snowflake.snowpark.DataFrame.unpivot

DataFrame.unpivot(value_column: str, name_column: str, column_list: List[Union[Column, str]], include_nulls: bool = False) → DataFrame[source] (https://github.com/snowflakedb/snowpark-python/blob/v1.26.0/snowpark-python/src/snowflake/snowpark/dataframe.py#L2377-L2454)

Rotates a table by transforming columns into rows. UNPIVOT is a relational operator that accepts two columns (from a table or subquery), along with a list of columns, and generates a row for each column specified in the list. In a query, it is specified in the FROM clause after the table name or subquery. Note that UNPIVOT is not exactly the reverse of PIVOT as it cannot undo aggregations made by PIVOT.

Parameters:

  • value_column – The name to assign to the generated column that will be populated with the values from the columns in the column list.

  • name_column – The name to assign to the generated column that will be populated with the names of the columns in the column list.

  • column_list – The names of the columns in the source table or subequery that will be narrowed into a single pivot column. The column names will populate name_column, and the column values will populate value_column.

  • include_nulls – If True, include rows with NULL values in name_column. The default value is False.

Example:

>>> df = session.create_dataframe([
...     (1, 'electronics', 100, 200),
...     (2, 'clothes', 100, 300)
... ], schema=["empid", "dept", "jan", "feb"])
>>> df = df.unpivot("sales", "month", ["jan", "feb"]).sort("empid")
>>> df.show()
---------------------------------------------
|"EMPID"  |"DEPT"       |"MONTH"  |"SALES"  |
---------------------------------------------
|1        |electronics  |JAN      |100      |
|1        |electronics  |FEB      |200      |
|2        |clothes      |JAN      |100      |
|2        |clothes      |FEB      |300      |
---------------------------------------------
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