InsuranceDataset_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Central Tendency and Dispersion measures over Insurance Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3756.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8240.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7281.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>29.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6406.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>28923.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2721.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>female</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>27808.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1826.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>56</td>\n",
       "      <td>female</td>\n",
       "      <td>39.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>11090.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>39611.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>24.6</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1837.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10797.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2395.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>40.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10602.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>36837.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13228.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>32.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4149.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1137.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>37701.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6203.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>59</td>\n",
       "      <td>female</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>14001.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>14451.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>55</td>\n",
       "      <td>female</td>\n",
       "      <td>32.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>12268.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2775.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>36.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>38711.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>25</td>\n",
       "      <td>female</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>33900.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>41</td>\n",
       "      <td>male</td>\n",
       "      <td>32.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>6875.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>26.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6940.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>33</td>\n",
       "      <td>female</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4571.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>34</td>\n",
       "      <td>male</td>\n",
       "      <td>42.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4536.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>34.7</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>36397.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>23.7</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>18765.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>28.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>11272.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1731.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>53.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1163.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>39.7</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>19496.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>26.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7201.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>31.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>5425.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>28101.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>38.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>12981.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>40.4</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>43896.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4239.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13143.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7050.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>51</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>9377.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>24.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>22395.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>52</td>\n",
       "      <td>male</td>\n",
       "      <td>38.6</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10325.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>25.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>12629.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10795.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>44.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>11411.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1338 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "0      19  female  27.9         0    yes  southwest  16884.92\n",
       "1      18    male  33.8         1     no  southeast   1725.55\n",
       "2      28    male  33.0         3     no  southeast   4449.46\n",
       "3      33    male  22.7         0     no  northwest  21984.47\n",
       "4      32    male  28.9         0     no  northwest   3866.86\n",
       "5      31  female  25.7         0     no  southeast   3756.62\n",
       "6      46  female  33.4         1     no  southeast   8240.59\n",
       "7      37  female  27.7         3     no  northwest   7281.51\n",
       "8      37    male  29.8         2     no  northeast   6406.41\n",
       "9      60  female  25.8         0     no  northwest  28923.14\n",
       "10     25    male  26.2         0     no  northeast   2721.32\n",
       "11     62  female  26.3         0    yes  southeast  27808.73\n",
       "12     23    male  34.4         0     no  southwest   1826.84\n",
       "13     56  female  39.8         0     no  southeast  11090.72\n",
       "14     27    male  42.1         0    yes  southeast  39611.76\n",
       "15     19    male  24.6         1     no  southwest   1837.24\n",
       "16     52  female  30.8         1     no  northeast  10797.34\n",
       "17     23    male  23.8         0     no  northeast   2395.17\n",
       "18     56    male  40.3         0     no  southwest  10602.39\n",
       "19     30    male  35.3         0    yes  southwest  36837.47\n",
       "20     60  female  36.0         0     no  northeast  13228.85\n",
       "21     30  female  32.4         1     no  southwest   4149.74\n",
       "22     18    male  34.1         0     no  southeast   1137.01\n",
       "23     34  female  31.9         1    yes  northeast  37701.88\n",
       "24     37    male  28.0         2     no  northwest   6203.90\n",
       "25     59  female  27.7         3     no  southeast  14001.13\n",
       "26     63  female  23.1         0     no  northeast  14451.84\n",
       "27     55  female  32.8         2     no  northwest  12268.63\n",
       "28     23    male  17.4         1     no  northwest   2775.19\n",
       "29     31    male  36.3         2    yes  southwest  38711.00\n",
       "...   ...     ...   ...       ...    ...        ...       ...\n",
       "1308   25  female  30.2         0    yes  southwest  33900.65\n",
       "1309   41    male  32.2         2     no  southwest   6875.96\n",
       "1310   42    male  26.3         1     no  northwest   6940.91\n",
       "1311   33  female  26.7         0     no  northwest   4571.41\n",
       "1312   34    male  42.9         1     no  southwest   4536.26\n",
       "1313   19  female  34.7         2    yes  southwest  36397.58\n",
       "1314   30  female  23.7         3    yes  northwest  18765.88\n",
       "1315   18    male  28.3         1     no  northeast  11272.33\n",
       "1316   19  female  20.6         0     no  southwest   1731.68\n",
       "1317   18    male  53.1         0     no  southeast   1163.46\n",
       "1318   35    male  39.7         4     no  northeast  19496.72\n",
       "1319   39  female  26.3         2     no  northwest   7201.70\n",
       "1320   31    male  31.1         3     no  northwest   5425.02\n",
       "1321   62    male  26.7         0    yes  northeast  28101.33\n",
       "1322   62    male  38.8         0     no  southeast  12981.35\n",
       "1323   42  female  40.4         2    yes  southeast  43896.38\n",
       "1324   31    male  25.9         1     no  northwest   4239.89\n",
       "1325   61    male  33.5         0     no  northeast  13143.34\n",
       "1326   42  female  32.9         0     no  northeast   7050.02\n",
       "1327   51    male  30.0         1     no  southeast   9377.90\n",
       "1328   23  female  24.2         2     no  northeast  22395.74\n",
       "1329   52    male  38.6         2     no  southwest  10325.21\n",
       "1330   57  female  25.7         2     no  southeast  12629.17\n",
       "1331   23  female  33.4         0     no  southwest  10795.94\n",
       "1332   52  female  44.7         3     no  southwest  11411.69\n",
       "1333   50    male  31.0         3     no  northwest  10600.55\n",
       "1334   18  female  31.9         0     no  northeast   2205.98\n",
       "1335   18  female  36.9         0     no  southeast   1629.83\n",
       "1336   21  female  25.8         0     no  southwest   2007.95\n",
       "1337   61  female  29.1         0    yes  northwest  29141.36\n",
       "\n",
       "[1338 rows x 7 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"Datasets/insurance.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1338"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dividing First 30% and the rest 70% data of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "402"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first = int(0.3*data.shape[0])+1\n",
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "936"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sec = data.shape[0]-first\n",
    "sec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>25.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3756.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8240.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7281.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>29.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6406.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>28923.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2721.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>62</td>\n",
       "      <td>female</td>\n",
       "      <td>26.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>27808.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1826.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>56</td>\n",
       "      <td>female</td>\n",
