Pivot Transform

Anmerkung

Transforms are a part of the underlying language, which is not directly accessible to users. This content is maintained for reference purposes only. For more information on the user-accessible equivalent to transforms, see Transformation Reference.

When you pivot data, the values from the selected column or columns become new columns in the dataset, each of which contains a summary calculation that you specify.

This calculation can be based on all rows for totals across the dataset or based on group of rows you define in the transform.
When your data has been pivoted, the unused columns are dropped, leaving only the operative data for faster evaluation.
Transform accepts one or more columns as inputs to the pivot.

Tipp

This transform is very useful in Preview mode for quick discovery and analysis.

Basic Usage

pivot col: Dates value:SUM(Sales) group: prodId

Output: Reshapes your dataset to include a ProdId column and new columns for each distinct value in the Dates column. Each distinct ProdId value is represented by a separate row in the reshaped dataset. Within each row, the new columns contain a sum of sales for the product for each date in the dataset.

Syntax and Parameters

pivot col:column_ref value: FUNCTION(arg1,arg2) [group: group_col] [limit:int_num]

Token	Required?	Data Type	Description
pivot	Y	transform	Name of the transform
col	N	string	Source column name or names
value	N	string	Expression containing the aggregation function or functions used to pivot the source column data
group	N	string	Column name or names containing the values by which to group for calculation
limit	N	integer (positive)	Maximum number of unique values in a group. Default is 50.

For more information on syntax standards, see Language Documentation Syntax Notes.

col

Identifies the column or columns to which to apply the transform.

Single-column example:

The specified column contains the values that become new columns in the dataset.

pivot col: autoBrand value:AVERAGE(autoPrice) group:State

Output: Reshapes the dataset to contain the State column followed by columns for each distinct value in the autoBrand column. Each value in those columns is the average value in the autoPrice column, as grouped by State value.

Multi-column example:

For multiple input columns, the transform generates new columns for each combination of the inputs. See the example below.

Usage Notes:

Required?	Data Type
No	String (column name)

value

For this transform, the value parameter contains the aggregation function and its parameters, which define the set of rows to which the function is applied.

Anmerkung

For the value parameter, you can only use aggregation functions. Nested functions are not supported. For more information, see Aggregate Functions.

Usage Notes:

Required?	Data Type
No	String containing a list of aggregation functions each containing one column reference

group

For this transform, this parameter specifies the column whose values are used to group the dataset prior to applying the specified function. You can specify multiple columns as comma-separated values.

Anmerkung

Transforms that use the group parameter can result in non-deterministic re-ordering in the data grid. However, you should apply the group parameter, particularly on larger datasets, or your job may run out of memory and fail. To enforce row ordering, you can use the sort transform. For more information, see Sort Transform.

Usage Notes:

Required?	Data Type
No	String (column name)

limit

The limit parameter defines the maximum number of unique values permitted in a group. If it is not specified, the default value for this parameter is 50.

Anmerkung

Be careful setting this parameter too high. In some cases, the application can run out of memory generating the results, and your results can fail.

Usage Notes:

Required?	Data Type
No. Default value is `50`.	Integer (positive)

Examples

Tipp

For additional examples, see Common Tasks.

Example - Basic Pivot

Source:

The following dataset contains information about sales across one weekend and two states for three different products.

Date	State	ProdId	Sales
3/9/16	CA	Big Trike 9000	500
3/9/16	NV	Fast GoKart 5000	200
3/9/16	CA	SuperQuick Scooter	700
3/9/16	CA	Fast GoKart 5000	900
3/9/16	NV	Big Trike 9000	300
3/9/16	NV	SuperQuick Scooter	250
3/10/16	NV	Fast GoKart 5000	50
3/10/16	NV	Big Trike 9000	400
3/10/16	NV	SuperQuick Scooter	150
3/10/16	CA	Big Trike 9000	600
3/10/16	CA	SuperQuick Scooter	800
3/10/16	CA	Fast GoKart 5000	1100

Transformation 1: Sum of sales by State for each Date

Apply this transformation in Preview only, just so you can see the results:

Transformation Name	`Pivot columns`
Parameter: Column labels	Date
Parameter: Row labels	State
Parameter: Values	sum(Sales)

Cancel the transformation update.

State	sum_Sales_03/09/2016	sum_Sales_03/10/2016
CA	2100	2500
NV	750	600

Transform 2: Sum of Sales by Date for each State

Transformation Name	`Pivot columns`
Parameter: Column labels	State
Parameter: Row labels	Date
Parameter: Values	sum(Sales)

Date	sum_Sales_CA	sum_Sales_NV
03/09/2016	2100	750
03/10/2016	2500	600

Cancel the transformation update again.

Transform 3: Sum of Sales by product ID for each State

Transformation Name	`Pivot columns`
Parameter: Column labels	State
Parameter: Row labels	ProdId
Parameter: Values	sum(Sales)

ProdId	sum_Sales_CA	sum_Sales_NV
Big Trike 9000	1100	700
Fast GoKart 5000	2000	500
SuperQuick Scooter	1500	300

Example - Multi-column Pivot

The Pivot Columns transformation supports the ability to use the values in multiple columns to specify the columns that are generated. When two or more columns are used in the pivot, columns containing values of all possible combinations from the source columns are generated.

In the following source data, sales data on individual products is organized by brand, product name, and month:

Brand	Product	Month	Sales
AAA	Towels	January	95
AAA	Napkins	January	113
B	Towels	January	99
B	Tissues	January	88
AAA	Towels	February	108
AAA	Napkins	February	91
B	Towels	February	85
B	Tissues	February	105
AAA	Towels	March	81
AAA	Napkins	March	92
B	Towels	March	112
B	Tissues	March	104

Transformation:

If you wanted to create summary sales information for each product by month, you might choose to create a pivot on the Product column. However, if you look at the column values, you might notice that the Paper towels product is available for both the AAA and B brands. In this case, you must perform a multi-column pivot on these two columns, like the following:

Transformation Name	`Pivot columns`
Parameter: Column labels	Brand,Product
Parameter: Row labels	Month
Parameter: Values	average(Sales)

Results:

Month	avg_Sales_B_Towels	avg_Sales_B_Tissues	avg_Sales_AAA_Napkins	avg_Sales_AAA_Towels
January	99	88	113	95
February	85	105	91	108
March	112	104	92	81

Example - Aggregate Values

You can use the pivot transform to perform aggregation calculations on values in a column.

Source:

In the following table, you can review test results from three different tests for 10 students:

StudentId	TestId	Score
s001	t001	98
s001	t002	98
s001	t003	87
s002	t001	92
s002	t002	96
s002	t003	79
s003	t001	99
s003	t002	76
s003	t003	94
s004	t001	93
s004	t002	99
s004	t003	80
s005	t001	79
s005	t002	80
s005	t003	84
s006	t001	93
s006	t002	74
s006	t003	89
s007	t001	86
s007	t002	81
s007	t003	97
s008	t001	82
s008	t002	73
s008	t003	79
s009	t001	96
s009	t002	97
s009	t003	79
s010	t001	80
s010	t002	79
s010	t003	75

Transformation:

For each student, you're interested in a student's average score and maximum score. You create the following pivot transform, which groups computations of average score and maximum score columns by studentId value.

Transformation Name	`Pivot columns`
Parameter: Row labels	StudentId
Parameter: Values	average(Score),max(Score)
Parameter: Max number of columns to create	50

For the generated average column, you want results rounded to two decimal places:

Transformation Name	`Edit column with formula`
Parameter: Columns	average_Score
Parameter: Formula	round(average_Score, 2)

Results:

StudentId	average_Score	max_Score
s001	94.33	98
s002	89	96
s003	89.67	99
s004	90.67	99
s005	81	84
s006	85.33	93
s007	88	97
s008	78	82
s009	90.67	97
s010	78	80

In diesem Abschnitt:

Pivot Transform

Basic Usage

Syntax and Parameters

col

value

group

limit

Examples

Example - Basic Pivot

Example - Multi-column Pivot

Example - Aggregate Values

Suchresultat