diff --git a/doc/source/whatsnew/v0.24.0.rst b/doc/source/whatsnew/v0.24.0.rst
index 4ff3cc728f7f7..d36ddc075b91e 100644
--- a/doc/source/whatsnew/v0.24.0.rst
+++ b/doc/source/whatsnew/v0.24.0.rst
@@ -1420,6 +1420,7 @@ Groupby/Resample/Rolling
 - Bug in :meth:`DataFrame.expanding` in which the ``axis`` argument was not being respected during aggregations (:issue:`23372`)
 - Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.transform` which caused missing values when the input function can accept a :class:`DataFrame` but renames it (:issue:`23455`).
 - Bug in :func:`pandas.core.groupby.GroupBy.nth` where column order was not always preserved (:issue:`20760`)
+- Bug in :meth:`pandas.core.groupby.DataFrameGroupBy.rank` with ``method='dense'`` and ``pct=True`` when a group has only one member would raise a ``ZeroDivisionError`` (:issue:`23666`).
 
 Reshaping
 ^^^^^^^^^
diff --git a/pandas/_libs/groupby_helper.pxi.in b/pandas/_libs/groupby_helper.pxi.in
index 523d43f893aad..abac9f147848e 100644
--- a/pandas/_libs/groupby_helper.pxi.in
+++ b/pandas/_libs/groupby_helper.pxi.in
@@ -587,7 +587,7 @@ def group_rank_{{name}}(ndarray[float64_t, ndim=2] out,
                 # rankings, so we assign them percentages of NaN.
                 if out[i, 0] != out[i, 0] or out[i, 0] == NAN:
                     out[i, 0] = NAN
-                else:
+                elif grp_sizes[i, 0] != 0:
                     out[i, 0] = out[i, 0] / grp_sizes[i, 0]
 {{endif}}
 {{endfor}}
diff --git a/pandas/tests/groupby/test_rank.py b/pandas/tests/groupby/test_rank.py
index e7e91572c56d1..aaac614761083 100644
--- a/pandas/tests/groupby/test_rank.py
+++ b/pandas/tests/groupby/test_rank.py
@@ -290,3 +290,18 @@ def test_rank_empty_group():
     result = df.groupby(column).rank(pct=True)
     expected = DataFrame({"B": [0.5, np.nan, 1.0]})
     tm.assert_frame_equal(result, expected)
+
+
+@pytest.mark.parametrize("input_key,input_value,output_value", [
+    ([1, 2], [1, 1], [1.0, 1.0]),
+    ([1, 1, 2, 2], [1, 2, 1, 2], [0.5, 1.0, 0.5, 1.0]),
+    ([1, 1, 2, 2], [1, 2, 1, np.nan], [0.5, 1.0, 1.0, np.nan]),
+    ([1, 1, 2], [1, 2, np.nan], [0.5, 1.0, np.nan])
+])
+def test_rank_zero_div(input_key, input_value, output_value):
+    # GH 23666
+    df = DataFrame({"A": input_key, "B": input_value})
+
+    result = df.groupby("A").rank(method="dense", pct=True)
+    expected = DataFrame({"B": output_value})
+    tm.assert_frame_equal(result, expected)