Pivot transform table（数据透视转换表（Pivot transform table））¶
The pivot transform table is similar to the object set pivot table card. A transform table is chosen as input and columns of that table are chosen as row and column properties; the resulting data is grouped by these properties and aggregated based on the configuration you set in the editor panel.
Row properties¶
Row properties are the properties with values that will be the row headers of the table.
Column properties¶
Column properties are the properties with values that will be the column headers of the table.
Aggregations¶
Aggregation configurations allow you to set the way you want the data to be aggregated for each cell, grouped by row and column properties. The pivot transform table card provides numerous configuration options for aggregations, such as:
- Count
- Sum of a numeric property
- Min of a numeric property
- Max of a numeric property
- Unique count of a property
For example, suppose you have a dataset of daily precipitation by city in the United States. If you select city as a row property, year as a column property, and sum of precipitation as an aggregation, then the column headers will be New York, Chicago, and Los Angeles, the row headers will be 2015, 2016, and 2017, and the values will be the total precipitation in that city during that year.
The only difference between row and column properties is on which edge of the table they appear. For example, if you have configured row properties A and B with no column properties, the data will be the same as if you had row property A and column property B, or column properties A and B with no row properties. Only the layout of the data will be different.
If you would like to do further processing on the columns of a pivot table, or use advanced formatting options like conditional coloring, you can convert a pivot table back to a transform table.
See transform table computation differences for more details on how the output of this card may differ from the pivot table card for similar input.
Input type¶
Transform table
Output type¶
Pivot table, transform table
Usage information¶
| Functionality | Availability |
|---|---|
| Standard Quiver card | Supported |
| Transform table transform | Unsupported |
中文翻译¶
数据透视转换表（Pivot transform table）¶
数据透视转换表与对象集数据透视表卡片类似。选择转换表作为输入，并选取该表的列作为行和列属性；结果数据将按这些属性分组，并根据您在编辑面板中配置的聚合方式进行汇总。
行属性（Row properties）¶
行属性是指其值将作为表格行标题的属性。
列属性（Column properties）¶
列属性是指其值将作为表格列标题的属性。
聚合方式（Aggregations）¶
聚合配置允许您设置每个单元格的数据按行和列属性分组后的汇总方式。数据透视转换表卡片为聚合提供了多种配置选项，例如：
- 计数（Count）
- 数值属性求和（Sum of a numeric property）
- 数值属性最小值（Min of a numeric property）
- 数值属性最大值（Max of a numeric property）
- 属性唯一值计数（Unique count of a property）
例如，假设您有一个美国各城市每日降水量的数据集。如果您选择 city 作为行属性，year 作为列属性，并选择 sum of precipitation 作为聚合方式，那么列标题将是 New York、Chicago 和 Los Angeles，行标题将是 2015、2016 和 2017，而单元格值将是该城市在该年份的总降水量。
行属性和列属性之间的唯一区别在于它们在表格中的显示位置。例如，如果您配置了行属性 A 和 B，但没有列属性，那么数据结果将与设置行属性 A 和列属性 B，或设置列属性 A 和 B 且无行属性时相同。仅数据的布局会有所不同。
如果您希望对数据透视表的列进行进一步处理，或使用条件着色等高级格式选项，可以将数据透视表转换回转换表。
有关此卡片输出与数据透视表卡片在类似输入下可能存在的差异，请参阅转换表计算差异。
输入类型¶
转换表（Transform table）
输出类型¶
数据透视表（Pivot table），转换表（Transform table）
使用信息¶
| 功能 | 可用性 |
|---|---|
| 标准 Quiver 卡片 | 支持 |
| 转换表转换 | 不支持 |