Data_Wangle_Phase_II_ Clinical_Trial_Dataset .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients = pd.read_csv('patients.csv')\n",
    "treatments = pd.read_csv('treatments.csv')\n",
    "adverse_reactions = pd.read_csv('adverse_reactions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>contact</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>Zoe</td>\n",
       "      <td>Wellish</td>\n",
       "      <td>576 Brown Bear Drive</td>\n",
       "      <td>Rancho California</td>\n",
       "      <td>California</td>\n",
       "      <td>92390.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>951-719-9170ZoeWellish@superrito.com</td>\n",
       "      <td>7/10/1976</td>\n",
       "      <td>121.7</td>\n",
       "      <td>66</td>\n",
       "      <td>19.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>Pamela</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2370 University Hill Road</td>\n",
       "      <td>Armstrong</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>61812.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>PamelaSHill@cuvox.de+1 (217) 569-3204</td>\n",
       "      <td>4/3/1967</td>\n",
       "      <td>118.8</td>\n",
       "      <td>66</td>\n",
       "      <td>19.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>Jae</td>\n",
       "      <td>Debord</td>\n",
       "      <td>1493 Poling Farm Road</td>\n",
       "      <td>York</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>68467.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>402-363-6804JaeMDebord@gustr.com</td>\n",
       "      <td>2/19/1980</td>\n",
       "      <td>177.8</td>\n",
       "      <td>71</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>Liêm</td>\n",
       "      <td>Phan</td>\n",
       "      <td>2335 Webster Street</td>\n",
       "      <td>Woodbridge</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7095.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>PhanBaLiem@jourrapide.com+1 (732) 636-8246</td>\n",
       "      <td>7/26/1951</td>\n",
       "      <td>220.9</td>\n",
       "      <td>70</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Neudorf</td>\n",
       "      <td>1428 Turkey Pen Lane</td>\n",
       "      <td>Dothan</td>\n",
       "      <td>AL</td>\n",
       "      <td>36303.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>334-515-7487TimNeudorf@cuvox.de</td>\n",
       "      <td>2/18/1928</td>\n",
       "      <td>192.3</td>\n",
       "      <td>27</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>male</td>\n",
       "      <td>Rafael</td>\n",
       "      <td>Costa</td>\n",
       "      <td>1140 Willis Avenue</td>\n",
       "      <td>Daytona Beach</td>\n",
       "      <td>Florida</td>\n",
       "      <td>32114.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>386-334-5237RafaelCardosoCosta@gustr.com</td>\n",
       "      <td>8/31/1931</td>\n",
       "      <td>183.9</td>\n",
       "      <td>70</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>female</td>\n",
       "      <td>Mary</td>\n",
       "      <td>Adams</td>\n",
       "      <td>3145 Sheila Lane</td>\n",
       "      <td>Burbank</td>\n",
       "      <td>NV</td>\n",
       "      <td>84728.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>775-533-5933MaryBAdams@einrot.com</td>\n",
       "      <td>11/19/1969</td>\n",
       "      <td>146.3</td>\n",
       "      <td>65</td>\n",
       "      <td>24.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>female</td>\n",
       "      <td>Xiuxiu</td>\n",
       "      <td>Chang</td>\n",
       "      <td>2687 Black Oak Hollow Road</td>\n",
       "      <td>Morgan Hill</td>\n",
       "      <td>CA</td>\n",
       "      <td>95037.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>XiuxiuChang@einrot.com1 408 778 3236</td>\n",
       "      <td>8/13/1958</td>\n",
       "      <td>158.0</td>\n",
       "      <td>60</td>\n",
       "      <td>30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
       "      <td>Dsvid</td>\n",
       "      <td>Gustafsson</td>\n",
       "      <td>1790 Nutter Street</td>\n",
       "      <td>Kansas City</td>\n",
       "      <td>MO</td>\n",
       "      <td>64105.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>816-265-9578DavidGustafsson@armyspy.com</td>\n",
       "      <td>3/6/1937</td>\n",
       "      <td>163.9</td>\n",
       "      <td>66</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>Sophie</td>\n",
       "      <td>Cabrera</td>\n",
       "      <td>3303 Anmoore Road</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>10011.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>SophieCabreraIbarra@teleworm.us1 718 795 9124</td>\n",
       "      <td>12/3/1930</td>\n",
       "      <td>194.7</td>\n",
       "      <td>64</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>female</td>\n",
       "      <td>Sandy</td>\n",
       "      <td>Gunnarsson</td>\n",
       "      <td>87 Wood Duck Drive</td>\n",
       "      <td>Rudyard</td>\n",
       "      <td>MI</td>\n",
       "      <td>49780.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>906-478-8949SandyGunnarsson@dayrep.com</td>\n",
       "      <td>7/16/1974</td>\n",
       "      <td>199.3</td>\n",
       "      <td>62</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>Abdul-Nur</td>\n",
       "      <td>Isa</td>\n",
       "      <td>1092 Farm Meadow Drive</td>\n",
       "      <td>Brentwood</td>\n",
       "      <td>TN</td>\n",
       "      <td>37027.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>Abdul-NurMummarIsa@rhyta.com1 931 207 0839</td>\n",
       "      <td>2/3/1954</td>\n",
       "      <td>238.7</td>\n",
       "      <td>73</td>\n",
       "      <td>31.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>male</td>\n",
       "      <td>Omeokachie</td>\n",
       "      <td>Ibeamaka</td>\n",
       "      <td>2544 Worley Avenue</td>\n",
       "      <td>Lynchburg</td>\n",
       "      <td>VA</td>\n",
       "      <td>24504.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>OmeokachieIbeamaka@einrot.com434-509-2614</td>\n",
       "      <td>8/5/1957</td>\n",
       "      <td>224.2</td>\n",
       "      <td>69</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>Anenechi</td>\n",
       "      <td>Chidi</td>\n",
       "      <td>826 Broad Street</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>AL</td>\n",
       "      <td>35203.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>AnenechiChidi@armyspy.com+1 (205) 417-8095</td>\n",
       "      <td>3/7/1961</td>\n",
       "      <td>228.4</td>\n",
       "      <td>67</td>\n",
       "      <td>35.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>female</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Woźniak</td>\n",
       "      <td>4970 Heather Sees Way</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>OK</td>\n",
       "      <td>74105.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>AsiaWozniak@rhyta.com918-712-3469</td>\n",
       "      <td>8/15/1997</td>\n",
       "      <td>112.0</td>\n",
       "      <td>65</td>\n",
       "      <td>18.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>male</td>\n",
       "      <td>Søren</td>\n",
       "      <td>Lund</td>\n",
       "      <td>2438 Shady Pines Drive</td>\n",
       "      <td>Kingsport</td>\n",
       "      <td>VA</td>\n",
       "      <td>37660.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>276-225-1955SrenFLund@gustr.com</td>\n",
       "      <td>8/23/1922</td>\n",
       "      <td>201.5</td>\n",
       "      <td>64</td>\n",
       "      <td>34.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>female</td>\n",
       "      <td>Tám</td>\n",
       "      <td>Liễu</td>\n",
       "      <td>2152 Heritage Road</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>California</td>\n",
       "      <td>93706.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>LieuThiThuTam@dayrep.com1 559 765 7836</td>\n",
       "      <td>11/14/1952</td>\n",
       "      <td>183.9</td>\n",
       "      <td>61</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>Roxanne</td>\n",
       "      <td>Andreyeva</td>\n",
       "      <td>2103 Edington Drive</td>\n",
       "      <td>Smyrna</td>\n",
       "      <td>GA</td>\n",
       "      <td>30082.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>RoxanneAndreyeva@armyspy.com678-829-8578</td>\n",
       "      <td>7/24/1922</td>\n",
       "      <td>129.1</td>\n",
       "      <td>60</td>\n",
       "      <td>25.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>William</td>\n",
       "      <td>Oates</td>\n",
       "      <td>441 Tibbs Avenue</td>\n",
       "      <td>Ekalaka</td>\n",
       "      <td>MT</td>\n",
       "      <td>59324.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>406-775-2696WilliamVOates@armyspy.com</td>\n",
       "      <td>9/4/1949</td>\n",
       "      <td>202.2</td>\n",
       "      <td>64</td>\n",
       "      <td>34.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>male</td>\n",
       "      <td>Zak</td>\n",
       "      <td>Kelly</td>\n",
       "      <td>994 Hill Croft Farm Road</td>\n",
       "      <td>Oroville</td>\n",
       "      <td>California</td>\n",
       "      <td>95966.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>ZakKelly@rhyta.com1 530 532 8397</td>\n",
       "      <td>12/13/1988</td>\n",
       "      <td>208.8</td>\n",
       "      <td>70</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>Sofia</td>\n",
       "      <td>Karlsen</td>\n",
       "      <td>2931 Romano Street</td>\n",
       "      <td>Whitman</td>\n",
       "      <td>MA</td>\n",
       "      <td>2382.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>SofiaTKarlsen@teleworm.us1 781 447 1763</td>\n",
       "      <td>9/24/1934</td>\n",
       "      <td>153.1</td>\n",
       "      <td>66</td>\n",
       "      <td>24.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>Samúel</td>\n",
       "      <td>Guðbrandsson</td>\n",
       "      <td>1904 Granville Lane</td>\n",
       "      <td>Elmsford</td>\n",
       "      <td>NJ</td>\n",
       "      <td>10523.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>973-445-5341SamuelGubrandsson@teleworm.us</td>\n",
       "      <td>4/12/1983</td>\n",
       "      <td>223.7</td>\n",
       "      <td>69</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>male</td>\n",
       "      <td>Manchu</td>\n",
       "      <td>Su</td>\n",
       "      <td>1092 Deans Lane</td>\n",
       "      <td>Pleasantville</td>\n",
       "      <td>NY</td>\n",
       "      <td>10570.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>914-745-6108ManchuSu@einrot.com</td>\n",
       "      <td>1/19/1936</td>\n",
       "      <td>130.7</td>\n",
       "      <td>65</td>\n",
       "      <td>21.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>Lovre</td>\n",
       "      <td>Galić</td>\n",
       "      <td>4941 Marion Drive</td>\n",
       "      <td>Winter Haven</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33830.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>LovreGalic@gustr.com1 813 355 9476</td>\n",
       "      <td>5/26/1960</td>\n",
       "      <td>222.9</td>\n",
       "      <td>66</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>Jakob</td>\n",
       "      <td>Jakobsen</td>\n",
       "      <td>648 Old Dear Lane</td>\n",
       "      <td>Port Jervis</td>\n",
       "      <td>New York</td>\n",
       "      <td>12771.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>JakobCJakobsen@einrot.com+1 (845) 858-7707</td>\n",
       "      <td>8/1/1985</td>\n",
       "      <td>155.8</td>\n",
       "      <td>67</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>male</td>\n",
       "      <td>Gregor</td>\n",
       "      <td>Bole</td>\n",
       "      <td>922 Chapmans Lane</td>\n",
       "      <td>Albuquerque</td>\n",
       "      <td>NM</td>\n",
       "      <td>87109.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>GregorBole@gustr.com505-828-4955</td>\n",
       "      <td>6/19/1922</td>\n",
       "      <td>180.8</td>\n",
       "      <td>67</td>\n",
       "      <td>28.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>female</td>\n",
       "      <td>Ella</td>\n",
       "      <td>Lund</td>\n",
       "      <td>1207 Garfield Road</td>\n",
       "      <td>Peoria</td>\n",
       "      <td>IL</td>\n",
       "      <td>61602.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>309-671-8852EllaLund@armyspy.com</td>\n",
       "      <td>12/19/1933</td>\n",
       "      <td>144.8</td>\n",
       "      <td>61</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>Joseph</td>\n",
       "      <td>Tucker</td>\n",
       "      <td>4982 Wood Street</td>\n",
       "      <td>Venice</td>\n",
       "      <td>LA</td>\n",
       "      <td>70091.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>985-814-7603JosephNTucker@rhyta.com</td>\n",
       "      <td>4/10/1959</td>\n",
       "      <td>175.8</td>\n",
       "      <td>72</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>male</td>\n",
       "      <td>Robert</td>\n",
       "      <td>Wolf</td>\n",
       "      <td>2386 Linda Street</td>\n",
       "      <td>Fort Washington</td>\n",
       "      <td>PA</td>\n",
       "      <td>19034.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>RobertWolf@fleckens.hu1 267 895 7462</td>\n",
       "      <td>6/26/1937</td>\n",
       "      <td>206.6</td>\n",
       "      <td>70</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Jakobsen</td>\n",
       "      <td>648 Old Dear Lane</td>\n",
       "      <td>Port Jervis</td>\n",
       "      <td>New York</td>\n",
       "      <td>12771.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>JakobCJakobsen@einrot.com+1 (845) 858-7707</td>\n",
       "      <td>8/1/1985</td>\n",
       "      <td>155.8</td>\n",
       "      <td>67</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>474</td>\n",
       "      <td>female</td>\n",
       "      <td>Kate</td>\n",
       "      <td>Wilkinson</td>\n",
       "      <td>664 Lyon Avenue</td>\n",
       "      <td>South Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>2127.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>KateWilkinson@armyspy.com1 508 905 2371</td>\n",
       "      <td>7/18/1998</td>\n",
       "      <td>175.3</td>\n",
       "      <td>65</td>\n",
       "      <td>29.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>475</td>\n",
       "      <td>female</td>\n",
       "      <td>Esperanza</td>\n",
       "      <td>Labrosse</td>\n",
       "      <td>1370 Flint Street</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>30303.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>EsperanzaLabrosse@armyspy.com678-263-3564</td>\n",
       "      <td>10/7/1961</td>\n",
       "      <td>181.5</td>\n",
       "      <td>63</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>475</th>\n",
       "      <td>476</td>\n",
       "      <td>male</td>\n",
       "      <td>Malik</td>\n",
       "      <td>Vaneker</td>\n",
       "      <td>1270 Haul Road</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>California</td>\n",
       "      <td>94041.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>MalikVaneker@superrito.com650-962-7179</td>\n",
       "      <td>9/25/1953</td>\n",
       "      <td>214.4</td>\n",
       "      <td>67</td>\n",
       "      <td>33.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>477</td>\n",
       "      <td>female</td>\n",
       "      <td>Berta</td>\n",
       "      <td>Napolitani</td>\n",
       "      <td>1815 Garrett Street</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>PA</td>\n",
       "      <td>19108.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>267-972-3749BertaNapolitani@rhyta.com</td>\n",
       "      <td>12/2/1958</td>\n",
       "      <td>153.3</td>\n",
       "      <td>63</td>\n",
       "      <td>27.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>478</td>\n",
       "      <td>male</td>\n",
       "      <td>Juliusz</td>\n",
       "      <td>Majewski</td>\n",
       "      <td>4435 Poe Road</td>\n",
       "      <td>Florence</td>\n",
       "      <td>SC</td>\n",
       "      <td>29501.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>JuliuszMajewski@superrito.com+1 (843) 212-6421</td>\n",
       "      <td>9/29/1966</td>\n",
       "      <td>212.1</td>\n",
       "      <td>69</td>\n",
       "      <td>31.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>479</td>\n",
       "      <td>female</td>\n",
       "      <td>Edelma</td>\n",
       "      <td>Villalpando</td>\n",
       "      <td>312 Jim Rosa Lane</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>CA</td>\n",
       "      <td>95134.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>EdelmaVillalpandoSantillan@teleworm.us+1 (415)...</td>\n",
       "      <td>6/24/1977</td>\n",
       "      <td>109.6</td>\n",
       "      <td>63</td>\n",
       "      <td>19.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>480</td>\n",
       "      <td>male</td>\n",
       "      <td>Tapa</td>\n",
       "      <td>Arsanukayev</td>\n",
       "      <td>4720 Gordon Street</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>California</td>\n",
       "      <td>91762.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>TapaArsanukayev@dayrep.com1 909 458 2515</td>\n",
       "      <td>9/15/1955</td>\n",
       "      <td>220.0</td>\n",
       "      <td>65</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>481</td>\n",
       "      <td>male</td>\n",
       "      <td>Nasser</td>\n",
       "      <td>Mansour</td>\n",
       "      <td>547 Weekley Street</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>TX</td>\n",
       "      <td>78212.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>NasserMazinMansour@fleckens.hu1 210 326 5509</td>\n",
       "      <td>3/25/1938</td>\n",
       "      <td>183.5</td>\n",
       "      <td>66</td>\n",
       "      <td>29.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>482</td>\n",
       "      <td>male</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Kristensen</td>\n",
       "      <td>1614 Heather Sees Way</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>OK</td>\n",
       "      <td>74116.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>MichaelKristensen@gustr.com1 918 706 2776</td>\n",
       "      <td>8/10/1930</td>\n",
       "      <td>154.7</td>\n",
       "      <td>65</td>\n",
       "      <td>25.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>male</td>\n",
       "      <td>Diogo</td>\n",
       "      <td>Souza</td>\n",
       "      <td>4033 White Avenue</td>\n",
       "      <td>Corpus Christi</td>\n",
       "      <td>TX</td>\n",
       "      <td>78401.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>361-693-4960DiogoBarrosSouza@jourrapide.com</td>\n",
       "      <td>3/3/1945</td>\n",
       "      <td>220.0</td>\n",
       "      <td>65</td>\n",
       "      <td>36.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>484</td>\n",
       "      <td>female</td>\n",
       "      <td>Angel</td>\n",
       "      <td>Grant</td>\n",
       "      <td>990 Melville Street</td>\n",
       "      <td>Memphis</td>\n",
       "      <td>TN</td>\n",
       "      <td>38118.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>731-577-0292AngelGrant@fleckens.hu</td>\n",
       "      <td>8/14/1987</td>\n",
       "      <td>123.9</td>\n",
       "      <td>61</td>\n",
       "      <td>23.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>485</td>\n",
       "      <td>male</td>\n",
       "      <td>Placido</td>\n",
       "      <td>Udinesi</td>\n",
       "      <td>1094 Jones Avenue</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>NC</td>\n",
       "      <td>28716.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>336-697-2005PlacidoUdinesi@dayrep.com</td>\n",
       "      <td>5/31/1934</td>\n",
       "      <td>175.8</td>\n",
       "      <td>65</td>\n",
       "      <td>29.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>486</td>\n",
       "      <td>male</td>\n",
       "      <td>Trifon</td>\n",
       "      <td>Izmailov</td>\n",
       "      <td>3697 Drainer Avenue</td>\n",
       "      <td>Fort Walton Beach</td>\n",
       "      <td>FL</td>\n",
       "      <td>32548.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>TrifonIzmailov@fleckens.hu1 850 659 0417</td>\n",
       "      <td>2/15/1973</td>\n",
       "      <td>255.9</td>\n",
       "      <td>74</td>\n",
       "      <td>32.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>487</td>\n",
       "      <td>male</td>\n",
       "      <td>Samuel</td>\n",
       "      <td>Blix</td>\n",
       "      <td>3488 Clair Street</td>\n",
       "      <td>Waco</td>\n",
       "      <td>TX</td>\n",
       "      <td>76706.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>254-681-4504SamuelBlix@dayrep.com</td>\n",
       "      <td>7/6/1983</td>\n",
       "      <td>211.4</td>\n",
       "      <td>74</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>488</td>\n",
       "      <td>male</td>\n",
       "      <td>Ivar</td>\n",
       "      <td>Löfgren</td>\n",
       "      <td>1346 Nicholas Street</td>\n",
       "      <td>Ottawa</td>\n",
       "      <td>KS</td>\n",
       "      <td>66067.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>IvarLofgren@armyspy.com1 785 229 1188</td>\n",
       "      <td>11/7/1962</td>\n",
       "      <td>242.4</td>\n",
       "      <td>77</td>\n",
       "      <td>28.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>489</td>\n",
       "      <td>male</td>\n",
       "      <td>Mika</td>\n",
       "      <td>Martinsson</td>\n",
       "      <td>962 George Street</td>\n",
       "      <td>Ocala</td>\n",
       "      <td>Florida</td>\n",
       "      <td>34471.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>352-453-4601MikaMartinsson@armyspy.com</td>\n",
       "      <td>1/27/1970</td>\n",
       "      <td>165.0</td>\n",
       "      <td>67</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>490</td>\n",
       "      <td>female</td>\n",
       "      <td>Jasmine</td>\n",
       "      <td>Sykes</td>\n",
       "      <td>2607 Water Street</td>\n",
       "      <td>Lafayette</td>\n",
       "      <td>California</td>\n",
       "      <td>94549.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>JasmineSykes@jourrapide.com925-283-5425</td>\n",
       "      <td>12/1/1988</td>\n",
       "      <td>187.2</td>\n",
       "      <td>63</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>491</td>\n",
       "      <td>male</td>\n",
       "      <td>Jackson</td>\n",
       "      <td>Addison</td>\n",
       "      <td>1160 Taylor Street</td>\n",
       "      <td>New Rochelle</td>\n",
       "      <td>New York</td>\n",
       "      <td>10801.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>914-636-9304JacksonAddison@armyspy.com</td>\n",
       "      <td>5/29/1953</td>\n",
       "      <td>192.7</td>\n",
       "      <td>69</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>492</td>\n",
       "      <td>female</td>\n",
       "      <td>Vanessa</td>\n",
       "      <td>Ferguson</td>\n",
       "      <td>241 Freshour Circle</td>\n",
       "      <td>San Antonio</td>\n",
       "      <td>TX</td>\n",
