Issues with TfidifVecotorizer() for fake news project

Question

I am following Dataflair for a fake news project and using Jupyter notebook. I am following along the code that is provided and have been able to fix some errors but I am having an issue with the tfidf_train, it gives me an error as well as np.nan is an invalid document, expected byte or unicode string.

 import numpy as np
    import pandas as pd
    import itertools
    from sklearn.model_selection import train_test_split
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.linear_model import PassiveAggressiveClassifier
    from sklearn.metrics import accuracy_score, confusion_matrix
    from sklearn.feature_extraction.text import CountVectorizer`

I imported the tools as above

Then i imported the database provided by Dataflair:

df=pd.read_csv('C:/Users/cmisl/OneDrive/Desktop/fake_news.csv')
    df.shape
    df.head()

the table does show NaN values. Is there a way to remove them?

Next I used:

x_train, x_test, y_train, y_test = train_test_split(df['text'], labels, test_size=0.2,       random_state=7)
    tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)
    count_vectorizer = CountVectorizer(stop_words='english')
    tfidf_train= vectorize.fit_transform(x_train) 
    tfidf_test = vectorize.transform(x_test)

And these are the errors I encounter:

ValueError                                Traceback (most recent call last)
    Cell In[28], line 3
      1 tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)
      2 count_vectorizer = CountVectorizer(stop_words='english')
----> 3 tfidf_train= vectorize.fit_transform(x_train) 
      4 tfidf_test = vectorize.transform(x_test)

    File ~\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:2121, in     TfidfVectorizer.fit_transform(self, raw_documents, y)
   2114 self._check_params()
   2115 self._tfidf = TfidfTransformer(
   2116     norm=self.norm,
   2117     use_idf=self.use_idf,
   2118     smooth_idf=self.smooth_idf,
   2119     sublinear_tf=self.sublinear_tf,
   2120 )
-> 2121 X = super().fit_transform(raw_documents)
   2122 self._tfidf.fit(X)
   2123 # X is already a transformed view of raw_documents so
   2124 # we set copy to False

    File ~\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:1377, in CountVectorizer.fit_transform(self, raw_documents, y)
   1369             warnings.warn(
   1370                 "Upper case characters found in"
   1371                 " vocabulary while 'lowercase'"
   1372                 " is True. These entries will not"
   1373                 " be matched with any documents"
   1374             )
   1375             break
-> 1377 vocabulary, X = self._count_vocab(raw_documents, self.fixed_vocabulary_)
   1379 if self.binary:
   1380     X.data.fill(1)

    File ~\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:1264, in CountVectorizer._count_vocab(self, raw_documents, fixed_vocab)
   1262 for doc in raw_documents:
   1263     feature_counter = {}
-> 1264     for feature in analyze(doc):
   1265         try:
   1266             feature_idx = vocabulary[feature]

    File ~\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:106, in _analyze(doc, analyzer, tokenizer, ngrams, preprocessor, decoder, stop_words)
     84 """Chain together an optional series of text processing steps to go from
     85 a single document to ngrams, with or without tokenizing or preprocessing.
     86 
   (...)
    102     A sequence of tokens, possibly with pairs, triples, etc.
    103 """
    105 if decoder is not None:
--> 106     doc = decoder(doc)
    107 if analyzer is not None:
    108     doc = analyzer(doc)

    File ~\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py:239, in _VectorizerMixin.decode(self, doc)
    236     doc = doc.decode(self.encoding, self.decode_error)
    238 if doc is np.nan:
--> 239     raise ValueError(
    240         "np.nan is an invalid document, expected byte or unicode string."
    241     )
    243 return doc

    

ValueError: np.nan is an invalid document, expected byte or unicode string.

Not sure if I should fill in the Nan values? Thanks for your help!