Question

我有一个包含大约40列的数据集，并在其中5个使用.apply(word_tokenize)，如下所示：df['token_column'] = df.column.apply(word_tokenize)。

我只为其中一列获取了TypeError，我们会调用此problem_column

TypeError: expected string or bytes-like object

这里是完整的错误（剥离的df和列名，以及pii），我是Python的新手，我仍在试图弄清楚错误消息的哪些部分是相关的：

TypeError                                 Traceback (most recent call last)
<ipython-input-51-22429aec3622> in <module>()
----> 1 df['token_column'] = df.problem_column.apply(word_tokenize)

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)
   2353             else:
   2354                 values = self.asobject
-> 2355                 mapped = lib.map_infer(values, f, convert=convert_dtype)
   2356 
   2357         if len(mapped) and isinstance(mapped[0], Series):

pandas\_libs\src\inference.pyx in pandas._libs.lib.map_infer (pandas\_libs\lib.c:66440)()

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\__init__.py in word_tokenize(text, language, preserve_line)
    128     :type preserver_line: bool
    129     """
--> 130     sentences = [text] if preserve_line else sent_tokenize(text, language)
    131     return [token for sent in sentences
    132             for token in _treebank_word_tokenizer.tokenize(sent)]

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\__init__.py in sent_tokenize(text, language)
     95     """
     96     tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
---> 97     return tokenizer.tokenize(text)
     98 
     99 # Standard word tokenizer.

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in tokenize(self, text, realign_boundaries)
   1233         Given a text, returns a list of the sentences in that text.
   1234         """
-> 1235         return list(self.sentences_from_text(text, realign_boundaries))
   1236 
   1237     def debug_decisions(self, text):

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in sentences_from_text(self, text, realign_boundaries)
   1281         follows the period.
   1282         """
-> 1283         return [text[s:e] for s, e in self.span_tokenize(text, realign_boundaries)]
   1284 
   1285     def _slices_from_text(self, text):

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in span_tokenize(self, text, realign_boundaries)
   1272         if realign_boundaries:
   1273             slices = self._realign_boundaries(text, slices)
-> 1274         return [(sl.start, sl.stop) for sl in slices]
   1275 
   1276     def sentences_from_text(self, text, realign_boundaries=True):

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in <listcomp>(.0)
   1272         if realign_boundaries:
   1273             slices = self._realign_boundaries(text, slices)
-> 1274         return [(sl.start, sl.stop) for sl in slices]
   1275 
   1276     def sentences_from_text(self, text, realign_boundaries=True):

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in _realign_boundaries(self, text, slices)
   1312         """
   1313         realign = 0
-> 1314         for sl1, sl2 in _pair_iter(slices):
   1315             sl1 = slice(sl1.start + realign, sl1.stop)
   1316             if not sl2:

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in _pair_iter(it)
    310     """
    311     it = iter(it)
--> 312     prev = next(it)
    313     for el in it:
    314         yield (prev, el)

C:\Users\egagne\AppData\Local\Continuum\Anaconda3\lib\site-packages\nltk\tokenize\punkt.py in _slices_from_text(self, text)
   1285     def _slices_from_text(self, text):
   1286         last_break = 0
-> 1287         for match in self._lang_vars.period_context_re().finditer(text):
   1288             context = match.group() + match.group('after_tok')
   1289             if self.text_contains_sentbreak(context):

TypeError: expected string or bytes-like object

5列都是字符/字符串（在SQL Server，SAS中验证并使用.select_dtypes(include=[object]))。

为了更好的衡量，我使用.to_string()来确保problem_column确实不是除了字符串之外的任何东西，但我继续得到错误。如果我单独处理列，则good_column1-good_column4继续工作，而problem_column仍会生成错误。

我已经用Google搜索了一下，除了剥离集合中的任何数字（我无法做到，因为这些都是有意义的）我还没有找到任何额外的修复。

Answer 1

问题是您的DF中有无（NA）类型。试试这个：

df['label'].dropna(inplace=True)
tokens = df['label'].apply(word_tokenize)

Answer 2

尝试

from nltk.tokenize import word_tokenize as WordTokenizer

def word_tokenizer(data, col):
    token=[]
    for item in data[col]:
         token.append(WordTokenizer(item))

    return token

token = word_tokenizer(df, column)
df. insert(index, 'token_column', token)

Answer 3

这就是我得到了理想的结果。

def custom_tokenize(text):
    if not text:
        print('The text to be tokenized is a None type. Defaulting to blank string.')
        text = ''
    return word_tokenize(text)
df['tokenized_column'] = df.column.apply(custom_tokenize)

Answer 4

可能显示错误，因为word_tokenize()一次只接受1个字符串。您可以循环遍历字符串，然后对其进行标记。

例如：

text = "This is the first sentence. This is the second one. And this is the last one."
sentences = sent_tokenize(text)
words = [word_tokenize(sent) for sent in sentences]
print(words)

TypeError：期望字符串或类字节对象 - 使用Python / NLTK word_tokenize

4 个答案: