8th day of python challenges 111-117

venv/lib/python3.6/site-packages/pandas/tests/extension/arrow/bool.py

@@ -0,0 +1,144 @@
+"""Rudimentary Apache Arrow-backed ExtensionArray.
+
+At the moment, just a boolean array / type is implemented.
+Eventually, we'll want to parametrize the type and support
+multiple dtypes. Not all methods are implemented yet, and the
+current implementation is not efficient.
+"""
+import copy
+import itertools
+
+import numpy as np
+import pyarrow as pa
+
+import pandas as pd
+from pandas.api.extensions import (
+    ExtensionArray,
+    ExtensionDtype,
+    register_extension_dtype,
+    take,
+)
+
+
+@register_extension_dtype
+class ArrowBoolDtype(ExtensionDtype):
+
+    type = np.bool_
+    kind = "b"
+    name = "arrow_bool"
+    na_value = pa.NULL
+
+    @classmethod
+    def construct_from_string(cls, string):
+        if string == cls.name:
+            return cls()
+        else:
+            raise TypeError("Cannot construct a '{}' from '{}'".format(cls, string))
+
+    @classmethod
+    def construct_array_type(cls):
+        return ArrowBoolArray
+
+    def _is_boolean(self):
+        return True
+
+
+class ArrowBoolArray(ExtensionArray):
+    def __init__(self, values):
+        if not isinstance(values, pa.ChunkedArray):
+            raise ValueError
+
+        assert values.type == pa.bool_()
+        self._data = values
+        self._dtype = ArrowBoolDtype()
+
+    def __repr__(self):
+        return "ArrowBoolArray({})".format(repr(self._data))
+
+    @classmethod
+    def from_scalars(cls, values):
+        arr = pa.chunked_array([pa.array(np.asarray(values))])
+        return cls(arr)
+
+    @classmethod
+    def from_array(cls, arr):
+        assert isinstance(arr, pa.Array)
+        return cls(pa.chunked_array([arr]))
+
+    @classmethod
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        return cls.from_scalars(scalars)
+
+    def __getitem__(self, item):
+        if pd.api.types.is_scalar(item):
+            return self._data.to_pandas()[item]
+        else:
+            vals = self._data.to_pandas()[item]
+            return type(self).from_scalars(vals)
+
+    def __len__(self):
+        return len(self._data)
+
+    def astype(self, dtype, copy=True):
+        # needed to fix this astype for the Series constructor.
+        if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        return super().astype(dtype, copy)
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @property
+    def nbytes(self):
+        return sum(
+            x.size
+            for chunk in self._data.chunks
+            for x in chunk.buffers()
+            if x is not None
+        )
+
+    def isna(self):
+        nas = pd.isna(self._data.to_pandas())
+        return type(self).from_scalars(nas)
+
+    def take(self, indices, allow_fill=False, fill_value=None):
+        data = self._data.to_pandas()
+
+        if allow_fill and fill_value is None:
+            fill_value = self.dtype.na_value
+
+        result = take(data, indices, fill_value=fill_value, allow_fill=allow_fill)
+        return self._from_sequence(result, dtype=self.dtype)
+
+    def copy(self):
+        return type(self)(copy.copy(self._data))
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        chunks = list(itertools.chain.from_iterable(x._data.chunks for x in to_concat))
+        arr = pa.chunked_array(chunks)
+        return cls(arr)
+
+    def __invert__(self):
+        return type(self).from_scalars(~self._data.to_pandas())
+
+    def _reduce(self, method, skipna=True, **kwargs):
+        if skipna:
+            arr = self[~self.isna()]
+        else:
+            arr = self
+
+        try:
+            op = getattr(arr, method)
+        except AttributeError:
+            raise TypeError
+        return op(**kwargs)
+
+    def any(self, axis=0, out=None):
+        return self._data.to_pandas().any()
+
+    def all(self, axis=0, out=None):
+        return self._data.to_pandas().all()

venv/lib/python3.6/site-packages/pandas/tests/extension/arrow/test_bool.py

@@ -0,0 +1,70 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.tests.extension import base
+import pandas.util.testing as tm
+
+pytest.importorskip("pyarrow", minversion="0.10.0")
+
+from .bool import ArrowBoolArray, ArrowBoolDtype  # isort:skip
+
+
+@pytest.fixture
+def dtype():
+    return ArrowBoolDtype()
+
+
+@pytest.fixture
+def data():
+    values = np.random.randint(0, 2, size=100, dtype=bool)
+    values[1] = ~values[0]
+    return ArrowBoolArray.from_scalars(values)
+
+
+@pytest.fixture
+def data_missing():
+    return ArrowBoolArray.from_scalars([None, True])
+
+
+class BaseArrowTests:
+    pass
+
+
+class TestDtype(BaseArrowTests, base.BaseDtypeTests):
+    def test_array_type_with_arg(self, data, dtype):
+        pytest.skip("GH-22666")
+
+
+class TestInterface(BaseArrowTests, base.BaseInterfaceTests):
+    def test_copy(self, data):
+        # __setitem__ does not work, so we only have a smoke-test
+        data.copy()
+
+
+class TestConstructors(BaseArrowTests, base.BaseConstructorsTests):
+    def test_from_dtype(self, data):
+        pytest.skip("GH-22666")
+
+    # seems like some bug in isna on empty BoolArray returning floats.
+    @pytest.mark.xfail(reason="bad is-na for empty data")
+    def test_from_sequence_from_cls(self, data):
+        super().test_from_sequence_from_cls(data)
+
+
+class TestReduce(base.BaseNoReduceTests):
+    def test_reduce_series_boolean(self):
+        pass
+
+
+class TestReduceBoolean(base.BaseBooleanReduceTests):
+    pass
+
+
+def test_is_bool_dtype(data):
+    assert pd.api.types.is_bool_dtype(data)
+    assert pd.core.common.is_bool_indexer(data)
+    s = pd.Series(range(len(data)))
+    result = s[data]
+    expected = s[np.asarray(data)]
+    tm.assert_series_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/__init__.py

@@ -0,0 +1,60 @@
+"""Base test suite for extension arrays.
+
+These tests are intended for third-party libraries to subclass to validate
+that their extension arrays and dtypes satisfy the interface. Moving or
+renaming the tests should not be done lightly.
+
+Libraries are expected to implement a few pytest fixtures to provide data
+for the tests. The fixtures may be located in either
+
+* The same module as your test class.
+* A ``conftest.py`` in the same directory as your test class.
+
+The full list of fixtures may be found in the ``conftest.py`` next to this
+file.
+
+.. code-block:: python
+
+   import pytest
+   from pandas.tests.extension.base import BaseDtypeTests
+
+
+   @pytest.fixture
+   def dtype():
+       return MyDtype()
+
+
+   class TestMyDtype(BaseDtypeTests):
+       pass
+
+
+Your class ``TestDtype`` will inherit all the tests defined on
+``BaseDtypeTests``. pytest's fixture discover will supply your ``dtype``
+wherever the test requires it. You're free to implement additional tests.
+
+All the tests in these modules use ``self.assert_frame_equal`` or
+``self.assert_series_equal`` for dataframe or series comparisons. By default,
+they use the usual ``pandas.testing.assert_frame_equal`` and
+``pandas.testing.assert_series_equal``. You can override the checks used
+by defining the staticmethods ``assert_frame_equal`` and
+``assert_series_equal`` on your base test class.
+
+"""
+from .casting import BaseCastingTests  # noqa
+from .constructors import BaseConstructorsTests  # noqa
+from .dtype import BaseDtypeTests  # noqa
+from .getitem import BaseGetitemTests  # noqa
+from .groupby import BaseGroupbyTests  # noqa
+from .interface import BaseInterfaceTests  # noqa
+from .io import BaseParsingTests  # noqa
+from .methods import BaseMethodsTests  # noqa
+from .missing import BaseMissingTests  # noqa
+from .ops import BaseArithmeticOpsTests, BaseComparisonOpsTests, BaseOpsUtil  # noqa
+from .printing import BasePrintingTests  # noqa
+from .reduce import (  # noqa
+    BaseBooleanReduceTests,
+    BaseNoReduceTests,
+    BaseNumericReduceTests,
+)
+from .reshaping import BaseReshapingTests  # noqa
+from .setitem import BaseSetitemTests  # noqa

venv/lib/python3.6/site-packages/pandas/tests/extension/base/base.py

@@ -0,0 +1,9 @@
+import pandas.util.testing as tm
+
+
+class BaseExtensionTests:
+
+    assert_equal = staticmethod(tm.assert_equal)
+    assert_series_equal = staticmethod(tm.assert_series_equal)
+    assert_frame_equal = staticmethod(tm.assert_frame_equal)
+    assert_extension_array_equal = staticmethod(tm.assert_extension_array_equal)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/casting.py

@@ -0,0 +1,23 @@
+import pandas as pd
+from pandas.core.internals import ObjectBlock
+
+from .base import BaseExtensionTests
+
+
+class BaseCastingTests(BaseExtensionTests):
+    """Casting to and from ExtensionDtypes"""
+
+    def test_astype_object_series(self, all_data):
+        ser = pd.Series({"A": all_data})
+        result = ser.astype(object)
+        assert isinstance(result._data.blocks[0], ObjectBlock)
+
+    def test_tolist(self, data):
+        result = pd.Series(data).tolist()
+        expected = list(data)
+        assert result == expected
+
+    def test_astype_str(self, data):
+        result = pd.Series(data[:5]).astype(str)
+        expected = pd.Series(data[:5].astype(str))
+        self.assert_series_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/constructors.py

@@ -0,0 +1,76 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.internals import ExtensionBlock
+
+from .base import BaseExtensionTests
+
+
+class BaseConstructorsTests(BaseExtensionTests):
+    def test_from_sequence_from_cls(self, data):
+        result = type(data)._from_sequence(data, dtype=data.dtype)
+        self.assert_extension_array_equal(result, data)
+
+        data = data[:0]
+        result = type(data)._from_sequence(data, dtype=data.dtype)
+        self.assert_extension_array_equal(result, data)
+
+    def test_array_from_scalars(self, data):
+        scalars = [data[0], data[1], data[2]]
+        result = data._from_sequence(scalars)
+        assert isinstance(result, type(data))
+
+    def test_series_constructor(self, data):
+        result = pd.Series(data)
+        assert result.dtype == data.dtype
+        assert len(result) == len(data)
+        assert isinstance(result._data.blocks[0], ExtensionBlock)
+        assert result._data.blocks[0].values is data
+
+        # Series[EA] is unboxed / boxed correctly
+        result2 = pd.Series(result)
+        assert result2.dtype == data.dtype
+        assert isinstance(result2._data.blocks[0], ExtensionBlock)
+
+    @pytest.mark.parametrize("from_series", [True, False])
+    def test_dataframe_constructor_from_dict(self, data, from_series):
+        if from_series:
+            data = pd.Series(data)
+        result = pd.DataFrame({"A": data})
+        assert result.dtypes["A"] == data.dtype
+        assert result.shape == (len(data), 1)
+        assert isinstance(result._data.blocks[0], ExtensionBlock)
+
+    def test_dataframe_from_series(self, data):
+        result = pd.DataFrame(pd.Series(data))
+        assert result.dtypes[0] == data.dtype
+        assert result.shape == (len(data), 1)
+        assert isinstance(result._data.blocks[0], ExtensionBlock)
+
+    def test_series_given_mismatched_index_raises(self, data):
+        msg = "Length of passed values is 3, index implies 5"
+        with pytest.raises(ValueError, match=msg):
+            pd.Series(data[:3], index=[0, 1, 2, 3, 4])
+
+    def test_from_dtype(self, data):
+        # construct from our dtype & string dtype
+        dtype = data.dtype
+
+        expected = pd.Series(data)
+        result = pd.Series(list(data), dtype=dtype)
+        self.assert_series_equal(result, expected)
+
+        result = pd.Series(list(data), dtype=str(dtype))
+        self.assert_series_equal(result, expected)
+
+    def test_pandas_array(self, data):
+        # pd.array(extension_array) should be idempotent...
+        result = pd.array(data)
+        self.assert_extension_array_equal(result, data)
+
+    def test_pandas_array_dtype(self, data):
+        # ... but specifying dtype will override idempotency
+        result = pd.array(data, dtype=np.dtype(object))
+        expected = pd.arrays.PandasArray(np.asarray(data, dtype=object))
+        self.assert_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/dtype.py

@@ -0,0 +1,102 @@
+import warnings
+
+import numpy as np
+import pytest
+
+import pandas as pd
+
+from .base import BaseExtensionTests
+
+
+class BaseDtypeTests(BaseExtensionTests):
+    """Base class for ExtensionDtype classes"""
+
+    def test_name(self, dtype):
+        assert isinstance(dtype.name, str)
+
+    def test_kind(self, dtype):
+        valid = set("biufcmMOSUV")
+        if dtype.kind is not None:
+            assert dtype.kind in valid
+
+    def test_construct_from_string_own_name(self, dtype):
+        result = dtype.construct_from_string(dtype.name)
+        assert type(result) is type(dtype)
+
+        # check OK as classmethod
+        result = type(dtype).construct_from_string(dtype.name)
+        assert type(result) is type(dtype)
+
+    def test_is_dtype_from_name(self, dtype):
+        result = type(dtype).is_dtype(dtype.name)
+        assert result is True
+
+    def test_is_dtype_unboxes_dtype(self, data, dtype):
+        assert dtype.is_dtype(data) is True
+
+    def test_is_dtype_from_self(self, dtype):
+        result = type(dtype).is_dtype(dtype)
+        assert result is True
+
+    def test_is_not_string_type(self, dtype):
+        return not pd.api.types.is_string_dtype(dtype)
+
+    def test_is_not_object_type(self, dtype):
+        return not pd.api.types.is_object_dtype(dtype)
+
+    def test_eq_with_str(self, dtype):
+        assert dtype == dtype.name
+        assert dtype != dtype.name + "-suffix"
+
+    def test_eq_with_numpy_object(self, dtype):
+        assert dtype != np.dtype("object")
+
+    def test_eq_with_self(self, dtype):
+        assert dtype == dtype
+        assert dtype != object()
+
+    def test_array_type(self, data, dtype):
+        assert dtype.construct_array_type() is type(data)
+
+    def test_check_dtype(self, data):
+        dtype = data.dtype
+
+        # check equivalency for using .dtypes
+        df = pd.DataFrame(
+            {"A": pd.Series(data, dtype=dtype), "B": data, "C": "foo", "D": 1}
+        )
+
+        # np.dtype('int64') == 'Int64' == 'int64'
+        # so can't distinguish
+        if dtype.name == "Int64":
+            expected = pd.Series([True, True, False, True], index=list("ABCD"))
+        else:
+            expected = pd.Series([True, True, False, False], index=list("ABCD"))
+
+        # XXX: This should probably be *fixed* not ignored.
+        # See libops.scalar_compare
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", DeprecationWarning)
+            result = df.dtypes == str(dtype)
+
+        self.assert_series_equal(result, expected)
+
+        expected = pd.Series([True, True, False, False], index=list("ABCD"))
+        result = df.dtypes.apply(str) == str(dtype)
+        self.assert_series_equal(result, expected)
+
+    def test_hashable(self, dtype):
+        hash(dtype)  # no error
+
+    def test_str(self, dtype):
+        assert str(dtype) == dtype.name
+
+    def test_eq(self, dtype):
+        assert dtype == dtype.name
+        assert dtype != "anonther_type"
+
+    def test_construct_from_string(self, dtype):
+        dtype_instance = dtype.__class__.construct_from_string(dtype.name)
+        assert isinstance(dtype_instance, dtype.__class__)
+        with pytest.raises(TypeError):
+            dtype.__class__.construct_from_string("another_type")

venv/lib/python3.6/site-packages/pandas/tests/extension/base/getitem.py

@@ -0,0 +1,262 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+
+from .base import BaseExtensionTests
+
+
+class BaseGetitemTests(BaseExtensionTests):
+    """Tests for ExtensionArray.__getitem__."""
+
+    def test_iloc_series(self, data):
+        ser = pd.Series(data)
+        result = ser.iloc[:4]
+        expected = pd.Series(data[:4])
+        self.assert_series_equal(result, expected)
+
+        result = ser.iloc[[0, 1, 2, 3]]
+        self.assert_series_equal(result, expected)
+
+    def test_iloc_frame(self, data):
+        df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
+        expected = pd.DataFrame({"A": data[:4]})
+
+        # slice -> frame
+        result = df.iloc[:4, [0]]
+        self.assert_frame_equal(result, expected)
+
+        # sequence -> frame
+        result = df.iloc[[0, 1, 2, 3], [0]]
+        self.assert_frame_equal(result, expected)
+
+        expected = pd.Series(data[:4], name="A")
+
+        # slice -> series
+        result = df.iloc[:4, 0]
+        self.assert_series_equal(result, expected)
+
+        # sequence -> series
+        result = df.iloc[:4, 0]
+        self.assert_series_equal(result, expected)
+
+    def test_loc_series(self, data):
+        ser = pd.Series(data)
+        result = ser.loc[:3]
+        expected = pd.Series(data[:4])
+        self.assert_series_equal(result, expected)
+
+        result = ser.loc[[0, 1, 2, 3]]
+        self.assert_series_equal(result, expected)
+
+    def test_loc_frame(self, data):
+        df = pd.DataFrame({"A": data, "B": np.arange(len(data), dtype="int64")})
+        expected = pd.DataFrame({"A": data[:4]})
+
+        # slice -> frame
+        result = df.loc[:3, ["A"]]
+        self.assert_frame_equal(result, expected)
+
+        # sequence -> frame
+        result = df.loc[[0, 1, 2, 3], ["A"]]
+        self.assert_frame_equal(result, expected)
+
+        expected = pd.Series(data[:4], name="A")
+
+        # slice -> series
+        result = df.loc[:3, "A"]
+        self.assert_series_equal(result, expected)
+
+        # sequence -> series
+        result = df.loc[:3, "A"]
+        self.assert_series_equal(result, expected)
+
+    def test_loc_iloc_frame_single_dtype(self, data):
+        # GH#27110 bug in ExtensionBlock.iget caused df.iloc[n] to incorrectly
+        #  return a scalar
+        df = pd.DataFrame({"A": data})
+        expected = pd.Series([data[2]], index=["A"], name=2, dtype=data.dtype)
+
+        result = df.loc[2]
+        self.assert_series_equal(result, expected)
+
+        expected = pd.Series(
+            [data[-1]], index=["A"], name=len(data) - 1, dtype=data.dtype
+        )
+        result = df.iloc[-1]
+        self.assert_series_equal(result, expected)
+
+    def test_getitem_scalar(self, data):
+        result = data[0]
+        assert isinstance(result, data.dtype.type)
+
+        result = pd.Series(data)[0]
+        assert isinstance(result, data.dtype.type)
+
+    def test_getitem_scalar_na(self, data_missing, na_cmp, na_value):
+        result = data_missing[0]
+        assert na_cmp(result, na_value)
+
+    def test_getitem_mask(self, data):
+        # Empty mask, raw array
+        mask = np.zeros(len(data), dtype=bool)
+        result = data[mask]
+        assert len(result) == 0
+        assert isinstance(result, type(data))
+
+        # Empty mask, in series
+        mask = np.zeros(len(data), dtype=bool)
+        result = pd.Series(data)[mask]
+        assert len(result) == 0
+        assert result.dtype == data.dtype
+
+        # non-empty mask, raw array
+        mask[0] = True
+        result = data[mask]
+        assert len(result) == 1
+        assert isinstance(result, type(data))
+
+        # non-empty mask, in series
+        result = pd.Series(data)[mask]
+        assert len(result) == 1
+        assert result.dtype == data.dtype
+
+    def test_getitem_slice(self, data):
+        # getitem[slice] should return an array
+        result = data[slice(0)]  # empty
+        assert isinstance(result, type(data))
+
+        result = data[slice(1)]  # scalar
+        assert isinstance(result, type(data))
+
+    def test_get(self, data):
+        # GH 20882
+        s = pd.Series(data, index=[2 * i for i in range(len(data))])
+        assert s.get(4) == s.iloc[2]
+
+        result = s.get([4, 6])
+        expected = s.iloc[[2, 3]]
+        self.assert_series_equal(result, expected)
+
+        result = s.get(slice(2))
+        expected = s.iloc[[0, 1]]
+        self.assert_series_equal(result, expected)
+
+        assert s.get(-1) is None
+        assert s.get(s.index.max() + 1) is None
+
+        s = pd.Series(data[:6], index=list("abcdef"))
+        assert s.get("c") == s.iloc[2]
+
+        result = s.get(slice("b", "d"))
+        expected = s.iloc[[1, 2, 3]]
+        self.assert_series_equal(result, expected)
+
+        result = s.get("Z")
+        assert result is None
+
+        assert s.get(4) == s.iloc[4]
+        assert s.get(-1) == s.iloc[-1]
+        assert s.get(len(s)) is None
+
+        # GH 21257
+        s = pd.Series(data)
+        s2 = s[::2]
+        assert s2.get(1) is None
+
+    def test_take_sequence(self, data):
+        result = pd.Series(data)[[0, 1, 3]]
+        assert result.iloc[0] == data[0]
+        assert result.iloc[1] == data[1]
+        assert result.iloc[2] == data[3]
+
+    def test_take(self, data, na_value, na_cmp):
+        result = data.take([0, -1])
+        assert result.dtype == data.dtype
+        assert result[0] == data[0]
+        assert result[1] == data[-1]
+
+        result = data.take([0, -1], allow_fill=True, fill_value=na_value)
+        assert result[0] == data[0]
+        assert na_cmp(result[1], na_value)
+
+        with pytest.raises(IndexError, match="out of bounds"):
+            data.take([len(data) + 1])
+
+    def test_take_empty(self, data, na_value, na_cmp):
+        empty = data[:0]
+
+        result = empty.take([-1], allow_fill=True)
+        assert na_cmp(result[0], na_value)
+
+        with pytest.raises(IndexError):
+            empty.take([-1])
+
+        with pytest.raises(IndexError, match="cannot do a non-empty take"):
+            empty.take([0, 1])
+
+    def test_take_negative(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20640
+        n = len(data)
+        result = data.take([0, -n, n - 1, -1])
+        expected = data.take([0, 0, n - 1, n - 1])
+        self.assert_extension_array_equal(result, expected)
+
+    def test_take_non_na_fill_value(self, data_missing):
+        fill_value = data_missing[1]  # valid
+        na = data_missing[0]
+
+        array = data_missing._from_sequence([na, fill_value, na])
+        result = array.take([-1, 1], fill_value=fill_value, allow_fill=True)
+        expected = array.take([1, 1])
+        self.assert_extension_array_equal(result, expected)
+
+    def test_take_pandas_style_negative_raises(self, data, na_value):
+        with pytest.raises(ValueError):
+            data.take([0, -2], fill_value=na_value, allow_fill=True)
+
+    @pytest.mark.parametrize("allow_fill", [True, False])
+    def test_take_out_of_bounds_raises(self, data, allow_fill):
+        arr = data[:3]
+        with pytest.raises(IndexError):
+            arr.take(np.asarray([0, 3]), allow_fill=allow_fill)
+
+    def test_take_series(self, data):
+        s = pd.Series(data)
+        result = s.take([0, -1])
+        expected = pd.Series(
+            data._from_sequence([data[0], data[len(data) - 1]], dtype=s.dtype),
+            index=[0, len(data) - 1],
+        )
+        self.assert_series_equal(result, expected)
+
+    def test_reindex(self, data, na_value):
+        s = pd.Series(data)
+        result = s.reindex([0, 1, 3])
+        expected = pd.Series(data.take([0, 1, 3]), index=[0, 1, 3])
+        self.assert_series_equal(result, expected)
+
+        n = len(data)
+        result = s.reindex([-1, 0, n])
+        expected = pd.Series(
+            data._from_sequence([na_value, data[0], na_value], dtype=s.dtype),
+            index=[-1, 0, n],
+        )
+        self.assert_series_equal(result, expected)
+
+        result = s.reindex([n, n + 1])
+        expected = pd.Series(
+            data._from_sequence([na_value, na_value], dtype=s.dtype), index=[n, n + 1]
+        )
+        self.assert_series_equal(result, expected)
+
+    def test_reindex_non_na_fill_value(self, data_missing):
+        valid = data_missing[1]
+        na = data_missing[0]
+
+        array = data_missing._from_sequence([na, valid])
+        ser = pd.Series(array)
+        result = ser.reindex([0, 1, 2], fill_value=valid)
+        expected = pd.Series(data_missing._from_sequence([na, valid, valid]))
+
+        self.assert_series_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/groupby.py

@@ -0,0 +1,91 @@
+import pytest
+
+import pandas as pd
+import pandas.util.testing as tm
+
+from .base import BaseExtensionTests
+
+
+class BaseGroupbyTests(BaseExtensionTests):
+    """Groupby-specific tests."""
+
+    def test_grouping_grouper(self, data_for_grouping):
+        df = pd.DataFrame(
+            {"A": ["B", "B", None, None, "A", "A", "B", "C"], "B": data_for_grouping}
+        )
+        gr1 = df.groupby("A").grouper.groupings[0]
+        gr2 = df.groupby("B").grouper.groupings[0]
+
+        tm.assert_numpy_array_equal(gr1.grouper, df.A.values)
+        tm.assert_extension_array_equal(gr2.grouper, data_for_grouping)
+
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_groupby_extension_agg(self, as_index, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        result = df.groupby("B", as_index=as_index).A.mean()
+        _, index = pd.factorize(data_for_grouping, sort=True)
+
+        index = pd.Index(index, name="B")
+        expected = pd.Series([3, 1, 4], index=index, name="A")
+        if as_index:
+            self.assert_series_equal(result, expected)
+        else:
+            expected = expected.reset_index()
+            self.assert_frame_equal(result, expected)
+
+    def test_groupby_extension_no_sort(self, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        result = df.groupby("B", sort=False).A.mean()
+        _, index = pd.factorize(data_for_grouping, sort=False)
+
+        index = pd.Index(index, name="B")
+        expected = pd.Series([1, 3, 4], index=index, name="A")
+        self.assert_series_equal(result, expected)
+
+    def test_groupby_extension_transform(self, data_for_grouping):
+        valid = data_for_grouping[~data_for_grouping.isna()]
+        df = pd.DataFrame({"A": [1, 1, 3, 3, 1, 4], "B": valid})
+
+        result = df.groupby("B").A.transform(len)
+        expected = pd.Series([3, 3, 2, 2, 3, 1], name="A")
+
+        self.assert_series_equal(result, expected)
+
+    def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        df.groupby("B").apply(groupby_apply_op)
+        df.groupby("B").A.apply(groupby_apply_op)
+        df.groupby("A").apply(groupby_apply_op)
+        df.groupby("A").B.apply(groupby_apply_op)
+
+    def test_groupby_apply_identity(self, data_for_grouping):
+        df = pd.DataFrame({"A": [1, 1, 2, 2, 3, 3, 1, 4], "B": data_for_grouping})
+        result = df.groupby("A").B.apply(lambda x: x.array)
+        expected = pd.Series(
+            [
+                df.B.iloc[[0, 1, 6]].array,
+                df.B.iloc[[2, 3]].array,
+                df.B.iloc[[4, 5]].array,
+                df.B.iloc[[7]].array,
+            ],
+            index=pd.Index([1, 2, 3, 4], name="A"),
+            name="B",
+        )
+        self.assert_series_equal(result, expected)
+
+    def test_in_numeric_groupby(self, data_for_grouping):
+        df = pd.DataFrame(
+            {
+                "A": [1, 1, 2, 2, 3, 3, 1, 4],
+                "B": data_for_grouping,
+                "C": [1, 1, 1, 1, 1, 1, 1, 1],
+            }
+        )
+        result = df.groupby("A").sum().columns
+
+        if data_for_grouping.dtype._is_numeric:
+            expected = pd.Index(["B", "C"])
+        else:
+            expected = pd.Index(["C"])
+
+        tm.assert_index_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/interface.py

@@ -0,0 +1,77 @@
+import numpy as np
+
+from pandas.core.dtypes.common import is_extension_array_dtype
+from pandas.core.dtypes.dtypes import ExtensionDtype
+
+import pandas as pd
+import pandas.util.testing as tm
+
+from .base import BaseExtensionTests
+
+
+class BaseInterfaceTests(BaseExtensionTests):
+    """Tests that the basic interface is satisfied."""
+
+    # ------------------------------------------------------------------------
+    # Interface
+    # ------------------------------------------------------------------------
+
+    def test_len(self, data):
+        assert len(data) == 100
+
+    def test_ndim(self, data):
+        assert data.ndim == 1
+
+    def test_can_hold_na_valid(self, data):
+        # GH-20761
+        assert data._can_hold_na is True
+
+    def test_memory_usage(self, data):
+        s = pd.Series(data)
+        result = s.memory_usage(index=False)
+        assert result == s.nbytes
+
+    def test_array_interface(self, data):
+        result = np.array(data)
+        assert result[0] == data[0]
+
+        result = np.array(data, dtype=object)
+        expected = np.array(list(data), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_is_extension_array_dtype(self, data):
+        assert is_extension_array_dtype(data)
+        assert is_extension_array_dtype(data.dtype)
+        assert is_extension_array_dtype(pd.Series(data))
+        assert isinstance(data.dtype, ExtensionDtype)
+
+    def test_no_values_attribute(self, data):
+        # GH-20735: EA's with .values attribute give problems with internal
+        # code, disallowing this for now until solved
+        assert not hasattr(data, "values")
+        assert not hasattr(data, "_values")
+
+    def test_is_numeric_honored(self, data):
+        result = pd.Series(data)
+        assert result._data.blocks[0].is_numeric is data.dtype._is_numeric
+
+    def test_isna_extension_array(self, data_missing):
+        # If your `isna` returns an ExtensionArray, you must also implement
+        # _reduce. At the *very* least, you must implement any and all
+        na = data_missing.isna()
+        if is_extension_array_dtype(na):
+            assert na._reduce("any")
+            assert na.any()
+
+            assert not na._reduce("all")
+            assert not na.all()
+
+            assert na.dtype._is_boolean
+
+    def test_copy(self, data):
+        # GH#27083 removing deep keyword from EA.copy
+        assert data[0] != data[1]
+        result = data.copy()
+
+        data[1] = data[0]
+        assert result[1] != result[0]

venv/lib/python3.6/site-packages/pandas/tests/extension/base/io.py

@@ -0,0 +1,20 @@
+from io import StringIO
+
+import numpy as np
+import pytest
+
+import pandas as pd
+
+from .base import BaseExtensionTests
+
+
+class BaseParsingTests(BaseExtensionTests):
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data):
+        df = pd.DataFrame({"with_dtype": pd.Series(data, dtype=str(data.dtype))})
+        csv_output = df.to_csv(index=False, na_rep=np.nan)
+        result = pd.read_csv(
+            StringIO(csv_output), dtype={"with_dtype": str(data.dtype)}, engine=engine
+        )
+        expected = df
+        self.assert_frame_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/methods.py

@@ -0,0 +1,360 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.sorting import nargsort
+import pandas.util.testing as tm
+
+from .base import BaseExtensionTests
+
+
+class BaseMethodsTests(BaseExtensionTests):
+    """Various Series and DataFrame methods."""
+
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_value_counts(self, all_data, dropna):
+        all_data = all_data[:10]
+        if dropna:
+            other = np.array(all_data[~all_data.isna()])
+        else:
+            other = all_data
+
+        result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
+        expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
+
+        self.assert_series_equal(result, expected)
+
+    def test_count(self, data_missing):
+        df = pd.DataFrame({"A": data_missing})
+        result = df.count(axis="columns")
+        expected = pd.Series([0, 1])
+        self.assert_series_equal(result, expected)
+
+    def test_series_count(self, data_missing):
+        # GH#26835
+        ser = pd.Series(data_missing)
+        result = ser.count()
+        expected = 1
+        assert result == expected
+
+    def test_apply_simple_series(self, data):
+        result = pd.Series(data).apply(id)
+        assert isinstance(result, pd.Series)
+
+    def test_argsort(self, data_for_sorting):
+        result = pd.Series(data_for_sorting).argsort()
+        expected = pd.Series(np.array([2, 0, 1], dtype=np.int64))
+        self.assert_series_equal(result, expected)
+
+    def test_argsort_missing_array(self, data_missing_for_sorting):
+        result = data_missing_for_sorting.argsort()
+        expected = np.array([2, 0, 1], dtype=np.dtype("int"))
+        # we don't care whether it's int32 or int64
+        result = result.astype("int64", casting="safe")
+        expected = expected.astype("int64", casting="safe")
+        tm.assert_numpy_array_equal(result, expected)
+
+    def test_argsort_missing(self, data_missing_for_sorting):
+        result = pd.Series(data_missing_for_sorting).argsort()
+        expected = pd.Series(np.array([1, -1, 0], dtype=np.int64))
+        self.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "na_position, expected",
+        [
+            ("last", np.array([2, 0, 1], dtype=np.dtype("intp"))),
+            ("first", np.array([1, 2, 0], dtype=np.dtype("intp"))),
+        ],
+    )
+    def test_nargsort(self, data_missing_for_sorting, na_position, expected):
+        # GH 25439
+        result = nargsort(data_missing_for_sorting, na_position=na_position)
+        tm.assert_numpy_array_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values(self, data_for_sorting, ascending):
+        ser = pd.Series(data_for_sorting)
+        result = ser.sort_values(ascending=ascending)
+        expected = ser.iloc[[2, 0, 1]]
+        if not ascending:
+            expected = expected[::-1]
+
+        self.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_missing(self, data_missing_for_sorting, ascending):
+        ser = pd.Series(data_missing_for_sorting)
+        result = ser.sort_values(ascending=ascending)
+        if ascending:
+            expected = ser.iloc[[2, 0, 1]]
+        else:
+            expected = ser.iloc[[0, 2, 1]]
+        self.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_frame(self, data_for_sorting, ascending):
+        df = pd.DataFrame({"A": [1, 2, 1], "B": data_for_sorting})
+        result = df.sort_values(["A", "B"])
+        expected = pd.DataFrame(
+            {"A": [1, 1, 2], "B": data_for_sorting.take([2, 0, 1])}, index=[2, 0, 1]
+        )
+        self.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
+    @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
+    def test_unique(self, data, box, method):
+        duplicated = box(data._from_sequence([data[0], data[0]]))
+
+        result = method(duplicated)
+
+        assert len(result) == 1
+        assert isinstance(result, type(data))
+        assert result[0] == duplicated[0]
+
+    @pytest.mark.parametrize("na_sentinel", [-1, -2])
+    def test_factorize(self, data_for_grouping, na_sentinel):
+        labels, uniques = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
+        expected_labels = np.array(
+            [0, 0, na_sentinel, na_sentinel, 1, 1, 0, 2], dtype=np.intp
+        )
+        expected_uniques = data_for_grouping.take([0, 4, 7])
+
+        tm.assert_numpy_array_equal(labels, expected_labels)
+        self.assert_extension_array_equal(uniques, expected_uniques)
+
+    @pytest.mark.parametrize("na_sentinel", [-1, -2])
+    def test_factorize_equivalence(self, data_for_grouping, na_sentinel):
+        l1, u1 = pd.factorize(data_for_grouping, na_sentinel=na_sentinel)
+        l2, u2 = data_for_grouping.factorize(na_sentinel=na_sentinel)
+
+        tm.assert_numpy_array_equal(l1, l2)
+        self.assert_extension_array_equal(u1, u2)
+
+    def test_factorize_empty(self, data):
+        labels, uniques = pd.factorize(data[:0])
+        expected_labels = np.array([], dtype=np.intp)
+        expected_uniques = type(data)._from_sequence([], dtype=data[:0].dtype)
+
+        tm.assert_numpy_array_equal(labels, expected_labels)
+        self.assert_extension_array_equal(uniques, expected_uniques)
+
+    def test_fillna_copy_frame(self, data_missing):
+        arr = data_missing.take([1, 1])
+        df = pd.DataFrame({"A": arr})
+
+        filled_val = df.iloc[0, 0]
+        result = df.fillna(filled_val)
+
+        assert df.A.values is not result.A.values
+
+    def test_fillna_copy_series(self, data_missing):
+        arr = data_missing.take([1, 1])
+        ser = pd.Series(arr)
+
+        filled_val = ser[0]
+        result = ser.fillna(filled_val)
+
+        assert ser._values is not result._values
+        assert ser._values is arr
+
+    def test_fillna_length_mismatch(self, data_missing):
+        msg = "Length of 'value' does not match."
+        with pytest.raises(ValueError, match=msg):
+            data_missing.fillna(data_missing.take([1]))
+
+    def test_combine_le(self, data_repeated):
+        # GH 20825
+        # Test that combine works when doing a <= (le) comparison
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 <= x2)
+        expected = pd.Series(
+            [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))]
+        )
+        self.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 <= x2)
+        expected = pd.Series([a <= val for a in list(orig_data1)])
+        self.assert_series_equal(result, expected)
+
+    def test_combine_add(self, data_repeated):
+        # GH 20825
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 + x2)
+        with np.errstate(over="ignore"):
+            expected = pd.Series(
+                orig_data1._from_sequence(
+                    [a + b for (a, b) in zip(list(orig_data1), list(orig_data2))]
+                )
+            )
+        self.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 + x2)
+        expected = pd.Series(
+            orig_data1._from_sequence([a + val for a in list(orig_data1)])
+        )
+        self.assert_series_equal(result, expected)
+
+    def test_combine_first(self, data):
+        # https://github.com/pandas-dev/pandas/issues/24147
+        a = pd.Series(data[:3])
+        b = pd.Series(data[2:5], index=[2, 3, 4])
+        result = a.combine_first(b)
+        expected = pd.Series(data[:5])
+        self.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize("frame", [True, False])
+    @pytest.mark.parametrize(
+        "periods, indices",
+        [(-2, [2, 3, 4, -1, -1]), (0, [0, 1, 2, 3, 4]), (2, [-1, -1, 0, 1, 2])],
+    )
+    def test_container_shift(self, data, frame, periods, indices):
+        # https://github.com/pandas-dev/pandas/issues/22386
+        subset = data[:5]
+        data = pd.Series(subset, name="A")
+        expected = pd.Series(subset.take(indices, allow_fill=True), name="A")
+
+        if frame:
+            result = data.to_frame(name="A").assign(B=1).shift(periods)
+            expected = pd.concat(
+                [expected, pd.Series([1] * 5, name="B").shift(periods)], axis=1
+            )
+            compare = self.assert_frame_equal
+        else:
+            result = data.shift(periods)
+            compare = self.assert_series_equal
+
+        compare(result, expected)
+
+    @pytest.mark.parametrize(
+        "periods, indices",
+        [[-4, [-1, -1]], [-1, [1, -1]], [0, [0, 1]], [1, [-1, 0]], [4, [-1, -1]]],
+    )
+    def test_shift_non_empty_array(self, data, periods, indices):
+        # https://github.com/pandas-dev/pandas/issues/23911
+        subset = data[:2]
+        result = subset.shift(periods)
+        expected = subset.take(indices, allow_fill=True)
+        self.assert_extension_array_equal(result, expected)
+
+    @pytest.mark.parametrize("periods", [-4, -1, 0, 1, 4])
+    def test_shift_empty_array(self, data, periods):
+        # https://github.com/pandas-dev/pandas/issues/23911
+        empty = data[:0]
+        result = empty.shift(periods)
+        expected = empty
+        self.assert_extension_array_equal(result, expected)
+
+    def test_shift_fill_value(self, data):
+        arr = data[:4]
+        fill_value = data[0]
+        result = arr.shift(1, fill_value=fill_value)
+        expected = data.take([0, 0, 1, 2])
+        self.assert_extension_array_equal(result, expected)
+
+        result = arr.shift(-2, fill_value=fill_value)
+        expected = data.take([2, 3, 0, 0])
+        self.assert_extension_array_equal(result, expected)
+
+    def test_hash_pandas_object_works(self, data, as_frame):
+        # https://github.com/pandas-dev/pandas/issues/23066
+        data = pd.Series(data)
+        if as_frame:
+            data = data.to_frame()
+        a = pd.util.hash_pandas_object(data)
+        b = pd.util.hash_pandas_object(data)
+        self.assert_equal(a, b)
+
+    def test_searchsorted(self, data_for_sorting, as_series):
+        b, c, a = data_for_sorting
+        arr = type(data_for_sorting)._from_sequence([a, b, c])
+
+        if as_series:
+            arr = pd.Series(arr)
+        assert arr.searchsorted(a) == 0
+        assert arr.searchsorted(a, side="right") == 1
+
+        assert arr.searchsorted(b) == 1
+        assert arr.searchsorted(b, side="right") == 2
+
+        assert arr.searchsorted(c) == 2
+        assert arr.searchsorted(c, side="right") == 3
+
+        result = arr.searchsorted(arr.take([0, 2]))
+        expected = np.array([0, 2], dtype=np.intp)
+
+        tm.assert_numpy_array_equal(result, expected)
+
+        # sorter
+        sorter = np.array([1, 2, 0])
+        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0
+
+    def test_where_series(self, data, na_value, as_frame):
+        assert data[0] != data[1]
+        cls = type(data)
+        a, b = data[:2]
+
+        ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
+        cond = np.array([True, True, False, False])
+
+        if as_frame:
+            ser = ser.to_frame(name="a")
+            cond = cond.reshape(-1, 1)
+
+        result = ser.where(cond)
+        expected = pd.Series(
+            cls._from_sequence([a, a, na_value, na_value], dtype=data.dtype)
+        )
+
+        if as_frame:
+            expected = expected.to_frame(name="a")
+        self.assert_equal(result, expected)
+
+        # array other
+        cond = np.array([True, False, True, True])
+        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
+        if as_frame:
+            other = pd.DataFrame({"a": other})
+            cond = pd.DataFrame({"a": cond})
+        result = ser.where(cond, other)
+        expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
+        if as_frame:
+            expected = expected.to_frame(name="a")
+        self.assert_equal(result, expected)
+
+    @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
+    def test_repeat(self, data, repeats, as_series, use_numpy):
+        arr = type(data)._from_sequence(data[:3], dtype=data.dtype)
+        if as_series:
+            arr = pd.Series(arr)
+
+        result = np.repeat(arr, repeats) if use_numpy else arr.repeat(repeats)
+
+        repeats = [repeats] * 3 if isinstance(repeats, int) else repeats
+        expected = [x for x, n in zip(arr, repeats) for _ in range(n)]
+        expected = type(data)._from_sequence(expected, dtype=data.dtype)
+        if as_series:
+            expected = pd.Series(expected, index=arr.index.repeat(repeats))
+
+        self.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "repeats, kwargs, error, msg",
+        [
+            (2, dict(axis=1), ValueError, "'axis"),
+            (-1, dict(), ValueError, "negative"),
+            ([1, 2], dict(), ValueError, "shape"),
+            (2, dict(foo="bar"), TypeError, "'foo'"),
+        ],
+    )
+    def test_repeat_raises(self, data, repeats, kwargs, error, msg, use_numpy):
+        with pytest.raises(error, match=msg):
+            if use_numpy:
+                np.repeat(data, repeats, **kwargs)
+            else:
+                data.repeat(repeats, **kwargs)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/missing.py

