Quora EDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;font-size:30px;\" > Quora Question Pairs </h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script><script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window._Plotly) {require(['plotly'],function(plotly) {window._Plotly=plotly;});}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<module 'ntpath' from 'C:\\\\Users\\\\ankan\\\\Anaconda3\\\\lib\\\\ntpath.py'>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from subprocess import check_output\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "import os\n",
    "import gc\n",
    "\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "import distance\n",
    "from nltk.stem import PorterStemmer\n",
    "from bs4 import BeautifulSoup\n",
    "os.path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\ankan\\\\Desktop\\\\Quora')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\ankan\\\\Desktop\\\\Quora'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 404290\n",
      "~-> Total number of question pairs for training <-- :\n",
      "   404290 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  \n",
       "0  What is the step by step guide to invest in sh...             0  \n",
       "1  What would happen if the Indian government sto...             0  \n",
       "2  How can Internet speed be increased by hacking...             0  \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0  \n",
       "4            Which fish would survive in salt water?             0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"train.csv\")\n",
    "\n",
    "print(\"Number of data points:\",df.shape[0])\n",
    "\n",
    "print('~-> Total number of question pairs for training <-- :\\n   {} '.format(len(df)))\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 404290 entries, 0 to 404289\n",
      "Data columns (total 6 columns):\n",
      "id              404290 non-null int64\n",
      "qid1            404290 non-null int64\n",
      "qid2            404290 non-null int64\n",
      "question1       404289 non-null object\n",
      "question2       404288 non-null object\n",
      "is_duplicate    404290 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 18.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = [10, 5]\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~> Question pairs are not Similar (is_duplicate = 0):\n",
      "   63.08%\n",
      "\n",
      "~> Question pairs are Similar (is_duplicate = 1):\n",
      "   36.92%\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 339,
       "width": 697
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(\"is_duplicate\")['id'].count().plot.bar()\n",
    "\n",
    "print('~> Question pairs are not Similar (is_duplicate = 0):\\n   {}%'.format(100 - round(df['is_duplicate'].mean()*100, 2)))\n",
    "print('\\n~> Question pairs are Similar (is_duplicate = 1):\\n   {}%'.format(round(df['is_duplicate'].mean()*100, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of  Unique Questions are: 537933\n",
      "\n",
      "Number of unique questions that appear more than one time: 111780 (20.77953945937505%)\n",
      "\n",
      "Max number of times a single question is repeated: 157\n",
      "\n"
     ]
    }
   ],
   "source": [
    "qids = pd.Series(df['qid1'].tolist() + df['qid2'].tolist())\n",
    "unique_qs = len(np.unique(qids))\n",
    "qs_morethan_onetime = np.sum(qids.value_counts() > 1)\n",
    "print ('Total number of  Unique Questions are: {}\\n'.format(unique_qs))\n",
