I am working on a NLP problem https://www.kaggle.com/c/nlp-getting-started. I want to perform vectorization after train_test_split but when I do that, the resulting sparse matrix has size = 1 which cannot be right. Below is my code:

def clean_text(text):
    tokens = nltk.word_tokenize(text)    #tokenizing the words
    lower = [word.lower() for word in tokens]  #converting words to lowercase
    remove_stopwords = [word for word in lower if word not in set(stopwords.words('english'))]  
    remove_char = [word for word in remove_stopwords if word.isalpha()]
    lemm_text = [ps.stem(word) for word in remove_char]     #lemmatizing the words
    cleaned_data = " ".join([str(word) for word in lemm_text])
    return cleaned_data

x['clean_text']= x["text"].map(clean_text)

x.drop(['text'], axis = 1, inplace = True)

from sklearn.model_selection import train_test_split
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size = 0.2, random_state = 69, 
stratify = y)

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
tfidf = TfidfVectorizer()
train_x_vect = tfidf.fit_transform(train_x)
test_x1 = tfidf.transform(test_x)


When I try to convert the sparse matrix (with size = 1) into a dataframe, it gives me error

ValueError: Index length mismatch: 4064 vs. 1

The dataframe x has size = 4064 and my sparse matrix has size = 1 which is why it is giving me error. Any help will be aprreciated!

  • $\begingroup$ What is the size of train_x_vect ? If it not same as train_x, then there is the problem. If it is same, then where you are converting it to dataframe is the problem. $\endgroup$
    – Ankit Seth
    Aug 23, 2021 at 16:24
  • 1
    $\begingroup$ The size of train_x_vect is (1,1). $\endgroup$
    – spectre
    Aug 24, 2021 at 7:40
  • $\begingroup$ Check your train_x size before using tfidf. Given tfidf is working properly, train_x may not be what you think it is. $\endgroup$
    – Ankit Seth
    Aug 24, 2021 at 12:27

1 Answer 1


You can convert the train_x to a list and pass that to your tfidf.fit_transform.

Approach 1

The updated code is:

from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
tfidf = TfidfVectorizer()
train_x_vect = tfidf.fit_transform(train_x['text'].tolist())

Approach 2

Rather than converting this to a list you can simply pass the training data along with the column name like this train_x['text'] inside your fit_transform


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