Determines whether the specified object is equal to the current object.
The object to compare with the current object.
true if the specified object is equal to the current object; otherwise, false.
Serves as the default hash function.
A hash code for the current object.
Returns a string that represents the current object.
A string that represents the current object.
StaticEqualsDetermines whether the specified object instances are considered equal.
The first object to compare.
The second object to compare.
true if the objects are considered equal; otherwise, false. If both objA and objB are null, the method returns true.
Category columns values are used for grouping data into result columns. There will be an aggregated result data column for each distinct combined value of the Category columns.
Identity columns are passed through and identify each row of the result data.
Naming expression for the aggregated result data columns.
This naming expression is a string containing a placeholder to be
replaced with values during Pivot as follows:
%M is replaced with the aggregation method name (empty string for None).
%V is replaced with the value column name.
%C is replaced with a concatenated list of category values separated by Category separator.
TransferColumns are aggregated over the identity columns and stored into result transfer columns. See Spotfire.Dxp.Data.Transformations.ColumnAggregation.
Naming expression for the aggregated transfer result columns.
This naming expression is a string containing a placeholder to be
replaced with values during Pivot as follows:
%A is replaced with the aggregation method name (empty string for None).
%T is replaced with the transfer value column name.
Each value column data is aggregated over identity columns and category columns and stored into the result data columns. See Spotfire.Dxp.Data.Transformations.ColumnAggregation.
Connects to the input reader. A Spotfire.Dxp.Data.DataRowReader can then be retrieved from the Spotfire.Dxp.Data.DataTransformationConnection.
The import context.
The input reader.
The connected transformation.
Pivots tall-skinny data into short-wide data.
Pivot is based on identity columns and category columns, where each distinct identity denotes a row, and each distinct category, together with an aggregation method and a value column, denotes a column, in a result matrix of data cells. Each cell is the aggregated result of all data values having the same identity and category. Additional transfer columns may exist, holding values aggregated by identity only, and not by category.
Remark
If nothing but identity columns are specified, the result of this Pivot transformation will be the distinct values of the identity columns.
If nothing but identity and transfer columns are specified, the result of this Pivot transformation will be an aggregation of the transfer columns over the identity columns.
Since
2.0