Assignment_Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Random</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-02</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-01-09</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-01-10</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-01-11</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-01-14</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-01-15</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-01-16</td>\n",
       "      <td>223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2019-01-17</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2019-01-18</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2019-01-21</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2019-01-22</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2019-01-23</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2019-01-24</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2019-01-25</td>\n",
       "      <td>106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2019-01-28</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2019-01-29</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2019-01-30</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2019-01-31</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-02-04</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-02-05</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-02-06</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2019-11-20</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2019-11-21</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2019-11-22</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2019-11-25</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2019-11-26</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2019-11-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2019-11-28</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2019-11-29</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2019-12-02</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2019-12-03</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2019-12-04</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2019-12-05</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2019-12-06</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2019-12-09</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2019-12-10</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2019-12-11</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2019-12-12</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2019-12-13</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2019-12-16</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2019-12-17</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2019-12-18</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>2019-12-19</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>2019-12-20</td>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>2019-12-23</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>2019-12-24</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>2019-12-25</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>2019-12-26</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>2019-12-27</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>2019-12-30</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>261 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Random\n",
       "0   2019-01-01     135\n",
       "1   2019-01-02      84\n",
       "2   2019-01-03     247\n",
       "3   2019-01-04     124\n",
       "4   2019-01-07     154\n",
       "5   2019-01-08     141\n",
       "6   2019-01-09     232\n",
       "7   2019-01-10     166\n",
       "8   2019-01-11      82\n",
       "9   2019-01-14     248\n",
       "10  2019-01-15     238\n",
       "11  2019-01-16     223\n",
       "12  2019-01-17      63\n",
       "13  2019-01-18     131\n",
       "14  2019-01-21     237\n",
       "15  2019-01-22      49\n",
       "16  2019-01-23      55\n",
       "17  2019-01-24      62\n",
       "18  2019-01-25     106\n",
       "19  2019-01-28      50\n",
       "20  2019-01-29      78\n",
       "21  2019-01-30     146\n",
       "22  2019-01-31     136\n",
       "23  2019-02-01     147\n",
       "24  2019-02-04     200\n",
       "25  2019-02-05     166\n",
       "26  2019-02-06     141\n",
       "27  2019-02-07     237\n",
       "28  2019-02-08     135\n",
       "29  2019-02-11     177\n",
       "..         ...     ...\n",
       "231 2019-11-20      27\n",
       "232 2019-11-21     118\n",
       "233 2019-11-22     233\n",
       "234 2019-11-25     218\n",
       "235 2019-11-26      17\n",
       "236 2019-11-27       1\n",
       "237 2019-11-28      20\n",
       "238 2019-11-29      15\n",
       "239 2019-12-02      40\n",
       "240 2019-12-03      61\n",
       "241 2019-12-04      51\n",
       "242 2019-12-05     239\n",
       "243 2019-12-06     109\n",
       "244 2019-12-09     186\n",
       "245 2019-12-10     115\n",
       "246 2019-12-11      82\n",
       "247 2019-12-12     146\n",
       "248 2019-12-13     242\n",
       "249 2019-12-16     187\n",
       "250 2019-12-17     100\n",
