verticapy.vDataColumn.rename¶

vDataColumn.rename(new_name: str, inplace: bool = True) → vDataFrame¶

Renames the vDataColumn. This function is not directly applied to the input object.

Warning

SQL code generation will be slower if the vDataFrame has been transformed multiple times, so it’s better to use this method when first preparing your data. It is even recommended to use the vDataFrame.select() method directly and perform all renaming within a single operation.

Parameters¶

new_name: str: The new vDataColumn alias.
inplace: bool, optional: If set to True, the vDataFrame is replaced with the new relation.

Returns¶

vDataFrame: result.

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.

Let us create a dummy dataset and rename one of its columns:

vdf = vp.vDataFrame(
    {
        "val" : [0, 10, 20],
        "cat": ['a', 'b', 'c'],
    },
)

We can copy the “val” column, and name the new column:

vdf["val"].rename("value")

	Abc cat Varchar(1) 100%	123 value Integer 100%
1	a	0
2	b	10
3	c	20