Оптимизация кода

import math
from collections import Counter
 
def read_data(data=True):
    df = open('war_peace_processed.txt', 'rt').read()
    if data:
        return df.split('\n')
    else:
        df = df.split('[new chapter]')
        return [i.split('\n') for i in df]
 
def df_calc(target_word):
    corpus = read_data(data=False) # corpus
    number_of_documents_with_target_word = 0
    for chapter in corpus:
        if target_word in chapter:
            number_of_documents_with_target_word += 1
    return number_of_documents_with_target_word / len(corpus)
 
def tf_calc(target_word, target_chapter):
    corpus = read_data(data=False) # corpus
    sum_word_in_chapter = 0
    n_target_word_in_chapter = len(corpus[target_chapter])
    for term in corpus[target_chapter]:
        if term == target_word:
            sum_word_in_chapter += 1
    return sum_word_in_chapter / n_target_word_in_chapter
 
def tfidf(target_word, target_chapter):
    tf = tf_calc(target_word, target_chapter)
    idf = 1 / df_calc(target_word)
    return math.log(1 + tf) * math.log(idf)
 
def calc_all_tfidf(target_chapter):
    tfidf_dict = {}
    corp = read_data(data=False)[target_chapter]
    for w in corp:
        tfidf_dict[w] = tfidf(w, target_chapter)
        res = dict(Counter(tfidf_dict).most_common(5))
        ds = {k: v for k, v in sorted(res.items(), key=lambda item: item[1])}
    return list(ds.keys())[4:1:-1] (edited)

import math
from collections import Counter
 
 
def read_data(data=True):
    df = open('war_peace_processed.txt', 'rt').read()
    if data:
        return df.split('\n')
    else:
        df = df.split('[new chapter]')
        return [i.split('\n') for i in df]
 
 
def df_calc(target_word):
    corpus = read_data(data=False)  # corpus
    number_of_documents_with_target_word = 0
    for chapter in corpus:
        if target_word in chapter:
            number_of_documents_with_target_word += 1
    return number_of_documents_with_target_word / len(corpus)
 
 
def tf_calc(target_word, target_chapter):
    corpus = read_data(data=False)  # corpus
    sum_word_in_chapter = 0
    n_target_word_in_chapter = len(corpus[target_chapter])
    for term in corpus[target_chapter]:
        if term == target_word:
            sum_word_in_chapter += 1
    return sum_word_in_chapter / n_target_word_in_chapter
 
 
def tfidf(target_word, target_chapter):
    tf = tf_calc(target_word, target_chapter)
    idf = 1 / df_calc(target_word)
    return math.log(1 + tf) * math.log(idf)
 
 
def calc_all_tfidf(target_chapter):
    tfidf_dict = {}
    corp = read_data(data=False)[target_chapter]
    for w in corp:
        tfidf_dict[w] = tfidf(w, target_chapter)
    res = dict(Counter(tfidf_dict).most_common(5))
    ds = {k: v for k, v in sorted(res.items(), key=lambda item: item[1])}
    return list(ds.keys())[4:1:-1]

def get_corpus():
    with open('war_peace_processed.txt', 'r') as f:
        return [i.split('\n') for i in f.read().split('[new chapter]')]
 
 
def df_calc(corpus, target_word):
    number_of_documents_with_target_word = 0
    for chapter in corpus:
        if target_word in chapter:
            number_of_documents_with_target_word += 1
    return number_of_documents_with_target_word / len(corpus)
 
 
def tf_calc(corpus, target_word, target_chapter):
    sum_word_in_chapter = 0
    n_target_word_in_chapter = len(corpus[target_chapter])
    for term in corpus[target_chapter]:
        if term == target_word:
            sum_word_in_chapter += 1
    return sum_word_in_chapter / n_target_word_in_chapter
 
 
def tfidf(corpus, target_word, target_chapter):
    tf = tf_calc(corpus, target_word, target_chapter)
    idf = 1 / df_calc(corpus, target_word)
    return math.log(1 + tf) * math.log(idf)