vastorbit.VastFrame.narrow¶

VastFrame.narrow(index: Annotated[str | list[str], 'STRING representing one column or a list of columns'], columns: Annotated[str | list[str], 'STRING representing one column or a list of columns'] | None = None, col_name: str = 'column', val_name: str = 'value') → VastFrame¶

Returns the Narrow Table of the VastFrame using the input VastColumns.

Parameters:

index (SQLColumns) – Index(es) used to identify the Row.
columns (SQLColumns, optional) – List of the VastColumns names. If empty, all VastColumns except the index(es) are used.
col_name (str, optional) – Alias of the VastColumn representing the different input VastColumns names as categories.
val_name (str, optional) – Alias of the VastColumn representing the different input VastColumns values.

Returns:

the narrow table object.

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.

For this example, let’s generate a dataset which has multiple columns:

vdf = vo.VastFrame(
    {
        "id": [12, 11, 13],
        "state": [12, 11, 13],
        "size":[100, 120, 140],
        "score": [9, 9.5, 4],
        "extra_info": ['Grey', 'Black', 'White'],
    }
)

	123 id Integer	123 state Integer	123 size Integer	123 score Decimal(11, 1)	Abc extra_info Varchar(5)
1	11	11	120	9.5	Black
2	13	13	140	4.0	White
3	12	12	100	9.0	Grey

Rows: 1-3 | Columns: 5

To focus only on the quantities of interest, we can utilize the narrow function:

vdf.narrow("id", col_name = "state", val_name = "score")

	123 id Integer	Abc state Varchar(5)	123 score Decimal(11, 1)
1	11	score	9.5
2	12	state	12.0
3	13	state	13.0
4	12	score	9.0
5	13	size	140.0
6	12	size	100.0
7	11	size	120.0
8	11	state	11.0
9	13	score	4.0

Rows: 1-9 | Columns: 3

Note

The inverse function of pivot is narrow. With both, you can preprocess the table either vertically or horizontally. These functions utilize pure SQL statements to perform the job.