Convert CountVectorizer and TfidfTransformer Sparse Matrices into Separate Pandas Dataframe Rows

Question

Question: What is the best way to convert sparse matrices resulting from sklearn's CountVectorizer and TfidfTransformer into Pandas DataFrame columns with a separate row for each bigram and its corresponding frequency and tf-idf score?

Pipeline: Bring in text data from a SQL DB, split text into bigrams and calculate the frequency per document and the tf-idf per bigram per document, load the results back into the SQL DB.

Current State:

Two columns of data are brought in (number, text). text is cleaned to produce a third column cleanText:

   number                               text              cleanText
0     123            The farmer plants grain    farmer plants grain
1     234  The farmer and his son go fishing  farmer son go fishing
2     345            The fisher catches tuna    fisher catches tuna

This DataFrame is fed into sklearn's feature extraction:

cv = CountVectorizer(token_pattern=r"(?u)\b\w+\b", stop_words=None, ngram_range=(2,2), analyzer='word')
dt_mat = cv.fit_transform(data.cleanText)

tfidf_transformer = TfidfTransformer()
tfidf_mat = tfidf_transformer.fit_transform(dt_mat)

Then the matrices are fed back into the original DataFrame after converting them to an array:

data['frequency'] = list(dt_mat.toarray())
data['tfidf_score']=list(tfidf_mat.toarray())

Output:

   number                               text              cleanText  \
0     123            The farmer plants grain    farmer plants grain   
1     234  The farmer and his son go fishing  farmer son go fishing   
2     345            The fisher catches tuna    fisher catches tuna   

               frequency                                        tfidf_score  

0  [0, 1, 0, 0, 0, 1, 0]  [0.0, 0.707106781187, 0.0, 0.0, 0.0, 0.7071067...  
1  [0, 0, 1, 0, 1, 0, 1]  [0.0, 0.0, 0.57735026919, 0.0, 0.57735026919, ...  
2  [1, 0, 0, 1, 0, 0, 0]  [0.707106781187, 0.0, 0.0, 0.707106781187, 0.0... 

Problems:

1. The feature names (i.e. bigrams) are not in the DataFrame
2. The frequency and tfidf_score are not on separate lines for each bigram

Desired Output:

       number                    bigram         frequency      tfidf_score
0     123            farmer plants                 1              0.70  
0     123            plants grain                  1              0.56
1     234            farmer son                    1              0.72
1     234            son go                        1              0.63
1     234            go fishing                    1              0.34
2     345            fisher catches                1              0.43
2     345            catches tuna                  1              0.43

I managed to get one of the numeric columns assigned to separate rows of the DataFrame with this code:

data.reset_index(inplace=True)
rows = []
_ = data.apply(lambda row: [rows.append([row['number'], nn]) 
                         for nn in row.tfidf_score], axis=1)
df_new = pd.DataFrame(rows, columns=['number', 'tfidf_score'])

Output:

    number  tfidf_score
0      123     0.000000
1      123     0.707107
2      123     0.000000
3      123     0.000000
4      123     0.000000
5      123     0.707107
6      123     0.000000
7      234     0.000000
8      234     0.000000
9      234     0.577350
10     234     0.000000
11     234     0.577350
12     234     0.000000
13     234     0.577350
14     345     0.707107
15     345     0.000000
16     345     0.000000
17     345     0.707107
18     345     0.000000
19     345     0.000000
20     345     0.000000

However, I am unsure how to do this for both numeric columns, and this doesn't bring in the bigrams (feature names) themselves. Also, this method requires an array (which is why I converted the sparse matrices to arrays in the first place), and I would like to avoid this if possible due to performance issues and the fact that I would then have to strip the meaningless rows.

Any insight is greatly appreciated! Thank you very much for taking the time to read this question - I apologize for the length. Please let me know if there's anything I can do to improve the question or clarify my process.

Answer 1

The bigram names can be captured using CountVectorizer's get_feature_names(). From there it's just a series of melt and merge operations:

print(data)

   number                               text              cleanText
0     123            The farmer plants grain    farmer plants grain
1     234  The farmer and his son go fishing  farmer son go fishing
2     345            The fisher catches tuna    fisher catches tuna

from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer

cv = CountVectorizer(token_pattern=r"(?u)\b\w+\b", stop_words=None, ngram_range=(2,2), analyzer='word')
dt_mat = cv.fit_transform(data.cleanText)

tfidf_transformer = TfidfTransformer()
tfidf_mat = tfidf_transformer.fit_transform(dt_mat)

The CountVectorizer feature names are, in this case, the bigrams:

print(cv.get_feature_names())

[u'catches tuna',
 u'farmer plants',
 u'farmer son',
 u'fisher catches',
 u'go fishing',
 u'plants grain',
 u'son go']

CountVectorizer.fit_transform() returns a sparse matrix. We can convert it to a dense representation, wrap it in a DataFrame, and then tack on the feature names as columns:

bigrams = pd.DataFrame(dt_mat.todense(), index=data.index, columns=cv.get_feature_names())
bigrams['number'] = data.number
print(bigrams)

   catches tuna  farmer plants  farmer son  fisher catches  go fishing  \
0             0              1           0               0           0   
1             0              0           1               0           1   
2             1              0           0               1           0   

   plants grain  son go  number  
0             1       0     123  
1             0       1     234  
2             0       0     345  

To go from wide to long format, use melt().
Then restrict the results to bigram matches (query() is useful here):

bigrams_long = (pd.melt(bigrams.reset_index(), 
                       id_vars=['index','number'],
                       value_name='bigram_ct')
                 .query('bigram_ct > 0')
                 .sort_values(['index','number']))

    index  number        variable  bigram_ct
3       0     123   farmer plants          1
15      0     123    plants grain          1
7       1     234      farmer son          1
13      1     234      go fishing          1
19      1     234          son go          1
2       2     345    catches tuna          1
11      2     345  fisher catches          1

Now repeat the process for tfidf:

tfidf = pd.DataFrame(tfidf_mat.todense(), index=data.index, columns=cv.get_feature_names())
tfidf['number'] = data.number

tfidf_long = pd.melt(tfidf.reset_index(), 
                     id_vars=['index','number'], 
                     value_name='tfidf').query('tfidf > 0')

Finally, merge bigrams and tfidf:

fulldf = (bigrams_long.merge(tfidf_long, 
                             on=['index','number','variable'])
                      .set_index('index'))

       number        variable  bigram_ct     tfidf
index                                             
0         123   farmer plants          1  0.707107
0         123    plants grain          1  0.707107
1         234      farmer son          1  0.577350
1         234      go fishing          1  0.577350
1         234          son go          1  0.577350
2         345    catches tuna          1  0.707107
2         345  fisher catches          1  0.707107