    "#print len(np.unique(qids))\n",
    "\n",
    "print ('Number of unique questions that appear more than one time: {} ({}%)\\n'.format(qs_morethan_onetime,qs_morethan_onetime/unique_qs*100))\n",
    "\n",
    "print ('Max number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) \n",
    "\n",
    "q_vals=qids.value_counts()\n",
    "\n",
    "q_vals=q_vals.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 403,
       "width": 697
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of duplicate questions 0\n"
     ]
    }
   ],
   "source": [
    "x = [\"unique_questions\" , \"Repeated Questions\"]\n",
    "y =  [unique_qs , qs_morethan_onetime]\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title (\"Plot representing unique and repeated questions  \")\n",
    "sns.barplot(x,y)\n",
    "plt.show()\n",
    "\n",
    "#checking whether there are any repeated pair of questions\n",
    "\n",
    "pair_duplicates = df[['qid1','qid2','is_duplicate']].groupby(['qid1','qid2']).count().reset_index()\n",
    "\n",
    "print (\"Number of duplicate questions\",(pair_duplicates).shape[0] - df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum number of times a single question is repeated: 157\n",
      "\n",
      "            id    qid1    qid2                         question1  \\\n",
      "105780  105780  174363  174364    How can I develop android app?   \n",
      "201841  201841  303951  174364  How can I create an Android app?   \n",
      "363362  363362  493340  493341                               NaN   \n",
      "\n",
      "                                                question2  is_duplicate  \n",
      "105780                                                NaN             0  \n",
      "201841                                                NaN             0  \n",
      "363362  My Chinese name is Haichao Yu. What English na...             0  \n",
      "------>There are two rows with null values in question2<------ \n"
     ]
    },
    {
     "data": {
      "image/png": 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KITIyEkFBQUKgJSsrC4sXL8bbt2+xZMkSrcf44YcfsGbNGtEyExMTeHh4wMnJCSkpKXj48CGePXuGzMxMTJ06FZUrV87zOaWnp6Nfv364d+8egJyqdR4eHnBwcEBycjIeP36stI1iGMXIyAjVq1dHtWrVULp0aWRnZyM2NhZ3797F27dvhfXu3r2L7t274+zZszpXYp0+fTr27NkDAChRogS8vLzg4OCAtLQ0YdrxXMeOHcPSpUsxdepU7N69G5MnTxZ+VrduXdSoUQPGxsbC1Ki5nj17hpEjR+LAgQM69U0qQ489BSk9PR09e/bE1atXRcutra3h5eUFGxsbvH79GkFBQUhNTUV0dDR69uyJESNGFFKPNTtw4ACGDRuG9PR00fJatWqhRo0asLKyQlxcHIKCgoSA3IcPH4SqqxMmTFC77/Hjx+PkyZOiZTY2Nvjkk09gb28PExMTJCYm4uXLlwgNDUVaWpr+TzCfkpOT8fnnnyuNy1ZWVvD09ETZsmURFxeHGzdu4N27d8LPT5w4gS5duuDQoUM6Ta9+69YtjBw5EikpKQByKqm6u7vD2toasbGxuHbtmih0+eeff8LNzQ0TJ07M55kqUwz72NnZwdnZGba2tjA3N0diYiLCwsJEIZzU1FQMHToU1tbW+PTTT3U63qVLlzBu3Dgh7FmhQgXUq1cPlpaWiIqKQlBQkCgI+uuvv6JEiRKYM2eOTseZMmWKqKJqvXr1UK1aNbx//x6hoaGi80lISECfPn1w4MABeHl5ad337NmzsXr1atEyExMTuLm5wcnJCaampoiKisKNGzeEgFh8fDy++OIL7N27F97e3mr3XRjPn2nTpmH//v0AcsZsNzc3VK5cGcbGxnj+/LnSfaGqnw4ODqhduzZsbGxgamoqTAcu/0cWb968Qa9evXDq1Cm4ubnp1Mdjx45hxIgRwnGrVKkCFxcXWFpa4uXLl7h+/boojLdmzRo0aNAAPXr00Lrv3377DdOmTVM6p9ypusuWLYv09HRER0fj/v37wn2rzZUrV9C7d2+l0HGlSpVQt25dlC5dGgkJCbh58yZiYmKEn69evRrJyclYvny5pOPo6vvvv8fKlSuVljs6OqJOnTqwtbVFSkoKIiMj8eDBA40hR3kTJ07E5s2bRctMTEzg6ekJR0dHJCcn4+7du6I/0njy5Ak6dOiAI0eOFMmptjds2IDvv/9eaDs7O6NGjRowNzfHixcvcOPGDeH9cHZ2NqZPn4769eujcePGBulPUf4MQUREREREpArDkURERERERKR3ly5dUgoQfPbZZ1i4cKHS9HOXL1/GxIkTERoaCiAnHDJ06FBcunQJZcqUUXuMmTNnioKRZcqUwfz589GzZ0+YmPzfx920tDT8/vvvmDdvHp4+fYrFixfr4xQ/eidPnhRVj6pZsyZWrFihdurd9PR0XLhwAX/99ZfwWsk7fvw4MjMzERQUhG+++UZYPmfOHHTp0kXlPuVfp1xpaWkYMmSIKBjp5OSEJUuWwNfXFzKZTFgeExODH3/8EQEBAcKy33//HU2bNsUXX3yh9twvXryo9IX8gAED4O/vDzs7O9Hyy5cv49tvv8XDhw8xatQotfvUZsOGDUhKSkKJEiUwefJkjB49GpaWlqJ1VFXRMzMzQ/fu3dG5c2e0bNkSpUqVUlonKysLp06dgr+/vxBCfPLkCX788UcsXbpUch/v3bsnTLU4cuRITJ06VTSNeFZWFtauXQt/f38hyLRy5Up06NBBCK21adMGixYtQq1atUT7Pn36NAYOHCiEM86dO4eTJ0/qHGjSxlBjT+71DQAHDx7E7NmzhZ9t3LhRZZBJH1OHL168WBSMLFWqFH744Qd8/fXXoilAk5KS8PPPP2PFihWIiooyWJglP0JCQjBixAhRMLJfv36YNGkSqlWrJlo3MzMT27dvx6xZs4RrZs6cOWjSpIkwxb3ivnfv3i207e3tsWzZMnTs2FHlNNCZmZkIDAzEoUOHcPToUaWfb9q0CWlpaYiKisJnn30mLB8xYgRGjhyp+8lLNG3aNFHYxNzcHDNnzsSQIUNgZmYmLJd/ruWGPG/cuIFZs2bp9NqPHz8eKSkpqFWrFhYsWIC2bduKxtikpCT4+/tj06ZNwrLFixdj0KBBorFBH2QyGRo1aoSePXuiffv2aqeq/ffff7Fs2TJs27YNQE5IZ+TIkbh9+7bK8VGd6dOnIzMzE3Z2dli8eDG6desmulZiY2Px/fffC8cBcsJibdu2RatWrSQdQ35MbdGiBZYvX46aNWuK1rl8+TImTJggvJ9JTk7G8OHDcenSJdFrruiPP/4QBSPNzc0xZcoUDB48WOm1iY+Px7Jly7BmzRpkZ2cjLS0Nw4YN0/o+qyCfP7dv3xZ+V19++SW+//57VKpUSbSOqmekkZER2rRpg+7du6N9+/Zqx927d+9i7ty5OHHiBICc9zTDhg3D5cuXRde8NiNHjkRWVhY8PT2xcOFCNGzYUPTz2NhYTJgwAYcPHxaW+fv7o1u3bjA2Nla737NnzyoFI728vODv7w9vb2+lcezDhw+4ePEidu3apRQKl/fq1SsMGDBAFIz87LPPMGPGDKVgaHZ2Ng4ePIipU6cKVRw3bdoEb29vje+p8uKPP/5Qeh/Wrl07zJw5Ew0aNFBaPz09Hf/88w927typseLwrl27lIKR/fv3h7+/P8qVKycsy87OxrFjxzB58mQhJBkbG4tvvvkGp0+fljTFdkF58+YNZsyYASDndzR37lzUqVNHtE54eDhGjBghVO7OysrC7Nmz8ffffyvtTx/PN31/hiAiIiIiIjI0hiOJiIiIiIhIr7KzszFhwgTRF7x9+/bFmjVrVH4B3axZM5w4cQKdOnUSpoONiorCnDlz1IY8bt26JQosWFlZ4a+//oK7u7vSumZmZhg7dixcXFzQp08ftdNvG1pCQoLGL3TVMdT0w8ePHxf+bWJign379qkNowA5lQ7btWuHdu3aqay45ujoCABK00Da2tpq3K+itWvXiqbTdHJywvHjx+Hk5KS0brly5bBmzRo4ODiIqkVOnjwZvr6+aiuofffdd6IqZVOmTMHMmTNVrtusWTMcP34cHTt2lDzNpypJSUkwMjLCli1b0LFjR5XrKP6eKlSogHv37ikFNhUZGRmhffv2aNasGbp27YobN24AALZt24aZM2dqDL/IS0hIAJATfho2bJjK44wdOxZRUVHCNMcpKSno2rUrUlJS0L17d2zYsEFlAKRNmzZYu3ataPrP7du36zUcacixJ/f6BnKuaXnly5fX6RqX6tmzZ1i1apXQLlmyJHbu3KkyfGBpaYlZs2ahevXqGDVqlMbpdQtDdnY2hgwZIkx7K5PJsHbtWvj5+alc38TEBAMGDECDBg3g6+uL5ORkfPjwATNnzsQ///yjtH5u4CnXli1bVIYo5ffv7e0Nb29vpSqjgPpgq7W1tUFeawC4evUq/vjjD6FtamqK7du3o3Xr1krryj/XvvrqK6Ei16ZNm+Dn5yep6iCQE5qrV68eDh48qDLsaGlpieXLlyM2NhYHDx4EkFOtcd++faIQvD6sX79eUnXe6tWrY+3atXB2doa/vz+AnFDTzp07depTQkICbGxscPjwYaWgEZBTuXLt2rWws7MThbgmTZqEoKAgSYG63DG1Q4cO2LZtm8o/CMh9xnz22WdCaOjJkydYvXo1pkyZonK/ERERmDp1qtC2sbHBoUOH1FZBtLGxwZw5c1CrVi1hSuKoqCj8/PPPKq9/oOCfP0lJSQCAsWPHqq3OqereO3r0qKTrxs3NDbt27cLkyZOxYcMGADmh6lOnTun0HIqPj0fbtm2xbds2leFVOzs7bNmyBR06dEBQUBAAIDIyEqdPn1Z7nNTUVAwbNkz07PLz88OqVatUXjMAYGxsjJYtW6Jly5Z4/fq12v6OGzdOVA1yxowZ+O6771SuK5PJ0LVrV3h6eqJdu3ZCQHL27Nno3LkzSpQoofY4uoiKilK6tidPnqxxCvKSJUuiY8eO6Nixo9rzTU5OFt0XQE4IWnEZkHOuHTt2hJubG3x9fREZGQkgJ9D8yy+/aKxSXNByq/f27dsXq1evVhn4r1KlCvbs2YOmTZsKVVKvXbuG0NBQpUqY+ni+6fszBBERERERkaEpf5IiIiIiIiIiyodTp04hLCxMaNeoUQPLly/XGCSwsbHBhg0bRF8C79y5U23AJ/eL7VyzZs1SGYyU165dO5WBr4Ly66+/wt3dXef/5Ku66FNERITwbzc3N50CP5qqWeVHZmYmNm7cKFq2du1alcFIeTNmzBAFod68eYNdu3apXPfy5cuikGODBg0wffp0jfu3sbHBunXrNFZ9kuKbb75RG4xUxcLCQmswRZ6lpSV+/vlnoZ2amqqyKp4m7du313qfjB07VnQ/JyQkoEKFCli1apXG31Hnzp1RvXp1oX3x4kWd+qZNQYw9BWnz5s2iaSjHjRuntipTLj8/P0nTtxa0Y8eOCQFUIKdClbpgpDw3NzchAAfkVEe8du2a0nry41nZsmU1BiMVGWo809Wvv/4qao8fP15lMFJeu3btMHbsWNGy3OCyFCVKlMDmzZu1VoFUDI/nVvjTJykBN3ljx45FvXr1hPaff/6p8zHnzJmjMhgpz9/fH66urkL7yZMnOH36tORj2NjY4JdfflEbcgNyAtfr1q0TjVWbN28WpslV9Msvv4imVF67dq2k6aEHDBiAzp07C+3//e9/aqdmLoznj6urq2jqYCl0vW7mzp2LChUqCG1drxtbW1v8/vvvGscNY2NjpUCepntm69atosBfo0aNsHr1ao3XjLzy5curXH7v3j1RcLxTp05qg5HynJycsGLFCqEdGRmJAwcOSOqLFGvXrhUF5Lp166YxGKlI3fkqPrubNWumMhgpr1KlSkpT0//+++9CpeiiolatWli+fLnKYGQuS0tLpVCnIcZqoGh+hiAiIiIiItKE4UgiIiIiIiLSK/mpTYGcajAlS5bUup2Li4so1JOSkiJUqpKXlZUlWm5jY4OBAwdK6tu4cePyHXArjuSrChWmCxcuiCpl+vj4oGXLllq3k8lkSgFHxesw1/79+0Xt8ePHa/yyOZe7uzvatGmjdT1NfRw9enSet5fKzc1NFBbJreIllWLQSpWKFSuidu3aomUDBw6ElZWV1m3lp6ONiYnBq1evdOqfJoYeewqa/PmYm5tLem0AaA2DFAb50HPJkiV16uPXX38tqgKrWCVS0bt37z66ylQpKSmiaXhVhVzUmThxomi644MHDwoVOrXp2rUratSooXU9Z2dnUfjl3r17kvZvSLmV33LdunVLbZhQFUdHR/Tt21fresbGxpg0aZJombrwvSrffPMNypYtq3U9Dw8PtGvXTmhHRUXhwoULSutlZGSIKox6eHigU6dOkvszYsQI4d8JCQm4evWq5G21ye/zZ+TIkZIDgXllZmYmepbr2seBAwcqVQ9WpVWrVqKpmTXdM1u2bBG1FyxYoJf3qop/bDJ79mzJ23722WeoWrWq0NY27kr14cMH0fVrYmKC+fPn62Xfiu8B1FUEV9SmTRs0bdpUaEdGRhosVJhXo0ePlvR+pn379qJ2QYzVReUzBBERERERkSYMRxIREREREZFeyX/RXrJkSXTp0kXytr179xa1VVUICw0NRWJiotD29fWV9IUhkDNNZKNGjST3pzirVauW8O+IiAidqo0ZimJIo2fPnpK3bdGiBSpWrCi0b968iYyMDKX1cqe5BHKmrfX19ZV8DF2uZUUuLi6ioEF+paamIiYmBs+fP0d4eLjoP/kgzqNHjyTv08LCQhQQ0KRatWqittTgqHzlSEC/X6obeuwpSM+fP8fLly+Fdtu2bVG6dGlJ29auXVtU6a6wvX//HleuXBHaLVq00FqpUJ6ZmRkaNGggtAMDA5XWkR/PMjIy1E7LW1QFBweLqoR27NhRFHjUxMrKSlRh+P3797h586akbXWZTlh+atbY2FjJ2+VXZmYm4uPjERERoTTWyVcgS05OFqbGlaJr166SgvFATlDM3NxcaOsyPnTv3j3P66o6TnBwMN69eye0u3btKnn/ANCwYUPR9Miq7idtDPH8AaCfELe5AAAgAElEQVTXStnv37/Hmzdv8OLFC6U+yt9bjx8/Fk1nrY3Ue6ZEiRKi5526eyY2NhYhISFC+5NPPoGnp6fk/mhy7tw54d/Ozs5aq6Qqkn8/kJfrRJU7d+6I3sN/+umnovdueZWRkYFbt24JbUdHRzRr1kzy9orvAfQZGtYHqdddpUqVRNe3ocbqovgZgoiIiIiISBPD/ikmERERERER/ae8ffsWz58/F9ouLi6SAx4A4OnpCZlMhuzsbAAQfdGZS35qVgBap9NWVL9+fVFQR5XMzEzJIQtra2tJQZ+pU6dqnb5ZlfDwcJ3PUYoePXpg7dq1Qnv69Ok4evQo/Pz88Omnn+o0naa+KL7eXl5ekreVyWTw8PAQKk+mpaXh4cOHomlXAfH14+zsrNP0fvXr15e8riIpU55qcv/+fezbtw9XrlzB/fv3ReECTXSZHrpq1aqSq3YpBvXkvyjXZbukpCRpndOiIMaegnT79m1R28PDQ6ftPTw8ikR1PyAnDCM/da+TkxPCw8N12oelpaXwb1Xbdu7cGf7+/khPTweQM23rxYsXMWDAAHTo0AFOTk557H3ByM/Yl7v+3r17RfuTEgySDzxqY21tLfxb6viTF/Hx8Thw4ACOHz+O+/fv48WLF8J9KWVbqdMs63JPmZmZwcXFBcHBwQCAZ8+eIT4+Xuuz39zcHC4uLpKPo9gnVeOQYmjLzs5O5/updOnSiIuLA6D6flJUEM8fJycnSRUZ1YmOjsaff/6J06dP4/79+6JwuSZZWVlITEyUHNjW9z0TFBQkur6bN28uef+axMTE4N9//xXaVatW1fk6kQ8ER0REICsrS3KgWJ3r16+L2vo63wcPHgjjP/B/z3SpFMfcwn4PIM/S0hKOjo6S1y9dujSSk5MBGG6sLoqfIYiIiIiIiDRhOJKIiIiIiIj0RrFCSc2aNXXa3traGuXLlxem2s398l7emzdvRG1dQy9SvmCMjIyUHEjMa+ixsHl4eGD48OFYv369sOzChQvCNJ7Ozs5o1KgRmjVrBh8fnwIJFym+3rpeP4oBPcX9JSUliapJ6npO+fkdlCtXLk/bvXz5ElOnTs3zNM/yFca0kQ9yaKM45afUbRW3k6+Wlx8FMfYUJMWKmooVN7WRMlVyQVEMmm/evBmbN2/O8/5UBa4qVqwIf39/0TSqt2/fxqRJkzBp0iRUqVIFjRs3FsazovT7AQw/9qmjSwVP+eB0Zmam5O2kys7Oxtq1a7Fo0SKdxi15umyn6z1VvXp1IRwJ5Iw52n5/VapU0Wl6ZMU+qar6png/jR49WvL+VXn79q3anxXk8yevYar09HQsWrQIa9asUVktWop3795Jvhfyes+oe9a9fv1a1Na1uqM6itfJiRMn8jU1dnZ2NuLj4/MVYAUMd76FNYYWBF2uOUDadZdfRfEzBBERERERkSYMRxIREREREZHeKIZWpE4Dq7hNbkBJ1Zf2ilVQrKysdN4/5Vi4cCEqVqyIJUuWKFXwCw0NRWhoKP744w8AOVV1Bg0ahN69e0uuLqgr+evHzMwMpqamOm2v+NoqXj/5vXZ0XV+eLlUMcz1//hyff/65qCKirnSZLjQ/FaHyW00qvwpi7ClICQkJovbHPM7pUj1OCnXVRkePHo0yZcpg9uzZSsGW3Ol0d+/eDQCoW7cu+vfvj4EDB4qqoxWW/F6/2sY+dXSprGZI2dnZGDt2LAICAvK1H13Gu/zeU1Kua12PYWpqCjMzM6Slpak9hr7vp9wKc4oK+vmTl2dkeno6+vbti1OnTum8rbyCek6qovgHP7r8kYIm+r5OgJyxN7/hyII6X13H0JIlS6JkyZJC9cnCfg8gr6iM04qK2mcIIiIiIiIiTQr3/1wTERERERFRsZbfL/RUba8YmNO1UpD8tHv/dTKZDOPHj8ft27cxd+5cNG3aVG0gMSgoCKNHj0bLli3x5MmTAumbvvfxsV07Y8aMEQVTLC0tMWDAAGzcuBHnz5/H48ePERUVhbdv3yI+Pl74T1/TVH7MDDH2FCZd+yN1GuKCYKjKVar4+fnh1q1b+Pnnn9G6dWtYWFioXO/BgweYPn06GjVqhBs3bhRY/6QqbtevNjt37lQKRnp7e2PBggU4fvw47t69i4iICMTExIjGOvlpXXVVEPdUXl4HbcfR9/2k7ngfw/Pn559/FgUjjYyM4OvrixUrVuDUqVMICQlBREQE4uLiRH2cOnVqgfVRV/q6dw0x7hriuWKoseq/NoYWhqL8GYKIiIiIiEgR/0yLiIiIiIiI9EZx6jfFSn1SyG+jaio5xWW6VseRsn6VKlUMUnWnqCpbtizGjBmDMWPGIDU1FcHBwQgMDMTFixdx6dIlUYjw/v376Nq1K86fP5/vCkKK5F/b1NRUZGRk6FQ9UvF6U7xWCuLa0ZfAwECcP39eaDs7O