DEPR: Index.is_monotonic for Index.is_monotonic_increasing

asv_bench/benchmarks/index_cached_properties.py

@@ -56,9 +56,6 @@ def time_values(self, index_type):
     def time_shape(self, index_type):
         self.idx.shape
 
-    def time_is_monotonic(self, index_type):
-        self.idx.is_monotonic
-
     def time_is_monotonic_decreasing(self, index_type):
         self.idx.is_monotonic_decreasing
 

asv_bench/benchmarks/multiindex_object.py

@@ -112,7 +112,7 @@ def time_get_indexer_and_pad(self):
         self.mi_int.get_indexer(self.other_mi_many_mismatches, method="pad")
 
     def time_is_monotonic(self):
-        self.mi_int.is_monotonic
+        self.mi_int.is_monotonic_increasing
 
 
 class Duplicated:

doc/source/whatsnew/v1.5.0.rst

@@ -147,6 +147,7 @@ Other Deprecations
 - Deprecated behavior of :meth:`SparseArray.astype`, :meth:`Series.astype`, and :meth:`DataFrame.astype` with :class:`SparseDtype` when passing a non-sparse ``dtype``. In a future version, this will cast to that non-sparse dtype instead of wrapping it in a :class:`SparseDtype` (:issue:`34457`)
 - Deprecated behavior of :meth:`DatetimeIndex.intersection` and :meth:`DatetimeIndex.symmetric_difference` (``union`` behavior was already deprecated in version 1.3.0) with mixed timezones; in a future version both will be cast to UTC instead of object dtype (:issue:`39328`, :issue:`45357`)
 - Deprecated :meth:`DataFrame.iteritems`, :meth:`Series.iteritems`, :meth:`HDFStore.iteritems` in favor of :meth:`DataFrame.items`, :meth:`Series.items`, :meth:`HDFStore.items`  (:issue:`45321`)
+- Deprecated :meth:`Series.is_monotonic` and :meth:`Index.is_monotonic` in favor of :meth:`Series.is_monotonic_increasing` and :meth:`Index.is_monotonic_increasing` (:issue:`45422`, :issue:`21335`)
 - Deprecated the ``__array_wrap__`` method of DataFrame and Series, rely on standard numpy ufuncs instead (:issue:`45451`)
 -
 

pandas/core/base.py

@@ -16,6 +16,7 @@
     final,
     overload,
 )
+import warnings
 
 import numpy as np
 
@@ -35,6 +36,7 @@
     cache_readonly,
     doc,
 )
+from pandas.util._exceptions import find_stack_level
 
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
@@ -1050,17 +1052,27 @@ def is_monotonic(self) -> bool:
         -------
         bool
         """
-        from pandas import Index
-
-        return Index(self).is_monotonic
+        warnings.warn(
+            "is_monotonic is deprecated and will be removed in a future version. "
+            "Use is_monotonic_increasing instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
+        return self.is_monotonic_increasing
 
     @property
     def is_monotonic_increasing(self) -> bool:
         """
-        Alias for is_monotonic.
+        Return boolean if values in the object are
+        monotonic_increasing.
+
+        Returns
+        -------
+        bool
         """
-        # mypy complains if we alias directly
-        return self.is_monotonic
+        from pandas import Index
+
+        return Index(self).is_monotonic_increasing
 
     @property
     def is_monotonic_decreasing(self) -> bool:

pandas/core/generic.py

@@ -7161,7 +7161,7 @@ def asof(self, where, subset=None):
         if isinstance(where, str):
             where = Timestamp(where)
 
-        if not self.index.is_monotonic:
+        if not self.index.is_monotonic_increasing:
             raise ValueError("asof requires a sorted index")
 
         is_series = isinstance(self, ABCSeries)

pandas/core/groupby/grouper.py

@@ -400,7 +400,7 @@ def _set_grouper(self, obj: NDFrame, sort: bool = False):
                         raise ValueError(f"The level {level} is not valid")
 
         # possibly sort
-        if (self.sort or sort) and not ax.is_monotonic:
+        if (self.sort or sort) and not ax.is_monotonic_increasing:
             # use stable sort to support first, last, nth
             # TODO: why does putting na_position="first" fix datetimelike cases?
             indexer = self.indexer = ax.array.argsort(

pandas/core/groupby/ops.py

@@ -823,7 +823,7 @@ def groups(self) -> dict[Hashable, np.ndarray]:
     @cache_readonly
     def is_monotonic(self) -> bool:
         # return if my group orderings are monotonic
-        return Index(self.group_info[0]).is_monotonic
+        return Index(self.group_info[0]).is_monotonic_increasing
 
     @cache_readonly
     def group_info(self) -> tuple[npt.NDArray[np.intp], npt.NDArray[np.intp], int]:

pandas/core/indexes/base.py

@@ -2172,6 +2172,12 @@ def is_monotonic(self) -> bool:
         """
         Alias for is_monotonic_increasing.
         """
+        warnings.warn(
+            "is_monotonic is deprecated and will be removed in a future version. "
+            "Use is_monotonic_increasing instead.",
+            FutureWarning,
+            stacklevel=find_stack_level(),
+        )
         return self.is_monotonic_increasing
 
     @property
@@ -3235,8 +3241,8 @@ def _union(self, other: Index, sort):
 
         if (
             sort is None
-            and self.is_monotonic
-            and other.is_monotonic
+            and self.is_monotonic_increasing
+            and other.is_monotonic_increasing
             and not (self.has_duplicates and other.has_duplicates)
             and self._can_use_libjoin
         ):
@@ -3274,7 +3280,7 @@ def _union(self, other: Index, sort):
         else:
             result = lvals
 
-        if not self.is_monotonic or not other.is_monotonic:
+        if not self.is_monotonic_increasing or not other.is_monotonic_increasing:
             # if both are monotonic then result should already be sorted
             result = _maybe_try_sort(result, sort)
 
@@ -3379,7 +3385,11 @@ def _intersection(self, other: Index, sort=False):
         """
         intersection specialized to the case with matching dtypes.
         """
-        if self.is_monotonic and other.is_monotonic and self._can_use_libjoin:
+        if (
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
+            and self._can_use_libjoin
+        ):
             try:
                 result = self._inner_indexer(other)[0]
             except TypeError:
@@ -4475,15 +4485,15 @@ def join(
         if not self.is_unique and not other.is_unique:
             return self._join_non_unique(other, how=how)
         elif not self.is_unique or not other.is_unique:
-            if self.is_monotonic and other.is_monotonic:
+            if self.is_monotonic_increasing and other.is_monotonic_increasing:
                 if self._can_use_libjoin:
                     # otherwise we will fall through to _join_via_get_indexer
                     return self._join_monotonic(other, how=how)
             else:
                 return self._join_non_unique(other, how=how)
         elif (
-            self.is_monotonic
-            and other.is_monotonic
+            self.is_monotonic_increasing
+            and other.is_monotonic_increasing
             and self._can_use_libjoin
             and (
                 not isinstance(self, ABCMultiIndex)

pandas/core/indexes/multi.py

@@ -1557,7 +1557,7 @@ def is_monotonic_increasing(self) -> bool:
         if any(-1 in code for code in self.codes):
             return False
 
-        if all(level.is_monotonic for level in self.levels):
+        if all(level.is_monotonic_increasing for level in self.levels):
             # If each level is sorted, we can operate on the codes directly. GH27495
             return libalgos.is_lexsorted(
                 [x.astype("int64", copy=False) for x in self.codes]
@@ -1574,11 +1574,11 @@ def is_monotonic_increasing(self) -> bool:
             #  int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float,
             #  complex, str, bytes]]]"  [arg-type]
             sort_order = np.lexsort(values)  # type: ignore[arg-type]
-            return Index(sort_order).is_monotonic
+            return Index(sort_order).is_monotonic_increasing
         except TypeError:
 
             # we have mixed types and np.lexsort is not happy
-            return Index(self._values).is_monotonic
+            return Index(self._values).is_monotonic_increasing
 
