这意思是不是用1次 35，用多次的话20一次，如果一次性买50次，每次只用10元？
经过检查，该data element的domain并未assign任何conversion exit. 从dialog module传进来就已经是被100除之...
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[1240] * * * SAP很多云产品，例如SAP Hybris Revenue Cloud，基于微服务架构开发而成。...比如Revenue Cloud的客户主数据列表，就是通过部署在revcloud.XXX.eu10.revenue.cloud.sap上的一个微服务返回的。...下面邀请我的同事，SAP成都研究院Revenue Cloud开发团队的陈文心(Chen Vicky)给大家简单介绍Revenue Cloud目前已经发布的一些功能。...我从她的朋友圈盗了一张图： [1240] SAP Hybris Revenue Cloud功能概述 大家好，我是陈文心，现在工作于SAP成都研究院Revenue Cloud开发团队。...SAP Hybris Revenue Cloud由三个主要功能组成： 订阅式订单生成 订阅式订单管理 订阅式订单计费（包括使用费和一次性费用） 登陆SAP Hybris Revenue Cloud 进入主页面可以看到业务流和主数据的配置
---- SAP很多云产品，例如SAP Hybris Revenue Cloud，基于微服务架构开发而成。...比如Revenue Cloud的客户主数据列表，就是通过部署在revcloud.XXX.eu10.revenue.cloud.sap上的一个微服务返回的。...下面邀请我的同事，SAP成都研究院Revenue Cloud开发团队的陈文心(Chen Vicky)给大家简单介绍Revenue Cloud目前已经发布的一些功能。...SAP Hybris Revenue Cloud功能概述 大家好，我是陈文心，现在工作于SAP成都研究院Revenue Cloud开发团队。...SAP Hybris Revenue Cloud由三个主要功能组成： 订阅式订单生成 订阅式订单管理 订阅式订单计费（包括使用费和一次性费用） 登陆SAP Hybris Revenue Cloud 进入主页面可以看到业务流和主数据的配置
SAP Hybris Revenue Cloud SAP Hybris Revenue Cloud是SAP公司旗下的订阅和收入管理解决方案。...随着时间的推移，该产品在功能和范围上不断扩展，并改名为SAP Hybris Revenue Cloud。...功能和特点： 订阅管理：SAP Hybris Revenue Cloud允许企业创建和管理各种类型的订阅产品和服务。...SAP Hybris Revenue Cloud 和 SAP Subscription Billing 的关联： SAP Hybris Revenue Cloud和SAP Subscription Billing...在开始阶段，ABC科技公司采用了SAP Hybris Revenue Cloud来管理客户订阅和收入。
Budget allocation - pseudo-revenue - first-revenue assumption - regressions 用上了回归模型来计算，Revenue~cost 举例了两种做法...= {} predictions_cost_revenue = {} errors_cost_revenue = {} displayDf = pd.DataFrame() res_cost_revenue...[k][Revenue_col]) predictions_cost_revenue[k] = model_cost_revenue[k].predict(valData[k][Cost_col...]) errors_cost_revenue[k] = errorL1(predictions_cost_revenue[k], valData[k][Revenue_col]) res_cost_revenue.append...[k][Revenue_col].sum(), \ model_sessions_revenue[k].param, errors_sessions_revenue[k]
| Feb_Revenue | Mar_Revenue | ... | Dec_Revenue | +------+-------------+-------------+-------------+...= 'Apr' then revenue else null end) as Apr_Revenue, sum(case when month = 'May' then revenue else...null end) as May_Revenue, sum(case when month = 'Jun' then revenue else null end) as Jun_Revenue...= 'Aug' then revenue else null end) as Aug_Revenue, sum(case when month = 'Sep' then revenue else...null end) as Sep_Revenue, sum(case when month = 'Oct' then revenue else null end) as Oct_Revenue
