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预算分配Budget Allocation：Morphl-AI的营销科学解决方案（一）
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]
1.1K20编辑于 2021-12-07 - 来自专栏信息技术智库
「SQL面试题库」 No_75 重新格式化部门表
| 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
41720编辑于 2023-05-23 - 来自专栏Michael阿明学习之路
LeetCode MySQL 1179. 重新格式化部门表
| 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
93110发布于 2021-02-19 - 来自专栏数据小魔方
R语言可视化——图表美化与套用主题（下）
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
1.5K60发布于 2018-04-11 使用 Vanna + Ollama + ChromaDB，实现 自然语言查询 / ChatBI
="查询2024年各部门总营业额") vn.train(sql="SELECT year, AVG(revenue_amount) as avg_revenue FROM revenue GROUP BY ELSE 0 END) as revenue_2023, SUM(CASE WHEN r.year = 2024 THEN r.revenue_amount ELSE 0 END) as revenue ELSE 0 END) as revenue_2023,\n SUM(CASE WHEN r.year = 2024 THEN r.revenue_amount ELSE 0 END) as revenue ELSE 0 END) as revenue_2023, SUM(CASE WHEN r.year = 2024 THEN r.revenue_amount ELSE 0 END) as revenue END) as revenue_2023, SUM(CASE WHEN r.year = 2024 THEN r.revenue_amount ELSE 0 END) as revenue_2024
34910编辑于 2026-03-25- 来自专栏Hank’s Blog
LeetCode SQL
(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
38510发布于 2020-09-17 - 来自专栏全栈程序员必看
Mysql 行转列 + json
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
2.8K20编辑于 2022-09-05 - 来自专栏nginx
程序化广告ADX中的关键指标计算与优化策略
= (impressions / 1000) * cpm revenue_from_clicks = clicks * cpc total_revenue = revenue_from_impressions + revenue_from_clicks return round(total_revenue, 2) # 示例数据 impressions = 500000 clicks = 10000 """ profit = revenue - cost profit_margin = (profit / revenue) * 100 if revenue ! = channel_cost * (1 - adx_fee_rate) return round(media_revenue, 2) media_revenue = calculate_media_revenue """ profit = revenue - cost profit_margin = (profit / revenue) * 100 if revenue !
50710编辑于 2025-11-16 - 来自专栏架构师修炼
6亿数据秒级查询，ClickHouse太快了！
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
3.1K20发布于 2021-04-12 - 来自专栏公众号：Lucifer三思而后行
⭐️ LeetCode解题系列 ⭐️ 1179. 重新格式化部门表（Oracle Pivot 行转列函数）
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
81010发布于 2021-08-17 - 来自专栏信息技术智库
【每日SQL打卡】DAY 14丨重新格式化部门表【难度中等】
--+---------+ | 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 | +------+-------------+-------------+-------------+
34030编辑于 2022-07-29 - 来自专栏wayn的程序开发
行转列不再复杂：SQL高手都在用的技巧揭秘
一、行转列原理：从行到列的数学逻辑 问题场景： 假设有一张销售表 sales： year quarter revenue 2023 Q1 5000 2023 Q2 7000 2023 Q3 6000 2023 'THEN revenue ELSE0END) AS Q2, SUM(CASEWHENquarter = 'Q3'THEN revenue ELSE0END) AS Q3, SUM(CASEWHENquarter 自动聚合：SUM()会自动对每个季度的revenue求和。 优势： 简洁性：代码行数减少 60%。 可读性：逻辑更直观。 局限性： 不支持动态列：列名必须预先定义（如 Q1-Q4）。 (CASEWHENquarter = 'Q2'THEN revenue END) AS Q2, SUM(CASEWHENquarter = 'Q3'THEN revenue END) AS Q3, SUM (CASEWHENquarter = 'Q4'THEN revenue END) AS Q4, -- 计算累计值 SUM(revenue) OVER (PARTITIONBYyearORDERBYquarter
2.2K10编辑于 2025-04-04 - 来自专栏浊酒清味
Python进阶之Pandas入门(四) 数据清理
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。
2.4K60发布于 2020-02-10 - 来自专栏程序猿人
计算广告——收入分解
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；
6.4K21发布于 2020-08-11 - 来自专栏bisal的个人杂货铺
