CLN: remove pyarrow<1 compat

ci/deps/actions-38-locale.yaml

@@ -35,7 +35,7 @@ dependencies:
   - xlsxwriter
   - xlwt
   - moto
-  - pyarrow=1.0.0
+  - pyarrow=1.0.1
   - pip
   - pip:
     - pyxlsb

pandas/compat/__init__.py

@@ -20,7 +20,7 @@
     np_version_under1p20,
 )
 from pandas.compat.pyarrow import (
-    pa_version_under1p0,
+    pa_version_under1p01,
     pa_version_under2p0,
     pa_version_under3p0,
     pa_version_under4p0,
@@ -153,7 +153,7 @@ def get_lzma_file(lzma):
     "np_datetime64_compat",
     "np_version_under1p19",
     "np_version_under1p20",
-    "pa_version_under1p0",
+    "pa_version_under1p01",
     "pa_version_under2p0",
     "pa_version_under3p0",
     "pa_version_under4p0",

pandas/compat/_optional.py

@@ -21,7 +21,7 @@
     "odfpy": "1.4.1",
     "openpyxl": "3.0.2",
     "pandas_gbq": "0.14.0",
-    "pyarrow": "0.17.0",
+    "pyarrow": "1.0.1",
     "pytest": "6.0",
     "pyxlsb": "1.0.6",
     "s3fs": "0.4.0",

pandas/compat/pyarrow.py

@@ -7,14 +7,14 @@
 
     _pa_version = pa.__version__
     _palv = Version(_pa_version)
-    pa_version_under1p0 = _palv < Version("1.0.0")
+    pa_version_under1p01 = _palv < Version("1.0.1")
     pa_version_under2p0 = _palv < Version("2.0.0")
     pa_version_under3p0 = _palv < Version("3.0.0")
     pa_version_under4p0 = _palv < Version("4.0.0")
     pa_version_under5p0 = _palv < Version("5.0.0")
     pa_version_under6p0 = _palv < Version("6.0.0")
 except ImportError:
-    pa_version_under1p0 = True
+    pa_version_under1p01 = True
     pa_version_under2p0 = True
     pa_version_under3p0 = True
     pa_version_under4p0 = True

pandas/core/arrays/string_.py

@@ -19,7 +19,7 @@
     Scalar,
     type_t,
 )
-from pandas.compat import pa_version_under1p0
+from pandas.compat import pa_version_under1p01
 from pandas.compat.numpy import function as nv
 
 from pandas.core.dtypes.base import (
@@ -104,11 +104,10 @@ def __init__(self, storage=None):
             raise ValueError(
                 f"Storage must be 'python' or 'pyarrow'. Got {storage} instead."
             )
-        if storage == "pyarrow" and pa_version_under1p0:
+        if storage == "pyarrow" and pa_version_under1p01:
             raise ImportError(
                 "pyarrow>=1.0.0 is required for PyArrow backed StringArray."
             )
-
         self.storage = storage
 
     @property

pandas/core/arrays/string_arrow.py

@@ -27,7 +27,7 @@
     npt,
 )
 from pandas.compat import (
-    pa_version_under1p0,
+    pa_version_under1p01,
     pa_version_under2p0,
     pa_version_under3p0,
     pa_version_under4p0,
@@ -63,10 +63,7 @@
 )
 from pandas.core.strings.object_array import ObjectStringArrayMixin
 
-# PyArrow backed StringArrays are available starting at 1.0.0, but this
-# file is imported from even if pyarrow is < 1.0.0, before pyarrow.compute
-# and its compute functions existed. GH38801
-if not pa_version_under1p0:
+if not pa_version_under1p01:
     import pyarrow as pa
     import pyarrow.compute as pc
 
@@ -87,7 +84,7 @@
 
 
 def _chk_pyarrow_available() -> None:
-    if pa_version_under1p0:
+    if pa_version_under1p01:
         msg = "pyarrow>=1.0.0 is required for PyArrow backed StringArray."
         raise ImportError(msg)
 

pandas/tests/arrays/masked/test_arrow_compat.py

@@ -1,12 +1,10 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 import pandas._testing as tm
 
