Modeling Result Overview.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# original\n",
    "a = [83.0441,\n",
    "82.2983,\n",
    "82.8203,\n",
    "82.5559,\n",
    "82.4542,\n",
    "83.2441]\n",
    "# fare\n",
    "b = [82.7017,\n",
    "82.6746,\n",
    "82.2542,\n",
    "82.1390,\n",
    "82.0983,\n",
    "82.8576,]\n",
    "# family size & alone\n",
    "c = [83.3898,\n",
    "82.9288,\n",
    "82.9051,\n",
    "83.1186,\n",
    "81.9695,\n",
    "83.6441,]\n",
    "# ipSp & Parch\n",
    "d = [82.9898,\n",
    "83.0746,\n",
    "82.7051,\n",
    "82.6102,\n",
    "82.0271,\n",
    "83.0508]\n",
    "# XYZ\n",
    "e = [82.6746,\n",
    "83.2814,\n",
    "82.8102,\n",
    "82.6407,\n",
    "82.6814,\n",
    "83.3085,]\n",
    "\n",
    "f= [82.8542,\n",
    "82.5322,\n",
    "82.5390,\n",
    "82.7763,\n",
    "81.7153,\n",
    "83.2136]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(a,columns=['Original'])\n",
    "df2 = pd.DataFrame(b,columns=['log fare'])\n",
    "df3 = pd.DataFrame(c,columns=['no family size & alone'])\n",
    "df4 = pd.DataFrame(d,columns=['no SipSp & Parch'])\n",
    "df5 = pd.DataFrame(e,columns=['CabinXYZ'])\n",
    "df6 = pd.DataFrame(f,columns=['Cabin + no size and alone'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "summary = pd.concat([df1,df2,df3,df4,df5, df6],axis=1)\n",
    "summary.index = ['RFC','XGB','DAC','LRC','GBC','voting']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Original</th>        <th class=\"col_heading level0 col1\" >log fare</th>        <th class=\"col_heading level0 col2\" >no family size & alone</th>        <th class=\"col_heading level0 col3\" >no SipSp & Parch</th>        <th class=\"col_heading level0 col4\" >CabinXYZ</th>        <th class=\"col_heading level0 col5\" >Cabin + no size and alone</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row0\" class=\"row_heading level0 row0\" >RFC</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col0\" class=\"data row0 col0\" >83.0441</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col1\" class=\"data row0 col1\" >82.7017</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col2\" class=\"data row0 col2\" >83.3898</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col3\" class=\"data row0 col3\" >82.9898</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col4\" class=\"data row0 col4\" >82.6746</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row0_col5\" class=\"data row0 col5\" >82.8542</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row1\" class=\"row_heading level0 row1\" >XGB</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col0\" class=\"data