Quant plot hotfixes

pyam/compute.py

@@ -25,7 +25,8 @@ def __init__(self, df):
         self._df = df
 
     def quantiles(
-        self, quantiles, weights=None, level=["model", "scenario"], append=False
+        self, quantiles, weights=None, level=["model", "scenario"], drop_zeros=False,
+        append=False
     ):
         """Compute the optionally weighted quantiles of data grouped by `level`.
 
@@ -44,6 +45,8 @@ def quantiles(
             Series indexed by `level`
         level : collection, optional
             The index columns to compute quantiles over
+        drop_zeros : bool, optional
+            Whether to drop data rows if all elements are 0
         append : bool, optional
             Whether to append computed timeseries data to this instance.
 
@@ -72,6 +75,8 @@ def quantiles(
             raise ValueError("weights pd.Series must have name 'weight'")
 
         df = self_df.timeseries()
+        if drop_zeros:
+            df = df[~(df == 0).all(axis=1)]
         model = (
             "Quantiles" if weights is None else "Weighted Quantiles"
         )  # can make this a kwarg

pyam/plotting.py

@@ -710,8 +710,7 @@ def box(df, y="value", x=None, by=None, legend=True, title=None, ax=None, **kwar
         if "palette" not in kwargs and "color" in rc and by in rc["color"]:
             # TODO this only works if all categories are defined in run_control
             palette = rc["color"][by]
-            df[by] = df[by].astype("category")
-            df[by].cat.set_categories(list(palette), inplace=True)
+            df[by] = df[by].astype("category").cat.set_categories(list(palette))
             kwargs["palette"] = palette
         else:
             df.sort_values(by, inplace=True)