feat: add rolling window support to 'Big Number with Trendline' viz (a…

…pache#9107)

* Rolling big number

* addressing comment

superset-frontend/src/explore/controlPanels/BigNumber.js

@@ -17,6 +17,7 @@
  * under the License.
  */
 import { t } from '@superset-ui/translation';
+import React from 'react';
 
 export default {
   controlPanelSections: [
@@ -43,6 +44,14 @@ export default {
         ['subheader_font_size'],
       ],
     },
+    {
+      label: t('Advanced Analytics'),
+      expanded: false,
+      controlSetRows: [
+        [<h1 className="section-header">{t('Rolling Window')}</h1>],
+        ['rolling_type', 'rolling_periods', 'min_periods'],
+      ],
+    },
   ],
   controlOverrides: {
     y_axis_format: {

superset-frontend/src/explore/controlPanels/sections.jsx

@@ -75,7 +75,7 @@ export const NVD3TimeSeries = [
         'of query results',
     ),
     controlSetRows: [
-      [<h1 className="section-header">{t('Moving Average')}</h1>],
+      [<h1 className="section-header">{t('Rolling Window')}</h1>],
       ['rolling_type', 'rolling_periods', 'min_periods'],
       [<h1 className="section-header">{t('Time Comparison')}</h1>],
       ['time_compare', 'comparison_type'],

superset-frontend/src/explore/controls.jsx

@@ -1126,7 +1126,7 @@ export const controls = {
 
   rolling_type: {
     type: 'SelectControl',
-    label: t('Rolling'),
+    label: t('Rolling Function'),
     default: 'None',
     choices: formatSelectOptions(['None', 'mean', 'sum', 'std', 'cumsum']),
     description: t(

superset/examples/birth_names.py

@@ -106,22 +106,23 @@ def load_birth_names(only_metadata=False, force=False):
     obj.fetch_metadata()
     tbl = obj
 