row1 col0\" >82.2983</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col1\" class=\"data row1 col1\" >82.6746</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col2\" class=\"data row1 col2\" >82.9288</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col3\" class=\"data row1 col3\" >83.0746</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col4\" class=\"data row1 col4\" >83.2814</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row1_col5\" class=\"data row1 col5\" >82.5322</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row2\" class=\"row_heading level0 row2\" >DAC</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col0\" class=\"data row2 col0\" >82.8203</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col1\" class=\"data row2 col1\" >82.2542</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col2\" class=\"data row2 col2\" >82.9051</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col3\" class=\"data row2 col3\" >82.7051</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col4\" class=\"data row2 col4\" >82.8102</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row2_col5\" class=\"data row2 col5\" >82.539</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row3\" class=\"row_heading level0 row3\" >LRC</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col0\" class=\"data row3 col0\" >82.5559</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col1\" class=\"data row3 col1\" >82.139</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col2\" class=\"data row3 col2\" >83.1186</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col3\" class=\"data row3 col3\" >82.6102</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col4\" class=\"data row3 col4\" >82.6407</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row3_col5\" class=\"data row3 col5\" >82.7763</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row4\" class=\"row_heading level0 row4\" >GBC</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col0\" class=\"data row4 col0\" >82.4542</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col1\" class=\"data row4 col1\" >82.0983</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col2\" class=\"data row4 col2\" >81.9695</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col3\" class=\"data row4 col3\" >82.0271</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col4\" class=\"data row4 col4\" >82.6814</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row4_col5\" class=\"data row4 col5\" >81.7153</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5level0_row5\" class=\"row_heading level0 