@@ -0,0 +1,129 @@
+import numpy as np
+
+import pandas as pd
+import pandas.util.testing as tm
+
+from .base import BaseExtensionTests
+
+
+class BaseMissingTests(BaseExtensionTests):
+    def test_isna(self, data_missing):
+        expected = np.array([True, False])
+
+        result = pd.isna(data_missing)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = pd.Series(data_missing).isna()
+        expected = pd.Series(expected)
+        self.assert_series_equal(result, expected)
+
+        # GH 21189
+        result = pd.Series(data_missing).drop([0, 1]).isna()
+        expected = pd.Series([], dtype=bool)
+        self.assert_series_equal(result, expected)
+
+    def test_dropna_array(self, data_missing):
+        result = data_missing.dropna()
+        expected = data_missing[[1]]
+        self.assert_extension_array_equal(result, expected)
+
+    def test_dropna_series(self, data_missing):
+        ser = pd.Series(data_missing)
+        result = ser.dropna()
+        expected = ser.iloc[[1]]
+        self.assert_series_equal(result, expected)
+
+    def test_dropna_frame(self, data_missing):
+        df = pd.DataFrame({"A": data_missing})
+
+        # defaults
+        result = df.dropna()
+        expected = df.iloc[[1]]
+        self.assert_frame_equal(result, expected)
+
+        # axis = 1
+        result = df.dropna(axis="columns")
+        expected = pd.DataFrame(index=[0, 1])
+        self.assert_frame_equal(result, expected)
+
+        # multiple
+        df = pd.DataFrame({"A": data_missing, "B": [1, np.nan]})
+        result = df.dropna()
+        expected = df.iloc[:0]
+        self.assert_frame_equal(result, expected)
+
+    def test_fillna_scalar(self, data_missing):
+        valid = data_missing[1]
+        result = data_missing.fillna(valid)
+        expected = data_missing.fillna(valid)
+        self.assert_extension_array_equal(result, expected)
+
+    def test_fillna_limit_pad(self, data_missing):
+        arr = data_missing.take([1, 0, 0, 0, 1])
+        result = pd.Series(arr).fillna(method="ffill", limit=2)
+        expected = pd.Series(data_missing.take([1, 1, 1, 0, 1]))
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_limit_backfill(self, data_missing):
+        arr = data_missing.take([1, 0, 0, 0, 1])
+        result = pd.Series(arr).fillna(method="backfill", limit=2)
+        expected = pd.Series(data_missing.take([1, 0, 1, 1, 1]))
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_series(self, data_missing):
+        fill_value = data_missing[1]
+        ser = pd.Series(data_missing)
+
+        result = ser.fillna(fill_value)
+        expected = pd.Series(
+            data_missing._from_sequence(
+                [fill_value, fill_value], dtype=data_missing.dtype
+            )
+        )
+        self.assert_series_equal(result, expected)
+
+        # Fill with a series
+        result = ser.fillna(expected)
+        self.assert_series_equal(result, expected)
+
+        # Fill with a series not affecting the missing values
+        result = ser.fillna(ser)
+        self.assert_series_equal(result, ser)
+
+    def test_fillna_series_method(self, data_missing, fillna_method):
+        fill_value = data_missing[1]
+
+        if fillna_method == "ffill":
+            data_missing = data_missing[::-1]
+
+        result = pd.Series(data_missing).fillna(method=fillna_method)
+        expected = pd.Series(
+            data_missing._from_sequence(
+                [fill_value, fill_value], dtype=data_missing.dtype
+            )
+        )
+
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_frame(self, data_missing):
+        fill_value = data_missing[1]
+
+        result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
+
+        expected = pd.DataFrame(
+            {
+                "A": data_missing._from_sequence(
+                    [fill_value, fill_value], dtype=data_missing.dtype
+                ),
+                "B": [1, 2],
+            }
+        )
+
+        self.assert_frame_equal(result, expected)
+
+    def test_fillna_fill_other(self, data):
+        result = pd.DataFrame({"A": data, "B": [np.nan] * len(data)}).fillna({"B": 0.0})
+
+        expected = pd.DataFrame({"A": data, "B": [0.0] * len(result)})
+
+        self.assert_frame_equal(result, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/ops.py

@@ -0,0 +1,173 @@
+import operator
+
+import pytest
+
+import pandas as pd
+from pandas.core import ops
+
+from .base import BaseExtensionTests
+
+
+class BaseOpsUtil(BaseExtensionTests):
+    def get_op_from_name(self, op_name):
+        short_opname = op_name.strip("_")
+        try:
+            op = getattr(operator, short_opname)
+        except AttributeError:
+            # Assume it is the reverse operator
+            rop = getattr(operator, short_opname[1:])
+            op = lambda x, y: rop(y, x)
+
+        return op
+
+    def check_opname(self, s, op_name, other, exc=Exception):
+        op = self.get_op_from_name(op_name)
+
+        self._check_op(s, op, other, op_name, exc)
+
+    def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
+        if exc is None:
+            result = op(s, other)
+            expected = s.combine(other, op)
+            self.assert_series_equal(result, expected)
+        else:
+            with pytest.raises(exc):
+                op(s, other)
+
+    def _check_divmod_op(self, s, op, other, exc=Exception):
+        # divmod has multiple return values, so check separately
+        if exc is None:
+            result_div, result_mod = op(s, other)
+            if op is divmod:
+                expected_div, expected_mod = s // other, s % other
+            else:
+                expected_div, expected_mod = other // s, other % s
+            self.assert_series_equal(result_div, expected_div)
+            self.assert_series_equal(result_mod, expected_mod)
+        else:
+            with pytest.raises(exc):
+                divmod(s, other)
+
+
+class BaseArithmeticOpsTests(BaseOpsUtil):
+    """Various Series and DataFrame arithmetic ops methods.
+
+    Subclasses supporting various ops should set the class variables
+    to indicate that they support ops of that kind
+
+    * series_scalar_exc = TypeError
+    * frame_scalar_exc = TypeError
+    * series_array_exc = TypeError
+    * divmod_exc = TypeError
+    """
+
+    series_scalar_exc = TypeError
+    frame_scalar_exc = TypeError
+    series_array_exc = TypeError
+    divmod_exc = TypeError
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        # series & scalar
+        op_name = all_arithmetic_operators
+        s = pd.Series(data)
+        self.check_opname(s, op_name, s.iloc[0], exc=self.series_scalar_exc)
+
+    @pytest.mark.xfail(run=False, reason="_reduce needs implementation")
+    def test_arith_frame_with_scalar(self, data, all_arithmetic_operators):
+        # frame & scalar
+        op_name = all_arithmetic_operators
+        df = pd.DataFrame({"A": data})
+        self.check_opname(df, op_name, data[0], exc=self.frame_scalar_exc)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        # ndarray & other series
+        op_name = all_arithmetic_operators
+        s = pd.Series(data)
+        self.check_opname(
+            s, op_name, pd.Series([s.iloc[0]] * len(s)), exc=self.series_array_exc
+        )
+
+    def test_divmod(self, data):
+        s = pd.Series(data)
+        self._check_divmod_op(s, divmod, 1, exc=self.divmod_exc)
+        self._check_divmod_op(1, ops.rdivmod, s, exc=self.divmod_exc)
+
+    def test_divmod_series_array(self, data, data_for_twos):
+        s = pd.Series(data)
+        self._check_divmod_op(s, divmod, data)
+
+        other = data_for_twos
+        self._check_divmod_op(other, ops.rdivmod, s)
+
+        other = pd.Series(other)
+        self._check_divmod_op(other, ops.rdivmod, s)
+
+    def test_add_series_with_extension_array(self, data):
+        s = pd.Series(data)
+        result = s + data
+        expected = pd.Series(data + data)
+        self.assert_series_equal(result, expected)
+
+    def test_error(self, data, all_arithmetic_operators):
+        # invalid ops
+        op_name = all_arithmetic_operators
+        with pytest.raises(AttributeError):
+            getattr(data, op_name)
+
+    def test_direct_arith_with_series_returns_not_implemented(self, data):
+        # EAs should return NotImplemented for ops with Series.
+        # Pandas takes care of unboxing the series and calling the EA's op.
+        other = pd.Series(data)
+        if hasattr(data, "__add__"):
+            result = data.__add__(other)
+            assert result is NotImplemented
+        else:
+            raise pytest.skip(
+                "{} does not implement add".format(data.__class__.__name__)
+            )
+
+
+class BaseComparisonOpsTests(BaseOpsUtil):
+    """Various Series and DataFrame comparison ops methods."""
+
+    def _compare_other(self, s, data, op_name, other):
+        op = self.get_op_from_name(op_name)
+        if op_name == "__eq__":
+            assert getattr(data, op_name)(other) is NotImplemented
+            assert not op(s, other).all()
+        elif op_name == "__ne__":
+            assert getattr(data, op_name)(other) is NotImplemented
+            assert op(s, other).all()
+
+        else:
+
+            # array
+            assert getattr(data, op_name)(other) is NotImplemented
+
+            # series
+            s = pd.Series(data)
+            with pytest.raises(TypeError):
+                op(s, other)
+
+    def test_compare_scalar(self, data, all_compare_operators):
+        op_name = all_compare_operators
+        s = pd.Series(data)
+        self._compare_other(s, data, op_name, 0)
+
+    def test_compare_array(self, data, all_compare_operators):
+        op_name = all_compare_operators
+        s = pd.Series(data)
+        other = pd.Series([data[0]] * len(data))
+        self._compare_other(s, data, op_name, other)
+
+    def test_direct_arith_with_series_returns_not_implemented(self, data):
+        # EAs should return NotImplemented for ops with Series.
+        # Pandas takes care of unboxing the series and calling the EA's op.
+        other = pd.Series(data)
+        if hasattr(data, "__eq__"):
+            result = data.__eq__(other)
+            assert result is NotImplemented
+        else:
+            raise pytest.skip(
+                "{} does not implement __eq__".format(data.__class__.__name__)
+            )

venv/lib/python3.6/site-packages/pandas/tests/extension/base/printing.py

@@ -0,0 +1,43 @@
+import io
+
+import pytest
+
+import pandas as pd
+
+from .base import BaseExtensionTests
+
+
+class BasePrintingTests(BaseExtensionTests):
+    """Tests checking the formatting of your EA when printed."""
+
+    @pytest.mark.parametrize("size", ["big", "small"])
+    def test_array_repr(self, data, size):
+        if size == "small":
+            data = data[:5]
+        else:
+            data = type(data)._concat_same_type([data] * 5)
+
+        result = repr(data)
+        assert data.__class__.__name__ in result
+        assert "Length: {}".format(len(data)) in result
+        assert str(data.dtype) in result
+        if size == "big":
+            assert "..." in result
+
+    def test_array_repr_unicode(self, data):
+        result = str(data)
+        assert isinstance(result, str)
+
+    def test_series_repr(self, data):
+        ser = pd.Series(data)
+        assert data.dtype.name in repr(ser)
+
+    def test_dataframe_repr(self, data):
+        df = pd.DataFrame({"A": data})
+        repr(df)
+
+    def test_dtype_name_in_info(self, data):
+        buf = io.StringIO()
+        pd.DataFrame({"A": data}).info(buf=buf)
+        result = buf.getvalue()
+        assert data.dtype.name in result

venv/lib/python3.6/site-packages/pandas/tests/extension/base/reduce.py

@@ -0,0 +1,60 @@
+import warnings
+
+import pytest
+
+import pandas as pd
+import pandas.util.testing as tm
+
+from .base import BaseExtensionTests
+
+
+class BaseReduceTests(BaseExtensionTests):
+    """
+    Reduction specific tests. Generally these only
+    make sense for numeric/boolean operations.
+    """
+
+    def check_reduce(self, s, op_name, skipna):
+        result = getattr(s, op_name)(skipna=skipna)
+        expected = getattr(s.astype("float64"), op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+
+class BaseNoReduceTests(BaseReduceTests):
+    """ we don't define any reductions """
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna):
+        op_name = all_numeric_reductions
+        s = pd.Series(data)
+
+        with pytest.raises(TypeError):
+            getattr(s, op_name)(skipna=skipna)
+
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna):
+        op_name = all_boolean_reductions
+        s = pd.Series(data)
+
+        with pytest.raises(TypeError):
+            getattr(s, op_name)(skipna=skipna)
+
+
+class BaseNumericReduceTests(BaseReduceTests):
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series(self, data, all_numeric_reductions, skipna):
+        op_name = all_numeric_reductions
+        s = pd.Series(data)
+
+        # min/max with empty produce numpy warnings
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            self.check_reduce(s, op_name, skipna)
+
+
+class BaseBooleanReduceTests(BaseReduceTests):
+    @pytest.mark.parametrize("skipna", [True, False])
+    def test_reduce_series(self, data, all_boolean_reductions, skipna):
+        op_name = all_boolean_reductions
+        s = pd.Series(data)
+        self.check_reduce(s, op_name, skipna)

venv/lib/python3.6/site-packages/pandas/tests/extension/base/reshaping.py

@@ -0,0 +1,297 @@
+import itertools
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.internals import ExtensionBlock
+
+from .base import BaseExtensionTests
+
+
+class BaseReshapingTests(BaseExtensionTests):
+    """Tests for reshaping and concatenation."""
+
+    @pytest.mark.parametrize("in_frame", [True, False])
+    def test_concat(self, data, in_frame):
+        wrapped = pd.Series(data)
+        if in_frame:
+            wrapped = pd.DataFrame(wrapped)
+        result = pd.concat([wrapped, wrapped], ignore_index=True)
+
+        assert len(result) == len(data) * 2
+
+        if in_frame:
+            dtype = result.dtypes[0]
+        else:
+            dtype = result.dtype
+
+        assert dtype == data.dtype
+        assert isinstance(result._data.blocks[0], ExtensionBlock)
+
+    @pytest.mark.parametrize("in_frame", [True, False])
+    def test_concat_all_na_block(self, data_missing, in_frame):
+        valid_block = pd.Series(data_missing.take([1, 1]), index=[0, 1])
+        na_block = pd.Series(data_missing.take([0, 0]), index=[2, 3])
+        if in_frame:
+            valid_block = pd.DataFrame({"a": valid_block})
+            na_block = pd.DataFrame({"a": na_block})
+        result = pd.concat([valid_block, na_block])
+        if in_frame:
+            expected = pd.DataFrame({"a": data_missing.take([1, 1, 0, 0])})
+            self.assert_frame_equal(result, expected)
+        else:
+            expected = pd.Series(data_missing.take([1, 1, 0, 0]))
+            self.assert_series_equal(result, expected)
+
+    def test_concat_mixed_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20762
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"A": [1, 2, 3]})
+        df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
+        dfs = [df1, df2, df3]
+
+        # dataframes
+        result = pd.concat(dfs)
+        expected = pd.concat([x.astype(object) for x in dfs])
+        self.assert_frame_equal(result, expected)
+
+        # series
+        result = pd.concat([x["A"] for x in dfs])
+        expected = pd.concat([x["A"].astype(object) for x in dfs])
+        self.assert_series_equal(result, expected)
+
+        # simple test for just EA and one other
+        result = pd.concat([df1, df2])
+        expected = pd.concat([df1.astype("object"), df2.astype("object")])
+        self.assert_frame_equal(result, expected)
+
+        result = pd.concat([df1["A"], df2["A"]])
+        expected = pd.concat([df1["A"].astype("object"), df2["A"].astype("object")])
+        self.assert_series_equal(result, expected)
+
+    def test_concat_columns(self, data, na_value):
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"B": [1, 2, 3]})
+
+        expected = pd.DataFrame({"A": data[:3], "B": [1, 2, 3]})
+        result = pd.concat([df1, df2], axis=1)
+        self.assert_frame_equal(result, expected)
+        result = pd.concat([df1["A"], df2["B"]], axis=1)
+        self.assert_frame_equal(result, expected)
+
+        # non-aligned
+        df2 = pd.DataFrame({"B": [1, 2, 3]}, index=[1, 2, 3])
+        expected = pd.DataFrame(
+            {
+                "A": data._from_sequence(list(data[:3]) + [na_value], dtype=data.dtype),
+                "B": [np.nan, 1, 2, 3],
+            }
+        )
+
+        result = pd.concat([df1, df2], axis=1)
+        self.assert_frame_equal(result, expected)
+        result = pd.concat([df1["A"], df2["B"]], axis=1)
+        self.assert_frame_equal(result, expected)
+
+    def test_align(self, data, na_value):
+        a = data[:3]
+        b = data[2:5]
+        r1, r2 = pd.Series(a).align(pd.Series(b, index=[1, 2, 3]))
+
+        # Assumes that the ctor can take a list of scalars of the type
+        e1 = pd.Series(data._from_sequence(list(a) + [na_value], dtype=data.dtype))
+        e2 = pd.Series(data._from_sequence([na_value] + list(b), dtype=data.dtype))
+        self.assert_series_equal(r1, e1)
+        self.assert_series_equal(r2, e2)
+
+    def test_align_frame(self, data, na_value):
+        a = data[:3]
+        b = data[2:5]
+        r1, r2 = pd.DataFrame({"A": a}).align(pd.DataFrame({"A": b}, index=[1, 2, 3]))
+
+        # Assumes that the ctor can take a list of scalars of the type
+        e1 = pd.DataFrame(
+            {"A": data._from_sequence(list(a) + [na_value], dtype=data.dtype)}
+        )
+        e2 = pd.DataFrame(
+            {"A": data._from_sequence([na_value] + list(b), dtype=data.dtype)}
+        )
+        self.assert_frame_equal(r1, e1)
+        self.assert_frame_equal(r2, e2)
+
+    def test_align_series_frame(self, data, na_value):
+        # https://github.com/pandas-dev/pandas/issues/20576
+        ser = pd.Series(data, name="a")
+        df = pd.DataFrame({"col": np.arange(len(ser) + 1)})
+        r1, r2 = ser.align(df)
+
+        e1 = pd.Series(
+            data._from_sequence(list(data) + [na_value], dtype=data.dtype),
+            name=ser.name,
+        )
+
+        self.assert_series_equal(r1, e1)
+        self.assert_frame_equal(r2, df)
+
+    def test_set_frame_expand_regular_with_extension(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        df["B"] = data
+        expected = pd.DataFrame({"A": [1] * len(data), "B": data})
+        self.assert_frame_equal(df, expected)
+
+    def test_set_frame_expand_extension_with_regular(self, data):
+        df = pd.DataFrame({"A": data})
+        df["B"] = [1] * len(data)
+        expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
+        self.assert_frame_equal(df, expected)
+
+    def test_set_frame_overwrite_object(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20555
+        df = pd.DataFrame({"A": [1] * len(data)}, dtype=object)
+        df["A"] = data
+        assert df.dtypes["A"] == data.dtype
+
+    def test_merge(self, data, na_value):
+        # GH-20743
+        df1 = pd.DataFrame({"ext": data[:3], "int1": [1, 2, 3], "key": [0, 1, 2]})
+        df2 = pd.DataFrame({"int2": [1, 2, 3, 4], "key": [0, 0, 1, 3]})
+
+        res = pd.merge(df1, df2)
+        exp = pd.DataFrame(
+            {
+                "int1": [1, 1, 2],
+                "int2": [1, 2, 3],
+                "key": [0, 0, 1],
+                "ext": data._from_sequence(
+                    [data[0], data[0], data[1]], dtype=data.dtype
+                ),
+            }
+        )
+        self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
+
+        res = pd.merge(df1, df2, how="outer")
+        exp = pd.DataFrame(
+            {
+                "int1": [1, 1, 2, 3, np.nan],
+                "int2": [1, 2, 3, np.nan, 4],
+                "key": [0, 0, 1, 2, 3],
+                "ext": data._from_sequence(
+                    [data[0], data[0], data[1], data[2], na_value], dtype=data.dtype
+                ),
+            }
+        )
+        self.assert_frame_equal(res, exp[["ext", "int1", "key", "int2"]])
+
+    def test_merge_on_extension_array(self, data):
+        # GH 23020
+        a, b = data[:2]
+        key = type(data)._from_sequence([a, b], dtype=data.dtype)
+
+        df = pd.DataFrame({"key": key, "val": [1, 2]})
+        result = pd.merge(df, df, on="key")
+        expected = pd.DataFrame({"key": key, "val_x": [1, 2], "val_y": [1, 2]})
+        self.assert_frame_equal(result, expected)
+
+        # order
+        result = pd.merge(df.iloc[[1, 0]], df, on="key")
+        expected = expected.iloc[[1, 0]].reset_index(drop=True)
+        self.assert_frame_equal(result, expected)
+
+    def test_merge_on_extension_array_duplicates(self, data):
+        # GH 23020
+        a, b = data[:2]
+        key = type(data)._from_sequence([a, b, a], dtype=data.dtype)
+        df1 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
+        df2 = pd.DataFrame({"key": key, "val": [1, 2, 3]})
+
+        result = pd.merge(df1, df2, on="key")
+        expected = pd.DataFrame(
+            {
+                "key": key.take([0, 0, 0, 0, 1]),
+                "val_x": [1, 1, 3, 3, 2],
+                "val_y": [1, 3, 1, 3, 2],
+            }
+        )
+        self.assert_frame_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "columns",
+        [
+            ["A", "B"],
+            pd.MultiIndex.from_tuples(
+                [("A", "a"), ("A", "b")], names=["outer", "inner"]
+            ),
+        ],
+    )
+    def test_stack(self, data, columns):
+        df = pd.DataFrame({"A": data[:5], "B": data[:5]})
+        df.columns = columns
+        result = df.stack()
+        expected = df.astype(object).stack()
+        # we need a second astype(object), in case the constructor inferred
+        # object -> specialized, as is done for period.
+        expected = expected.astype(object)
+
+        if isinstance(expected, pd.Series):
+            assert result.dtype == df.iloc[:, 0].dtype
+        else:
+            assert all(result.dtypes == df.iloc[:, 0].dtype)
+
+        result = result.astype(object)
+        self.assert_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "index",
+        [
+            # Two levels, uniform.
+            pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"]),
+            # non-uniform
+            pd.MultiIndex.from_tuples([("A", "a"), ("A", "b"), ("B", "b")]),
+            # three levels, non-uniform
+            pd.MultiIndex.from_product([("A", "B"), ("a", "b", "c"), (0, 1, 2)]),
+            pd.MultiIndex.from_tuples(
+                [
+                    ("A", "a", 1),
+                    ("A", "b", 0),
+                    ("A", "a", 0),
+                    ("B", "a", 0),
+                    ("B", "c", 1),
+                ]
+            ),
+        ],
+    )
+    @pytest.mark.parametrize("obj", ["series", "frame"])
+    def test_unstack(self, data, index, obj):
+        data = data[: len(index)]
+        if obj == "series":
+            ser = pd.Series(data, index=index)
+        else:
+            ser = pd.DataFrame({"A": data, "B": data}, index=index)
+
+        n = index.nlevels
+        levels = list(range(n))
+        # [0, 1, 2]
+        # [(0,), (1,), (2,), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1)]
+        combinations = itertools.chain.from_iterable(
+            itertools.permutations(levels, i) for i in range(1, n)
+        )
+
+        for level in combinations:
+            result = ser.unstack(level=level)
+            assert all(
+                isinstance(result[col].array, type(data)) for col in result.columns
+            )
+            expected = ser.astype(object).unstack(level=level)
+            result = result.astype(object)
+
+            self.assert_frame_equal(result, expected)
+
+    def test_ravel(self, data):
+        # as long as EA is 1D-only, ravel is a no-op
+        result = data.ravel()
+        assert type(result) == type(data)
+
+        # Check that we have a view, not a copy
+        result[0] = result[1]
+        assert data[0] == data[1]

