What is the simplest way to get tfidf with pandas dataframe?

Question

I want to calculate tf-idf from the documents below. I'm using python and pandas.

import pandas as pd
df = pd.DataFrame({'docId': [1,2,3], 
               'sent': ['This is the first sentence','This is the second sentence', 'This is the third sentence']})

First, I thought I would need to get word_count for each row. So I wrote a simple function:

def word_count(sent):
    word2cnt = dict()
    for word in sent.split():
        if word in word2cnt: word2cnt[word] += 1
        else: word2cnt[word] = 1
return word2cnt

And then, I applied it to each row.

df['word_count'] = df['sent'].apply(word_count)

But now I'm lost. I know there's an easy method to calculate tf-idf if I use Graphlab, but I want to stick with an open source option. Both Sklearn and gensim look overwhelming. What's the simplest solution to get tf-idf?

Answer 1

Scikit-learn implementation is really easy :

from sklearn.feature_extraction.text import TfidfVectorizer
v = TfidfVectorizer()
x = v.fit_transform(df['sent'])

There are plenty of parameters you can specify. See the documentation here

The output of fit_transform will be a sparse matrix, if you want to visualize it you can do x.toarray()

In [44]: x.toarray()
Out[44]: 
array([[ 0.64612892,  0.38161415,  0.        ,  0.38161415,  0.38161415,
         0.        ,  0.38161415],
       [ 0.        ,  0.38161415,  0.64612892,  0.38161415,  0.38161415,
         0.        ,  0.38161415],
       [ 0.        ,  0.38161415,  0.        ,  0.38161415,  0.38161415,
         0.64612892,  0.38161415]])