     @cache_readonly
     def is_monotonic_decreasing(self) -> bool:
@@ -1946,15 +1946,15 @@ def _sort_levels_monotonic(self) -> MultiIndex:
                     ('b', 'bb')],
                    )
         """
-        if self._is_lexsorted() and self.is_monotonic:
+        if self._is_lexsorted() and self.is_monotonic_increasing:
             return self
 
         new_levels = []
         new_codes = []
 
         for lev, level_codes in zip(self.levels, self.codes):
 
-            if not lev.is_monotonic:
+            if not lev.is_monotonic_increasing:
                 try:
                     # indexer to reorder the levels
                     indexer = lev.argsort()

pandas/core/missing.py

@@ -161,7 +161,7 @@ def clean_interp_method(method: str, index: Index, **kwargs) -> str:
         raise ValueError(f"method must be one of {valid}. Got '{method}' instead.")
 
     if method in ("krogh", "piecewise_polynomial", "pchip"):
-        if not index.is_monotonic:
+        if not index.is_monotonic_increasing:
             raise ValueError(
                 f"{method} interpolation requires that the index be monotonic."
             )

pandas/core/reshape/merge.py

@@ -1929,14 +1929,14 @@ def flip(xs) -> np.ndarray:
         tolerance = self.tolerance
 
         # we require sortedness and non-null values in the join keys
-        if not Index(left_values).is_monotonic:
+        if not Index(left_values).is_monotonic_increasing:
             side = "left"
             if isna(left_values).any():
                 raise ValueError(f"Merge keys contain null values on {side} side")
             else:
                 raise ValueError(f"{side} keys must be sorted")
 
-        if not Index(right_values).is_monotonic:
+        if not Index(right_values).is_monotonic_increasing:
             side = "right"
             if isna(right_values).any():
                 raise ValueError(f"Merge keys contain null values on {side} side")

pandas/tests/frame/methods/test_sort_index.py

@@ -25,7 +25,7 @@ def test_sort_index_and_reconstruction_doc_example(self):
             ),
         )
         assert df.index._is_lexsorted()
-        assert not df.index.is_monotonic
+        assert not df.index.is_monotonic_increasing
 
         # sort it
         expected = DataFrame(
@@ -35,23 +35,21 @@ def test_sort_index_and_reconstruction_doc_example(self):
             ),
         )
         result = df.sort_index()
-        assert result.index.is_monotonic
-
+        assert result.index.is_monotonic_increasing
         tm.assert_frame_equal(result, expected)
 
         # reconstruct
         result = df.sort_index().copy()
         result.index = result.index._sort_levels_monotonic()
-        assert result.index.is_monotonic
-
+        assert result.index.is_monotonic_increasing
         tm.assert_frame_equal(result, expected)
 
     def test_sort_index_non_existent_label_multiindex(self):
         # GH#12261
         df = DataFrame(0, columns=[], index=MultiIndex.from_product([[], []]))
         df.loc["b", "2"] = 1
         df.loc["a", "3"] = 1
-        result = df.sort_index().index.is_monotonic
+        result = df.sort_index().index.is_monotonic_increasing
         assert result is True
 
     def test_sort_index_reorder_on_ops(self):
@@ -549,7 +547,7 @@ def test_sort_index_and_reconstruction(self):
             index=MultiIndex.from_product([[0.5, 0.8], list("ab")]),
         )
         result = result.sort_index()
-        assert result.index.is_monotonic
+        assert result.index.is_monotonic_increasing
 
         tm.assert_frame_equal(result, expected)
 
@@ -567,7 +565,7 @@ def test_sort_index_and_reconstruction(self):
         concatted = pd.concat([df, df], keys=[0.8, 0.5])
         result = concatted.sort_index()
 
-        assert result.index.is_monotonic
+        assert result.index.is_monotonic_increasing
 
         tm.assert_frame_equal(result, expected)
 
@@ -583,11 +581,11 @@ def test_sort_index_and_reconstruction(self):
         df.columns = df.columns.set_levels(
             pd.to_datetime(df.columns.levels[1]), level=1
         )
-        assert not df.columns.is_monotonic
+        assert not df.columns.is_monotonic_increasing
         result = df.sort_index(axis=1)
-        assert result.columns.is_monotonic
+        assert result.columns.is_monotonic_increasing
         result = df.sort_index(axis=1, level=1)
-        assert result.columns.is_monotonic
+        assert result.columns.is_monotonic_increasing
 