row5\" >voting</th>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col0\" class=\"data row5 col0\" >83.2441</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col1\" class=\"data row5 col1\" >82.8576</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col2\" class=\"data row5 col2\" >83.6441</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col3\" class=\"data row5 col3\" >83.0508</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col4\" class=\"data row5 col4\" >83.3085</td>\n",
       "                        <td id=\"T_7fa009f4_676c_11e9_972d_b06ebfbf33c5row5_col5\" class=\"data row5 col5\" >83.2136</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2cdc8b7f1d0>"
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     },
     "execution_count": 28,
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   ],
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    "summary.style.background_gradient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
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     "data": {
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dO9q3b8/zzz8PwH//93/XjDZvyqeffkqbNm1o164d77//Pk8++eQ2q+eCCy7g7LPP5rvf/S7Nm9de7vrNN9+kuLiYq6++mrKyMl577bUNHr/rrrtYvnw511xzTc22QYMG8cADD/DZZ1XXpnnnnXf44IMPNuh38MEH89prrzF37lzatGnDL3/5S6666ipOOOGEWif/5XL8vXv35tlnn+XDDz9kzZo1PPTQQ/W+rgMHDuTOO++suV9eXp7ZXpIkKVc5jTA3Ng29DNzmGD9+PMOGDWPFihUcdNBBPPjgg5ntu3XrRvfu3SkqKuKggw7i8MMP32a1nHDCCZx33nl1TscAGDduHM888wzNmzfn0EMP5dhjj+Xdd9+teXzs2LG0bNmyZurJsGHDGDZsGPPnz+cb3/gGUHXS3a9//Wu+/OUv1/Rr374948eP55xzziGlRLt27ZgwYQLXXnstffv25Zvf/OZmHf8+++zDT37yE4466ihSShx33HGceOKJmcd+++23M3z4cEpKSqisrKRv3741J0pKkiRtjcj6+jsfysrK0owZMzbYNn/+fLp06ZKnipqOGTNmMGrUqJoRbzU8fzclSWq6ImJmSqneC0A0yRFm1XbLLbfwi1/8os65y5IkSdpyzmHeQVxzzTW8/fbbHHHEEfkuRZIkaYdiYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScrQJFfJmH/Itl3Gq8tr87fp/jb2+uuvc/HFF/Pxxx/zxRdfcOSRR3LvvfcyY8YMfvWrX3H77bdvsu/atWsZOXIkU6ZMISIoKCjgkUceoXPnzjk//3333cfYsWNp0aIFw4cP53vf+16d7caMGcN9991HYWEhlZWV/PjHP665VPfW6NSpEzNmzKBDhw5bvS9JkqTtrUkG5qZmxIgRjBo1qubiG7NnV114paysjLKy7KX/Hn74YZYsWUJFRQXNmjVj8eLFtGnTJufnrqys5LrrruONN95gt9124+23385sP2rUKK688krmz5/PkUceyQcffECzZvV/EVFZWUmLFv46SZKkHY9TMnKwcOFCunTpwoUXXkhRUREDBw5k5cqVQNUlmPv06UNJSQknn3wyH330Ua3+7777Lh07dqy5X1xcDMDUqVMZPHgwUDW6e84553D00Udz8MEHc99999X03WeffWpCa8eOHWnfvj1QddW9K664gh49ejBgwACWLl1aZ/2VlZUsW7aMiKBTp045HXOXLl1o0aIFH374IX/4wx/o3bs33bt355hjjuH999+vqfmiiy5i4MCBnHvuuaxZs4Yrr7yS4uJiSkpKuOOOO2r2d8cdd9CjRw+Ki4trXZJbkiSpMTMw52jBggUMHz6cuXPnssceezBp0iQAzj33XH76059SUVFBcXExN954Y62+o0aN4uijj+bYY4/ltttu4+OPP67zOSoqKvjTn/7EtGnTuOmmm1iyZAnf/e53+cMf/kBpaSlXXHEFr7zySk37zz//nB49ejBr1iz69etX53NXVlZSUlLCSSedxD//+c+cj/ell16iWbNmFBYWcsQRR/DXv/