-pa = pytest.importorskip("pyarrow", minversion="0.17.0")
+pa = pytest.importorskip("pyarrow", minversion="1.0.1")
 
 from pandas.core.arrays._arrow_utils import pyarrow_array_to_numpy_and_mask
 
@@ -29,7 +27,6 @@ def test_arrow_array(data):
     assert arr.equals(expected)
 
 
-@td.skip_if_no("pyarrow")
 def test_arrow_roundtrip(data):
     df = pd.DataFrame({"a": data})
     table = pa.table(df)
@@ -39,7 +36,6 @@ def test_arrow_roundtrip(data):
     tm.assert_frame_equal(result, df)
 
 
-@td.skip_if_no("pyarrow")
 def test_arrow_load_from_zero_chunks(data):
     # GH-41040
 
@@ -54,7 +50,6 @@ def test_arrow_load_from_zero_chunks(data):
     tm.assert_frame_equal(result, df)
 
 
-@td.skip_if_no("pyarrow")
 def test_arrow_from_arrow_uint():
     # https://github.com/pandas-dev/pandas/issues/31896
     # possible mismatch in types
@@ -66,7 +61,6 @@ def test_arrow_from_arrow_uint():
     tm.assert_extension_array_equal(result, expected)
 
 
-@td.skip_if_no("pyarrow")
 def test_arrow_sliced(data):
     # https://github.com/pandas-dev/pandas/issues/38525
 
@@ -161,7 +155,6 @@ def test_pyarrow_array_to_numpy_and_mask(np_dtype_to_arrays):
     tm.assert_numpy_array_equal(mask, mask_expected_empty)
 
 
-@td.skip_if_no("pyarrow")
 def test_from_arrow_type_error(request, data):
     # ensure that __from_arrow__ returns a TypeError when getting a wrong
     # array type

pandas/tests/arrays/period/test_arrow_compat.py

@@ -1,7 +1,5 @@
 import pytest
 
-import pandas.util._test_decorators as td
-
 from pandas.core.dtypes.dtypes import PeriodDtype
 
 import pandas as pd
@@ -11,10 +9,9 @@
     period_array,
 )
 
-pyarrow_skip = td.skip_if_no("pyarrow", min_version="0.17.0")
+pa = pytest.importorskip("pyarrow", minversion="1.0.1")
 
 
-@pyarrow_skip
 def test_arrow_extension_type():
     from pandas.core.arrays._arrow_utils import ArrowPeriodType
 
@@ -29,7 +26,6 @@ def test_arrow_extension_type():
     assert not hash(p1) == hash(p3)
 
 
-@pyarrow_skip
 @pytest.mark.parametrize(
     "data, freq",
     [
@@ -38,8 +34,6 @@ def test_arrow_extension_type():
     ],
 )
 def test_arrow_array(data, freq):
-    import pyarrow as pa
-
     from pandas.core.arrays._arrow_utils import ArrowPeriodType
 
     periods = period_array(data, freq=freq)
@@ -62,10 +56,7 @@ def test_arrow_array(data, freq):
         pa.array(periods, type=ArrowPeriodType("T"))
 
 
-@pyarrow_skip
 def test_arrow_array_missing():
-    import pyarrow as pa
-
     from pandas.core.arrays._arrow_utils import ArrowPeriodType
 
     arr = PeriodArray([1, 2, 3], freq="D")
@@ -78,10 +69,7 @@ def test_arrow_array_missing():
     assert result.storage.equals(expected)
 
 
-@pyarrow_skip
 def test_arrow_table_roundtrip():
-    import pyarrow as pa
-
     from pandas.core.arrays._arrow_utils import ArrowPeriodType
 
     arr = PeriodArray([1, 2, 3], freq="D")
@@ -100,10 +88,8 @@ def test_arrow_table_roundtrip():
     tm.assert_frame_equal(result, expected)
 
 
-@pyarrow_skip
 def test_arrow_load_from_zero_chunks():
     # GH-41040
-    import pyarrow as pa
 
     from pandas.core.arrays._arrow_utils import ArrowPeriodType
 
@@ -120,10 +106,7 @@ def test_arrow_load_from_zero_chunks():
     tm.assert_frame_equal(result, df)
 
