Source code for featuretools.primitives.standard.transform.natural_language.mean_characters_per_word

# -*- coding: utf-8 -*-

import re

import numpy as np
import pandas as pd
from woodwork.column_schema import ColumnSchema
from woodwork.logical_types import Double, NaturalLanguage

from featuretools.primitives.base import TransformPrimitive

PUNCTUATION = re.escape("!,.:;?")

def _mean_characters_per_word(value):
    if pd.isna(value):
        return np.nan

    # replace end-of-sentence punctuation with space
    value = END_OF_SENTENCE_PUNCT_RE.sub(" ", value)
    words = value.split()
    character_count = [len(x) for x in words]

    return np.mean(character_count) if len(character_count) else 0

[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(series): return series.apply(_mean_characters_per_word) return mean_characters_per_word