Add PivotTable Transform (#630)

lumen/transforms/base.py

@@ -544,6 +544,42 @@ def apply(self, table: DataFrame) -> DataFrame:
         return pivot_table
 
 
+class PivotTable(Transform):
+    """
+    `PivotTable` applies pandas.pivot_table` to the data.
+    """
+
+    values = param.ListSelector(default=[], doc="""
+        Column or columns to aggregate.""")
+
+    index = param.ListSelector(default=[], doc="""
+        Column, Grouper, array, or list of the previous
+        Keys to group by on the pivot table index. If a list is passed,
+        it can contain any of the other types (except list). If an array is
+        passed, it must be the same length as the data and will be used in
+        the same manner as column values.""")
+
+    columns = param.ListSelector(default=[], doc="""
+        Column, Grouper, array, or list of the previous
+        Keys to group by on the pivot table column. If a list is passed,
+        it can contain any of the other types (except list). If an array is
+        passed, it must be the same length as the data and will be used in
+        the same manner as column values.""")
+
+    aggfunc = param.String(default="mean", doc="""
+        Function, list of functions, dict, default 'mean'""")
+
+    _field_params: ClassVar[List[str]] = ['values', 'index', 'columns']
+
+    def apply(self, table: DataFrame) -> DataFrame:
+        values = self.values if len(self.values) > 1 else self.values[0]
+        columns = self.columns if len(self.columns) > 1 else self.columns[0]
+        return pd.pivot_table(
+            table, values=values, index=self.index, columns=columns,
+            aggfunc=self.aggfunc
+        )
+
+
 class Melt(Transform):
     """
     `Melt` applies the `pandas.melt` operation given the `id_vars` and `value_vars`.