python - 无法标记数据框中的多个列

Question

这个问题已经有答案了:

How to apply NLTK word_tokenize library on a Pandas dataframe for Twitter data? (1 个回答)

已关闭 4 年前。

我有一个表，其中包含数字和字符串数据，但位于不同的列中。该表是网络表单的答案，包含空单元格。我想对字符串列使用文本处理。我无法删除包含空单元格的行，因此对于空字符串列，我用 aplhabet 'a' 替换了 NaN。

示例数据

colmun_name1    column_name2     column_name3 column_name4 classify
This is a cat   This is a dog    1            2            0
This is a rat   This is a mouse  45           32           1
a               Good mouse       0            0            0 

我使用以下代码来确保字符串列中的所有数据实际上都是字符串数据。

df2=df[[column_name1, column_name2]]
for i in range(0,len(df2)):
cell=df2.iloc[i]
cell=str(str)
df2.iloc[i]=cell

然后当我标记化时，我收到错误

    <ipython-input-64-24a99733ba19> in <module>
      1 from nltk.tokenize import word_tokenize
----> 2 tokenized_word=word_tokenize(df2)
      3 print(tokenized_word)

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/__init__.py in word_tokenize(text, language, preserve_line)
    126     :type preserver_line: bool
    127     """
--> 128     sentences = [text] if preserve_line else sent_tokenize(text, language)
    129     return [token for sent in sentences
    130             for token in _treebank_word_tokenizer.tokenize(sent)]

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/__init__.py in sent_tokenize(text, language)
     93     """
     94     tokenizer = load('tokenizers/punkt/{0}.pickle'.format(language))
---> 95     return tokenizer.tokenize(text)
     96 
     97 # Standard word tokenizer.

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in tokenize(self, text, realign_boundaries)
   1239         Given a text, returns a list of the sentences in that text.
   1240         """
-> 1241         return list(self.sentences_from_text(text, realign_boundaries))
   1242 
   1243     def debug_decisions(self, text):

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in sentences_from_text(self, text, realign_boundaries)
   1289         follows the period.
   1290         """
-> 1291         return [text[s:e] for s, e in self.span_tokenize(text, realign_boundaries)]
   1292 
   1293     def _slices_from_text(self, text):

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in <listcomp>(.0)
   1289         follows the period.
   1290         """
-> 1291         return [text[s:e] for s, e in self.span_tokenize(text, realign_boundaries)]
   1292 
   1293     def _slices_from_text(self, text):

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in span_tokenize(self, text, realign_boundaries)
   1279         if realign_boundaries:
   1280             slices = self._realign_boundaries(text, slices)
-> 1281         for sl in slices:
   1282             yield (sl.start, sl.stop)
   1283 

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in _realign_boundaries(self, text, slices)
   1320         """
   1321         realign = 0
-> 1322         for sl1, sl2 in _pair_iter(slices):
   1323             sl1 = slice(sl1.start + realign, sl1.stop)
   1324             if not sl2:

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in _pair_iter(it)
    311     """
    312     it = iter(it)
--> 313     prev = next(it)
    314     for el in it:
    315         yield (prev, el)

/anaconda3/lib/python3.6/site-packages/nltk/tokenize/punkt.py in _slices_from_text(self, text)
   1293     def _slices_from_text(self, text):
   1294         last_break = 0
-> 1295         for match in self._lang_vars.period_context_re().finditer(text):
   1296             context = match.group() + match.group('after_tok')
   1297             if self.text_contains_sentbreak(context):

TypeError: expected string or bytes-like object

我尝试改变

df2=df[column_name1][column_name2]

但我遇到了同样的错误。

我应该做什么？

Answer 1

请参阅How to apply NLTK word_tokenize library on a Pandas dataframe for Twitter data?

TL;DR

# Creates a `colmun_name1_tokenized` column by 
# taking the `colmun_name1` column and 
# applying the word_tokenize function on every cell in the column. 

>>> df['colmun_name1_tokenized'] = df['colmun_name1'].apply(word_tokenize)

>>> df.head()
    colmun_name1     column_name2  column_name3  column_name4  classify  \
0  This is a cat    This is a dog             1             2         0   
1  This is a rat  This is a mouse            45            32         1   
2              a       Good mouse             0             0         0   

  colmun_name1_tokenized  
0     [This, is, a, cat]  
1     [This, is, a, rat]  
2                    [a]  

如果您需要对多个列进行标记，并且希望使用标记化输出覆盖该列:

>>> with StringIO(file_str) as fin:
...     df = pd.read_csv(fin, sep='\t')
... 
>>> for col_name in ['colmun_name1', 'column_name2']:
...     df[col_name] = df[col_name].apply(word_tokenize)
... 
>>> df.head()
         colmun_name1          column_name2  column_name3  column_name4  \
0  [This, is, a, cat]    [This, is, a, dog]             1             2   
1  [This, is, a, rat]  [This, is, a, mouse]            45            32   
2                 [a]         [Good, mouse]             0             0   

   classify  
0         0  
1         1  
2         0  

只是代码:

from io import StringIO

import pandas as pd

from nltk import word_tokenize

file_str = """colmun_name1\tcolumn_name2\tcolumn_name3\tcolumn_name4\tclassify
This is a cat\tThis is a dog\t1\t2\t0
This is a rat\tThis is a mouse\t45\t32\t1
a\tGood mouse\t0\t0\t0 """

with StringIO(file_str) as fin:
    df = pd.read_csv(fin, sep='\t')

for col_name in ['colmun_name1', 'column_name2']:
    df[col_name] = df[col_name].apply(word_tokenize)