6VV155hTPOOIOf/exnNe1mzpzJ448/zm9+8xvuvfde3nrrLV555RUqKio466yzatp16NCBWbNmcckllzB27Nic65AkSco3A3OOOnfuTGlpKQA9e/Zk4cKFfPLJJ3z88cf069cPgCFDhvDcc8/V6nveeecxf/58TjvtNKZOnUqfPn344osvarU78cQTad26NR06dOCoo47i5ZdfpmPHjrz++uv85Cc/oVmzZgwYMICnn34agGbNmnH66acDcPbZZ/PCCy/U2ue1117LOeecwxVXXMF3vvMdVqxYwSOPPMJVV11V53HedtttlJaWcuWVV/Lwww8TESxevJhBgwZRXFzMrbfeyty5c2van3DCCbRu3RqAp556imHDhtVMzfjSl75U0+6UU07Z4LWTJElqKpx0mqNWrVrV3G7evHnNlIxc7bvvvpx//vmcf/75dO3alTlz5tRqExF13m/VqhXHHnssxx57LHvttRePPfYYAwYMqLc/wF/+8hcuu+wyOnXqxAcffMBpp51GmzZtNhmY181hXt/3v/99Lr/8ck444QSmTp3KmDFjah5bfz51SqnOGtYdA1S9dpWVlXW2kSRJaowcYd4K7dq1o3379jz//PMA/Pd//3fNaPP6/vznP7N69WoA3nvvPZYtW8Z+++1Xq93jjz/OqlWrWLZsGVOnTqVXr17MmjWLJUuWAFUrZlRUVHDggQfW3J84cSIAv/nNb+q8LHb37t351a9+BcDll1/O8uXLmTt3Lj179sz5OD/55JOaesePH7/JdgMHDuSee+6pCcSbMwVEkiSpsWqSI8wNvQzc5hg/fjzDhg1jxYoVHHTQQTz44IO12kyePJnLLruMgoICAG699Vb23nvvWie/HXbYYRx//PEsWrSI66+/nn333ZeKigouvPDCmikchx12GJdeeilQNbq7Lvy2a9eOhx9+uNZzjxs3josvvpiioiJat27NySefzIIFCxg1ahT/9V//ldMxjhkzhtNOO4399tuPPn368NZbb9XZ7oILLuBvf/sbJSUltGzZkgsvvLCmVkmSpKYqUkr5rmEDZWVlacaMGRtsmz9/Pl26bNu1lxubMWPG0LZt21rTIbK0bduWzz77rAGrUn12ht9NSZJ2VBExM6WUvcYvTsmQJEmSMjXJKRk7ovVPpMuVo8uSJEkNzxFmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDE3ypL+7hk3Zpvsbfs/RObV77733GDlyJNOnT6dVq1Z06tSJcePG8bWvfa1W24ULFzJ48OA6r+h3wQUXcPnll3PooYdu8rlGjBhBYWEh119/PQA333wzS5YsoVu3bjz99NM1ay5/+umndO/encmTJ3PqqadusI/FixczYMCAOtdnliRJUm6aZGDOh5QSJ598MkOGDOG3v/0tAOXl5bz//vt1BuYs999/f71t/uM//oPS0lLOOussIoL777+fV155hXbt2jF+/HieeuopjjnmGG644QbOP/98vvKVr1BeXl7T/9133+Wwww6rCdySJEnaMk7JyNEzzzxDy5YtGTZsWM220tJSunfvzoABA+jRowfFxcU8/vjjNY9XVlYyZMgQSkpK+Ld/+zdWrFgBQP/+/Vl3cZa2bdty3XXX0a1bN/r06cP7778PwO67787NN9/MpZdeyvDhw7npppvYY489iAh+8YtfMHLkSGbMmMHTTz/NVVddtUGtKSWGDBnCVVddRdeuXRv6pZEkSdqhGZhzNGfOHHr27Flre0FBAY8++iizZs3imWee4YorrmDd1RNff/11LrroIioqKth99925++67a/X//PPP6dOnD6+++ip9+/blvvvuq3nszDPP5KOPPuLTTz/lnHPOqdleUlLCoEGDGDBgALfffju77LLLBvu87bbbaNGiBd///ve31eFLkiTttAzMWymlxA9/+ENKSko45phjeOedd2pGiffff38OP/xwAM4++2xeeOGFWv132WUXBg8eDEDPnj1ZuHBhzWOLFy/mvffeY8mSJbUuUjJ8+HD2228/jjrqqA22v/rqq4wbN44HH3yQiNiWhypJkrRTMjDnqKioiJkzZ9baPmHCBJYuXcrMmTMpLy9nr732YtWqVQC1AmtdAbZly5Y125s3b05lZWXNY5dddhljxozhu9/9LjfeeOMG/Zo1a0azZhu+fStXruSss87i7rvvZq+99tqyA5UkSdIGDMw5Ovroo/niiy82mDIxffp03n77bb785S/TsmVLnnnmGd5+++2axxctWsS0adMAeOihhzjiiCNyfr4nn3ySDz74gHPPPZfrr7+eRx99lHnz5mX2ufLKK+nXr1/NiLUkSZK2XpNcJSPXZeC2pYjg0UcfZeTIkdxyyy0UFBTQqVMnxowZw4gRIygrK6O0tJRDDjmkpk+XLl0YP348F198MQcffDCXXHJJTs+1atUqRo4cycSJE4kI2rRpw89+9jMuvfRSpkype0m9JUuWcPfdd3PIIYdQWlpas72oqIgJEyZs3cFLkiTtxGLdCWqNRVlZWVq3gsQ68+fPp0uXLnmqSNo0fzclSWq6ImJmSqmsvnZOyZAkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDE1yHeafn75