       "      <td>39.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>11090.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27</td>\n",
       "      <td>male</td>\n",
       "      <td>42.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>39611.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>24.6</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1837.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>30.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10797.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2395.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>40.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10602.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>36837.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13228.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>32.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4149.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1137.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>34</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>37701.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>28.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6203.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>59</td>\n",
       "      <td>female</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>14001.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>23.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>14451.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>55</td>\n",
       "      <td>female</td>\n",
       "      <td>32.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>12268.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>17.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2775.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>36.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>38711.00</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>33.2</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7639.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>26</td>\n",
       "      <td>male</td>\n",
       "      <td>32.9</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>36085.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1391.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>18033.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>24.9</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>21659.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>40.2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>38126.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>64</td>\n",
       "      <td>female</td>\n",
       "      <td>30.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>16455.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>31.5</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>27000.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>380</th>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>15006.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>42303.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>20781.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>383</th>\n",
       "      <td>35</td>\n",
       "      <td>female</td>\n",
       "      <td>43.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5846.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>384</th>\n",
       "      <td>44</td>\n",
       "      <td>male</td>\n",
       "      <td>22.1</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>8302.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>34.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1261.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>386</th>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "      <td>39.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>11856.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>25.4</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>30284.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3176.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>24</td>\n",
       "      <td>female</td>\n",
       "      <td>30.2</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4618.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>35.6</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10736.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2138.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>31.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>8964.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>31.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9290.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>32.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9411.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>395</th>\n",
       "      <td>46</td>\n",
       "      <td>male</td>\n",
       "      <td>19.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7526.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>43</td>\n",
       "      <td>female</td>\n",
       "      <td>34.4</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>8522.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>21</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>16586.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>64</td>\n",
       "      <td>male</td>\n",
       "      <td>25.6</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>14988.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>38.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1631.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>51</td>\n",
       "      <td>female</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>9264.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>47</td>\n",
       "      <td>male</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8083.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>402 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age     sex   bmi  children smoker     region  expenses\n",
       "0     19  female  27.9         0    yes  southwest  16884.92\n",
       "1     18    male  33.8         1     no  southeast   1725.55\n",
       "2     28    male  33.0         3     no  southeast   4449.46\n",
       "3     33    male  22.7         0     no  northwest  21984.47\n",
       "4     32    male  28.9         0     no  northwest   3866.86\n",
       "5     31  female  25.7         0     no  southeast   3756.62\n",
       "6     46  female  33.4         1     no  southeast   8240.59\n",
       "7     37  female  27.7         3     no  northwest   7281.51\n",
       "8     37    male  29.8         2     no  northeast   6406.41\n",
       "9     60  female  25.8         0     no  northwest  28923.14\n",
       "10    25    male  26.2         0     no  northeast   2721.32\n",
       "11    62  female  26.3         0    yes  southeast  27808.73\n",
       "12    23    male  34.4         0     no  southwest   1826.84\n",
       "13    56  female  39.8         0     no  southeast  11090.72\n",
       "14    27    male  42.1         0    yes  southeast  39611.76\n",
       "15    19    male  24.6         1     no  southwest   1837.24\n",
       "16    52  female  30.8         1     no  northeast  10797.34\n",
       "17    23    male  23.8         0     no  northeast   2395.17\n",
       "18    56    male  40.3         0     no  southwest  10602.39\n",
       "19    30    male  35.3         0    yes  southwest  36837.47\n",
       "20    60  female  36.0         0     no  northeast  13228.85\n",
       "21    30  female  32.4         1     no  southwest   4149.74\n",
       "22    18    male  34.1         0     no  southeast   1137.01\n",
       "23    34  female  31.9         1    yes  northeast  37701.88\n",
       "24    37    male  28.0         2     no  northwest   6203.90\n",
       "25    59  female  27.7         3     no  southeast  14001.13\n",
       "26    63  female  23.1         0     no  northeast  14451.84\n",
       "27    55  female  32.8         2     no  northwest  12268.63\n",
       "28    23    male  17.4         1     no  northwest   2775.19\n",
       "29    31    male  36.3         2    yes  southwest  38711.00\n",
       "..   ...     ...   ...       ...    ...        ...       ...\n",
       "372   42  female  33.2         1     no  northeast   7639.42\n",
       "373   26    male  32.9         2    yes  southwest  36085.22\n",
       "374   20    male  33.3         0     no  southeast   1391.53\n",
       "375   23  female  28.3         0    yes  northwest  18033.97\n",
       "376   39  female  24.9         3    yes  northeast  21659.93\n",
       "377   24    male  40.2         0    yes  southeast  38126.25\n",
       "378   64  female  30.1         3     no  northwest  16455.71\n",
       "379   62    male  31.5         1     no  southeast  27000.98\n",
       "380   27  female  18.0         2    yes  northeast  15006.58\n",
       "381   55    male  30.7         0    yes  northeast  42303.69\n",
       "382   55    male  33.0         0     no  southeast  20781.49\n",
       "383   35  female  43.3         2     no  southeast   5846.92\n",
       "384   44    male  22.1         2     no  northeast   8302.54\n",
       "385   19    male  34.4         0     no  southwest   1261.86\n",
       "386   58  female  39.1         0     no  southeast  11856.41\n",
       "387   50    male  25.4         2     no  northwest  30284.64\n",
       "388   26  female  22.6         0     no  northwest   3176.82\n",
       "389   24  female  30.2         3     no  northwest   4618.08\n",
       "390   48    male  35.6         4     no  northeast  10736.87\n",
       "391   19  female  37.4         0     no  northwest   2138.07\n",
       "392   48    male  31.4         1     no  northeast   8964.06\n",
       "393   49    male  31.4         1     no  northeast   9290.14\n",
       "394   46  female  32.3         2     no  northeast   9411.01\n",
       "395   46    male  19.9         0     no  northwest   7526.71\n",
       "396   43  female  34.4         3     no  southwest   8522.00\n",
       "397   21    male  31.0         0     no  southeast  16586.50\n",
       "398   64    male  25.6         2     no  southwest  14988.43\n",
       "399   18  female  38.2         0     no  southeast   1631.67\n",
       "400   51  female  20.6         0     no  southwest   9264.80\n",
       "401   47    male  47.5         1     no  southeast   8083.92\n",
       "\n",
       "[402 rows x 7 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = data.head(n=first)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>64</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>14692.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>49</td>\n",
       "      <td>male</td>\n",
       "      <td>32.3</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10269.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>20.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>3260.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>38.4</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>11396.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>33</td>\n",
       "      <td>female</td>\n",
       "      <td>24.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4185.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>23.6</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>8539.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>21.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>6652.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4074.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>17.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1621.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>44</td>\n",
       "      <td>female</td>\n",
       "      <td>20.2</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>19594.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>14455.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>23.9</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>5080.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>414</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>35.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2134.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>415</th>\n",
       "      <td>43</td>\n",
       "      <td>female</td>\n",
       "      <td>35.6</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>7345.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>52</td>\n",
       "      <td>male</td>\n",
       "      <td>34.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>9140.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>22.6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>18608.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>418</th>\n",
       "      <td>64</td>\n",
       "      <td>male</td>\n",
       "      <td>39.2</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>14418.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>28950.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>64</td>\n",
       "      <td>male</td>\n",
       "      <td>33.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>46889.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>35.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>46599.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>32.8</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>39125.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>30.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2727.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>424</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>30.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>8968.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>24.3</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>9788.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>38</td>\n",
       "      <td>female</td>\n",
       "      <td>27.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6555.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>29.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7323.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>16.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>3167.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>30.4</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>18804.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>33.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>23082.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>29</td>\n",
       "      <td>female</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4906.41</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>25</td>\n",
       "      <td>female</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>33900.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>41</td>\n",
       "      <td>male</td>\n",
       "      <td>32.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>6875.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>26.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6940.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>33</td>\n",
       "      <td>female</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4571.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>34</td>\n",
       "      <td>male</td>\n",
       "      <td>42.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4536.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>34.7</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>36397.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1314</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>23.7</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>18765.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1315</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>28.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>11272.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1731.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>53.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1163.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>39.7</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>19496.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1319</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>26.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7201.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1320</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>31.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>5425.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>28101.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>38.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>12981.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1323</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>40.4</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>43896.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1324</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4239.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>33.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>13143.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7050.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1327</th>\n",
       "      <td>51</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>9377.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>24.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>22395.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1329</th>\n",
       "      <td>52</td>\n",
       "      <td>male</td>\n",
       "      <td>38.6</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10325.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1330</th>\n",
       "      <td>57</td>\n",
       "      <td>female</td>\n",
       "      <td>25.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>12629.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10795.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1332</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>44.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>11411.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1333</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>31.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10600.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2205.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1336</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2007.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1337</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29141.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "402    64  female  33.0         0     no  northwest  14692.67\n",
       "403    49    male  32.3         3     no  northwest  10269.46\n",
       "404    31    male  20.4         0     no  southwest   3260.20\n",
       "405    52  female  38.4         2     no  northeast  11396.90\n",
       "406    33  female  24.3         0     no  southeast   4185.10\n",
       "407    47  female  23.6         1     no  southwest   8539.67\n",
       "408    38    male  21.1         3     no  southeast   6652.53\n",
       "409    32    male  30.0         1     no  southeast   4074.45\n",
       "410    19    male  17.5         0     no  northwest   1621.34\n",
       "411    44  female  20.2         1    yes  northeast  19594.81\n",
       "412    26  female  17.2         2    yes  northeast  14455.64\n",
       "413    25    male  23.9         5     no  southwest   5080.10\n",
       "414    19  female  35.2         0     no  northwest   2134.90\n",
       "415    43  female  35.6         1     no  southeast   7345.73\n",
       "416    52    male  34.1         0     no  southeast   9140.95\n",
       "417    36  female  22.6         2    yes  southwest  18608.26\n",
       "418    64    male  39.2         1     no  southeast  14418.28\n",
       "419    63  female  27.0         0    yes  northwest  28950.47\n",
       "420    64    male  33.9         0    yes  southeast  46889.26\n",
       "421    61    male  35.9         0    yes  southeast  46599.11\n",
       "422    40    male  32.8         1    yes  northeast  39125.33\n",
       "423    25    male  30.6         0     no  northeast   2727.40\n",
       "424    48    male  30.2         2     no  southwest   8968.33\n",
       "425    45    male  24.3         5     no  southeast   9788.87\n",
       "426    38  female  27.3         1     no  northeast   6555.07\n",
       "427    18  female  29.2         0     no  northeast   7323.73\n",
       "428    21  female  16.8         1     no  northeast   3167.46\n",
       "429    27  female  30.4         3     no  northwest  18804.75\n",
       "430    19    male  33.1         0     no  southwest  23082.96\n",
       "431    29  female  20.2         2     no  northwest   4906.41\n",
       "...   ...     ...   ...       ...    ...        ...       ...\n",
       "1308   25  female  30.2         0    yes  southwest  33900.65\n",
       "1309   41    male  32.2         2     no  southwest   6875.96\n",
       "1310   42    male  26.3         1     no  northwest   6940.91\n",
       "1311   33  female  26.7         0     no  northwest   4571.41\n",
       "1312   34    male  42.9         1     no  southwest   4536.26\n",
       "1313   19  female  34.7         2    yes  southwest  36397.58\n",
       "1314   30  female  23.7         3    yes  northwest  18765.88\n",
       "1315   18    male  28.3         1     no  northeast  11272.33\n",
       "1316   19  female  20.6         0     no  southwest   1731.68\n",
       "1317   18    male  53.1         0     no  southeast   1163.46\n",
       "1318   35    male  39.7         4     no  northeast  19496.72\n",
       "1319   39  female  26.3         2     no  northwest   7201.70\n",
       "1320   31    male  31.1         3     no  northwest   5425.02\n",
       "1321   62    male  26.7         0    yes  northeast  28101.33\n",
       "1322   62    male  38.8         0     no  southeast  12981.35\n",
       "1323   42  female  40.4         2    yes  southeast  43896.38\n",
       "1324   31    male  25.9         1     no  northwest   4239.89\n",
       "1325   61    male  33.5         0     no  northeast  13143.34\n",
       "1326   42  female  32.9         0     no  northeast   7050.02\n",
