verticapy.vDataColumn.apply_fun#

vDataColumn.apply_fun(func: Literal['abs', 'acos', 'asin', 'atan', 'avg', 'cbrt', 'ceil', 'contain', 'count', 'cos', 'cosh', 'cot', 'dim', 'exp', 'find', 'floor', 'len', 'length', 'ln', 'log', 'log10', 'max', 'mean', 'mod', 'min', 'pow', 'round', 'sign', 'sin', 'sinh', 'sum', 'sqrt', 'tan', 'tanh'], x: bool | float | str | timedelta | datetime = 2) → vDataFrame#

Applies a default function to the vDataColumn.

Parameters#

func: str

Function to use to transform the vDataColumn.

abs:
absolute value
acos:
trigonometric inverse cosine
asin:
trigonometric inverse sine
atan:
trigonometric inverse tangent
avg / mean:
average
cbrt:
cube root
ceil:
value up to the next whole number
contain:
checks if x is in the collection
count:
number of non-null elements
cos:
trigonometric cosine
cosh:
hyperbolic cosine
cot:
trigonometric cotangent
dim:
dimension (only for arrays)
exp:
exponential function
find:
returns the ordinal position of a specified element in an array (only for arrays)
floor:
value down to the next whole number
len / length:
length
ln:
natural logarithm
log:
logarithm
log10:
base 10 logarithm
max:
maximum
min:
minimum
mod:
remainder of a division operation
pow:
number raised to the power of another number
round:
rounds a value to a specified number of decimal places
sign:
arithmetic sign
sin:
trigonometric sine
sinh:
hyperbolic sine
sqrt:
arithmetic square root
sum:
sum
tan:
trigonometric tangent
tanh:
hyperbolic tangent

x: PythonScalar, optional

If the function has two arguments (example, power or mod), x represents the second argument.

Returns#

vDataFrame: self._parent

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 with float values:

vdf = vp.vDataFrame({"val" : [0.2, 10.6, 20.1]})

	123 val Numeric(5) 100%
1	0.2
2	10.6
3	20.1

A ceil function can be conveniently applied using the apply_fun function. Below, we can round off the values of “val” column:

vdf["val"].apply_fun("ceil")

	123 val Numeric(5) 100%
1	1.0
2	11.0
3	21.0

Note

Applying a function will alter the vDataColumn structure. It’s advisable to check the current relation of the vDataFrame to ensure it aligns with the intended outcome. For more information on achieving that, check out the current_relation documentation.