| Feb_Revenue | Mar_Revenue | ... | Dec_Revenue | +------+-------------+-------------+-------------+...Feb' THEN revenue END) Feb_Revenue, SUM(CASE `month` WHEN 'Mar' THEN revenue END) Mar_Revenue, SUM(CASE...`month` WHEN 'Apr' THEN revenue END) Apr_Revenue, SUM(CASE `month` WHEN 'May' THEN revenue END) May_Revenue...Sep' THEN revenue END) Sep_Revenue, SUM(CASE `month` WHEN 'Oct' THEN revenue END) Oct_Revenue, SUM(CASE...`month` WHEN 'Nov' THEN revenue END) Nov_Revenue, SUM(CASE `month` WHEN 'Dec' THEN revenue END) Dec_Revenue
(IF (month = "Feb", revenue, null)) AS "Feb_Revenue", SUM(IF (month = "Mar", revenue, null)) AS "Mar_Revenue...", SUM(IF (month = "Apr", revenue, null)) AS "Apr_Revenue", SUM(IF (month = "May", revenue, null)) AS..."May_Revenue", SUM(IF (month = "Jun", revenue, null)) AS "Jun_Revenue", SUM(IF (month = "Jul", revenue...Sep", revenue, null)) AS "Sep_Revenue", SUM(IF (month = "Oct", revenue, null)) AS "Oct_Revenue", SUM(...IF (month = "Nov", revenue, null)) AS "Nov_Revenue", SUM(IF (month = "Dec", revenue, null)) AS "Dec_Revenue
ggplot(data,aes(reorder(conpany,-Revenue),Revenue))+geom_bar(stat="identity")+labs(x="Company",y="The...主题：theme_wsj() ggplot(data,aes(reorder(conpany,-Revenue),Revenue))+geom_bar(stat="identity")+labs(x=...主题+颜色主题：theme_wsj()+scale_fill_wsj("rgby", "") ggplot(data,aes(reorder(conpany,-Revenue),Revenue,fill...下面一个一个解决： ggplot(data,aes(reorder(conpany,-Revenue),Revenue,fill="steelbule"))+geom_bar(stat="identity...当然，如果我们找到了更好的一组配色，我们也可以仅仅使用华尔街日报的主题，而使用我们自己准备好的调色板： ggplot(data,aes(reorder(conpany,-Revenue),Revenue
SET @EE = ''; SET @str_tmp = ''; SET @Revenue_JSON = ''; SET @Revenue_JSON_tmp..._', fsRevenueTypeName, ''',' ) AS aa , @Revenue_JSON := CONCAT ( @Revenue_JSON...,','',"' ,fsRevenueTypeName,'":'',' ,'revenue_',fsRevenueTypeName ) AS bb INTO @str_tmp...,4),','',"汇总":'',','revenue_汇总',',''}''') INTO @Revenue_JSON_tmp; SET @QQ = CONCAT(...' CREATE TEMPORARY TABLE TempRevenueType1 ( SELECT fsSellNo3,CONCAT(',@Revenue_JSON_tmp,') as revenue_info
编写一个 SQL 查询来重新格式化表，使得新的表中有一个部门 id 列和一些对应 每个月 的收入（revenue）列。...查询结果格式如下面的示例所示： Department 表： +------+---------+-------+ | id | revenue | month | +------+---------...| Feb_Revenue | Mar_Revenue | ... | Dec_Revenue | +------+-------------+-------------+-------------+...执行 Pivot 行转列函数 根据题意：已确定需要查出的列为 ID 和 12个月份，月份列对应的 REVENUE 的值需要进行汇总 (SUM) 显示。...SELECT * FROM department PIVOT (SUM(revenue) as "Revenue" for month in ( 'Jan' as "Jan", 'Feb' as "Feb
--+---------+ | Column Name | Type | +---------------+---------+ | id | int | | revenue...编写一个 SQL 查询来重新格式化表，使得新的表中有一个部门 id 列和一些对应 每个月 的收入（revenue）列。...查询结果格式如下面的示例所示： Department 表： +------+---------+-------+ | id | revenue | month | +------+---------...---+ 查询得到的结果表： +------+-------------+-------------+-------------+-----+-------------+ | id | Jan_Revenue... | Feb_Revenue | Mar_Revenue | ... | Dec_Revenue | +------+-------------+-------------+-------------+