       "1327   51    male  30.0         1     no  southeast   9377.90\n",
       "1328   23  female  24.2         2     no  northeast  22395.74\n",
       "1329   52    male  38.6         2     no  southwest  10325.21\n",
       "1330   57  female  25.7         2     no  southeast  12629.17\n",
       "1331   23  female  33.4         0     no  southwest  10795.94\n",
       "1332   52  female  44.7         3     no  southwest  11411.69\n",
       "1333   50    male  31.0         3     no  northwest  10600.55\n",
       "1334   18  female  31.9         0     no  northeast   2205.98\n",
       "1335   18  female  36.9         0     no  southeast   1629.83\n",
       "1336   21  female  25.8         0     no  southwest   2007.95\n",
       "1337   61  female  29.1         0    yes  northwest  29141.36\n",
       "\n",
       "[936 rows x 7 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = data.tail(n=sec)\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Randomly dividing 30% and 70% of the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Randomly diving 30% and 70% of the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "df1_r, df2_r = train_test_split(data, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1116</th>\n",
       "      <td>41</td>\n",
       "      <td>male</td>\n",
       "      <td>29.6</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9222.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>53.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1163.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>17.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>12829.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>24.8</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>17904.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>26</td>\n",
       "      <td>female</td>\n",
       "      <td>29.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3392.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>524</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>26.1</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>38245.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1208</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>20296.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>34.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>7418.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1129</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1728.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1246</th>\n",
       "      <td>45</td>\n",
       "      <td>female</td>\n",
       "      <td>25.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>9101.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>31</td>\n",
       "      <td>female</td>\n",
       "      <td>32.7</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4738.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>59</td>\n",
       "      <td>male</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>12913.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>38</td>\n",
       "      <td>female</td>\n",
       "      <td>40.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5400.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1107</th>\n",
       "      <td>50</td>\n",
       "      <td>female</td>\n",
       "      <td>26.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>10493.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1837.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>787</th>\n",
       "      <td>21</td>\n",
       "      <td>male</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1917.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1227</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>37.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>7162.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>30.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>1639.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>919</th>\n",
       "      <td>35</td>\n",
       "      <td>female</td>\n",
       "      <td>34.2</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5245.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>31.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4433.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1105</th>\n",
       "      <td>54</td>\n",
       "      <td>female</td>\n",
       "      <td>31.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>10338.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>740</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>8604.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>39</td>\n",
       "      <td>male</td>\n",
       "      <td>29.6</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>7512.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>37.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>48824.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>28.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2331.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>17.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6877.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4074.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>40</td>\n",
       "      <td>female</td>\n",
       "      <td>25.5</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7077.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>25</td>\n",
       "      <td>female</td>\n",
       "      <td>20.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>3208.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>53</td>\n",
       "      <td>male</td>\n",
       "      <td>36.1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10085.85</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>890</th>\n",
       "      <td>64</td>\n",
       "      <td>female</td>\n",
       "      <td>26.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>29330.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>918</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>28.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>13041.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>29.9</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>30942.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>847</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>50.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>2438.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1100</th>\n",
       "      <td>33</td>\n",
       "      <td>female</td>\n",
       "      <td>19.1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>16776.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>42</td>\n",
       "      <td>male</td>\n",
       "      <td>31.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6358.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>28.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>13770.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>64</td>\n",
       "      <td>female</td>\n",
       "      <td>39.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>14901.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>25</td>\n",
       "      <td>female</td>\n",
       "      <td>30.2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>33900.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>24.1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>26236.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>836</th>\n",
       "      <td>36</td>\n",
       "      <td>male</td>\n",
       "      <td>31.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4402.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>13887.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>31.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>3056.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>46.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4686.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1065</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>25.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>7045.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>845</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>45008.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>22</td>\n",
       "      <td>female</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>2166.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1066</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>37.3</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8978.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1136.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1094</th>\n",
       "      <td>50</td>\n",
       "      <td>female</td>\n",
       "      <td>33.7</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>11299.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>44</td>\n",
       "      <td>male</td>\n",
       "      <td>34.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>7147.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>20.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1242.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>975</th>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>22.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>16138.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1321</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>26.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>28101.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>32.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4149.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>1964.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>24</td>\n",
       "      <td>female</td>\n",
       "      <td>30.2</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4618.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>36.9</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>46661.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>5472.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>25</td>\n",
       "      <td>female</td>\n",
       "      <td>32.2</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>18218.16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "1116   41    male  29.6         5     no  northeast   9222.40\n",
       "1317   18    male  53.1         0     no  southeast   1163.46\n",
       "250    18    male  17.3         2    yes  northeast  12829.46\n",
       "799    33    male  24.8         0    yes  northeast  17904.53\n",
       "218    26  female  29.9         1     no  southeast   3392.98\n",
       "524    42    male  26.1         1    yes  southeast  38245.59\n",
       "1208   37  female  25.6         1    yes  northeast  20296.86\n",
       "996    39  female  34.1         3     no  southwest   7418.52\n",
       "1129   19  female  18.6         0     no  southwest   1728.90\n",
       "1246   45  female  25.7         3     no  southwest   9101.80\n",
       "440    31  female  32.7         1     no  northwest   4738.27\n",
       "580    59    male  25.5         1     no  northeast  12913.99\n",
       "257    38  female  40.2         0     no  southeast   5400.98\n",
       "1107   50  female  26.2         2     no  northwest  10493.95\n",
       "358    23    male  41.9         0     no  southeast   1837.28\n",
       "787    21    male  36.9         0     no  northwest   1917.32\n",
       "1227   42    male  37.2         2     no  southeast   7162.01\n",
       "581    19    male  30.6         0     no  northwest   1639.56\n",
       "919    35  female  34.2         1     no  southeast   5245.23\n",
       "1005   29    male  31.7         2     no  northwest   4433.39\n",
       "1105   54  female  31.2         0     no  southeast  10338.93\n",
       "740    45    male  24.0         2     no  northeast   8604.48\n",
       "450    39    male  29.6         4     no  southwest   7512.27\n",
       "175    63  female  37.7         0    yes  southwest  48824.45\n",
       "106    19  female  28.4         1     no  southwest   2331.52\n",
       "1029   37  female  17.3         2     no  northeast   6877.98\n",
       "409    32    male  30.0         1     no  southeast   4074.45\n",
       "154    40  female  25.5         1     no  northeast   7077.19\n",
       "1302   25  female  20.8         1     no  southwest   3208.79\n",
       "630    53    male  36.1         1     no  southwest  10085.85\n",
       "...   ...     ...   ...       ...    ...        ...       ...\n",
       "890    64  female  26.9         0    yes  northwest  29330.98\n",
       "918    61  female  28.2         0     no  southwest  13041.92\n",
       "103    61  female  29.9         3    yes  southeast  30942.19\n",
       "847    23    male  50.4         1     no  southeast   2438.06\n",
       "1100   33  female  19.1         2    yes  northeast  16776.30\n",
       "948    42    male  31.3         0     no  northwest   6358.78\n",
       "33     63    male  28.3         0     no  northwest  13770.10\n",
       "199    64  female  39.3         0     no  northeast  14901.52\n",
       "1308   25  female  30.2         0    yes  southwest  33900.65\n",
       "688    47  female  24.1         1     no  southwest  26236.58\n",
       "836    36    male  31.5         0     no  southwest   4402.23\n",
       "542    63  female  36.3         0     no  southeast  13887.20\n",
       "541    20  female  31.8         2     no  southeast   3056.39\n",
       "454    32    male  46.5         2     no  southeast   4686.39\n",
       "1065   42  female  25.3         1     no  southwest   7045.50\n",
       "845    60  female  32.5         0    yes  southeast  45008.96\n",
       "139    22  female  36.0         0     no  southwest   2166.73\n",
       "1066   48    male  37.3         2     no  southeast   8978.19\n",
       "663    18    male  33.7         0     no  southeast   1136.40\n",
       "1094   50  female  33.7         4     no  southwest  11299.34\n",
       "1289   44    male  34.3         1     no  southeast   7147.47\n",
       "681    19    male  20.3         0     no  southwest   1242.26\n",
       "975    29    male  22.9         0    yes  northeast  16138.76\n",
       "1321   62    male  26.7         0    yes  northeast  28101.33\n",
       "21     30  female  32.4         1     no  southwest   4149.74\n",
       "1295   20    male  22.0         1     no  southwest   1964.78\n",
       "389    24  female  30.2         3     no  northwest   4618.08\n",
       "1122   53  female  36.9         3    yes  northwest  46661.44\n",
       "1287   36  female  25.9         1     no  southwest   5472.45\n",
       "858    25  female  32.2         1     no  southeast  18218.16\n",
       "\n",
       "[936 rows x 7 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>41.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5662.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>33.7</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>7445.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1331</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>10795.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>45</td>\n",
       "      <td>male</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>62592.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>4137.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>64</td>\n",
       "      <td>female</td>\n",
       "      <td>31.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>47291.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>615</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>36.6</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>42969.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>936</th>\n",
       "      <td>44</td>\n",
       "      <td>male</td>\n",
       "      <td>29.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>32108.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>35.6</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>37465.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>43921.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>40.2</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>38126.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>877</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>33.4</td>\n",
       "      <td>5</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>6653.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>381</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>30.7</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>42303.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>58</td>\n",
       "      <td>female</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>24227.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>45.3</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>8569.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1146</th>\n",
       "      <td>60</td>\n",
       "      <td>male</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>52590.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>41</td>\n",
       "      <td>female</td>\n",
       "      <td>33.1</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>7749.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>33.7</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>8823.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>20.6</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>2803.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>26.6</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9715.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>46</td>\n",
       "      <td>female</td>\n",
       "      <td>34.6</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>41661.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>31</td>\n",
       "      <td>male</td>\n",
       "      <td>30.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>3857.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>41</td>\n",
       "      <td>male</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5699.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>778</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>34.3</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>5934.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>942</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>40.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>2217.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1037</th>\n",
       "      <td>45</td>\n",
       "      <td>female</td>\n",
       "      <td>30.5</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>39725.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>54</td>\n",
       "      <td>female</td>\n",
       "      <td>31.9</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>27322.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>60</td>\n",
       "      <td>female</td>\n",
       "      <td>28.7</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>13224.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>47</td>\n",
       "      <td>female</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>20878.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>978</th>\n",
       "      <td>45</td>\n",
       "      <td>female</td>\n",
       "      <td>40.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>9704.67</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1035</th>\n",
       "      <td>54</td>\n",
       "      <td>female</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>12094.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1335</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>36.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1629.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>465</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>28.4</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>19521.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>61</td>\n",
       "      <td>male</td>\n",
       "      <td>36.3</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>47403.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>40</td>\n",
       "      <td>female</td>\n",
       "      <td>32.8</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>40003.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>62</td>\n",
       "      <td>male</td>\n",
       "      <td>30.9</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>46718.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>24.4</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>18259.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1631.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>34</td>\n",
       "      <td>male</td>\n",
       "      <td>22.4</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>27375.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>37.2</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4673.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>957</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>26.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>12609.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>57</td>\n",
       "      <td>male</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>11356.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1119</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>20.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>5693.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>952</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>28.4</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>4527.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1326</th>\n",
       "      <td>42</td>\n",
       "      <td>female</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7050.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1258</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>37.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>30063.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>63</td>\n",
       "      <td>male</td>\n",
