1.修改设备LTV计算公式
2.修改多次付费占比公式 3.优化超限范围的异常问题 4.修改下载多次付费占比数据没有百分比的问题 5.优化看板设置调整后提示文本异常问题
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@ -46,9 +46,10 @@ async def get_permissions_for_user_in_domain(
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"""
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获取域内用户或角色的权限
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"""
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#data为列表
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data = casbin_enforcer.get_permissions_for_user_in_domain(data_in.role_id, data_in.game)
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paths = {i[2] for i in data}
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#列表形式的coll_name
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all_api = await crud.api_list.all_api(db)
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for item in all_api:
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if item['path'] in paths:
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@ -151,10 +151,11 @@ async def event_model(
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res = []
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is_hide = []
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for idx, item in enumerate(sqls):
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for idx, item in enumerate(sqls): #列出索引下标
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if item.get('is_show') == False:
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is_hide.append(idx)
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#event_name:事件名，日充总额
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#format：float浮点型
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q = {
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'groups': [],
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'values': [],
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@ -167,7 +168,7 @@ async def event_model(
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'end_date': item['end_date'],
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'time_particle': item['time_particle']
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}
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# 处理组合问题
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# 处理组合问题，如combination_event不存在则跳过
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if item.get('combination_event'):
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combination_event = CombinationEvent(res, item.get('combination_event'), item['format'])
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values, sum_, avg = combination_event.parse()
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@ -181,13 +182,14 @@ async def event_model(
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break
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res.append(q)
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continue
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#sql语句
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sql = item['sql']
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groupby = item['groupby']
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date_range = item['date_range']
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q['date_range'] = date_range
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df = await ckdb.query_dataframe(sql)
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df.fillna(0, inplace=True)
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date_range = item['date_range'] #获取的要查询的每一天的时间
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q['date_range'] = date_range #把要查询的时间加入q字典中
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df = await ckdb.query_dataframe(sql) #以sql语句查出数据，df是二维列表
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df.fillna(0, inplace=True)#以0填补空数据
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#获取第一矩阵的长度
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if df.shape[0] == 0:
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df = pd.DataFrame({'date': date_range, 'values': 0 * len(date_range)})
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# continue
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@ -214,6 +216,7 @@ async def event_model(
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if groupby and (set(groupby) & set(df)) == set(groupby):
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# 有分组
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for group, df_group in df.groupby(groupby):
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#在原数据上将索引重新转换为列，新索引的列删除
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df_group.reset_index(drop=True, inplace=True)
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q['groups'].append(str(group))
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@ -238,7 +241,9 @@ async def event_model(
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concat_data = []
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for i in set(date_range) - set(df['date']):
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concat_data.append((i, 0))
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#纵向拼接两个表
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df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
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#在原数据上按data排序
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df.sort_values('date', inplace=True)
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if len(df) >= 2:
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q['chain_ratio'] = division((df.iloc[-1, 1] - df.iloc[-2, 1]) * 100, df.iloc[-2, 1], 2)
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@ -249,7 +254,9 @@ async def event_model(
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if last_value > 0:
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q['last_value'] = float(last_value)
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break
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#求所有值的和
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q['sum'].append(round(float(df['values'].sum()), 2))
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#求平均值
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q['avg'].append(round(float(df['values'].mean()), 2))
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# q['eventNameDisplay']=item['event_name_display']
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@ -257,7 +264,7 @@ async def event_model(
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# 按总和排序
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for item in res:
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try:
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if item['time_particle'] in ('P1D', 'P1W'):
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if item['time_particle'] in ('P1D', 'P1W'): #按格式修改年月日
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item['date_range'] = [d.strftime('%Y-%m-%d') for d in item['date_range']]
