# Source code for featuretools.primitives.standard.transform.exponential.exponential_weighted_variance

```from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Double

from featuretools.primitives.base import TransformPrimitive

[docs]class ExponentialWeightedVariance(TransformPrimitive):
"""Computes the exponentially weighted moving variance for a series of numbers

Description:
Returns the exponentially weighted moving variance for a series of
numbers. Exactly one of center of mass (com), span, half-life, and
alpha must be provided. Missing values can be ignored when calculating
weights by setting 'ignore_na' to True.

Args:
com (float): Specify decay in terms of center of mass for com >= 0.
Default is None.

span (float): Specify decay in terms of span for span >= 1.
Default is None.

halflife (float): Specify decay in terms of half-life for halflife > 0.
Default is None.

alpha (float): Specify smoothing factor alpha directly. Alpha should be
greater than 0 and less than or equal to 1. Default is None.

ignore_na (bool): Ignore missing values when calculating weights.
Default is False.

Examples:
>>> exponential_weighted_variance = ExponentialWeightedVariance(com=0.5)
>>> exponential_weighted_variance([1, 2, 3, 4]).tolist()
[nan, 0.49999999999999983, 0.8461538461538459, 1.1230769230769233]

Missing values can be ignored

>>> ewmv_ignorena = ExponentialWeightedVariance(com=0.5, ignore_na=True)
>>> ewmv_ignorena([1, 2, 3, None, 4]).tolist()
[nan, 0.49999999999999983, 0.8461538461538459, 0.8461538461538459, 1.1230769230769233]
"""

name = "exponential_weighted_variance"
input_types = [ColumnSchema(semantic_tags={"numeric"})]
return_type = ColumnSchema(logical_type=Double, semantic_tags={"numeric"})
uses_full_dataframe = True

[docs]    def __init__(self, com=None, span=None, halflife=None, alpha=None, ignore_na=False):
if all(x is None for x in [com, span, halflife, alpha]):
com = 0.5
self.com = com
self.span = span
self.halflife = halflife
self.alpha = alpha
self.ignore_na = ignore_na

def get_function(self):
def exponential_weighted_average(x):
return x.ewm(
com=self.com,
span=self.span,
halflife=self.halflife,
alpha=self.alpha,
ignore_na=self.ignore_na,
).var()

return exponential_weighted_average
```