       "      <td>35.1</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>47055.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>22.9</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>23244.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1019</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>32.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>26018.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>56</td>\n",
       "      <td>male</td>\n",
       "      <td>26.7</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>26109.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>21.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>4718.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>37.1</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6334.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>37.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>9634.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>842</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>32.8</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>36021.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1815.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>691</th>\n",
       "      <td>47</td>\n",
       "      <td>male</td>\n",
       "      <td>36.2</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>8068.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>4883.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>46</td>\n",
       "      <td>male</td>\n",
       "      <td>25.7</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>9301.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>54</td>\n",
       "      <td>male</td>\n",
       "      <td>34.2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>44260.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>402 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "790    39  female  41.8         0     no  southeast   5662.23\n",
       "820    45    male  33.7         1     no  southwest   7445.92\n",
       "1331   23  female  33.4         0     no  southwest  10795.94\n",
       "1300   45    male  30.4         0    yes  southeast  62592.87\n",
       "1032   30  female  27.9         0     no  northeast   4137.52\n",
       "94     64  female  31.3         2    yes  southwest  47291.06\n",
       "615    47  female  36.6         1    yes  southeast  42969.85\n",
       "936    44    male  29.7         2     no  northeast  32108.66\n",
       "1186   20    male  35.6         3    yes  northwest  37465.34\n",
       "256    56    male  33.6         0    yes  northwest  43921.18\n",
       "377    24    male  40.2         0    yes  southeast  38126.25\n",
       "877    33    male  33.4         5     no  southeast   6653.79\n",
       "381    55    male  30.7         0    yes  northeast  42303.69\n",
       "227    58  female  41.9         0     no  southeast  24227.34\n",
       "1024   47  female  45.3         1     no  southeast   8569.86\n",
       "1146   60    male  32.8         0    yes  southwest  52590.83\n",
       "309    41  female  33.1         2     no  northwest   7749.16\n",
       "595    46  female  33.7         1     no  northeast   8823.99\n",
       "276    19    male  20.6         2     no  northwest   2803.70\n",
       "275    47  female  26.6         2     no  northeast   9715.84\n",
       "1218   46  female  34.6         1    yes  southwest  41661.60\n",
       "519    31    male  30.9         0     no  northeast   3857.76\n",
       "1197   41    male  33.6         0     no  southeast   5699.84\n",
       "778    35    male  34.3         3     no  southeast   5934.38\n",
       "942    18  female  40.2         0     no  northeast   2217.47\n",
       "1037   45  female  30.5         1    yes  northwest  39725.52\n",
       "138    54  female  31.9         3     no  southeast  27322.73\n",
       "466    60  female  28.7         1     no  southwest  13224.69\n",
       "473    47  female  33.3         0     no  northeast  20878.78\n",
       "978    45  female  40.0         3     no  northeast   9704.67\n",
       "...   ...     ...   ...       ...    ...        ...       ...\n",
       "1035   54  female  23.0         3     no  southwest  12094.48\n",
       "1335   18  female  36.9         0     no  southeast   1629.83\n",
       "465    30  female  28.4         1    yes  southeast  19521.97\n",
       "1284   61    male  36.3         1    yes  southwest  47403.88\n",
       "667    40  female  32.8         2    yes  northwest  40003.33\n",
       "4      32    male  28.9         0     no  northwest   3866.86\n",
       "1301   62    male  30.9         3    yes  northwest  46718.16\n",
       "780    30    male  24.4         3    yes  southwest  18259.22\n",
       "181    18  female  38.3         0     no  southeast   1631.82\n",
       "140    34    male  22.4         2     no  northeast  27375.90\n",
       "934    32    male  37.2         2     no  southeast   4673.39\n",
       "957    24    male  26.8         1     no  northwest  12609.89\n",
       "75     57    male  34.0         0     no  northwest  11356.66\n",
       "1119   30  female  20.0         3     no  northwest   5693.43\n",
       "952    30  female  28.4         1     no  northwest   4527.18\n",
       "1326   42  female  32.9         0     no  northeast   7050.02\n",
       "1258   55    male  37.7         3     no  northwest  30063.58\n",
       "109    63    male  35.1         0    yes  southeast  47055.53\n",
       "58     53  female  22.9         1    yes  southeast  23244.79\n",
       "1019   21  female  32.7         2     no  northwest  26018.95\n",
       "444    56    male  26.7         1    yes  northwest  26109.33\n",
       "1220   30  female  21.9         1     no  northeast   4718.20\n",
       "152    32  female  37.1         3     no  northeast   6334.34\n",
       "127    52  female  37.4         0     no  southwest   9634.54\n",
       "842    23  female  32.8         2    yes  southeast  36021.01\n",
       "804    23    male  26.5         0     no  southeast   1815.88\n",
       "691    47    male  36.2         1     no  southwest   8068.19\n",
       "191    36  female  26.2         0     no  southwest   4883.87\n",
       "1293   46    male  25.7         3     no  northwest   9301.89\n",
       "252    54    male  34.2         2    yes  southeast  44260.75\n",
       "\n",
       "[402 rows x 7 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Sub Samples with Replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>61</td>\n",
       "      <td>female</td>\n",
       "      <td>33.3</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>36580.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>3645.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>28</td>\n",
       "      <td>female</td>\n",
       "      <td>33.4</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>3172.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>55</td>\n",
       "      <td>male</td>\n",
       "      <td>37.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>20630.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>36</td>\n",
       "      <td>female</td>\n",
       "      <td>25.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>5266.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>43</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>6837.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>712</th>\n",
       "      <td>43</td>\n",
       "      <td>female</td>\n",
       "      <td>30.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>8310.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>655</th>\n",
       "      <td>52</td>\n",
       "      <td>female</td>\n",
       "      <td>25.3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>southeast</td>\n",
       "      <td>24667.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1261</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>37.1</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southwest</td>\n",
       "      <td>3277.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>37</td>\n",
       "      <td>female</td>\n",
       "      <td>30.8</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>northeast</td>\n",
       "      <td>37270.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "1012   61  female  33.3         4     no  southeast  36580.28\n",
       "101    30    male  25.5         0     no  northeast   3645.09\n",
       "290    28  female  33.4         0     no  southwest   3172.02\n",
       "45     55    male  37.3         0     no  southwest  20630.28\n",
       "831    36  female  25.8         0     no  northwest   5266.37\n",
       "501    43    male  26.0         0     no  northeast   6837.37\n",
       "712    43  female  30.7         2     no  northwest   8310.84\n",
       "655    52  female  25.3         2    yes  southeast  24667.42\n",
       "1261   28    male  37.1         1     no  southwest   3277.16\n",
       "850    37  female  30.8         0    yes  northeast  37270.15"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_samples_r= data.sample(n=10,replace=True)\n",
    "sub_samples_r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Sub Samples without Replacement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7173.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>30</td>\n",
       "      <td>female</td>\n",
       "      <td>21.9</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>4718.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>39.7</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>19496.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>54</td>\n",
       "      <td>male</td>\n",
       "      <td>32.8</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10435.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>35</td>\n",
       "      <td>female</td>\n",
       "      <td>26.1</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>5227.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10564.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6455.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>45702.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>35.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4890.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>28</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4349.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "633    40    male  22.7         2     no  northeast   7173.36\n",
       "1220   30  female  21.9         1     no  northeast   4718.20\n",
       "1318   35    male  39.7         4     no  northeast  19496.72\n",
       "317    54    male  32.8         0     no  northeast  10435.07\n",
       "1165   35  female  26.1         0     no  northeast   5227.99\n",
       "221    53  female  33.3         0     no  northeast  10564.88\n",
       "176    38    male  27.8         2     no  northwest   6455.86\n",
       "569    48    male  40.6         2    yes  northwest  45702.02\n",
       "346    33    male  35.8         2     no  southeast   4890.00\n",
       "303    28  female  33.0         2     no  southeast   4349.46"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_samples_wr= data.sample(n=10,replace=False)\n",