       "251 2019-12-18     224\n",
       "252 2019-12-19     125\n",
       "253 2019-12-20     239\n",
       "254 2019-12-23     109\n",
       "255 2019-12-24      22\n",
       "256 2019-12-25      47\n",
       "257 2019-12-26     220\n",
       "258 2019-12-27     245\n",
       "259 2019-12-30     115\n",
       "260 2019-12-31     218\n",
       "\n",
       "[261 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dl_weekdayc=[]\n",
    "idx = pd.date_range('2019-01-01', periods=365, freq='D')\n",
    "dl_weekday = [wd for wd in idx if wd.weekday() < 5]\n",
    "df_date = pd.DataFrame({'Date': dl_weekday, \n",
    "                       'Random': np.random.randint(1,len(dl_weekday)+1,size=len(dl_weekday))})\n",
    "df_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>48</td>\n",
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      "text/plain": [
       "   height  weight\n",
       "0      23      71\n",
       "1      42      32\n",
       "2      55      48"
      ]
     },
     "execution_count": 41,
     "metadata": {},
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    }
   ],
   "source": [
    "height = [23,42,55] \n",
    "weight = [71,32,48]\n",
    "df_h_w = pd.DataFrame({'height': height,\n",
    "                       'weight': weight})\n",
    "df_h_w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series([2,3,4,5])\n",
    "ser2 = pd.Series([5,6,7,8])\n",
    "ser3 = [k for k in ser1 if k not in ser2]\n",
    "list(set(ser1) - set(ser2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q4  - With droping NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Age-   80.0\n",
      "Min Age-   0.42\n",
      "Numpy Median-   28.0\n",
      "Q25-   20.125\n",
      "Q75-   38.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from numpy import percentile\n",
    "df = pd.read_csv('DataSets\\\\titanic.csv')\n",
    "print(\"Max Age-   \" + str(max(df['Age'].dropna())))\n",
    "print(\"Min Age-   \" + str(min(df['Age'].dropna())))\n",
    "print(\"Numpy Median-   \" + str(np.median(df['Age'].dropna())))\n",
    "# calculate interquartile range\n",
    "q25, q75 = percentile(df['Age'].dropna(), 25), percentile(df['Age'].dropna(), 75)\n",
    "print(\"Q25-   \" + str(q25))\n",
    "print(\"Q75-   \" + str(q75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q4  - With 0.0 inplace of NAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Age-   80.0\n",
      "Min Age-   0.0\n",
      "Numpy Median-   24.0\n",
      "Q25-   6.0\n",
      "Q75-   35.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "from numpy import percentile\n",
    "df = pd.read_csv('DataSets\\\\titanic.csv')\n",
    "print(\"Max Age-   \" + str(max(df['Age'].fillna(0))))\n",
    "print(\"Min Age-   \" + str(min(df['Age'].fillna(0))))\n",
    "print(\"Numpy Median-   \" + str(np.median(df['Age'].fillna(0))))\n",
    "# calculate interquartile range\n",
    "q25, q75 = percentile(df['Age'].fillna(0), 25), percentile(df['Age'].fillna(0), 75)\n",
    "print(\"Q25-   \" + str(q25))\n",
    "print(\"Q75-   \" + str(q75))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "4     18\n",
       "3     16\n",
       "8      7\n",
       "5      5\n",
       "Name: SibSp, dtype: int64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SibSp'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           S\n",
       "1           C\n",
       "2           S\n",
       "3           S\n",
       "4           S\n",
       "5      Others\n",
       "6           S\n",
       "7           S\n",
       "8           S\n",
       "9           C\n",
       "10          S\n",
       "11          S\n",
       "12          S\n",
       "13          S\n",
       "14          S\n",
       "15          S\n",
       "16     Others\n",
       "17          S\n",
       "18          S\n",
       "19          C\n",
       "20          S\n",
       "21          S\n",
       "22     Others\n",
       "23          S\n",
       "24          S\n",
       "25          S\n",
       "26          C\n",
       "27          S\n",
       "28     Others\n",
       "29          S\n",
       "        ...  \n",
       "861         S\n",
       "862         S\n",
       "863         S\n",
       "864         S\n",
       "865         S\n",
       "866         C\n",
       "867         S\n",
       "868         S\n",
       "869         S\n",
       "870         S\n",
       "871         S\n",
       "872         S\n",
       "873         S\n",
       "874         C\n",
       "875         C\n",
       "876         S\n",
       "877         S\n",
       "878         S\n",
       "879         C\n",
       "880         S\n",
       "881         S\n",
       "882         S\n",
       "883         S\n",
       "884         S\n",
       "885    Others\n",
       "886         S\n",
       "887         S\n",
       "888         S\n",
       "889         C\n",
       "890    Others\n",
       "Name: Embarked_Other, Length: 891, dtype: object"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dl_Other = df['Embarked'].value_counts(ascending=False).index.tolist()[:2]\n",
    "df[~df.Embarked.isin(dl_Other)] = 'Others'\n",
