Cleaner way of updating column names

Rename the column to what the previous code expected.

app.py

@@ -38,11 +38,12 @@ def hdi(values, ci=0.95):
 #
 def load(repo):
     parameter = 'parameter'
+    model = 'model'
     items = [
         'chain',
         'sample',
         parameter,
-        'parameter_value',
+        model,
         'value',
     ]
     dataset = load_dataset(repo)
@@ -50,6 +51,7 @@ def load(repo):
     return (dataset
             .get('train')
             .to_pandas()
+            .rename(columns={'parameter_value': model})
             .filter(items=items)
             .query(f'{parameter} == "alpha"')
             .drop(columns=parameter))
@@ -60,7 +62,7 @@ def summarize(df, ci=0.95):
         interval = hdi(values, ci)
 
         agg = {
-            'parameter_value': i,
+            'model': i,
             'ability': values.median(),
             'uncertainty': interval.upper - interval.lower,
         }
@@ -68,7 +70,7 @@ def summarize(df, ci=0.95):
 
         return agg
 
-    groups = df.groupby('parameter_value', sort=False)
+    groups = df.groupby('model', sort=False)
     records = it.starmap(_aggregate, groups)
 
     return pd.DataFrame.from_records(records)
@@ -84,7 +86,7 @@ def rank(df, ascending, name='rank'):
     return df.reset_index(names=name)
 
 def compare(df, model_1, model_2):
-    mcol = 'parameter_value'
+    mcol = 'model'
     models = [
         model_1,
         model_2,
@@ -141,7 +143,7 @@ class RankPlotter(DataPlotter):
                   xmax=self.df['upper'],
                   alpha=0.5)
         ax.set_ylabel('')
-        ax.set_yticks(self.y, self.df['parameter_value'])
+        ax.set_yticks(self.y, self.df['model'])
 
 class ComparisonPlotter(DataPlotter):
     def __init__(self, df, model_1, model_2, ci=0.95):
@@ -219,9 +221,8 @@ with gr.Blocks() as demo:
 
                 ''')
             with gr.Column():
-                models = df['parameter_value'].unique()
-                choices = sorted(models, key=lambda x: x.lower())
-                drops = ft.partial(gr.Dropdown, choices=choices)
+                models = sorted(df['model'].unique(), key=lambda x: x.lower())
+                drops = ft.partial(gr.Dropdown, choices=models)
                 inputs = [ drops(label=f'Model {x}') for x in range(1, 3) ]
 
                 button = gr.Button(value='Compare!')