+    metrics = [
+        {
+            "expressionType": "SIMPLE",
+            "column": {"column_name": "num", "type": "BIGINT"},
+            "aggregate": "SUM",
+            "label": "Births",
+            "optionName": "metric_11",
+        }
+    ]
+    metric = "sum__num"
+
     defaults = {
         "compare_lag": "10",
         "compare_suffix": "o10Y",
         "limit": "25",
         "granularity_sqla": "ds",
         "groupby": [],
-        "metric": "sum__num",
-        "metrics": [
-            {
-                "expressionType": "SIMPLE",
-                "column": {"column_name": "num", "type": "BIGINT"},
-                "aggregate": "SUM",
-                "label": "Births",
-                "optionName": "metric_11",
-            }
-        ],
         "row_limit": config["ROW_LIMIT"],
         "since": "100 years ago",
         "until": "now",
@@ -144,14 +145,17 @@ def load_birth_names(only_metadata=False, force=False):
                 granularity_sqla="ds",
                 compare_lag="5",
                 compare_suffix="over 5Y",
+                metric=metric,
             ),
         ),
         Slice(
             slice_name="Genders",
             viz_type="pie",
             datasource_type="table",
             datasource_id=tbl.id,
-            params=get_slice_json(defaults, viz_type="pie", groupby=["gender"]),
+            params=get_slice_json(
+                defaults, viz_type="pie", groupby=["gender"], metric=metric
+            ),
         ),
         Slice(
             slice_name="Trends",
@@ -165,6 +169,7 @@ def load_birth_names(only_metadata=False, force=False):
                 granularity_sqla="ds",
                 rich_tooltip=True,
                 show_legend=True,
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -215,6 +220,7 @@ def load_birth_names(only_metadata=False, force=False):
                 adhoc_filters=[gen_filter("gender", "girl")],
                 row_limit=50,
                 timeseries_limit_metric="sum__num",
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -231,6 +237,7 @@ def load_birth_names(only_metadata=False, force=False):
                 rotation="square",
                 limit="100",
                 adhoc_filters=[gen_filter("gender", "girl")],
+                metric=metric,
             ),
         ),
         Slice(
@@ -243,6 +250,7 @@ def load_birth_names(only_metadata=False, force=False):
                 groupby=["name"],
                 adhoc_filters=[gen_filter("gender", "boy")],
                 row_limit=50,
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -259,6 +267,7 @@ def load_birth_names(only_metadata=False, force=False):
                 rotation="square",
                 limit="100",
                 adhoc_filters=[gen_filter("gender", "boy")],
+                metric=metric,
             ),
         ),
         Slice(
@@ -276,6 +285,7 @@ def load_birth_names(only_metadata=False, force=False):
                 time_grain_sqla="P1D",
                 viz_type="area",
                 x_axis_forma="smart_date",
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -293,6 +303,7 @@ def load_birth_names(only_metadata=False, force=False):
                 time_grain_sqla="P1D",
                 viz_type="area",
                 x_axis_forma="smart_date",
+                metrics=metrics,
             ),
         ),
     ]
@@ -314,14 +325,15 @@ def load_birth_names(only_metadata=False, force=False):
                 },
                 metric_2="sum__num",
                 granularity_sqla="ds",
+                metrics=metrics,
             ),
         ),
         Slice(
             slice_name="Num Births Trend",
             viz_type="line",
             datasource_type="table",
             datasource_id=tbl.id,
-            params=get_slice_json(defaults, viz_type="line"),
+            params=get_slice_json(defaults, viz_type="line", metrics=metrics),
         ),
         Slice(
             slice_name="Daily Totals",
@@ -335,6 +347,7 @@ def load_birth_names(only_metadata=False, force=False):
                 since="40 years ago",
                 until="now",
                 viz_type="table",
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -397,6 +410,7 @@ def load_birth_names(only_metadata=False, force=False):
             datasource_id=tbl.id,
             params=get_slice_json(
                 defaults,
+                metrics=metrics,
                 groupby=["name"],
                 row_limit=50,
                 timeseries_limit_metric={
@@ -417,6 +431,7 @@ def load_birth_names(only_metadata=False, force=False):
             datasource_id=tbl.id,
             params=get_slice_json(
                 defaults,
+                metric=metric,
                 viz_type="big_number_total",
                 granularity_sqla="ds",
                 adhoc_filters=[gen_filter("gender", "girl")],
@@ -429,7 +444,11 @@ def load_birth_names(only_metadata=False, force=False):
             datasource_type="table",
             datasource_id=tbl.id,
             params=get_slice_json(
-                defaults, viz_type="pivot_table", groupby=["name"], columns=["state"]
+                defaults,
+                viz_type="pivot_table",
+                groupby=["name"],
+                columns=["state"],
+                metrics=metrics,
             ),
         ),
     ]

superset/examples/world_bank.py

@@ -97,31 +97,32 @@ def load_world_bank_health_n_pop(
     db.session.commit()
     tbl.fetch_metadata()
 
