如何替换整个csv文件中的特定单词？

Question

我有一个大的CSV文件，其中有许多简短的单词，我需要把它们变成一个完整的单词。我在这里发现了一些帖子，比如1、2，但大多数帖子要么改变整行，要么需要逐个手动完成。你知道吗

我的CSV文件看起来像：

infoID               messages
 111     we need to fix the car mag but we can't
 113         we need a shf to perform eng change
 115                      gr is needed to change
 116                            bat needs change
 117                    car towed for ext change 
 118                              car ml is high
  .
  .

我的另一个文件包含所有简短单词的完整单词，我想在我的文档中使用该文件，其形式为：

shf:shaft
gr:gear
ml:mileage

如果你能提供你的代码，我也可以在我身边运行的帮助，那就太好了。谢谢

Answer 1

将文本文件作为一个系列读入

s

0    mag:magnitude
1        shf:shaft
2          gr:gear
3      bat:battery
4      ext:exhaust
5       ml:mileage
Name: 0, dtype: object

在冒号上拆分并将序列转换为字典映射键以替换：

dict(s.str.split(':').tolist())

# {'bat': 'battery',
#  'ext': 'exhaust',
#  'gr': 'gear',
#  'mag': 'magnitude',
#  'ml': 'mileage',
#  'shf': 'shaft'}

使用此命令对regex=True执行^{}操作：

df['messages'].replace(dict(s.str.split(':').tolist()), regex=True)

0    we need to fix the car magnitude but we can't
1            we need a shaft to perform eng change
2                         gear is needed to change
3                             battery needs change
4                     car towed for exhaust change
5                              car mileage is high
Name: messages, dtype: object

---

请注意，如果这些是严格意义上的整词替换，则可以通过将关键字字符串转换为使用词边界的正则表达式来扩展此解决方案。为了更好地测量，也要对字符串进行转义：

import re

mapping = {fr'\b{re.escape(k)}\b': v for k, v in s.str.split(':').tolist()}
df['messages'].replace(mapping, regex=True)

0    we need to fix the car magnitude but we can't
1            we need a shaft to perform eng change
2                         gear is needed to change
3                             battery needs change
4                     car towed for exhaust change
5                              car mileage is high
Name: messages, dtype: object

Answer 2

另一种使用pd.Series.apply的方法：

d = dict(i.split(':') for i in d.split('\n'))
#{'bat': 'battery',
# 'ext': 'exhaust',
# 'gr': 'gear',
# 'mag': 'magnitude',
# 'ml': 'mileage',
# 'shf': 'shaft'}

df['messages'].apply(lambda x : ' '.join(d.get(i, i) for i in x.split()), 1)

输出：

0    we need to fix the car magnitude but we can't
1            we need a shaft to perform eng change
2                         gear is needed to change
3                             battery needs change
4                     car towed for exhaust change
5                              car mileage is high
Name: messages, dtype: object