Complex Dtype Support for Hashmap Algos

doc/source/whatsnew/v1.4.0.rst

@@ -96,6 +96,7 @@ Other enhancements
 - :meth:`Series.ewm`, :meth:`DataFrame.ewm`, now support a ``method`` argument with a ``'table'`` option that performs the windowing operation over an entire :class:`DataFrame`. See :ref:`Window Overview <window.overview>` for performance and functional benefits (:issue:`42273`)
 - :meth:`.GroupBy.cummin` and :meth:`.GroupBy.cummax` now support the argument ``skipna`` (:issue:`34047`)
 - :meth:`read_table` now supports the argument ``storage_options`` (:issue:`39167`)
+- Methods that relied on hashmap based algos such as :meth:`DataFrameGroupBy.value_counts`, :meth:`DataFrameGroupBy.count` and :func:`factorize` ignored imaginary component for complex numbers (:issue:`17927`)
 
 .. ---------------------------------------------------------------------------
 

pandas/_libs/algos.pyi

@@ -148,6 +148,8 @@ def diff_2d(
 ) -> None: ...
 def ensure_platform_int(arr: object) -> np.ndarray: ...
 def ensure_object(arr: object) -> np.ndarray: ...
+def ensure_complex64(arr: object, copy=True) -> np.ndarray: ...
+def ensure_complex128(arr: object, copy=True) -> np.ndarray: ...
 def ensure_float64(arr: object, copy=True) -> np.ndarray: ...
 def ensure_float32(arr: object, copy=True) -> np.ndarray: ...
 def ensure_int8(arr: object, copy=True) -> np.ndarray: ...

pandas/_libs/algos.pyx

@@ -15,6 +15,8 @@ import numpy as np
 
 cimport numpy as cnp
 from numpy cimport (
+    NPY_COMPLEX64,
+    NPY_COMPLEX128,
     NPY_FLOAT32,
     NPY_FLOAT64,
     NPY_INT8,

pandas/_libs/algos_common_helper.pxi.in

@@ -47,6 +47,8 @@ dtypes = [('float64', 'FLOAT64', 'float64'),
           ('uint16', 'UINT16', 'uint16'),
           ('uint32', 'UINT32', 'uint32'),
           ('uint64', 'UINT64', 'uint64'),
+          ('complex64', 'COMPLEX64', 'complex64'),
+          ('complex128', 'COMPLEX128', 'complex128')
           # ('platform_int', 'INT', 'int_'),
           # ('object', 'OBJECT', 'object_'),
 ]

pandas/core/dtypes/common.py

@@ -97,6 +97,8 @@ def ensure_float(arr):
 ensure_int32 = algos.ensure_int32
 ensure_int16 = algos.ensure_int16
 ensure_int8 = algos.ensure_int8
+ensure_complex64 = algos.ensure_complex64
+ensure_complex128 = algos.ensure_complex128
 ensure_platform_int = algos.ensure_platform_int
 ensure_object = algos.ensure_object
 

pandas/tests/groupby/test_groupby.py

@@ -1009,6 +1009,36 @@ def test_groupby_complex():
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "frame,expected",
+    [
+        (
+            DataFrame(
+                [
+                    {"a": 1, "b": 1 + 1j},
+                    {"a": 1, "b": 1 + 2j},
+                    {"a": 4, "b": 1},
+                ]
+            ),
+            DataFrame(
+                np.array([1, 1, 1], dtype=np.int64),
+                index=Index([(1 + 1j), (1 + 2j), (1 + 0j)], dtype="object", name="b"),
+                columns=Index(["a"], dtype="object"),
+            ),
+        )
+    ],
+)
+def test_groupby_complex_numbers(frame, expected):
+    result = frame.groupby("b", sort=False).count()
+    tm.assert_frame_equal(result, expected)
+
+    # Sorted by the magnitude of the complex numbers
+    # Complex Index dtype is cast to object
+    expected.index = Index([(1 + 0j), (1 + 1j), (1 + 2j)], dtype="object", name="b")
+    result = frame.groupby("b", sort=True).count()
+    tm.assert_frame_equal(result, expected)
+
+
 def test_groupby_series_indexed_differently():
     s1 = Series(
         [5.0, -9.0, 4.0, 100.0, -5.0, 55.0, 6.7],

pandas/tests/indexes/multi/test_duplicates.py

@@ -8,6 +8,7 @@
 from pandas import (
     DatetimeIndex,
     MultiIndex,
+    Series,
 )
 import pandas._testing as tm
 
@@ -299,6 +300,54 @@ def test_duplicated_drop_duplicates():
     tm.assert_index_equal(idx.drop_duplicates(keep=False), expected)
 
 
+@pytest.mark.parametrize(
+    "array,expected,dtype",
+    [
+        (
+            [
+                np.nan + np.nan * 1j,
+                0,
+                1j,
+                1j,
+                1,
+                1 + 1j,
+                1 + 2j,
+                1 + 1j,
+                np.nan,
+                np.nan + np.nan * 1j,
+            ],
+            Series(
+                [False, False, False, True, False, False, False, True, False, True],
+                dtype=bool,
+            ),
+            np.complex64,
+        ),
+        (
+            [
+                np.nan + np.nan * 1j,
+                0,
+                1j,
+                1j,
+                1,
+                1 + 1j,
+                1 + 2j,
+                1 + 1j,
+                np.nan,
+                np.nan + np.nan * 1j,
+            ],
+            Series(
+                [False, False, False, True, False, False, False, True, False, True],
+                dtype=bool,
+            ),
+            np.complex128,
+        ),
+    ],
+)
+def test_duplicated_series_complex_numbers(array, expected, dtype):
+    result = Series(array, dtype=dtype).duplicated()
+    tm.assert_series_equal(result, expected)
+
+
 def test_multi_drop_duplicates_pos_args_deprecation():
     # GH#41485
     idx = MultiIndex.from_arrays([[1, 2, 3, 1], [1, 2, 3, 1]])

pandas/tests/indexes/period/methods/test_factorize.py

@@ -1,6 +1,9 @@
 import numpy as np
 
-from pandas import PeriodIndex
+from pandas import (
+    PeriodIndex,
+    factorize,
+)
 import pandas._testing as tm
 
