How 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]]) 

Answer 2

A simple solution is to use texthero:

import texthero as hero df['tfidf'] = hero.tfidf(df['sent']) 

In [5]: df.head() Out[5]:    docId                         sent                                              tfidf 0      1   This is the first sentence  [0.3816141458138271, 0.6461289150464732, 0.381... 1      2  This is the second sentence  [0.3816141458138271, 0.0, 0.3816141458138271, ... 2      3   This is the third sentence  [0.3816141458138271, 0.0, 0.3816141458138271, ...