datachain: implement to_parquet (#97)

First part of #91, critical for the release.
It uses `to_pandas()`, so it cannot write to a parquet
file than what memory allows.

src/datachain/lib/dc.py

@@ -1,10 +1,12 @@
 import copy
+import os
 import re
 from collections.abc import Iterable, Iterator, Sequence
 from functools import wraps
 from typing import (
     TYPE_CHECKING,
     Any,
+    BinaryIO,
     Callable,
     ClassVar,
     Literal,
@@ -1178,6 +1180,25 @@ def from_parquet(
             partitioning=partitioning,
         )
 
+    def to_parquet(
+        self,
+        path: Union[str, os.PathLike[str], BinaryIO],
+        partition_cols: Optional[Sequence[str]] = None,
+        **kwargs,
+    ) -> None:
+        """Save chain to parquet file.
+
+        Parameters:
+            path : Path or a file-like binary object to save the file.
+            partition_cols : Column names by which to partition the dataset.
+        """
+        _partition_cols = list(partition_cols) if partition_cols else None
+        return self.to_pandas().to_parquet(
+            path,
+            partition_cols=_partition_cols,
+            **kwargs,
+        )
+
     @classmethod
     def create_empty(
         cls,

tests/unit/lib/test_datachain.py

@@ -817,6 +817,34 @@ def test_from_parquet_partitioned(tmp_dir, catalog):
     assert df1.equals(df)
 
 
+def test_to_parquet(tmp_dir, catalog):
+    df = pd.DataFrame(DF_DATA)
+    dc = DataChain.from_pandas(df)
+
+    path = tmp_dir / "test.parquet"
+    dc.to_parquet(path)
+
+    assert path.is_file()
+    pd.testing.assert_frame_equal(pd.read_parquet(path), df)
+
+
+def test_to_parquet_partitioned(tmp_dir, catalog):
+    df = pd.DataFrame(DF_DATA)
+    dc = DataChain.from_pandas(df)
+
+    path = tmp_dir / "parquets"
+    dc.to_parquet(path, partition_cols=["first_name"])
+
+    assert set(path.iterdir()) == {
+        path / f"first_name={name}" for name in df["first_name"]
+    }
+    df1 = pd.read_parquet(path)
+    df1 = df1.reindex(columns=df.columns)
+    df1["first_name"] = df1["first_name"].astype("str")
+    df1 = df1.sort_values("first_name").reset_index(drop=True)
+    pd.testing.assert_frame_equal(df1, df)
+
+
 @pytest.mark.parametrize("processes", [False, 2, True])
 def test_parallel(processes, catalog):
     prefix = "t & "