Add CSV parsing options (#813)

* Update dc.py

Adding support for CSV files where values can span several lines, pyarrow parser already supports it

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update dc.py

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* adding csv parse options config

* naming of parse_options_config to parse_options

* typo

* fix tests, address PR review

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ivan Shcheklein <[email protected]>

src/datachain/lib/dc.py

@@ -1942,7 +1942,7 @@ def parse_tabular(
     def from_csv(
         cls,
         path,
-        delimiter: str = ",",
+        delimiter: Optional[str] = None,
         header: bool = True,
         output: OutputType = None,
         object_name: str = "",
@@ -1952,14 +1952,16 @@ def from_csv(
         session: Optional[Session] = None,
         settings: Optional[dict] = None,
         column_types: Optional[dict[str, "Union[str, ArrowDataType]"]] = None,
+        parse_options: Optional[dict[str, "Union[str, Union[bool, Callable]]"]] = None,
         **kwargs,
     ) -> "DataChain":
         """Generate chain from csv files.
 
         Parameters:
             path : Storage URI with directory. URI must start with storage prefix such
                 as `s3://`, `gs://`, `az://` or "file:///".
-            delimiter : Character for delimiting columns.
+            delimiter : Character for delimiting columns. Takes precedence if also
+                specified in `parse_options`. Defaults to ",".
             header : Whether the files include a header row.
             output : Dictionary or feature class defining column names and their
                 corresponding types. List of column names is also accepted, in which
@@ -1973,6 +1975,8 @@ def from_csv(
             column_types : Dictionary of column names and their corresponding types.
                 It is passed to CSV reader and for each column specified type auto
                 inference is disabled.
+            parse_options: Tells the parser how to process lines.
+                See https://arrow.apache.org/docs/python/generated/pyarrow.csv.ParseOptions.html
 
         Example:
             Reading a csv file:
@@ -1990,6 +1994,12 @@ def from_csv(
         from pyarrow.dataset import CsvFileFormat
         from pyarrow.lib import type_for_alias
 
+        parse_options = parse_options or {}
+        if "delimiter" not in parse_options:
+            parse_options["delimiter"] = ","
+        if delimiter:
+            parse_options["delimiter"] = delimiter
+
         if column_types:
             column_types = {
                 name: type_for_alias(typ) if isinstance(typ, str) else typ
@@ -2017,7 +2027,7 @@ def from_csv(
                 msg = f"error parsing csv - incompatible output type {type(output)}"
                 raise DatasetPrepareError(chain.name, msg)
 
-        parse_options = ParseOptions(delimiter=delimiter)
+        parse_options = ParseOptions(**parse_options)
         read_options = ReadOptions(column_names=column_names)
         convert_options = ConvertOptions(
             strings_can_be_null=True,

tests/unit/lib/test_datachain.py

@@ -1332,6 +1332,34 @@ def test_from_csv_column_types(tmp_dir, test_session):
     assert df1["age"].dtype == pd.StringDtype
 
 
+def test_from_csv_parse_options(tmp_dir, test_session):
+    def skip_comment(row):
+        if row.text.startswith("# "):
+            return "skip"
+        return "error"
+
+    s = (
+        "animals;n_legs;entry\n"
+        "Flamingo;2;2022-03-01\n"
+        "# Comment here:\n"
+        "Horse;4;2022-03-02\n"
+        "Brittle stars;5;2022-03-03\n"
+        "Centipede;100;2022-03-04"
+    )
+
+    path = tmp_dir / "test.csv"
+    path.write_text(s)
+
+    dc = DataChain.from_csv(
+        path.as_uri(),
+        session=test_session,
+        parse_options={"invalid_row_handler": skip_comment, "delimiter": ";"},
+    )
+
+    df = dc.select("animals", "n_legs", "entry").to_pandas()
+    assert set(df["animals"]) == {"Horse", "Centipede", "Brittle stars", "Flamingo"}
+
+
 def test_to_csv_features(tmp_dir, test_session):
     dc_to = DataChain.from_values(
         f1=features, num=range(len(features)), session=test_session