       "      <td>78205.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>210-222-8684VanessaFerguson@jourrapide.com</td>\n",
       "      <td>9/21/1950</td>\n",
       "      <td>149.8</td>\n",
       "      <td>67</td>\n",
       "      <td>23.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>493</td>\n",
       "      <td>male</td>\n",
       "      <td>Poldi</td>\n",
       "      <td>Tar</td>\n",
       "      <td>3958 Liberty Avenue</td>\n",
       "      <td>Burbank</td>\n",
       "      <td>California</td>\n",
       "      <td>91505.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>714-496-2264TarPoldi@superrito.com</td>\n",
       "      <td>5/23/1970</td>\n",
       "      <td>184.6</td>\n",
       "      <td>70</td>\n",
       "      <td>26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>494</td>\n",
       "      <td>female</td>\n",
       "      <td>Fen</td>\n",
       "      <td>Chin</td>\n",
       "      <td>1826 Poplar Chase Lane</td>\n",
       "      <td>Boise</td>\n",
       "      <td>ID</td>\n",
       "      <td>83702.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>FenChin@gustr.com+1 (208) 388-1065</td>\n",
       "      <td>3/18/1997</td>\n",
       "      <td>195.1</td>\n",
       "      <td>68</td>\n",
       "      <td>29.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>495</td>\n",
       "      <td>female</td>\n",
       "      <td>Sirkka</td>\n",
       "      <td>Piirainen</td>\n",
       "      <td>4102 Ritter Avenue</td>\n",
       "      <td>Roseville</td>\n",
       "      <td>MI</td>\n",
       "      <td>48066.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>SirkkaPiirainen@teleworm.us+1 (586) 790-0975</td>\n",
       "      <td>1/16/1942</td>\n",
       "      <td>126.3</td>\n",
       "      <td>67</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>496</td>\n",
       "      <td>male</td>\n",
       "      <td>Hajime</td>\n",
       "      <td>Tsukada</td>\n",
       "      <td>4111 Thunder Road</td>\n",
       "      <td>San Mateo</td>\n",
       "      <td>CA</td>\n",
       "      <td>94403.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>650-570-4896HajimeTsukada@dayrep.com</td>\n",
       "      <td>9/5/1972</td>\n",
       "      <td>168.1</td>\n",
       "      <td>66</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>497</td>\n",
       "      <td>male</td>\n",
       "      <td>Alexander</td>\n",
       "      <td>Hueber</td>\n",
       "      <td>3868 Freed Drive</td>\n",
       "      <td>Stockton</td>\n",
       "      <td>California</td>\n",
       "      <td>95204.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>AlexanderHueber@jourrapide.com1 209 762 2320</td>\n",
       "      <td>9/12/1942</td>\n",
       "      <td>194.0</td>\n",
       "      <td>72</td>\n",
       "      <td>26.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>498</td>\n",
       "      <td>male</td>\n",
       "      <td>Masataka</td>\n",
       "      <td>Murakami</td>\n",
       "      <td>1179 Patton Lane</td>\n",
       "      <td>Tulsa</td>\n",
       "      <td>OK</td>\n",
       "      <td>74116.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>MasatakaMurakami@einrot.com+1 (918) 984-9171</td>\n",
       "      <td>8/19/1937</td>\n",
       "      <td>155.1</td>\n",
       "      <td>72</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>499</td>\n",
       "      <td>male</td>\n",
       "      <td>Mustafa</td>\n",
       "      <td>Lindström</td>\n",
       "      <td>2530 Victoria Court</td>\n",
       "      <td>Milton Mills</td>\n",
       "      <td>ME</td>\n",
       "      <td>3852.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>207-477-0579MustafaLindstrom@jourrapide.com</td>\n",
       "      <td>4/10/1959</td>\n",
       "      <td>181.1</td>\n",
       "      <td>72</td>\n",
       "      <td>24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>500</td>\n",
       "      <td>male</td>\n",
       "      <td>Ruman</td>\n",
       "      <td>Bisliev</td>\n",
       "      <td>494 Clarksburg Park Road</td>\n",
       "      <td>Sedona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>86341.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>928-284-4492RumanBisliev@gustr.com</td>\n",
       "      <td>3/26/1948</td>\n",
       "      <td>239.6</td>\n",
       "      <td>70</td>\n",
       "      <td>34.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>501</td>\n",
       "      <td>female</td>\n",
       "      <td>Jinke</td>\n",
       "      <td>de Keizer</td>\n",
       "      <td>649 Nutter Street</td>\n",
       "      <td>Overland Park</td>\n",
       "      <td>MO</td>\n",
       "      <td>64110.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>816-223-6007JinkedeKeizer@teleworm.us</td>\n",
       "      <td>1/13/1971</td>\n",
       "      <td>171.2</td>\n",
       "      <td>67</td>\n",
       "      <td>26.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>502</td>\n",
       "      <td>female</td>\n",
       "      <td>Chidalu</td>\n",
       "      <td>Onyekaozulu</td>\n",
       "      <td>3652 Boone Crockett Lane</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>98109.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>ChidaluOnyekaozulu@jourrapide.com1 360 443 2060</td>\n",
       "      <td>2/13/1952</td>\n",
       "      <td>176.9</td>\n",
       "      <td>67</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>503</td>\n",
       "      <td>male</td>\n",
       "      <td>Pat</td>\n",
       "      <td>Gersten</td>\n",
       "      <td>2778 North Avenue</td>\n",
       "      <td>Burr</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>68324.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>PatrickGersten@rhyta.com402-848-4923</td>\n",
       "      <td>5/3/1954</td>\n",
       "      <td>138.2</td>\n",
       "      <td>71</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>503 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex  given_name       surname  \\\n",
       "0             1       female         Zoe       Wellish   \n",
       "1             2       female      Pamela          Hill   \n",
       "2             3         male         Jae        Debord   \n",
       "3             4         male        Liêm          Phan   \n",
       "4             5         male         Tim       Neudorf   \n",
       "5             6         male      Rafael         Costa   \n",
       "6             7       female        Mary         Adams   \n",
       "7             8       female      Xiuxiu         Chang   \n",
       "8             9         male       Dsvid    Gustafsson   \n",
       "9            10       female      Sophie       Cabrera   \n",
       "10           11       female       Sandy    Gunnarsson   \n",
       "11           12         male   Abdul-Nur           Isa   \n",
       "12           13         male  Omeokachie      Ibeamaka   \n",
       "13           14       female    Anenechi         Chidi   \n",
       "14           15       female        Asia       Woźniak   \n",
       "15           16         male       Søren          Lund   \n",
       "16           17       female         Tám          Liễu   \n",
       "17           18       female     Roxanne     Andreyeva   \n",
       "18           19         male     William         Oates   \n",
       "19           20         male         Zak         Kelly   \n",
       "20           21       female       Sofia       Karlsen   \n",
       "21           22         male      Samúel  Guðbrandsson   \n",
       "22           23         male      Manchu            Su   \n",
       "23           24         male       Lovre         Galić   \n",
       "24           25         male       Jakob      Jakobsen   \n",
       "25           26         male      Gregor          Bole   \n",
       "26           27       female        Ella          Lund   \n",
       "27           28         male      Joseph        Tucker   \n",
       "28           29         male      Robert          Wolf   \n",
       "29           30         male        Jake      Jakobsen   \n",
       "..          ...          ...         ...           ...   \n",
       "473         474       female        Kate     Wilkinson   \n",
       "474         475       female   Esperanza      Labrosse   \n",
       "475         476         male       Malik       Vaneker   \n",
       "476         477       female       Berta    Napolitani   \n",
       "477         478         male     Juliusz      Majewski   \n",
       "478         479       female      Edelma   Villalpando   \n",
       "479         480         male        Tapa   Arsanukayev   \n",
       "480         481         male      Nasser       Mansour   \n",
       "481         482         male     Michael    Kristensen   \n",
       "482         483         male       Diogo         Souza   \n",
       "483         484       female       Angel         Grant   \n",
       "484         485         male     Placido       Udinesi   \n",
       "485         486         male      Trifon      Izmailov   \n",
       "486         487         male      Samuel          Blix   \n",
       "487         488         male        Ivar       Löfgren   \n",
       "488         489         male        Mika    Martinsson   \n",
       "489         490       female     Jasmine         Sykes   \n",
       "490         491         male     Jackson       Addison   \n",
       "491         492       female     Vanessa      Ferguson   \n",
       "492         493         male       Poldi           Tar   \n",
       "493         494       female         Fen          Chin   \n",
       "494         495       female      Sirkka     Piirainen   \n",
       "495         496         male      Hajime       Tsukada   \n",
       "496         497         male   Alexander        Hueber   \n",
       "497         498         male    Masataka      Murakami   \n",
       "498         499         male     Mustafa     Lindström   \n",
       "499         500         male       Ruman       Bisliev   \n",
       "500         501       female       Jinke     de Keizer   \n",
       "501         502       female     Chidalu   Onyekaozulu   \n",
       "502         503         male         Pat       Gersten   \n",
       "\n",
       "                        address               city       state  zip_code  \\\n",
       "0          576 Brown Bear Drive  Rancho California  California   92390.0   \n",
       "1     2370 University Hill Road          Armstrong    Illinois   61812.0   \n",
       "2         1493 Poling Farm Road               York    Nebraska   68467.0   \n",
       "3           2335 Webster Street         Woodbridge          NJ    7095.0   \n",
       "4          1428 Turkey Pen Lane             Dothan          AL   36303.0   \n",
       "5            1140 Willis Avenue      Daytona Beach     Florida   32114.0   \n",
       "6              3145 Sheila Lane            Burbank          NV   84728.0   \n",
       "7    2687 Black Oak Hollow Road        Morgan Hill          CA   95037.0   \n",
       "8            1790 Nutter Street        Kansas City          MO   64105.0   \n",
       "9             3303 Anmoore Road           New York    New York   10011.0   \n",
       "10           87 Wood Duck Drive            Rudyard          MI   49780.0   \n",
       "11       1092 Farm Meadow Drive          Brentwood          TN   37027.0   \n",
       "12           2544 Worley Avenue          Lynchburg          VA   24504.0   \n",
       "13             826 Broad Street         Birmingham          AL   35203.0   \n",
       "14        4970 Heather Sees Way              Tulsa          OK   74105.0   \n",
       "15       2438 Shady Pines Drive          Kingsport          VA   37660.0   \n",
