vastorbit.VastFrame.sort¶

VastFrame.sort(columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | dict) → VastFrame¶

Sorts the VastFrame using the input VastColumn.

Parameters:: columns (SQLColumns | dict) – List of the VastColumn used to sort the data, using asc order or dictionary of all sorting methods. For example, to sort by “column1” ASC and “column2” DESC, write: {"column1": "asc", "column2": "desc"}
Returns:: self
Return type:: VastFrame

Examples

Let’s begin by importing vastorbit.

import vastorbit as vo

Hint

By assigning an alias to vastorbit, we mitigate the risk of code collisions with other libraries. This precaution is necessary because vastorbit uses commonly known function names like “average” and “median”, which can potentially lead to naming conflicts. The use of an alias ensures that the functions from vastorbit are used as intended without interfering with functions from other libraries.

Let us create a VastFrame which we can sort:

vdf = vo.VastFrame(
    {
        "sales": [10, 11, 9, 20, 6],
        "cat": ['C', 'B', 'A', 'A', 'B'],
    },
)

	123 sales Integer	Abc cat Varchar(1)
1	10	C
2	9	A
3	6	B
4	11	B
5	20	A

Rows: 1-5 | Columns: 2

We can conveniently sort the VastFrame using a particular column:

vdf.sort({"sales": "asc"})

	123 sales Integer	Abc cat Varchar(1)
1	6	B
2	9	A
3	10	C
4	11	B
5	20	A

Rows: 1-5 | Columns: 2

The same operation can also be performed in descending order.

vdf.sort({"sales": "desc"})

	123 sales Integer	Abc cat Varchar(1)
1	20	A
2	11	B
3	10	C
4	9	A
5	6	B

Rows: 1-5 | Columns: 2

Note

Sorting the data is crucial to ensure consistent output. While VAST forgoes the use of indexes for enhanced performance, it does not guarantee a specific order of data retrieval.