留存时长修复
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wuaho
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0cadeb283a
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@ -287,46 +287,54 @@ async def retention_model(request: Request,
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res = await analysis.retention_model_sql2()
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sql = res['sql']
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df = await ckdb.query_dataframe(sql)
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if len(df) == 0:
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if df.empty:
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return schemas.Msg(code=0, msg='无数据', data=None)
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title = f'用户数'
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date_range = res['date_range']
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unit_num = res['unit_num']
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retention_n = res['retention_n']
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filter_item_type = res['filter_item_type']
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filter_item = res['filter_item']
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df.set_index('reg_date', inplace=True)
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for i in set(date_range) - set(df.index):
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df.loc[i] = 0
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df.sort_index(inplace=True)
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days = [i for i in range(1, unit_num )]
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summary_values = {}
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today = datetime.datetime.today().date()
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for d in set(res['date_range']) - set(df.index):
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df.loc[d] = 0
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for date, value in df.T.items():
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tmp = summary_values.setdefault(date.strftime('%Y-%m-%d'), dict())
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tmp['d0'] = int(value.cnt0)
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df.sort_index(inplace=True)
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summary_values = {'均值': {}}
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max_retention_n = 1
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avg = {}
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avgo = {}
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for date, v in df.T.items():
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tmp = summary_values.setdefault(date, dict())
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tmp['d0'] = int(v.cnt0)
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tmp['p'] = []
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tmp['n'] = []
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tmp['p_outflow'] = []
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tmp['n_outflow'] = []
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for i in range(1, (today - date).days):
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if i >= unit_num:
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break
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p = float(getattr(value, f'p{i + 1}'))
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n = int(getattr(value, f'cnt{i + 1}'))
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p_outflow = round(100 - p, 2)
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n_outflow = value.cnt0 - n
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tmp['p'].append(p)
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tmp['n'].append(n)
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tmp['p_outflow'].append(p_outflow)
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tmp['n_outflow'].append(n_outflow)
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for i in retention_n:
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n = (pd.Timestamp.now().date() - date).days
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if i > n:
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continue
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max_retention_n = i if i > max_retention_n else max_retention_n
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avg[i] = avg.setdefault(i, 0) + v[f'cnt{i}']
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avgo[i] = avgo.setdefault(i, 0) + v[f'on{i}']
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tmp['p'].append(v[f'p{i}'])
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tmp['n'].append(v[f'cnt{i}'])
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tmp['p_outflow'].append(v[f'op{i}'])
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tmp['n_outflow'].append(v[f'on{i}'])
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tmp = summary_values['均值']
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tmp['d0'] = int(df['cnt0'].sum())
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tmp['p'] = (pd.Series(avg) * 100 / tmp['d0']).round(2).tolist()
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tmp['n'] = pd.Series(avg).values.tolist()
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tmp['p_outflow'] = (pd.Series(avgo) * 100 / tmp['d0']).round(2).tolist()
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tmp['n_outflow'] = pd.Series(avgo).values.tolist()
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title = ['日期', '用户数', '次留', *[f'{i}留' for i in retention_n[1:] if i <= max_retention_n]]
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resp = {
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'summary_values': summary_values,
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# 'values': values,
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'days': days,
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'date_range': [d.strftime('%Y-%m-%d') for d in date_range][:unit_num + 1],
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'date_range': [d.strftime('%Y-%m-%d') for d in date_range],
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'title': title,
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'filter_item_type': filter_item_type,
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'filter_item': filter_item,
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@ -757,7 +765,6 @@ async def scatter_model(
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resp['label'].append(f'[{quota_interval_arr[i]},{v})')
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bins.append(v)
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# 这是整体的
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for key, tmp_df in df.groupby('date'):
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bins_s = pd.cut(tmp_df['values'], bins=bins,
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@ -784,7 +791,8 @@ async def scatter_model(
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title = '.'.join(key[1:])
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date = key[0]
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resp['list'][date.strftime('%Y-%m-%d')][title] = {'n': bins_s.to_list(), 'total': total,
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'p': round((bins_s * 100 / total).fillna(0), 2).to_list(),
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'p': round((bins_s * 100 / total).fillna(0),
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2).to_list(),
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'title': title
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}
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return schemas.Msg(code=0, msg='ok', data=resp)
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@ -801,7 +809,7 @@ async def scatter_model(
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for key, tmp_df in df.groupby(['date']):
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total = len(tmp_df)
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if res['time_particle'] == 'total':
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dt='合计'
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dt = '合计'
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else:
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dt = key.strftime('%Y-%m-%d')
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labels_dict = {}
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@ -772,10 +772,21 @@ ORDER BY values desc"""
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days = (arrow.get(self.end_date).date() - arrow.get(self.start_date).date()).days
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keep = []
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cnt = []
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for i in range(1, days + 1):
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retention_n = [*[k for k in range(1, 31)], 45, 60, 90, 120, 180, 240, 300, 360]
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"""
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cnt0-cnt1 as on1,
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round(on1 * 100 / cnt0, 2) as `0p1`,
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"""
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for i in retention_n:
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keep.append(
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f"""cnt{i + 1},round(cnt{i + 1} * 100 / cnt0, 2) as `p{i + 1}`""")
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cnt.append(f"""sum(if(dateDiff('day',a.reg_date,b.visit_date)={i},1,0)) as cnt{i + 1}""")
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f"""cnt{i},
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round(cnt{i} * 100 / cnt0, 2) as `p{i}`,
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cnt0-cnt{i} as on{i},
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round(on{i} * 100 / cnt0, 2) as `op{i}`
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""")
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cnt.append(f"""sum(if(dateDiff('day',a.reg_date,b.visit_date)={i},1,0)) as cnt{i}""")
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keep_str = ','.join(keep)
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cnt_str = ','.join(cnt)
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@ -797,7 +808,7 @@ group by date) reg left join
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(select a.reg_date,
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{cnt_str}
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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
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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
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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
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a.visit = b.visit
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group by a.reg_date) log on reg.date=log.reg_date
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"""
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@ -806,6 +817,7 @@ group by a.reg_date) log on reg.date=log.reg_date
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'sql': sql,
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'date_range': self.date_range,
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'unit_num': self.unit_num,
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'retention_n':retention_n,
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'filter_item_type': filter_item_type,
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'filter_item': filter_item,
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'time_particle': self.time_particle,
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