2Pfvn2SpibM65S0H7OCGHsKkmI1LV3Pp6CuASkV1xTHieXLl2Pw4MGG6hKsrKwwcOBADBw4EBkZGbhz5w4CAwNx6dIlnD9/XlQp78WLF+jevTvOnDlTqFNt5/f61Tb2FXVLliwR/m1kZISNGzeie/fuWrfLz3We33tKyu9Y12NkZGSIQviqjqF4P127dg21a9fW6TjafAzPn7S0NKxevVpoW1hYYM+ePZLCmUXpGVmmTBlRW1/vMxSvkwkTJuCHH37Qy77zw1Dnm98xND09Xeu9l18fPnzQ+z6LgqLyGYKIiIiIiEgTVo4kIiIiIiIivbGzsxO1da0OkpiYiNevXwvtsmXLKq2j+OX8w4cPdTqGruv/15ibm6N58+aYNGkS9u/fj8ePH2PRokWiL4ojIiKwatUqvR9b8fX+999/ddr+8ePHGvdnYmKCChUqCO2ifO2cOHFC1F6xYoWkYAoA0T30X1EQY09BKleunKit672gy/nLT3Gpa3hDcfpvVRTPpSCrRpmamsLLywtjxozBjh078OTJE6xbtw6Ojo7COomJiZg3b16B9UkVxetN19+RtrGvKAsLCxNd335+fpKCkUD+xjpd7ynF9RXHHFXCw8N1uqekHKMg7qeP4flz6dIl0VS+EyZMkFy1sig9I+3t7UXt0NBQvey3MMddTQx1vgU1hipOCa3L/S3lefmxK8zPEERERERERJowHElERERERER6U6ZMGVSuXFloP3jwACkpKZK3v3Hjhmjavvr16yut4+HhIZru7uLFi5L3n5WVhStXrkhen4DSpUtj+PDhCAgIEP3ejx07pnL9/ExFqPh6X79+XfK22dnZCA4OFtpmZmZwcXFRWs/Dw0P4d3R0tNIX4ppcunRJ8rr5JR+SKVWqFJo2bSppuxcvXiA6OtpQ3SqyCmLsKUju7u6itvy1LYUu61tZWQn/1qXaVmRkpCicpE79+vVRokQJoX3u3DnJx9A3MzMzfPXVV9i/f79oyu2///5bZRXMgppaNT9jH5AzZamm/RVlioHAdu3aSd5W19+TPF3ukbS0NISEhAjtqlWrSqosl5qaKtpO1z6peh0bNmwoahvifvoYnj+Fdd3oW8OGDUXjjL7eZ1SuXFn0xyCXLl0qEpULGzVqJGrr63zr1q2LkiVLCm3FMVEbqWOo/PMS0C3wqK8gqL4UxPNN188QREREREREhsJwJBEREREREelV48aNhX+npaXh8OHDkrfdvXu3qK34JSqQM9Wdm5ub0L527ZrkLxxPnz6NqKgoyf2h/+Pt7Y0qVaoI7efPn6tcT/7LaQB4//695GPIXzsAsG/fPsnbXrx4EZGRkUK7QYMGokBWrhYtWojaAQEBkvafkZGhdH0aknxIzcrKSvKX2Dt37jRUl4o8Q489Baly5cpwcHAQ2v/884/k4OKjR49w7949yceSr06XlJQkuo80OXnypKT1LC0t4eXlJbTv3buHW7duSe6fIdSuXVvUp6SkJMTFxSmtl5/xTBceHh6i8erYsWNITU2VtG1SUpIoaGJqaooGDRrovY+Gonhdly5dWtJ2z58/x+XLl/N83AMHDkiaFh5Qfj10GR/279+f53VVHadp06ai63L//v06BcGl+BieP3m9bq5cuYLw8HBDdClPypYtK/pDjnv37ukchldH/v3OmzdvcOTIEb3sNz/c3NxgbW0ttE+ePImXL1/me7+mpqaiQGNERAQCAwMlb6/4HkDx/WguxWqujx49krT/x48f49mzZ5L7UxAK6vkGSP8MQUREREREZCgMRxIREREREZFe9erVS9RetmyZpC/cQkNDsXfvXqFtYWGBLl26qFx3wIABova0adNEVd9USU9Px+zZs7X2g9STDx+YmpqqXEf+S28AOlWR8vHxQcWKFYX22bNnJYdf5s+fL2r37t1b5Xq9e/eGmZmZ0F6/fr2k6VVXrlyJiIgISX3RB/nfY0xMDOLj47Vu8+rVK/z666+G7FaRVhBjT0GSP5/U1FSsXr1a0naLFi3S6TjyYXNAWugxPT0da9askXyMwYMHi9ozZsxAZmam5O0NQTFMpWpMU1zHUFXxLCws8PnnnwvtxMREyb/fn3/+WVTBs0uXLjA3N9d7Hw1F8ZkhtZrv3Llz81UJLzIyEtu2bdO63ocPH7Bs2TLRMnXPF1U2btyoMnirKDg4GKdOnRLaFStWhI+Pj9J6FhYW6NOnj9B+9eqVUv/y62N4/uTlusnKysKcOXMM1aU8GzRokKg9Y8YMvVR5/Oabb0TtOXPmSKr2a0jGxsai9/Dv37/HrFmz9LJvxfcACxYskLTduXPnRBUsnZyc4O3trXLdvDwvAej9HtWHgnq+qTqeus8QREREREREhsJwJBEREREREelVu3btUKtWLaEdGhqKadOmadwmISEBQ4YMEQWZvvrqK7XTVvbp0weOjo5C+8yZM5gwYQIyMjJUrv/u3TsMGDBAp+kti7sNGzYgJiZG8vohISGianTyr7G8qlWr5nkKXRMTEwwZMkS0bNSoUVq/sF24cKFounRbW1ulL8lzlSlTBgMHDhTaqamp6NGjh8aKPlu3bpX8Jbu+fPLJJ8K/P3z4gHXr1mlc/927dxg4cKCkEE5xVRBjT0EaOHAgTExMhPaqVatw4cIFjdvs2LFDp4qrAJQCWCtXrtRYtTArKwvffvutTlPS9+jRA87OzkL78uXLGDt2rNoxW5Xs7GwcP35c5bi1bds2narBvX79GufPnxfa5cuXVwpbAYC5uTmcnJyEtiGnph05cqSovWzZMq1Tzp45cwarVq0SLRsxYoTe+2ZI8mMdkPNs0lY185dfftFLJd/Zs2fj4cOHGtf56aefRM++GjVqoHXr1pKPER8fj1GjRmkMA799+xYjR44U/ZHHoEGDYGxsrHL9b7/9VhQuWr58OTZt2iS5TwCQkpKCPXv2qPzZx/D8Ubxu1q5dq7US6MyZM/NVbdRQ+vXrB3t7e6EdGBiIiRMnSh5rXr9+rXJ548aN0aZNG6EdFhaGr7/+WnIV4lyXLl3SabzXZtSoUaIA9759+3QK9as73z59+oie3efOncPKlSs17isiIgKjR48WLRs6dKjae69WrVqi6coPHDig9bNFQEAAduzYoXGdwpCf55uhPkMQEREREREZCsORREREREREJAgPD8/Tf/JTOspkMvz8888wMvq/j5wbN27E119/rbLyXmBgIHx9fXH37l1hmYODg8ZKMpaWlli+fLlo2ZYtW+Dj44N169bhxo0bePz4MQIDA7Fs2TI0btwYJ06cgEwmQ9euXfPzKyo2Vq5cCVdXVwwePBgHDhxAQkKCyvWysrJw7Ngx9OjRQxQ8UBc+NDU1FU1Xe+XKFYwaNQpnzpzB48ePRdeNqul7R40ahbp16wrtZ8+eoX379jh58qRSddDY2FiMGzcOCxcuFC1funQpLCws1J777NmzRdP7PX36FM2aNcP06dNx9uxZhIWF4c6dO9i1axe6deuGcePGISsrC926dVO7T33r3LmzaCrTxYsXY9myZUhLSxOtl52djbNnz6J9+/a4cuUKZDIZbG1tC6yfRUlBjD0FqVq1ahg/frzQTk9PR58+fbBhwwalUGFSUhLmzZuHMWPGAFCuqqZJ7dq1RdP3Pn36FL169cKLFy+U1n348CF69eqFbdu2wdTUVON9Js/IyAgbN25EqVKlhGU7duxA69atcfDgQbUVPrOysnD37l0sXLgQDRs2RJ8+ffD27Vul9bZv3w4PDw/07t0bO3bsQGxsrNq+XL58GV26dBEFhNSNZwDQvHlz4d/h4eHo27cvjh8/jkePHik9C/OjUaNG6N+/v9BOS0tDr169sH79eqSnp4vWTUtLw9q1a+Hn5ye6FgYPHiwafz8Gjo6Ooj4/evQIvXr1UhlYj4qKwujRozFjxgwAOVMS55W1tTXi4+PRuXNn7N+/XylYFxsbizFjxiiFq5YtWyYaY7QdAwBOnDiBHj164MmTJ0rrXLlyBb6+vggNDRWWVa9eHWPHjlW73ypVqmDp0qVCOzs7G99++y38/PwQFBSkdrv09HScO3cOU6ZMwSeffIKZM2eqXO9jeP40bNhQ9EcyFy5cwDfffKMyOBcWFoY+ffoIIc/8XDeGYG5ujvXr14uuq61bt+Kzzz7DxYsXVVZG//DhA86dO4eRI0eiWbNmave9Zs0aUfDyn3/+gY+PDwICAjSGkMPCwrBq1Sq0aNECnTp10uuU0A4ODliyZIlo2YIFC9CrVy/cunVL5Tbp6ek4evQo+vfvj549e6pcp1SpUkohy++//x6TJk3CmzdvRMtzw/a+vr6i9weurq5KQXV5RkZG8PPzE9rv379Hz549VU7hHRcXh+nTpwv3clH4owtFeX2+GeozBBERERERkaGYaF+FiIiIiIiI/ivc3d3ztF1AQIBoStDmzZtj9uzZ+PHHH4VlBw4cwKFDh1C/fn1UqVIFGRkZCAkJUZrS2MLCAr///jvKlCmj8ZgdOnTAvHnzRF/uh4aGYvr06Wq3GT9+PGrVqoUDBw4Iy+QDAP816enp+PPPP/Hnn39CJpOhevXqqFq1KmxsbGBkZITY2FjcuXNHqRqUp6en0jS58oYPHy6q5Lh9+3Zs375dab1KlSqJgmkAYGZmhg0bNuDzzz8Xvsx+/vw5evbsCScnJ7i6uqJUqVKIjIxEUFCQUjWuoUOH4osvvtB43qVKlcLu3bvRuXNnIUiRkpKCdevWqa2QVaNGDaxatQp//fWXsMyQ106dOnXw1VdfCb+37OxszJkzB6tWrYKXlxfKli2LhIQE3LlzBy9fvhS2GzNmDIKDg7VWnCuuCmLsKUjfffcdLl68iKtXrwIAkpOTMXnyZMydOxdeXl6wsbFBTEwMrl+/LoTUK1SogFGjRsHf31/ycebOnQtfX18hvHDhwgU0aNAAXl5ecHJyQlpaGsLCwkQBrvnz52PlypWicLwmrq6u2LhxIwYPHixsc//+fQwYMAAWFhZwd3dHuXLlYGZmhsTERLx+/RoPHz6UvP8PHz7gxIkTOHHiBICcqVFr1qyJMmXKoESJEnj79i3u3bsnul+AnKDZd999p3a/Q4cOxZ49e4TfzfHjx3H8+HGV60qZfliTBQsW4O7du0JAKDk5GVOnTsW8efPg5eUFW1tbvHnzBkFBQUrV3zw9PTF37tx8Hb+w+Pv7o1u3bqLrz8PDA/Xr10e1atWQmZmJ8PBw3L59WwiKVa9eHUOHDtX4zNdk/vz5GDduHGJiYjBo0CBUqFAB7u7usLS0RFRUFK5fv670fBkzZgxatWol+Riurq6oVq0aAgICcO7cOXh6esLd3R3VqlXD+/fv8fDhQ6XAZKlSpbB+/XqYmZlp3PeAAQPw4sULUcjs6NGjOHr0KMqWLQs3NzfY2tpCJpMhMTERERERCAsLE51T+fLlVe77Y3j+mJiYYObMmRg1apSwbP/+/Thy5Ag8PT3h5OSE1NRUPHnyRFTZr1GjRvDx8Sly0xy3atUKCxYswLRp04Rr/Nq1a/j8889hZ2eHevXqwdbWFhkZGYiOjsa9e/eEsVFxemR5FStWxPbt29GrVy/hvVx4eDjGjBmDb7/9Fm5ubnBwcICFhQWSkpIQFxeHkJAQnatL6qpfv34IDQ3F6tWrhWV///03/v77bzg5OcHFxQVlypRBSkoKIiMjcf/+fSEI7urqqna/vXv3RmBgIDZv3iws27hxI7Zs2YKGDRuiYsWKSEpKwr1795T+QMfOzg4bN27UOuXzuHHjsH37dqGqeUREBHx9feHi4gJnZ2fIZDJEREQgODhYqMTYrVs3mJubF7kKkvl5vhnqMwQREREREZEhMBxJREREREREBjFx4kRYWVlhxowZwheaWVlZCA4ORnBwsMptypcvj23btqFhw4aSjjF69GjY2dlh+vTpSlVh5JUoUQJz587F8OHDsXHjRtHPLC0tJZ5R8ZadnY0nT56orGwlr2XLltiyZYtoul9FudUWV69erbLikTZ169bFiRMn0KtXLzx9+lRYHhERobICIJBTzWfSpElqK2EpcnZ2xrFjxzBmzBhRkFOV9u3bY/369aLpwgHDXzvLli3Ds2fPRNOAJiQk4J9//lG5/qBBg/Djjz+ic+fOBu1XUVcQY09BKVmyJPbs2YNevXqJKlPFx8fj1KlTSuuXK1cOu3fvVgoda9OoUSMsXrwYU6ZMEe7ZzMxMldWwZDIZfvjhBwwZMkTrlKWKfH198ffff2PQoEEICwsTlqekpGi9D4Gc8HTJkiUlHUvTeJHLzc0NO3bs0Bgu8vLywvz58zFr1iyNUyPrg6WlJQ4dOoRBgwaJXt/ExEScPn1a7XYdOnTApk2bJFfyLGpatGiBBQsWYPr06UJIR9M9W6tWLezZsydfITxvb2+sWbMGY8eORWZmJqKjo4WwkyrDhw/PU/h0+fLliI2NFQJ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      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 657,
       "width": 1315
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "\n",
    "plt.hist(qids.value_counts(), bins=160)\n",
    "\n",
    "plt.yscale('log', nonposy='clip')\n",
    "\n",
    "plt.title('Log-Histogram of question appearance counts')\n",
    "\n",
    "plt.xlabel('Number of occurences of question')\n",
    "\n",
    "plt.ylabel('Number of questions')\n",
    "\n",
    "print ('Maximum number of times a single question is repeated: {}\\n'.format(max(qids.value_counts()))) \n",
    "\n",
