Source code for featuretools.primitives.standard.aggregation.count_below_mean

import numpy as np
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import IntegerNullable

from featuretools.primitives.base.aggregation_primitive_base import AggregationPrimitive

[docs]class CountBelowMean(AggregationPrimitive): """Determines the number of values that are below the mean. Args: skipna (bool): Determines if to use NA/null values. Defaults to True to skip NA/null. Examples: >>> count_below_mean = CountBelowMean() >>> count_below_mean([1, 2, 3, 4, 10]) 3 The way NaNs are treated can be controlled. >>> count_below_mean_skipna = CountBelowMean(skipna=False) >>> count_below_mean_skipna([1, 2, 3, 4, 5, None]) nan """ name = "count_below_mean" input_types = [ColumnSchema(semantic_tags={"numeric"})] return_type = ColumnSchema(logical_type=IntegerNullable, semantic_tags={"numeric"}) stack_on_self = False
[docs] def __init__(self, skipna=True): self.skipna = skipna
def get_function(self): def count_below_mean(x): mean = x.mean(skipna=self.skipna) if np.isnan(mean): return np.nan return len(x[x < mean]) return count_below_mean