Avoid aggregating in lineplot when not necessary (#3081)

* Avoid aggregating in lineplot when not necessary

* Update release notes

doc/whatsnew/v0.12.1.rst

@@ -24,4 +24,6 @@ v0.12.1 (Unreleased)
 
 - |Fix| Fixed a regression in :func:`kdeplot` where passing `cmap` for an unfilled bivariate plot would raise an exception (:pr:`3065`).
 
+- |Fix| Addressed a performance regression in :func:`lineplot` with a large number of unique x values (:pr:`3081`).
+
 - |Build| Seaborn no longer contains doctest-style examples, simplifying the testing infrastructure (:pr:`3034`).

seaborn/relational.py

@@ -427,7 +427,10 @@ def plot(self, ax, kws):
                 sort_cols = [var for var in sort_vars if var in self.variables]
                 sub_data = sub_data.sort_values(sort_cols)
 
-            if self.estimator is not None:
+            if (
+                self.estimator is not None
+                and sub_data[orient].value_counts().max() > 1
+            ):
                 if "units" in self.variables:
                     # TODO eventually relax this constraint
                     err = "estimator must be None when specifying units"
@@ -436,6 +439,9 @@ def plot(self, ax, kws):
                 # Could pass as_index=False instead of reset_index,
                 # but that fails on a corner case with older pandas.
                 sub_data = grouped.apply(agg, other).reset_index()
+            else:
+                sub_data[f"{other}min"] = np.nan
+                sub_data[f"{other}max"] = np.nan
 
             # TODO this is pretty ad hoc ; see GH2409
             for var in "xy":

tests/test_relational.py

@@ -1070,6 +1070,15 @@ def test_plot(self, long_df, repeated_df):
         ax.clear()
         p.plot(ax, {})
 
+    def test_non_aggregated_data(self):
+
+        x = [1, 2, 3, 4]
+        y = [2, 4, 6, 8]
+        ax = lineplot(x=x, y=y)
+        line, = ax.lines
+        assert_array_equal(line.get_xdata(), x)
+        assert_array_equal(line.get_ydata(), y)
+
     def test_orient(self, long_df):
 
         long_df = long_df.drop("x", axis=1).rename(columns={"s": "y", "y": "x"})