两个 python 循环看起来应该做同样的事情，但输出不同的结果？

Question

我昨天试图完成 Udacity 关于文本矢量化的第 11 课。我检查了代码，一切似乎都正常工作 - 我拿了一些电子邮件，打开它们，删除了一些签名词和 return 每封电子邮件的词干词到列表中。

这是循环 1：

for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
    for path in from_person:
        ### only look at first 200 emails when developing
        ### once everything is working, remove this line to run over full dataset
#        temp_counter += 1
    if temp_counter < 200:
        path = os.path.join('/xxx', path[:-1])
        email = open(path, "r")

        ### use parseOutText to extract the text from the opened email

        email_stemmed = parseOutText(email)

        ### use str.replace() to remove any instances of the words
        ### ["sara", "shackleton", "chris", "germani"]

        email_stemmed.replace("sara","")
        email_stemmed.replace("shackleton","")
        email_stemmed.replace("chris","")
        email_stemmed.replace("germani","")

    ### append the text to word_data

    word_data.append(email_stemmed.replace('\n', ' ').strip())

    ### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
        if from_person == "sara":
            from_data.append(0)
        elif from_person == "chris":
            from_data.append(1)

    email.close()

这是循环 2：

for name, from_person in [("sara", from_sara), ("chris", from_chris)]:
    for path in from_person:
        ### only look at first 200 emails when developing
        ### once everything is working, remove this line to run over full dataset
#        temp_counter += 1
        if temp_counter < 200:
            path = os.path.join('/xxx', path[:-1])
            email = open(path, "r")

            ### use parseOutText to extract the text from the opened email
            stemmed_email = parseOutText(email)

            ### use str.replace() to remove any instances of the words
            ### ["sara", "shackleton", "chris", "germani"]
            signature_words = ["sara", "shackleton", "chris", "germani"]
            for each_word in signature_words:
                stemmed_email = stemmed_email.replace(each_word, '')         #careful here, dont use another variable, I did and broke my head to solve it

            ### append the text to word_data
            word_data.append(stemmed_email)

            ### append a 0 to from_data if email is from Sara, and 1 if email is from Chris
            if name == "sara":
                from_data.append(0)
            else: # its chris
                from_data.append(1)


            email.close()

代码的下一部分按预期工作：

print("emails processed")
from_sara.close()
from_chris.close()

pickle.dump( word_data, open("/xxx/your_word_data.pkl", "wb") )
pickle.dump( from_data, open("xxx/your_email_authors.pkl", "wb") )


print("Answer to Lesson 11 quiz 19: ")
print(word_data[152])


### in Part 4, do TfIdf vectorization here

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction import stop_words
print("SKLearn has this many Stop Words: ")
print(len(stop_words.ENGLISH_STOP_WORDS))

vectorizer = TfidfVectorizer(stop_words="english", lowercase=True)
vectorizer.fit_transform(word_data)

feature_names = vectorizer.get_feature_names()

print('Number of different words: ')
print(len(feature_names))

但是当我用循环1计算单词总数时，我得到了错误的结果。当我使用循环 2 执行此操作时，我得到了正确的结果。

我查看这段代码太久了，我看不出其中的区别 - 我在循环 1 中做错了什么？

郑重声明，我一直得到的错误答案是 38825。正确答案应该是 38757。

非常感谢你的帮助，好心的陌生人！

Answer 1

这些行什么都不做：

email_stemmed.replace("sara","")
email_stemmed.replace("shackleton","")
email_stemmed.replace("chris","")
email_stemmed.replace("germani","")

replace return 是一个新字符串，不会修改 email_stemmed。相反，您应该将 return 值设置为 email_stemmed:

email_stemmed = email_stemmed.replace("sara", "")

依此类推。

循环二实际上在 for 循环中设置了 return 值：

for each_word in signature_words:
    stemmed_email = stemmed_email.replace(each_word, '')

上面的代码片段不等同，因为在第一个片段的末尾 email_stemmed 由于 replace 被正确使用而完全没有改变，而在第二个片段的末尾stemmed_email实际上每个字都被去掉了。

两个 python 循环看起来应该做同样的事情，但输出不同的结果？

Two python loops that look like they should do the same thing, but output different results?

python-3.x

machine-learning

tfidfvectorizer