留存时长修复

api/api_v1/endpoints/query.py

@@ -287,46 +287,54 @@ async def retention_model(request: Request,
     res = await analysis.retention_model_sql2()
     sql = res['sql']
     df = await ckdb.query_dataframe(sql)
-    if len(df) == 0:
+    if df.empty:
         return schemas.Msg(code=0, msg='无数据', data=None)
 
-    title = f'用户数'
     date_range = res['date_range']
     unit_num = res['unit_num']
+    retention_n = res['retention_n']
     filter_item_type = res['filter_item_type']
     filter_item = res['filter_item']
     df.set_index('reg_date', inplace=True)
-    for i in set(date_range) - set(df.index):
+    for d in set(res['date_range']) - set(df.index):
-        df.loc[i] = 0
+        df.loc[d] = 0
-    df.sort_index(inplace=True)
-    days = [i for i in range(1, unit_num )]
-    summary_values = {}
-    today = datetime.datetime.today().date()
 
-    for date, value in df.T.items():
+    df.sort_index(inplace=True)
-        tmp = summary_values.setdefault(date.strftime('%Y-%m-%d'), dict())
+    summary_values = {'均值': {}}
-        tmp['d0'] = int(value.cnt0)
+    max_retention_n = 1
+    avg = {}
+    avgo = {}
+    for date, v in df.T.items():
+        tmp = summary_values.setdefault(date, dict())
+        tmp['d0'] = int(v.cnt0)
         tmp['p'] = []
         tmp['n'] = []
         tmp['p_outflow'] = []
         tmp['n_outflow'] = []
-        for i in range(1, (today - date).days):
+        for i in retention_n:
-            if i >= unit_num:
+            n = (pd.Timestamp.now().date() - date).days
-                break
+            if i > n:
-            p = float(getattr(value, f'p{i + 1}'))
+                continue
-            n = int(getattr(value, f'cnt{i + 1}'))
+            max_retention_n = i if i > max_retention_n else max_retention_n
-            p_outflow = round(100 - p, 2)
+            avg[i] = avg.setdefault(i, 0) + v[f'cnt{i}']
-            n_outflow = value.cnt0 - n
+            avgo[i] = avgo.setdefault(i, 0) + v[f'on{i}']
-            tmp['p'].append(p)
+            tmp['p'].append(v[f'p{i}'])
-            tmp['n'].append(n)
+            tmp['n'].append(v[f'cnt{i}'])
-            tmp['p_outflow'].append(p_outflow)
+            tmp['p_outflow'].append(v[f'op{i}'])
-            tmp['n_outflow'].append(n_outflow)
+            tmp['n_outflow'].append(v[f'on{i}'])
+    tmp = summary_values['均值']
+    tmp['d0'] = int(df['cnt0'].sum())
+    tmp['p'] = (pd.Series(avg) * 100 / tmp['d0']).round(2).tolist()
+    tmp['n'] = pd.Series(avg).values.tolist()
+    tmp['p_outflow'] = (pd.Series(avgo) * 100 / tmp['d0']).round(2).tolist()
+    tmp['n_outflow'] = pd.Series(avgo).values.tolist()
+
+    title = ['日期', '用户数', '次留', *[f'{i}留' for i in retention_n[1:] if i <= max_retention_n]]
 
     resp = {
         'summary_values': summary_values,
         # 'values': values,
-        'days': days,
+        'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
-        'date_range': [d.strftime('%Y-%m-%d') for d in date_range][:unit_num + 1],
         'title': title,
         'filter_item_type': filter_item_type,
         'filter_item': filter_item,
@@ -757,7 +765,6 @@ async def scatter_model(
                 resp['label'].append(f'[{quota_interval_arr[i]},{v})')
                 bins.append(v)
 
-
         # 这是整体的
         for key, tmp_df in df.groupby('date'):
             bins_s = pd.cut(tmp_df['values'], bins=bins,
@@ -784,7 +791,8 @@ async def scatter_model(
                 title = '.'.join(key[1:])
                 date = key[0]
                 resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
-                                                                  'p': round((bins_s * 100 / total).fillna(0), 2).to_list(),
+                                                                  'p': round((bins_s * 100 / total).fillna(0),
+                                                                             2).to_list(),
                                                                   'title': title
                                                                   }
         return schemas.Msg(code=0, msg='ok', data=resp)

models/behavior_analysis.py

@@ -772,10 +772,21 @@ ORDER BY values desc"""
         days = (arrow.get(self.end_date).date() - arrow.get(self.start_date).date()).days
         keep = []
         cnt = []
-        for i in range(1, days + 1):
+        retention_n = [*[k for k in range(1, 31)], 45, 60, 90, 120, 180, 240, 300, 360]
+
+        """
+               cnt0-cnt1 as on1,
+       round(on1 * 100 / cnt0, 2)   as `0p1`,
+        """
+
+        for i in retention_n:
             keep.append(
-                f"""cnt{i + 1},round(cnt{i + 1} * 100 / cnt0, 2) as `p{i + 1}`""")
+                f"""cnt{i},
-            cnt.append(f"""sum(if(dateDiff('day',a.reg_date,b.visit_date)={i},1,0)) as cnt{i + 1}""")
+                round(cnt{i} * 100 / cnt0, 2) as `p{i}`,
+                cnt0-cnt{i} as on{i},
+                round(on{i} * 100 / cnt0, 2)   as `op{i}`
+                """)
+            cnt.append(f"""sum(if(dateDiff('day',a.reg_date,b.visit_date)={i},1,0)) as cnt{i}""")
         keep_str = ','.join(keep)
         cnt_str = ','.join(cnt)
 
@@ -797,7 +808,7 @@ group by date) reg left join
 (select a.reg_date,
        {cnt_str}
 from (select date as reg_date, visit from {self.game}.event where `#event_name` = start_event and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_a} group by reg_date, visit) a
-         left join (select date as visit_date, visit from {self.game}.event where retuen_visit and addHours(`#event_time`, {self.zone_time}) >= start_data  and addHours(`#event_time`, {self.zone_time}) <= end_data and {where_b} group by visit_date, visit) b on
+         left join (select date as visit_date, visit from {self.game}.event where retuen_visit and addHours(`#event_time`, {self.zone_time}) >= start_data group by visit_date, visit) b on
 a.visit = b.visit
 group by a.reg_date) log on reg.date=log.reg_date
 """
@@ -806,6 +817,7 @@ group by a.reg_date) log on reg.date=log.reg_date
             'sql': sql,
             'date_range': self.date_range,
             'unit_num': self.unit_num,
+            'retention_n':retention_n,
             'filter_item_type': filter_item_type,
             'filter_item': filter_item,
             'time_particle': self.time_particle,