scikit-learn 中的 TfidfVectorizer：ValueError：np.nan 是无效文档

我正在使用 scikit-learn 中的 TfidfVectorizer 从文本数据中提取一些特征。我有一个 CSV 文件，其中包含分数（可以是 +1 或 -1）和评论（文本）。我将这些数据提取到 DataFrame 中，以便可以运行 Vectorizer。

这是我的代码：

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'])

这是我得到的错误的回溯：

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.

我检查了 CSV 文件和 DataFrame 中是否有任何被读取为 NaN 的内容，但我找不到任何内容。有 18000 行，没有一行返回isnan为真。

这是什么df['Review'].head()好像：

  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

您需要转换数据类型object to unicode回溯中明确提到的字符串。

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

来自 TFIDF Vectorizer 的文档页面：

fit_transform(raw_documents, y=None)

参数： raw_documents ：可迭代
一个可迭代的，它产生str, unicode or 文件对象