content: import numpy as np
  import pytest
  
  import pandas as pd
  import pandas._testing as tm
  from pandas.core.arrays import FloatingArray
  
  
  @pytest.mark.parametrize("ufunc", [np.abs, np.sign])
  # np.sign emits a warning with nans, <https://github.com/numpy/numpy/issues/15127>
  @pytest.mark.filterwarnings("ignore:invalid value encountered in sign")
  def test_ufuncs_single_int(ufunc):
      a = pd.array([1, 2, -3, np.nan])
      result = ufunc(a)
      expected = pd.array(ufunc(a.astype(float)), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
      s = pd.Series(a)
      result = ufunc(s)
      expected = pd.Series(pd.array(ufunc(a.astype(float)), dtype="Int64"))
      tm.assert_series_equal(result, expected)
  
  
  @pytest.mark.parametrize("ufunc", [np.log, np.exp, np.sin, np.cos, np.sqrt])
  def test_ufuncs_single_float(ufunc):
      a = pd.array([1, 2, -3, np.nan])
      with np.errstate(invalid="ignore"):
          result = ufunc(a)
          expected = FloatingArray(ufunc(a.astype(float)), mask=a._mask)
      tm.assert_extension_array_equal(result, expected)
  
      s = pd.Series(a)
      with np.errstate(invalid="ignore"):
          result = ufunc(s)
      expected = pd.Series(expected)
      tm.assert_series_equal(result, expected)
  
  
  @pytest.mark.parametrize("ufunc", [np.add, np.subtract])
  def test_ufuncs_binary_int(ufunc):
      # two IntegerArrays
      a = pd.array([1, 2, -3, np.nan])
      result = ufunc(a, a)
      expected = pd.array(ufunc(a.astype(float), a.astype(float)), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
      # IntegerArray with numpy array
      arr = np.array([1, 2, 3, 4])
      result = ufunc(a, arr)
      expected = pd.array(ufunc(a.astype(float), arr), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
      result = ufunc(arr, a)
      expected = pd.array(ufunc(arr, a.astype(float)), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
      # IntegerArray with scalar
      result = ufunc(a, 1)
      expected = pd.array(ufunc(a.astype(float), 1), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
      result = ufunc(1, a)
      expected = pd.array(ufunc(1, a.astype(float)), dtype="Int64")
      tm.assert_extension_array_equal(result, expected)
  
  
  def test_ufunc_binary_output():
      a = pd.array([1, 2, np.nan])
      result = np.modf(a)
      expected = np.modf(a.to_numpy(na_value=np.nan, dtype="float"))
      expected = (pd.array(expected[0]), pd.array(expected[1]))
  
      assert isinstance(result, tuple)
      assert len(result) == 2
  
      for x, y in zip(result, expected):
          tm.assert_extension_array_equal(x, y)
  
  
  @pytest.mark.parametrize("values", [[0, 1], [0, None]])
  def test_ufunc_reduce_raises(values):
      a = pd.array(values)
      msg = r"The 'reduce' method is not supported."
      with pytest.raises(NotImplementedError, match=msg):
          np.add.reduce(a)
  
  
  @pytest.mark.parametrize(
      "pandasmethname, kwargs",
      [
          ("var", {"ddof": 0}),
          ("var", {"ddof": 1}),
          ("kurtosis", {}),
          ("skew", {}),
          ("sem", {}),
      ],
  )
  def test_stat_method(pandasmethname, kwargs):
      s = pd.Series(data=[1, 2, 3, 4, 5, 6, np.nan, np.nan], dtype="Int64")
      pandasmeth = getattr(s, pandasmethname)
      result = pandasmeth(**kwargs)
      s2 = pd.Series(data=[1, 2, 3, 4, 5, 6], dtype="Int64")
      pandasmeth = getattr(s2, pandasmethname)
      expected = pandasmeth(**kwargs)
      assert expected == result
  
  
  def test_value_counts_na():
      arr = pd.array([1, 2, 1, pd.NA], dtype="Int64")
      result = arr.value_counts(dropna=False)
      expected = pd.Series([2, 1, 1], index=[1, 2, pd.NA], dtype="Int64")
      tm.assert_series_equal(result, expected)
  
      result = arr.value_counts(dropna=True)
      expected = pd.Series([2, 1], index=[1, 2], dtype="Int64")
      tm.assert_series_equal(result, expected)
  
  
  def test_value_counts_empty():
      # https://github.com/pandas-dev/pandas/issues/33317
      s = pd.Series([], dtype="Int64")
      result = s.value_counts()
      # TODO: The dtype of the index seems wrong (it's int64 for non-empty)
      idx = pd.Index([], dtype="object")
      expected = pd.Series([], index=idx, dtype="Int64")
      tm.assert_series_equal(result, expected)
  
  
  def test_value_counts_with_normalize():
      # GH 33172
      s = pd.Series([1, 2, 1, pd.NA], dtype="Int64")
      result = s.value_counts(normalize=True)
      expected = pd.Series([2, 1], index=[1, 2], dtype="Float64") / 3
      tm.assert_series_equal(result, expected)
  
  
  @pytest.mark.parametrize("skipna", [True, False])
  @pytest.mark.parametrize("min_count", [0, 4])
  def test_integer_array_sum(skipna, min_count, any_nullable_int_dtype):
      dtype = any_nullable_int_dtype
      arr = pd.array([1, 2, 3, None], dtype=dtype)
      result = arr.sum(skipna=skipna, min_count=min_count)
      if skipna and min_count == 0:
          assert result == 6
      else:
          assert result is pd.NA
  
  
  @pytest.mark.parametrize("skipna", [True, False])
  @pytest.mark.parametrize("method", ["min", "max"])
  def test_integer_array_min_max(skipna, method, any_nullable_int_dtype):
      dtype = any_nullable_int_dtype
      arr = pd.array([0, 1, None], dtype=dtype)
      func = getattr(arr, method)
      result = func(skipna=skipna)
      if skipna:
          assert result == (0 if method == "min" else 1)
      else:
          assert result is pd.NA
  
  
  @pytest.mark.parametrize("skipna", [True, False])
  @pytest.mark.parametrize("min_count", [0, 9])
  def test_integer_array_prod(skipna, min_count, any_nullable_int_dtype):
      dtype = any_nullable_int_dtype
      arr = pd.array([1, 2, None], dtype=dtype)
      result = arr.prod(skipna=skipna, min_count=min_count)
      if skipna and min_count == 0:
          assert result == 2
      else:
          assert result is pd.NA
  
  
  @pytest.mark.parametrize(
      "values, expected", [([1, 2, 3], 6), ([1, 2, 3, None], 6), ([None], 0)]
  )
  def test_integer_array_numpy_sum(values, expected):
      arr = pd.array(values, dtype="Int64")
      result = np.sum(arr)
      assert result == expected
  
  
  @pytest.mark.parametrize("op", ["sum", "prod", "min", "max"])
  def test_dataframe_reductions(op):
      # https://github.com/pandas-dev/pandas/pull/32867
      # ensure the integers are not cast to float during reductions
      df = pd.DataFrame({"a": pd.array([1, 2], dtype="Int64")})
      result = df.max()
      assert isinstance(result["a"], np.int64)
  
  
  # TODO(jreback) - these need testing / are broken
  
  # shift
  
  # set_index (destroys type)
  