BoW.ipynb

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   "execution_count": 5,
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   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
    "\n",
    "#Consider the 3 documents containing below content\n",
    "doc1 = \"Natural Language processing heps us in playing with words in any language.\"\n",
    "doc2 = \"Natural language processing is ne important concept in machine learning use cases\"\n",
    "doc3 = \"We are here to help people to upgrade their skills and become expert in demanding technologies\"\n",
    "\n",
    "vectr = CountVectorizer()\n",
    "#CountVectorizer is used to tokenize a collection of text documents and build a known vocabulary of known words. It returns integer count of each word found in document.\n",
    "corpus = [doc1,doc2,doc3]\n",
    "cntvect = vectr.fit_transform(corpus)\n",
    "print(cntvect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
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   "source": [
    "df = pd.DataFrame(cntvect.toarray().transpose(),index = vectr.get_feature_names())\n",
    "df.columns = ['document1','document2','document3']\n",
    "df"
   ]
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   "source": [
    "vectr = TfidfVectorizer() # TfidfVectorizer is a class that returns TF-IDF value of each word in each document.\n",
    "cntvect = vectr.fit_transform(corpus)\n",
    "print(cntvect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>we</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.240085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>with</th>\n",
       "      <td>0.303369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>words</th>\n",
       "      <td>0.303369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              document1  document2  document3\n",
       "and            0.000000   0.000000   0.240085\n",
       "any            0.303369   0.000000   0.000000\n",
       "are            0.000000   0.000000   0.240085\n",
       "become         0.000000   0.000000   0.240085\n",
       "cases          0.000000   0.314908   0.000000\n",
       "concept        0.000000   0.314908   0.000000\n",
       "demanding      0.000000   0.000000   0.240085\n",
       "expert         0.000000   0.000000   0.240085\n",
       "help           0.000000   0.000000   0.240085\n",
       "heps           0.303369   0.000000   0.000000\n",
       "here           0.000000   0.000000   0.240085\n",
       "important      0.000000   0.314908   0.000000\n",
       "in             0.358349   0.185989   0.141798\n",
       "is             0.000000   0.314908   0.000000\n",
       "language       0.461440   0.239495   0.000000\n",
       "learning       0.000000   0.314908   0.000000\n",
       "machine        0.000000   0.314908   0.000000\n",
       "natural        0.230720   0.239495   0.000000\n",
       "ne             0.000000   0.314908   0.000000\n",
       "people         0.000000   0.000000   0.240085\n",
       "playing        0.303369   0.000000   0.000000\n",
       "processing     0.230720   0.239495   0.000000\n",
       "skills         0.000000   0.000000   0.240085\n",
       "technologies   0.000000   0.000000   0.240085\n",
       "their          0.000000   0.000000   0.240085\n",
       "to             0.000000   0.000000   0.480170\n",
       "upgrade        0.000000   0.000000   0.240085\n",
       "us             0.303369   0.000000   0.000000\n",
       "use            0.000000   0.314908   0.000000\n",
       "we             0.000000   0.000000   0.240085\n",
       "with           0.303369   0.000000   0.000000\n",
       "words          0.303369   0.000000   0.000000"
      ]
     },
     "execution_count": 7,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(cntvect.toarray().transpose(),index = vectr.get_feature_names())\n",
    "df.columns = ['document1','document2','document3']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
 ],
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