       "16           2152 Heritage Road             Fresno  California   93706.0   \n",
       "17          2103 Edington Drive             Smyrna          GA   30082.0   \n",
       "18             441 Tibbs Avenue            Ekalaka          MT   59324.0   \n",
       "19     994 Hill Croft Farm Road           Oroville  California   95966.0   \n",
       "20           2931 Romano Street            Whitman          MA    2382.0   \n",
       "21          1904 Granville Lane           Elmsford          NJ   10523.0   \n",
       "22              1092 Deans Lane      Pleasantville          NY   10570.0   \n",
       "23            4941 Marion Drive       Winter Haven     Florida   33830.0   \n",
       "24            648 Old Dear Lane        Port Jervis    New York   12771.0   \n",
       "25            922 Chapmans Lane        Albuquerque          NM   87109.0   \n",
       "26           1207 Garfield Road             Peoria          IL   61602.0   \n",
       "27             4982 Wood Street             Venice          LA   70091.0   \n",
       "28            2386 Linda Street    Fort Washington          PA   19034.0   \n",
       "29            648 Old Dear Lane        Port Jervis    New York   12771.0   \n",
       "..                          ...                ...         ...       ...   \n",
       "473             664 Lyon Avenue       South Boston          MA    2127.0   \n",
       "474           1370 Flint Street            Atlanta          GA   30303.0   \n",
       "475              1270 Haul Road      Mountain View  California   94041.0   \n",
       "476         1815 Garrett Street       Philadelphia          PA   19108.0   \n",
       "477               4435 Poe Road           Florence          SC   29501.0   \n",
       "478           312 Jim Rosa Lane           San Jose          CA   95134.0   \n",
       "479          4720 Gordon Street            Ontario  California   91762.0   \n",
       "480          547 Weekley Street        San Antonio          TX   78212.0   \n",
       "481       1614 Heather Sees Way              Tulsa          OK   74116.0   \n",
       "482           4033 White Avenue     Corpus Christi          TX   78401.0   \n",
       "483         990 Melville Street            Memphis          TN   38118.0   \n",
       "484           1094 Jones Avenue         Greensboro          NC   28716.0   \n",
       "485         3697 Drainer Avenue  Fort Walton Beach          FL   32548.0   \n",
       "486           3488 Clair Street               Waco          TX   76706.0   \n",
       "487        1346 Nicholas Street             Ottawa          KS   66067.0   \n",
       "488           962 George Street              Ocala     Florida   34471.0   \n",
       "489           2607 Water Street          Lafayette  California   94549.0   \n",
       "490          1160 Taylor Street       New Rochelle    New York   10801.0   \n",
       "491         241 Freshour Circle        San Antonio          TX   78205.0   \n",
       "492         3958 Liberty Avenue            Burbank  California   91505.0   \n",
       "493      1826 Poplar Chase Lane              Boise          ID   83702.0   \n",
       "494          4102 Ritter Avenue          Roseville          MI   48066.0   \n",
       "495           4111 Thunder Road          San Mateo          CA   94403.0   \n",
       "496            3868 Freed Drive           Stockton  California   95204.0   \n",
       "497            1179 Patton Lane              Tulsa          OK   74116.0   \n",
       "498         2530 Victoria Court       Milton Mills          ME    3852.0   \n",
       "499    494 Clarksburg Park Road             Sedona          AZ   86341.0   \n",
       "500           649 Nutter Street      Overland Park          MO   64110.0   \n",
       "501    3652 Boone Crockett Lane            Seattle          WA   98109.0   \n",
       "502           2778 North Avenue               Burr    Nebraska   68324.0   \n",
       "\n",
       "           country                                            contact  \\\n",
       "0    United States               951-719-9170ZoeWellish@superrito.com   \n",
       "1    United States              PamelaSHill@cuvox.de+1 (217) 569-3204   \n",
       "2    United States                   402-363-6804JaeMDebord@gustr.com   \n",
       "3    United States         PhanBaLiem@jourrapide.com+1 (732) 636-8246   \n",
       "4    United States                    334-515-7487TimNeudorf@cuvox.de   \n",
       "5    United States           386-334-5237RafaelCardosoCosta@gustr.com   \n",
       "6    United States                  775-533-5933MaryBAdams@einrot.com   \n",
       "7    United States               XiuxiuChang@einrot.com1 408 778 3236   \n",
       "8    United States            816-265-9578DavidGustafsson@armyspy.com   \n",
       "9    United States      SophieCabreraIbarra@teleworm.us1 718 795 9124   \n",
       "10   United States             906-478-8949SandyGunnarsson@dayrep.com   \n",
       "11   United States         Abdul-NurMummarIsa@rhyta.com1 931 207 0839   \n",
       "12   United States          OmeokachieIbeamaka@einrot.com434-509-2614   \n",
       "13   United States         AnenechiChidi@armyspy.com+1 (205) 417-8095   \n",
       "14   United States                  AsiaWozniak@rhyta.com918-712-3469   \n",
       "15   United States                    276-225-1955SrenFLund@gustr.com   \n",
       "16   United States             LieuThiThuTam@dayrep.com1 559 765 7836   \n",
       "17   United States           RoxanneAndreyeva@armyspy.com678-829-8578   \n",
       "18   United States              406-775-2696WilliamVOates@armyspy.com   \n",
       "19   United States                   ZakKelly@rhyta.com1 530 532 8397   \n",
       "20   United States            SofiaTKarlsen@teleworm.us1 781 447 1763   \n",
       "21   United States          973-445-5341SamuelGubrandsson@teleworm.us   \n",
       "22   United States                    914-745-6108ManchuSu@einrot.com   \n",
       "23   United States                 LovreGalic@gustr.com1 813 355 9476   \n",
       "24   United States         JakobCJakobsen@einrot.com+1 (845) 858-7707   \n",
       "25   United States                   GregorBole@gustr.com505-828-4955   \n",
       "26   United States                   309-671-8852EllaLund@armyspy.com   \n",
       "27   United States                985-814-7603JosephNTucker@rhyta.com   \n",
       "28   United States               RobertWolf@fleckens.hu1 267 895 7462   \n",
       "29   United States         JakobCJakobsen@einrot.com+1 (845) 858-7707   \n",
       "..             ...                                                ...   \n",
       "473  United States            KateWilkinson@armyspy.com1 508 905 2371   \n",
       "474  United States          EsperanzaLabrosse@armyspy.com678-263-3564   \n",
       "475  United States             MalikVaneker@superrito.com650-962-7179   \n",
       "476  United States              267-972-3749BertaNapolitani@rhyta.com   \n",
       "477  United States     JuliuszMajewski@superrito.com+1 (843) 212-6421   \n",
       "478  United States  EdelmaVillalpandoSantillan@teleworm.us+1 (415)...   \n",
       "479  United States           TapaArsanukayev@dayrep.com1 909 458 2515   \n",
       "480  United States       NasserMazinMansour@fleckens.hu1 210 326 5509   \n",
       "481  United States          MichaelKristensen@gustr.com1 918 706 2776   \n",
       "482  United States        361-693-4960DiogoBarrosSouza@jourrapide.com   \n",
       "483  United States                 731-577-0292AngelGrant@fleckens.hu   \n",
       "484  United States              336-697-2005PlacidoUdinesi@dayrep.com   \n",
       "485  United States           TrifonIzmailov@fleckens.hu1 850 659 0417   \n",
       "486  United States                  254-681-4504SamuelBlix@dayrep.com   \n",
       "487  United States              IvarLofgren@armyspy.com1 785 229 1188   \n",
       "488  United States             352-453-4601MikaMartinsson@armyspy.com   \n",
       "489  United States            JasmineSykes@jourrapide.com925-283-5425   \n",
       "490  United States             914-636-9304JacksonAddison@armyspy.com   \n",
       "491  United States         210-222-8684VanessaFerguson@jourrapide.com   \n",
       "492  United States                 714-496-2264TarPoldi@superrito.com   \n",
       "493  United States                 FenChin@gustr.com+1 (208) 388-1065   \n",
       "494  United States       SirkkaPiirainen@teleworm.us+1 (586) 790-0975   \n",
       "495  United States               650-570-4896HajimeTsukada@dayrep.com   \n",
       "496  United States       AlexanderHueber@jourrapide.com1 209 762 2320   \n",
       "497  United States       MasatakaMurakami@einrot.com+1 (918) 984-9171   \n",
       "498  United States        207-477-0579MustafaLindstrom@jourrapide.com   \n",
       "499  United States                 928-284-4492RumanBisliev@gustr.com   \n",
       "500  United States              816-223-6007JinkedeKeizer@teleworm.us   \n",
       "501  United States    ChidaluOnyekaozulu@jourrapide.com1 360 443 2060   \n",
       "502  United States               PatrickGersten@rhyta.com402-848-4923   \n",
       "\n",
       "      birthdate  weight  height   bmi  \n",
       "0     7/10/1976   121.7      66  19.6  \n",
       "1      4/3/1967   118.8      66  19.2  \n",
       "2     2/19/1980   177.8      71  24.8  \n",
       "3     7/26/1951   220.9      70  31.7  \n",
       "4     2/18/1928   192.3      27  26.1  \n",
       "5     8/31/1931   183.9      70  26.4  \n",
       "6    11/19/1969   146.3      65  24.3  \n",
       "7     8/13/1958   158.0      60  30.9  \n",
       "8      3/6/1937   163.9      66  26.5  \n",
       "9     12/3/1930   194.7      64  33.4  \n",
       "10    7/16/1974   199.3      62  36.4  \n",
       "11     2/3/1954   238.7      73  31.5  \n",
       "12     8/5/1957   224.2      69  33.1  \n",
       "13     3/7/1961   228.4      67  35.8  \n",
       "14    8/15/1997   112.0      65  18.6  \n",
       "15    8/23/1922   201.5      64  34.6  \n",
       "16   11/14/1952   183.9      61  34.7  \n",
       "17    7/24/1922   129.1      60  25.2  \n",
       "18     9/4/1949   202.2      64  34.7  \n",
       "19   12/13/1988   208.8      70  30.0  \n",
       "20    9/24/1934   153.1      66  24.7  \n",
       "21    4/12/1983   223.7      69  33.0  \n",
       "22    1/19/1936   130.7      65  21.7  \n",
       "23    5/26/1960   222.9      66  36.0  \n",
       "24     8/1/1985   155.8      67  24.4  \n",
       "25    6/19/1922   180.8      67  28.3  \n",
       "26   12/19/1933   144.8      61  27.4  \n",
       "27    4/10/1959   175.8      72  23.8  \n",
       "28    6/26/1937   206.6      70  29.6  \n",
       "29     8/1/1985   155.8      67  24.4  \n",
       "..          ...     ...     ...   ...  \n",
       "473   7/18/1998   175.3      65  29.2  \n",
       "474   10/7/1961   181.5      63  32.1  \n",
       "475   9/25/1953   214.4      67  33.6  \n",
       "476   12/2/1958   153.3      63  27.2  \n",
       "477   9/29/1966   212.1      69  31.3  \n",
       "478   6/24/1977   109.6      63  19.4  \n",
       "479   9/15/1955   220.0      65  36.6  \n",
       "480   3/25/1938   183.5      66  29.6  \n",
       "481   8/10/1930   154.7      65  25.7  \n",
       "482    3/3/1945   220.0      65  36.6  \n",
       "483   8/14/1987   123.9      61  23.4  \n",
       "484   5/31/1934   175.8      65  29.3  \n",
       "485   2/15/1973   255.9      74  32.9  \n",
       "486    7/6/1983   211.4      74  27.1  \n",
       "487   11/7/1962   242.4      77  28.7  \n",
       "488   1/27/1970   165.0      67  25.8  \n",
       "489   12/1/1988   187.2      63  33.2  \n",
       "490   5/29/1953   192.7      69  28.5  \n",
