From 5252ebafd207c0fc2800232c28511d8d918d3670 Mon Sep 17 00:00:00 2001
From: jbrockmendel <jbrockmendel@gmail.com>
Date: Sun, 5 Jan 2020 11:43:01 -0800
Subject: [PATCH] CLN: remove Index/Series._is_homogeneous_type

---
 pandas/core/base.py                           | 18 -------------
 pandas/core/indexes/multi.py                  | 25 -------------------
 .../indexing/multiindex/test_multiindex.py    | 12 ---------
 pandas/tests/series/test_dtypes.py            |  7 ------
 4 files changed, 62 deletions(-)

diff --git a/pandas/core/base.py b/pandas/core/base.py
index d38dbec684f35..7d499181c6ed1 100644
--- a/pandas/core/base.py
+++ b/pandas/core/base.py
@@ -628,24 +628,6 @@ def transpose(self, *args, **kwargs):
         """,
     )
 
-    @property
-    def _is_homogeneous_type(self) -> bool:
-        """
-        Whether the object has a single dtype.
-
-        By definition, Series and Index are always considered homogeneous.
-        A MultiIndex may or may not be homogeneous, depending on the
-        dtypes of the levels.
-
-        See Also
-        --------
-        DataFrame._is_homogeneous_type : Whether all the columns in a
-            DataFrame have the same dtype.
-        MultiIndex._is_homogeneous_type : Whether all the levels of a
-            MultiIndex have the same dtype.
-        """
-        return True
-
     @property
     def shape(self):
         """
diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py
index 19f36e071fd0e..d9e68b64f526d 100644
--- a/pandas/core/indexes/multi.py
+++ b/pandas/core/indexes/multi.py
@@ -661,31 +661,6 @@ def array(self):
             "'MultiIndex.to_numpy()' to get a NumPy array of tuples."
         )
 
-    @property
-    def _is_homogeneous_type(self) -> bool:
-        """
-        Whether the levels of a MultiIndex all have the same dtype.
-
-        This looks at the dtypes of the levels.
-
-        See Also
-        --------
-        Index._is_homogeneous_type : Whether the object has a single
-            dtype.
-        DataFrame._is_homogeneous_type : Whether all the columns in a
-            DataFrame have the same dtype.
-
-        Examples
-        --------
-        >>> MultiIndex.from_tuples([
-        ...     ('a', 'b'), ('a', 'c')])._is_homogeneous_type
-        True
-        >>> MultiIndex.from_tuples([
-        ...     ('a', 1), ('a', 2)])._is_homogeneous_type
-        False
-        """
-        return len({x.dtype for x in self.levels}) <= 1
-
     def _set_levels(
         self, levels, level=None, copy=False, validate=True, verify_integrity=False
     ):
diff --git a/pandas/tests/indexing/multiindex/test_multiindex.py b/pandas/tests/indexing/multiindex/test_multiindex.py
index 3339cb5f3150d..8163de8588232 100644
--- a/pandas/tests/indexing/multiindex/test_multiindex.py
+++ b/pandas/tests/indexing/multiindex/test_multiindex.py
@@ -1,5 +1,4 @@
 import numpy as np
-import pytest
 
 import pandas._libs.index as _index
 from pandas.errors import PerformanceWarning
@@ -47,17 +46,6 @@ def test_multiindex_contains_dropped(self):
         assert "a" in idx.levels[0]
         assert "a" not in idx
 
-    @pytest.mark.parametrize(
-        "data, expected",
-        [
-            (MultiIndex.from_product([(), ()]), True),
-            (MultiIndex.from_product([(1, 2), (3, 4)]), True),
-            (MultiIndex.from_product([("a", "b"), (1, 2)]), False),
-        ],
-    )
-    def test_multiindex_is_homogeneous_type(self, data, expected):
-        assert data._is_homogeneous_type is expected
-
     def test_indexing_over_hashtable_size_cutoff(self):
         n = 10000
 
diff --git a/pandas/tests/series/test_dtypes.py b/pandas/tests/series/test_dtypes.py
index 4bf2f1bd82eff..a57ec2ba05d54 100644
--- a/pandas/tests/series/test_dtypes.py
+++ b/pandas/tests/series/test_dtypes.py
@@ -465,13 +465,6 @@ def test_infer_objects_series(self):
         assert actual.dtype == "object"
         tm.assert_series_equal(actual, expected)
 
-    def test_is_homogeneous_type(self):
-        with tm.assert_produces_warning(DeprecationWarning, check_stacklevel=False):
-            empty = Series()
-        assert empty._is_homogeneous_type
-        assert Series([1, 2])._is_homogeneous_type
-        assert Series(pd.Categorical([1, 2]))._is_homogeneous_type
-
     @pytest.mark.parametrize(
         "data",
         [