 
@@ -35,3 +38,13 @@ def test_factorize(self):
         arr, idx = idx2.factorize()
         tm.assert_numpy_array_equal(arr, exp_arr)
         tm.assert_index_equal(idx, exp_idx)
+
+    def test_factorize_complex(self):
+        array = [1, 2, 2 + 1j]
+        labels, uniques = factorize(array)
+
+        expected_labels = np.array([0, 1, 2], dtype=np.intp)
+        tm.assert_numpy_array_equal(labels, expected_labels)
+
+        expected_uniques = np.array([(1 + 0j), (2 + 0j), (2 + 1j)], dtype=object)
+        tm.assert_numpy_array_equal(uniques, expected_uniques)

pandas/tests/reductions/test_reductions.py

@@ -1487,3 +1487,49 @@ def test_mode_boolean_with_na(self):
         result = ser.mode()
         expected = Series({0: True}, dtype="boolean")
         tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "array,expected,dtype",
+        [
+            (
+                [0, 1j, 1, 1, 1 + 1j, 1 + 2j],
+                Series([1], dtype=np.complex128),
+                np.complex128,
+            ),
+            (
+                [0, 1j, 1, 1, 1 + 1j, 1 + 2j],
+                Series([1], dtype=np.complex64),
+                np.complex64,
+            ),
+            (
+                [1 + 1j, 2j, 1 + 1j],
+                Series([1 + 1j], dtype=np.complex128),
+                np.complex128,
+            ),
+        ],
+    )
+    def test_unimode_complex(self, array, expected, dtype):
+        result = Series(array, dtype=dtype).mode()
+        tm.assert_series_equal(result, expected)
+
+    @pytest.mark.parametrize(
+        "array,expected,dtype",
+        [
+            (
+                # no modes
+                [0, 1j, 1, 1 + 1j, 1 + 2j],
+                Series([0j, 1j, 1 + 0j, 1 + 1j, 1 + 2j], dtype=np.complex128),
+                np.complex128,
+            ),
+            (
+                [1 + 1j, 2j, 1 + 1j, 2j, 3],
+                Series([2j, 1 + 1j], dtype=np.complex64),
+                np.complex64,
+            ),
+        ],
+    )
+    def test_multimode_complex(self, array, expected, dtype):
+        # mode tries to sort multimodal series.
+        # Complex numbers are sorted by their magnitude
+        result = Series(array, dtype=dtype).mode()
+        tm.assert_series_equal(result, expected)

pandas/tests/series/methods/test_isin.py

@@ -186,3 +186,17 @@ def test_isin_large_series_mixed_dtypes_and_nan():
     result = ser.isin({"foo", "bar"})
     expected = Series([False] * 3 * 1_000_000)
     tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+    "array,expected",
+    [
+        (
+            [0, 1j, 1j, 1, 1 + 1j, 1 + 2j, 1 + 1j],
+            Series([False, True, True, False, True, True, True], dtype=bool),
+        )
+    ],
+)
+def test_isin_complex_numbers(array, expected):
+    result = Series(array).isin([1j, 1 + 1j, 1 + 2j])
+    tm.assert_series_equal(result, expected)

pandas/tests/series/methods/test_value_counts.py

@@ -207,3 +207,21 @@ def test_value_counts_bool_with_nan(self, ser, dropna, exp):
         # GH32146
         out = ser.value_counts(dropna=dropna)
         tm.assert_series_equal(out, exp)
+
+    @pytest.mark.parametrize(
+        "input_array,expected",
+        [
+            (
+                [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j],
+                Series([3, 2, 1], index=pd.Index([3j, 1 + 1j, 1], dtype=np.complex128)),
+            ),
+            (
+                [1 + 1j, 1 + 1j, 1, 3j, 3j, 3j],
+                Series([3, 2, 1], index=pd.Index([3j, 1 + 1j, 1], dtype=np.complex64)),
+            ),
+        ],
+    )
+    def test_value_counts_complex_numbers(self, input_array, expected):
+        # Complex Index dtype is cast to object
+        result = Series(input_array).value_counts()
+        tm.assert_series_equal(result, expected)

pandas/tests/test_algos.py

@@ -1513,6 +1513,19 @@ def test_unique_tuples(self, arr, uniques):
         result = pd.unique(arr)
         tm.assert_numpy_array_equal(result, expected)
 
+    @pytest.mark.parametrize(
+        "array,expected",
+        [
+            (
+                [1 + 1j, 0, 1, 1j, 1 + 2j, 1 + 2j],
+                np.array([(1 + 1j), 0j, (1 + 0j), 1j, (1 + 2j)], dtype=object),
+            )
+        ],
+    )
+    def test_unique_complex_numbers(self, array, expected):
+        result = pd.unique(array)
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestHashTable:
     def test_string_hashtable_set_item_signature(self):