       "491   9/21/1950   149.8      67  23.5  \n",
       "492   5/23/1970   184.6      70  26.5  \n",
       "493   3/18/1997   195.1      68  29.7  \n",
       "494   1/16/1942   126.3      67  19.8  \n",
       "495    9/5/1972   168.1      66  27.1  \n",
       "496   9/12/1942   194.0      72  26.3  \n",
       "497   8/19/1937   155.1      72  21.0  \n",
       "498   4/10/1959   181.1      72  24.6  \n",
       "499   3/26/1948   239.6      70  34.4  \n",
       "500   1/13/1971   171.2      67  26.8  \n",
       "501   2/13/1952   176.9      67  27.7  \n",
       "502    5/3/1954   138.2      71  19.3  \n",
       "\n",
       "[503 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>auralin</th>\n",
       "      <th>novodra</th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>veronika</td>\n",
       "      <td>jindrová</td>\n",
       "      <td>41u - 48u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elliot</td>\n",
       "      <td>richardson</td>\n",
       "      <td>-</td>\n",
       "      <td>40u - 45u</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.09</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yukitaka</td>\n",
       "      <td>takenaka</td>\n",
       "      <td>-</td>\n",
       "      <td>39u - 36u</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.25</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skye</td>\n",
       "      <td>gormanston</td>\n",
       "      <td>33u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.97</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alissa</td>\n",
       "      <td>montez</td>\n",
       "      <td>-</td>\n",
       "      <td>33u - 29u</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jasmine</td>\n",
       "      <td>sykes</td>\n",
       "      <td>-</td>\n",
       "      <td>42u - 44u</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.18</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sophia</td>\n",
       "      <td>haugen</td>\n",
       "      <td>37u - 42u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eddie</td>\n",
       "      <td>archer</td>\n",
       "      <td>31u - 38u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>saber</td>\n",
       "      <td>ménard</td>\n",
       "      <td>-</td>\n",
       "      <td>54u - 54u</td>\n",
       "      <td>8.08</td>\n",
       "      <td>7.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asia</td>\n",
       "      <td>woźniak</td>\n",
       "      <td>30u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.37</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>joseph</td>\n",
       "      <td>day</td>\n",
       "      <td>29u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>kristiina</td>\n",
       "      <td>hyypiä</td>\n",
       "      <td>-</td>\n",
       "      <td>36u - 38u</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.49</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>roxanne</td>\n",
       "      <td>andreyeva</td>\n",
       "      <td>29u - 38u</td>\n",
       "      <td>-</td>\n",
       "      <td>9.54</td>\n",
       "      <td>9.14</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gregor</td>\n",
       "      <td>bole</td>\n",
       "      <td>-</td>\n",
       "      <td>47u - 45u</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.16</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>simone</td>\n",
       "      <td>baumgaertner</td>\n",
       "      <td>27u - 37u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.74</td>\n",
       "      <td>7.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>enco</td>\n",
       "      <td>žibrik</td>\n",
       "      <td>55u - 68u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>camilla</td>\n",
       "      <td>zaitseva</td>\n",
       "      <td>28u - 37u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.53</td>\n",
       "      <td>7.13</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gina</td>\n",
       "      <td>cain</td>\n",
       "      <td>-</td>\n",
       "      <td>36u - 36u</td>\n",
       "      <td>7.88</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>addolorata</td>\n",
       "      <td>lombardi</td>\n",
       "      <td>-</td>\n",
       "      <td>49u - 46u</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.33</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>khalid</td>\n",
       "      <td>johnsrud</td>\n",
       "      <td>-</td>\n",
       "      <td>54u - 54u</td>\n",
       "      <td>8.35</td>\n",
       "      <td>7.94</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>mile</td>\n",
       "      <td>stanić</td>\n",
       "      <td>-</td>\n",
       "      <td>47u - 48u</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tekla</td>\n",
       "      <td>walczak</td>\n",
       "      <td>29u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>brancaleone</td>\n",
       "      <td>russo</td>\n",
       "      <td>53u - 60u</td>\n",
       "      <td>-</td>\n",
       "      <td>8.61</td>\n",
       "      <td>8.18</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>chiemela</td>\n",
       "      <td>tobeolisa</td>\n",
       "      <td>-</td>\n",
       "      <td>43u - 47u</td>\n",
       "      <td>7.59</td>\n",
       "      <td>7.17</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>isac</td>\n",
       "      <td>berg</td>\n",
       "      <td>31u - 41u</td>\n",
       "      <td>-</td>\n",
       "      <td>9.68</td>\n",
       "      <td>9.29</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>benoît</td>\n",
       "      <td>bonami</td>\n",
       "      <td>-</td>\n",
       "      <td>44u - 43u</td>\n",
       "      <td>9.82</td>\n",
       "      <td>9.40</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>suhaim</td>\n",
       "      <td>rahal</td>\n",
       "      <td>-</td>\n",
       "      <td>49u - 47u</td>\n",
       "      <td>7.94</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>mizuki</td>\n",
       "      <td>iwata</td>\n",
       "      <td>-</td>\n",
       "      <td>45u - 46u</td>\n",
       "      <td>7.70</td>\n",
       "      <td>7.23</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>clinton</td>\n",
       "      <td>miller</td>\n",
       "      <td>42u - 51u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>eugene</td>\n",
       "      <td>mironov</td>\n",
       "      <td>42u - 49u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.81</td>\n",
       "      <td>7.48</td>\n",
       "      <td>0.33</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>250</th>\n",
       "      <td>chen</td>\n",
       "      <td>yao</td>\n",
       "      <td>-</td>\n",
       "      <td>56u - 57u</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>aksel</td>\n",
       "      <td>vestergaard</td>\n",
       "      <td>-</td>\n",
       "      <td>42u - 38u</td>\n",
       "      <td>9.62</td>\n",
       "      <td>9.29</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>ellen</td>\n",
       "      <td>luman</td>\n",
       "      <td>-</td>\n",
       "      <td>40u - 39u</td>\n",
       "      <td>9.27</td>\n",
       "      <td>8.77</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>albino</td>\n",
       "      <td>schiavone</td>\n",
       "      <td>35u - 43u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.15</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>jose</td>\n",
       "      <td>combs</td>\n",
       "      <td>-</td>\n",
       "      <td>39u - 36u</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.42</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>jia li</td>\n",
       "      <td>teng</td>\n",
       "      <td>48u - 54u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.32</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>ilija</td>\n",
       "      <td>horvat</td>\n",
       "      <td>42u - 50u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>mathilde</td>\n",
       "      <td>nørgaard</td>\n",
       "      <td>-</td>\n",
       "      <td>27u - 28u</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>csilla</td>\n",
       "      <td>herczegh</td>\n",
       "      <td>-</td>\n",
       "      <td>43u - 46u</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>aaliyah</td>\n",
       "      <td>rice</td>\n",
       "      <td>-</td>\n",
       "      <td>31u - 31u</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.33</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>david</td>\n",
       "      <td>beauvais</td>\n",
       "      <td>-</td>\n",
       "      <td>26u - 23u</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.47</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>caroline</td>\n",
       "      <td>shuler</td>\n",
       "      <td>-</td>\n",
       "      <td>50u - 54u</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>alex</td>\n",
       "      <td>crawford</td>\n",
       "      <td>51u - 62u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>rebecca</td>\n",
       "      <td>jephcott</td>\n",
       "      <td>53u - 63u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.96</td>\n",
       "      <td>7.57</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>chukwumoge</td>\n",
       "      <td>ogochukwu</td>\n",
       "      <td>-</td>\n",
       "      <td>41u - 39u</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>fearne</td>\n",
       "      <td>mcgregor</td>\n",
       "      <td>-</td>\n",
       "      <td>27u - 29u</td>\n",
       "      <td>7.83</td>\n",
       "      <td>7.48</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>ursula</td>\n",
       "      <td>freud</td>\n",
       "      <td>42u - 54u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>leon</td>\n",
       "      <td>scholz</td>\n",
       "      <td>-</td>\n",
       "      <td>38u - 32u</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>yasmin</td>\n",
       "      <td>araujo</td>\n",
       "      <td>-</td>\n",
       "      <td>51u - 54u</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>hiromu</td>\n",
       "      <td>horikawa</td>\n",
       "      <td>-</td>\n",
       "      <td>47u - 46u</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.28</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>mika</td>\n",
       "      <td>martinsson</td>\n",
       "      <td>34u - 43u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.50</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>leo</td>\n",
       "      <td>vieira</td>\n",
       "      <td>-</td>\n",
       "      <td>30u - 33u</td>\n",
       "      <td>7.74</td>\n",
       "      <td>7.36</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>steven</td>\n",
       "      <td>roy</td>\n",
       "      <td>-</td>\n",
       "      <td>41u - 43u</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>kate</td>\n",
       "      <td>wilkinson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.20</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>naja</td>\n",
       "      <td>enoksen</td>\n",
       "      <td>43u - 50u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.59</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>albina</td>\n",
       "      <td>zetticci</td>\n",
       "      <td>45u - 51u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>john</td>\n",
       "      <td>teichelmann</td>\n",
       "      <td>-</td>\n",
       "      <td>49u - 49u</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.58</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>mathea</td>\n",
       "      <td>lillebø</td>\n",
       "      <td>23u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>9.04</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>vallie</td>\n",