+    metric = "sum__SP_POP_TOTL"
+    metrics = ["sum__SP_POP_TOTL"]
+    secondary_metric = {
+        "aggregate": "SUM",
+        "column": {
+            "column_name": "SP_RUR_TOTL",
+            "optionName": "_col_SP_RUR_TOTL",
+            "type": "DOUBLE",
+        },
+        "expressionType": "SIMPLE",
+        "hasCustomLabel": True,
+        "label": "Rural Population",
+    }
+
     defaults = {
         "compare_lag": "10",
         "compare_suffix": "o10Y",
         "limit": "25",
         "granularity_sqla": "year",
         "groupby": [],
-        "metric": "sum__SP_POP_TOTL",
-        "metrics": ["sum__SP_POP_TOTL"],
         "row_limit": config["ROW_LIMIT"],
         "since": "2014-01-01",
         "until": "2014-01-02",
         "time_range": "2014-01-01 : 2014-01-02",
         "markup_type": "markdown",
         "country_fieldtype": "cca3",
-        "secondary_metric": {
-            "aggregate": "SUM",
-            "column": {
-                "column_name": "SP_RUR_TOTL",
-                "optionName": "_col_SP_RUR_TOTL",
-                "type": "DOUBLE",
-            },
-            "expressionType": "SIMPLE",
-            "hasCustomLabel": True,
-            "label": "Rural Population",
-        },
         "entity": "country_code",
         "show_bubbles": True,
     }
@@ -207,6 +208,7 @@ def load_world_bank_health_n_pop(
                 viz_type="world_map",
                 metric="sum__SP_RUR_TOTL_ZS",
                 num_period_compare="10",
+                secondary_metric=secondary_metric,
             ),
         ),
         Slice(
@@ -264,6 +266,8 @@ def load_world_bank_health_n_pop(
                 groupby=["region", "country_name"],
                 since="2011-01-01",
                 until="2011-01-01",
+                metric=metric,
+                secondary_metric=secondary_metric,
             ),
         ),
         Slice(
@@ -277,6 +281,7 @@ def load_world_bank_health_n_pop(
                 until="now",
                 viz_type="area",
                 groupby=["region"],
+                metrics=metrics,
             ),
         ),
         Slice(
@@ -292,6 +297,7 @@ def load_world_bank_health_n_pop(
                 x_ticks_layout="staggered",
                 viz_type="box_plot",
                 groupby=["region"],
+                metrics=metrics,
             ),
         ),
         Slice(

superset/viz.py

@@ -178,6 +178,26 @@ def run_extra_queries(self):
         """
         pass
 
+    def apply_rolling(self, df):
+        fd = self.form_data
+        rolling_type = fd.get("rolling_type")
+        rolling_periods = int(fd.get("rolling_periods") or 0)
+        min_periods = int(fd.get("min_periods") or 0)
+
+        if rolling_type in ("mean", "std", "sum") and rolling_periods:
+            kwargs = dict(window=rolling_periods, min_periods=min_periods)
+            if rolling_type == "mean":
+                df = df.rolling(**kwargs).mean()
+            elif rolling_type == "std":
+                df = df.rolling(**kwargs).std()
+            elif rolling_type == "sum":
+                df = df.rolling(**kwargs).sum()
+        elif rolling_type == "cumsum":
+            df = df.cumsum()
+        if min_periods:
+            df = df[min_periods:]
+        return df
+
     def get_samples(self):
         query_obj = self.query_obj()
         query_obj.update(
@@ -1101,6 +1121,18 @@ def query_obj(self):
         self.form_data["metric"] = metric
         return d
 
+    def get_data(self, df: pd.DataFrame) -> VizData:
+        df = df.pivot_table(
+            index=DTTM_ALIAS,
+            columns=[],
+            values=self.metric_labels,
+            fill_value=0,
+            aggfunc=sum,
+        )
+        df = self.apply_rolling(df)
+        df[DTTM_ALIAS] = df.index
+        return super().get_data(df)
+
 
 class BigNumberTotalViz(BaseViz):
 
@@ -1225,23 +1257,7 @@ def process_data(self, df: pd.DataFrame, aggregate: bool = False) -> VizData:
             dfs.sort_values(ascending=False, inplace=True)
             df = df[dfs.index]
 
-        rolling_type = fd.get("rolling_type")
-        rolling_periods = int(fd.get("rolling_periods") or 0)
-        min_periods = int(fd.get("min_periods") or 0)
-
-        if rolling_type in ("mean", "std", "sum") and rolling_periods:
-            kwargs = dict(window=rolling_periods, min_periods=min_periods)
-            if rolling_type == "mean":
-                df = df.rolling(**kwargs).mean()
-            elif rolling_type == "std":
-                df = df.rolling(**kwargs).std()
-            elif rolling_type == "sum":
-                df = df.rolling(**kwargs).sum()
-        elif rolling_type == "cumsum":
-            df = df.cumsum()
-        if min_periods:
-            df = df[min_periods:]
-
+        df = self.apply_rolling(df)
         if fd.get("contribution"):
             dft = df.T
             df = (dft / dft.sum()).T