    "df['Embarked_Other']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 25]       542\n",
       "(25, 50]      174\n",
       "(50, 100]     107\n",
       "(100, 200]     33\n",
       "(200, 600]     20\n",
       "Name: price_bins, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('DataSets\\\\titanic.csv')\n",
    "df['price_bins'] = pd.cut(df['Fare'],[0,25, 50,100,200,600])\n",
    "df['price_bins'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "price_bins      15\n",
       "dtype: int64"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "116"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = sorted(df['Age'].fillna(0), reverse=True)[4]\n",
    "select_indices = list(np.where(df[\"Age\"] == i))\n",
    "select_indices[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.750</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141511</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.750</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.391357</td>\n",
       "      <td>4.421769</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154686</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.375</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.036443</td>\n",
       "      <td>6.190476</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157126</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.165095</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.750</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.012289</td>\n",
       "      <td>1.156463</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.411357</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.217308</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.586943</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.325962</td>\n",
       "      <td>0.068027</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.518221</td>\n",
       "      <td>6.598639</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.157126</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.875</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.610447</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.750</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153304</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.312299</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.568482</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253743</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.875</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.351337</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141023</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.507486</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253743</td>\n",
       "      <td>2.380952</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156720</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.692914</td>\n",
       "      <td>9.047619</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.000</td>\n",
       "      <td>3.75</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.411357</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.750</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.612643</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141023</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.375</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>5.133418</td>\n",
       "      <td>5.646259</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.153792</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154116</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>861</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.625</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.224465</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>862</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>6.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.506104</td>\n",
       "      <td>3.537415</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>863</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>10.00</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.357526</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>864</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253743</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>865</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253743</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>866</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.375</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.270496</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>867</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.985612</td>\n",
       "      <td>9.523810</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>868</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.185428</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.217308</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154116</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>871</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5.875</td>\n",
       "      <td>1.25</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.025790</td>\n",
       "      <td>2.993197</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>872</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.125</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.097594</td>\n",
