267 lines
11 KiB
Python
267 lines
11 KiB
Python
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 re
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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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from common import *
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from api import deps
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from db import get_database
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from db.ckdb import get_ck_db, CKDrive
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from db.redisdb import get_redis_pool, RedisDrive
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from models.behavior_analysis import BehaviorAnalysis
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from models.user_analysis import UserAnalysis
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from models.x_analysis import XAnalysis
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from utils import DfToStream, get_bijiao
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router = APIRouter()
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@router.post("/ltv_model_sql")
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async def ltv_model_sql(
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request: Request,
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game: str,
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analysis: XAnalysis = Depends(XAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" ltv模型sql """
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await analysis.init(data_where=current_user.data_where)
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data = analysis.ltv_model_sql()
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return schemas.Msg(code=0, msg='ok', data=[data])
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@router.post("/ltv_model")
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async def ltv_model_sql(
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request: Request,
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game: str,
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analysis: XAnalysis = Depends(XAnalysis),
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ckdb: CKDrive = Depends(get_ck_db),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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""" ltv模型sql """
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await analysis.init(data_where=current_user.data_where)
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res = analysis.ltv_model_sql()
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sql = res['sql']
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#仅一条筛选条件则是把GM过滤后获取全部数据
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if len(analysis.global_filters)==1 and analysis.global_filters[0]['strftv']=='GM':
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df = await ckdb.query_dataframe(sql)
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#多条筛选条件则合成新的sql
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else:
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new_sql=""""""
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#拆分sql
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split_sql = sql.split('AND 1')
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#获取每一条筛选条件
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for i in analysis.global_filters:
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#剔除GM
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if i['strftv'] != 'GM':
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#获取筛选条件的包含关系
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bijiao=get_bijiao(i["comparator"])
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#获取筛选条件的值
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condition=tuple(i['ftv'])
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#获取事件名
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columnName=i['columnName']
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dd = f""" AND {game}.event.{columnName} {bijiao} {condition}"""
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new_sql+=dd
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split_="""AND 1 """
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news_sql = split_sql[0] + split_+new_sql + split_sql[1] + split_+new_sql+ split_sql[2]+split_+split_sql[3]
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df = await ckdb.query_dataframe(news_sql)
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# 判断11月23号之前的数据
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list_data_range=analysis.date_range
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liststr_data_range=[]
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for i in list_data_range:
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liststr_data_range.append(str(i))
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quota = analysis.event_view['quota']
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#判断是设备LTV则执行下面代码，如是角色实充LTV则不执行
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if quota == '#distinct_id':
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if '2021-11-22' in liststr_data_range or '2021-11-22' >=liststr_data_range[-1]:
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#取搜索最后为11.23号之前的数据
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if '2021-11-22' >=liststr_data_range[-1]:
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news_sql=""""""
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split_sql=sql.split('AND is_new_device = 1')
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new_sql=split_sql[0]+split_sql[1]+split_sql[2]
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news_sql+=new_sql
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df_twenty_three=await ckdb.query_dataframe(news_sql)
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#取包含有11.23号之前和23号之后的那一段
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else:
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start_date=str(list_data_range[0])
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end_date='2021-11-22'
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news_sql = """"""
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split_sql = sql.split('AND is_new_device = 1')
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for i in split_sql:
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news_sql += i
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#用正则表达式切时间
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zhengze_time=r'\d{4}-\d{1,2}-\d{1,2}'
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zhengze_sql=re.split(zhengze_time,news_sql)
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zz_new_sql=zhengze_sql[0]+start_date+zhengze_sql[1]+end_date+zhengze_sql[2]+start_date+zhengze_sql[3]+end_date+zhengze_sql[4]
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zz_news_sql=""""""
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zz_news_sql+=zz_new_sql
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df_twenty_three = await ckdb.query_dataframe(zz_news_sql)
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#上下合并两组数据，忽略以前的索引下标
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df= pd.concat([df,df_twenty_three], axis=0, ignore_index=True)
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df.sort_values('date', inplace=True)
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#去重
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#df.drop_duplicates(inplace=True)
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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) #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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#在有效日期最后一行补充行数据（值都为'-'），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 = (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) #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': #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) #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) #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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# 去除sumpay_1的值为0的列
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new_df2 = (df2.drop(df2[(df2.sumpay_2 == 0)].index))
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#为new_df2排序
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new_df2=new_df2.reset_index(drop=True)
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#求相差天数
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str_time =new_df2['注册日期'][0]
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str_time01=str(str_time)
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split_time = str_time01.split('-')
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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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#计算均值
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#avgLtv = (new_df2[[f'sumpay_{i}']][0:newday + 1 - i].sum() / new_df2[cat][0:newday + 1 - i].sum()).round(2)
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#12.20号计算LTV均值的时候分母包括当天未充值新增设备数，比剔除掉的计算值偏小
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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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#取出均值
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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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if new_avgLtv01 == 'NaN':
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_listData[f'sumpay_{i}'] = '-'
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else:
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_listData[f'sumpay_{i}'] = new_avgLtv01
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#原代码
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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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# if new_avgLtv01 == 'NaN':
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# _listData[f'sumpay_{i}'] = '-'
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# else:
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# _listData[f'sumpay_{i}'] = new_avgLtv01
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else:
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_listData[f'sumpay_{i}']='-'
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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 or v!= '-':
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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 = (new_df2[[max_nmu]].sum() / new_df2[cat].sum()).round(2)
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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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#原代码
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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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#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.LTV1 == '-')].index)
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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(),
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#'rows': df1.values.tolist(),
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'title': df3.columns.tolist(),
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'rows': df3.values.tolist(),
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'start_date': res['start_date'],
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'end_date': res['end_date']
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}
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return schemas.Msg(code=0, msg='ok', data=data)
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@router.post("/ltv_model_export")
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async def ltv_model_export(request: Request,
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game: str,
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ckdb: CKDrive = Depends(get_ck_db),
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analysis: XAnalysis = Depends(XAnalysis),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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):
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""" ltv分析 数据导出"""
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await analysis.init(data_where=current_user.data_where)
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data = analysis.ltv_model_sql()
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file_name = quote(f'lvt.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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sql = data['sql']
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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_to_stream = DfToStream((df, 'ltv'))
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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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