       "      <td>prince</td>\n",
       "      <td>31u - 38u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.28</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>samúel</td>\n",
       "      <td>guðbrandsson</td>\n",
       "      <td>53u - 56u</td>\n",
       "      <td>-</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.64</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>280 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      given_name       surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0       veronika      jindrová  41u - 48u          -         7.63       7.20   \n",
       "1         elliot    richardson          -  40u - 45u         7.56       7.09   \n",
       "2       yukitaka      takenaka          -  39u - 36u         7.68       7.25   \n",
       "3           skye    gormanston  33u - 36u          -         7.97       7.62   \n",
       "4         alissa        montez          -  33u - 29u         7.78       7.46   \n",
       "5        jasmine         sykes          -  42u - 44u         7.56       7.18   \n",
       "6         sophia        haugen  37u - 42u          -         7.65       7.27   \n",
       "7          eddie        archer  31u - 38u          -         7.89       7.55   \n",
       "8          saber        ménard          -  54u - 54u         8.08       7.70   \n",
       "9           asia       woźniak  30u - 36u          -         7.76       7.37   \n",
       "10        joseph           day  29u - 36u          -         7.70       7.19   \n",
       "11     kristiina        hyypiä          -  36u - 38u         7.87       7.49   \n",
       "12       roxanne     andreyeva  29u - 38u          -         9.54       9.14   \n",
       "13        gregor          bole          -  47u - 45u         7.61       7.16   \n",
       "14        simone  baumgaertner  27u - 37u          -         7.74       7.30   \n",
       "15          enco        žibrik  55u - 68u          -         7.78       7.34   \n",
       "16       camilla      zaitseva  28u - 37u          -         7.53       7.13   \n",
       "17          gina          cain          -  36u - 36u         7.88       7.40   \n",
       "18    addolorata      lombardi          -  49u - 46u         7.75       7.33   \n",
       "19        khalid      johnsrud          -  54u - 54u         8.35       7.94   \n",
       "20          mile        stanić          -  47u - 48u         7.66       7.24   \n",
       "21         tekla       walczak  29u - 39u          -         7.61       7.29   \n",
       "22   brancaleone         russo  53u - 60u          -         8.61       8.18   \n",
       "23      chiemela     tobeolisa          -  43u - 47u         7.59       7.17   \n",
       "24          isac          berg  31u - 41u          -         9.68       9.29   \n",
       "25        benoît        bonami          -  44u - 43u         9.82       9.40   \n",
       "26        suhaim         rahal          -  49u - 47u         7.94       7.50   \n",
       "27        mizuki         iwata          -  45u - 46u         7.70       7.23   \n",
       "28       clinton        miller  42u - 51u          -         7.79       7.40   \n",
       "29        eugene       mironov  42u - 49u          -         7.81       7.48   \n",
       "..           ...           ...        ...        ...          ...        ...   \n",
       "250         chen           yao          -  56u - 57u         7.90       7.51   \n",
       "251        aksel   vestergaard          -  42u - 38u         9.62       9.29   \n",
       "252        ellen         luman          -  40u - 39u         9.27       8.77   \n",
       "253       albino     schiavone  35u - 43u          -         7.56       7.15   \n",
       "254         jose         combs          -  39u - 36u         7.89       7.42   \n",
       "255       jia li          teng  48u - 54u          -         7.66       7.32   \n",
       "256        ilija        horvat  42u - 50u          -         7.77       7.38   \n",
       "257     mathilde      nørgaard          -  27u - 28u         8.50       8.10   \n",
       "258       csilla      herczegh          -  43u - 46u         7.71       7.27   \n",
       "259      aaliyah          rice          -  31u - 31u         7.64       7.33   \n",
       "260        david      beauvais          -  26u - 23u         7.87       7.47   \n",
       "261     caroline        shuler          -  50u - 54u         7.63       7.27   \n",
       "262         alex      crawford  51u - 62u          -         7.69       7.30   \n",
       "263      rebecca      jephcott  53u - 63u          -         7.96       7.57   \n",
       "264   chukwumoge     ogochukwu          -  41u - 39u         7.95       7.56   \n",
       "265       fearne      mcgregor          -  27u - 29u         7.83       7.48   \n",
       "266       ursula         freud  42u - 54u          -         7.75       7.46   \n",
       "267         leon        scholz          -  38u - 32u         7.72       7.29   \n",
       "268       yasmin        araujo          -  51u - 54u         7.82       7.36   \n",
       "269       hiromu      horikawa          -  47u - 46u         7.77       7.28   \n",
       "270         mika    martinsson  34u - 43u          -         7.50       7.17   \n",
       "271          leo        vieira          -  30u - 33u         7.74       7.36   \n",
       "272       steven           roy          -  41u - 43u         7.87       7.43   \n",
       "273         kate     wilkinson  36u - 39u          -         7.72       7.20   \n",
       "274         naja       enoksen  43u - 50u          -         7.98       7.59   \n",
       "275       albina      zetticci  45u - 51u          -         7.93       7.73   \n",
       "276         john   teichelmann          -  49u - 49u         7.90       7.58   \n",
       "277       mathea       lillebø  23u - 36u          -         9.04       8.67   \n",
       "278       vallie        prince  31u - 38u          -         7.64       7.28   \n",
       "279       samúel  guðbrandsson  53u - 56u          -         8.00       7.64   \n",
       "\n",
       "     hba1c_change  \n",
       "0             NaN  \n",
       "1            0.97  \n",
       "2             NaN  \n",
       "3            0.35  \n",
       "4            0.32  \n",
       "5            0.38  \n",
       "6            0.38  \n",
       "7            0.34  \n",
       "8             NaN  \n",
       "9             NaN  \n",
       "10            NaN  \n",
       "11           0.38  \n",
       "12            NaN  \n",
       "13           0.95  \n",
       "14            NaN  \n",
       "15            NaN  \n",
       "16            NaN  \n",
       "17           0.98  \n",
       "18            NaN  \n",
       "19            NaN  \n",
       "20           0.92  \n",
       "21           0.32  \n",
       "22            NaN  \n",
       "23            NaN  \n",
       "24           0.39  \n",
       "25           0.92  \n",
       "26           0.94  \n",
       "27           0.97  \n",
       "28           0.39  \n",
       "29           0.33  \n",
       "..            ...  \n",
       "250          0.39  \n",
       "251           NaN  \n",
       "252          0.50  \n",
       "253           NaN  \n",
       "254           NaN  \n",
       "255          0.34  \n",
       "256          0.39  \n",
       "257          0.90  \n",
       "258           NaN  \n",
       "259          0.31  \n",
       "260           NaN  \n",
       "261           NaN  \n",
       "262          0.39  \n",
       "263          0.39  \n",
       "264          0.39  \n",
       "265          0.35  \n",
       "266          0.29  \n",
       "267          0.93  \n",
       "268          0.96  \n",
       "269           NaN  \n",
       "270          0.33  \n",
       "271           NaN  \n",
       "272          0.94  \n",
       "273           NaN  \n",
       "274           NaN  \n",
       "275          0.20  \n",
       "276           NaN  \n",
       "277          0.37  \n",
       "278          0.36  \n",
       "279          0.36  \n",
       "\n",
       "[280 rows x 7 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>adverse_reaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>berta</td>\n",
       "      <td>napolitani</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lena</td>\n",
       "      <td>baer</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>joseph</td>\n",
       "      <td>day</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>flavia</td>\n",
       "      <td>fiorentino</td>\n",
       "      <td>cough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>manouck</td>\n",
       "      <td>wubbels</td>\n",
       "      <td>throat irritation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>jasmine</td>\n",
       "      <td>sykes</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>louise</td>\n",
       "      <td>johnson</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>albinca</td>\n",
       "      <td>komavec</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>noe</td>\n",
       "      <td>aranda</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>sofia</td>\n",
       "      <td>hermansen</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>tegan</td>\n",
       "      <td>johnson</td>\n",
       "      <td>headache</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>abel</td>\n",
       "      <td>yonatan</td>\n",
       "      <td>cough</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>abdul-nur</td>\n",
       "      <td>isa</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>leon</td>\n",
       "      <td>scholz</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gabriele</td>\n",
       "      <td>saenger</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>jia li</td>\n",
       "      <td>teng</td>\n",
       "      <td>nausea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>jakob</td>\n",
       "      <td>jakobsen</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>christopher</td>\n",
       "      <td>woodward</td>\n",
       "      <td>nausea</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ole</td>\n",
       "      <td>petersen</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>finley</td>\n",
       "      <td>chandler</td>\n",
       "      <td>headache</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>anenechi</td>\n",
       "      <td>chidi</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>miłosław</td>\n",
       "      <td>wiśniewski</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>lixue</td>\n",
       "      <td>hsueh</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>merci</td>\n",
       "      <td>leroux</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>kang</td>\n",
       "      <td>mai</td>\n",
       "      <td>injection site discomfort</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>elliot</td>\n",
       "      <td>richardson</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>clinton</td>\n",
       "      <td>miller</td>\n",
       "      <td>throat irritation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>idalia</td>\n",
       "      <td>moore</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>xiuxiu</td>\n",
       "      <td>chang</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>alex</td>\n",
       "      <td>crawford</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>monika</td>\n",
       "      <td>lončar</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>steven</td>\n",
       "      <td>roy</td>\n",
       "      <td>headache</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>cecilie</td>\n",
       "      <td>nilsen</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>krisztina</td>\n",