venv/lib/python3.6/site-packages/pandas/tests/extension/base/setitem.py

@@ -0,0 +1,188 @@
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+
+from .base import BaseExtensionTests
+
+
+class BaseSetitemTests(BaseExtensionTests):
+    def test_setitem_scalar_series(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        data[0] = data[1]
+        assert data[0] == data[1]
+
+    def test_setitem_sequence(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        original = data.copy()
+
+        data[[0, 1]] = [data[1], data[0]]
+        assert data[0] == original[1]
+        assert data[1] == original[0]
+
+    def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
+        ser = pd.Series(data)
+        original = ser.copy()
+        value = [data[0]]
+        if as_array:
+            value = data._from_sequence(value)
+
+        xpr = "cannot set using a {} indexer with a different length"
+        with pytest.raises(ValueError, match=xpr.format("list-like")):
+            ser[[0, 1]] = value
+        # Ensure no modifications made before the exception
+        self.assert_series_equal(ser, original)
+
+        with pytest.raises(ValueError, match=xpr.format("slice")):
+            ser[slice(3)] = value
+        self.assert_series_equal(ser, original)
+
+    def test_setitem_empty_indxer(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        original = data.copy()
+        data[np.array([], dtype=int)] = []
+        self.assert_equal(data, original)
+
+    def test_setitem_sequence_broadcasts(self, data, box_in_series):
+        if box_in_series:
+            data = pd.Series(data)
+        data[[0, 1]] = data[2]
+        assert data[0] == data[2]
+        assert data[1] == data[2]
+
+    @pytest.mark.parametrize("setter", ["loc", "iloc"])
+    def test_setitem_scalar(self, data, setter):
+        arr = pd.Series(data)
+        setter = getattr(arr, setter)
+        operator.setitem(setter, 0, data[1])
+        assert arr[0] == data[1]
+
+    def test_setitem_loc_scalar_mixed(self, data):
+        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
+        df.loc[0, "B"] = data[1]
+        assert df.loc[0, "B"] == data[1]
+
+    def test_setitem_loc_scalar_single(self, data):
+        df = pd.DataFrame({"B": data})
+        df.loc[10, "B"] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_loc_scalar_multiple_homogoneous(self, data):
+        df = pd.DataFrame({"A": data, "B": data})
+        df.loc[10, "B"] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_mixed(self, data):
+        df = pd.DataFrame({"A": np.arange(len(data)), "B": data})
+        df.iloc[0, 1] = data[1]
+        assert df.loc[0, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_single(self, data):
+        df = pd.DataFrame({"B": data})
+        df.iloc[10, 0] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
+        df = pd.DataFrame({"A": data, "B": data})
+        df.iloc[10, 1] = data[1]
+        assert df.loc[10, "B"] == data[1]
+
+    @pytest.mark.parametrize("as_callable", [True, False])
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_aligned(self, data, as_callable, setter):
+        ser = pd.Series(data)
+        mask = np.zeros(len(data), dtype=bool)
+        mask[:2] = True
+
+        if as_callable:
+            mask2 = lambda x: mask
+        else:
+            mask2 = mask
+
+        if setter:
+            # loc
+            target = getattr(ser, setter)
+        else:
+            # Series.__setitem__
+            target = ser
+
+        operator.setitem(target, mask2, data[5:7])
+
+        ser[mask2] = data[5:7]
+        assert ser[0] == data[5]
+        assert ser[1] == data[6]
+
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_broadcast(self, data, setter):
+        ser = pd.Series(data)
+        mask = np.zeros(len(data), dtype=bool)
+        mask[:2] = True
+
+        if setter:  # loc
+            target = getattr(ser, setter)
+        else:  # __setitem__
+            target = ser
+
+        operator.setitem(target, mask, data[10])
+        assert ser[0] == data[10]
+        assert ser[1] == data[10]
+
+    def test_setitem_expand_columns(self, data):
+        df = pd.DataFrame({"A": data})
+        result = df.copy()
+        result["B"] = 1
+        expected = pd.DataFrame({"A": data, "B": [1] * len(data)})
+        self.assert_frame_equal(result, expected)
+
+        result = df.copy()
+        result.loc[:, "B"] = 1
+        self.assert_frame_equal(result, expected)
+
+        # overwrite with new type
+        result["B"] = data
+        expected = pd.DataFrame({"A": data, "B": data})
+        self.assert_frame_equal(result, expected)
+
+    def test_setitem_expand_with_extension(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        result = df.copy()
+        result["B"] = data
+        expected = pd.DataFrame({"A": [1] * len(data), "B": data})
+        self.assert_frame_equal(result, expected)
+
+        result = df.copy()
+        result.loc[:, "B"] = data
+        self.assert_frame_equal(result, expected)
+
+    def test_setitem_frame_invalid_length(self, data):
+        df = pd.DataFrame({"A": [1] * len(data)})
+        xpr = "Length of values does not match length of index"
+        with pytest.raises(ValueError, match=xpr):
+            df["B"] = data[:5]
+
+    @pytest.mark.xfail(reason="GH#20441: setitem on extension types.")
+    def test_setitem_tuple_index(self, data):
+        s = pd.Series(data[:2], index=[(0, 0), (0, 1)])
+        expected = pd.Series(data.take([1, 1]), index=s.index)
+        s[(0, 1)] = data[1]
+        self.assert_series_equal(s, expected)
+
+    def test_setitem_slice_mismatch_length_raises(self, data):
+        arr = data[:5]
+        with pytest.raises(ValueError):
+            arr[:1] = arr[:2]
+
+    def test_setitem_slice_array(self, data):
+        arr = data[:5].copy()
+        arr[:5] = data[-5:]
+        self.assert_extension_array_equal(arr, data[-5:])
+
+    def test_setitem_scalar_key_sequence_raise(self, data):
+        arr = data[:5].copy()
+        with pytest.raises(ValueError):
+            arr[0] = arr[[0, 1]]

venv/lib/python3.6/site-packages/pandas/tests/extension/conftest.py

@@ -0,0 +1,178 @@
+import operator
+
+import pytest
+
+from pandas import Series
+
+
+@pytest.fixture
+def dtype():
+    """A fixture providing the ExtensionDtype to validate."""
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data():
+    """Length-100 array for this type.
+
+    * data[0] and data[1] should both be non missing
+    * data[0] and data[1] should not be equal
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_for_twos():
+    """Length-100 array in which all the elements are two."""
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_missing():
+    """Length-2 array with [NA, Valid]"""
+    raise NotImplementedError
+
+
+@pytest.fixture(params=["data", "data_missing"])
+def all_data(request, data, data_missing):
+    """Parametrized fixture giving 'data' and 'data_missing'"""
+    if request.param == "data":
+        return data
+    elif request.param == "data_missing":
+        return data_missing
+
+
+@pytest.fixture
+def data_repeated(data):
+    """
+    Generate many datasets.
+
+    Parameters
+    ----------
+    data : fixture implementing `data`
+
+    Returns
+    -------
+    Callable[[int], Generator]:
+        A callable that takes a `count` argument and
+        returns a generator yielding `count` datasets.
+    """
+
+    def gen(count):
+        for _ in range(count):
+            yield data
+
+    return gen
+
+
+@pytest.fixture
+def data_for_sorting():
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, C, A] with
+    A < B < C
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, NA, A] with
+    A < B and NA missing.
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture
+def na_cmp():
+    """Binary operator for comparing NA values.
+
+    Should return a function of two arguments that returns
+    True if both arguments are (scalar) NA for your type.
+
+    By default, uses ``operator.is_``
+    """
+    return operator.is_
+
+
+@pytest.fixture
+def na_value():
+    """The scalar missing value for this type. Default 'None'"""
+    return None
+
+
+@pytest.fixture
+def data_for_grouping():
+    """Data for factorization, grouping, and unique tests.
+
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing
+    """
+    raise NotImplementedError
+
+
+@pytest.fixture(params=[True, False])
+def box_in_series(request):
+    """Whether to box the data in a Series"""
+    return request.param
+
+
+@pytest.fixture(
+    params=[
+        lambda x: 1,
+        lambda x: [1] * len(x),
+        lambda x: Series([1] * len(x)),
+        lambda x: x,
+    ],
+    ids=["scalar", "list", "series", "object"],
+)
+def groupby_apply_op(request):
+    """
+    Functions to test groupby.apply().
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_frame(request):
+    """
+    Boolean fixture to support Series and Series.to_frame() comparison testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_series(request):
+    """
+    Boolean fixture to support arr and Series(arr) comparison testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def use_numpy(request):
+    """
+    Boolean fixture to support comparison testing of ExtensionDtype array
+    and numpy array.
+    """
+    return request.param
+
+
+@pytest.fixture(params=["ffill", "bfill"])
+def fillna_method(request):
+    """
+    Parametrized fixture giving method parameters 'ffill' and 'bfill' for
+    Series.fillna(method=<method>) testing.
+    """
+    return request.param
+
+
+@pytest.fixture(params=[True, False])
+def as_array(request):
+    """
+    Boolean fixture to support ExtensionDtype _from_sequence method testing.
+    """
+    return request.param

venv/lib/python3.6/site-packages/pandas/tests/extension/decimal/__init__.py

@@ -0,0 +1,3 @@
+from .array import DecimalArray, DecimalDtype, make_data, to_decimal
+
+__all__ = ["DecimalArray", "DecimalDtype", "to_decimal", "make_data"]

venv/lib/python3.6/site-packages/pandas/tests/extension/decimal/array.py

@@ -0,0 +1,189 @@
+import decimal
+import numbers
+import random
+import sys
+
+import numpy as np
+
+from pandas.core.dtypes.base import ExtensionDtype
+
+import pandas as pd
+from pandas.api.extensions import register_extension_dtype
+from pandas.core.arrays import ExtensionArray, ExtensionScalarOpsMixin
+
+
+@register_extension_dtype
+class DecimalDtype(ExtensionDtype):
+    type = decimal.Decimal
+    name = "decimal"
+    na_value = decimal.Decimal("NaN")
+    _metadata = ("context",)
+
+    def __init__(self, context=None):
+        self.context = context or decimal.getcontext()
+
+    def __repr__(self):
+        return "DecimalDtype(context={})".format(self.context)
+
+    @classmethod
+    def construct_array_type(cls):
+        """Return the array type associated with this dtype
+
+        Returns
+        -------
+        type
+        """
+        return DecimalArray
+
+    @classmethod
+    def construct_from_string(cls, string):
+        if string == cls.name:
+            return cls()
+        else:
+            raise TypeError("Cannot construct a '{}' from '{}'".format(cls, string))
+
+    @property
+    def _is_numeric(self):
+        return True
+
+
+class DecimalArray(ExtensionArray, ExtensionScalarOpsMixin):
+    __array_priority__ = 1000
+
+    def __init__(self, values, dtype=None, copy=False, context=None):
+        for val in values:
+            if not isinstance(val, decimal.Decimal):
+                raise TypeError("All values must be of type " + str(decimal.Decimal))
+        values = np.asarray(values, dtype=object)
+
+        self._data = values
+        # Some aliases for common attribute names to ensure pandas supports
+        # these
+        self._items = self.data = self._data
+        # those aliases are currently not working due to assumptions
+        # in internal code (GH-20735)
+        # self._values = self.values = self.data
+        self._dtype = DecimalDtype(context)
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @classmethod
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        return cls(scalars)
+
+    @classmethod
+    def _from_sequence_of_strings(cls, strings, dtype=None, copy=False):
+        return cls._from_sequence([decimal.Decimal(x) for x in strings], dtype, copy)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls(values)
+
+    _HANDLED_TYPES = (decimal.Decimal, numbers.Number, np.ndarray)
+
+    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
+        #
+        if not all(
+            isinstance(t, self._HANDLED_TYPES + (DecimalArray,)) for t in inputs
+        ):
+            return NotImplemented
+
+        inputs = tuple(x._data if isinstance(x, DecimalArray) else x for x in inputs)
+        result = getattr(ufunc, method)(*inputs, **kwargs)
+
+        def reconstruct(x):
+            if isinstance(x, (decimal.Decimal, numbers.Number)):
+                return x
+            else:
+                return DecimalArray._from_sequence(x)
+
+        if isinstance(result, tuple):
+            return tuple(reconstruct(x) for x in result)
+        else:
+            return reconstruct(result)
+
+    def __getitem__(self, item):
+        if isinstance(item, numbers.Integral):
+            return self._data[item]
+        else:
+            return type(self)(self._data[item])
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        from pandas.api.extensions import take
+
+        data = self._data
+        if allow_fill and fill_value is None:
+            fill_value = self.dtype.na_value
+
+        result = take(data, indexer, fill_value=fill_value, allow_fill=allow_fill)
+        return self._from_sequence(result)
+
+    def copy(self):
+        return type(self)(self._data.copy())
+
+    def astype(self, dtype, copy=True):
+        if isinstance(dtype, type(self.dtype)):
+            return type(self)(self._data, context=dtype.context)
+        return np.asarray(self, dtype=dtype)
+
+    def __setitem__(self, key, value):
+        if pd.api.types.is_list_like(value):
+            if pd.api.types.is_scalar(key):
+                raise ValueError("setting an array element with a sequence.")
+            value = [decimal.Decimal(v) for v in value]
+        else:
+            value = decimal.Decimal(value)
+        self._data[key] = value
+
+    def __len__(self):
+        return len(self._data)
+
+    @property
+    def nbytes(self):
+        n = len(self)
+        if n:
+            return n * sys.getsizeof(self[0])
+        return 0
+
+    def isna(self):
+        return np.array([x.is_nan() for x in self._data], dtype=bool)
+
+    @property
+    def _na_value(self):
+        return decimal.Decimal("NaN")
+
+    def _formatter(self, boxed=False):
+        if boxed:
+            return "Decimal: {0}".format
+        return repr
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        return cls(np.concatenate([x._data for x in to_concat]))
+
+    def _reduce(self, name, skipna=True, **kwargs):
+
+        if skipna:
+            raise NotImplementedError("decimal does not support skipna=True")
+
+        try:
+            op = getattr(self.data, name)
+        except AttributeError:
+            raise NotImplementedError(
+                "decimal does not support the {} operation".format(name)
+            )
+        return op(axis=0)
+
+
+def to_decimal(values, context=None):
+    return DecimalArray([decimal.Decimal(x) for x in values], context=context)
+
+
+def make_data():
+    return [decimal.Decimal(random.random()) for _ in range(100)]
+
+
+DecimalArray._add_arithmetic_ops()
+DecimalArray._add_comparison_ops()

