兽化熊猫（Python）

Posted 2021-03-19

我是Pandas的初学者，我试图弄清楚如何对数据框的单个列进行定标。以下面的示例为例（这是（非）常用词去除后的一些文本，我想对其进行词形化处理：

0良好的需求变化使天然微酿啤酒...

1个新的收藏夹给人惊喜的发现...

2个最喜欢的红酱享受强劲的单宁好拉...

3种品质出色的1800年代21世纪尝试饮品...

4红初次尝试恋爱100优秀交融...

这是我用来进行词法化的代码（摘自here：

df['words'] = df['words'].apply(lambda x: "".join([Word(word).lemmatize() for word in x]))
df['words'].head()

但是运行此代码后，输出不会更改：

0好的需要改变维尔吉尔自然微酿造的酒...

1个新的收藏夹给人惊喜的发现...

2个最喜欢的红酱享受强劲的单宁好拉...

3种品质出色的1800年代21世纪尝试饮品...

4红初次尝试恋爱100优秀交融...

任何帮助将不胜感激：）

P.S：words是标记词的列表

答案

您可能不再需要解决方案，但是如果您想在许多pos上进行定理，则可以使用：

如果需要更多，可以尝试以下代码：

import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem.wordnet import WordNetLemmatizer
from nltk.corpus import wordnet
lemmatizer = nltk.stem.WordNetLemmatizer()
wordnet_lemmatizer = WordNetLemmatizer()
stop = stopwords.words('english')


def nltk_tag_to_wordnet_tag(nltk_tag):
    if nltk_tag.startswith('J'):
        return wordnet.ADJ
    elif nltk_tag.startswith('V'):
        return wordnet.VERB
    elif nltk_tag.startswith('N'):
        return wordnet.NOUN
    elif nltk_tag.startswith('R'):
        return wordnet.ADV
    else:
        return None

def lemmatize_sentence(sentence):
    #tokenize the sentence and find the POS tag for each token
    nltk_tagged = nltk.pos_tag(nltk.word_tokenize(sentence))
    #tuple of (token, wordnet_tag)
    wordnet_tagged = map(lambda x: (x[0], nltk_tag_to_wordnet_tag(x[1])), nltk_tagged)
    lemmatized_sentence = []
    for word, tag in wordnet_tagged:
        if tag is None:
            #if there is no available tag, append the token as is
            lemmatized_sentence.append(word)
        else:
            #else use the tag to lemmatize the token
            lemmatized_sentence.append(lemmatizer.lemmatize(word, tag))
    return " ".join(lemmatized_sentence)



# Lemmatizing
df['Lemmatize'] = df['word'].apply(lambda x: lemmatize_sentence(x))
print(df.head())

df结果：

         word                       |        Lemmatize

0  Best scores, good cats, it rocks | Best score , good cat , it rock

1          You received best scores |          You receive best score

2                         Good news |                       Good news

3                          Bad news |                        Bad news

4                    I am loving it |                    I be love it

5                    it rocks a lot |                   it rock a lot

6     it is still good to do better |     it be still good to do good