Return mode even if single value (#15714)

Author: Robin

pandas/_libs/hashtable_func_helper.pxi.in

@@ -233,7 +233,7 @@ def mode_{{dtype}}(ndarray[{{ctype}}] values):
 def mode_{{dtype}}({{ctype}}[:] values):
 {{endif}}
     cdef:
-        int count, max_count = 2
+        int count, max_count = 1
         int j = -1 # so you can do +=
         Py_ssize_t k
         kh_{{table_type}}_t *table

pandas/core/categorical.py

@@ -1876,8 +1876,7 @@ def mode(self):
         """
         Returns the mode(s) of the Categorical.
 
-        Empty if nothing occurs at least 2 times.  Always returns `Categorical`
-        even if only one value.
+        Always returns `Categorical` even if only one value.
 
         Returns
         -------

pandas/core/frame.py

@@ -5168,9 +5168,8 @@ def _get_agg_axis(self, axis_num):
 
     def mode(self, axis=0, numeric_only=False):
         """
-        Gets the mode(s) of each element along the axis selected. Empty if
-        nothing has 2+ occurrences. Adds a row for each mode per label, fills
-        in gaps with nan.
+        Gets the mode(s) of each element along the axis selected. Adds a row
+        for each mode per label, fills in gaps with nan.
 
         Note that there could be multiple values returned for the selected
         axis (when more than one item share the maximum frequency), which is

pandas/core/series.py

@@ -1192,8 +1192,7 @@ def count(self, level=None):
     def mode(self):
         """Return the mode(s) of the dataset.
 
-        Empty if nothing occurs at least 2 times. Always returns Series even
-        if only one value is returned.
+        Always returns Series even if only one value is returned.
 
         Returns
         -------

pandas/tests/test_algos.py

@@ -1262,6 +1262,15 @@ def test_no_mode(self):
         exp = Series([], dtype=np.object)
         tm.assert_series_equal(algos.mode(['a', 'b', 'c']), exp)
 
+    def test_mode_single(self):
+        exp_single = [1]
+        data_single = [1]
+
+        for dt in np.typecodes['AllInteger'] + np.typecodes['Float']:
+            s = Series(data_single, dtype=dt)
+            exp = Series(exp_single, dtype=dt)
+            tm.assert_series_equal(algos.mode(s), exp)
+
     def test_number_mode(self):
         exp_single = [1]
         data_single = [1] * 5 + [2] * 3