[26660] compare int with double by subtracting during describe percen…

doc/source/whatsnew/v0.25.0.rst

@@ -97,7 +97,7 @@ Other Enhancements
 - :func:`merge_asof` now gives a more clear error message when merge keys are categoricals that are not equal (:issue:`26136`)
 - :meth:`pandas.core.window.Rolling` supports exponential (or Poisson) window type (:issue:`21303`)
 - :class:`DatetimeIndex` and :class:`TimedeltaIndex` now have a `mean` method (:issue:`24757`)
--
+- :meth:`DataFrame.describe` now formats integer percentiles without decimal point (:issue:`26660`)
 
 .. _whatsnew_0250.api_breaking:
 

pandas/io/formats/format.py

@@ -1246,7 +1246,7 @@ def format_percentiles(percentiles):
             raise ValueError("percentiles should all be in the interval [0,1]")
 
     percentiles = 100 * percentiles
-    int_idx = (percentiles.astype(int) == percentiles)
+    int_idx = np.isclose(percentiles.astype(int), percentiles)
 
     if np.all(int_idx):
         out = percentiles.astype(int).astype(str)

pandas/tests/frame/test_analytics.py

@@ -704,6 +704,17 @@ def test_describe_tz_values(self, tz_naive_fixture):
         result = df.describe(include='all')
         tm.assert_frame_equal(result, expected)
 
+    def test_describe_percentiles_integer_idx(self):
+        # Issue 26660
+        df = pd.DataFrame({'x': [1]})
+        pct = np.linspace(0, 1, 10 + 1)
+        result = df.describe(percentiles=pct)
+
+        expected = DataFrame(
+            {'x': [1.0, 1.0, np.NaN, 1.0, *[1.0 for _ in pct], 1.0]},
+            index=['count', 'mean', 'std', 'min', '0%', '10%', '20%', '30%',
+                   '40%', '50%', '60%', '70%', '80%', '90%', '100%', 'max'])
+        tm.assert_frame_equal(result, expected)
     # ---------------------------------------------------------------------
     # Reductions
 

pandas/tests/io/formats/test_format.py

@@ -2740,6 +2740,14 @@ def test_format_percentiles():
         fmt.format_percentiles([0.1, 0.5, 'a'])
 
 
+def test_format_percentiles_integer_idx():
+    # Issue #26660
+    result = fmt.format_percentiles(np.linspace(0, 1, 10 + 1))
+    expected = ['0%', '10%', '20%', '30%', '40%', '50%',
+                '60%', '70%', '80%', '90%', '100%']
+    assert result == expected
+
+
 def test_repr_html_ipython_config(ip):
     code = textwrap.dedent("""\
     import pandas as pd