venv/lib/python3.6/site-packages/pandas/tests/extension/decimal/test_decimal.py

@@ -0,0 +1,439 @@
+import decimal
+import math
+import operator
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.tests.extension import base
+import pandas.util.testing as tm
+
+from .array import DecimalArray, DecimalDtype, make_data, to_decimal
+
+
+@pytest.fixture
+def dtype():
+    return DecimalDtype()
+
+
+@pytest.fixture
+def data():
+    return DecimalArray(make_data())
+
+
+@pytest.fixture
+def data_for_twos():
+    return DecimalArray([decimal.Decimal(2) for _ in range(100)])
+
+
+@pytest.fixture
+def data_missing():
+    return DecimalArray([decimal.Decimal("NaN"), decimal.Decimal(1)])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return DecimalArray(
+        [decimal.Decimal("1"), decimal.Decimal("2"), decimal.Decimal("0")]
+    )
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return DecimalArray(
+        [decimal.Decimal("1"), decimal.Decimal("NaN"), decimal.Decimal("0")]
+    )
+
+
+@pytest.fixture
+def na_cmp():
+    return lambda x, y: x.is_nan() and y.is_nan()
+
+
+@pytest.fixture
+def na_value():
+    return decimal.Decimal("NaN")
+
+
+@pytest.fixture
+def data_for_grouping():
+    b = decimal.Decimal("1.0")
+    a = decimal.Decimal("0.0")
+    c = decimal.Decimal("2.0")
+    na = decimal.Decimal("NaN")
+    return DecimalArray([b, b, na, na, a, a, b, c])
+
+
+class BaseDecimal:
+    def assert_series_equal(self, left, right, *args, **kwargs):
+        def convert(x):
+            # need to convert array([Decimal(NaN)], dtype='object') to np.NaN
+            # because Series[object].isnan doesn't recognize decimal(NaN) as
+            # NA.
+            try:
+                return math.isnan(x)
+            except TypeError:
+                return False
+
+        if left.dtype == "object":
+            left_na = left.apply(convert)
+        else:
+            left_na = left.isna()
+        if right.dtype == "object":
+            right_na = right.apply(convert)
+        else:
+            right_na = right.isna()
+
+        tm.assert_series_equal(left_na, right_na)
+        return tm.assert_series_equal(left[~left_na], right[~right_na], *args, **kwargs)
+
+    def assert_frame_equal(self, left, right, *args, **kwargs):
+        # TODO(EA): select_dtypes
+        tm.assert_index_equal(
+            left.columns,
+            right.columns,
+            exact=kwargs.get("check_column_type", "equiv"),
+            check_names=kwargs.get("check_names", True),
+            check_exact=kwargs.get("check_exact", False),
+            check_categorical=kwargs.get("check_categorical", True),
+            obj="{obj}.columns".format(obj=kwargs.get("obj", "DataFrame")),
+        )
+
+        decimals = (left.dtypes == "decimal").index
+
+        for col in decimals:
+            self.assert_series_equal(left[col], right[col], *args, **kwargs)
+
+        left = left.drop(columns=decimals)
+        right = right.drop(columns=decimals)
+        tm.assert_frame_equal(left, right, *args, **kwargs)
+
+
+class TestDtype(BaseDecimal, base.BaseDtypeTests):
+    def test_hashable(self, dtype):
+        pass
+
+
+class TestInterface(BaseDecimal, base.BaseInterfaceTests):
+    pass
+
+
+class TestConstructors(BaseDecimal, base.BaseConstructorsTests):
+    @pytest.mark.skip(reason="not implemented constructor from dtype")
+    def test_from_dtype(self, data):
+        # construct from our dtype & string dtype
+        pass
+
+
+class TestReshaping(BaseDecimal, base.BaseReshapingTests):
+    pass
+
+
+class TestGetitem(BaseDecimal, base.BaseGetitemTests):
+    def test_take_na_value_other_decimal(self):
+        arr = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
+        result = arr.take([0, -1], allow_fill=True, fill_value=decimal.Decimal("-1.0"))
+        expected = DecimalArray([decimal.Decimal("1.0"), decimal.Decimal("-1.0")])
+        self.assert_extension_array_equal(result, expected)
+
+
+class TestMissing(BaseDecimal, base.BaseMissingTests):
+    pass
+
+
+class Reduce:
+    def check_reduce(self, s, op_name, skipna):
+
+        if skipna or op_name in ["median", "skew", "kurt"]:
+            with pytest.raises(NotImplementedError):
+                getattr(s, op_name)(skipna=skipna)
+
+        else:
+            result = getattr(s, op_name)(skipna=skipna)
+            expected = getattr(np.asarray(s), op_name)()
+            tm.assert_almost_equal(result, expected)
+
+
+class TestNumericReduce(Reduce, base.BaseNumericReduceTests):
+    pass
+
+
+class TestBooleanReduce(Reduce, base.BaseBooleanReduceTests):
+    pass
+
+
+class TestMethods(BaseDecimal, base.BaseMethodsTests):
+    @pytest.mark.parametrize("dropna", [True, False])
+    @pytest.mark.xfail(reason="value_counts not implemented yet.")
+    def test_value_counts(self, all_data, dropna):
+        all_data = all_data[:10]
+        if dropna:
+            other = np.array(all_data[~all_data.isna()])
+        else:
+            other = all_data
+
+        result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
+        expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
+
+        tm.assert_series_equal(result, expected)
+
+
+class TestCasting(BaseDecimal, base.BaseCastingTests):
+    pass
+
+
+class TestGroupby(BaseDecimal, base.BaseGroupbyTests):
+    @pytest.mark.xfail(
+        reason="needs to correctly define __eq__ to handle nans, xref #27081."
+    )
+    def test_groupby_apply_identity(self, data_for_grouping):
+        super().test_groupby_apply_identity(data_for_grouping)
+
+
+class TestSetitem(BaseDecimal, base.BaseSetitemTests):
+    pass
+
+
+class TestPrinting(BaseDecimal, base.BasePrintingTests):
+    def test_series_repr(self, data):
+        # Overriding this base test to explicitly test that
+        # the custom _formatter is used
+        ser = pd.Series(data)
+        assert data.dtype.name in repr(ser)
+        assert "Decimal: " in repr(ser)
+
+
+# TODO(extension)
+@pytest.mark.xfail(
+    reason=(
+        "raising AssertionError as this is not implemented, though easy enough to do"
+    )
+)
+def test_series_constructor_coerce_data_to_extension_dtype_raises():
+    xpr = (
+        "Cannot cast data to extension dtype 'decimal'. Pass the "
+        "extension array directly."
+    )
+    with pytest.raises(ValueError, match=xpr):
+        pd.Series([0, 1, 2], dtype=DecimalDtype())
+
+
+def test_series_constructor_with_dtype():
+    arr = DecimalArray([decimal.Decimal("10.0")])
+    result = pd.Series(arr, dtype=DecimalDtype())
+    expected = pd.Series(arr)
+    tm.assert_series_equal(result, expected)
+
+    result = pd.Series(arr, dtype="int64")
+    expected = pd.Series([10])
+    tm.assert_series_equal(result, expected)
+
+
+def test_dataframe_constructor_with_dtype():
+    arr = DecimalArray([decimal.Decimal("10.0")])
+
+    result = pd.DataFrame({"A": arr}, dtype=DecimalDtype())
+    expected = pd.DataFrame({"A": arr})
+    tm.assert_frame_equal(result, expected)
+
+    arr = DecimalArray([decimal.Decimal("10.0")])
+    result = pd.DataFrame({"A": arr}, dtype="int64")
+    expected = pd.DataFrame({"A": [10]})
+    tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("frame", [True, False])
+def test_astype_dispatches(frame):
+    # This is a dtype-specific test that ensures Series[decimal].astype
+    # gets all the way through to ExtensionArray.astype
+    # Designing a reliable smoke test that works for arbitrary data types
+    # is difficult.
+    data = pd.Series(DecimalArray([decimal.Decimal(2)]), name="a")
+    ctx = decimal.Context()
+    ctx.prec = 5
+
+    if frame:
+        data = data.to_frame()
+
+    result = data.astype(DecimalDtype(ctx))
+
+    if frame:
+        result = result["a"]
+
+    assert result.dtype.context.prec == ctx.prec
+
+
+class TestArithmeticOps(BaseDecimal, base.BaseArithmeticOpsTests):
+    def check_opname(self, s, op_name, other, exc=None):
+        super().check_opname(s, op_name, other, exc=None)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        op_name = all_arithmetic_operators
+        s = pd.Series(data)
+
+        context = decimal.getcontext()
+        divbyzerotrap = context.traps[decimal.DivisionByZero]
+        invalidoptrap = context.traps[decimal.InvalidOperation]
+        context.traps[decimal.DivisionByZero] = 0
+        context.traps[decimal.InvalidOperation] = 0
+
+        # Decimal supports ops with int, but not float
+        other = pd.Series([int(d * 100) for d in data])
+        self.check_opname(s, op_name, other)
+
+        if "mod" not in op_name:
+            self.check_opname(s, op_name, s * 2)
+
+        self.check_opname(s, op_name, 0)
+        self.check_opname(s, op_name, 5)
+        context.traps[decimal.DivisionByZero] = divbyzerotrap
+        context.traps[decimal.InvalidOperation] = invalidoptrap
+
+    def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
+        # We implement divmod
+        super()._check_divmod_op(s, op, other, exc=None)
+
+    def test_error(self):
+        pass
+
+
+class TestComparisonOps(BaseDecimal, base.BaseComparisonOpsTests):
+    def check_opname(self, s, op_name, other, exc=None):
+        super().check_opname(s, op_name, other, exc=None)
+
+    def _compare_other(self, s, data, op_name, other):
+        self.check_opname(s, op_name, other)
+
+    def test_compare_scalar(self, data, all_compare_operators):
+        op_name = all_compare_operators
+        s = pd.Series(data)
+        self._compare_other(s, data, op_name, 0.5)
+
+    def test_compare_array(self, data, all_compare_operators):
+        op_name = all_compare_operators
+        s = pd.Series(data)
+
+        alter = np.random.choice([-1, 0, 1], len(data))
+        # Randomly double, halve or keep same value
+        other = pd.Series(data) * [decimal.Decimal(pow(2.0, i)) for i in alter]
+        self._compare_other(s, data, op_name, other)
+
+
+class DecimalArrayWithoutFromSequence(DecimalArray):
+    """Helper class for testing error handling in _from_sequence."""
+
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        raise KeyError("For the test")
+
+
+class DecimalArrayWithoutCoercion(DecimalArrayWithoutFromSequence):
+    @classmethod
+    def _create_arithmetic_method(cls, op):
+        return cls._create_method(op, coerce_to_dtype=False)
+
+
+DecimalArrayWithoutCoercion._add_arithmetic_ops()
+
+
+def test_combine_from_sequence_raises():
+    # https://github.com/pandas-dev/pandas/issues/22850
+    ser = pd.Series(
+        DecimalArrayWithoutFromSequence(
+            [decimal.Decimal("1.0"), decimal.Decimal("2.0")]
+        )
+    )
+    result = ser.combine(ser, operator.add)
+
+    # note: object dtype
+    expected = pd.Series(
+        [decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
+    )
+    tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "class_", [DecimalArrayWithoutFromSequence, DecimalArrayWithoutCoercion]
+)
+def test_scalar_ops_from_sequence_raises(class_):
+    # op(EA, EA) should return an EA, or an ndarray if it's not possible
+    # to return an EA with the return values.
+    arr = class_([decimal.Decimal("1.0"), decimal.Decimal("2.0")])
+    result = arr + arr
+    expected = np.array(
+        [decimal.Decimal("2.0"), decimal.Decimal("4.0")], dtype="object"
+    )
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "reverse, expected_div, expected_mod",
+    [(False, [0, 1, 1, 2], [1, 0, 1, 0]), (True, [2, 1, 0, 0], [0, 0, 2, 2])],
+)
+def test_divmod_array(reverse, expected_div, expected_mod):
+    # https://github.com/pandas-dev/pandas/issues/22930
+    arr = to_decimal([1, 2, 3, 4])
+    if reverse:
+        div, mod = divmod(2, arr)
+    else:
+        div, mod = divmod(arr, 2)
+    expected_div = to_decimal(expected_div)
+    expected_mod = to_decimal(expected_mod)
+
+    tm.assert_extension_array_equal(div, expected_div)
+    tm.assert_extension_array_equal(mod, expected_mod)
+
+
+def test_ufunc_fallback(data):
+    a = data[:5]
+    s = pd.Series(a, index=range(3, 8))
+    result = np.abs(s)
+    expected = pd.Series(np.abs(a), index=range(3, 8))
+    tm.assert_series_equal(result, expected)
+
+
+def test_formatting_values_deprecated():
+    class DecimalArray2(DecimalArray):
+        def _formatting_values(self):
+            return np.array(self)
+
+    ser = pd.Series(DecimalArray2([decimal.Decimal("1.0")]))
+
+    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        repr(ser)
+
+
+def test_array_ufunc():
+    a = to_decimal([1, 2, 3])
+    result = np.exp(a)
+    expected = to_decimal(np.exp(a._data))
+    tm.assert_extension_array_equal(result, expected)
+
+
+def test_array_ufunc_series():
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+    result = np.exp(s)
+    expected = pd.Series(to_decimal(np.exp(a._data)))
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_ufunc_series_scalar_other():
+    # check _HANDLED_TYPES
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+    result = np.add(s, decimal.Decimal(1))
+    expected = pd.Series(np.add(a, decimal.Decimal(1)))
+    tm.assert_series_equal(result, expected)
+
+
+def test_array_ufunc_series_defer():
+    a = to_decimal([1, 2, 3])
+    s = pd.Series(a)
+
+    expected = pd.Series(to_decimal([2, 4, 6]))
+    r1 = np.add(s, a)
+    r2 = np.add(a, s)
+
+    tm.assert_series_equal(r1, expected)
+    tm.assert_series_equal(r2, expected)

venv/lib/python3.6/site-packages/pandas/tests/extension/json/__init__.py

@@ -0,0 +1,3 @@
+from .array import JSONArray, JSONDtype, make_data
+
+__all__ = ["JSONArray", "JSONDtype", "make_data"]

venv/lib/python3.6/site-packages/pandas/tests/extension/json/array.py

@@ -0,0 +1,198 @@
+"""Test extension array for storing nested data in a pandas container.
+
+The JSONArray stores lists of dictionaries. The storage mechanism is a list,
+not an ndarray.
+
+Note:
+
+We currently store lists of UserDicts. Pandas has a few places
+internally that specifically check for dicts, and does non-scalar things
+in that case. We *want* the dictionaries to be treated as scalars, so we
+hack around pandas by using UserDicts.
+"""
+from collections import UserDict, abc
+import itertools
+import numbers
+import random
+import string
+import sys
+
+import numpy as np
+
+from pandas.core.dtypes.base import ExtensionDtype
+
+from pandas.core.arrays import ExtensionArray
+
+
+class JSONDtype(ExtensionDtype):
+    type = abc.Mapping
+    name = "json"
+    na_value = UserDict()
+
+    @classmethod
+    def construct_array_type(cls):
+        """Return the array type associated with this dtype
+
+        Returns
+        -------
+        type
+        """
+        return JSONArray
+
+    @classmethod
+    def construct_from_string(cls, string):
+        if string == cls.name:
+            return cls()
+        else:
+            raise TypeError("Cannot construct a '{}' from '{}'".format(cls, string))
+
+
+class JSONArray(ExtensionArray):
+    dtype = JSONDtype()
+    __array_priority__ = 1000
+
+    def __init__(self, values, dtype=None, copy=False):
+        for val in values:
+            if not isinstance(val, self.dtype.type):
+                raise TypeError("All values must be of type " + str(self.dtype.type))
+        self.data = values
+
+        # Some aliases for common attribute names to ensure pandas supports
+        # these
+        self._items = self._data = self.data
+        # those aliases are currently not working due to assumptions
+        # in internal code (GH-20735)
+        # self._values = self.values = self.data
+
+    @classmethod
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        return cls(scalars)
+
+    @classmethod
+    def _from_factorized(cls, values, original):
+        return cls([UserDict(x) for x in values if x != ()])
+
+    def __getitem__(self, item):
+        if isinstance(item, numbers.Integral):
+            return self.data[item]
+        elif isinstance(item, np.ndarray) and item.dtype == "bool":
+            return self._from_sequence([x for x, m in zip(self, item) if m])
+        elif isinstance(item, abc.Iterable):
+            # fancy indexing
+            return type(self)([self.data[i] for i in item])
+        else:
+            # slice
+            return type(self)(self.data[item])
+
+    def __setitem__(self, key, value):
+        if isinstance(key, numbers.Integral):
+            self.data[key] = value
+        else:
+            if not isinstance(value, (type(self), abc.Sequence)):
+                # broadcast value
+                value = itertools.cycle([value])
+
+            if isinstance(key, np.ndarray) and key.dtype == "bool":
+                # masking
+                for i, (k, v) in enumerate(zip(key, value)):
+                    if k:
+                        assert isinstance(v, self.dtype.type)
+                        self.data[i] = v
+            else:
+                for k, v in zip(key, value):
+                    assert isinstance(v, self.dtype.type)
+                    self.data[k] = v
+
+    def __len__(self):
+        return len(self.data)
+
+    @property
+    def nbytes(self):
+        return sys.getsizeof(self.data)
+
+    def isna(self):
+        return np.array([x == self.dtype.na_value for x in self.data], dtype=bool)
+
+    def take(self, indexer, allow_fill=False, fill_value=None):
+        # re-implement here, since NumPy has trouble setting
+        # sized objects like UserDicts into scalar slots of
+        # an ndarary.
+        indexer = np.asarray(indexer)
+        msg = (
+            "Index is out of bounds or cannot do a "
+            "non-empty take from an empty array."
+        )
+
+        if allow_fill:
+            if fill_value is None:
+                fill_value = self.dtype.na_value
+            # bounds check
+            if (indexer < -1).any():
+                raise ValueError
+            try:
+                output = [
+                    self.data[loc] if loc != -1 else fill_value for loc in indexer
+                ]
+            except IndexError:
+                raise IndexError(msg)
+        else:
+            try:
+                output = [self.data[loc] for loc in indexer]
+            except IndexError:
+                raise IndexError(msg)
+
+        return self._from_sequence(output)
+
+    def copy(self):
+        return type(self)(self.data[:])
+
+    def astype(self, dtype, copy=True):
+        # NumPy has issues when all the dicts are the same length.
+        # np.array([UserDict(...), UserDict(...)]) fails,
+        # but np.array([{...}, {...}]) works, so cast.
+
+        # needed to add this check for the Series constructor
+        if isinstance(dtype, type(self.dtype)) and dtype == self.dtype:
+            if copy:
+                return self.copy()
+            return self
+        return np.array([dict(x) for x in self], dtype=dtype, copy=copy)
+
+    def unique(self):
+        # Parent method doesn't work since np.array will try to infer
+        # a 2-dim object.
+        return type(self)(
+            [dict(x) for x in list({tuple(d.items()) for d in self.data})]
+        )
+
+    @classmethod
+    def _concat_same_type(cls, to_concat):
+        data = list(itertools.chain.from_iterable([x.data for x in to_concat]))
+        return cls(data)
+
+    def _values_for_factorize(self):
+        frozen = self._values_for_argsort()
+        if len(frozen) == 0:
+            # _factorize_array expects 1-d array, this is a len-0 2-d array.
+            frozen = frozen.ravel()
+        return frozen, ()
+
+    def _values_for_argsort(self):
+        # Disable NumPy's shape inference by including an empty tuple...
+        # If all the elemnts of self are the same size P, NumPy will
+        # cast them to an (N, P) array, instead of an (N,) array of tuples.
+        frozen = [()] + [tuple(x.items()) for x in self]
+        return np.array(frozen, dtype=object)[1:]
+
+
+def make_data():
+    # TODO: Use a regular dict. See _NDFrameIndexer._setitem_with_indexer
+    return [
+        UserDict(
+            [
+                (random.choice(string.ascii_letters), random.randint(0, 100))
+                for _ in range(random.randint(0, 10))
+            ]
+        )
+        for _ in range(100)
+    ]