tL8xxxcN/rLfNe++9x8iRI5k+fTqtWrWiU6dOjBs3jq997Wt1tl+4cCGDBw9mzpw5tR674IILuPzyyzn00EO3uvbtYcmSJYwYMYKJEyfmtY5OnToxY8YMOnTokFP7/v37M3bsWMrK6l0tRpIkaZMcYc5BSomTTz6Z/v378+abbzJv3jx+/OMf8/7772/R/u6///4tDstDhw5l6tSpW9R3S+277755D8uSJEn5YmDOwTPPPEPLli0ZNmxYzbbS0lKOPPJIPvvsMwYMGECPHj0oLi7m8ccfr2lTWVnJkCFDKCkp4d/+7d9YsWIFUDXyue7iLG3btuW6666jW7du9OnTZ4tD+Po2tc+3336bAQMGUFJSwoABA1i0aFGtvs8++yylpaWUlpbSvXt3li9fzsKFC+natStQNTq+7vHCwkJuvPFGAG699VZ69epFSUkJo0ePrrOuSy65hLKyMoqKijZo06lTJ0aPHl3zGr722msALFu2jIEDB9K9e3cuvvhiNnWRnU3td30PPfQQxcXFdO3alauvvrre12rp0qWceuqp9OrVi169evHiiy9mvuaSJGnHZWDOwZw5c+jZs2edjxUUFPDoo48ya9YsnnnmGa644oqaYPf6669z0UUXUVFRwe67787dd99dq//nn39Onz59ePXVV+nbty/33XffVte7qX1eeumlnHvuuVRUVHDWWWcxYsSIWn3Hjh3LXXfdRXl5Oc8//zytW7fe4PH777+f8vJyHn/8cfbcc0+GDh3K5MmTWbBgAS+//DLl5eXMnDmT5557rta+b775ZmbMmEFFRQXPPvssFRUVNY916NCBWbNmcckllzB27FgAbrzxRo444gheeeUVTjjhhDoDfn37haopJVdffTVTpkyhvLyc6dOn89hjj2W+VpdddhmjRo1i+vTpTJo0iQsuuCDXl1+SJO1gmuQc5sYkpcQPf/hDnnvuOZo1a8Y777xTM0q5//77c/jhhwNw9tlnc/vtt3PllVdu0H+XXXZh8OCqOdk9e/bkf//3f2s9x1/+8peaUdFFixbxwgsv0LZtW1q1asVLL71Uq/2m9jlt2jR+97vfAXDOOefwgx/8oFbfww8/nMsvv5yzzjqLU045hY4dO9Zqs2rVKk477TTuvPNODjzwQO644w4mT55M9+7dAfjss89YsGABffv23aDfI488wr333ktlZSXvvvsu8+bNo6SkBIBTTjmlpt51NT733HM1t48//njat29fq5b69gswffp0+vfvT2FhIQBnnXUWzz33HCeddNImX6unnnqKefPm1ezj008/Zfny5ey222511iBJUlNTPL643jazh8zeDpU0fgbmHBQVFW1yDu+ECRNYunQpM2fOpGXLlnTq1IlVq1YBEBEbtN34PkDLli1rtjdv3pzKyspabQYNGsSgQYOAqjnMQ4cOpX///pusN5d9bqqea665huOPP54nnniCPn368NRTT1FQULBBm2HDhnHKKadwzDHHAFV/NFx77bVcfPHFm6zprbfeYuzYsUyfPp327dszdOjQmtcJoFWrVnXWW1eNm7PfdfVtyqZeq7Vr1zJt2rRaI+ySJGnn45SMHBx99NF88cUXG0yXmD59Os8++yyffPIJX/7yl2nZsiXPPPMMb7/9dk2bRYsWMW3aNKBqDu0RRxyx3Wtf3ze/+U1++9vfAlVBv6563nzzTYqLi7n66qspKyurmU+8zl133cXy5cu55pprarYNGjSIBx54gM8++wyAd955hw8++GCDfp9++ilt2rShXbt2vP/++zz55JP11tu3b18mTJgAwJNPPslHH31Uq00u++3duzfPPvssH374IWvWrOGhhx6iX79+mc89cOBA7rzzzpr75eXl9dYrSZJ2TE1yhDmXZeC2pYjg0UcfZeTIkdxyyy0UFBTULCtXVFTEd77StAhZAAAaWUlEQVTzHcrKyigtLeWQQw6p6delSxfGjx/PxRdfzMEHH8wll1yyXeve2O23387555/PrbfeSmFhIQ8++GCtNuPGjeOZZ56hefPmHHrooRx77LG8++67NY+PHTuWli1bUlpaClSNNg8bNoz58+fzjW98A6g6ke7Xv/41X/7yl2v6devWje7du1NUVMRBBx1UM1Uly+jRoznzzDPp0aMH/fr144ADDqjVJpf97rPPPvzkJz/hqKOOIqXEcccdx4knnljvazV8+HBKSkqorKykb9++3HPPPfXWLEmSdjyR9XV1PpSVlaV1K0isM3/+fLp06ZKniqRN83dTktRUOYcZImJmSqneCzY4JUOSJEnKYGCWJEmSMjSZwNzYpo5I/k5KkrRzaBKBuaCggGXLlhlQ1GiklFi2bFmtJfckSdKOp0msktGxY0cWL17M0qVL812KVKOgoKDOC7tIkqQdS5MIzC1btqRz5875LkOSJEk7oSYxJUOSJEnKFwOzJEmSlMHALEmSJGUwMEuSJEkZDMySJElShpwCc0SMioi5ETEnIh6KiIKI+GVEvBoRFRExMSLa1tGvU0SsjIjy6p97tv0hSJIkSQ2n3sAcEfsBI4CylFJXoDlwBjAqpdQtpVQCLAIu3cQu3kwplVb/DNtWhUuSJEnbQ65TMloArSOiBbArsCSl9ClARATQGvAyfJIkSdrh1BuYU0rvAGOpGkV+