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elif item['time_particle'] in ('P1M',):
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item['date_range'] = [d.strftime('%Y-%m') for d in item['date_range']]
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@ -266,7 +273,7 @@ async def event_model(
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except:
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pass
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sort_key = np.argsort(np.array(item['sum']))[::-1]
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sort_key = np.argsort(np.array(item['sum']))[::-1]#将sum中的元素从小到大排列后的结果，提取其对应的索引，然后倒着输出到变量之中
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if item.get('groups'):
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item['groups'] = np.array(item['groups'])[sort_key].tolist()
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item['values'] = np.array(item['values'])[sort_key].tolist()
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@ -299,17 +306,17 @@ async def retention_model(request: Request,
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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await analysis.init(data_where=current_user.data_where)
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res = await analysis.retention_model_sql2()
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sql = res['sql']
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res = await analysis.retention_model_sql2() #初始化开始时间结束时间，sql语句 字典
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sql = res['sql'] #获取到sql语句
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='无数据', data=None)
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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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date_range = res['date_range'] #时间 列表
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unit_num = res['unit_num'] #int
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retention_n = res['retention_n'] #列表 int
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filter_item_type = res['filter_item_type'] #all
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filter_item = res['filter_item'] #列表 0,1,3,7,14,21,30
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df.set_index('reg_date', inplace=True)
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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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@ -320,6 +327,7 @@ async def retention_model(request: Request,
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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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#字典中data存在时不替换，否则将data替换成空字典
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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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@ -516,6 +524,7 @@ async def funnel_model(
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await analysis.init(data_where=current_user.data_where)
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res = await analysis.funnel_model_sql()
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sql = res['sql']
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#查询的时间
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date_range = res['date_range']
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cond_level = res['cond_level']
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groupby = res['groupby']
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@ -530,6 +539,7 @@ async def funnel_model(
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for item in not_exists_level:
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key = key if isinstance(key, tuple) else (key,)
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concat_data.append((*key, item, 0))
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#合并数据
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df = pd.concat([df, pd.DataFrame(concat_data, columns=df.columns)])
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# df.set_index('date',inplace=True)
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@ -539,7 +549,9 @@ async def funnel_model(
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return schemas.Msg(code=0, msg='ok', data={'list': data_list, 'level': cond_level})
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tmp = {'title': '总体'}
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#以level分组后的和
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tmp_df = df[['level', 'values']].groupby('level').sum()
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#在原数据上对索引进行排序
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tmp_df.sort_index(inplace=True)
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for i in tmp_df.index:
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tmp_df.loc[i, 'values'] = tmp_df.loc[i:]['values'].sum()
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@ -785,10 +797,18 @@ async def scatter_model(
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await analysis.init(data_where=current_user.data_where)
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res = await analysis.scatter_model_sql()
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sql = res['sql']
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#查询买量渠道owner为kuaiyou3的日注册玩家等级分布
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# sql_list=sql.split("GROUP BY")
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# sql01 = """and xiangsu.event.owner_name='kuaiyou3'GROUP BY"""""
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# new_sql=sql_list[0]+sql01+sql_list[1]
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='无数据', data=None)
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df.fillna(0, inplace=True)
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#转换数据类型为int
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df['values'] = df['values'].astype(int)
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interval_type = res['interval_type']
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analysis = res['analysis']
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@ -866,14 +886,37 @@ async def scatter_model(
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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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for key2, tmp_df2 in tmp_df.groupby('values'):
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label = str(key2)
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n = len(tmp_df2)
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labels_dict[label] = n
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resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
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'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
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#如需要2之后所有之和，则执行下面代码，返回值为字典的labels_dict01
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labels_dict01={}