       "      <td>7.619048</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>873</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175668</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.468449</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>875</th>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.141023</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.192177</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154116</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>878</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154116</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>879</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>1.623142</td>\n",
       "      <td>5.170068</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>880</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>0.507486</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>881</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.125</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154116</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.750</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.205272</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>883</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.500</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.204946</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.125</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.137607</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.875</td>\n",
       "      <td>0.00</td>\n",
       "      <td>8.333333</td>\n",
       "      <td>0.568482</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>886</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.253743</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>887</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.375</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.585561</td>\n",
       "      <td>7.891156</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>0.457714</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>889</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.250</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.585561</td>\n",
       "      <td>5.850340</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.151270</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Sex    Age  SibSp     Parch      Fare      Cabin  \\\n",
       "0         0.0    10.0   0.0  2.750   1.25  0.000000  0.141511  10.000000   \n",
       "1        10.0     0.0  10.0  4.750   1.25  0.000000  1.391357   4.421769   \n",
       "2        10.0    10.0  10.0  3.250   0.00  0.000000  0.154686  10.000000   \n",
       "3        10.0     0.0  10.0  4.375   1.25  0.000000  1.036443   6.190476   \n",
       "4         0.0    10.0   0.0  4.375   0.00  0.000000  0.157126  10.000000   \n",
       "5         0.0    10.0   0.0  0.000   0.00  0.000000  0.165095  10.000000   \n",
       "6         0.0     0.0   0.0  6.750   0.00  0.000000  1.012289   1.156463   \n",
       "7         0.0    10.0   0.0  0.250   3.75  1.666667  0.411357  10.000000   \n",
       "8        10.0    10.0  10.0  3.375   0.00  3.333333  0.217308  10.000000   \n",
       "9        10.0     5.0  10.0  1.750   1.25  0.000000  0.586943  10.000000   \n",
       "10       10.0    10.0  10.0  0.500   1.25  1.666667  0.325962   0.068027   \n",
       "11       10.0     0.0  10.0  7.250   0.00  0.000000  0.518221   6.598639   \n",
       "12        0.0    10.0   0.0  2.500   0.00  0.000000  0.157126  10.000000   \n",
       "13        0.0    10.0   0.0  4.875   1.25  8.333333  0.610447  10.000000   \n",
       "14        0.0    10.0  10.0  1.750   0.00  0.000000  0.153304  10.000000   \n",
       "15       10.0     5.0  10.0  6.875   0.00  0.000000  0.312299  10.000000   \n",
       "16        0.0    10.0   0.0  0.250   5.00  1.666667  0.568482  10.000000   \n",
       "17       10.0     5.0   0.0  0.000   0.00  0.000000  0.253743  10.000000   \n",
       "18        0.0    10.0  10.0  3.875   1.25  0.000000  0.351337  10.000000   \n",
       "19       10.0    10.0  10.0  0.000   0.00  0.000000  0.141023  10.000000   \n",
       "20        0.0     5.0   0.0  4.375   0.00  0.000000  0.507486  10.000000   \n",
       "21       10.0     5.0   0.0  4.250   0.00  0.000000  0.253743   2.380952   \n",
       "22       10.0    10.0  10.0  1.875   0.00  0.000000  0.156720  10.000000   \n",
       "23       10.0     0.0   0.0  3.500   0.00  0.000000  0.692914   9.047619   \n",
       "24        0.0    10.0  10.0  1.000   3.75  1.666667  0.411357  10.000000   \n",
       "25       10.0    10.0  10.0  4.750   1.25  8.333333  0.612643  10.000000   \n",
       "26        0.0    10.0   0.0  0.000   0.00  0.000000  0.141023  10.000000   \n",
       "27        0.0     0.0   0.0  2.375   3.75  3.333333  5.133418   5.646259   \n",
       "28       10.0    10.0  10.0  0.000   0.00  0.000000  0.153792  10.000000   \n",
       "29        0.0    10.0   0.0  0.000   0.00  0.000000  0.154116  10.000000   \n",
       "..        ...     ...   ...    ...    ...       ...       ...        ...   \n",
