Rewriting python code

Hello guys
my friend asked me if he can get my code and i wanna give it to him but im sure he won’t be able to rewrite it. Is it possible if somebody could rewrite my following code:

from math import log

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

vectorizer = TfidfVectorizer()

posting_list = imdb_all.iloc[intersection].sort_values('rating', ascending=False)


def create_query_term_count(posting_list, query_terms):
    posting_list['term_count'] = posting_list['plotterms'].apply(lambda x: len(x.split()))

    # count how many times any of the query terms appear in the plot
    posting_list['query_term_count'] = posting_list['plotterms'].apply(lambda x: len([item for item in x.split() if item in query_terms.split()]))


    # order by the query_term_count column and filter if query_term_count is greater than 0
    return posting_list[posting_list['query_term_count'] > 0].sort_values('query_term_count', ascending=False)



def inverse_document_frequency(all_documents, terms):
    num_documents_with_this_term = 0
    for term in terms.split():
        for index, doc in all_documents.iterrows():
            if term in doc['plotterms'].split():
                num_documents_with_this_term += 1
                if num_documents_with_this_term > 0:
                    return 1.0 + log(float(len(all_documents)) / num_documents_with_this_term)
                else:
                    return 1.0


def cos_sim(df, query):
    X = vectorizer.fit_transform(imdb_all['plotterms'])
    query_vec = vectorizer.transform([query])

    results = cosine_similarity(X,query_vec)
    results = results.flatten()
    imdb_all['cos_sim'] = results


def tf_idf(posting_list, query):
# calculate the term frequency
    tf = posting_list['query_term_count'] / posting_list['term_count']
    posting_list['term_frequency'] = tf
# calculate the inverse document frequency
    idf = inverse_document_frequency(imdb_all, query)
# calculate the TF-IDF score
    posting_list['tf_idf'] = tf * idf


def search(query):
    query_list = create_query_term_count(posting_list, query)
    tf_idf(query_list, query)
    cos_sim(query_list, query)

    return query_list


queries = ['american dream', 'american', 'dream']
for query in queries:
    query_list = search(query)
display(query_list)

best regards
willien

Rewrite your code to do what?

I already rewrote it so its okay