So I have a dataframe X which looks something like this:
X.head()
0 My wife took me here on my birthday for breakf...
1 I have no idea why some people give bad review...
3 Rosie, Dakota, and I LOVE Chaparral Dog Park!!...
4 General Manager Scott Petello is a good egg!!!...
6 Drop what you're doing and drive here. After I...
Name: text, dtype: object
And then,
from sklearn.feature_extraction.text import CountVectorizer
cv = CountVectorizer()
X = cv.fit_transform(X)
But I get this error:
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-61-8ff79b91e317> in <module>()
----> 1 X = cv.fit_transform(X)
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in fit_transform(self, raw_documents, y)
867
868 vocabulary, X = self._count_vocab(raw_documents,
--> 869 self.fixed_vocabulary_)
870
871 if self.binary:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in _count_vocab(self, raw_documents, fixed_vocab)
790 for doc in raw_documents:
791 feature_counter = {}
--> 792 for feature in analyze(doc):
793 try:
794 feature_idx = vocabulary[feature]
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(doc)
264
265 return lambda doc: self._word_ngrams(
--> 266 tokenize(preprocess(self.decode(doc))), stop_words)
267
268 else:
~/anaconda3/lib/python3.6/site-packages/sklearn/feature_extraction/text.py in <lambda>(x)
230
231 if self.lowercase:
--> 232 return lambda x: strip_accents(x.lower())
233 else:
234 return strip_accents
~/anaconda3/lib/python3.6/site-packages/scipy/sparse/base.py in __getattr__(self, attr)
574 return self.getnnz()
575 else:
--> 576 raise AttributeError(attr + " not found")
577
578 def transpose(self, axes=None, copy=False):
AttributeError: lower not found
No idea why.
You need to specify the column name of the text data even if the dataframe has single column.
X_countMatrix = cv.fit_transform(X['text'])
Because a CountVectorizer expects an iterable as input and when you supply a dataframe as an argument, only thing thats iterated is the column names. So even if you did not have any errors, that would be incorrect. Lucky that you got an error and got a chance to correct it.