venv/lib/python3.6/site-packages/pandas/tests/extension/json/test_json.py

@@ -0,0 +1,312 @@
+import collections
+import operator
+
+import pytest
+
+from pandas.compat import PY36
+
+import pandas as pd
+from pandas.tests.extension import base
+import pandas.util.testing as tm
+
+from .array import JSONArray, JSONDtype, make_data
+
+
+@pytest.fixture
+def dtype():
+    return JSONDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 PeriodArray for semantics test."""
+    data = make_data()
+
+    # Why the while loop? NumPy is unable to construct an ndarray from
+    # equal-length ndarrays. Many of our operations involve coercing the
+    # EA to an ndarray of objects. To avoid random test failures, we ensure
+    # that our data is coercible to an ndarray. Several tests deal with only
+    # the first two elements, so that's what we'll check.
+
+    while len(data[0]) == len(data[1]):
+        data = make_data()
+
+    return JSONArray(data)
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return JSONArray([{}, {"a": 10}])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return JSONArray([{"b": 1}, {"c": 4}, {"a": 2, "c": 3}])
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return JSONArray([{"b": 1}, {}, {"a": 4}])
+
+
+@pytest.fixture
+def na_value(dtype):
+    return dtype.na_value
+
+
+@pytest.fixture
+def na_cmp():
+    return operator.eq
+
+
+@pytest.fixture
+def data_for_grouping():
+    return JSONArray(
+        [
+            {"b": 1},
+            {"b": 1},
+            {},
+            {},
+            {"a": 0, "c": 2},
+            {"a": 0, "c": 2},
+            {"b": 1},
+            {"c": 2},
+        ]
+    )
+
+
+class BaseJSON:
+    # NumPy doesn't handle an array of equal-length UserDicts.
+    # The default assert_series_equal eventually does a
+    # Series.values, which raises. We work around it by
+    # converting the UserDicts to dicts.
+    def assert_series_equal(self, left, right, **kwargs):
+        if left.dtype.name == "json":
+            assert left.dtype == right.dtype
+            left = pd.Series(
+                JSONArray(left.values.astype(object)), index=left.index, name=left.name
+            )
+            right = pd.Series(
+                JSONArray(right.values.astype(object)),
+                index=right.index,
+                name=right.name,
+            )
+        tm.assert_series_equal(left, right, **kwargs)
+
+    def assert_frame_equal(self, left, right, *args, **kwargs):
+        tm.assert_index_equal(
+            left.columns,
+            right.columns,
+            exact=kwargs.get("check_column_type", "equiv"),
+            check_names=kwargs.get("check_names", True),
+            check_exact=kwargs.get("check_exact", False),
+            check_categorical=kwargs.get("check_categorical", True),
+            obj="{obj}.columns".format(obj=kwargs.get("obj", "DataFrame")),
+        )
+
+        jsons = (left.dtypes == "json").index
+
+        for col in jsons:
+            self.assert_series_equal(left[col], right[col], *args, **kwargs)
+
+        left = left.drop(columns=jsons)
+        right = right.drop(columns=jsons)
+        tm.assert_frame_equal(left, right, *args, **kwargs)
+
+
+class TestDtype(BaseJSON, base.BaseDtypeTests):
+    pass
+
+
+class TestInterface(BaseJSON, base.BaseInterfaceTests):
+    def test_custom_asserts(self):
+        # This would always trigger the KeyError from trying to put
+        # an array of equal-length UserDicts inside an ndarray.
+        data = JSONArray(
+            [
+                collections.UserDict({"a": 1}),
+                collections.UserDict({"b": 2}),
+                collections.UserDict({"c": 3}),
+            ]
+        )
+        a = pd.Series(data)
+        self.assert_series_equal(a, a)
+        self.assert_frame_equal(a.to_frame(), a.to_frame())
+
+        b = pd.Series(data.take([0, 0, 1]))
+        with pytest.raises(AssertionError):
+            self.assert_series_equal(a, b)
+
+        with pytest.raises(AssertionError):
+            self.assert_frame_equal(a.to_frame(), b.to_frame())
+
+
+class TestConstructors(BaseJSON, base.BaseConstructorsTests):
+    @pytest.mark.skip(reason="not implemented constructor from dtype")
+    def test_from_dtype(self, data):
+        # construct from our dtype & string dtype
+        pass
+
+
+class TestReshaping(BaseJSON, base.BaseReshapingTests):
+    @pytest.mark.skip(reason="Different definitions of NA")
+    def test_stack(self):
+        """
+        The test does .astype(object).stack(). If we happen to have
+        any missing values in `data`, then we'll end up with different
+        rows since we consider `{}` NA, but `.astype(object)` doesn't.
+        """
+
+    @pytest.mark.xfail(reason="dict for NA")
+    def test_unstack(self, data, index):
+        # The base test has NaN for the expected NA value.
+        # this matches otherwise
+        return super().test_unstack(data, index)
+
+
+class TestGetitem(BaseJSON, base.BaseGetitemTests):
+    pass
+
+
+class TestMissing(BaseJSON, base.BaseMissingTests):
+    @pytest.mark.skip(reason="Setting a dict as a scalar")
+    def test_fillna_series(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
+
+    @pytest.mark.skip(reason="Setting a dict as a scalar")
+    def test_fillna_frame(self):
+        """We treat dictionaries as a mapping in fillna, not a scalar."""
+
+
+unhashable = pytest.mark.skip(reason="Unhashable")
+unstable = pytest.mark.skipif(
+    not PY36, reason="Dictionary order unstable"  # 3.6 or higher
+)
+
+
+class TestReduce(base.BaseNoReduceTests):
+    pass
+
+
+class TestMethods(BaseJSON, base.BaseMethodsTests):
+    @unhashable
+    def test_value_counts(self, all_data, dropna):
+        pass
+
+    @unhashable
+    def test_sort_values_frame(self):
+        # TODO (EA.factorize): see if _values_for_factorize allows this.
+        pass
+
+    @unstable
+    def test_argsort(self, data_for_sorting):
+        super().test_argsort(data_for_sorting)
+
+    @unstable
+    def test_argsort_missing(self, data_missing_for_sorting):
+        super().test_argsort_missing(data_missing_for_sorting)
+
+    @unstable
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values(self, data_for_sorting, ascending):
+        super().test_sort_values(data_for_sorting, ascending)
+
+    @unstable
+    @pytest.mark.parametrize("ascending", [True, False])
+    def test_sort_values_missing(self, data_missing_for_sorting, ascending):
+        super().test_sort_values_missing(data_missing_for_sorting, ascending)
+
+    @pytest.mark.skip(reason="combine for JSONArray not supported")
+    def test_combine_le(self, data_repeated):
+        pass
+
+    @pytest.mark.skip(reason="combine for JSONArray not supported")
+    def test_combine_add(self, data_repeated):
+        pass
+
+    @pytest.mark.skip(reason="combine for JSONArray not supported")
+    def test_combine_first(self, data):
+        pass
+
+    @unhashable
+    def test_hash_pandas_object_works(self, data, kind):
+        super().test_hash_pandas_object_works(data, kind)
+
+    @pytest.mark.skip(reason="broadcasting error")
+    def test_where_series(self, data, na_value):
+        # Fails with
+        # *** ValueError: operands could not be broadcast together
+        # with shapes (4,) (4,) (0,)
+        super().test_where_series(data, na_value)
+
+    @pytest.mark.skip(reason="Can't compare dicts.")
+    def test_searchsorted(self, data_for_sorting):
+        super().test_searchsorted(data_for_sorting)
+
+
+class TestCasting(BaseJSON, base.BaseCastingTests):
+    @pytest.mark.skip(reason="failing on np.array(self, dtype=str)")
+    def test_astype_str(self):
+        """This currently fails in NumPy on np.array(self, dtype=str) with
+
+        *** ValueError: setting an array element with a sequence
+        """
+
+
+# We intentionally don't run base.BaseSetitemTests because pandas'
+# internals has trouble setting sequences of values into scalar positions.
+
+
+class TestGroupby(BaseJSON, base.BaseGroupbyTests):
+    @unhashable
+    def test_groupby_extension_transform(self):
+        """
+        This currently fails in Series.name.setter, since the
+        name must be hashable, but the value is a dictionary.
+        I think this is what we want, i.e. `.name` should be the original
+        values, and not the values for factorization.
+        """
+
+    @unhashable
+    def test_groupby_extension_apply(self):
+        """
+        This fails in Index._do_unique_check with
+
+        >   hash(val)
+        E   TypeError: unhashable type: 'UserDict' with
+
+        I suspect that once we support Index[ExtensionArray],
+        we'll be able to dispatch unique.
+        """
+
+    @unstable
+    @pytest.mark.parametrize("as_index", [True, False])
+    def test_groupby_extension_agg(self, as_index, data_for_grouping):
+        super().test_groupby_extension_agg(as_index, data_for_grouping)
+
+
+class TestArithmeticOps(BaseJSON, base.BaseArithmeticOpsTests):
+    def test_error(self, data, all_arithmetic_operators):
+        pass
+
+    def test_add_series_with_extension_array(self, data):
+        ser = pd.Series(data)
+        with pytest.raises(TypeError, match="unsupported"):
+            ser + data
+
+    def test_divmod_series_array(self):
+        # GH 23287
+        # skipping because it is not implemented
+        pass
+
+    def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
+        return super()._check_divmod_op(s, op, other, exc=TypeError)
+
+
+class TestComparisonOps(BaseJSON, base.BaseComparisonOpsTests):
+    pass
+
+
+class TestPrinting(BaseJSON, base.BasePrintingTests):
+    pass

venv/lib/python3.6/site-packages/pandas/tests/extension/test_categorical.py

@@ -0,0 +1,245 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import string
+
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas import Categorical
+from pandas.api.types import CategoricalDtype
+from pandas.tests.extension import base
+import pandas.util.testing as tm
+
+
+def make_data():
+    while True:
+        values = np.random.choice(list(string.ascii_letters), size=100)
+        # ensure we meet the requirements
+        # 1. first two not null
+        # 2. first and second are different
+        if values[0] != values[1]:
+            break
+    return values
+
+
+@pytest.fixture
+def dtype():
+    return CategoricalDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 array for this type.
+
+    * data[0] and data[1] should both be non missing
+    * data[0] and data[1] should not gbe equal
+    """
+    return Categorical(make_data())
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return Categorical([np.nan, "A"])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return Categorical(["A", "B", "C"], categories=["C", "A", "B"], ordered=True)
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return Categorical(["A", None, "B"], categories=["B", "A"], ordered=True)
+
+
+@pytest.fixture
+def na_value():
+    return np.nan
+
+
+@pytest.fixture
+def data_for_grouping():
+    return Categorical(["a", "a", None, None, "b", "b", "a", "c"])
+
+
+class TestDtype(base.BaseDtypeTests):
+    pass
+
+
+class TestInterface(base.BaseInterfaceTests):
+    @pytest.mark.skip(reason="Memory usage doesn't match")
+    def test_memory_usage(self, data):
+        # Is this deliberate?
+        super().test_memory_usage(data)
+
+
+class TestConstructors(base.BaseConstructorsTests):
+    pass
+
+
+class TestReshaping(base.BaseReshapingTests):
+    def test_ravel(self, data):
+        # GH#27199 Categorical.ravel returns self until after deprecation cycle
+        with tm.assert_produces_warning(FutureWarning):
+            data.ravel()
+
+
+class TestGetitem(base.BaseGetitemTests):
+    skip_take = pytest.mark.skip(reason="GH-20664.")
+
+    @pytest.mark.skip(reason="Backwards compatibility")
+    def test_getitem_scalar(self, data):
+        # CategoricalDtype.type isn't "correct" since it should
+        # be a parent of the elements (object). But don't want
+        # to break things by changing.
+        super().test_getitem_scalar(data)
+
+    @skip_take
+    def test_take(self, data, na_value, na_cmp):
+        # TODO remove this once Categorical.take is fixed
+        super().test_take(data, na_value, na_cmp)
+
+    @skip_take
+    def test_take_negative(self, data):
+        super().test_take_negative(data)
+
+    @skip_take
+    def test_take_pandas_style_negative_raises(self, data, na_value):
+        super().test_take_pandas_style_negative_raises(data, na_value)
+
+    @skip_take
+    def test_take_non_na_fill_value(self, data_missing):
+        super().test_take_non_na_fill_value(data_missing)
+
+    @skip_take
+    def test_take_out_of_bounds_raises(self, data, allow_fill):
+        return super().test_take_out_of_bounds_raises(data, allow_fill)
+
+    @pytest.mark.skip(reason="GH-20747. Unobserved categories.")
+    def test_take_series(self, data):
+        super().test_take_series(data)
+
+    @skip_take
+    def test_reindex_non_na_fill_value(self, data_missing):
+        super().test_reindex_non_na_fill_value(data_missing)
+
+    @pytest.mark.skip(reason="Categorical.take buggy")
+    def test_take_empty(self, data, na_value, na_cmp):
+        super().test_take_empty(data, na_value, na_cmp)
+
+    @pytest.mark.skip(reason="test not written correctly for categorical")
+    def test_reindex(self, data, na_value):
+        super().test_reindex(data, na_value)
+
+
+class TestSetitem(base.BaseSetitemTests):
+    pass
+
+
+class TestMissing(base.BaseMissingTests):
+    @pytest.mark.skip(reason="Not implemented")
+    def test_fillna_limit_pad(self, data_missing):
+        super().test_fillna_limit_pad(data_missing)
+
+    @pytest.mark.skip(reason="Not implemented")
+    def test_fillna_limit_backfill(self, data_missing):
+        super().test_fillna_limit_backfill(data_missing)
+
+
+class TestReduce(base.BaseNoReduceTests):
+    pass
+
+
+class TestMethods(base.BaseMethodsTests):
+    @pytest.mark.skip(reason="Unobserved categories included")
+    def test_value_counts(self, all_data, dropna):
+        return super().test_value_counts(all_data, dropna)
+
+    def test_combine_add(self, data_repeated):
+        # GH 20825
+        # When adding categoricals in combine, result is a string
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 + x2)
+        expected = pd.Series(
+            ([a + b for (a, b) in zip(list(orig_data1), list(orig_data2))])
+        )
+        self.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 + x2)
+        expected = pd.Series([a + val for a in list(orig_data1)])
+        self.assert_series_equal(result, expected)
+
+    @pytest.mark.skip(reason="Not Applicable")
+    def test_fillna_length_mismatch(self, data_missing):
+        super().test_fillna_length_mismatch(data_missing)
+
+    def test_searchsorted(self, data_for_sorting):
+        if not data_for_sorting.ordered:
+            raise pytest.skip(reason="searchsorted requires ordered data.")
+
+
+class TestCasting(base.BaseCastingTests):
+    pass
+
+
+class TestArithmeticOps(base.BaseArithmeticOpsTests):
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+
+        op_name = all_arithmetic_operators
+        if op_name != "__rmod__":
+            super().test_arith_series_with_scalar(data, op_name)
+        else:
+            pytest.skip("rmod never called when string is first argument")
+
+    def test_add_series_with_extension_array(self, data):
+        ser = pd.Series(data)
+        with pytest.raises(TypeError, match="cannot perform"):
+            ser + data
+
+    def test_divmod_series_array(self):
+        # GH 23287
+        # skipping because it is not implemented
+        pass
+
+    def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
+        return super()._check_divmod_op(s, op, other, exc=TypeError)
+
+
+class TestComparisonOps(base.BaseComparisonOpsTests):
+    def _compare_other(self, s, data, op_name, other):
+        op = self.get_op_from_name(op_name)
+        if op_name == "__eq__":
+            result = op(s, other)
+            expected = s.combine(other, lambda x, y: x == y)
+            assert (result == expected).all()
+
+        elif op_name == "__ne__":
+            result = op(s, other)
+            expected = s.combine(other, lambda x, y: x != y)
+            assert (result == expected).all()
+
+        else:
+            with pytest.raises(TypeError):
+                op(data, other)
+
+
+class TestParsing(base.BaseParsingTests):
+    pass

venv/lib/python3.6/site-packages/pandas/tests/extension/test_common.py

@@ -0,0 +1,81 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes import dtypes
+from pandas.core.dtypes.common import is_extension_array_dtype
+
+import pandas as pd
+from pandas.core.arrays import ExtensionArray
+import pandas.util.testing as tm
+
+
+class DummyDtype(dtypes.ExtensionDtype):
+    pass
+
+
+class DummyArray(ExtensionArray):
+    def __init__(self, data):
+        self.data = data
+
+    def __array__(self, dtype):
+        return self.data
+
+    @property
+    def dtype(self):
+        return DummyDtype()
+
+    def astype(self, dtype, copy=True):
+        # we don't support anything but a single dtype
+        if isinstance(dtype, DummyDtype):
+            if copy:
+                return type(self)(self.data)
+            return self
+
+        return np.array(self, dtype=dtype, copy=copy)
+
+
+class TestExtensionArrayDtype:
+    @pytest.mark.parametrize(
+        "values",
+        [
+            pd.Categorical([]),
+            pd.Categorical([]).dtype,
+            pd.Series(pd.Categorical([])),
+            DummyDtype(),
+            DummyArray(np.array([1, 2])),
+        ],
+    )
+    def test_is_extension_array_dtype(self, values):
+        assert is_extension_array_dtype(values)
+
+    @pytest.mark.parametrize("values", [np.array([]), pd.Series(np.array([]))])
+    def test_is_not_extension_array_dtype(self, values):
+        assert not is_extension_array_dtype(values)
+
+
+def test_astype():
+
+    arr = DummyArray(np.array([1, 2, 3]))
+    expected = np.array([1, 2, 3], dtype=object)
+
+    result = arr.astype(object)
+    tm.assert_numpy_array_equal(result, expected)
+
+    result = arr.astype("object")
+    tm.assert_numpy_array_equal(result, expected)
+
+
+def test_astype_no_copy():
+    arr = DummyArray(np.array([1, 2, 3], dtype=np.int64))
+    result = arr.astype(arr.dtype, copy=False)
+
+    assert arr is result
+
+    result = arr.astype(arr.dtype)
+    assert arr is not result
+
+
+@pytest.mark.parametrize("dtype", [dtypes.CategoricalDtype(), dtypes.IntervalDtype()])
+def test_is_extension_array_dtype(dtype):
+    assert isinstance(dtype, dtypes.ExtensionDtype)
+    assert is_extension_array_dtype(dtype)

venv/lib/python3.6/site-packages/pandas/tests/extension/test_datetime.py

@@ -0,0 +1,230 @@
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import DatetimeTZDtype
+
+import pandas as pd
+from pandas.core.arrays import DatetimeArray
+from pandas.tests.extension import base
+
+
+@pytest.fixture(params=["US/Central"])
+def dtype(request):
+    return DatetimeTZDtype(unit="ns", tz=request.param)
+
+
+@pytest.fixture
+def data(dtype):
+    data = DatetimeArray(pd.date_range("2000", periods=100, tz=dtype.tz), dtype=dtype)
+    return data
+
+
+@pytest.fixture
+def data_missing(dtype):
+    return DatetimeArray(
+        np.array(["NaT", "2000-01-01"], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    c = pd.Timestamp("2000-01-03")
+    return DatetimeArray(np.array([b, c, a], dtype="datetime64[ns]"), dtype=dtype)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    return DatetimeArray(np.array([b, "NaT", a], dtype="datetime64[ns]"), dtype=dtype)
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    """
+        Expected to be like [B, B, NA, NA, A, A, B, C]
+
+        Where A < B < C and NA is missing
+    """
+    a = pd.Timestamp("2000-01-01")
+    b = pd.Timestamp("2000-01-02")
+    c = pd.Timestamp("2000-01-03")
+    na = "NaT"
+    return DatetimeArray(
+        np.array([b, b, na, na, a, a, b, c], dtype="datetime64[ns]"), dtype=dtype
+    )
+
+
+@pytest.fixture
+def na_cmp():
+    def cmp(a, b):
+        return a is pd.NaT and a is b
+
+    return cmp
+
+
+@pytest.fixture
+def na_value():
+    return pd.NaT
+
+
+# ----------------------------------------------------------------------------
+class BaseDatetimeTests:
+    pass
+
+
+# ----------------------------------------------------------------------------
+# Tests
+class TestDatetimeDtype(BaseDatetimeTests, base.BaseDtypeTests):
+    pass
+
+
+class TestConstructors(BaseDatetimeTests, base.BaseConstructorsTests):
+    pass
+
+
+class TestGetitem(BaseDatetimeTests, base.BaseGetitemTests):
+    pass
+
+
+class TestMethods(BaseDatetimeTests, base.BaseMethodsTests):
+    @pytest.mark.skip(reason="Incorrect expected")
+    def test_value_counts(self, all_data, dropna):
+        pass
+
+    def test_combine_add(self, data_repeated):
+        # Timestamp.__add__(Timestamp) not defined
+        pass
+
+
+class TestInterface(BaseDatetimeTests, base.BaseInterfaceTests):
+    def test_array_interface(self, data):
+        if data.tz:
+            # np.asarray(DTA) is currently always tz-naive.
+            pytest.skip("GH-23569")
+        else:
+            super().test_array_interface(data)
+
+
+class TestArithmeticOps(BaseDatetimeTests, base.BaseArithmeticOpsTests):
+    implements = {"__sub__", "__rsub__"}
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        if all_arithmetic_operators in self.implements:
+            s = pd.Series(data)
+            self.check_opname(s, all_arithmetic_operators, s.iloc[0], exc=None)
+        else:
+            # ... but not the rest.
+            super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_add_series_with_extension_array(self, data):
+        # Datetime + Datetime not implemented
+        s = pd.Series(data)
+        msg = "cannot add DatetimeArray and DatetimeArray"
+        with pytest.raises(TypeError, match=msg):
+            s + data
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        if all_arithmetic_operators in self.implements:
+            s = pd.Series(data)
+            self.check_opname(s, all_arithmetic_operators, s.iloc[0], exc=None)
+        else:
+            # ... but not the rest.
+            super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_error(self, data, all_arithmetic_operators):
+        pass
+
+    def test_divmod_series_array(self):
+        # GH 23287
+        # skipping because it is not implemented
+        pass
+
+    @pytest.mark.xfail(reason="different implementation", strict=False)
+    def test_direct_arith_with_series_returns_not_implemented(self, data):
+        # Right now, we have trouble with this. Returning NotImplemented
+        # fails other tests like
+        # tests/arithmetic/test_datetime64::TestTimestampSeriesArithmetic::
+        # test_dt64_seris_add_intlike
+        return super(
+            TestArithmeticOps, self
+        ).test_direct_arith_with_series_returns_not_implemented(data)
+
+
+class TestCasting(BaseDatetimeTests, base.BaseCastingTests):
+    pass
+
+
+class TestComparisonOps(BaseDatetimeTests, base.BaseComparisonOpsTests):
+    def _compare_other(self, s, data, op_name, other):
+        # the base test is not appropriate for us. We raise on comparison
+        # with (some) integers, depending on the value.
+        pass
+
+    @pytest.mark.xfail(reason="different implementation", strict=False)
+    def test_direct_arith_with_series_returns_not_implemented(self, data):
+        return super(
+            TestComparisonOps, self
+        ).test_direct_arith_with_series_returns_not_implemented(data)
+
+
+class TestMissing(BaseDatetimeTests, base.BaseMissingTests):
+    pass
+
+
+class TestReshaping(BaseDatetimeTests, base.BaseReshapingTests):
+    @pytest.mark.skip(reason="We have DatetimeTZBlock")
+    def test_concat(self, data, in_frame):
+        pass
+
+    def test_concat_mixed_dtypes(self, data):
+        # concat(Series[datetimetz], Series[category]) uses a
+        # plain np.array(values) on the DatetimeArray, which
+        # drops the tz.
+        super().test_concat_mixed_dtypes(data)
+
+    @pytest.mark.parametrize("obj", ["series", "frame"])
+    def test_unstack(self, obj):
+        # GH-13287: can't use base test, since building the expected fails.
+        data = DatetimeArray._from_sequence(
+            ["2000", "2001", "2002", "2003"], tz="US/Central"
+        )
+        index = pd.MultiIndex.from_product(([["A", "B"], ["a", "b"]]), names=["a", "b"])
+
+        if obj == "series":
+            ser = pd.Series(data, index=index)
+            expected = pd.DataFrame(
+                {"A": data.take([0, 1]), "B": data.take([2, 3])},
+                index=pd.Index(["a", "b"], name="b"),
+            )
+            expected.columns.name = "a"
+
+        else:
+            ser = pd.DataFrame({"A": data, "B": data}, index=index)
+            expected = pd.DataFrame(
+                {
+                    ("A", "A"): data.take([0, 1]),
+                    ("A", "B"): data.take([2, 3]),
+                    ("B", "A"): data.take([0, 1]),
+                    ("B", "B"): data.take([2, 3]),
+                },
+                index=pd.Index(["a", "b"], name="b"),
+            )
+            expected.columns.names = [None, "a"]
+
+        result = ser.unstack(0)
+        self.assert_equal(result, expected)
+
+
+class TestSetitem(BaseDatetimeTests, base.BaseSetitemTests):
+    pass
+
+
+class TestGroupby(BaseDatetimeTests, base.BaseGroupbyTests):
+    pass
+
+
+class TestPrinting(BaseDatetimeTests, base.BasePrintingTests):
+    pass