       "861       0.0     5.0   0.0  2.625   1.25  0.000000  0.224465  10.000000   \n",
       "862      10.0     0.0  10.0  6.000   0.00  0.000000  0.506104   3.537415   \n",
       "863       0.0    10.0  10.0  0.000  10.00  3.333333  1.357526  10.000000   \n",
       "864       0.0     5.0   0.0  3.000   0.00  0.000000  0.253743  10.000000   \n",
       "865      10.0     5.0  10.0  5.250   0.00  0.000000  0.253743  10.000000   \n",
       "866      10.0     5.0  10.0  3.375   1.25  0.000000  0.270496  10.000000   \n",
       "867       0.0     0.0   0.0  3.875   0.00  0.000000  0.985612   9.523810   \n",
       "868       0.0    10.0   0.0  0.000   0.00  0.000000  0.185428  10.000000   \n",
       "869      10.0    10.0   0.0  0.500   1.25  1.666667  0.217308  10.000000   \n",
       "870       0.0    10.0   0.0  3.250   0.00  0.000000  0.154116  10.000000   \n",
       "871      10.0     0.0  10.0  5.875   1.25  1.666667  1.025790   2.993197   \n",
       "872       0.0     0.0   0.0  4.125   0.00  0.000000  0.097594   7.619048   \n",
       "873       0.0    10.0   0.0  5.875   0.00  0.000000  0.175668  10.000000   \n",
       "874      10.0     5.0  10.0  3.500   1.25  0.000000  0.468449  10.000000   \n",
       "875      10.0    10.0  10.0  1.875   0.00  0.000000  0.141023  10.000000   \n",
       "876       0.0    10.0   0.0  2.500   0.00  0.000000  0.192177  10.000000   \n",
       "877       0.0    10.0   0.0  2.375   0.00  0.000000  0.154116  10.000000   \n",
       "878       0.0    10.0   0.0  0.000   0.00  0.000000  0.154116  10.000000   \n",
       "879      10.0     0.0  10.0  7.000   0.00  1.666667  1.623142   5.170068   \n",
       "880      10.0     5.0  10.0  3.125   0.00  1.666667  0.507486  10.000000   \n",
       "881       0.0    10.0   0.0  4.125   0.00  0.000000  0.154116  10.000000   \n",
       "882       0.0    10.0  10.0  2.750   0.00  0.000000  0.205272  10.000000   \n",
       "883       0.0     5.0   0.0  3.500   0.00  0.000000  0.204946  10.000000   \n",
       "884       0.0    10.0   0.0  3.125   0.00  0.000000  0.137607  10.000000   \n",
       "885       0.0    10.0  10.0  4.875   0.00  8.333333  0.568482  10.000000   \n",
       "886       0.0     5.0   0.0  3.375   0.00  0.000000  0.253743  10.000000   \n",
       "887      10.0     0.0  10.0  2.375   0.00  0.000000  0.585561   7.891156   \n",
       "888       0.0    10.0  10.0  0.000   1.25  3.333333  0.457714  10.000000   \n",
       "889      10.0     0.0   0.0  3.250   0.00  0.000000  0.585561   5.850340   \n",
       "890       0.0    10.0   0.0  4.000   0.00  0.000000  0.151270  10.000000   \n",
       "\n",
       "     Embarked  \n",
       "0    0.000000  \n",
       "1    6.666667  \n",
       "2    0.000000  \n",
       "3    0.000000  \n",
       "4    0.000000  \n",
       "5    3.333333  \n",
       "6    0.000000  \n",
       "7    0.000000  \n",
       "8    0.000000  \n",
       "9    6.666667  \n",
       "10   0.000000  \n",
       "11   0.000000  \n",
       "12   0.000000  \n",
       "13   0.000000  \n",
       "14   0.000000  \n",
       "15   0.000000  \n",
       "16   3.333333  \n",
       "17   0.000000  \n",
       "18   0.000000  \n",
       "19   6.666667  \n",
       "20   0.000000  \n",
       "21   0.000000  \n",
       "22   3.333333  \n",
       "23   0.000000  \n",
       "24   0.000000  \n",
       "25   0.000000  \n",
       "26   6.666667  \n",
       "27   0.000000  \n",
       "28   3.333333  \n",
       "29   0.000000  \n",
       "..        ...  \n",
       "861  0.000000  \n",
       "862  0.000000  \n",
       "863  0.000000  \n",
       "864  0.000000  \n",
       "865  0.000000  \n",
       "866  6.666667  \n",
       "867  0.000000  \n",
       "868  0.000000  \n",
       "869  0.000000  \n",
       "870  0.000000  \n",
       "871  0.000000  \n",
       "872  0.000000  \n",
       "873  0.000000  \n",
       "874  6.666667  \n",
       "875  6.666667  \n",
       "876  0.000000  \n",
       "877  0.000000  \n",
       "878  0.000000  \n",
       "879  6.666667  \n",
       "880  0.000000  \n",
       "881  0.000000  \n",
       "882  0.000000  \n",
       "883  0.000000  \n",
       "884  0.000000  \n",
       "885  3.333333  \n",
       "886  0.000000  \n",
       "887  0.000000  \n",
       "888  0.000000  \n",
       "889  6.666667  \n",
       "890  3.333333  \n",
       "\n",
       "[891 rows x 9 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('DataSets\\\\titanic.csv')\n",
    "\n",
    "#Relapcing string Columns with 0,1,2 values like Sex, Embarked\n",
    "def mapping(data,feature):\n",
    "    featureMap=dict()\n",
    "    count=0\n",
    "    for i in sorted(data[feature].unique(), reverse=True):\n",
    "        featureMap[str(i)]=count\n",
    "        count=count+1\n",
    "    data[feature]=data[feature].map(featureMap)\n",
    "    return data\n",
    "\n",
    "df = mapping(df,\"Sex\")\n",
    "df[\"Embarked\"] = df[\"Embarked\"].fillna('0')\n",
    "df = mapping(df,\"Embarked\")\n",
    "df[\"Cabin\"] = df[\"Cabin\"].fillna('0')\n",
    "df = mapping(df,\"Cabin\")\n",
    "\n",
    "#Filling default values with 0\n",
    "df[\"Fare\"] = df[\"Fare\"].fillna(0)\n",
    "df[\"Age\"] = df[\"Age\"].fillna(0)\n",
