以下示例:Twitter data mining with Python and Gephi: Case synthetic biology
CSV to: df['Country', 'Responses']
'Country'
Italy
Italy
France
Germany
'Responses'
"Loren ipsum..."
"Loren ipsum..."
"Loren ipsum..."
"Loren ipsum..."
我可以完成步骤1和2,但在步骤3中出现错误:
TypeError: unhashable type: 'list'
我相信这是因为我在一个数据帧中工作,并进行了以下(可能是有害的)修改:
原始示例:
#divide to words
tokenizer = RegexpTokenizer(r'\w+')
words = tokenizer.tokenize(tweets)
我的代码:
#divide to words
tokenizer = RegexpTokenizer(r'\w+')
df['tokenized_sents'] = df['Responses'].apply(nltk.word_tokenize)
我的完整代码:
df = pd.read_csv('CountryResponses.csv', encoding='utf-8', skiprows=0, error_bad_lines=False)
tokenizer = RegexpTokenizer(r'\w+')
df['tokenized_sents'] = df['Responses'].apply(nltk.word_tokenize)
words = df['tokenized_sents']
#remove 100 most common words based on Brown corpus
fdist = FreqDist(brown.words())
mostcommon = fdist.most_common(100)
mclist = []
for i in range(len(mostcommon)):
mclist.append(mostcommon[i][0])
words = [w for w in words if w not in mclist]
Out: ['the',
',',
'.',
'of',
'and',
...]
#keep only most common words
fdist = FreqDist(words)
mostcommon = fdist.most_common(100)
mclist = []
for i in range(len(mostcommon)):
mclist.append(mostcommon[i][0])
words = [w for w in words if w not in mclist]
TypeError: unhashable type: 'list'
有很多问题都列在不好的清单上,但据我所知,没有一个问题是完全相同的。 有什么建议吗?谢谢
回溯
TypeError Traceback (most recent call last)
<ipython-input-164-a0d17b850b10> in <module>()
1 #keep only most common words
----> 2 fdist = FreqDist(words)
3 mostcommon = fdist.most_common(100)
4 mclist = []
5 for i in range(len(mostcommon)):
/home/*******/anaconda3/envs/*******/lib/python3.5/site-packages/nltk/probability.py in __init__(self, samples)
104 :type samples: Sequence
105 """
--> 106 Counter.__init__(self, samples)
107
108 def N(self):
/home/******/anaconda3/envs/******/lib/python3.5/collections/__init__.py in __init__(*args, **kwds)
521 raise TypeError('expected at most 1 arguments, got %d' % len(args))
522 super(Counter, self).__init__()
--> 523 self.update(*args, **kwds)
524
525 def __missing__(self, key):
/home/******/anaconda3/envs/******/lib/python3.5/collections/__init__.py in update(*args, **kwds)
608 super(Counter, self).update(iterable) # fast path when counter is empty
609 else:
--> 610 _count_elements(self, iterable)
611 if kwds:
612 self.update(kwds)
TypeError: unhashable type: 'list'
FreqDist
函数接受可散列对象的iterable(使其成为字符串,但它可能与任何对象一起工作)。您得到的错误是因为您传入了一个列表的iterable。正如您所建议的,这是因为您所做的更改:如果我正确理解了Pandas apply function documentation,那么这一行将
nltk.word_tokenize
函数应用于某个系列word-tokenize
返回单词列表作为解决方案,在尝试应用
FreqDist
之前,只需将列表添加到一起,如下所示:一个更完整的修订,做你想做的。如果您只需要识别第二组100,请注意
mclist
将在第二次识别相关问题 更多 >
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