     # TODO: better name, de-duplicate with test_sort_index_level above
     def test_sort_index_level2(self, multiindex_dataframe_random_data):

pandas/tests/frame/test_constructors.py

@@ -1530,7 +1530,7 @@ def test_constructor_mixed_dict_and_Series(self):
         data["B"] = Series([4, 3, 2, 1], index=["bar", "qux", "baz", "foo"])
 
         result = DataFrame(data)
-        assert result.index.is_monotonic
+        assert result.index.is_monotonic_increasing
 
         # ordering ambiguous, raise exception
         with pytest.raises(ValueError, match="ambiguous ordering"):

pandas/tests/indexes/datetimelike_/test_is_monotonic.py

@@ -7,7 +7,7 @@
 
 def test_is_monotonic_with_nat():
     # GH#31437
-    # PeriodIndex.is_monotonic should behave analogously to DatetimeIndex,
+    # PeriodIndex.is_monotonic_increasing should behave analogously to DatetimeIndex,
     #  in particular never be monotonic when we have NaT
     dti = date_range("2016-01-01", periods=3)
     pi = dti.to_period("D")
@@ -16,7 +16,7 @@ def test_is_monotonic_with_nat():
     for obj in [pi, pi._engine, dti, dti._engine, tdi, tdi._engine]:
         if isinstance(obj, Index):
             # i.e. not Engines
-            assert obj.is_monotonic
+            assert obj.is_monotonic_increasing
         assert obj.is_monotonic_increasing
         assert not obj.is_monotonic_decreasing
         assert obj.is_unique
@@ -28,7 +28,7 @@ def test_is_monotonic_with_nat():
     for obj in [pi1, pi1._engine, dti1, dti1._engine, tdi1, tdi1._engine]:
         if isinstance(obj, Index):
             # i.e. not Engines
-            assert not obj.is_monotonic
+            assert not obj.is_monotonic_increasing
         assert not obj.is_monotonic_increasing
         assert not obj.is_monotonic_decreasing
         assert obj.is_unique
@@ -40,7 +40,7 @@ def test_is_monotonic_with_nat():
     for obj in [pi2, pi2._engine, dti2, dti2._engine, tdi2, tdi2._engine]:
         if isinstance(obj, Index):
             # i.e. not Engines
-            assert not obj.is_monotonic
+            assert not obj.is_monotonic_increasing
         assert not obj.is_monotonic_increasing
         assert not obj.is_monotonic_decreasing
         assert obj.is_unique

pandas/tests/indexes/datetimelike_/test_sort_values.py

@@ -59,17 +59,16 @@ def test_argmin_argmax(self, non_monotonic_idx):
     def test_sort_values(self, non_monotonic_idx):
         idx = non_monotonic_idx
         ordered = idx.sort_values()
-        assert ordered.is_monotonic
-
+        assert ordered.is_monotonic_increasing
         ordered = idx.sort_values(ascending=False)
-        assert ordered[::-1].is_monotonic
+        assert ordered[::-1].is_monotonic_increasing
 
         ordered, dexer = idx.sort_values(return_indexer=True)
-        assert ordered.is_monotonic
+        assert ordered.is_monotonic_increasing
         tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))
 
         ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
-        assert ordered[::-1].is_monotonic
+        assert ordered[::-1].is_monotonic_increasing
         tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))
 
     def check_sort_values_with_freq(self, idx):

pandas/tests/indexes/datetimes/test_join.py

@@ -89,7 +89,7 @@ def test_join_nonunique(self):
         idx1 = to_datetime(["2012-11-06 16:00:11.477563", "2012-11-06 16:00:11.477563"])
         idx2 = to_datetime(["2012-11-06 15:11:09.006507", "2012-11-06 15:11:09.006507"])
         rs = idx1.join(idx2, how="outer")
-        assert rs.is_monotonic
+        assert rs.is_monotonic_increasing
 
     @pytest.mark.parametrize("freq", ["B", "C"])
     def test_outer_join(self, freq):