如何在数据框中使用 word_tokenize

Question

我最近开始使用 nltk 模块进行文本分析。我被困在一个点上。我想在数据帧上使用 word_tokenize，以获得数据帧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，我想分离所有的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，但如何将它应用到整个数据帧上？

请帮忙！

提前致谢...

Answer 1

你可以使用DataFrame的apply方法API:

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，请再次尝试使用 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

Answer 2

pandas.Series.apply 比 pandas.DataFrame.apply

快

import pandas as pd
import nltk

df = pd.read_csv("/path/to/file.csv")

start = time.time()
df["unigrams"] = df["verbatim"].apply(nltk.word_tokenize)
print "series.apply", (time.time() - start)

start = time.time()
df["unigrams2"] = df.apply(lambda row: nltk.word_tokenize(row["verbatim"]), axis=1)
print "dataframe.apply", (time.time() - start)

在 125 MB 的示例 csv 文件中，

series.apply 144.428858995

dataframe.apply 201.884778976

编辑：您可能会想到 series.apply([=42= 之后的 Dataframe df ])体积较大，可能会影响下一次操作的运行时间dataframe.apply(nltk.word_tokenize).

Pandas 针对这种情况进行了底层优化。我仅通过单独执行 dataframe.apply(nltk.word_tokenize) 获得了类似的 200s 运行时间。

Answer 3

可能需要添加 str() 以将 pandas' 对象类型转换为字符串。

请记住，计算字数的更快方法通常是计算空格。

有趣的是分词器计算句点。可能想先删除那些，也许还删除数字。取消注释下面的行将导致计数相等，至少在这种情况下是这样。

import nltk
import pandas as pd

sentences = pd.Series([ 
    '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',
    'not a very helpful site in finding home decor. '
])

# remove anything but characters and spaces
sentences = sentences.str.replace('[^A-z ]','').str.replace(' +',' ').str.strip()

splitwords = [ nltk.word_tokenize( str(sentence) ) for sentence in sentences ]
print(splitwords)
    # output: [['This', 'is', 'a', 'very', 'good', 'site', 'I', 'will', 'recommend', 'it', 'to', 'others'], ['Can', 'you', 'please', 'give', 'me', 'a', 'call', 'at', 'have', 'issues', 'with', 'the', 'listings'], ['good', 'work', 'keep', 'it', 'up'], ['not', 'a', 'very', 'helpful', 'site', 'in', 'finding', 'home', 'decor']]

wordcounts = [ len(words) for words in splitwords ]
print(wordcounts)
    # output: [12, 13, 5, 9]

wordcounts2 = [ sentence.count(' ') + 1 for sentence in sentences ]
print(wordcounts2)
    # output: [12, 13, 5, 9]

如果您不使用 Pandas，您可能不需要 str()

Answer 4

我给你举个例子。假设您有一个名为 twitter_df 的 数据框 ，并且您在其中存储了情绪和文本。所以，首先我将文本数据提取到列表中，如下所示

 tweetText = twitter_df['text']

然后标记化

 from nltk.tokenize import word_tokenize

 tweetText = tweetText.apply(word_tokenize)
 tweetText.head()

我想这对你有帮助

Answer 5

使用 pandarallel

使其更快

使用Spacy

import spacy
from pandarallel import pandarallel

pandarallel.initialize(progress_bar=True)    
nlp = spacy.load("en_core_web_sm")

df['new_col'] = df['text'].parallel_apply(lambda x: nlp(x))

使用NLTK

import nltk
from pandarallel import pandarallel

pandarallel.initialize(progress_bar=True)

df['new_col'] = df['text'].parallel_apply(lambda x: nltk.word_tokenize(x))

如何在数据框中使用 word_tokenize

how to use word_tokenize in data frame

python

nltk

pandas