ENH: removed median percentile to be always included in describe GH #60550

doc/source/whatsnew/v2.3.0.rst

@@ -36,7 +36,8 @@ Other enhancements
   when using ``np.array()`` or ``np.asarray()`` on pandas objects) has been
   updated to work correctly with NumPy >= 2 (:issue:`57739`)
 - The :meth:`~Series.sum` reduction is now implemented for ``StringDtype`` columns (:issue:`59853`)
--
+- Median percentile is only included in :meth:`~Series.describe` when a blank
+  list is passed (:issue:`60550`).
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_230.notable_bug_fixes:

pandas/core/generic.py

@@ -10795,7 +10795,8 @@ def describe(
             The percentiles to include in the output. All should
             fall between 0 and 1. The default is
             ``[.25, .5, .75]``, which returns the 25th, 50th, and
-            75th percentiles.
+            75th percentiles. If a blank list is passed, then returns
+            only the 50th percentile value.
         include : 'all', list-like of dtypes or None (default), optional
             A white list of data types to include in the result. Ignored
             for ``Series``. Here are the options:

pandas/core/methods/describe.py

@@ -74,7 +74,8 @@ def describe_ndframe(
     percentiles : list-like of numbers, optional
         The percentiles to include in the output. All should fall between 0 and 1.
         The default is ``[.25, .5, .75]``, which returns the 25th, 50th, and
-        75th percentiles.
+        75th percentiles. If a blank list is passed, then returns only the
+        50th percentile value.
 
     Returns
     -------
@@ -351,13 +352,13 @@ def _refine_percentiles(
     # explicit conversion of `percentiles` to list
     percentiles = list(percentiles)
 
+    # median should be included only if blank iterable is passed
+    if len(percentiles) == 0:
+        return np.array([0.5])
+
     # get them all to be in [0, 1]
     validate_percentile(percentiles)
 
-    # median should always be included
-    if 0.5 not in percentiles:
-        percentiles.append(0.5)
-
     percentiles = np.asarray(percentiles)
 
     # sort and check for duplicates

pandas/tests/reductions/test_describe_ndframe.py

@@ -0,0 +1,25 @@
+# -*- encoding: utf-8 -*-
+
+"""
+We test the describe_ndframe function.
+"""
+
+import numpy as np
+import pytest
+
+from pandas.core.methods.describe import _refine_percentiles
+
+@pytest.mark.parametrize(
+    "percentiles_, expected", [
+        (None, np.array([0.25, 0.5, 0.75])),
+        ([], np.array([0.5])),
+        ([0.3, 0.6], np.array([0.3, 0.6])),
+    ]
+)
+def test_refine_percentiles(percentiles_, expected):
+    """
+    Check the performance of the _refine_percentiles when multiple
+    values are passed.
+    """
+
+    assert np.array_equal(_refine_percentiles(percentiles_), expected)