如何才能发挥数据库的真正作用？
(SELECT SUM(revenue) FROM monthly_revenue) as total_revenue, -- By category ( SELECT json_object_agg (category, category_revenue) FROM ( SELECT category, SUM(revenue) as category_revenue SELECT json_object_agg(region, region_revenue) FROM ( SELECT region, SUM(revenue) as region_revenue SELECT json_object_agg(month, month_revenue) FROM ( SELECT month, SUM(revenue) as month_revenue , SUM(revenue) OVER (ORDER BY date) as running_total, RANK() OVER (ORDER BY revenue DESC) as revenue_rank
11010编辑于 2026-03-12 - 来自专栏Spark学习技巧
clickhouse 亿级数据性能测试
查询性能测试详情 Query 1.1 SELECT sum(LO_EXTENDEDPRICE * LO_DISCOUNT) AS revenue FROM lineorder_flat WHERE (toYear 扫描行数：600,040,000 大约6亿 耗时(秒)：8.558 查询列数：3 结果行数：280 Query 2.2 SELECT sum(LO_REVENUE), toYear( ) AS revenue FROM lineorder_flat WHERE (C_REGION = 'ASIA') AND (S_REGION = 'ASIA') AND (year >= 1992) DESC ┌─C_NATION──┬─S_NATION──┬─year─┬──────revenue─┐ │ INDIA │ INDIA │ 1992 │ 537778456208 ) AS revenue FROM lineorder_flat WHERE (C_NATION = 'UNITED STATES') AND (S_NATION = 'UNITED STATES')
2.1K00编辑于 2022-06-09 - 来自专栏用户6988187的专栏
2019年北美SaaS行业M&A概况
5）EV/Revenue Multiple逐年增加。2019年，EV/Revenue Multiple达到8.2X，创新高。 6）EV/Revenue Multiple 分布 EV/Revenue Multiple达到15.0X或更高的公司，占比由2018年第4季度的6.5％增长至2019年Q4的17.8％。 9）前25％的上市SaaS公司表现 按EV /Revenue Multiple中值划分，Top 25%的上市SaaS公司EV/Revenue Multiple中值为17.3X，2019年营收中值, 第四季度的增长率为 10) 底部25%的 SaaS公司表现 按EV /Revenue Multiple中值划分，Bottom 25%的上市SaaS公司EV/Revenue Multiple中值为2.9X，2019年营收中值第四季度的增长率为 此外，超过一半的SaaS卖家公布其EV /Revenue 倍数不超过5.0X，仅有近三分之一的公司发公布的EV /Revenue 倍数超过7.0X。
59400发布于 2020-07-08 - 来自专栏小工匠聊架构
白话Elasticsearch36-深入聚合数据分析之案例实战Histogram Aggregation：按价格区间统计电视销量和销售额
"field": "price", "interval": 2000 }, "aggs":{ "revenue "price": { "buckets": [ { "key": 0, "doc_count": 3, "revenue : 3700 } }, { "key": 2000, "doc_count": 4, "revenue : 9500 } }, { "key": 4000, "doc_count": 0, "revenue value": 0 } }, { "key": 6000, "doc_count": 0, "revenue
56330发布于 2021-08-17 - 来自专栏YashanDB知识库
YashanDB LAST_VALUE函数
示例（HEAP表）-- finance_info表记录了分年、月、机构的收入情况SELECT year,month,branch,revenue_total FROM finance_info;YEAR MONTH BRANCH REVENUE_TOTAL ----- ----- ------ ------------- 2001 01 0201 28882021 01 ,LAST_VALUE(branch) OVER (PARTITION BY year ORDER BY revenue_total DESC) frFROM finance_info;YEAR REVENUE_TOTAL ,LAST_VALUE(branch) OVER (ORDER BY revenue_total DESC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW ,LAST_VALUE(branch) OVER (ORDER BY revenue_total DESC ROWS BETWEEN 1 PRECEDING AND 1 FOLLOWING) frFROM
18100编辑于 2025-06-10 - 来自专栏数据大宇宙
pandas每天一题-题目7：批量列计算
以下是原项目解法： df = pd.read_csv('chipotle.tsv',sep='\t',converters={'item_price': lambda x: float(x[1:-1])}) revenue = (df['quantity']* df['item_price']).sum() print('Revenue was: $' + str(np.round(revenue,2))) Revenue revenue = df.eval('quantity * item_price').sum() print('Revenue was: $' + str(np.round(revenue,2))) Revenue was: $39237.02 行1：df.eval 可以动态解析表达式 点评： pandas 官方测试中，当数据量较大时(10万行以上)，这种方式会得到一定优化加速(使用numba)
1K20发布于 2021-09-01
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