    "sub_samples_wr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating 10 Sub Samples of the above Sub Sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      age     sex   bmi  children smoker     region  expenses\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "317    54    male  32.8         0     no  northeast  10435.07\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "317    54    male  32.8         0     no  northeast  10435.07\n",
      "317    54    male  32.8         0     no  northeast  10435.07\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "317    54    male  32.8         0     no  northeast  10435.07\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "346    33    male  35.8         2     no  southeast   4890.00\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "1220   30  female  21.9         1     no  northeast   4718.20\n",
      "303    28  female  33.0         2     no  southeast   4349.46\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "633    40    male  22.7         2     no  northeast   7173.36\n",
      "      age     sex   bmi  children smoker     region  expenses\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "1318   35    male  39.7         4     no  northeast  19496.72\n",
      "176    38    male  27.8         2     no  northwest   6455.86\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "221    53  female  33.3         0     no  northeast  10564.88\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n",
      "317    54    male  32.8         0     no  northeast  10435.07\n",
      "1165   35  female  26.1         0     no  northeast   5227.99\n",
      "569    48    male  40.6         2    yes  northwest  45702.02\n"
     ]
    }
   ],
   "source": [
    "samplelist = []\n",
    "for i in range(10):\n",
    "    samplelist.append(sub_samples_wr.sample(n=10,replace=True))\n",
    "    print(samplelist[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating measures of central tendency\n",
    "\n",
    "Mean and Median for all numerical features and mode for all categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "numeric_features = ['age','bmi','children','expenses']\n",
    "categorical_features = ['sex','smoker','region']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[42.4, 34.74, 1.8, 20060.121999999996], [37.4, 31.810000000000002, 1.4, 11445.2], [35.9, 24.98, 1.4, 6176.307999999999], [38.8, 30.51, 1.4, 10376.724], [38.8, 33.699999999999996, 1.8, 16966.256], [38.1, 27.339999999999996, 1.0, 6532.687], [37.6, 30.429999999999996, 2.1, 10541.131], [39.1, 30.78, 1.4, 8277.893999999998], [35.2, 26.240000000000002, 1.6, 5833.565], [44.7, 34.09, 1.2, 20507.945]]\n",
      "\n",
      "39.20702541106129\n",
      "30.665470852017936\n",
      "1.0949177877429\n",
      "13270.422414050821\n"
     ]
    }
   ],
   "source": [
    "mean10 = []\n",
    "for i in range(10):\n",
    "    mean10.append([samplelist[i][numeric_features[0]].mean(),samplelist[i][numeric_features[1]].mean(),samplelist[i][numeric_features[2]].mean(),samplelist[i][numeric_features[3]].mean()])\n",
    "    \n",
    "print(mean10)\n",
    "print()\n",
    "print(statistics.mean(data['age']))\n",
    "print(statistics.mean(data['bmi']))\n",
    "print(statistics.mean(data['children']))\n",
    "print(statistics.mean(data['expenses']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[44.0, 34.55, 2.0, 10564.88], [34.0, 33.0, 1.5, 5058.995], [34.0, 22.7, 1.5, 5587.03], [35.5, 32.8, 2.0, 5672.93], [35.0, 33.3, 2.0, 10564.88], [36.5, 26.950000000000003, 1.0, 5841.924999999999], [35.0, 29.7, 2.0, 7173.36], [36.5, 33.15, 1.5, 6455.86], [36.5, 24.4, 2.0, 5841.924999999999], [48.0, 33.3, 1.0, 10564.88]]\n"
     ]
    }
   ],
   "source": [
    "median10 = []\n",
    "for i in range(10):\n",
    "    median10.append([samplelist[i][numeric_features[0]].median(),samplelist[i][numeric_features[1]].median(),samplelist[i][numeric_features[2]].median(),samplelist[i][numeric_features[3]].median()])\n",
    "    \n",
    "print(median10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0    male\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "1    northwest\n",
      "dtype: object], [0    female\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    female\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    male\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    female\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    female\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    male\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    female\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    male\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object], [0    male\n",
      "dtype: object, 0    no\n",
      "dtype: object, 0    northeast\n",
      "dtype: object]]\n"
     ]
    }
   ],
   "source": [
    "mode10 = []\n",
    "for i in range(10):\n",
    "    mode10.append([samplelist[i][categorical_features[0]].mode(),samplelist[i][categorical_features[1]].mode(),samplelist[i][categorical_features[2]].mode()])\n",
    "    \n",
    "print(mode10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculating Dispersion\n",
    "\n",
    "Variance, Standard Deviation, Range and IQR(Interquartile range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64.9"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statistics.variance(samplelist[i][numeric_features[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[77.15555555555555, 36.56044444444446, 1.288888888888889, 331251060.4000399], [105.37777777777778, 43.387666666666675, 1.6, 168108941.58459997], [60.98888888888889, 21.67066666666667, 0.4888888888888889, 3795748.9961955547], [98.62222222222222, 43.45211111111111, 0.7111111111111111, 159343560.10769328], [109.95555555555556, 49.666666666666686, 1.9555555555555557, 262738536.11047107], [81.21111111111111, 20.175999999999995, 0.8888888888888888, 5185012.771134444], [40.044444444444444, 62.84900000000002, 2.3222222222222224, 40967245.094098896], [102.76666666666667, 33.80177777777778, 1.6, 22271761.077782225], [21.733333333333334, 24.138222222222222, 0.48888888888888893, 1345259.5408277775], [64.9, 36.5298888888889, 1.9555555555555555, 318761539.7469833]]\n"
     ]
    }
   ],
   "source": [
    "var10 = []\n",
    "for i in range(10):\n",
    "    var10.append([statistics.variance(samplelist[i][numeric_features[0]]),statistics.variance(samplelist[i][numeric_features[1]]),statistics.variance(samplelist[i][numeric_features[2]]),statistics.variance(samplelist[i][numeric_features[3]])])\n",
    "    \n",
    "print(var10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.783823515733655, 6.046523335309677, 1.1352924243950935, 18200.303854607482], [10.265367883216742, 6.5869315061465965, 1.2649110640673518, 12965.683228607737], [7.809538327512637, 4.655176330351694, 0.699205898780101, 1948.2682043793545], [9.930872178324632, 6.59182153210409, 0.8432740427115678, 12623.135906251397], [10.485969461883606, 7.047458170621993, 1.3984117975602022, 16209.211458626576], [9.011720763045819, 4.491770252361533, 0.9428090415820634, 2277.0623116494735], [6.328067986711619, 7.927736120734596, 1.5238839267549948, 6400.5659979488455], [10.13738953906116, 5.813929633025995, 1.2649110640673518, 4719.296671939816], [4.661902329879224, 4.913066478506292, 0.6992058987801011, 1159.8532410731011], [8.056053624449133, 6.0439961026533515, 1.398411797560202, 17853.894245989675]]\n",
      "\n",
      "14.049960379216156\n",
      "6.098382190003363\n",
      "1.2054927397819137\n",
      "12110.011239706468\n"
     ]
    }
   ],
   "source": [
    "stddev10 = []\n",
    "for i in range(10):\n",
    "    stddev10.append([statistics.stdev(samplelist[i][numeric_features[0]]),statistics.stdev(samplelist[i][numeric_features[1]]),statistics.stdev(samplelist[i][numeric_features[2]]),statistics.stdev(samplelist[i][numeric_features[3]])])\n",
    "    \n",
    "print(stddev10)\n",
    "\n",
    "print()\n",
    "print(statistics.stdev(data['age']))\n",
    "print(statistics.stdev(data['bmi']))\n",
    "print(statistics.stdev(data['children']))\n",
    "print(statistics.stdev(data['expenses']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value = max(data['age'])\n",
    "max_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min_value = min(data['age'])\n",
    "min_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range_of_age = max_value - min_value\n",
    "range_of_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "[[46, 37.1, 5, 62648.56]]\n"
     ]
    }
   ],
   "source": [
    "rangedata = []\n",
    "for i in range(1):\n",
    "    max_value = max(data[numeric_features[0]])\n",
    "    min_value = min(data[numeric_features[0]])\n",
    "    rangei = max_value - min_value\n",
    "    max_value = max(data[numeric_features[1]])\n",
    "    min_value = min(data[numeric_features[1]])\n",
    "    rangei2 = max_value - min_value\n",
    "    max_value = max(data[numeric_features[2]])\n",
    "    min_value = min(data[numeric_features[2]])\n",
    "    rangei3 = max_value - min_value\n",
    "    max_value = max(data[numeric_features[3]])\n",
    "    min_value = min(data[numeric_features[3]])\n",
    "    rangei4 = max_value - min_value\n",
    "    rangedata.append([rangei,rangei2,rangei3,rangei4])\n",
    "\n",
    "print(numeric_features)\n",
    "print(rangedata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "[25, 17.900000000000002, 4, 41352.56]\n",
      "[26, 18.700000000000003, 4, 41352.56]\n",
      "[25, 11.399999999999999, 2, 6215.419999999999]\n",
      "[26, 18.700000000000003, 2, 41352.56]\n",
      "[25, 18.700000000000003, 4, 41352.56]\n",
      "[26, 11.399999999999999, 2, 6215.419999999999]\n",
      "[23, 17.800000000000004, 4, 14778.52]\n",
      "[25, 17.800000000000004, 4, 15147.260000000002]\n",
      "[12, 13.899999999999999, 2, 2823.8999999999996]\n",
      "[19, 14.5, 4, 40474.03]\n"
     ]
    }
   ],
   "source": [
    "range10 = []\n",
    "print(numeric_features)\n",
    "\n",
    "for i in range(10):\n",
    "    max_value = max(samplelist[i][numeric_features[0]])\n",
    "    min_value = min(samplelist[i][numeric_features[0]])\n",
    "    rangei = max_value - min_value\n",
    "    max_value = max(samplelist[i][numeric_features[1]])\n",
    "    min_value = min(samplelist[i][numeric_features[1]])\n",
    "    rangei2 = max_value - min_value\n",
    "    max_value = max(samplelist[i][numeric_features[2]])\n",
    "    min_value = min(samplelist[i][numeric_features[2]])\n",
    "    rangei3 = max_value - min_value\n",
    "    max_value = max(samplelist[i][numeric_features[3]])\n",
    "    min_value = min(samplelist[i][numeric_features[3]])\n",
    "    rangei4 = max_value - min_value\n",
    "    range10.append([rangei,rangei2,rangei3,rangei4])\n",
    "    print(range10[i])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.0"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import iqr\n",
    "iqr_age = iqr(data['age'])\n",
