verticapy.vDataFrame.narrow#

vDataFrame.narrow(index: str | list[str], columns: str | list[str] | None = None, col_name: str = 'column', val_name: str = 'value') → vDataFrame#

Returns the Narrow Table of the vDataFrame using the input vDataColumns.

Parameters#

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

Returns#

vDataFrame: the narrow table object.

Examples#

Let’s begin by importing VerticaPy.

import verticapy as vp

Hint

By assigning an alias to verticapy, we mitigate the risk of code collisions with other libraries. This precaution is necessary because verticapy 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 verticapy are used as intended without interfering with functions from other libraries.

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

vdf = vp.vDataFrame(
    {
        "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 100%	...	123 score Numeric(21) 100%	Abc extra_info Varchar(5) 100%
1	12	...	9.0	Grey
2	11	...	9.5	Black
3	13	...	4.0	White

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 100%	...	Abc state Varchar(5) 100%	123 score Numeric(21) 100%
1	12	...	state	12.0
2	11	...	state	11.0
3	13	...	state	13.0
4	12	...	size	100.0
5	11	...	size	120.0
6	13	...	size	140.0
7	12	...	score	9.0
8	11	...	score	9.5
9	13	...	score	4.0

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.