@@ -26,7 +26,7 @@ Keep reading for detailed descriptions of each type of transformation and the op
## Reduce
The _Reduce_ transformation will apply a calculation to each field in the frame and return a single value. Time fields are removed when applying
this transformation.
this transformation.
Consider the input:
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@@ -48,11 +48,11 @@ The reduce transformer has two modes:
-**Series to rows -** Creates a row for each field and a column for each calculation.
-**Reduce fields -** Keeps the existing frame structure, but collapses each field into a single value.
For example, if you used the **First** and **Last** calculation with a **Series to rows** transformation, then
For example, if you used the **First** and **Last** calculation with a **Series to rows** transformation, then
the result would be:
| Field | First | Last |
| ------- | ------- | ------- |
| Field | First | Last |
| ------- | ------- | ------- |
| Temp | 12.3 | 15.4 |
| Uptime | 256122 | 1230233 |
| AQI | 6.5 | 3.2 |
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@@ -104,7 +104,7 @@ Here is the result after applying the Merge transformation.
| 2020-07-07 11:34:20 | node | 15 | 25260122 |
| 2020-07-07 11:24:20 | postgre | 5 | 123001233 |
## Filter by name
## Filter data by name
Use this transformation to remove portions of the query results.
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@@ -214,22 +214,22 @@ If you selected Server as the **Value field name**, then you would get one field
The labels to fields transformer is internally two separate transformations. The first acts on single series and extracts labels to fields. The second is the [merge](#merge) transformation that joins all the results into a single table. The merge transformation tries to join on all matching fields. This merge step is required and cannot be turned off.
To illustrate this, here is an example where you have two queries that return time series with no overlapping labels.
To illustrate this, here is an example where you have two queries that return time series with no overlapping labels.