       "      <td>magyar</td>\n",
       "      <td>hypoglycemia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     given_name     surname           adverse_reaction\n",
       "0         berta  napolitani  injection site discomfort\n",
       "1          lena        baer               hypoglycemia\n",
       "2        joseph         day               hypoglycemia\n",
       "3        flavia  fiorentino                      cough\n",
       "4       manouck     wubbels          throat irritation\n",
       "5       jasmine       sykes               hypoglycemia\n",
       "6        louise     johnson               hypoglycemia\n",
       "7       albinca     komavec               hypoglycemia\n",
       "8           noe      aranda               hypoglycemia\n",
       "9         sofia   hermansen  injection site discomfort\n",
       "10        tegan     johnson                   headache\n",
       "11         abel     yonatan                      cough\n",
       "12    abdul-nur         isa               hypoglycemia\n",
       "13         leon      scholz  injection site discomfort\n",
       "14     gabriele     saenger               hypoglycemia\n",
       "15       jia li        teng                     nausea\n",
       "16        jakob    jakobsen               hypoglycemia\n",
       "17  christopher    woodward                     nausea\n",
       "18          ole    petersen               hypoglycemia\n",
       "19       finley    chandler                   headache\n",
       "20     anenechi       chidi               hypoglycemia\n",
       "21     miłosław  wiśniewski  injection site discomfort\n",
       "22        lixue       hsueh  injection site discomfort\n",
       "23        merci      leroux               hypoglycemia\n",
       "24         kang         mai  injection site discomfort\n",
       "25       elliot  richardson               hypoglycemia\n",
       "26      clinton      miller          throat irritation\n",
       "27       idalia       moore               hypoglycemia\n",
       "28       xiuxiu       chang               hypoglycemia\n",
       "29         alex    crawford               hypoglycemia\n",
       "30       monika      lončar               hypoglycemia\n",
       "31       steven         roy                   headache\n",
       "32      cecilie      nilsen               hypoglycemia\n",
       "33    krisztina      magyar               hypoglycemia"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adverse_reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 503 entries, 0 to 502\n",
      "Data columns (total 14 columns):\n",
      "patient_id      503 non-null int64\n",
      "assigned_sex    503 non-null object\n",
      "given_name      503 non-null object\n",
      "surname         503 non-null object\n",
      "address         491 non-null object\n",
      "city            491 non-null object\n",
      "state           491 non-null object\n",
      "zip_code        491 non-null float64\n",
      "country         491 non-null object\n",
      "contact         491 non-null object\n",
      "birthdate       503 non-null object\n",
      "weight          503 non-null float64\n",
      "height          503 non-null int64\n",
      "bmi             503 non-null float64\n",
      "dtypes: float64(3), int64(2), object(9)\n",
      "memory usage: 55.1+ KB\n"
     ]
    }
   ],
   "source": [
    "patients.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 280 entries, 0 to 279\n",
      "Data columns (total 7 columns):\n",
      "given_name      280 non-null object\n",
      "surname         280 non-null object\n",
      "auralin         280 non-null object\n",
      "novodra         280 non-null object\n",
      "hba1c_start     280 non-null float64\n",
      "hba1c_end       280 non-null float64\n",
      "hba1c_change    171 non-null float64\n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 15.4+ KB\n"
     ]
    }
   ],
   "source": [
    "treatments.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 34 entries, 0 to 33\n",
      "Data columns (total 3 columns):\n",
      "given_name          34 non-null object\n",
      "surname             34 non-null object\n",
      "adverse_reaction    34 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 896.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "adverse_reactions.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14    given_name\n",
       "15       surname\n",
       "21    given_name\n",
       "22       surname\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_columns = pd.Series(list(patients) + list(treatments) + list(adverse_reactions))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['patient_id',\n",
       " 'assigned_sex',\n",
       " 'given_name',\n",
       " 'surname',\n",
       " 'address',\n",
       " 'city',\n",
       " 'state',\n",
       " 'zip_code',\n",
       " 'country',\n",
       " 'contact',\n",
       " 'birthdate',\n",
       " 'weight',\n",
       " 'height',\n",
       " 'bmi']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(patients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>contact</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>210</td>\n",
       "      <td>female</td>\n",
       "      <td>Lalita</td>\n",
       "      <td>Eldarkhanov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8/14/1950</td>\n",
       "      <td>143.4</td>\n",
       "      <td>62</td>\n",
       "      <td>26.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>220</td>\n",
       "      <td>male</td>\n",
       "      <td>Mỹ</td>\n",
       "      <td>Quynh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/9/1978</td>\n",
       "      <td>237.8</td>\n",
       "      <td>69</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>231</td>\n",
       "      <td>female</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Knudsen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/23/1976</td>\n",
       "      <td>165.9</td>\n",
       "      <td>63</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>235</td>\n",
       "      <td>female</td>\n",
       "      <td>Martina</td>\n",
       "      <td>Tománková</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/7/1936</td>\n",
       "      <td>199.5</td>\n",
       "      <td>65</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>243</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>O'Brian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/25/1957</td>\n",
       "      <td>205.3</td>\n",
       "      <td>74</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>male</td>\n",
       "      <td>Benjamin</td>\n",
       "      <td>Mehler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/30/1951</td>\n",
       "      <td>146.5</td>\n",
       "      <td>69</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>male</td>\n",
       "      <td>Jin</td>\n",
       "      <td>Kung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/17/1995</td>\n",
       "      <td>231.7</td>\n",
       "      <td>69</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>female</td>\n",
       "      <td>Wafiyyah</td>\n",
       "      <td>Asfour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/3/1989</td>\n",
       "      <td>158.6</td>\n",
       "      <td>63</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>270</td>\n",
       "      <td>female</td>\n",
       "      <td>Flavia</td>\n",
       "      <td>Fiorentino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/9/1937</td>\n",
       "      <td>175.2</td>\n",
       "      <td>61</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>279</td>\n",
       "      <td>female</td>\n",
       "      <td>Generosa</td>\n",
       "      <td>Cabán</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/16/1962</td>\n",
       "      <td>124.3</td>\n",
       "      <td>69</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>287</td>\n",
       "      <td>male</td>\n",
       "      <td>Lewis</td>\n",
       "      <td>Webb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/1/1979</td>\n",
       "      <td>155.3</td>\n",
       "      <td>68</td>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>female</td>\n",
       "      <td>Chỉ</td>\n",
       "      <td>Lâm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/14/1990</td>\n",
       "      <td>181.1</td>\n",
       "      <td>63</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name      surname address city state  \\\n",
       "209         210       female     Lalita  Eldarkhanov     NaN  NaN   NaN   \n",
       "219         220         male         Mỹ        Quynh     NaN  NaN   NaN   \n",
       "230         231       female  Elisabeth      Knudsen     NaN  NaN   NaN   \n",
       "234         235       female    Martina    Tománková     NaN  NaN   NaN   \n",
       "242         243         male       John      O'Brian     NaN  NaN   NaN   \n",
       "249         250         male   Benjamin       Mehler     NaN  NaN   NaN   \n",
       "257         258         male        Jin         Kung     NaN  NaN   NaN   \n",
       "264         265       female   Wafiyyah       Asfour     NaN  NaN   NaN   \n",
       "269         270       female     Flavia   Fiorentino     NaN  NaN   NaN   \n",
       "278         279       female   Generosa        Cabán     NaN  NaN   NaN   \n",
       "286         287         male      Lewis         Webb     NaN  NaN   NaN   \n",
       "296         297       female        Chỉ          Lâm     NaN  NaN   NaN   \n",
       "\n",
       "     zip_code country contact   birthdate  weight  height   bmi  \n",
       "209       NaN     NaN     NaN   8/14/1950   143.4      62  26.2  \n",
       "219       NaN     NaN     NaN    4/9/1978   237.8      69  35.1  \n",
       "230       NaN     NaN     NaN   9/23/1976   165.9      63  29.4  \n",
       "234       NaN     NaN     NaN    4/7/1936   199.5      65  33.2  \n",
       "242       NaN     NaN     NaN   2/25/1957   205.3      74  26.4  \n",
       "249       NaN     NaN     NaN  10/30/1951   146.5      69  21.6  \n",
       "257       NaN     NaN     NaN   5/17/1995   231.7      69  34.2  \n",
       "264       NaN     NaN     NaN   11/3/1989   158.6      63  28.1  \n",
       "269       NaN     NaN     NaN   10/9/1937   175.2      61  33.1  \n",
       "278       NaN     NaN     NaN  12/16/1962   124.3      69  18.4  \n",
       "286       NaN     NaN     NaN    4/1/1979   155.3      68  23.6  \n",
       "296       NaN     NaN     NaN   5/14/1990   181.1      63  32.1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients[patients['address'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>503.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "      <td>503.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>49084.118126</td>\n",
       "      <td>173.434990</td>\n",
       "      <td>66.634195</td>\n",
       "      <td>27.483897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>145.347859</td>\n",
       "      <td>30265.807442</td>\n",
       "      <td>33.916741</td>\n",
       "      <td>4.411297</td>\n",
       "      <td>5.276438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1002.000000</td>\n",
       "      <td>48.800000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>17.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>126.500000</td>\n",
       "      <td>21920.500000</td>\n",
       "      <td>149.300000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>23.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>252.000000</td>\n",
       "      <td>48057.000000</td>\n",
       "      <td>175.300000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>27.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>377.500000</td>\n",
