Handle pandas timestamps

python/deltalake/schema.py

@@ -1,7 +1,42 @@
-from typing import Union
+from typing import TYPE_CHECKING, Tuple, Union
+
+import pyarrow as pa
+
+if TYPE_CHECKING:
+    import pandas as pd
 
 from ._internal import ArrayType, Field, MapType, PrimitiveType, Schema, StructType
 
 # Can't implement inheritance (see note in src/schema.rs), so this is next
 # best thing.
 DataType = Union["PrimitiveType", "MapType", "StructType", "ArrayType"]
+
+
+def delta_arrow_schema_from_pandas(
+    data: "pd.DataFrame",
+) -> Tuple[pa.Table, pa.Schema]:
+    """
+    Infers the schema for the delta table from the Pandas DataFrame.
+    Necessary because of issues such as:  https://github.com/delta-io/delta-rs/issues/686
+
+    :param data: Data to write.
+    :return: A PyArrow Table and the inferred schema for the Delta Table
+    """
+
+    table = pa.Table.from_pandas(data)
+    schema = table.schema
+    schema_out = []
+    for field in schema:
+        if isinstance(field.type, pa.TimestampType):
+            f = pa.field(
+                name=field.name,
+                type=pa.timestamp("us"),
+                nullable=field.nullable,
+                metadata=field.metadata,
+            )
+            schema_out.append(f)
+        else:
+            schema_out.append(field)
+    schema = pa.schema(schema_out, metadata=schema.metadata)
+    data = pa.Table.from_pandas(data, schema=schema)
+    return data, schema

python/deltalake/writer.py

@@ -35,6 +35,8 @@
 import pyarrow.fs as pa_fs
 from pyarrow.lib import RecordBatchReader
 
+from deltalake.schema import delta_arrow_schema_from_pandas
+
 from ._internal import DeltaDataChecker as _DeltaDataChecker
 from ._internal import PyDeltaTableError
 from ._internal import write_new_deltalake as _write_new_deltalake
@@ -132,8 +134,12 @@ def write_deltalake(
     :param overwrite_schema: If True, allows updating the schema of the table.
     :param storage_options: options passed to the native delta filesystem. Unused if 'filesystem' is defined.
     """
+
     if _has_pandas and isinstance(data, pd.DataFrame):
-        data = pa.Table.from_pandas(data)
+        if schema is not None:
+            data = pa.Table.from_pandas(data, schema=schema)
+        else:
+            data, schema = delta_arrow_schema_from_pandas(data)
 
     if schema is None:
         if isinstance(data, RecordBatchReader):

python/stubs/pyarrow/__init__.pyi

@@ -23,6 +23,8 @@ float16: Any
 float32: Any
 float64: Any
 dictionary: Any
+timestamp: Any
+TimestampType: Any
 
 py_buffer: Callable[[bytes], Any]
 NativeFile: Any

python/tests/test_writer.py

@@ -272,12 +272,16 @@ def test_fails_wrong_partitioning(existing_table: DeltaTable, sample_data: pa.Ta
 
 
 @pytest.mark.pandas
-def test_write_pandas(tmp_path: pathlib.Path, sample_data: pa.Table):
+@pytest.mark.parametrize("schema_provided", [True, False])
+def test_write_pandas(tmp_path: pathlib.Path, sample_data: pa.Table, schema_provided):
     # When timestamp is converted to Pandas, it gets casted to ns resolution,
     # but Delta Lake schemas only support us resolution.
-    sample_pandas = sample_data.to_pandas().drop(["timestamp"], axis=1)
-    write_deltalake(str(tmp_path), sample_pandas)
-
+    sample_pandas = sample_data.to_pandas()
+    if schema_provided is True:
+        schema = sample_data.schema
+    else:
+        schema = None
+    write_deltalake(str(tmp_path), sample_pandas, schema=schema)
     delta_table = DeltaTable(str(tmp_path))
     df = delta_table.to_pandas()
     assert_frame_equal(df, sample_pandas)