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v=-1
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for i in labels:
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v +=1
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if int(i) == 1:
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labels_dict01["1"]=labels_dict["1"]
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else:
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# for number in labels_dict.keys():
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# if number >=i:
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values=list(labels_dict.values())
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n=sum(values[v:])
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labels_dict01[i]=n
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#传入百分比数据
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list_p=[]
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for i in labels:
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number_int=round(labels_dict01.get(i, 0) * 100 / total, 2)
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number_str=str(number_int)+'%'
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list_p.append(number_str)
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resp['list'][dt] = {'总体': {'n': [labels_dict01.get(i, 0) for i in labels], 'total': total,
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'p': list_p}}
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# resp['list'][dt] = {'总体': {'n': [labels_dict.get(i, 0) for i in labels], 'total': total,
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# 'p': [round(labels_dict.get(i, 0) * 100 / total, 2) for i in labels]}}
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return schemas.Msg(code=0, msg='ok', data=resp)
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# bins_s = pd.cut(tmp_df['values'], bins=bins,
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@ -40,7 +40,8 @@ async def edit(
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"""编辑报表"""
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res = await crud.report.update_one(db, {'_id': data_in.report_id, 'user_id': request.user.id},
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{'$set': {'query': data_in.query, 'name': data_in.name, 'desc': data_in.desc}})
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if not res.matched_count:
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#if not res.matched_count:
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if res.matched_count:
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return schemas.Msg(code=-1, msg='只能报表所有者编辑')
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return schemas.Msg(code=0, msg='ok', data='编辑成功')
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@ -1,11 +1,14 @@
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import datetime
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import mimetypes
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from collections import defaultdict
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import time
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from urllib.parse import quote
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import pandas as pd
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import numpy as np
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from fastapi import APIRouter, Depends, Request
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from motor.motor_asyncio import AsyncIOMotorDatabase
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from pandas import DataFrame
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from starlette.responses import StreamingResponse
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import crud, schemas
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@ -50,50 +53,105 @@ async def ltv_model_sql(
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res = analysis.ltv_model_sql()
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sql = res['sql']
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quota = res['quota']
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quota = res['quota'] #字段名
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ltv_n = res['ltv_n']
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='查无数据')
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df.fillna(0, inplace=True)
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df.fillna(0, inplace=True) #修改原对象，以0填补空缺值
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# for d in set(res['date_range']) - set(df['date']): # 时间的差集运算 最后为空
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# df.loc[len(df)] = 0
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# df.loc[len(df) - 1, 'date'] = d
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# days = (pd.Timestamp.now().date() - d).days # 时间差
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# # if days + 2 >= ltv_len:
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# # continue
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# df.iloc[len(df) - 1, days + 3:] = '-'
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# df.sort_values('date', inplace=True) # 根据date进行倒叙排序
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for d in set(res['date_range']) - set(df['date']):
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df.loc[len(df)] = 0
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#在有效日期最后一行补充行数据（值都为0），补充的行数为两个集合的差集长度
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df.loc[len(df)] = '-'
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#在date此列补充多行数据（值为两个集合差集的子元素）
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df.loc[len(df) - 1, 'date'] = d
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days = (pd.Timestamp.now().date() - d).days
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# if days + 2 >= ltv_len:
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# continue
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df.iloc[len(df) - 1, days+3:] = '-'
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# days = (d-pd.Timestamp.now().date()).days
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# # if days + 2 >= ltv_len:
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# # continue
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# if days>0:
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# df.iloc[len(df) - 1, 1:] = '-'
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df.sort_values('date', inplace=True)
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df.rename(columns={'date': '注册日期'}, inplace=True)
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df.rename(columns={'date': '注册日期'}, inplace=True) #True为将结果返回赋值给原变量，修改原对象，columns为列名
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cat = '角色数'
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if quota == '#distinct_id':
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if quota == '#distinct_id': #如果字段名=字段名
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cat = '设备数'
