verticapy.sql.functions.decode#

verticapy.sql.functions.decode(expr: str | list[str] | StringSQL | list[StringSQL], *args) → StringSQL#

Compares the expressions to each search value.

Parameters#

expr: SQLExpression

Expression.

args: SQLExpression

Infinite number of Expressions. The expression generated will look like:

even:: CASE … WHEN expr = args[2 * i] THEN args[2 * i + 1] … END
odd:: CASE … WHEN expr = args[2 * i] THEN args[2 * i + 1] … ELSE args[n] END

Returns#

StringSQL: SQL string.

Examples#

First, let’s import the vDataFrame in order to create a dummy dataset.

from verticapy import vDataFrame

Now, let’s import the VerticaPy SQL functions.

import verticapy.sql.functions as vpf

We can now build a dummy dataset.

df = vDataFrame({"x": ['banana', 'apple', 'onion', 'potato']})

Now, let’s go ahead and apply the function.

df["type_x"] = vpf.decode(
    df["x"],
    'banana', 'fruit',
    'apple', 'fruit',
    'vegetable',
)
display(df)

	Abc x Varchar(6) 100%	Abc type_x Varchar(9) 100%
1	banana	fruit
2	apple	fruit
3	onion	vegetable
4	potato	vegetable

Note

It’s crucial to utilize VerticaPy SQL functions in coding, as they can be updated over time with new syntax. While SQL functions typically remain stable, they may vary across platforms or versions. VerticaPy effectively manages these changes, a task not achievable with pure SQL.