    "#Checking whether there are any rows with null values\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)\n",
    "\n",
    "print('------>There are two rows with null values in question2<------ ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2> Basic Feature Extraction (before cleaning) </h2>\n",
    "\n",
    "Let us now construct a few features like:\n",
    " - ____freq_qid1____ = Frequency of qid1's\n",
    " - ____freq_qid2____ = Frequency of qid2's \n",
    " - ____q1len____ = Length of q1\n",
    " - ____q2len____ = Length of q2\n",
    " - ____q1_n_words____ = Number of words in Question 1\n",
    " - ____q2_n_words____ = Number of words in Question 2\n",
    " - ____word_Common____ = (Number of common unique words in Question 1 and Question 2)\n",
    " - ____word_Total____ =(Total num of words in Question 1 + Total num of words in Question 2)\n",
    " - ____word_share____ = (word_common)/(word_Total)\n",
    " - ____freq_q1+freq_q2____ = sum total of frequency of qid1 and qid2 \n",
    " - ____freq_q1-freq_q2____ = absolute difference of frequency of qid1 and qid2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: [id, qid1, qid2, question1, question2, is_duplicate]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Filling the null values with ' '\n",
    "df = df.fillna('')\n",
    "nan_rows = df[df.isnull().any(1)]\n",
    "print (nan_rows)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>qid1</th>\n",
       "      <th>qid2</th>\n",
       "      <th>question1</th>\n",
       "      <th>question2</th>\n",
       "      <th>is_duplicate</th>\n",
       "      <th>freq_qid1</th>\n",
       "      <th>freq_qid2</th>\n",
       "      <th>q1len</th>\n",
       "      <th>q2len</th>\n",
       "      <th>q1_n_words</th>\n",
       "      <th>q2_n_words</th>\n",
       "      <th>word_Common</th>\n",
       "      <th>word_Total</th>\n",
       "      <th>word_share</th>\n",
       "      <th>freq_q1+q2</th>\n",
       "      <th>freq_q1-q2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>What is the step by step guide to invest in sh...</td>\n",
       "      <td>What is the step by step guide to invest in sh...</td>\n",
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       "      <td>66</td>\n",
       "      <td>57</td>\n",
       "      <td>14</td>\n",
       "      <td>12</td>\n",
       "      <td>10.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.434783</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
       "      <td>What would happen if the Indian government sto...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>51</td>\n",
       "      <td>88</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>How can I increase the speed of my internet co...</td>\n",
       "      <td>How can Internet speed be increased by hacking...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Why am I mentally very lonely? How can I solve...</td>\n",
       "      <td>Find the remainder when [math]23^{24}[/math] i...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>65</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>Which one dissolve in water quikly sugar, salt...</td>\n",
       "      <td>Which fish would survive in salt water?</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>76</td>\n",
       "      <td>39</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  qid1  qid2                                          question1  \\\n",
       "0   0     1     2  What is the step by step guide to invest in sh...   \n",
