python pandas dataframe words in context: 得到前后3个词

Question

我在 jupyter notebook 上工作，有一个 pandas 数据框 "data"：

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

我想遍历 "Answer" 列中的文本并获取单词 "data" 前后的三个单词。所以在这种情况下我会得到 "is very important"; "We value", "since we need".

有没有在 pandas 数据框中执行此操作的好方法？到目前为止，我只找到了 "Answer" 通过 python 代码（没有 pandas 数据帧）是它自己的文件运行的解决方案。虽然我意识到我需要使用 NLTK 库，但我以前没有使用过它，所以我不知道最好的方法是什么。（这是一个很好的例子 Extracting a word and its prior 10 word context to a dataframe in Python）

Answer 1

使用生成器表达式、re.findall和itertools.chain.from_iterable函数的解决方案：

import pandas as pd, re, itertools

data = pd.read_csv('test.csv')  # change with your current file path

data_adjacents = ((i for sublist in (list(filter(None,t))
                         for t in re.findall(r'(\w*?\s*\w*?\s*\w*?\s+)(?=\bdata\b)|(?<=\bdata\b)(\s+\w*\s*\w*\s*\w*)', l, re.I)) for i in sublist)
                            for l in data.Answer.tolist())

print(list(itertools.chain.from_iterable(data_adjacents)))

输出：

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

Answer 2

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']

python pandas dataframe words in context: 得到前后3个词

python pandas dataframe words in context: get 3 words before and after

python

text

word

pandas