# -*- coding: utf-8 -*-
import re
from featuretools.primitives.base import TransformPrimitive
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Double, NaturalLanguage
[docs]class MeanCharactersPerWord(TransformPrimitive):
"""Determines the mean number of characters per word.
Description:
Given list of strings, determine the mean number of
characters per word in each string. A word is defined as
a series of any characters not separated by white space.
Punctuation is removed before counting. If a string
is empty or `NaN`, return `NaN`.
Examples:
>>> x = ['This is a test file', 'This is second line', 'third line $1,000']
>>> mean_characters_per_word = MeanCharactersPerWord()
>>> mean_characters_per_word(x).tolist()
[3.0, 4.0, 5.0]
"""
name = "mean_characters_per_word"
input_types = [ColumnSchema(logical_type=NaturalLanguage)]
return_type = ColumnSchema(logical_type=Double, semantic_tags={'numeric'})
default_value = 0
def get_function(self):
def mean_characters_per_word(x):
x = x.reset_index(drop=True).fillna('')
# replace end-of-sentence punctuation with space
p = re.escape('!,.:;?')
end_of_sentence_punct = re.compile('[%s]+$|[%s]+ |[%s]+\n' % (p, p, p))
x = x.str.replace(end_of_sentence_punct, ' ')
# build DF of split words, and calculate avg length
df = x.str.split(expand=True)
df = df.reset_index()
df = df.melt(id_vars='index', var_name='word_index', value_name='word')
df['n_characters'] = df['word'].str.len()
results = df.groupby(by=['index']).mean(numeric_only=False)['n_characters']
return results
return mean_characters_per_word