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df.rename(columns={'cnt1': cat}, inplace=True)
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df1 = df[['注册日期', cat, *[f'LTV{i}' for i in ltv_n]]]
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df.rename(columns={'cnt1': cat}, inplace=True) #原数据基础上修改df里面列名为cnt1为设备数
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df1 = df[['注册日期', cat, *[f'LTV{i}' for i in ltv_n]]] #1, 2, 3, 4, 5, 6, 7, 8, 9, ~~到360
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df2 = df[['注册日期', cat, *[f'sumpay_{i}' for i in ltv_n]]]
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df2.replace('-', 0, inplace=True)
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avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
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df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
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df2.replace('-', 0, inplace=True) #True改变原数据，前面是需要替换的值，后面是替换后的值。 在原数据把下划线替换成0
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#修改下面代码
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#求相差天数
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str_time = str(res['date_range'][0])
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split_time = str_time.split('-')
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now_time = time.strftime("%Y-%m-%d", time.localtime())
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split_now_time = now_time.split('-')
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today = datetime.datetime(int(split_time[0]), int(split_time[1]), int(split_time[2]))
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now_day = datetime.datetime(int(split_now_time[0]), int(split_now_time[1]), int(split_now_time[2]))
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newday = (now_day - today).days + 1
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#计算方法运算每个LTV的均值
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_listData = {}
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for i in ltv_n:
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if i <=newday:
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avgLtv=(df2[[f'sumpay_{i}']][0:newday+1-i].sum()/df2[cat][0:newday+1-i].sum()).round(2)
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new_avgLtv=str(avgLtv).split('\n')[0].split(' ')
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new_avgLtv01=new_avgLtv[len(new_avgLtv)-1]
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_listData[f'sumpay_{i}'] = new_avgLtv01
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else:
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_listData[f'sumpay_{i}']=0
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avgLtvlist = pd.Series(_listData)
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_listname=[]
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#计算总累计LTV最后一个值
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for k, v in _listData.items():
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if v !=0:
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_listname.append(k)
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max_nmu=max(_listname)
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max_num=(df2[[max_nmu]].sum()/df2[cat].sum()).round(2)
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max_number=str(max_num[0])
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df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avgLtvlist]
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# avg_ltv = (df2[[f'sumpay_{i}' for i in ltv_n]].sum() / df2[cat].sum()).round(2)
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#df1.loc[len(df1)] = ['均值', df2[cat].sum(), *avg_ltv]
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df1.insert(2, '累计LTV', 0)
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last_ltv = []
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for items in df1.values:
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for item in items[::-1]:
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if item != '-':
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last_ltv.append(item)
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break
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df1['累计LTV'] = last_ltv
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days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
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#修改累计LTV中最后一个值
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last_ltv[-1]=max_number
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df1['累计LTV'] = last_ltv
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#把列中累计LTV等于0的值改成'-'
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df1.loc[df1['累计LTV']==0, '累计LTV'] = '-'
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#剔除行，列的累计LTV=='-'的剔除出去
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df3 = df1.drop(df1[(df1.累计LTV=='-')].index)
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days = (pd.Timestamp.now().date() - pd.to_datetime(res['start_date']).date()).days
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df1.iloc[len(df1) - 1, days + 4:] = '-'
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data = {
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'title': df1.columns.tolist(),
|
||||
'rows': df1.values.tolist(),
|
||||
#'title': df1.columns.tolist(),
|
||||
#'rows': df1.values.tolist(),
|
||||
'title': df3.columns.tolist(),
|
||||
'rows': df3.values.tolist(),
|
||||
|
||||
'start_date': res['start_date'],
|
||||
'end_date': res['end_date']
|
||||
}
|
||||
|
||||
1
main.py
1
main.py
@ -115,4 +115,5 @@ async def add_process_time_header(request: Request, call_next):
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
#uvicorn.run(app='main:app', host="10.0.0.240", port=7899, reload=True, debug=True)
|
||||
uvicorn.run(app='main:app', host="0.0.0.0", port=7899, reload=True, debug=True)
|
||||
@ -32,7 +32,7 @@ class XAnalysis:
|
||||
self.ext_filters = (self.data_in.ext_filter.get('filts', []), self.data_in.ext_filter.get('relation', 'and'))
|
||||
|
||||
def _get_global_filters(self):
|
||||
return self.event_view.get('filts') or []
|
||||
return self.event_view.get('filts') or [] #获取event_view字典里面filts的值，或返回空列表
|
||||
|
||||
async def init(self, *args, **kwargs):
|
||||
if self.data_in.report_id:
|
||||
@ -152,11 +152,12 @@ class XAnalysis:
|
||||
]
|
||||
if quota == '#distinct_id':
|
||||
where.append(sa.Column('is_new_device') == 1)
|
||||
|
||||
qry = sa.select().where(*where)
|
||||
sql = str(qry.compile(compile_kwargs={"literal_binds": True}))
|
||||
where_str = sql.split('WHERE ')[1]
|
||||
|
||||
where_order = self.handler_filts((self.global_filters, self.global_relation))
|
||||
where_order = self.handler_filts((self.global_filters, self.global_relation)) #global_relation就是 and
|
||||
where_order_str = 1
|
||||
if where_order:
|
||||
qry = sa.select().where(*where_order)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user