diff --git a/python/prophet/plot.py b/python/prophet/plot.py
index 3e7efb9d7..bb9bdc2a6 100644
--- a/python/prophet/plot.py
+++ b/python/prophet/plot.py
@@ -309,7 +309,10 @@ def seasonality_plot_df(
     -------
     A dataframe with seasonal components on ds.
     """
-    df_dict = {'ds': ds, 'cap': 1., 'floor': 0.}
+    cap = 1.0
+    if m.scaling == "minmax" and not m.logistic_floor and m.y_min is not None:
+        cap = float(m.y_min) + 1.0
+    df_dict = {'ds': ds, 'cap': cap, 'floor': 0.}
     for name in m.extra_regressors:
         df_dict[name] = 0.
     # Activate all conditional seasonality columns
diff --git a/python/prophet/tests/test_plot.py b/python/prophet/tests/test_plot.py
index e3df0489d..aa15fc17d 100644
--- a/python/prophet/tests/test_plot.py
+++ b/python/prophet/tests/test_plot.py
@@ -201,3 +201,17 @@ def test_plotly_draws_data(self, model_and_forecast):
         traces = get_seasonality_plotly_props(m, "weekly")["traces"]
         np.testing.assert_array_equal(pd.to_datetime(traces[0].x), data["ds"])
         np.testing.assert_array_equal(traces[0].y, data["weekly"])
+
+    def test_seasonality_plot_df_logistic_minmax_without_floor(self):
+        m = Prophet(growth="logistic", scaling="minmax")
+        df = pd.DataFrame({
+            "ds": pd.date_range("2020-01-01", periods=20),
+            "y": np.linspace(100.0, 150.0, 20),
+            "cap": 200.0,
+        })
+        m.fit(df)
+        days = pd.date_range("2020-01-01", periods=7)
+        plot_df = seasonality_plot_df(m, days)
+        assert len(plot_df) == 7
+        assert (plot_df["cap"] > plot_df["floor"]).all()
+