的数字，保存后， Expected Revenue的值被除以了100。...这个回调函数，然后自动把Expected Revenue根据相应的currency做处理（比如，currency为JPY即日元的话， Expected Revenue会除以100）。...场景2 如果在SAP CRM WebClient UI层输入了Currency，那么在UI层就会直接根据Currency转换Expected Revenue。...一种容易犯的配置错误是，客户同时输入了配置了currency determination 的 account， expected Revenue和Currency。...在UI层， UI根据已经输入的Currency把Expected Revenue做了转换， 然后后台又根据account做了determination， 把已经做了转换的Expected Revenue
下面对互联网广告的收入分解加以总结： Revenue = PV * eCPM PV（Page View）： 是系统一天的访问量（有的媒体公司，广告和内容分开，PV则代表他们的广告曝光，访问量用request...eCPM = CTR * ACP * 1000 Revenue = ACP * CLK CTR：点击率；点击率预估是一个广告投放引擎需要经常优化的模块； ACP：平均点击价格，ACP主要看市场和客户的成熟程度...，广告效果好，竞争激烈的领域ACP就会高，ACP也可通过限制低价，甚至MRP的方式来控制提高； CLK ：点击次数 Revenue = PV * CPM1 Revenue = PV * PVR * CPM3...Revenue = PV * PVR * ASN * CPM2 Revenue = PV * CTR1 * ACP Revenue = PV * PVR * CTR3 * ACP Revenue =...= 有消费的客户数 * ARPU ARPU = 客户平均点击 * ACP ARPU（AVerage Revenue Per Users ）： 每位客户的平均收入；
现在我们可以看到，我们的数据有128个revenue_millions缺失值和64个metascore缺失值。...让我们看看在revenue_millions列中输入缺失的值。...首先，我们将该列提取到它自己的变量: revenue = movies_df['revenue_millions'] 这里使用方括号是我们在DataFrame中选择列的一般方法。...这是平均值: revenue_mean = revenue.mean() print (revenue_mean) 运行结果： 82.95637614678897 有了均值，fillna()将会填充空值...: revenue.fillna(revenue_mean, inplace=True) 我们现在用列的平均值替换了所有的收益为空。
查询性能测试详情 ①Query 1.1 SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE (...②Query 1.2 SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE (toYYYYMM(...⑤Query 3.1 SELECT C_NATION, S_NATION, toYear(LO_ORDERDATE) AS year, sum(LO_REVENUE)...⑥Query 3.2 SELECT C_CITY, S_CITY, toYear(LO_ORDERDATE) AS year, sum(LO_REVENUE) AS revenue...DESC ┌─C_CITY─────┬─S_CITY─────┬─year─┬────revenue─┐ │ UNITED ST6 │ UNITED ST6 │ 1992 │ 5694246807
= pd.read_csv('my_data.csv', sep=',') data.head() 它的输出如下： id city department sms category 01 khi revenue...NaN 0 02 lhr revenue good 1 03 lhr revenue NaN 0 我想删除sms列为空/ NaN的所有行.什么是有效的方法呢？...NaN的列： data = data.dropna(subset=['sms']) print (data) id city department sms category 1 2 lhr revenue...的另一个解决方案： data = data[data['sms'].notnull()] print (data) id city department sms category 1 2 lhr revenue...good 1 替代query： print (data.query("sms == sms")) id city department sms category 1 2 lhr revenue
of the revenue....This analysis can be of great help to predict recurring revenue....The prediction of revenue is important for both manufacturing and SaaS services industries....Other than that, there are subscription types that offer you more than one revenue stream....Different revenue streams mean you have chances to earn more.
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