Power BI 中的瀑布图(教程)Waterfall charts in Power BI (Tutorial)

瀑布图显示随着值的增加或减少的不断变化的总数。A waterfall chart shows a running total as values are added or subtracted. 该图对于了解一系列正值和负值更改如何影响初始值(例如,净收益)很有用。It's useful for understanding how an initial value (for example, net income) is affected by a series of positive and negative changes.

列使用颜色编码,因此可以快速区分增加和减少。The columns are color coded so you can quickly tell increases and decreases. 初始值列和最终值列通常从水平轴开始,而中间值为浮动列。The initial and the final value columns often start on the horizontal axis, while the intermediate values are floating columns. 由于该图的外观,瀑布图也被称为桥图。Because of this "look", waterfall charts are also called bridge charts.

何时使用瀑布图When to use a waterfall chart

瀑布图适用情况:Waterfall charts are a great choice:

  • 跨时序或不同类别更改指标when you have changes for the measure across time series or different categories
  • 要审核对总值有影响的主要更改to audit the major changes contributing to the total value
  • 要通过显示各种收入来源和计算总利润(或损失)绘制公司的的年利润图。to plot your company's annual profit by showing various sources of revenue and arrive at the total profit (or loss).
  • 要说明一年中公司的起始和结束员工人数。to illustrate the beginning and the ending headcount for your company in a year
  • 要可视化你每月的收入和支出,以及你的帐户的不断变化的余额。to visualize how much money you make and spend each month, and the running balance for your account.

创建瀑布图Create a waterfall chart

我们将创建按月显示销售差额(比较估计销售额与实际销售额)的瀑布图。We'll create a waterfall chart that displays sales variance (estimated sales versus actual sales) by month. 要遵循示例执行操作,请登录到 Power BI,然后选择获取数据 > 示例 > 零售分析示例To follow along, sign in to Power BI and select Get Data > Samples > Retail Analysis Sample.

  1. 选择“数据集”选项卡并滚动到新的“零售分析示例”数据集。Select the Datasets tab and scroll to the new "Retail Analysis Sample" dataset. 选择“创建报表”图标,在报表编辑视图中打开数据集。Select the Create report icon to open the dataset in report editing view.

  2. 字段窗格,选择销售额 > 总销售差额From the Fields pane, select Sales > Total Sales Variance. 如果总销售差额不在 Y 轴区域中,请将其拖至该区域。If Total Sales Variance isn't in the Y Axis area, drag it there.
  3. 将图表转换为瀑布图Convert the chart to a Waterfall.

  4. 选择时间 > 财月以将它添加到类别框。Select Time > FiscalMonth to add it to the Category well.

  5. 按时间顺序对瀑布图排序。Sort the waterfall chart chronologically. 在图表右上角选择省略号 (...),然后选择“财月”。From the top-right corner of the chart, select the ellipses (...) and choose FiscalMonth.

  6. 进一步了解每月发生变化的最主要原因。Dig in a little more to see what's contributing most to the changes month to month. 将“应用商店” > “区域”拖动到“细目”桶中。Drag Store > Territory to the Breakdown bucket.

  7. 默认情况下,Power BI 按月将前 5 个地区添加到增加结果或减少结果中。By default, Power BI adds the top 5 contributors to increases or decreases by month. 但我们只关注前 2 个地区。But we're only interested in the top 2 contributors. 在“格式设置”窗格中,选择“细目”,并将“最大值”设置为 2。In the Formatting pane, select Breakdown and set Maximum to 2.

    快速浏览显示,在我们的瀑布图中,俄亥俄州和宾夕法尼亚州是正负增长变化幅度最大的两个区域。A quick review reveals that the territories of Ohio and Pennsylvania are the biggest contributors to movement, negative and positive, in our waterfall chart.

  8. 这个结果很有意思。This is an interesting finding. 俄亥俄州和宾夕法尼亚州具有如此显著的影响,是不是因为这两个区域的销售额远高于其他区域?Do Ohio and Pennsylvania have such a significant impact because sales in these 2 territories are much higher than the other territories? 我们可以来看看。We can check that. 创建一个按区域显示销售额的地图。Create a map that looks at sales by territory.

    地图验证了我们的结果。Our map supports our theory. 地图显示,这两个区域具有去年(气泡大小)和今年(气泡明暗度)的销售额最高值。It shows that these 2 territories had the highest value of sales last year (bubble size) and this year (bubble shading).

突出显示和交叉筛选Highlighting and cross-filtering

有关使用筛选器窗格的信息,请参阅向报表添加筛选器For information about using the Filters pane, see Add a filter to a report.

突出显示瀑布图中的列可交叉筛选报表页上的其他可视化效果,反之亦然。Highlighting a column in a waterfall chart cross-filters the other visualizations on the report page... and vice versa. 但是,“汇总”列不会触发突出显示或响应交叉筛选。However, the Total column does not trigger highlighting or respond to cross-filtering.

后续步骤Next steps

Power BI 中的报表Reports in Power BI

Power BI 中的可视化效果类型Visualization types in Power BI

Power BI 报表中的可视化效果Visualizations in Power BI reports

Power BI - 基本概念Power BI - Basic Concepts

更多问题?More questions? 尝试参与 Power BI 社区Try the Power BI Community