TfidfVectorizer in scikit-learn : ValueError: np.nan is an invalid document

I'm using TfidfVectorizer from scikit-learn to do some feature extraction from text data. I have a CSV file with a Score (can be +1 or -1) and a Review (text). I pulled this data into a DataFrame so I can run the Vectorizer.

This is my code:

import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer

df = pd.read_csv("train_new.csv",
             names = ['Score', 'Review'], sep=',')

# x = df['Review'] == np.nan
#
# print x.to_csv(path='FindNaN.csv', sep=',', na_rep = 'string', index=True)
#
# print df.isnull().values.any()

v = TfidfVectorizer(decode_error='replace', encoding='utf-8')
x = v.fit_transform(df['Review'])

This is the traceback for the error I get:

Traceback (most recent call last):
  File "/home/PycharmProjects/Review/src/feature_extraction.py", line 16, in <module>
x = v.fit_transform(df['Review'])
 File "/home/b/hw1/local/lib/python2.7/site-   packages/sklearn/feature_extraction/text.py", line 1305, in fit_transform
   X = super(TfidfVectorizer, self).fit_transform(raw_documents)
 File "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 817, in fit_transform
self.fixed_vocabulary_)
 File "/home/b/work/local/lib/python2.7/site- packages/sklearn/feature_extraction/text.py", line 752, in _count_vocab
   for feature in analyze(doc):
 File "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 238, in <lambda>
tokenize(preprocess(self.decode(doc))), stop_words)
 File "/home/b/work/local/lib/python2.7/site-packages/sklearn/feature_extraction/text.py", line 118, in decode
 raise ValueError("np.nan is an invalid document, expected byte or "
 ValueError: np.nan is an invalid document, expected byte or unicode string.

I checked the CSV file and DataFrame for anything that's being read as NaN but I can't find anything. There are 18000 rows, none of which return isnan as True.

This is what df['Review'].head() looks like:

  0    This book is such a life saver.  It has been s...
  1    I bought this a few times for my older son and...
  2    This is great for basics, but I wish the space...
  3    This book is perfect!  I'm a first time new mo...
  4    During your postpartum stay at the hospital th...
  Name: Review, dtype: object

You need to convert the dtype object to unicode string as is clearly mentioned in the traceback.

x = v.fit_transform(df['Review'].values.astype('U'))  ## Even astype(str) would work

From the Doc page of TFIDF Vectorizer:

fit_transform(raw_documents, y=None)

Parameters: raw_documents : iterable
an iterable which yields either str, unicode or file objects

I find a more efficient way to solve this problem.

x = v.fit_transform(df['Review'].apply(lambda x: np.str_(x)))

Of course you can use df['Review'].values.astype('U') to convert the entire Series. But I found using this function will consume much more memory if the Series you want to convert is really big. (I test this with a Series with 80w rows of data, and doing this astype('U') will consume about 96GB of memory)

Instead, if you use the lambda expression to only convert the data in the Series from str to numpy.str_, which the result will also be accepted by the fit_transform function, this will be faster and will not increase the memory usage.

I'm not sure why this will work because in the Doc page of TFIDF Vectorizer: