feat: support conversion from pyarrow RecordBatch to pandas DataFrame

db_dtypes/__init__.py

@@ -17,6 +17,7 @@
 
 import datetime
 import re
+from typing import Union
 
 import numpy
 import packaging.version
@@ -29,13 +30,16 @@
 import pandas.core.dtypes.generic
 import pandas.core.nanops
 import pyarrow
+import pyarrow.compute
 
 from db_dtypes.version import __version__
 from db_dtypes import core
 
 
 date_dtype_name = "dbdate"
 time_dtype_name = "dbtime"
+_EPOCH = datetime.datetime(1970, 1, 1)
+_NPEPOCH = numpy.datetime64(_EPOCH)
 
 pandas_release = packaging.version.parse(pandas.__version__).release
 
@@ -52,6 +56,33 @@ class TimeDtype(core.BaseDatetimeDtype):
     def construct_array_type(self):
         return TimeArray
 
+    @staticmethod
+    def __from_arrow__(
+        array: Union[pyarrow.Array, pyarrow.ChunkedArray]
+    ) -> "TimeArray":
+        """Convert to dbtime data from an Arrow array.
+
+        See:
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
+        """
+        # We can't call combine_chunks on an empty array, so short-circuit the
+        # rest of the function logic for this special case.
+        if len(array) == 0:
+            return TimeArray(numpy.array([], dtype="datetime64[ns]"))
+
+        # We can't cast to timestamp("ns"), but time64("ns") has the same
+        # memory layout: 64-bit integers representing the number of nanoseconds
+        # since the datetime epoch (midnight 1970-01-01).
+        array = pyarrow.compute.cast(array, pyarrow.time64("ns"))
+
+        # ChunkedArray has no "view" method, so combine into an Array.
+        if isinstance(array, pyarrow.ChunkedArray):
+            array = array.combine_chunks()
+
+        array = array.view(pyarrow.timestamp("ns"))
+        np_array = array.to_numpy(zero_copy_only=False)
+        return TimeArray(np_array)
+
 
 class TimeArray(core.BaseDatetimeArray):
     """
@@ -61,8 +92,6 @@ class TimeArray(core.BaseDatetimeArray):
     # Data are stored as datetime64 values with a date of Jan 1, 1970
 
     dtype = TimeDtype()
-    _epoch = datetime.datetime(1970, 1, 1)
-    _npepoch = numpy.datetime64(_epoch)
 
     @classmethod
     def _datetime(
@@ -75,8 +104,21 @@ def _datetime(
             r"(?:\.(?P<fraction>\d*))?)?)?\s*$"
         ).match,
     ):
-        if isinstance(scalar, datetime.time):
-            return datetime.datetime.combine(cls._epoch, scalar)
+        # Convert pyarrow values to datetime.time.
+        if isinstance(scalar, (pyarrow.Time32Scalar, pyarrow.Time64Scalar)):
+            scalar = (
+                scalar.cast(pyarrow.time64("ns"))
+                .cast(pyarrow.int64())
+                .cast(pyarrow.timestamp("ns"))
+                .as_py()
+            )
+
+        if scalar is None:
+            return None
+        elif isinstance(scalar, datetime.time):
+            return datetime.datetime.combine(_EPOCH, scalar)
+        elif isinstance(scalar, pandas.Timestamp):
+            return scalar.to_datetime64()
         elif isinstance(scalar, str):
             # iso string
             parsed = match_fn(scalar)
@@ -113,7 +155,7 @@ def _box_func(self, x):
     __return_deltas = {"timedelta", "timedelta64", "timedelta64[ns]", "<m8", "<m8[ns]"}
 
     def astype(self, dtype, copy=True):
-        deltas = self._ndarray - self._npepoch
+        deltas = self._ndarray - _NPEPOCH
         stype = str(dtype)
         if stype in self.__return_deltas:
             return deltas
@@ -122,15 +164,25 @@ def astype(self, dtype, copy=True):
         else:
             return super().astype(dtype, copy=copy)
 
-    if pandas_release < (1,):
+    def __arrow_array__(self, type=None):
+        """Convert to an Arrow array from dbtime data.
 
-        def to_numpy(self, dtype="object"):
-            return self.astype(dtype)
+        See:
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
+        """
+        array = pyarrow.array(self._ndarray, type=pyarrow.timestamp("ns"))
 
-    def __arrow_array__(self, type=None):
-        return pyarrow.array(
-            self.to_numpy(dtype="object"),
-            type=type if type is not None else pyarrow.time64("ns"),
+        # ChunkedArray has no "view" method, so combine into an Array.
+        array = (
+            array.combine_chunks() if isinstance(array, pyarrow.ChunkedArray) else array
+        )
+
+        # We can't cast to time64("ns"), but timestamp("ns") has the same
+        # memory layout: 64-bit integers representing the number of nanoseconds
+        # since the datetime epoch (midnight 1970-01-01).
+        array = array.view(pyarrow.time64("ns"))
+        return pyarrow.compute.cast(
+            array, type if type is not None else pyarrow.time64("ns"),
         )
 
 
@@ -146,6 +198,19 @@ class DateDtype(core.BaseDatetimeDtype):
     def construct_array_type(self):
         return DateArray
 
+    @staticmethod
+    def __from_arrow__(
+        array: Union[pyarrow.Array, pyarrow.ChunkedArray]
+    ) -> "DateArray":
+        """Convert to dbdate data from an Arrow array.
+
+        See:
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
+        """
+        array = pyarrow.compute.cast(array, pyarrow.timestamp("ns"))
+        np_array = array.to_numpy()
+        return DateArray(np_array)
+
 
 class DateArray(core.BaseDatetimeArray):
     """
@@ -161,7 +226,13 @@ def _datetime(
         scalar,
         match_fn=re.compile(r"\s*(?P<year>\d+)-(?P<month>\d+)-(?P<day>\d+)\s*$").match,
     ):
-        if isinstance(scalar, datetime.date):
+        # Convert pyarrow values to datetime.date.
+        if isinstance(scalar, (pyarrow.Date32Scalar, pyarrow.Date64Scalar)):
+            scalar = scalar.as_py()
+
+        if scalar is None:
+            return None
+        elif isinstance(scalar, datetime.date):
             return datetime.datetime(scalar.year, scalar.month, scalar.day)
         elif isinstance(scalar, str):
             match = match_fn(scalar)
@@ -197,16 +268,22 @@ def astype(self, dtype, copy=True):
         return super().astype(dtype, copy=copy)
 
     def __arrow_array__(self, type=None):
-        return pyarrow.array(
-            self._ndarray, type=type if type is not None else pyarrow.date32(),
+        """Convert to an Arrow array from dbdate data.
+
+        See:
+        https://pandas.pydata.org/pandas-docs/stable/development/extending.html#compatibility-with-apache-arrow
+        """
+        array = pyarrow.array(self._ndarray, type=pyarrow.timestamp("ns"))
+        return pyarrow.compute.cast(
+            array, type if type is not None else pyarrow.date32(),
         )
 
     def __add__(self, other):
         if isinstance(other, pandas.DateOffset):
             return self.astype("object") + other
 
         if isinstance(other, TimeArray):
-            return (other._ndarray - other._npepoch) + self._ndarray
+            return (other._ndarray - _NPEPOCH) + self._ndarray
 
         return super().__add__(other)
 

db_dtypes/core.py

@@ -17,6 +17,7 @@
 import numpy
 import pandas
 from pandas._libs import NaT
+import pandas.api.extensions
 import pandas.compat.numpy.function
 import pandas.core.algorithms
 import pandas.core.arrays
@@ -32,7 +33,7 @@
 pandas_release = pandas_backports.pandas_release
 
 
-class BaseDatetimeDtype(pandas.core.dtypes.base.ExtensionDtype):
+class BaseDatetimeDtype(pandas.api.extensions.ExtensionDtype):
     na_value = NaT
     kind = "o"
     names = None
@@ -60,10 +61,7 @@ def __init__(self, values, dtype=None, copy: bool = False):
 
     @classmethod
     def __ndarray(cls, scalars):
-        return numpy.array(
-            [None if scalar is None else cls._datetime(scalar) for scalar in scalars],
-            "M8[ns]",
-        )
+        return numpy.array([cls._datetime(scalar) for scalar in scalars], "M8[ns]",)
 
     @classmethod
     def _from_sequence(cls, scalars, *, dtype=None, copy=False):