how to use word_tokenize in data frame

Question

I have recently started using the nltk module for text analysis. I am stuck at a point. I want to use word_tokenize on a dataframe, so as to obtain all the words used in a p

Answer 1

pandas.Series.apply is faster than pandas.DataFrame.apply

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)

On a sample 125 MB csv file,

series.apply 144.428858995

dataframe.apply 201.884778976

Edit: You could be thinking the Dataframe df after series.apply(nltk.word_tokenize) is larger in size, which might affect the runtime for the next operation dataframe.apply(nltk.word_tokenize).

Pandas optimizes under the hood for such a scenario. I got a similar runtime of 200s by only performing dataframe.apply(nltk.word_tokenize) separately.

Answer 2

May need to add str() to convert to pandas' object type to a string.

Keep in mind a faster way to count words is often to count spaces.

Interesting that tokenizer counts periods. May want to remove those first, maybe also remove numbers. Un-commenting the line below will result in equal counts, at least in this case.

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]

If you aren't using Pandas, you might not need str()

Answer 3

I will show you an example. Suppose you have a data frame named twitter_df and you have stored sentiment and text within that. So, first I extract text data into a list as follows

 tweetText = twitter_df['text']

then to tokenize

 from nltk.tokenize import word_tokenize

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

I think this will help you

Answer 4

You can use apply method of DataFrame 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)

Output:

>>> 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]

For finding the length of each text try to use apply and lambda function again:

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