单事件属性漏斗概率

api/api_v1/endpoints/query.py

@@ -1061,7 +1061,8 @@ async def funnel_model(
     date_range = res['date_range']
     cond_level = res['cond_level']
     groupby = res['groupby']
-
+    switch_test = analysis.event_view.switchTest
+    if switch_test:
         df = await ckdb.query_dataframe(sql)
         if df.empty:
             return schemas.Msg(code=-9, msg='无数据', data=None)
@@ -1190,6 +1191,56 @@ async def funnel_model(
                 'date_data': date_data,
                 'title': title,
                 'level': cond_level,
+                'switch_test': switch_test,
+                'start_date': res['start_date'],
+                'end_date': res['end_date'],
+                'time_particle': res['time_particle']
+                }
+        return schemas.Msg(code=0, msg='ok', data=resp)
+    else:
+        try:
+            res = await analysis.guide_model_sql()
+        except Exception as e:
+            return schemas.Msg(code=-9, msg='报表配置参数异常')
+
+        sql = res['sql']
+        df = await ckdb.query_dataframe(sql)
+        if df.empty:
+            return schemas.Msg(code=-9, msg='无数据', data=None)
+
+        group_str = res['analysis']
+        # 转int
+        df[group_str] = df[group_str].astype(int)
+        step_list = [str(i) for i in sorted(df[group_str].unique())]
+        dict_k = {}
+        for k, nedf in df.groupby("date"):
+            ste_k = {}
+            for kk, ste_df in nedf.groupby(group_str):
+                value_list = ste_df.iloc[:, -1].to_list()
+                ste_k[str(kk)] = int(sum(value_list))
+            for ste in step_list:
+                if ste not in list(ste_k.keys()):
+                    ste_k[ste] = 0
+            dict_k[str(k)] = ste_k
+        p_data = {}
+        data = {}
+        for dict_key, dict_data in dict_k.items():
+            dict_data1 = deepcopy(dict_data)
+            dict_k1 = {int(k): v for k, v in dict_data1.items()}
+            sorted(dict_k1.keys())
+            data_values = list(dict_k1.values())
+            p_values = [round(i / sum(data_values), 2) or 0 for i in data_values]
+            p_values.insert(0, dict_key)
+            data_values.insert(0, dict_key)
+            data[dict_key] = data_values
+            p_data[dict_key] = p_values
+
+        step_list.insert(0, '日期')
+        resp = {'list': data,
+                'date_data': p_data,
+                'title': '1',
+                'level': step_list,
+                'switch_test': switch_test,
                 'start_date': res['start_date'],
                 'end_date': res['end_date'],
                 'time_particle': res['time_particle']