From 602a804bf9a00da63d3b7ba55554fa6aa9ccc924 Mon Sep 17 00:00:00 2001
From: Michael Tiemann <72577720+MichaelTiemannOSC@users.noreply.github.com>
Date: Fri, 11 Aug 2023 23:33:50 -0400
Subject: [PATCH] Keep up with pandas21_compat changes

Merge in important bugfixes for `na_value` handling.  And keep `black` happy.

Signed-off-by: Michael Tiemann <72577720+MichaelTiemannOSC@users.noreply.github.com>
---
 pint_pandas/pint_array.py                     | 10 +++---
 .../testsuite/test_pandas_extensiontests.py   | 32 +++++++++++++++----
 2 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/pint_pandas/pint_array.py b/pint_pandas/pint_array.py
index e75cb5c..6695b7d 100644
--- a/pint_pandas/pint_array.py
+++ b/pint_pandas/pint_array.py
@@ -601,9 +601,9 @@ def _values_for_factorize(self):
             if HAS_UNCERTAINTIES and arr.size > 0:
                 # Canonicalize uncertain NaNs and pd.NA to np.nan
                 arr = arr.map(
-                    lambda x: self.dtype.na_value.m if x is pd.NA or unp.isnan(x) else x
+                    lambda x: self.dtype.na_value if x is pd.NA or unp.isnan(x) else x
                 )
-            return np.array(arr, copy=False), self.dtype.na_value.m
+            return np.array(arr, copy=False), self.dtype.na_value
         return arr._values_for_factorize()
 
     def value_counts(self, dropna=True):
@@ -631,7 +631,7 @@ def value_counts(self, dropna=True):
         # compute counts on the data with no nans
         data = self._data
         nafilt = data.map(lambda x: x is pd.NA or unp.isnan(x))
-        na_value = self.dtype.na_value.m
+        na_value = self.dtype.na_value
         data = data[~nafilt]
         if HAS_UNCERTAINTIES and data.dtype.kind == "O":
             unique_data = []
@@ -661,7 +661,7 @@ def unique(self):
         from pandas import unique
 
         data = self._data
-        na_value = self.dtype.na_value.m
+        na_value = self.dtype.na_value
         if HAS_UNCERTAINTIES and data.dtype.kind == "O":
             unique_data = []
             for item in data:
@@ -824,7 +824,7 @@ def __array__(self, dtype=None, copy=False):
     def _to_array_of_quantity(self, copy=False):
         qtys = [
             self._Q(item, self._dtype.units)
-            if item is not self.dtype.na_value.m
+            if item is not self.dtype.na_value
             else self.dtype.na_value
             for item in self._data
         ]
diff --git a/pint_pandas/testsuite/test_pandas_extensiontests.py b/pint_pandas/testsuite/test_pandas_extensiontests.py
index d7060a4..672de1c 100644
--- a/pint_pandas/testsuite/test_pandas_extensiontests.py
+++ b/pint_pandas/testsuite/test_pandas_extensiontests.py
@@ -540,10 +540,18 @@ def _get_expected_exception(
         if op_name in ["__pow__", "__rpow__"]:
             return DimensionalityError
         complex128_dtype = pd.core.dtypes.dtypes.NumpyEADtype("complex128")
-        if ((isinstance(obj, pd.Series) and obj.dtype == complex128_dtype)
-            or (isinstance(obj, pd.DataFrame) and any([dtype == complex128_dtype for dtype in obj.dtypes]))
+        if (
+            (isinstance(obj, pd.Series) and obj.dtype == complex128_dtype)
+            or (
+                isinstance(obj, pd.DataFrame)
+                and any([dtype == complex128_dtype for dtype in obj.dtypes])
+            )
             or (isinstance(other, pd.Series) and other.dtype == complex128_dtype)
-            or (isinstance(other, pd.DataFrame) and any([dtype == complex128_dtype for dtype in other.dtypes]))):
+            or (
+                isinstance(other, pd.DataFrame)
+                and any([dtype == complex128_dtype for dtype in other.dtypes])
+            )
+        ):
             if op_name in ["__floordiv__", "__rfloordiv__", "__mod__", "__rmod__"]:
                 return TypeError
         return super()._get_expected_exception(op_name, obj, other)
@@ -611,7 +619,11 @@ class TestMissing(base.BaseMissingTests):
 class TestNumericReduce(base.BaseNumericReduceTests):
     def _supports_reduction(self, obj, op_name: str) -> bool:
         # Specify if we expect this reduction to succeed.
-        if USE_UNCERTAINTIES and op_name in _all_numeric_reductions and op_name not in _uncertain_numeric_reductions:
+        if (
+            USE_UNCERTAINTIES
+            and op_name in _all_numeric_reductions
+            and op_name not in _uncertain_numeric_reductions
+        ):
             if any([isinstance(v, UFloat) for v in obj.values.quantity._magnitude]):
                 pytest.skip(f"reduction {op_name} not implemented in uncertainties")
         return super()._supports_reduction(obj, op_name)
@@ -646,7 +658,11 @@ def test_reduce_scaling(
         This verifies that the result units are sensible.
         """
         op_name = all_numeric_reductions
-        if USE_UNCERTAINTIES and op_name in _all_numeric_reductions and op_name not in _uncertain_numeric_reductions:
+        if (
+            USE_UNCERTAINTIES
+            and op_name in _all_numeric_reductions
+            and op_name not in _uncertain_numeric_reductions
+        ):
             if any([isinstance(v, UFloat) for v in data.quantity._magnitude]):
                 pytest.skip(f"reduction {op_name} not implemented in uncertainties")
         s_nm = pd.Series(data)
@@ -683,7 +699,11 @@ def test_reduce_series(
         self, data, all_numeric_reductions, skipna, USE_UNCERTAINTIES
     ):
         op_name = all_numeric_reductions
-        if USE_UNCERTAINTIES and op_name in _all_numeric_reductions and op_name not in _uncertain_numeric_reductions:
+        if (
+            USE_UNCERTAINTIES
+            and op_name in _all_numeric_reductions
+            and op_name not in _uncertain_numeric_reductions
+        ):
             if any([isinstance(v, UFloat) for v in data.quantity._magnitude]):
                 pytest.skip(f"reduction {op_name} not implemented in uncertainties")
         s = pd.Series(data)