venv/lib/python3.6/site-packages/pandas/tests/extension/test_external_block.py

@@ -0,0 +1,74 @@
+import numpy as np
+import pytest
+
+import pandas as pd
+from pandas.core.internals import BlockManager, SingleBlockManager
+from pandas.core.internals.blocks import Block, NonConsolidatableMixIn
+
+
+class CustomBlock(NonConsolidatableMixIn, Block):
+
+    _holder = np.ndarray
+
+    def formatting_values(self):
+        return np.array(["Val: {}".format(i) for i in self.values])
+
+    def concat_same_type(self, to_concat, placement=None):
+        """
+        Always concatenate disregarding self.ndim as the values are
+        always 1D in this custom Block
+        """
+        values = np.concatenate([blk.values for blk in to_concat])
+        return self.make_block_same_class(
+            values, placement=placement or slice(0, len(values), 1)
+        )
+
+
+@pytest.fixture
+def df():
+    df1 = pd.DataFrame({"a": [1, 2, 3]})
+    blocks = df1._data.blocks
+    values = np.arange(3, dtype="int64")
+    custom_block = CustomBlock(values, placement=slice(1, 2))
+    blocks = blocks + (custom_block,)
+    block_manager = BlockManager(blocks, [pd.Index(["a", "b"]), df1.index])
+    return pd.DataFrame(block_manager)
+
+
+def test_custom_repr():
+    values = np.arange(3, dtype="int64")
+
+    # series
+    block = CustomBlock(values, placement=slice(0, 3))
+
+    s = pd.Series(SingleBlockManager(block, pd.RangeIndex(3)))
+    assert repr(s) == "0    Val: 0\n1    Val: 1\n2    Val: 2\ndtype: int64"
+
+    # dataframe
+    block = CustomBlock(values, placement=slice(0, 1))
+    blk_mgr = BlockManager([block], [["col"], range(3)])
+    df = pd.DataFrame(blk_mgr)
+    assert repr(df) == "      col\n0  Val: 0\n1  Val: 1\n2  Val: 2"
+
+
+def test_concat_series():
+    # GH17728
+    values = np.arange(3, dtype="int64")
+    block = CustomBlock(values, placement=slice(0, 3))
+    s = pd.Series(block, pd.RangeIndex(3), fastpath=True)
+
+    res = pd.concat([s, s])
+    assert isinstance(res._data.blocks[0], CustomBlock)
+
+
+def test_concat_dataframe(df):
+    # GH17728
+    res = pd.concat([df, df])
+    assert isinstance(res._data.blocks[1], CustomBlock)
+
+
+def test_concat_axis1(df):
+    # GH17954
+    df2 = pd.DataFrame({"c": [0.1, 0.2, 0.3]})
+    res = pd.concat([df, df2], axis=1)
+    assert isinstance(res._data.blocks[1], CustomBlock)

venv/lib/python3.6/site-packages/pandas/tests/extension/test_integer.py

@@ -0,0 +1,237 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.common import is_extension_array_dtype
+
+import pandas as pd
+from pandas.core.arrays import integer_array
+from pandas.core.arrays.integer import (
+    Int8Dtype,
+    Int16Dtype,
+    Int32Dtype,
+    Int64Dtype,
+    UInt8Dtype,
+    UInt16Dtype,
+    UInt32Dtype,
+    UInt64Dtype,
+)
+from pandas.tests.extension import base
+
+
+def make_data():
+    return list(range(1, 9)) + [np.nan] + list(range(10, 98)) + [np.nan] + [99, 100]
+
+
+@pytest.fixture(
+    params=[
+        Int8Dtype,
+        Int16Dtype,
+        Int32Dtype,
+        Int64Dtype,
+        UInt8Dtype,
+        UInt16Dtype,
+        UInt32Dtype,
+        UInt64Dtype,
+    ]
+)
+def dtype(request):
+    return request.param()
+
+
+@pytest.fixture
+def data(dtype):
+    return integer_array(make_data(), dtype=dtype)
+
+
+@pytest.fixture
+def data_for_twos(dtype):
+    return integer_array(np.ones(100) * 2, dtype=dtype)
+
+
+@pytest.fixture
+def data_missing(dtype):
+    return integer_array([np.nan, 1], dtype=dtype)
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    return integer_array([1, 2, 0], dtype=dtype)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    return integer_array([1, np.nan, 0], dtype=dtype)
+
+
+@pytest.fixture
+def na_cmp():
+    # we are np.nan
+    return lambda x, y: np.isnan(x) and np.isnan(y)
+
+
+@pytest.fixture
+def na_value():
+    return np.nan
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    b = 1
+    a = 0
+    c = 2
+    na = np.nan
+    return integer_array([b, b, na, na, a, a, b, c], dtype=dtype)
+
+
+class TestDtype(base.BaseDtypeTests):
+    @pytest.mark.skip(reason="using multiple dtypes")
+    def test_is_dtype_unboxes_dtype(self):
+        # we have multiple dtypes, so skip
+        pass
+
+
+class TestArithmeticOps(base.BaseArithmeticOpsTests):
+    def check_opname(self, s, op_name, other, exc=None):
+        # overwriting to indicate ops don't raise an error
+        super().check_opname(s, op_name, other, exc=None)
+
+    def _check_op(self, s, op, other, op_name, exc=NotImplementedError):
+        if exc is None:
+            if s.dtype.is_unsigned_integer and (op_name == "__rsub__"):
+                # TODO see https://github.com/pandas-dev/pandas/issues/22023
+                pytest.skip("unsigned subtraction gives negative values")
+
+            if (
+                hasattr(other, "dtype")
+                and not is_extension_array_dtype(other.dtype)
+                and pd.api.types.is_integer_dtype(other.dtype)
+            ):
+                # other is np.int64 and would therefore always result in
+                # upcasting, so keeping other as same numpy_dtype
+                other = other.astype(s.dtype.numpy_dtype)
+
+            result = op(s, other)
+            expected = s.combine(other, op)
+
+            if op_name in ("__rtruediv__", "__truediv__", "__div__"):
+                expected = expected.astype(float)
+                if op_name == "__rtruediv__":
+                    # TODO reverse operators result in object dtype
+                    result = result.astype(float)
+            elif op_name.startswith("__r"):
+                # TODO reverse operators result in object dtype
+                # see https://github.com/pandas-dev/pandas/issues/22024
+                expected = expected.astype(s.dtype)
+                result = result.astype(s.dtype)
+            else:
+                # combine method result in 'biggest' (int64) dtype
+                expected = expected.astype(s.dtype)
+                pass
+            if (op_name == "__rpow__") and isinstance(other, pd.Series):
+                # TODO pow on Int arrays gives different result with NA
+                # see https://github.com/pandas-dev/pandas/issues/22022
+                result = result.fillna(1)
+
+            self.assert_series_equal(result, expected)
+        else:
+            with pytest.raises(exc):
+                op(s, other)
+
+    def _check_divmod_op(self, s, op, other, exc=None):
+        super()._check_divmod_op(s, op, other, None)
+
+    @pytest.mark.skip(reason="intNA does not error on ops")
+    def test_error(self, data, all_arithmetic_operators):
+        # other specific errors tested in the integer array specific tests
+        pass
+
+
+class TestComparisonOps(base.BaseComparisonOpsTests):
+    def check_opname(self, s, op_name, other, exc=None):
+        super().check_opname(s, op_name, other, exc=None)
+
+    def _compare_other(self, s, data, op_name, other):
+        self.check_opname(s, op_name, other)
+
+
+class TestInterface(base.BaseInterfaceTests):
+    pass
+
+
+class TestConstructors(base.BaseConstructorsTests):
+    pass
+
+
+class TestReshaping(base.BaseReshapingTests):
+    pass
+
+    # for test_concat_mixed_dtypes test
+    # concat of an Integer and Int coerces to object dtype
+    # TODO(jreback) once integrated this would
+
+
+class TestGetitem(base.BaseGetitemTests):
+    pass
+
+
+class TestSetitem(base.BaseSetitemTests):
+    pass
+
+
+class TestMissing(base.BaseMissingTests):
+    pass
+
+
+class TestMethods(base.BaseMethodsTests):
+    @pytest.mark.parametrize("dropna", [True, False])
+    def test_value_counts(self, all_data, dropna):
+        all_data = all_data[:10]
+        if dropna:
+            other = np.array(all_data[~all_data.isna()])
+        else:
+            other = all_data
+
+        result = pd.Series(all_data).value_counts(dropna=dropna).sort_index()
+        expected = pd.Series(other).value_counts(dropna=dropna).sort_index()
+        expected.index = expected.index.astype(all_data.dtype)
+
+        self.assert_series_equal(result, expected)
+
+
+class TestCasting(base.BaseCastingTests):
+    pass
+
+
+class TestGroupby(base.BaseGroupbyTests):
+    pass
+
+
+class TestNumericReduce(base.BaseNumericReduceTests):
+    pass
+
+
+class TestBooleanReduce(base.BaseBooleanReduceTests):
+    pass
+
+
+class TestPrinting(base.BasePrintingTests):
+    pass
+
+
+class TestParsing(base.BaseParsingTests):
+    pass

venv/lib/python3.6/site-packages/pandas/tests/extension/test_interval.py

@@ -0,0 +1,161 @@
+"""
+This file contains a minimal set of tests for compliance with the extension
+array interface test suite, and should contain no other tests.
+The test suite for the full functionality of the array is located in
+`pandas/tests/arrays/`.
+
+The tests in this file are inherited from the BaseExtensionTests, and only
+minimal tweaks should be applied to get the tests passing (by overwriting a
+parent method).
+
+Additional tests should either be added to one of the BaseExtensionTests
+classes (if they are relevant for the extension interface for all dtypes), or
+be added to the array-specific tests in `pandas/tests/arrays/`.
+
+"""
+import numpy as np
+import pytest
+
+from pandas.core.dtypes.dtypes import IntervalDtype
+
+from pandas import Interval
+from pandas.core.arrays import IntervalArray
+from pandas.tests.extension import base
+
+
+def make_data():
+    N = 100
+    left = np.random.uniform(size=N).cumsum()
+    right = left + np.random.uniform(size=N)
+    return [Interval(l, r) for l, r in zip(left, right)]
+
+
+@pytest.fixture
+def dtype():
+    return IntervalDtype()
+
+
+@pytest.fixture
+def data():
+    """Length-100 PeriodArray for semantics test."""
+    return IntervalArray(make_data())
+
+
+@pytest.fixture
+def data_missing():
+    """Length 2 array with [NA, Valid]"""
+    return IntervalArray.from_tuples([None, (0, 1)])
+
+
+@pytest.fixture
+def data_for_sorting():
+    return IntervalArray.from_tuples([(1, 2), (2, 3), (0, 1)])
+
+
+@pytest.fixture
+def data_missing_for_sorting():
+    return IntervalArray.from_tuples([(1, 2), None, (0, 1)])
+
+
+@pytest.fixture
+def na_value():
+    return np.nan
+
+
+@pytest.fixture
+def data_for_grouping():
+    a = (0, 1)
+    b = (1, 2)
+    c = (2, 3)
+    return IntervalArray.from_tuples([b, b, None, None, a, a, b, c])
+
+
+class BaseInterval:
+    pass
+
+
+class TestDtype(BaseInterval, base.BaseDtypeTests):
+    pass
+
+
+class TestCasting(BaseInterval, base.BaseCastingTests):
+    pass
+
+
+class TestConstructors(BaseInterval, base.BaseConstructorsTests):
+    pass
+
+
+class TestGetitem(BaseInterval, base.BaseGetitemTests):
+    pass
+
+
+class TestGrouping(BaseInterval, base.BaseGroupbyTests):
+    pass
+
+
+class TestInterface(BaseInterval, base.BaseInterfaceTests):
+    pass
+
+
+class TestReduce(base.BaseNoReduceTests):
+    pass
+
+
+class TestMethods(BaseInterval, base.BaseMethodsTests):
+    @pytest.mark.skip(reason="addition is not defined for intervals")
+    def test_combine_add(self, data_repeated):
+        pass
+
+    @pytest.mark.skip(reason="Not Applicable")
+    def test_fillna_length_mismatch(self, data_missing):
+        pass
+
+
+class TestMissing(BaseInterval, base.BaseMissingTests):
+    # Index.fillna only accepts scalar `value`, so we have to skip all
+    # non-scalar fill tests.
+    unsupported_fill = pytest.mark.skip("Unsupported fillna option.")
+
+    @unsupported_fill
+    def test_fillna_limit_pad(self):
+        pass
+
+    @unsupported_fill
+    def test_fillna_series_method(self):
+        pass
+
+    @unsupported_fill
+    def test_fillna_limit_backfill(self):
+        pass
+
+    @unsupported_fill
+    def test_fillna_series(self):
+        pass
+
+    def test_non_scalar_raises(self, data_missing):
+        msg = "Got a 'list' instead."
+        with pytest.raises(TypeError, match=msg):
+            data_missing.fillna([1, 1])
+
+
+class TestReshaping(BaseInterval, base.BaseReshapingTests):
+    pass
+
+
+class TestSetitem(BaseInterval, base.BaseSetitemTests):
+    pass
+
+
+class TestPrinting(BaseInterval, base.BasePrintingTests):
+    @pytest.mark.skip(reason="custom repr")
+    def test_array_repr(self, data, size):
+        pass
+
+
+class TestParsing(BaseInterval, base.BaseParsingTests):
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data):
+        expected_msg = r".*must implement _from_sequence_of_strings.*"
+        with pytest.raises(NotImplementedError, match=expected_msg):
+            super().test_EA_types(engine, data)

venv/lib/python3.6/site-packages/pandas/tests/extension/test_numpy.py

@@ -0,0 +1,392 @@
+import numpy as np
+import pytest
+
+from pandas.compat.numpy import _np_version_under1p16
+
+import pandas as pd
+from pandas.core.arrays.numpy_ import PandasArray, PandasDtype
+import pandas.util.testing as tm
+
+from . import base
+
+
+@pytest.fixture(params=["float", "object"])
+def dtype(request):
+    return PandasDtype(np.dtype(request.param))
+
+
+@pytest.fixture
+def allow_in_pandas(monkeypatch):
+    """
+    A monkeypatch to tells pandas to let us in.
+
+    By default, passing a PandasArray to an index / series / frame
+    constructor will unbox that PandasArray to an ndarray, and treat
+    it as a non-EA column. We don't want people using EAs without
+    reason.
+
+    The mechanism for this is a check against ABCPandasArray
+    in each constructor.
+
+    But, for testing, we need to allow them in pandas. So we patch
+    the _typ of PandasArray, so that we evade the ABCPandasArray
+    check.
+    """
+    with monkeypatch.context() as m:
+        m.setattr(PandasArray, "_typ", "extension")
+        yield
+
+
+@pytest.fixture
+def data(allow_in_pandas, dtype):
+    if dtype.numpy_dtype == "object":
+        return pd.Series([(i,) for i in range(100)]).array
+    return PandasArray(np.arange(1, 101, dtype=dtype._dtype))
+
+
+@pytest.fixture
+def data_missing(allow_in_pandas, dtype):
+    # For NumPy <1.16, np.array([np.nan, (1,)]) raises
+    # ValueError: setting an array element with a sequence.
+    if dtype.numpy_dtype == "object":
+        if _np_version_under1p16:
+            raise pytest.skip("Skipping for NumPy <1.16")
+        return PandasArray(np.array([np.nan, (1,)]))
+    return PandasArray(np.array([np.nan, 1.0]))
+
+
+@pytest.fixture
+def na_value():
+    return np.nan
+
+
+@pytest.fixture
+def na_cmp():
+    def cmp(a, b):
+        return np.isnan(a) and np.isnan(b)
+
+    return cmp
+
+
+@pytest.fixture
+def data_for_sorting(allow_in_pandas, dtype):
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, C, A] with
+    A < B < C
+    """
+    if dtype.numpy_dtype == "object":
+        # Use an empty tuple for first element, then remove,
+        # to disable np.array's shape inference.
+        return PandasArray(np.array([(), (2,), (3,), (1,)])[1:])
+    return PandasArray(np.array([1, 2, 0]))
+
+
+@pytest.fixture
+def data_missing_for_sorting(allow_in_pandas, dtype):
+    """Length-3 array with a known sort order.
+
+    This should be three items [B, NA, A] with
+    A < B and NA missing.
+    """
+    if dtype.numpy_dtype == "object":
+        return PandasArray(np.array([(1,), np.nan, (0,)]))
+    return PandasArray(np.array([1, np.nan, 0]))
+
+
+@pytest.fixture
+def data_for_grouping(allow_in_pandas, dtype):
+    """Data for factorization, grouping, and unique tests.
+
+    Expected to be like [B, B, NA, NA, A, A, B, C]
+
+    Where A < B < C and NA is missing
+    """
+    if dtype.numpy_dtype == "object":
+        a, b, c = (1,), (2,), (3,)
+    else:
+        a, b, c = np.arange(3)
+    return PandasArray(np.array([b, b, np.nan, np.nan, a, a, b, c]))
+
+
+@pytest.fixture
+def skip_numpy_object(dtype):
+    """
+    Tests for PandasArray with nested data. Users typically won't create
+    these objects via `pd.array`, but they can show up through `.array`
+    on a Series with nested data. Many of the base tests fail, as they aren't
+    appropriate for nested data.
+
+    This fixture allows these tests to be skipped when used as a usefixtures
+    marker to either an individual test or a test class.
+    """
+    if dtype == "object":
+        raise pytest.skip("Skipping for object dtype.")
+
+
+skip_nested = pytest.mark.usefixtures("skip_numpy_object")
+
+
+class BaseNumPyTests:
+    pass
+
+
+class TestCasting(BaseNumPyTests, base.BaseCastingTests):
+    @skip_nested
+    def test_astype_str(self, data):
+        # ValueError: setting an array element with a sequence
+        super().test_astype_str(data)
+
+
+class TestConstructors(BaseNumPyTests, base.BaseConstructorsTests):
+    @pytest.mark.skip(reason="We don't register our dtype")
+    # We don't want to register. This test should probably be split in two.
+    def test_from_dtype(self, data):
+        pass
+
+    @skip_nested
+    def test_array_from_scalars(self, data):
+        # ValueError: PandasArray must be 1-dimensional.
+        super().test_array_from_scalars(data)
+
+
+class TestDtype(BaseNumPyTests, base.BaseDtypeTests):
+    @pytest.mark.skip(reason="Incorrect expected.")
+    # we unsurprisingly clash with a NumPy name.
+    def test_check_dtype(self, data):
+        pass
+
+
+class TestGetitem(BaseNumPyTests, base.BaseGetitemTests):
+    @skip_nested
+    def test_getitem_scalar(self, data):
+        # AssertionError
+        super().test_getitem_scalar(data)
+
+    @skip_nested
+    def test_take_series(self, data):
+        # ValueError: PandasArray must be 1-dimensional.
+        super().test_take_series(data)
+
+    @pytest.mark.xfail(reason="astype doesn't recognize data.dtype")
+    def test_loc_iloc_frame_single_dtype(self, data):
+        super().test_loc_iloc_frame_single_dtype(data)
+
+
+class TestGroupby(BaseNumPyTests, base.BaseGroupbyTests):
+    @skip_nested
+    def test_groupby_extension_apply(self, data_for_grouping, groupby_apply_op):
+        # ValueError: Names should be list-like for a MultiIndex
+        super().test_groupby_extension_apply(data_for_grouping, groupby_apply_op)
+
+
+class TestInterface(BaseNumPyTests, base.BaseInterfaceTests):
+    @skip_nested
+    def test_array_interface(self, data):
+        # NumPy array shape inference
+        super().test_array_interface(data)
+
+
+class TestMethods(BaseNumPyTests, base.BaseMethodsTests):
+    @pytest.mark.skip(reason="TODO: remove?")
+    def test_value_counts(self, all_data, dropna):
+        pass
+
+    @pytest.mark.skip(reason="Incorrect expected")
+    # We have a bool dtype, so the result is an ExtensionArray
+    # but expected is not
+    def test_combine_le(self, data_repeated):
+        super().test_combine_le(data_repeated)
+
+    @skip_nested
+    def test_combine_add(self, data_repeated):
+        # Not numeric
+        super().test_combine_add(data_repeated)
+
+    @skip_nested
+    def test_shift_fill_value(self, data):
+        # np.array shape inference. Shift implementation fails.
+        super().test_shift_fill_value(data)
+
+    @skip_nested
+    @pytest.mark.parametrize("box", [pd.Series, lambda x: x])
+    @pytest.mark.parametrize("method", [lambda x: x.unique(), pd.unique])
+    def test_unique(self, data, box, method):
+        # Fails creating expected
+        super().test_unique(data, box, method)
+
+    @skip_nested
+    def test_fillna_copy_frame(self, data_missing):
+        # The "scalar" for this array isn't a scalar.
+        super().test_fillna_copy_frame(data_missing)
+
+    @skip_nested
+    def test_fillna_copy_series(self, data_missing):
+        # The "scalar" for this array isn't a scalar.
+        super().test_fillna_copy_series(data_missing)
+
+    @skip_nested
+    def test_hash_pandas_object_works(self, data, as_frame):
+        # ndarray of tuples not hashable
+        super().test_hash_pandas_object_works(data, as_frame)
+
+    @skip_nested
+    def test_searchsorted(self, data_for_sorting, as_series):
+        # Test setup fails.
+        super().test_searchsorted(data_for_sorting, as_series)
+
+    @skip_nested
+    def test_where_series(self, data, na_value, as_frame):
+        # Test setup fails.
+        super().test_where_series(data, na_value, as_frame)
+
+    @skip_nested
+    @pytest.mark.parametrize("repeats", [0, 1, 2, [1, 2, 3]])
+    def test_repeat(self, data, repeats, as_series, use_numpy):
+        # Fails creating expected
+        super().test_repeat(data, repeats, as_series, use_numpy)
+
+
+@skip_nested
+class TestArithmetics(BaseNumPyTests, base.BaseArithmeticOpsTests):
+    divmod_exc = None
+    series_scalar_exc = None
+    frame_scalar_exc = None
+    series_array_exc = None
+
+    def test_divmod_series_array(self, data):
+        s = pd.Series(data)
+        self._check_divmod_op(s, divmod, data, exc=None)
+
+    @pytest.mark.skip("We implement ops")
+    def test_error(self, data, all_arithmetic_operators):
+        pass
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        super().test_arith_series_with_array(data, all_arithmetic_operators)
+
+
+class TestPrinting(BaseNumPyTests, base.BasePrintingTests):
+    pass
+
+
+@skip_nested
+class TestNumericReduce(BaseNumPyTests, base.BaseNumericReduceTests):
+    def check_reduce(self, s, op_name, skipna):
+        result = getattr(s, op_name)(skipna=skipna)
+        # avoid coercing int -> float. Just cast to the actual numpy type.
+        expected = getattr(s.astype(s.dtype._dtype), op_name)(skipna=skipna)
+        tm.assert_almost_equal(result, expected)
+
+
+@skip_nested
+class TestBooleanReduce(BaseNumPyTests, base.BaseBooleanReduceTests):
+    pass
+
+
+class TestMissing(BaseNumPyTests, base.BaseMissingTests):
+    @skip_nested
+    def test_fillna_scalar(self, data_missing):
+        # Non-scalar "scalar" values.
+        super().test_fillna_scalar(data_missing)
+
+    @skip_nested
+    def test_fillna_series_method(self, data_missing, fillna_method):
+        # Non-scalar "scalar" values.
+        super().test_fillna_series_method(data_missing, fillna_method)
+
+    @skip_nested
+    def test_fillna_series(self, data_missing):
+        # Non-scalar "scalar" values.
+        super().test_fillna_series(data_missing)
+
+    @skip_nested
+    def test_fillna_frame(self, data_missing):
+        # Non-scalar "scalar" values.
+        super().test_fillna_frame(data_missing)
+
+
+class TestReshaping(BaseNumPyTests, base.BaseReshapingTests):
+    @pytest.mark.skip("Incorrect parent test")
+    # not actually a mixed concat, since we concat int and int.
+    def test_concat_mixed_dtypes(self, data):
+        super().test_concat_mixed_dtypes(data)
+
+    @skip_nested
+    def test_merge(self, data, na_value):
+        # Fails creating expected
+        super().test_merge(data, na_value)
+
+    @skip_nested
+    def test_merge_on_extension_array(self, data):
+        # Fails creating expected
+        super().test_merge_on_extension_array(data)
+
+    @skip_nested
+    def test_merge_on_extension_array_duplicates(self, data):
+        # Fails creating expected
+        super().test_merge_on_extension_array_duplicates(data)
+
+
+class TestSetitem(BaseNumPyTests, base.BaseSetitemTests):
+    @skip_nested
+    def test_setitem_scalar_series(self, data, box_in_series):
+        # AssertionError
+        super().test_setitem_scalar_series(data, box_in_series)
+
+    @skip_nested
+    def test_setitem_sequence(self, data, box_in_series):
+        # ValueError: shape mismatch: value array of shape (2,1) could not
+        # be broadcast to indexing result of shape (2,)
+        super().test_setitem_sequence(data, box_in_series)
+
+    @skip_nested
+    def test_setitem_sequence_mismatched_length_raises(self, data, as_array):
+        # ValueError: PandasArray must be 1-dimensional.
+        super().test_setitem_sequence_mismatched_length_raises(data, as_array)
+
+    @skip_nested
+    def test_setitem_sequence_broadcasts(self, data, box_in_series):
+        # ValueError: cannot set using a list-like indexer with a different
+        # length than the value
+        super().test_setitem_sequence_broadcasts(data, box_in_series)
+
+    @skip_nested
+    def test_setitem_loc_scalar_mixed(self, data):
+        # AssertionError
+        super().test_setitem_loc_scalar_mixed(data)
+
+    @skip_nested
+    def test_setitem_loc_scalar_multiple_homogoneous(self, data):
+        # AssertionError
+        super().test_setitem_loc_scalar_multiple_homogoneous(data)
+
+    @skip_nested
+    def test_setitem_iloc_scalar_mixed(self, data):
+        # AssertionError
+        super().test_setitem_iloc_scalar_mixed(data)
+
+    @skip_nested
+    def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
+        # AssertionError
+        super().test_setitem_iloc_scalar_multiple_homogoneous(data)
+
+    @skip_nested
+    @pytest.mark.parametrize("setter", ["loc", None])
+    def test_setitem_mask_broadcast(self, data, setter):
+        # ValueError: cannot set using a list-like indexer with a different
+        # length than the value
+        super().test_setitem_mask_broadcast(data, setter)
+
+    @skip_nested
+    def test_setitem_scalar_key_sequence_raise(self, data):
+        # Failed: DID NOT RAISE <class 'ValueError'>
+        super().test_setitem_scalar_key_sequence_raise(data)
+
+
+@skip_nested
+class TestParsing(BaseNumPyTests, base.BaseParsingTests):
+    pass

