我想在我的数据集中去除无意义词。

我在StackOverflow上看到了一些类似这样的方法并尝试了：

import nltkwords = set(nltk.corpus.words.words())sent = "Io andiamo to the beach with my amico."" ".join(w for w in nltk.wordpunct_tokenize(sent) \     if w.lower() in words or not w.isalpha())

但现在我有一个数据框，我该如何遍历整个列呢？

我尝试了类似这样的方法：

import nltkwords = set(nltk.corpus.words.words())sent = df['Chats']df['Chats'] = df['Chats'].apply(lambda w:" ".join(w for w in nltk.wordpunct_tokenize(sent) \     if w.lower() in words or not w.isalpha()))

但我得到了一个错误 TypeError: expected string or bytes-like object

回答：

像下面这样的代码将生成一个名为Clean的新列，并将你的函数应用于Chats列

words = set(nltk.corpus.words.words())def clean_sent(sent):    return " ".join(w for w in nltk.wordpunct_tokenize(sent) \     if w.lower() in words or not w.isalpha())df['Clean'] = df['Chats'].apply(clean_sent)

要更新Chats列本身，可以使用原始列覆盖它：

df['Chats'] = df['Chats'].apply(clean_sent)