内容简介：翻译自：https://stackoverflow.com/questions/33098040/how-to-use-word-tokenize-in-data-frame
我最近开始使用nltk模块进行文本分析.我陷入了困境.我想在数据帧上使用word_tokenize,以便获取数据帧的特定行中使用的所有单词.
data example:
text
1. This is a very good site. I will recommend it to others.
2. Can you please give me a call at 9983938428. have issues with the listings.
3. good work! keep it up
4. not a very helpful site in finding home decor.
expected output:
1. 'This','is','a','very','good','site','.','I','will','recommend','it','to','others','.'
2. 'Can','you','please','give','me','a','call','at','9983938428','.','have','issues','with','the','listings'
3. 'good','work','!','keep','it','up'
4. 'not','a','very','helpful','site','in','finding','home','decor'
基本上,我想分离所有单词并找到数据框中每个文本的长度.
我知道word_tokenize可以用于字符串,但是如何将它应用到整个数据帧？
请帮忙！
提前致谢…
您可以使用DataFrame API的apply方法：
import pandas as pd
import nltk
df = pd.DataFrame({'sentences': ['This is a very good site. I will recommend it to others.', 'Can you please give me a call at 9983938428. have issues with the listings.', 'good work! keep it up']})
df['tokenized_sents'] = df.apply(lambda row: nltk.word_tokenize(row['sentences']), axis=1)
输出：
>>> df
sentences \
0 This is a very good site. I will recommend it ...
1 Can you please give me a call at 9983938428. h...
2 good work! keep it up
tokenized_sents
0 [This, is, a, very, good, site, ., I, will, re...
1 [Can, you, please, give, me, a, call, at, 9983...
2 [good, work, !, keep, it, up]
要查找每个文本的长度,请尝试再次使用apply和lambda函数：
df['sents_length'] = df.apply(lambda row: len(row['tokenized_sents']), axis=1)
>>> df
sentences \
0 This is a very good site. I will recommend it ...
1 Can you please give me a call at 9983938428. h...
2 good work! keep it up
tokenized_sents sents_length
0 [This, is, a, very, good, site, ., I, will, re... 14
1 [Can, you, please, give, me, a, call, at, 9983... 15
2 [good, work, !, keep, it, up] 6
翻译自：https://stackoverflow.com/questions/33098040/how-to-use-word-tokenize-in-data-frame
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