63 @@ 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
 
         """
         levels = list(self._levels_and_dtypes.keys())
         columns = list(self.columns.keys())
-        table = csv.read_csv(
-            path,
-            read_options=csv.ReadOptions(
-                column_names=levels + columns,
-                skip_rows=1,
-            ),
-            convert_options=csv.ConvertOptions(
-                column_types=self._pyarrow_csv_schema(),
-                strings_can_be_null=True,
-            ),
-        )
-        df = self._pyarrow_table_to_dataframe(table, from_csv=True)
+        try:
+            table = csv.read_csv(
+                path,
+                read_options=csv.ReadOptions(
+                    column_names=levels + columns,
+                    skip_rows=1,
+                ),
+                convert_options=csv.ConvertOptions(
+                    column_types=self._pyarrow_csv_schema(),
+                    strings_can_be_null=True,
+                ),
+            )
+            df = self._pyarrow_table_to_dataframe(table, from_csv=True)
+        except pa.lib.ArrowInvalid:
+            # If pyarrow fails to parse the CSV file
+            # https://github.com/audeering/audformat/issues/449
+
+            # 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 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[column] = to_pandas_dtype(dtype)
+
+            df = pd.read_csv(
+                path,
+                usecols=list(columns_and_dtypes.keys()),
+                dtype=dtypes_wo_converters,
+                index_col=levels,
+                converters=converters,
+                float_precision="round_trip",
+            )
 
         self._df = df
 

tests/test_table.py

@@ -1143,6 +1143,79 @@ def test_load(tmpdir):
         os.remove(f"{path_no_ext}.{ext}")
 
 
+class TestLoadBrokenCsv:
+    r"""Test loading of malformed csv files.
+
+    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.
+
+    See https://github.com/audeering/audformat/issues/449
+
+    """
+
+    def database_with_hidden_columns(self) -> audformat.Database:
+        r"""Database with hidden columns.
+
+        Create database with hidden columns
+        that are stored in csv,
+        but not in the header of the table.
+
+        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,
+        before switching to use pyarrow.csv.read_csv()
+        in https://github.com/audeering/audformat/pull/419.
+
+        Returns:
+            database
+
+        """
+        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"]["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"] = []
+        return db
+
+    def test_load_broken_csv(self, tmpdir):
+        r"""Test loading a database table from broken csv files.
+
+        Broken csv files
+        refer to csv tables,
+        that raise an error
+        when loading with ``pyarrow.csv.read_csv()``.
+
+        Args:
+            tmpdir: tmpdir fixture
+
+        """
+        db = self.database_with_hidden_columns()
+        build_dir = audeer.mkdir(tmpdir, "build")
+        db.save(build_dir, storage_format="csv")
+        db_loaded = audformat.Database.load(build_dir, load_data=True)
+        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):
     # We have stored string dtype as object dtype before
     # and have to fix this when loading old PKL files from cache.