"如何将用户定义的函数应用于熊猫数据框中的列？"

Question

I'm working on an NLP task. I defined a function to conduct aspect extraction and sentiment based on the aspect and dependency parsing. The function looks like:

import spacy
nlp = spacy.load('en_core_web_sm')

def aspect_sentiment(text):
    aspects = []
    for sentence in text:
        doc = nlp(sentence)
        descriptive_term = ''
        target = ''
        for token in doc:
            if token.dep_ == 'nsubj' and token.pos_ == 'NOUN':
                target = token.text
            if token.pos_ == 'ADJ':
                prepend = ''
                for child in token.children:
                    if child.pos_ != 'ADV':
                        continue
                    prepend += child.text + ' '
                descriptive_term = prepend + token.text
        aspects.append({'aspect': target, 'description': descriptive_term})
    
    for aspect in aspects:
        aspect['polarity'] = TextBlob(aspect['description']).sentiment.polarity
        aspect['subjectivity'] = TextBlob(aspect['description']).sentiment.subjectivity
    
    return(aspects)

Answer 1

You can use df[col].apply(fn)<\/code> instead, which will run the function once on each element in a pandas Series. Just need to adjust aspect_sentiment<\/code> a bit to expect individual sentences instead of a list of sentences.

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