       "1   1     3     4  What is the story of Kohinoor (Koh-i-Noor) Dia...   \n",
       "2   2     5     6  How can I increase the speed of my internet co...   \n",
       "3   3     7     8  Why am I mentally very lonely? How can I solve...   \n",
       "4   4     9    10  Which one dissolve in water quikly sugar, salt...   \n",
       "\n",
       "                                           question2  is_duplicate  freq_qid1  \\\n",
       "0  What is the step by step guide to invest in sh...             0          1   \n",
       "1  What would happen if the Indian government sto...             0          4   \n",
       "2  How can Internet speed be increased by hacking...             0          1   \n",
       "3  Find the remainder when [math]23^{24}[/math] i...             0          1   \n",
       "4            Which fish would survive in salt water?             0          3   \n",
       "\n",
       "   freq_qid2  q1len  q2len  q1_n_words  q2_n_words  word_Common  word_Total  \\\n",
       "0          1     66     57          14          12         10.0        23.0   \n",
       "1          1     51     88           8          13          4.0        20.0   \n",
       "2          1     73     59          14          10          4.0        24.0   \n",
       "3          1     50     65          11           9          0.0        19.0   \n",
       "4          1     76     39          13           7          2.0        20.0   \n",
       "\n",
       "   word_share  freq_q1+q2  freq_q1-q2  \n",
       "0    0.434783           2           0  \n",
       "1    0.200000           5           3  \n",
       "2    0.166667           2           0  \n",
       "3    0.000000           2           0  \n",
       "4    0.100000           4           2  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "if os.path.isfile('df_fe_without_preprocessing_train.csv'):\n",
    "    df = pd.read_csv(\"df_fe_without_preprocessing_train.csv\",encoding='latin-1')\n",
    "else:\n",
    "    df['freq_qid1'] = df.groupby('qid1')['qid1'].transform('count') \n",
    "    df['freq_qid2'] = df.groupby('qid2')['qid2'].transform('count')\n",
    "    df['q1len'] = df['question1'].str.len() \n",
    "    df['q2len'] = df['question2'].str.len()\n",
    "    df['q1_n_words'] = df['question1'].apply(lambda row: len(row.split(\" \")))\n",
    "    df['q2_n_words'] = df['question2'].apply(lambda row: len(row.split(\" \")))\n",
    "\n",
    "    def normalized_word_Common(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * len(w1 & w2)\n",
    "    df['word_Common'] = df.apply(normalized_word_Common, axis=1)\n",
    "\n",
    "    def normalized_word_Total(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * (len(w1) + len(w2))\n",
    "    df['word_Total'] = df.apply(normalized_word_Total, axis=1)\n",
    "\n",
    "    def normalized_word_share(row):\n",
    "        w1 = set(map(lambda word: word.lower().strip(), row['question1'].split(\" \")))\n",
    "        w2 = set(map(lambda word: word.lower().strip(), row['question2'].split(\" \")))    \n",
    "        return 1.0 * len(w1 & w2)/(len(w1) + len(w2))\n",
    "    df['word_share'] = df.apply(normalized_word_share, axis=1)\n",
    "\n",
    "    df['freq_q1+q2'] = df['freq_qid1']+df['freq_qid2']\n",
    "    df['freq_q1-q2'] = abs(df['freq_qid1']-df['freq_qid2'])\n",
    "\n",
    "    df.to_csv(\"df_fe_without_preprocessing_train.csv\", index=False)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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