The pivot operation works much the same as the SQL concept: "rotating" and expanding single columns of values into multiple columns (one for each value), creating a wider but shorter table. It is nearly the opposite of the unpivot operation, though it will merge duplicates and drop null values, which unpivot cannot undo. You can perform pivot operations using the pivot and pivot_values parameters in the options map of /aggregate/groupby or via the SQL PIVOT function. Visit the Aggregation documentation to review details on aggregating data.

A pivot operation could be used to convert a table like this:

Name Phone Type Phone Number
Jane Home 123-456-7890
Jane Work 111-222-3333
John Home 123-456-7890
John Cell 333-222-1111

into a table like this:

Name Home Phone Work Phone Cell Phone
Jane 123-456-7890 111-222-3333  
John 123-456-7890   333-222-1111



Given a source table customer, which lists each phone number for a customer as a separate record in the table:

name phone_type phone_number
... ... ...

A pivot operation can be performed in Python like so, creating a single record per customer with the home, work, & cell phone numbers as separate columns:

    table_name= "customer",
    column_names= ["name", "MAX(phone_number) as Phone"],
    offset= 0,
    limit= 1000,
    encoding= "json",
    options= {"pivot":"phone_type", "pivot_values":"Home, Work, Cell"}

The resulting table looks like this:

name Home_Phone Work_Phone Cell_Phone
... ... ... ...


Given a source table students, which lists each student's quarterly grade as a separate record in the table:

student_id quarter grade
... ... ...

Performing a pivot operation in Python on the student table converts it into a table with one record per student with each quarter grade as a separate column:

    table_name = "student",
    column_names= ["student_id", "MAX(grade) as grade"]
    offset= 0,
    limit= 1000,
    encoding= "json",
    options= {"pivot":"quarter", "pivot_values":"q1, q2, q3, q4"}

The resulting table looks like this:

student_id q1_grade q2_grade q3_grade q4_grade
... ... ... ... ...


  • Store-only columns cannot have Pivot operations applied to them, nor can they appear in the result set
  • Non-charN string column types cannot appear in the result set if the result_table option is used
  • Pivot values are case sensitive; e.g., a value of Home will not match records with values of HOME or home, and it will generate a new column with Home as the name (or part of the name if an alias was specified).
  • As the pivot operation is executed via /aggregate/groupby, all Aggregation Limitations also apply