F/gkpTQZICIeBN4DDgHu2MQuOkfEKxHxbEQcuW3KliRJkraPXKZktAdOBDoD+wJtIuJsgJTSedXb5gOn19H9XeCAlFJ34HLgNxGxex3PcVFEzIiIGV7+WpIkSY1JLlMyjgHeSiktTSmtBn4HfHPdgymlNcDDwKkbd0wpfZFSWlZ9eybwJvC1Otrdm1IqSymVFRYWbtmRSJIkSQ0gl8C8COgTEbtWz1ceAMyPiK9CzRzm7wCvbdwxIgojonn17YOAg4G/b6viJUmSpIbWor4GKaWXImIiMAuoBF4B7gWmVE+vCOBV4BKAiDiBqhU1bgD6AjdFRCWwBhiWUvpngxyJJEmS1ADqDcwAKaXRwOiNNh++iba/B35ffXsSMGlrCpQkSZLyySv9SZIkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRkMzJIkSVKGnC6NrdwUjy+ut83sIbO3QyWSJEnaVhxhliRJkjIYmCVJkqQMBmZJkiQpg4FZkiRJymBgliRJkjIYmCVJkqQMBmZJkiQpg4FZkiRJymBgliRJkjIYmCVJkqQMXhpbkiRJW+WuYVPqbTP8nqO3QyUNwxFmSZIkKYOBWZIkScrglAxpG9vRv5aSJGln4wizJEmSlMHALEmSJGUwMEuSJEkZDMySJElSBk/6kyRJaiI6XfOnnNotvOX4Bq5k5+IIsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgZP+pMkSVKD+/npg+ttc8XDf9wOlWw+R5glSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScrQIt8FSJJ2bsXji+ttM3vI7O1QiSTVzRFmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOrZEibYf4hXepv1P+uhi9EkiRtN44wS5IkSRkMzJIkSVIGA7MkSZKUwTnMkiRJqlNO5+7ADn/+zk4fmDtd86ec2i285fgGrkSSJEmNkVMyJEmSpAw5BeaIGBURcyNiTkQ8FBEFEfHLiHg1IioiYmJEtN1E32sj4o2IeD0iBm3b8iVJkqSGVW9gjoj9gBFAWUqpK9AcOAMYlVLqllIqARYBl9bR99DqtkXAt4G7I6L5NqxfkiRJalC5TsloAbSOiBbArsCSlNKnABERQGsg1dHvROC3KaUvUkpvAW8Ah2192ZIkSdL2UW9gTim9A4ylahT5XeCTlNJkgIh4EHgPOAS4o47u+wH/WO/+4uptkiRJUpOQy5SM9lSNFHcG9gXaRMTZACml86q3zQdOr6t7HdtqjURHxEURMSMiZixdunQzypckSZIaVi7Lyh0DvJVSWgoQEb8Dvgn8GiCltCYiHgauAh7cqO9iYP/17ncElmz8BCmle4F7AcrKyuqa2rHDyHU9wy6vzW/gSiRJkpSLXOYwLwL6RMSu1fOVBwDzI+KrUDOH+TvAa3X0/T1wRkS0iojOwMHAy9umdEmSJKnh1TvCnFJ6KSImArOASuAVqkaDp0TE7lRNu3gVuAQgIk6gakWNG1JKcyPiEWBedd/hKaU1DXMokiRJ0raX05X+UkqjgdEbbT58E21/T9XI8rr7NwM3b2mBkiRJUj55pT9JkiQpg4FZkiRJymBgliRJkjIYmCVJkqQMOZ30J+0MiscX19vmke1QhyRJalwcYZYkSZIyGJglSZKkDE7JkNTk5TKdZvaQ2duhEknSjsgRZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnK4LJyTdjPTx9cb5srHv7jdqhEkiQ1KmPa1d+m8wENX8cOwhFmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDJ70JylvOl3zp3rbLLzl+O1QiSRJm2ZglqT13DVsSr1tht9z9HaoRJLUWDglQ5IkScpgYJYkSZIyGJglSZKkDAZmSZIkKYMn/UmSdio/P31wvW2uePiP26ESSU2FI8ySJElSBgOzJEmSlMHALEmSJGUwMEuSJEkZDMySJElSBgOzJEmSlMHALEmSJGUwMEuSJEkZDMySJElSBq/0l6sx7epv0/mAhq9D0haZf0iX3Br2v6thC5EkNTmOMEuSJEkZDMySJElSBqdkSJKkvLtr2JR62wy/5+jtUIlUm4G5kcrlfxzKUS7zz8E56JIkqU5OyZAkSZIyGJglSZKkDAZmSZIkKYNzmCVJOwTP/ZDUUBxhliRJkjIYmCVJkqQMBmZJkiQpg4FZkiRJyuBJf5KkRm/+IV3qb9T/roYvRNJOyRFmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScrgsnKSpM3W6Zo/1dtm4S3Hb4dKJKnhOcIsSZIkZXCEWZI2089PH1xvmyse/uN2qESStD0YmKU8yCVwgaFLkqTGIKcpGRExKiLmRsSciHgoIgoiYkJEvF697YGIaLmJvmsiorz65/fbtnxJkiSpYdUbmCNiP2AEUJZS6go0B84AJgCHAMVAa+CCTexiZUqptPrnhG1TtiRJkrR95DolowXQOiJWA7sCS1JKk9c9GBEvAx0boD5JkiQpr