venv/lib/python3.6/site-packages/pandas/tests/extension/test_period.py

@@ -0,0 +1,161 @@
+import numpy as np
+import pytest
+
+from pandas._libs.tslib import iNaT
+
+from pandas.core.dtypes.dtypes import PeriodDtype
+
+import pandas as pd
+from pandas.core.arrays import PeriodArray
+from pandas.tests.extension import base
+
+
+@pytest.fixture
+def dtype():
+    return PeriodDtype(freq="D")
+
+
+@pytest.fixture
+def data(dtype):
+    return PeriodArray(np.arange(1970, 2070), freq=dtype.freq)
+
+
+@pytest.fixture
+def data_for_twos(dtype):
+    return PeriodArray(np.ones(100) * 2, freq=dtype.freq)
+
+
+@pytest.fixture
+def data_for_sorting(dtype):
+    return PeriodArray([2018, 2019, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_missing(dtype):
+    return PeriodArray([iNaT, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_missing_for_sorting(dtype):
+    return PeriodArray([2018, iNaT, 2017], freq=dtype.freq)
+
+
+@pytest.fixture
+def data_for_grouping(dtype):
+    B = 2018
+    NA = iNaT
+    A = 2017
+    C = 2019
+    return PeriodArray([B, B, NA, NA, A, A, B, C], freq=dtype.freq)
+
+
+@pytest.fixture
+def na_value():
+    return pd.NaT
+
+
+class BasePeriodTests:
+    pass
+
+
+class TestPeriodDtype(BasePeriodTests, base.BaseDtypeTests):
+    pass
+
+
+class TestConstructors(BasePeriodTests, base.BaseConstructorsTests):
+    pass
+
+
+class TestGetitem(BasePeriodTests, base.BaseGetitemTests):
+    pass
+
+
+class TestMethods(BasePeriodTests, base.BaseMethodsTests):
+    def test_combine_add(self, data_repeated):
+        # Period + Period is not defined.
+        pass
+
+
+class TestInterface(BasePeriodTests, base.BaseInterfaceTests):
+
+    pass
+
+
+class TestArithmeticOps(BasePeriodTests, base.BaseArithmeticOpsTests):
+    implements = {"__sub__", "__rsub__"}
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        # we implement substitution...
+        if all_arithmetic_operators in self.implements:
+            s = pd.Series(data)
+            self.check_opname(s, all_arithmetic_operators, s.iloc[0], exc=None)
+        else:
+            # ... but not the rest.
+            super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        if all_arithmetic_operators in self.implements:
+            s = pd.Series(data)
+            self.check_opname(s, all_arithmetic_operators, s.iloc[0], exc=None)
+        else:
+            # ... but not the rest.
+            super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
+        super()._check_divmod_op(s, op, other, exc=TypeError)
+
+    def test_add_series_with_extension_array(self, data):
+        # we don't implement + for Period
+        s = pd.Series(data)
+        msg = (
+            r"unsupported operand type\(s\) for \+: "
+            r"\'PeriodArray\' and \'PeriodArray\'"
+        )
+        with pytest.raises(TypeError, match=msg):
+            s + data
+
+    def test_error(self):
+        pass
+
+    def test_direct_arith_with_series_returns_not_implemented(self, data):
+        # Override to use __sub__ instead of __add__
+        other = pd.Series(data)
+        result = data.__sub__(other)
+        assert result is NotImplemented
+
+
+class TestCasting(BasePeriodTests, base.BaseCastingTests):
+    pass
+
+
+class TestComparisonOps(BasePeriodTests, base.BaseComparisonOpsTests):
+    def _compare_other(self, s, data, op_name, other):
+        # the base test is not appropriate for us. We raise on comparison
+        # with (some) integers, depending on the value.
+        pass
+
+
+class TestMissing(BasePeriodTests, base.BaseMissingTests):
+    pass
+
+
+class TestReshaping(BasePeriodTests, base.BaseReshapingTests):
+    pass
+
+
+class TestSetitem(BasePeriodTests, base.BaseSetitemTests):
+    pass
+
+
+class TestGroupby(BasePeriodTests, base.BaseGroupbyTests):
+    pass
+
+
+class TestPrinting(BasePeriodTests, base.BasePrintingTests):
+    pass
+
+
+class TestParsing(BasePeriodTests, base.BaseParsingTests):
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data):
+        super().test_EA_types(engine, data)

venv/lib/python3.6/site-packages/pandas/tests/extension/test_sparse.py

@@ -0,0 +1,370 @@
+import numpy as np
+import pytest
+
+from pandas.errors import PerformanceWarning
+
+import pandas as pd
+from pandas import SparseArray, SparseDtype
+from pandas.tests.extension import base
+import pandas.util.testing as tm
+
+
+def make_data(fill_value):
+    if np.isnan(fill_value):
+        data = np.random.uniform(size=100)
+    else:
+        data = np.random.randint(1, 100, size=100)
+        if data[0] == data[1]:
+            data[0] += 1
+
+    data[2::3] = fill_value
+    return data
+
+
+@pytest.fixture
+def dtype():
+    return SparseDtype()
+
+
+@pytest.fixture(params=[0, np.nan])
+def data(request):
+    """Length-100 PeriodArray for semantics test."""
+    res = SparseArray(make_data(request.param), fill_value=request.param)
+    return res
+
+
+@pytest.fixture
+def data_for_twos(request):
+    return SparseArray(np.ones(100) * 2)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_missing(request):
+    """Length 2 array with [NA, Valid]"""
+    return SparseArray([np.nan, 1], fill_value=request.param)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_repeated(request):
+    """Return different versions of data for count times"""
+
+    def gen(count):
+        for _ in range(count):
+            yield SparseArray(make_data(request.param), fill_value=request.param)
+
+    yield gen
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_for_sorting(request):
+    return SparseArray([2, 3, 1], fill_value=request.param)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_missing_for_sorting(request):
+    return SparseArray([2, np.nan, 1], fill_value=request.param)
+
+
+@pytest.fixture
+def na_value():
+    return np.nan
+
+
+@pytest.fixture
+def na_cmp():
+    return lambda left, right: pd.isna(left) and pd.isna(right)
+
+
+@pytest.fixture(params=[0, np.nan])
+def data_for_grouping(request):
+    return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param)
+
+
+class BaseSparseTests:
+    def _check_unsupported(self, data):
+        if data.dtype == SparseDtype(int, 0):
+            pytest.skip("Can't store nan in int array.")
+
+    @pytest.mark.xfail(reason="SparseArray does not support setitem")
+    def test_ravel(self, data):
+        super().test_ravel(data)
+
+
+class TestDtype(BaseSparseTests, base.BaseDtypeTests):
+    def test_array_type_with_arg(self, data, dtype):
+        assert dtype.construct_array_type() is SparseArray
+
+
+class TestInterface(BaseSparseTests, base.BaseInterfaceTests):
+    def test_no_values_attribute(self, data):
+        pytest.skip("We have values")
+
+    def test_copy(self, data):
+        # __setitem__ does not work, so we only have a smoke-test
+        data.copy()
+
+
+class TestConstructors(BaseSparseTests, base.BaseConstructorsTests):
+    pass
+
+
+class TestReshaping(BaseSparseTests, base.BaseReshapingTests):
+    def test_concat_mixed_dtypes(self, data):
+        # https://github.com/pandas-dev/pandas/issues/20762
+        # This should be the same, aside from concat([sparse, float])
+        df1 = pd.DataFrame({"A": data[:3]})
+        df2 = pd.DataFrame({"A": [1, 2, 3]})
+        df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
+        dfs = [df1, df2, df3]
+
+        # dataframes
+        result = pd.concat(dfs)
+        expected = pd.concat(
+            [x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs]
+        )
+        self.assert_frame_equal(result, expected)
+
+    def test_concat_columns(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_concat_columns(data, na_value)
+
+    def test_align(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align(data, na_value)
+
+    def test_align_frame(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align_frame(data, na_value)
+
+    def test_align_series_frame(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_align_series_frame(data, na_value)
+
+    def test_merge(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_merge(data, na_value)
+
+
+class TestGetitem(BaseSparseTests, base.BaseGetitemTests):
+    def test_get(self, data):
+        s = pd.Series(data, index=[2 * i for i in range(len(data))])
+        if np.isnan(s.values.fill_value):
+            assert np.isnan(s.get(4)) and np.isnan(s.iloc[2])
+        else:
+            assert s.get(4) == s.iloc[2]
+        assert s.get(2) == s.iloc[1]
+
+    def test_reindex(self, data, na_value):
+        self._check_unsupported(data)
+        super().test_reindex(data, na_value)
+
+
+# Skipping TestSetitem, since we don't implement it.
+
+
+class TestMissing(BaseSparseTests, base.BaseMissingTests):
+    def test_isna(self, data_missing):
+        expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
+        expected = SparseArray([True, False], dtype=expected_dtype)
+
+        result = pd.isna(data_missing)
+        self.assert_equal(result, expected)
+
+        result = pd.Series(data_missing).isna()
+        expected = pd.Series(expected)
+        self.assert_series_equal(result, expected)
+
+        # GH 21189
+        result = pd.Series(data_missing).drop([0, 1]).isna()
+        expected = pd.Series([], dtype=expected_dtype)
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_limit_pad(self, data_missing):
+        with tm.assert_produces_warning(PerformanceWarning):
+            super().test_fillna_limit_pad(data_missing)
+
+    def test_fillna_limit_backfill(self, data_missing):
+        with tm.assert_produces_warning(PerformanceWarning):
+            super().test_fillna_limit_backfill(data_missing)
+
+    def test_fillna_series_method(self, data_missing):
+        with tm.assert_produces_warning(PerformanceWarning):
+            super().test_fillna_limit_backfill(data_missing)
+
+    @pytest.mark.skip(reason="Unsupported")
+    def test_fillna_series(self):
+        # this one looks doable.
+        pass
+
+    def test_fillna_frame(self, data_missing):
+        # Have to override to specify that fill_value will change.
+        fill_value = data_missing[1]
+
+        result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
+
+        if pd.isna(data_missing.fill_value):
+            dtype = SparseDtype(data_missing.dtype, fill_value)
+        else:
+            dtype = data_missing.dtype
+
+        expected = pd.DataFrame(
+            {
+                "A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype),
+                "B": [1, 2],
+            }
+        )
+
+        self.assert_frame_equal(result, expected)
+
+
+class TestMethods(BaseSparseTests, base.BaseMethodsTests):
+    def test_combine_le(self, data_repeated):
+        # We return a Series[SparseArray].__le__ returns a
+        # Series[Sparse[bool]]
+        # rather than Series[bool]
+        orig_data1, orig_data2 = data_repeated(2)
+        s1 = pd.Series(orig_data1)
+        s2 = pd.Series(orig_data2)
+        result = s1.combine(s2, lambda x1, x2: x1 <= x2)
+        expected = pd.Series(
+            pd.SparseArray(
+                [a <= b for (a, b) in zip(list(orig_data1), list(orig_data2))],
+                fill_value=False,
+            )
+        )
+        self.assert_series_equal(result, expected)
+
+        val = s1.iloc[0]
+        result = s1.combine(val, lambda x1, x2: x1 <= x2)
+        expected = pd.Series(
+            pd.SparseArray([a <= val for a in list(orig_data1)], fill_value=False)
+        )
+        self.assert_series_equal(result, expected)
+
+    def test_fillna_copy_frame(self, data_missing):
+        arr = data_missing.take([1, 1])
+        df = pd.DataFrame({"A": arr})
+
+        filled_val = df.iloc[0, 0]
+        result = df.fillna(filled_val)
+
+        assert df.values.base is not result.values.base
+        assert df.A._values.to_dense() is arr.to_dense()
+
+    def test_fillna_copy_series(self, data_missing):
+        arr = data_missing.take([1, 1])
+        ser = pd.Series(arr)
+
+        filled_val = ser[0]
+        result = ser.fillna(filled_val)
+
+        assert ser._values is not result._values
+        assert ser._values.to_dense() is arr.to_dense()
+
+    @pytest.mark.skip(reason="Not Applicable")
+    def test_fillna_length_mismatch(self, data_missing):
+        pass
+
+    def test_where_series(self, data, na_value):
+        assert data[0] != data[1]
+        cls = type(data)
+        a, b = data[:2]
+
+        ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
+
+        cond = np.array([True, True, False, False])
+        result = ser.where(cond)
+
+        new_dtype = SparseDtype("float", 0.0)
+        expected = pd.Series(
+            cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype)
+        )
+        self.assert_series_equal(result, expected)
+
+        other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
+        cond = np.array([True, False, True, True])
+        result = ser.where(cond, other)
+        expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
+        self.assert_series_equal(result, expected)
+
+    def test_combine_first(self, data):
+        if data.dtype.subtype == "int":
+            # Right now this is upcasted to float, just like combine_first
+            # for Series[int]
+            pytest.skip("TODO(SparseArray.__setitem__ will preserve dtype.")
+        super().test_combine_first(data)
+
+    def test_searchsorted(self, data_for_sorting, as_series):
+        with tm.assert_produces_warning(PerformanceWarning):
+            super().test_searchsorted(data_for_sorting, as_series)
+
+
+class TestCasting(BaseSparseTests, base.BaseCastingTests):
+    pass
+
+
+class TestArithmeticOps(BaseSparseTests, base.BaseArithmeticOpsTests):
+    series_scalar_exc = None
+    frame_scalar_exc = None
+    divmod_exc = None
+    series_array_exc = None
+
+    def _skip_if_different_combine(self, data):
+        if data.fill_value == 0:
+            # arith ops call on dtype.fill_value so that the sparsity
+            # is maintained. Combine can't be called on a dtype in
+            # general, so we can't make the expected. This is tested elsewhere
+            raise pytest.skip("Incorrected expected from Series.combine")
+
+    def test_error(self, data, all_arithmetic_operators):
+        pass
+
+    def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
+        self._skip_if_different_combine(data)
+        super().test_arith_series_with_scalar(data, all_arithmetic_operators)
+
+    def test_arith_series_with_array(self, data, all_arithmetic_operators):
+        self._skip_if_different_combine(data)
+        super().test_arith_series_with_array(data, all_arithmetic_operators)
+
+
+class TestComparisonOps(BaseSparseTests, base.BaseComparisonOpsTests):
+    def _compare_other(self, s, data, op_name, other):
+        op = self.get_op_from_name(op_name)
+
+        # array
+        result = pd.Series(op(data, other))
+        # hard to test the fill value, since we don't know what expected
+        # is in general.
+        # Rely on tests in `tests/sparse` to validate that.
+        assert isinstance(result.dtype, SparseDtype)
+        assert result.dtype.subtype == np.dtype("bool")
+
+        with np.errstate(all="ignore"):
+            expected = pd.Series(
+                pd.SparseArray(
+                    op(np.asarray(data), np.asarray(other)),
+                    fill_value=result.values.fill_value,
+                )
+            )
+
+        tm.assert_series_equal(result, expected)
+
+        # series
+        s = pd.Series(data)
+        result = op(s, other)
+        tm.assert_series_equal(result, expected)
+
+
+class TestPrinting(BaseSparseTests, base.BasePrintingTests):
+    @pytest.mark.xfail(reason="Different repr", strict=True)
+    def test_array_repr(self, data, size):
+        super().test_array_repr(data, size)
+
+
+class TestParsing(BaseSparseTests, base.BaseParsingTests):
+    @pytest.mark.parametrize("engine", ["c", "python"])
+    def test_EA_types(self, engine, data):
+        expected_msg = r".*must implement _from_sequence_of_strings.*"
+        with pytest.raises(NotImplementedError, match=expected_msg):
+            super().test_EA_types(engine, data)