Python 查找“的总数”；停止语；归档
我正在尝试创建一个Python程序，它读取两个文本文件，一个包含一篇文章，另一个包含“停止词”（每行一个词）列表。我想确定我正在使用的包含文章的特定文本文件（每个“停止词”出现频率的累计总和）中有多少“停止词” 我尝试创建嵌套的Python 查找“的总数”；停止语；归档,python,for-loop,readfile,readlines,Python,For Loop,Readfile,Readlines,我正在尝试创建一个Python程序，它读取两个文本文件，一个包含一篇文章，另一个包含“停止词”（每行一个词）列表。我想确定我正在使用的包含文章的特定文本文件（每个“停止词”出现频率的累计总和）中有多少“停止词” 我尝试创建嵌套的for循环，以便在包含文章的文件的每一行中循环（外部为循环），在每一行中有一个for循环（内部为循环），循环“停止字”列表，并查看“停止字”是否在当前行中，如果是，频率如何。最后，我将单词在当前行中的频率添加到累加器中，累加器将跟踪在包含文章的文件中找到的停止词的累计总量
forimport string
def main():
analyzed_file = open('LearnToCode_LearnToThink.txt', 'r')
stop_word_file = open('stopwords.txt', 'r')
stop_word_accumulator = 0
for analyzed_line in analyzed_file.readlines():
formatted_line = remove_punctuation(analyzed_line)
for stop_word_line in stop_word_file.readlines():
stop_formatted_line = create_stopword_list(stop_word_line)
if stop_formatted_line in formatted_line:
stop_word_frequency = formatted_line.count(stop_formatted_line)
stop_word_accumulator += stop_word_frequency
print("there are ",stop_word_accumulator, " words")
stop_word_file.close()
analyzed_file.close()
def create_stopword_list(stop_word_text):
clean_words = [] # create an empty list
stop_word_text = stop_word_text.rstrip() # remove trailing whitespace characters
new_words = stop_word_text.split() # create a list of words from the text
for word in new_words: # normalize and add to list
clean_words.append(word.strip(string.punctuation).lower())
return clean_words
def remove_punctuation(text):
clean_words = [] # create an empty list
text = text.rstrip() # remove trailing whitespace characters
words = text.split() # create a list of words from the text
for word in words: # normalize and add to list
clean_words.append(word.strip(string.punctuation).lower())
return clean_words
main()
你有很多问题：
readlines另一个列表中的一个列表一个列表。计数（另一个列表）stop_words = get_stop_word_list(stop_words_file_name)
stop_word_count = 0
with open(other_file_name) as other_file: # note 'context manager' file handling
for line in other_file:
cleaned_line = clean(line)
for stop_word in stop_words:
if stop_word in cleaned_line:
stop_word_count += cleaned_line.count(stop_word)
有更有效的方法（例如使用
setcollections.Counterfrom nltk.corpus import stopwords
nltk.download('stopwords')
from nltk.tokenize import word_tokenize
nltk.download('punkt')
x = r"['Nel mezzo del cammin di nostra vita mi ritrovai per una selva oscura, ché la
diritta via era smarrita.Ahi quanto a dir qual era è cosa dura esta selva selvaggia
e aspra e forte che nel pensier rinova la paura! Tant' è amara che poco è più morte;
ma per trattar del ben ch'i' vi trovai, dirò de l altre cose chi v ho scorte.']"
word_tokens = word_tokenize(x) #splitta i pezzi
stopwords_x = [w for w in word_tokens if w in stopWords]
len(stopwords_x) / len(word_tokens) * 100
我建议将内部for循环替换为
stop\u word\u count+=sum（map（cleaned\u line.count，stop\u words））mapimapcount