 
-@pyarrow_skip
 def test_arrow_table_roundtrip_without_metadata():
-    import pyarrow as pa
-
     arr = PeriodArray([1, 2, 3], freq="H")
     arr[1] = pd.NaT
     df = pd.DataFrame({"a": arr})

pandas/tests/arrays/string_/test_string_arrow.py

@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from pandas.compat import pa_version_under1p0
+from pandas.compat import pa_version_under1p01
 
 import pandas as pd
 import pandas._testing as tm
@@ -14,7 +14,7 @@
 from pandas.core.arrays.string_arrow import ArrowStringArray
 
 skip_if_no_pyarrow = pytest.mark.skipif(
-    pa_version_under1p0,
+    pa_version_under1p01,
     reason="pyarrow>=1.0.0 is required for PyArrow backed StringArray",
 )
 
@@ -118,7 +118,7 @@ def test_from_sequence_wrong_dtype_raises():
 
 
 @pytest.mark.skipif(
-    not pa_version_under1p0,
+    not pa_version_under1p01,
     reason="pyarrow is installed",
 )
 def test_pyarrow_not_installed_raises():

pandas/tests/extension/arrow/test_bool.py

@@ -6,7 +6,7 @@
 from pandas.api.types import is_bool_dtype
 from pandas.tests.extension import base
 
-pytest.importorskip("pyarrow", minversion="0.13.0")
+pytest.importorskip("pyarrow", minversion="1.0.1")
 
 from pandas.tests.extension.arrow.arrays import (  # isort:skip
     ArrowBoolArray,

pandas/tests/extension/arrow/test_timestamp.py

@@ -12,7 +12,7 @@
     register_extension_dtype,
 )
 
-pytest.importorskip("pyarrow", minversion="0.13.0")
+pytest.importorskip("pyarrow", minversion="1.0.1")
 
 import pyarrow as pa  # isort:skip
 

pandas/tests/io/test_feather.py

@@ -2,14 +2,12 @@
 import numpy as np
 import pytest
 
-import pandas.util._test_decorators as td
-
 import pandas as pd
 import pandas._testing as tm
 
 from pandas.io.feather_format import read_feather, to_feather  # isort:skip
 
-pyarrow = pytest.importorskip("pyarrow")
+pyarrow = pytest.importorskip("pyarrow", minversion="1.0.1")
 
 
 filter_sparse = pytest.mark.filterwarnings("ignore:The Sparse")
@@ -120,7 +118,6 @@ def test_read_columns(self):
         columns = ["col1", "col3"]
         self.check_round_trip(df, expected=df[columns], columns=columns)
 
-    @td.skip_if_no("pyarrow", min_version="0.17.1")
     def read_columns_different_order(self):
         # GH 33878
         df = pd.DataFrame({"A": [1, 2], "B": ["x", "y"], "C": [True, False]})
@@ -180,12 +177,10 @@ def test_path_localpath(self):
         result = tm.round_trip_localpath(df.to_feather, read_feather)
         tm.assert_frame_equal(df, result)
 
-    @td.skip_if_no("pyarrow", min_version="0.17.0")
     def test_passthrough_keywords(self):
         df = tm.makeDataFrame().reset_index()
         self.check_round_trip(df, write_kwargs={"version": 1})
 
-    @td.skip_if_no("pyarrow")
     @tm.network
     def test_http_path(self, feather_file):
         # GH 29055

pandas/tests/io/test_parquet.py

@@ -15,7 +15,6 @@
 
 from pandas.compat import is_platform_windows
 from pandas.compat.pyarrow import (
-    pa_version_under1p0,
     pa_version_under2p0,
     pa_version_under5p0,
 )
@@ -784,11 +783,7 @@ def test_s3_roundtrip_for_dir(
         # only used if partition field is string, but this changed again to use
         # category dtype for all types (not only strings) in pyarrow 2.0.0
         if partition_col:
-            partition_col_type = (
-                "int32"
-                if (not pa_version_under1p0) and pa_version_under2p0
-                else "category"
-            )
+            partition_col_type = "int32" if pa_version_under2p0 else "category"
 
             expected_df[partition_col] = expected_df[partition_col].astype(
                 partition_col_type