    "iqr_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12.25"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr_age = iqr(samplelist[0]['age'])\n",
    "iqr_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10564.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>38</td>\n",
       "      <td>male</td>\n",
       "      <td>27.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>6455.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1318</th>\n",
       "      <td>35</td>\n",
       "      <td>male</td>\n",
       "      <td>39.7</td>\n",
       "      <td>4</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>19496.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>28</td>\n",
       "      <td>female</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4349.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>45702.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>45702.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>53</td>\n",
       "      <td>female</td>\n",
       "      <td>33.3</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>10564.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>569</th>\n",
       "      <td>48</td>\n",
       "      <td>male</td>\n",
       "      <td>40.6</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>northwest</td>\n",
       "      <td>45702.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>35.8</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4890.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>633</th>\n",
       "      <td>40</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>northeast</td>\n",
       "      <td>7173.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      age     sex   bmi  children smoker     region  expenses\n",
       "221    53  female  33.3         0     no  northeast  10564.88\n",
       "176    38    male  27.8         2     no  northwest   6455.86\n",
       "1318   35    male  39.7         4     no  northeast  19496.72\n",
       "303    28  female  33.0         2     no  southeast   4349.46\n",
       "569    48    male  40.6         2    yes  northwest  45702.02\n",
       "569    48    male  40.6         2    yes  northwest  45702.02\n",
       "221    53  female  33.3         0     no  northeast  10564.88\n",
       "569    48    male  40.6         2    yes  northwest  45702.02\n",
       "346    33    male  35.8         2     no  southeast   4890.00\n",
       "633    40    male  22.7         2     no  northeast   7173.36"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samplelist[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "[12.25, 7.299999999999997, 0.0, 32515.46]\n",
      "[14.75, 7.399999999999995, 1.75, 5814.2275]\n",
      "[10.0, 4.6250000000000036, 1.0, 2455.16]\n",
      "[15.25, 11.124999999999996, 1.0, 4858.4925]\n",
      "[18.0, 6.700000000000003, 1.0, 14778.52]\n",
      "[8.25, 8.0, 2.0, 2148.3375000000005]\n",
      "[5.0, 16.025000000000002, 2.25, 11549.427500000002]\n",
      "[18.5, 5.4999999999999964, 1.75, 5803.73]\n",
      "[8.75, 5.100000000000001, 0.75, 2232.835]\n",
      "[16.0, 11.325, 2.0, 31700.0325]\n"
     ]
    }
   ],
   "source": [
    "iqr10 = []\n",
    "print(numeric_features)\n",
    "\n",
    "for i in range(10):\n",
    "    iqr10.append([iqr(samplelist[i][numeric_features[0]]),iqr(samplelist[i][numeric_features[1]]),iqr(samplelist[i][numeric_features[2]]),iqr(samplelist[i][numeric_features[3]])])\n",
    "    print(iqr10[i])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iqr([1,2,3,4,5,6,7])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "data1 = [1,2,3,4,5,6,7]\n",
    "plt.boxplot(data1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n"
     ]
    }
   ],
   "source": [
    "print(numeric_features)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "39.0\n",
      "30.4\n",
      "1.0\n",
      "9382.029999999999\n"
     ]
    }
   ],
   "source": [
    "iqr = []\n",
    "print(numeric_features)\n",
    "\n",
    "for i in numeric_features:\n",
    "    iqr1 = data[i].quantile()\n",
    "    print(iqr1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box Plots for all numeric features of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in numeric_features:\n",
    "    plt.title(\"Boxplot of \"+ i)\n",
    "    plt.boxplot(data[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#70,30 randomly\n",
    "#70\n",
    "for i in numeric_features:\n",
    "    plt.title(\"Boxplot of \"+ i)\n",
    "    plt.boxplot(df1_r[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#30\n",
    "for i in numeric_features:\n",
    "    plt.title(\"Boxplot of \"+ i)\n",
    "    plt.boxplot(df2_r[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEICAYAAAB/Dx7IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADaBJREFUeJzt3X+w5XVdx/Hny13BYhEW2VR+XNa0mNBSZ+7oH1opWoOFP/pHYdLQcdqapLSccUxTsdEam6apUcfcSQZzESTUGSOzkCLGGcUWlQbCRiUIWpPFXUYwxaB3f5zv0uFy7p7vsvfs5b37fMyc4Zx7Pt/zfd/D3ef97vecezdVhSSpj0et9wCSpANjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdx6REhyUZJ3HaJ9/VKS25Lck+SZB7DdBUl27Of+G5M8b+TaW5K88IAGlwaGWw8yBOV7Q9T2JvmbJKeu91zTklSSpxzEQ/wxcH5VbaqqL6/VXFX11Kq6eq0eT1qN4dYsL66qTcATgW8B713nedbaacCN6z3EapJsXO8Z9MhmuLWqqvo+cDlwxr6PJTkuyV8m2Z3k1iS/l+RRw30fSHL51Nr3JLkqE89LcnuStyS5cziy/+XV9p3kV5N8PcmeJJ9KctLw8WuGJdcPfyt4xYxtHzXMdWuSO4Z5j0tydJJ7gA3D9t9YZd9PTXLlsO9vJXnL1N1HDY9393BqZHlqu1VPfyR51TDPt5O8dcV9FyS5PMmOJN8BXj18Dm9O8o1hm8uSnDCs3zr8reO8JP8xPJ9vnbVfHZ4Mt1aV5IeBVwBfmPrwe4HjgB8Ffhb4FeA1w31vBH4qyauT/DTwWuC8+v/fq/AE4ETgZOA8YHuS02fs90zgD4GXMznqvxW4FKCqfmZY9vThVMfHZoz+6uHy/GHOTcD7qure4W8S+7Z/8ox9Hwt8FvgMcBLwFOCqqSUvGWY5HvgU8L4Z+1/5mGcAHwBeNTzm44BTVix7KZNvkscDFwO/BbyMyXN8ErAXeP+KbZ4LnA68AHh7kp+YN4sOE1XlxcsDF+AW4B7gLuA+YBfwk8N9G4B7gTOm1v8acPXU7WcBe5jE9typjz9veLxjpj52GfC24fpFwLuG6x8C/mhq3Sbgf4Ctw+0CnrKfz+Eq4Dembp8+bL9x3vbAucCXV7nvAuCzU7fPAL634rl74dTaHcP1twOXTq07BvjBirXXrNjXTcALpm4/cd/nAGwdPodTpu7/InDOen/9eDk0F4+4NcvLqup44GjgfOCfkuw7Wj6KSZT3uZXJETQAVfVF4GYgTMI8bW9VfXfFtifN2P9J0/uoqnuAb0/vZ44HbT9c3wg8fsS2pwIzT6EM/mvq+n8DjxlxTvok4LZ9N4bn4Nsr1ty24vZpwCeT3JXkLiYhv58Hfw4rZ9mEjgiGW6uqqvur6hNMgvFc4E4mR32nTS1bAv5z340kr2MS/F3Am1Y85OYkx6zYdteMXe+a3sewzeOm9zPHg7Yf9nMfkxda57kNeMgplIP0TSbfEIAHTkE9bsWalb+m8zbgRVV1/NTlMVU19jnQYcxwa1XDi4ovBTYDN1XV/UyOot+d5NgkpwG/A+wY1v848C7glUzO574pyTNWPOw7kxw1nAM/G/irGbv+KPCaJM9IcjTwB8C1VXXLcP+3mJy7Xs0lwG8neVKSTcP2H6uq+0Z82lcAT0jyhuHFzGOTPHvEdvtzOXB2kucmOQr4feb/2ftzJs/zaQBJtgz/LyTDrZn+enj3xXeAdzN5gXHf2+d+E/guk9Mhn2MS2QuH0wU7gPdU1fVV9TXgLcBHhvjC5K/2e5kcEV8M/HpVfXXlzqvqKuBtwMeZHK0+GThnaskFwIeH0wgvnzH/hcBHgGuAfwe+P8w9V1XdDfwc8OJh3q8xeZHzYRueu9cxea6+yeQ5uH3OZn/G5MXPv09yN5MXiA/2G4gOE6nyH1LQ4mXyE4U7qmrluykkHSCPuCWpGcMtSc14qkSSmvGIW5KaWcgvsznxxBNr69ati3hoSTosXXfddXdW1ZYxaxcS7q1bt7Jz585FPLQkHZaS3Dp/1YSnSiSpGcMtSc0YbklqxnBLUjOGW5KaGfWukiS3AHcz+fWe91XV8v63kCQtyoG8HfD5VXXnwiaRJI3iqRJJambsEXcx+b3ABXywqravXJBkG7ANYGlpae0mlFaR5JDty9/po0eSseF+TlXtSvIjwJVJvlpV10wvGGK+HWB5edmvci3cw4lpEiOs9kadKqmqXcN/7wA+yeRf8pYkrYO54U5yTJJj910Hfh64YdGDSZJmG3Oq5PHAJ4fziRuBj1bVZxY6lSRpVXPDXVU3A08/BLNIkkbw7YCS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM6PDnWRDki8nuWKRA0mS9u9AjrhfD9y0qEEkSeOMCneSU4BfBP5iseNIkuYZe8T9p8CbgP9dbUGSbUl2Jtm5e/fuNRlOkvRQc8Od5Gzgjqq6bn/rqmp7VS1X1fKWLVvWbEBJ0oONOeJ+DvCSJLcAlwJnJtmx0KkkSauaG+6q+t2qOqWqtgLnAP9QVa9c+GSSpJl8H7ckNbPxQBZX1dXA1QuZRJI0ikfcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqZm64kzwmyReTXJ/kxiTvPBSDSZJm2zhizb3AmVV1T5JHA59L8rdV9YUFzyZJmmFuuKuqgHuGm48eLrXIoSRJqxt1jjvJhiRfAe4Arqyqa2es2ZZkZ5Kdu3fvXus5JUmDUeGuqvur6hnAKcCzkjxtxprtVbVcVctbtmxZ6zklSYMDeldJVd0FXA2ctZBpJElzjXlXyZYkxw/Xfwh4IfDVRQ8mSZptzLtKngh8OMkGJqG/rKquWOxYkqTVjHlXyb8AzzwEs0iSRvAnJyWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRm5oY7yalJ/jHJTUluTPL6QzGYJGm2jSPW3Ae8saq+lORY4LokV1bVvy54NknSDHOPuKvqm1X1peH63cBNwMmLHkySNNuYI+4HJNkKPBO4dsZ924BtAEtLS2swmo40J5xwAnv37l34fpIs9PE3b97Mnj17FroPHdlGhzvJJuDjwBuq6jsr76+q7cB2gOXl5VqzCXXE2Lt3L1X9v3QW/Y1BGvWukiSPZhLti6vqE4sdSZK0P2PeVRLgQ8BNVfUnix9JkrQ/Y464nwO8CjgzyVeGyy8seC5J0irmnuOuqs8BnrSTpEcIf3JSkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNzA13kguT3JHkhkMxkCRp/8YccV8EnLXgOSRJI80Nd1VdA+w5BLNIkkbYuFYPlGQbsA1gaWlprR5WR5B6x2PhguPWe4yDVu947HqPoMNcqmr+omQrcEVVPW3Mgy4vL9fOnTsPbjIdcZIw5uvxke5w+Tx0aCW5rqqWx6z1XSWS1IzhlqRmxrwd8BLg88DpSW5P8trFjyVJWs3cFyer6txDMYgkaRxPlUhSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNGG5JasZwS1IzhluSmjHcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjOGW5KaMdyS1IzhlqRmDLckNWO4JakZwy1JzRhuSWrGcEtSM4Zbkpox3JLUjOGWpGYMtyQ1Y7glqRnDLUnNjAp3krOS/FuSryd586KHkiStbm64k2wA3g+8CDgDODfJGYseTJI025gj7mcBX6+qm6vqB8ClwEsXO5YkaTUbR6w5Gbht6vbtwLNXLkqyDdgGsLS0tCbD6ciTZL1HOGibN29e7xF0mBsT7ll/kuohH6jaDmwHWF5efsj90jxVftlIY4w5VXI7cOrU7VOAXYsZR5I0z5hw/zPwY0melOQo4BzgU4sdS5K0mrmnSqrqviTnA38HbAAurKobFz6ZJGmmMee4qapPA59e8CySpBH8yUlJasZwS1IzhluSmjHcktRMFvFDD0l2A7eu+QNLB+9E4M71HkKa4bSq2jJm4ULCLT1SJdlZVcvrPYd0MDxVIknNGG5JasZw60izfb0HkA6W57glqRmPuCWpGcMtSc0Ybh0RklyY5I4kN6z3LNLBMtw6UlwEnLXeQ0hrwXDriFBV1wB71nsOaS0YbklqxnBLUjOGW5KaMdyS1Izh1hEhySXA54HTk9ye5LXrPZP0cPkj75LUjEfcktSM4ZakZgy3JDVjuCWpGcMtSc0YbklqxnBLUjP/B47WW6f00HE2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#30 Sequence\n",
    "for i in numeric_features:\n",
    "    plt.title(\"Boxplot of \"+ i)\n",
    "    plt.boxplot(df1[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#70 Sequence\n",
    "for i in numeric_features:\n",
    "    plt.title(\"Boxplot of \"+ i)\n",
    "    plt.boxplot(df2[i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.0"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age'].quantile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "Name: 0.5, dtype: float64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "mylist=[1,2,3,4,5,6,7]\n",
    "mylist = pd.DataFrame(mylist)\n",
    "mylist.quantile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "39.0\n",
      "30.4\n",
      "1.0\n",
      "9382.029999999999\n"
     ]
    }
   ],
   "source": [
    "iqr = []\n",
    "print(numeric_features)\n",
    "\n",
    "for i in numeric_features:\n",
    "    iqr1 = data[i].quantile()\n",
    "    print(iqr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'bmi', 'children', 'expenses']\n",
      "[44.0, 34.55, 2.0, 10564.88]\n",
      "[34.0, 33.0, 1.5, 5058.995]\n",
      "[34.0, 22.7, 1.5, 5587.03]\n",
      "[35.5, 32.8, 2.0, 5672.93]\n",
      "[35.0, 33.3, 2.0, 10564.88]\n",
      "[36.5, 26.950000000000003, 1.0, 5841.924999999999]\n",
      "[35.0, 29.7, 2.0, 7173.36]\n",
      "[36.5, 33.15, 1.5, 6455.86]\n",
      "[36.5, 24.4, 2.0, 5841.924999999999]\n",
      "[48.0, 33.3, 1.0, 10564.88]\n"
     ]
    }
   ],
   "source": [
    "iqr10 = []\n",
    "print(numeric_features)\n",
    "\n",
    "for i in range(10):\n",
    "    #iqr10.append([iqr(samplelist[i][numeric_features[0]]),iqr(samplelist[i][numeric_features[1]]),iqr(samplelist[i][numeric_features[2]]),iqr(samplelist[i][numeric_features[3]])])\n",
    "    iqr10.append([samplelist[i]['age'].quantile(),samplelist[i]['bmi'].quantile(),samplelist[i]['children'].quantile(),samplelist[i]['expenses'].quantile()])\n",
    "    print(iqr10[i])\n"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}