    "\n",
    "df=df.drop([\"PassengerId\",\"Name\",\"Ticket\"],axis=1)\n",
    "\n",
    "#Standard Deviation Normalization\n",
    "normalized_df_std=(df-df.mean())/df.std()\n",
    "normalized_df_std\n",
    "\n",
    "#Max Min Normalization on scale of 10\n",
    "normalized_df=(df-df.min())/(df.max()-df.min())*10\n",
    "normalized_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 5    1\n",
       " dtype: int64, 3: 4    3\n",
       " dtype: int64, 10: 0    10\n",
       " dtype: int64, 13: 8    13\n",
       " dtype: int64}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series([10,9,6,5,3,1,12,8,13])\n",
    "ser2 = pd.Series([1,3,10,13])\n",
    "ser1_dict = dict()\n",
    "for item in ser2:\n",
    "    ser1_dict[item] = ser1[ser1==item]\n",
    "ser1_dict        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-01 00:00:00', '2010-02-02 00:00:00',\n",
       "               '2012-03-03 00:00:00', '2013-04-04 00:00:00',\n",
       "               '2014-05-05 00:00:00', '2015-06-06 12:20:00'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_list = ['01 Jan 2010','02-02-2010','20120303','2013/04/04','2014-05-05','2015-06-06T12:20']\n",
    "newdate_list = pd.to_datetime(date_list)\n",
    "newdate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Date</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Day</th>\n",
       "      <th>Week</th>\n",
       "      <th>DayofYear</th>\n",
       "      <th>DayofWeek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01 00:00:00</td>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-02-02 00:00:00</td>\n",
       "      <td>2010</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-03 00:00:00</td>\n",
       "      <td>2012</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>63</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-04-04 00:00:00</td>\n",
       "      <td>2013</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>94</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-05-05 00:00:00</td>\n",
       "      <td>2014</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-06-06 12:20:00</td>\n",
       "      <td>2015</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>157</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  Year  Month  Day  Week  DayofYear  DayofWeek\n",
       "0 2010-01-01 00:00:00  2010      1    1    53          1          4\n",
       "1 2010-02-02 00:00:00  2010      2    2     5         33          1\n",
       "2 2012-03-03 00:00:00  2012      3    3     9         63          5\n",
       "3 2013-04-04 00:00:00  2013      4    4    14         94          3\n",
       "4 2014-05-05 00:00:00  2014      5    5    19        125          0\n",
       "5 2015-06-06 12:20:00  2015      6    6    23        157          5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "ser = pd.Series(['01 Jan 2010','02-02-2010','20120303','2013/04/04','2014-05-05','2015-06-06T12:20'])\n",
    "dl = pd.DataFrame(pd.to_datetime(ser.tolist()), columns=['Date'])\n",
    "dl['Year']=  dl['Date'].dt.year\n",
    "dl['Month']=  dl['Date'].dt.month\n",
    "dl['Day']=  dl['Date'].dt.day\n",
    "dl['Week']=  dl['Date'].dt.week\n",
    "dl['DayofYear'] = dl['Date'].dt.dayofyear \n",
    "dl['DayofWeek'] = dl['Date'].dt.dayofweek\n",
    "dl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[datetime.datetime(2000, 1, 1, 0, 0),\n",
       " datetime.datetime(2000, 1, 2, 0, 0),\n",
       " datetime.datetime(2000, 1, 8, 0, 0),\n",
       " datetime.datetime(2000, 1, 9, 0, 0),\n",
       " datetime.datetime(2000, 1, 15, 0, 0),\n",
       " datetime.datetime(2000, 1, 16, 0, 0),\n",
       " datetime.datetime(2000, 1, 22, 0, 0),\n",
       " datetime.datetime(2000, 1, 23, 0, 0),\n",
       " datetime.datetime(2000, 1, 29, 0, 0),\n",
       " datetime.datetime(2000, 1, 30, 0, 0)]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "from datetime import timedelta\n",
    "startvalue =  \"2000-01-01\"\n",
    "startDate = datetime.strptime(startvalue, '%Y-%m-%d')\n",
    "dl_Weekend =[]\n",
    "weekendcounter = 0\n",
    "while (weekendcounter < 10):\n",
    "    if(startDate.weekday() > 4):        \n",
    "        dl_Weekend.append(startDate)\n",
    "        weekendcounter = weekendcounter + 1\n",
    "    startDate = startDate + timedelta(days=1)    \n",
    "dl_Weekend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>MEDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>0.21977</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.91</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.448</td>\n",
       "      <td>5.602</td>\n",
       "      <td>62.0</td>\n",
       "      <td>6.0877</td>\n",
       "      <td>3.0</td>\n",
       "      <td>233.0</td>\n",
       "      <td>17.9</td>\n",
       "      <td>396.90</td>\n",
       "      <td>16.20</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.08187</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.445</td>\n",
       "      <td>7.820</td>\n",
       "      <td>36.9</td>\n",
       "      <td>3.4952</td>\n",
       "      <td>2.0</td>\n",
       "      <td>276.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>393.53</td>\n",
       "      <td>3.57</td>\n",
       "      <td>43.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2.36862</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.871</td>\n",
       "      <td>4.926</td>\n",
       "      <td>95.7</td>\n",
       "      <td>1.4608</td>\n",
       "      <td>5.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>14.7</td>\n",
       "      <td>391.71</td>\n",
       "      <td>29.53</td>\n",
       "      <td>14.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0.04011</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.404</td>\n",
       "      <td>7.287</td>\n",
       "      <td>34.1</td>\n",
       "      <td>7.3090</td>\n",
       "      <td>2.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>12.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.08</td>\n",
       "      <td>33.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>0.19133</td>\n",
       "      <td>22.0</td>\n",
       "      <td>5.86</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.431</td>\n",
       "      <td>5.605</td>\n",
       "      <td>70.2</td>\n",
       "      <td>7.9549</td>\n",
       "      <td>7.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>19.1</td>\n",
       "      <td>389.13</td>\n",
       "      <td>18.46</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>0.08199</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.437</td>\n",
       "      <td>6.009</td>\n",
       "      <td>42.3</td>\n",
       "      <td>5.5027</td>\n",
       "      <td>4.0</td>\n",
       "      <td>289.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>396.90</td>\n",
       "      <td>10.40</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0.02543</td>\n",
       "      <td>55.0</td>\n",
       "      <td>3.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.484</td>\n",
       "      <td>6.696</td>\n",
       "      <td>56.4</td>\n",
       "      <td>5.7321</td>\n",
       "      <td>5.0</td>\n",
       "      <td>370.0</td>\n",
       "      <td>17.6</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.18</td>\n",
       "      <td>23.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>11.57790</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.700</td>\n",
       "      <td>5.036</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.7700</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>396.90</td>\n",
       "      <td>25.68</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>9.72418</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.740</td>\n",
       "      <td>6.406</td>\n",
       "      <td>97.2</td>\n",
       "      <td>2.0651</td>\n",
       "      <td>24.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>20.2</td>\n",
       "      <td>385.96</td>\n",
       "      <td>19.52</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>0.20746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.609</td>\n",
       "      <td>5.093</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.8226</td>\n",
       "      <td>4.0</td>\n",
       "      <td>711.0</td>\n",
       "      <td>20.1</td>\n",
       "      <td>318.43</td>\n",
       "      <td>29.68</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS   RAD    TAX  \\\n",
       "0     0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900   1.0  296.0   \n",
       "49    0.21977   0.0   6.91   0.0  0.448  5.602  62.0  6.0877   3.0  233.0   \n",
       "98    0.08187   0.0   2.89   0.0  0.445  7.820  36.9  3.4952   2.0  276.0   \n",
       "147   2.36862   0.0  19.58   0.0  0.871  4.926  95.7  1.4608   5.0  403.0   \n",
       "196   0.04011  80.0   1.52   0.0  0.404  7.287  34.1  7.3090   2.0  329.0   \n",
       "245   0.19133  22.0   5.86   0.0  0.431  5.605  70.2  7.9549   7.0  330.0   \n",
       "294   0.08199   0.0  13.92   0.0  0.437  6.009  42.3  5.5027   4.0  289.0   \n",
       "343   0.02543  55.0   3.78   0.0  0.484  6.696  56.4  5.7321   5.0  370.0   \n",
       "392  11.57790   0.0  18.10   0.0  0.700  5.036  97.0  1.7700  24.0  666.0   \n",
       "441   9.72418   0.0  18.10   0.0  0.740  6.406  97.2  2.0651  24.0  666.0   \n",
       "490   0.20746   0.0  27.74   0.0  0.609  5.093  98.0  1.8226   4.0  711.0   \n",
       "\n",
       "     PTRATIO       B  LSTAT  MEDV  \n",
       "0       15.3  396.90   4.98  24.0  \n",
       "49      17.9  396.90  16.20  19.4  \n",
       "98      18.0  393.53   3.57  43.8  \n",
       "147     14.7  391.71  29.53  14.6  \n",
       "196     12.6  396.90   4.08  33.3  \n",
       "245     19.1  389.13  18.46  18.5  \n",
       "294     16.0  396.90  10.40  21.7  \n",
       "343     17.6  396.90   7.18  23.9  \n",
       "392     20.2  396.90  25.68   9.7  \n",
       "441     20.2  385.96  19.52  17.1  \n",
       "490     20.1  318.43  29.68   8.1  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('DataSets\\\\boston_house_price.csv')\n",
    "rowCounter = 0\n",
    "rowsindex = []\n",
    "while rowCounter <= df.shape[0]:\n",
    "    rowsindex.append(rowCounter)\n",
    "    rowCounter = rowCounter + 49\n",
    "rowsindex\n",
    "df_new = df.loc[rowsindex]\n",
    "df_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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