verticapy.sql.functions.log#

verticapy.sql.functions.log(expr: str | list[str] | StringSQL | list[StringSQL], base: int = 10) → StringSQL#

Logarithm.

Parameters#

expr: SQLExpression: Expression.
base: int: Specifies the base.

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": [2, 10, 16, 33]})
df["x"].astype("float")

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

df["log_x"] = vpf.log(df["x"])
display(df)

	123 x Float 100%	123 log_x Float(22) 100%
1	2.0	0.301029995663981
2	10.0	1.0
3	16.0	1.20411998265592
4	33.0	1.51851393987789

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.