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

import pandas as pd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import IntegerNullable

from featuretools.primitives.base.aggregation_primitive_base import AggregationPrimitive
from featuretools.utils.gen_utils import Library, import_or_none

dd = import_or_none("dask.dataframe")

[docs]class NumUnique(AggregationPrimitive): """Determines the number of distinct values, ignoring `NaN` values. Examples: >>> num_unique = NumUnique() >>> num_unique(['red', 'blue', 'green', 'yellow']) 4 `NaN` values will be ignored. >>> num_unique(['red', 'blue', 'green', 'yellow', None]) 4 """ name = "num_unique" input_types = [ColumnSchema(semantic_tags={"category"})] return_type = ColumnSchema(logical_type=IntegerNullable, semantic_tags={"numeric"}) stack_on_self = False compatibility = [Library.PANDAS, Library.DASK, Library.SPARK] description_template = "the number of unique elements in {}" def get_function(self, agg_type=Library.PANDAS): if agg_type == Library.DASK: def chunk(s): def inner_chunk(x): x = x[:].dropna() return set(x.unique()) return s.agg(inner_chunk) def agg(s): def inner_agg(x): x = x[:].dropna() return set().union(*x.values) return s.agg(inner_agg) def finalize(s): return s.apply(lambda x: len(x)) return dd.Aggregation(, chunk=chunk, agg=agg, finalize=finalize) elif agg_type == Library.SPARK: return "nunique" return pd.Series.nunique