Need to create dictionary of idf values, associating words with their idf values - tfidfvectorizer

Question

I understand how to get the idf values and vocabulary using the vectorizer. With vocabulary the frequency of the word is the value and the word is the key of a dictionary, however, what I want the value to be is the idf value.
I haven't been able to try anything because I don't know how to work with sklearn.
from sklearn.feature_extraction.text import TfidfVectorizer
# list of text documents
text = ["The quick brown fox jumped over the lazy dog.",
"The dog.",
"The fox"]
# create the transform
vectorizer = TfidfVectorizer()
# tokenize and build vocab
vectorizer.fit(text)
# summarize
print(vectorizer.vocabulary_)
print(vectorizer.idf_)
# encode document
vector = vectorizer.transform([text[0]])
# summarize encoded vector
print(vector.shape)
print(vector.toarray())
The code provided above is what I was originally trying to work with.
I have since come up with a new solution that does not use scikit:
for string in text_array:
for word in string:
if word not in total_dict.keys(): # build up a word frequency in the dictionary
total_dict[word] = 1
else:
total_dict[word] += 1
for word in total_dict.keys(): # calculate the tf-idf of each word in the dictionary using this url: https://nlpforhackers.io/tf-idf/
total_dict[word] = math.log(len(text_array) / float(1 + total_dict[word]))
print("word", word, ":" , total_dict[word])
Let me know if the code snippet above is enough to allow a reasonable estimation of what is going on. I included a link to what I was using for guidance.

Answer 1

You can directly use vectorizer.fit_transform(text) for the first time.
What it does is build a vocabulary set according to all the word/tokens in the text.
And then you can use vectorizer.transform(anothertext) to vectorize another text with the same mapping as the previous text.
More explanation:
fit() is to learn vocabulary and idf from training set. transform() is to transform the documents based on the learned vocabulary from the previous fit().
So you should only do fit() once, and can transform many times.