Fix duration typo, add is_v1() api to pandas shim

python/pyarrow/array.pxi

@@ -1682,10 +1682,8 @@ cdef _array_like_to_pandas(obj, options, types_mapper):
  original_type = obj.type
  name = obj._name
 
- # ARROW-33321 reenables support for date/timestamp conversion in pandas >= 2.0
- from pyarrow.vendored.version import Version
- if pandas_api.loose_version < Version('2.0.0'):
- # ARROW-3789(wesm): Convert date/timestamp types to datetime64[ns]
+ if pandas_api.is_v1():
+ # ARROW-3789: Convert date/timestamp types to datetime64[ns]
  c_options.coerce_temporal_nanoseconds = True
 
  if isinstance(obj, Array):

python/pyarrow/pandas-shim.pxi

@@ -58,6 +58,7 @@ cdef class _PandasAPIShim(object):
  self._pd = pd
  self._version = pd.__version__
  self._loose_version = Version(pd.__version__)
+ self._is_v1 = False
 
  if self._loose_version < Version('1.0.0'):
  self._have_pandas = False
@@ -72,6 +73,8 @@ cdef class _PandasAPIShim(object):
  "installed. Therefore, pandas-specific integration is not "
  "used.".format(self._version), stacklevel=2)
  return
+ elif self._loose_version < Version('2.0.0'):
+ self._is_v1 = True
 
  self._compat_module = pdcompat
  self._data_frame = pd.DataFrame
@@ -150,6 +153,10 @@ cdef class _PandasAPIShim(object):
  self._check_import()
  return self._version
 
+ def is_v1(self):
+ self._check_import()
+ return self._is_v1
+
  @property
  def categorical_type(self):
  self._check_import()

python/pyarrow/pandas_compat.py

@@ -787,10 +787,8 @@ def _reconstruct_block(item, columns=None, extension_columns=None):
 
 
 def make_datetimetz(unit, tz):
- from pyarrow.vendored.version import Version
- # ARROW-3789: Convert date/timestamp types to datetime64[ns]
- if _pandas_api.loose_version < Version('2.0.0'):
- unit = 'ns'
+ if _pandas_api.is_v1():
+ unit = 'ns' # ARROW-3789: Convert date/timestamp types to datetime64[ns]
  tz = pa.lib.string_to_tzinfo(tz)
  return _pandas_api.datetimetz_type(unit, tz=tz)
 

python/pyarrow/table.pxi

@@ -2892,10 +2892,8 @@ def table_to_blocks(options, Table table, categories, extension_columns):
  c_options.extension_columns = {tobytes(col)
  for col in extension_columns}
 
- # ARROW-33321 reenables support for date/timestamp conversion in pandas >= 2.0
- from pyarrow.vendored.version import Version
- if pandas_api.loose_version < Version('2.0.0'):
- # ARROW-3789(wesm); Convert date/timestamp types to datetime64[ns]
+ if pandas_api.is_v1():
+ # ARROW-3789: Convert date/timestamp types to datetime64[ns]
  c_options.coerce_temporal_nanoseconds = True
 
  if c_options.self_destruct:

python/pyarrow/types.pxi

@@ -42,7 +42,8 @@ cdef dict _pandas_type_map = {
  _Type_HALF_FLOAT: np.float16,
  _Type_FLOAT: np.float32,
  _Type_DOUBLE: np.float64,
- _Type_DATE32: np.dtype('datetime64[D]'),
+ # Pandas does not support [D]ay, so default to [s]econd for date32
+ _Type_DATE32: np.dtype('datetime64[s]'),
  _Type_DATE64: np.dtype('datetime64[ms]'),
  _Type_TIMESTAMP: {
  's': np.dtype('datetime64[s]'),
@@ -131,9 +132,8 @@ def _get_pandas_type(type, unit=None):
  if type not in _pandas_type_map:
  return None
  if type in [_Type_DATE32, _Type_DATE64, _Type_TIMESTAMP, _Type_DURATION]:
- from pyarrow.vendored.version import Version
- # ARROW-3789: Convert date/timestamp types to datetime64[ns]
- if _pandas_api.loose_version < Version('2.0.0'):
+ if _pandas_api.is_v1():
+ # ARROW-3789: Convert date/timestamp types to datetime64[ns]
  if type == _Type_DURATION:
  return np.dtype('timedelta64[ns]')
  return np.dtype('datetime64[ns]')
@@ -1161,7 +1161,7 @@ cdef class DurationType(DataType):
  >>> d.to_pandas_dtype()
  timedelta64[ms]
  """
- return _get_pandas_type(_Type_TIMESTAMP, self.unit)
+ return _get_pandas_type(_Type_DURATION, self.unit)
 
 
 cdef class FixedSizeBinaryType(DataType):