上下文中的python pandas数据框单词:前后获取3个单词

问题描述

我在 jupyter notebook 中工作并且有一个 Pandas 数据框data":

I am working in jupyter notebook and have a pandas dataframe "data":

Question_ID | Customer_ID | Answer
      1           234         Data is very important to use because ...
      2           234         We value data since we need it ...

我想通过答案"列中的文本并获取数据"一词前后的三个词.所以在这种情况下，我会得到非常重要"；我们重视"、因为我们需要".

I want to go through the text in column "Answer" and get the three words before and after the word "data".So in this scenario I would have gotten "is very important"; "We value", "since we need".

在 Pandas 数据框中有什么好方法可以做到这一点吗?到目前为止，我只找到了解决方案，其中答案"将是通过 python 代码运行的自己的文件(没有熊猫数据框).虽然我意识到我需要使用 NLTK 库，但我之前没有使用过它，所以我不知道最好的方法是什么.(这是一个很好的例子 在 Python 中将单词及其前 10 个单词的上下文提取到数据框)

Is there an good way to do this within a pandas dataframe? So far I only found solutions where "Answer" would be its own file run through python code (without a pandas dataframe). While I realize that I need to use the NLTK library, I haven't used it before, so I don't know what the best approach would be. (This was a great example Extracting a word and its prior 10 word context to a dataframe in Python)

推荐答案

这可能有效:

import pandas as pd
import re

df = pd.read_csv('data.csv')

for value in df.Answer.values:
    non_data = re.split('Data|data', value) # split text removing "data"
    terms_list = [term for term in non_data if len(term) > 0] # skip empty terms
    substrs = [term.split()[0:3] for term in terms_list]  # slice and grab first three terms
    result = [' '.join(term) for term in substrs] # combine the terms back into substrings
    print result

输出:

['is very important']
['We value', 'since we need']

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