Add DROPPED_AES to suppress expected dropped-aesthetic warnings

Declares which aesthetics each stat is expected to discard during
processing so the warning introduced in 315abf1 only fires for
unexpected drops.

closes #1049

Author: has2k1

plotnine/stats/stat.py

@@ -57,6 +57,15 @@ class stat(ABC, metaclass=Register):
     DEFAULT_PARAMS: dict[str, Any] = {}
     """Required parameters for the stat"""
 
+    DROPPED_AES: list[str] = []
+    """
+    Aesthetics that may be dropped during processing.
+
+    These are typically common aesthetics that a particular stat does not use.
+    If a value is mapped to any of these aesthetics, the stat may discard them
+    silently (without issuing a warning).
+    """
+
     CREATES: set[str] = set()
     """
     Stats may modify existing columns or create extra
@@ -315,7 +324,9 @@ def compute_panel(self, data: pd.DataFrame, scales: pos_scales):
             stats.append(group_result)
 
         stats = pd.concat(stats, axis=0, ignore_index=True)
-        dropped = data.columns.difference(stats.columns).to_list()
+        dropped = data.columns.difference(
+            stats.columns.union(self.DROPPED_AES)
+        ).to_list()
         if dropped:
             warn(DROPPED_TPL.format(dropped=dropped))
         # Note: If the data coming in has columns with non-unique

plotnine/stats/stat_bin.py

@@ -87,6 +87,7 @@ class stat_bin(stat):
     }
     DEFAULT_AES = {"y": after_stat("count"), "weight": None}
     CREATES = {"width", "count", "density", "ncount", "ndensity", "ngroup"}
+    DROPPED_AES = ["weight"]
 
     def setup_params(self, data):
         params = self.params

plotnine/stats/stat_bin_2d.py

@@ -66,6 +66,7 @@ class stat_bin_2d(stat):
     }
     DEFAULT_AES = {"fill": after_stat("count"), "weight": None}
     CREATES = {"xmin", "xmax", "ymin", "ymax", "count", "density"}
+    DROPPED_AES = ["weight"]
 
     def setup_params(self, data):
         params = self.params

plotnine/stats/stat_bindot.py

@@ -103,6 +103,7 @@ class stat_bindot(stat):
     }
     DEFAULT_AES = {"y": after_stat("count")}
     CREATES = {"width", "count", "density", "ncount", "ndensity"}
+    DROPPED_AES = ["weight", "bin", "bincenter"]
 
     def setup_params(self, data):
         params = self.params

plotnine/stats/stat_boxplot.py

@@ -80,6 +80,7 @@ class stat_boxplot(stat):
         "relvarwidth",
         "n",
     }
+    DROPPED_AES: list[str] = ["x", "y", "weight"]
 
     def setup_data(self, data):
         if "x" not in data:

plotnine/stats/stat_count.py

@@ -47,6 +47,7 @@ class stat_count(stat):
     }
     DEFAULT_AES = {"y": after_stat("count")}
     CREATES = {"count", "prop"}
+    DROPPED_AES = ["weight"]
 
     def setup_params(self, data):
         if self.params["width"] is None:

plotnine/stats/stat_density.py

@@ -123,6 +123,7 @@ class stat_density(stat):
     }
     DEFAULT_AES = {"y": after_stat("density")}
     CREATES = {"density", "count", "scaled", "n"}
+    DROPPED_AES = ["weight"]
 
     def setup_params(self, data):
         params = self.params

plotnine/stats/stat_ecdf.py

@@ -43,6 +43,7 @@ class stat_ecdf(stat):
     DEFAULT_PARAMS = {"geom": "step", "n": None, "pad": True}
     DEFAULT_AES = {"y": after_stat("ecdf")}
     CREATES = {"ecdf"}
+    DROPPED_AES = ["weight"]
 
     def compute_group(self, data, scales):
         from statsmodels.distributions.empirical_distribution import ECDF

plotnine/stats/stat_ellipse.py

@@ -50,6 +50,7 @@ class stat_ellipse(stat):
         "level": 0.95,
         "segments": 51,
     }
+    DROPPED_AES = ["weight"]
 
     def compute_group(self, data, scales):
         import scipy.stats as stats

plotnine/stats/stat_qq_line.py

@@ -62,6 +62,7 @@ class stat_qq_line(stat):
         "fullrange": False,
     }
     CREATES = {"x", "y"}
+    DROPPED_AES = ["sample"]
 
     def setup_params(self, data):
         if len(self.params["line_p"]) != 2: