1.新增日充总额订单详情下载功能
2.新增分布分析-分组项可按自定义区间，默认区间进行分组
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@ -111,8 +111,36 @@ async def event_model_export(request: Request,
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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@router.post("/event_model_pay")
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async def event_model_export(request: Request,
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game: str,
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data_in: schemas.Times,
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ckdb: CKDrive = Depends(get_ck_db)
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):
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"""下载日充总额详细订单数据"""
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sql=f"""select * FROM {game}.event WHERE addHours(`#event_time`, 8) >= '{data_in.start_time}' and addHours(`#event_time`, 8) <= '{data_in.end_time}' and `#event_name` = 'pay' and
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orderid NOT LIKE '%GM%' order by `#event_time`"""
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df = await ckdb.query_dataframe(sql)
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list_columns=list(df.columns.values)
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drop_list=[]
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for i in list_columns:
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aa=type(df[i][0])
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if df[i][0] == None or df[i][0] == [] or df[i][0] == '':
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drop_list.append(i)
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else:
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if 'time' in i :
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df[i] = df[i].astype(str)
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for nu in range(len(df)):
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df.replace(to_replace=df[i][nu],value=df[i][nu].split('+')[0],inplace=True)
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df.drop(drop_list, axis=1, inplace=True)
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file_name=quote(f'订单详情.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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df_to_stream = DfToStream((df, '订单详情'))
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with df_to_stream as d:
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export = d.to_stream()
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return StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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#
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# @router.get("/event_model_export")
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# async def event_model_export(request: Request,
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# game: str,
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@ -1136,9 +1164,11 @@ async def scatter_model(
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res = await analysis.scatter_model_sql()
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except Exception as e:
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return schemas.Msg(code=-9, msg='报表配置参数异常')
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event_type = analysis.events[0]['eventName']
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where = analysis.events[-1]['quotaname']
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sql=res['sql']
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columnName = analysis.event_view['groupBy'][-1]['columnName']
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if analysis.event_view['groupBy'] != []:
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columnName=analysis.event_view['groupBy'][-1]['columnName']
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sql=sql.replace(f'toDate(addHours({game}.event."#event_time", 8)) AS date', f'max(`{columnName}`) as va', 1)
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sql=sql.replace(f'toDate(addHours({game}.event."#event_time", 8)),','',1)
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df = await ckdb.query_dataframe(sql)
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@ -1204,6 +1234,69 @@ async def scatter_model(
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'p': p,
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'title': '总体'}
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return schemas.Msg(code=0, msg='ok', data=resp)
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else:
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resp = {'list': {}, 'label': [],
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'start_date': res['start_date'],
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'end_date': res['end_date'],
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'time_particle': res['time_particle'],
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'biaotou':columnName
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}
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labels = [str(i) for i in sorted(df['values'].unique())]
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resp['label'] = labels
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for key, tmp_df in df.groupby(['va']):
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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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else:
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dt = key
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#dt = key.strftime('%Y-%m-%d')
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#dt='合计'
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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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if event_type == 'pay':
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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] = {'n': [labels_dict01.get(i, 0) for i in labels], 'total': total,
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'p': list_p}
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else:
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list_p = []
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for i in labels:
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number_int = round(labels_dict.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] = {'n': [labels_dict.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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if where == "step_id" and event_type == "guide":
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sql = f"""SELECT toDate(addHours({game}.event."#event_time", 8)) AS date, count(DISTINCT {game}.event."#account_id") AS values FROM {game}.event WHERE addHours({game}.event."#event_time", 8) >= '{start_date}' AND addHours({game}.event."#event_time", 8) <= '{end_date}' AND {game}.event."#event_name" = 'create_account' GROUP BY toDate(addHours({game}.event."#event_time", 8)) ORDER BY date"""
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df = await ckdb.query_dataframe(sql)
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for i in range(len(df)):
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resp['list'][str(df['date'][i])]['总体']['total'] = int(df['values'][i])
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return schemas.Msg(code=0, msg='ok', data=resp)
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else:
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return schemas.Msg(code=-9, msg='没有添加分组项', data='')
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@ -31,3 +31,7 @@ class Ck_solo_user(BaseModel):
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start_time: str # 开始时间 例：2022-04-02
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end_time: str # 结束时间
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event_list: List[Dict] =None#事件名
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class Times(BaseModel):
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start_time: str # 开始时间 例：2022-04-02 00:00:00
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end_time: str # 结束时间
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