hi i wanna see the tfidf of each term for each document by using scikit :

```
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import Pipeline
import pandas as pd
import numpy as np
import os,glob

pipeline = Pipeline([('tfidf', TfidfVectorizer())])

corpus = []
os.chdir("/home/yougha/workspace/RecPrec/corpusH_test")
for file in glob.glob("*.ann"):
    f=open(file,'r').read().splitlines()
    corpus.append(str(' '.join(f)))

X = pipeline.fit_transform(corpus)
vec = pipeline.named_steps['tfidf']
features = vec.get_feature_names()

```

the problem is that i get this result set:

```
(0, 3014)   0.0377805822964
(0, 408)    0.0350786341362
(0, 1674)   0.0315760656866
(0, 2894)   0.0684834084489
(0, 414)    0.0702763812732
(0, 5435)   0.259268251165
(0, 4931)   0.0386510244744
(0, 5878)   0.0625452304837
(0, 4129)   0.0515443519972
(0, 867)    0.0537265899836
(0, 847)    0.0550074477291
(0, 5296)   0.046810389078

```

I absolutely don't know what to do with that ,

Is it possible to have something like that

```
Name of Doc |word1 |word2 |word3
doc1        |0.4   |0.2   |0.3

```

what i wanna do is to identify the document and printing the tfidf for each word occuring in this document.