+odYU4pvQOMBRYB7wKfbBSWWwLnAH/exC4KImJGRPw1Ik7aBjVLkiRJ200uUzLaAycCnYF9gTYRcfZ6Te4GnkspPb+JXRyQUioD/h0YFxFfqeM5LqoO1TOWLl262QchSZIkNZRcTvo7BngrpbQ0pbQa+B3wTYCIGA0UApdvqnNKaUn1f/8OTAW619Hm3pRSWUqprLCwcLMPQpIkSWoouQTmRUCfiNg1IgIYAMyPiAuAQcCZKaW1dXWMiPYR0ar6dgfgcGDetildkiRJani5zGF+CZgIzAJmV/e5F7gH2AuYVr1k3A0AEVEWEfdXd+8CzIiIV4FngFtSSgZmSZIkNRk5rZKRUhoNjM6lb0ppBtVLzKWU/g9Vy85JkiRJTVJOFy6RJEmSdlZeGluSpJ1Mp2v+VG+bhbccvx0qkZoGR5glSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIytMh3AdLW6HTNn+pts7BgOxQiSZJ2WI4wS5IkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IkSRlcJUNS4zamXf1tOh/Q8HVIknZajjBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpTBwCxJkiRlMDBLkiRJGQzMkiRJUgYDsyRJkpShRb4LkCTtoMa0y61d5wMatg5J2kqOMEuSJEkZDMySJElSBgOzJEmSlMHALEmSJGXwpD9JklRbLidtjvmk4euQGgFHmCVJkqQMBmZJkiQpg4FZkiRJymBgliRJkjIYmCVJkqQMBmZJkiQpg4FZkiRJymBgliRJkjIYmCVJkqQMOV3pLyJGARcACZgNnAf8EigDVgMvAxenlFbX0XcI8P9U3/2PlNL4bVC3JElqIuYf0qX+Rv3vavhCpC1U7whzROwHjADKUkpdgebAGcAE4BCgGGhNVaDeuO+XgNFAb+AwYHREtN9m1UuSJEkNLNcpGS2A1hHRAtgVWJJSeiJVo2qEuWMd/QYB/5tS+mdK6SPgf4Fvb4vCJUmSpO2h3sCcUnoHGAssAt4FPkkpTV73eES0BM4B/lxH9/2Af6x3f3H1tg1ExEURMSMiZixdunTzjkCSJElqQLlMyWgPnAh0BvYF2kTE2es1uRt4LqX0fF3d69iWam1I6d6UUllKqaywsDC3yiVJkqTtIJcpGccAb6WUllaf1Pc74JsAETEaKAQu30TfxcD+693vCCzZ8nIlSZKk7SuXwLwI6BMRu0ZEAAOA+RFxAVVzlM9MKa3dRN+/AAMjon31SPXA6m2SJElSk5DLHOaXgInALKqWlGsG3AvcA+wFTIuI8oi4ASAiyiLi/uq+/wR+BEyv/rmpepskSZLUJOS0DnNKaTRVy8PV2zelNIP1lphLKT0APLClBUqSpMapeHxxTu0eaeA6pIbmlf4kSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDAZmSZIkKYOBWZIkScpgYJYkSZIyGJglSZKkDC3yXYAkSVIufn764JzaXfHwHxu4Eu1sHGGWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMhiYJUmSpAwGZkmSJCmDgVmSJEnKYGCWJEmSMuQUmCNiVETMjYg5EfFQRBRExKUR8UZEpIjokNF3TUSUV//8ftuVLkmSJDW8FvU1iIj9gBHAoSmllRHxCHAG8CLwR2BqPbtYmVIq3dpCJUmSpHyoNzCv1651RKwGdgWWpJReAYiIhqpNkiRJyrtIKdXfKOIy4GZgJTA5pXTWeo8tBMpSSh9uom8lUA5UAreklB6ro81FwEXVd78OvL55h9GkdADqfK3UJPj+NV2+d02b71/T5vvXdO3o792BKaXC+hrVG5gjoj0wCTgd+Bj4H2BiSunX1Y8vJDsw75tSWhIRBwFTgAEppTc350h2JBExI6VUlu86tGV8/5ou37umzfevafP9a7p876rkctLfMcBbKaWlKaXVwO+Ab+b6BCmlJdX//TtV8527b0GdkiRJUl7kEpgXAX0iYteomrA8AJify84jon1EtKq+3QE4HJi3pcVKkiRJ21u9gTml9BIwEZgFzK7uc29EjIiIxUBHoCIi7geIiLJ1t4EuwIyIeBV4hqo5zDt7YL433wVoq/j+NV2+d02b71/T5vvXdPnekeNJf5IkSdLOyiv9SZIkSRkMzJIkSVIGA7MkSZKUwcAsSZIkZTAwS5IalYg4OyLOqWP7hRHx7/moSdLOzVUyGlBE/Az4e0rpno22jwL2TildnZ/KVJ+IKKDq6pYfAX8AfgAcCbwJ/GhTV7ZU4+Bnr2mLiFeAviml5Rtt3w2YmlLqmZ/KVB8/ezuGiLi8js2fADNTSuXbu57GwMDcgCJiHtA1pbR2o+3NgIqUUtf8VKb6RMQjwGqgDdAemENVcD4CKE0pDc5jeaqHn72mLSIqUkolm/uY8s/P3o4hIn4DlFH17x7A8cB04BDgf1JKP8tXbfnSIt8F7ODSxv/TqN64tvqqiWq8Dk0pdY2IFsDilFK/6u1/rr4Qjxo3P3tNW8uIaJNS+nz9jdUjzLvkqSblxs/ejmFPoEdK6TOAiBhN1