CountVectorizer_TF-IDF Vectorizer.ipynb

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   "source": [
    "import pandas as pd"
   ]
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   "execution_count": 15,
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    "corpus = [\n",
    "     'This is the first document awesome food.',\n",
    "     'This is the second second document.',\n",
    "     'And the third one the is mission impossible.',\n",
    "     'Is this the first document?',\n",
    "]"
   ]
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   "cell_type": "code",
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       "                                           Text\n",
       "0      This is the first document awesome food.\n",
       "1           This is the second second document.\n",
       "2  And the third one the is mission impossible.\n",
       "3                   Is this the first document?"
      ]
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   "source": [
    "df = pd.DataFrame({'Text':corpus})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CountVectorizer: Convert a collection of text documents to a matrix of token counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
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   "source": [
    "cv.fit_transform(df.Text).toarray()"
   ]
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'and': 0,\n",
       " 'awesome': 1,\n",
       " 'document': 2,\n",
       " 'first': 3,\n",
       " 'food': 4,\n",
       " 'impossible': 5,\n",
       " 'is': 6,\n",
       " 'mission': 7,\n",
       " 'one': 8,\n",
       " 'second': 9,\n",
       " 'the': 10,\n",
       " 'third': 11,\n",
       " 'this': 12}"
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     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "cv.vocabulary_"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### TF-IDF Vectorizer :\n",
    "Convert a collection of raw documents to a matrix of TF-IDF features.\n",
    "Equivalent to CountVectorizer followed by TfidfTransformer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), norm='l2', preprocessor=None, smooth_idf=True,\n",
       "        stop_words='english', strip_accents=None, sublinear_tf=False,\n",
       "        token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
       "        vocabulary=None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.fit(df.Text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "array([[ 0.64450299,  0.41137791,  0.64450299,  0.        ,  0.        ,\n",
       "         0.        ],\n",
       "       [ 0.        ,  0.30403549,  0.        ,  0.        ,  0.        ,\n",
       "         0.9526607 ],\n",
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       "         0.        ]])"
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     "execution_count": 24,
     "metadata": {},
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   "source": [
    "vectorizer.transform(df.Text).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['awesome', 'document', 'food', 'impossible', 'mission', 'second']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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    "collapsed": true
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