Series.isconstant() method added

Author: pedrocariellof

doc/source/whatsnew/v3.0.0.rst

@@ -43,6 +43,7 @@ Other enhancements
 - :meth:`DataFrame.cummin`, :meth:`DataFrame.cummax`, :meth:`DataFrame.cumprod` and :meth:`DataFrame.cumsum` methods now have a ``numeric_only`` parameter (:issue:`53072`)
 - :meth:`DataFrame.fillna` and :meth:`Series.fillna` can now accept ``value=None``; for non-object dtype the corresponding NA value will be used (:issue:`57723`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
+- :meth:`Series.isconstant` checks if a :class:`Series` has constant values, with an optional parameter to drop NaN values (:issue:`58806`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
 - Support reading Stata 110-format (Stata 7) dta files (:issue:`47176`)
 -
@@ -200,6 +201,7 @@ starting with 3.0, so it can be safely removed from your code.
 Other Deprecations
 ^^^^^^^^^^^^^^^^^^
 
+- Deprecated :meth:`series.values`, now don't drops the ``tz`` from ``dt64tz``, converts ``interval`` to ``object``, converts ``period`` to ``object``.
 - Deprecated :meth:`Timestamp.utcfromtimestamp`, use ``Timestamp.fromtimestamp(ts, "UTC")`` instead (:issue:`56680`)
 - Deprecated :meth:`Timestamp.utcnow`, use ``Timestamp.now("UTC")`` instead (:issue:`56680`)
 - Deprecated allowing non-keyword arguments in :meth:`DataFrame.all`, :meth:`DataFrame.min`, :meth:`DataFrame.max`, :meth:`DataFrame.sum`, :meth:`DataFrame.prod`, :meth:`DataFrame.mean`, :meth:`DataFrame.median`, :meth:`DataFrame.sem`, :meth:`DataFrame.var`, :meth:`DataFrame.std`, :meth:`DataFrame.skew`, :meth:`DataFrame.kurt`, :meth:`Series.all`,  :meth:`Series.min`, :meth:`Series.max`, :meth:`Series.sum`, :meth:`Series.prod`, :meth:`Series.mean`, :meth:`Series.median`, :meth:`Series.sem`, :meth:`Series.var`, :meth:`Series.std`, :meth:`Series.skew`, and :meth:`Series.kurt`. (:issue:`57087`)

pandas/core/series.py

@@ -25,6 +25,7 @@
 import warnings
 
 import numpy as np
+import numba
 
 from pandas._libs import (
     lib,
@@ -5423,6 +5424,46 @@ def isnull(self) -> Series:
         """
         return super().isnull()
 
+    def isconstant(self, dropna = False):
+        """
+        Return if the series has constant values.
+
+        Parameters
+        ----------
+        dropna : bool, default False
+        If True, NaN values will be ignored. If False, NaN values will be considered 
+        in the determination of whether the series has constant values.
+
+        Returns
+        -------
+        bool
+            True if the series has constant values, False otherwise.
+
+        Examples
+        --------
+        >>> s = pd.Series([2, 2, 2, 2])
+        >>> s.isconstant()
+        True
+
+        >>> s = pd.Series([2, 2, 3])
+        >>> s.isconstant()
+        False
+
+        >>> s = pd.Series([2, 2, 2, np.nan])
+        >>> s.isconstant(dropna=True)
+        True
+
+        >>> s = pd.Series([2, 2, 2, np.nan])
+        >>> s.isconstant(dropna=False)
+        False
+        """
+        v = self.to_numpy()
+        if dropna:
+            v = remove_na_arraylike(v)
+        if v.shape[0] == 0 or not notna(v).any():
+            return True
+        return (v[0] == v).all()    
+
     # error: Cannot determine type of 'notna'
     @doc(NDFrame.notna, klass=_shared_doc_kwargs["klass"])  # type: ignore[has-type]
     def notna(self) -> Series:

pandas/tests/series/methods/test_isconstant.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pytest
+
+from pandas import (
+    Series,
+)
+
+class TestSeriesIsConstant:
+    def test_isconstant(self):
+        s = Series([2, 2, 2, 2])
+        result = s.isconstant()
+        assert result
+
+        s = Series([2, 2, 2, 3])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([])
+        result = s.isconstant()
+        assert result
+
+        s = Series([5])
+        result = s.isconstant()
+        assert result
+
+    def test_isconstant_with_nan(self):
+        s = Series([np.nan])
+        result = s.isconstant()
+        assert result
+
+        s = Series([np.nan,np.nan])
+        result = s.isconstant()
+        assert result
+
+        s = Series([np.nan,1])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([np.nan, np.nan, 1])
+        result = s.isconstant()
+        assert not result
+
+    def test_isconstant_with_nan_dropna(self):
+        s = Series([np.nan])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan,np.nan])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan,1])
+        result = s.isconstant(True)
+        assert result
+
+        s = Series([np.nan, np.nan, 1])
+        result = s.isconstant(True)
+        assert result
+
+    def test_isconstant_mixed_types(self):
+        s = Series([2, '2', 2])
+        result = s.isconstant()
+        assert not result
+
+        s = Series([2, 2.0, 2])
+        result = s.isconstant()
+        assert result