UXs+gIzgZ0uMDuHuWGtiIiDN95YvW1lHupR7v4FkFKqBJZs9Nia7V+ONpOfvabtl8DEiOi0bkP17d9WP6bGy8/ejuEAqv8drLYaODCltBL4Ij8l5ZcjzA3rBuDJiPgPqv4ig6qvOK4FRuatKuWiY0TcDsR6t6m+v1/+ylKO/Ow1YSmlsRHxGfBsRLQFEvA5cEtK6Rf5rU718LO3Y/gN8NeIeLz6/neAhyKiDTAvf2Xlj3OYG1hEdAWuAtbN25oDjE0pzc5fVapPRAzJejylNH571aItU8dnby5wq5+9pqU6MMfGJwCq8fKzt2OIiDLgcKoGil5IKc3Ic0l5ZWBuQBHRovorfUmNQPXqJ99JKf1PvmtRtohoDrRftyJNROwCDAVGpZS65LM2aWdQ/Rnci/VmI6SUFuWvovxyDnPDenndjYi4I5+FaPNExBERce569ydGxJTqn6PzWZs2T0Q0j4hjI+JXwNtULReoRiwizgD+CVRExLMRcRTwd+BY4Ky8FqdMEdEhIkZHxIiIaBsRv4iIORHxeER8Nd/1KTcR8X3gfeB/gT8Cf6r+707LEeYGFBGvpJS6V9+elVLqke+alJuIeBr4fkppXvX92VSNbrUBfphS+nYey1MOIqIv8O9ULYf0MlVfLR6UUlqR18JUr4iYA5yUUnojInoA04AzUkqP5rk01SMiJgMzgN2AAcD/B/yeqnXsz0op9c9bccpZRLwB9E4pLct3LY2FJ/01LP8aabp2XxeWqy1IKc0EiIif5Kkm5SgiFgOLgF8AV6WUlkfEW4blJuNfKaU3AFJKs6rfO8Ny07BXSumH1UvIvb3eer2vRcTwfBamzfIPqi5UomoG5oZ1SERUUDVh/ivVt6m+n1x8v1HbY/07KaVT1ru713auRZtvEnASVdMv1lSf6e0fsE3Hlze60ljb9e+nlP4zDzUpN2ug6h+4iNj4iqi11mdWo/V3YGpE/In1lpHbmT97BuaG5YkpTddrEXF8SulP62+MiMHA63mqSTlKKV0WESOBo4AzgVuB3SPiu8AT6xbjV6N1H1Vf6dd13z98GreDIuL3VA0MrbtN9f3O+StLm2lR9c8ueLEgwDnMeVF95ukZKaUJ+a5FdateZP+PwP8BZlVv7gl8ExicUvpbvmrT5ouIllSdMHYGMDCl1CHPJWkLRcTIlNK4fNehukVEvzo2rwsakVJ6dnvWI20rBuYGFBG7A8OputDF76k62/RS4EqgPKV0Yh7LU4aI2B/4gKoz8ouqN8+lajH3Ximl5/NVmzZPRBQCpJSWVt9vXX21KjVBEbEopXRAvutQ3SLiRKBjSumu6vsvA4VUhearXdKxcYuIcSmlkRHxB+r4NieldEIeymoUDMwNqHre5EdUneE9AGhP1Vcbl6WUyvNZm7JFxN+Be4D/XLeWdkTsBfwc+HpKqVc+61O26hOORlP1B2ozqr4OrgTuSCndlM/atHUi4h8ppf3zXYfqFhEvUvUN6j+q75dT9e9fG+DBlNKAfNanbBHRM6U0cxPfFLAzf0PgOswN66CU0tCU0v9L1TzKMqq+zjcsN349ga8Ar0TE0RFxGVVLk00Deue1MuViJFXLyPVKKX0ppdSeqvft8IgYld/StJUc5WncdlkXlqu9kFJaVn3Bizb5Kkq5WbcaFFCaUnp2/R+gNJ+15ZsjzA1o47WXXYu56akOyrcBS4A+KaXFeS5JOYiIV4BvrbtK3HrbC4HJ69ZHV+MUEcupOxgH0Dql5AnrjVREvJFSqvMCJRHxZkrpK9u7Jm2+uvLK+teW2Bn5P52G1S0iPq2+HUDr6vvrlpXbPX+lKUtE7AH8lKpRyW8DxwFPRsRlKaUpeS1OuWi5cViGqnnM1ScAqhFLKe1Wfys1Ui9FxIUppfvW3xgRF7Pe1W/VOEXEmVRd8KnzeiucQNUqNTv1RUwMzA0opdQ83zVoi80C7gaGV89hnhwRpcDdEfF2SunM/JanevxrCx+TtHVGAY9FxL+z4QpDrahaG12N2/8B3gU6UHXOzjrLgYo6e+wknJIh1SEiOm5q+kVdoydqXCJiDfB5XQ8BBSklR5mlBhQRR7PeCkN+M9f0VJ/ovu4E95dTSh/ks558MzBLkiSpRkScBowFplI10HAkcFVKaWI+68onA7MkSZJqRMSrVJ04/cH/be+OTSIKoiiA3jE31NQO1ibswMACxMBs67EBzcwUQcESBFswEGFt4Rn8RT6KP33CnpNNdsMb3JnZng+SPFbVcW+yPp6VAwBgbu/HBGOTHe+MLv0BADB3P8Z4SHK9PZ8luWvM005hBgBg7j3Ja6bPSkaSq6q67Y3US2EGAGBuP8l5ks8kN5mem9tpLv0BAPDLGGOVaY5xmuStqk6aI7XZ6QE3AAB/+sg0z9gkOWzO0kphBgDg2xjjcozxnOQp069/F1W16k3Vy4YZAIC5oyTrqnrpDvJf2DADAMACkwwAAFigMAMAwAKFGQAAFijMAACw4Au8CmxGBKdBTAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary.plot(kind='bar',figsize=(12,7),ylim=(81.5,84.3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}