熊猫替换了ndarray的缩写

Question

我有一个numpy.ndarray，我想使用以下字典替换其中的所有缩写？我该怎么做，以使输出以与输入相同的格式获得。目前，这是我在做的

X_trying=array([[" And my account number His Okay It is Arrow My name with a K Last name Is and another phone numbers That's okay it's just number Yes <unk> at Gmail Dot com that is a lower "],
       ["Hi Amber I'm relocating so I need a insurance card for my car First name <unk> last name is D key No for brand new isn't"]],
      dtype='<U97064')
X_trying

#notice double quotes
array([[" And my account number His Okay It is Arrow My name with a K Last name Is and another phone numbers That's okay it's just number Yes <unk> at Gmail Dot com that is a lower "],
       ["Hi Amber I'm relocating so I need a insurance card for my car First name <unk> last name is D key No for brand new isn't"]],
      dtype='<U97064')



df_for_abbreviations = pd.DataFrame(X_trying, columns = ['text'])#converting to a dataframe
df_for_abbreviations['text_lower']=df_for_abbreviations['text'].apply(lambda x:x.lower())#converting to lowercase so it works with dictionary
df_for_abbreviations["unabbreviated_text"] = df_for_abbreviations["text_lower"].replace(abbreviations_master, regex=True)
#then when i convert back to ndarray format gets screwed up - quotes change from double to single and it causes in donstream code
x=df_for_abbreviations['unabbreviated_text'].to_numpy(dtype='<U97064').reshape(df_for_abbreviations.shape[0],1)
x

#notice，引用到单语引号

array([[' and my account number his okay it is arrow my name with a k last name is and another phone numbers that is okay it is just number yes <unk> at gmail dot com that is a lower '],
       ['hi amber i am relocating so i need a insurance card for my car first name <unk> last name is d key no for brand new is not']],
      dtype='<U97064')

single quotes affect affect the downstream output

，我有一个我想替换为下面的单词词典

abbreviations_master={}
abbreviations_master["i'm"]="i am"
abbreviations_master["it's"]="it is"
abbreviations_master["that's"]="that is"
abbreviations_master["don't"]="do not"
abbreviations_master["i'll"]="i will"
abbreviations_master["i've"]="i have"
abbreviations_master["we're"]="we are"
abbreviations_master["didn't"]="did not"
abbreviations_master["ma'am"]="madam"
abbreviations_master["you're"]="you are"
abbreviations_master["there's"]="there is "
abbreviations_master["let's"]="let us"
abbreviations_master["they're"]="they are"
abbreviations_master["can't"]="can not"
abbreviations_master["he's"]="he is"
abbreviations_master["doesn't"]="does not"
abbreviations_master["she's"]="she is"
abbreviations_master["what's"]="what is"
abbreviations_master["i'd"]="I would "
abbreviations_master["haven't"]="have not"
abbreviations_master["wasn't"]="was not"
abbreviations_master["we'll"]="we will"
abbreviations_master["won't"]="will not"
abbreviations_master["it'll"]="it will"
abbreviations_master["we've"]="we have"
abbreviations_master["wouldn't"]="would not"
abbreviations_master["that'd"]="that would "
abbreviations_master["you've"]="you have"
abbreviations_master["couldn't"]="could not"
abbreviations_master["that'll"]="that will"
abbreviations_master["y'all"]="you all"
abbreviations_master["isn't"]="is not"
abbreviations_master["it'd"]="it would"
abbreviations_master["would've"]="would have"
abbreviations_master["'cause"]="because"
abbreviations_master["hasn't"]="has not"
abbreviations_master["they've"]="they have"
abbreviations_master["you'll"]="you will"
abbreviations_master["here's"]="here is"
abbreviations_master["name's"]="name is"
abbreviations_master["shouldn't"]="should not"
abbreviations_master["wife's"]="?"
abbreviations_master["driver's"]="?"
abbreviations_master["they'll"]="they will"
abbreviations_master["everything's"]="?"
abbreviations_master["husband's"]="?"
abbreviations_master["there'll"]="there will"
abbreviations_master["should've"]="should have"
abbreviations_master["we'd"]="we would"
abbreviations_master["'bout"]="about"
abbreviations_master["she'll"]="she will"
abbreviations_master["he'll"]="he will"
abbreviations_master["you'd"]="you would"
abbreviations_master["one's"]="?"
abbreviations_master["who's"]="who has"
abbreviations_master["weren't"]="were not"
abbreviations_master["aren't"]="are not"
abbreviations_master["how's"]="how is"
abbreviations_master["how're"]="how are"
abbreviations_master["hadn't"]="had not"

Answer 1

您可以使用re.split在保持分离器时打破输入（因为您的某些示例以>开始），并检查是否有任何单词是在您的字典中，否则，只要保留这个词。下面的代码不是很优雅，因为您的输入是np.array。如果您可以使其成为简单的字符串列表，则可以简化代码。

import re
import numpy as np

output_array = []
for input_line in X_trying:
    output_array.append([''.join(abbreviations_master[word] if word in abbreviations_master else word
                                  for word in re.split('( )', str(input_line[0]).lower()))])
output_array = np.array(output_array, dtype='<U97064')

输出格式类似于输入：

array([[' and my account number his okay it is arrow my name with a k last name is and another phone numbers that is okay it is just number yes <unk> at gmail dot com that is a lower '],
       ['hi amber i am relocating so i need a insurance card for my car first name <unk> last name is d key no for brand new is not']],
      dtype='<U97064')

请注意，（）在split中很重要。如果您有更多的分离器，则可以将它们添加为：re.split（'（| \。|，）。但是您的示例没有其他标点符号，所以我没有添加它。