       "      <td>75679.000000</td>\n",
       "      <td>199.500000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>31.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>503.000000</td>\n",
       "      <td>99701.000000</td>\n",
       "      <td>255.900000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>37.700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       patient_id      zip_code      weight      height         bmi\n",
       "count  503.000000    491.000000  503.000000  503.000000  503.000000\n",
       "mean   252.000000  49084.118126  173.434990   66.634195   27.483897\n",
       "std    145.347859  30265.807442   33.916741    4.411297    5.276438\n",
       "min      1.000000   1002.000000   48.800000   27.000000   17.100000\n",
       "25%    126.500000  21920.500000  149.300000   63.000000   23.300000\n",
       "50%    252.000000  48057.000000  175.300000   67.000000   27.200000\n",
       "75%    377.500000  75679.000000  199.500000   70.000000   31.750000\n",
       "max    503.000000  99701.000000  255.900000   79.000000   37.700000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>280.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>171.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.985929</td>\n",
       "      <td>7.589286</td>\n",
       "      <td>0.546023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.568638</td>\n",
       "      <td>0.569672</td>\n",
       "      <td>0.279555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>7.500000</td>\n",
       "      <td>7.010000</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.660000</td>\n",
       "      <td>7.270000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.800000</td>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.970000</td>\n",
       "      <td>7.570000</td>\n",
       "      <td>0.920000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.950000</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.990000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       hba1c_start   hba1c_end  hba1c_change\n",
       "count   280.000000  280.000000    171.000000\n",
       "mean      7.985929    7.589286      0.546023\n",
       "std       0.568638    0.569672      0.279555\n",
       "min       7.500000    7.010000      0.200000\n",
       "25%       7.660000    7.270000      0.340000\n",
       "50%       7.800000    7.420000      0.380000\n",
       "75%       7.970000    7.570000      0.920000\n",
       "max       9.950000    9.580000      0.990000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>contact</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>259</td>\n",
       "      <td>male</td>\n",
       "      <td>Abel</td>\n",
       "      <td>Yonatan</td>\n",
       "      <td>2621 Koontz Lane</td>\n",
       "      <td>Burbank</td>\n",
       "      <td>California</td>\n",
       "      <td>91502.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>AbelYonatan@teleworm.us1 818 841 7660</td>\n",
       "      <td>4/29/1952</td>\n",
       "      <td>137.9</td>\n",
       "      <td>66</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>male</td>\n",
       "      <td>Frydryk</td>\n",
       "      <td>Adamski</td>\n",
       "      <td>1845 Saint Marys Avenue</td>\n",
       "      <td>Syracuse</td>\n",
       "      <td>New York</td>\n",
       "      <td>13221.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>FrydrykAdamski@armyspy.com1 315 869 4408</td>\n",
       "      <td>10/12/1941</td>\n",
       "      <td>230.8</td>\n",
       "      <td>76</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>123</td>\n",
       "      <td>male</td>\n",
       "      <td>Bội</td>\n",
       "      <td>Tạ</td>\n",
       "      <td>2389 Rubaiyat Road</td>\n",
       "      <td>Grand Rapids</td>\n",
       "      <td>MI</td>\n",
       "      <td>49503.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>TaHaBoi@superrito.com231-607-3625</td>\n",
       "      <td>5/30/1929</td>\n",
       "      <td>211.0</td>\n",
       "      <td>69</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>111</th>\n",
       "      <td>112</td>\n",
       "      <td>female</td>\n",
       "      <td>Nicole</td>\n",
       "      <td>Zimmerman</td>\n",
       "      <td>909 Williams Avenue</td>\n",
       "      <td>Newhall</td>\n",
       "      <td>California</td>\n",
       "      <td>91321.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>661-291-1812NicoleZimmerman@rhyta.com</td>\n",
       "      <td>1/14/1984</td>\n",
       "      <td>225.9</td>\n",
       "      <td>74</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>354</td>\n",
       "      <td>male</td>\n",
       "      <td>Ole</td>\n",
       "      <td>Petersen</td>\n",
       "      <td>3650 Graystone Lakes</td>\n",
       "      <td>Dudley</td>\n",
       "      <td>GA</td>\n",
       "      <td>31022.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>OlePetersen@armyspy.com478-676-7058</td>\n",
       "      <td>7/14/1931</td>\n",
       "      <td>136.8</td>\n",
       "      <td>69</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name    surname                  address  \\\n",
       "258         259         male       Abel    Yonatan         2621 Koontz Lane   \n",
       "121         122         male    Frydryk    Adamski  1845 Saint Marys Avenue   \n",
       "122         123         male        Bội         Tạ       2389 Rubaiyat Road   \n",
       "111         112       female     Nicole  Zimmerman      909 Williams Avenue   \n",
       "353         354         male        Ole   Petersen     3650 Graystone Lakes   \n",
       "\n",
       "             city       state  zip_code        country  \\\n",
       "258       Burbank  California   91502.0  United States   \n",
       "121      Syracuse    New York   13221.0  United States   \n",
       "122  Grand Rapids          MI   49503.0  United States   \n",
       "111       Newhall  California   91321.0  United States   \n",
       "353        Dudley          GA   31022.0  United States   \n",
       "\n",
       "                                      contact   birthdate  weight  height  \\\n",
       "258     AbelYonatan@teleworm.us1 818 841 7660   4/29/1952   137.9      66   \n",
       "121  FrydrykAdamski@armyspy.com1 315 869 4408  10/12/1941   230.8      76   \n",
       "122         TaHaBoi@superrito.com231-607-3625   5/30/1929   211.0      69   \n",
       "111     661-291-1812NicoleZimmerman@rhyta.com   1/14/1984   225.9      74   \n",
       "353       OlePetersen@armyspy.com478-676-7058   7/14/1931   136.8      69   \n",
       "\n",
       "      bmi  \n",
       "258  22.3  \n",
       "121  28.1  \n",
       "122  31.2  \n",
       "111  29.0  \n",
       "353  20.2  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Doe             6\n",
       "Jakobsen        3\n",
       "Taylor          3\n",
       "Kadyrov         2\n",
       "Correia         2\n",
       "Tucker          2\n",
       "Silva           2\n",
       "Tạ              2\n",
       "Gersten         2\n",
       "Grímsdóttir     2\n",
       "Ogochukwu       2\n",
       "Schiavone       2\n",
       "Cindrić         2\n",
       "Lund            2\n",
       "Souza           2\n",
       "Berg            2\n",
       "Bùi             2\n",
       "Lương           2\n",
       "Liễu            2\n",
       "Lâm             2\n",
       "Dratchev        2\n",
       "Hueber          2\n",
       "Kowalczyk       2\n",
       "Johnson         2\n",
       "Collins         2\n",
       "Batukayev       2\n",
       "Aranda          2\n",
       "Cabrera         2\n",
       "Nilsen          2\n",
       "Parker          2\n",
       "               ..\n",
       "Lindgren        1\n",
       "Gormanston      1\n",
       "Adams           1\n",
       "Haraguchi       1\n",
       "Richardson      1\n",
       "Mathiesen       1\n",
       "Day             1\n",
       "Mata            1\n",
       "Štěpánek        1\n",
       "Aličajić        1\n",
       "Wiśniewska      1\n",
       "Rokavc          1\n",
       "Kašná           1\n",
       "Azevedo         1\n",
       "Ščančar         1\n",
       "Efrem           1\n",
       "Thạch           1\n",
       "Pak             1\n",
       "Nnonso          1\n",
       "Jakšić          1\n",
       "Stanić          1\n",
       "House           1\n",
       "Frederiksen     1\n",
       "Guðbrandsson    1\n",
       "Reynoso         1\n",
       "Sokołowska      1\n",
       "Rushisvili      1\n",
       "Yuan            1\n",
       "Tomaszewski     1\n",
       "Lorenzo         1\n",
       "Name: surname, Length: 466, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.surname.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123 Main Street               6\n",
       "648 Old Dear Lane             2\n",
       "2476 Fulton Street            2\n",
       "2778 North Avenue             2\n",
       "3268 Karen Lane               1\n",
       "4310 Johnson Street           1\n",
       "3259 Roy Alley                1\n",
       "1384 Dovetail Estates         1\n",
       "2102 Geraldine Lane           1\n",
       "475 Preston Street            1\n",
       "4992 Moonlight Drive          1\n",
       "1270 Haul Road                1\n",
       "936 Lightning Point Drive     1\n",
       "4019 Cerullo Road             1\n",
       "4102 Ritter Avenue            1\n",
       "163 Hide A Way Road           1\n",
       "1389 Nutters Barn Lane        1\n",
       "3479 Elm Drive                1\n",
       "479 Elmwood Avenue            1\n",
       "4970 Heather Sees Way         1\n",
       "435 Pike Street               1\n",
       "2691 Kessla Way               1\n",
       "4237 Hamilton Drive           1\n",
       "1953 Rhapsody Street          1\n",
       "1904 Granville Lane           1\n",
       "883 Oakwood Circle            1\n",
       "4900 Philli Lane              1\n",
       "272 Boone Crockett Lane       1\n",
       "1147 Kelley Road              1\n",
       "796 Eagle Street              1\n",
       "                             ..\n",
       "2043 Jadewood Drive           1\n",
       "2651 Black Oak Hollow Road    1\n",
       "3402 Kildeer Drive            1\n",
       "3251 Radio Park Drive         1\n",
       "4643 Reeves Street            1\n",
       "1495 Post Farm Road           1\n",
       "3063 School House Road        1\n",
       "1463 Martha Ellen Drive       1\n",
       "4945 Jarvis Street            1\n",
       "4018 Poplar Avenue            1\n",
       "1140 Willis Avenue            1\n",
       "1240 Summit Park Avenue       1\n",
       "1956 Rosemont Avenue          1\n",
       "2873 John Calvin Drive        1\n",
       "4720 Gordon Street            1\n",
       "1424 Primrose Lane            1\n",
       "1690 Fannie Street            1\n",
       "1370 Flint Street             1\n",
       "1054 Zappia Drive             1\n",
       "1094 Jones Avenue             1\n",
       "3216 Lodgeville Road          1\n",
       "1350 Meadow Lane              1\n",
       "1271 Pretty View Lane         1\n",
       "989 Wayback Lane              1\n",
       "2126 Pearl Street             1\n",
       "2924 Stanley Avenue           1\n",
       "2754 Westwood Avenue          1\n",
       "4324 Oak Street               1\n",
       "1179 Patton Lane              1\n",
       "3434 Holt Street              1\n",
       "Name: address, Length: 483, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.address.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>contact</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>male</td>\n",
       "      <td>Jake</td>\n",
       "      <td>Jakobsen</td>\n",
       "      <td>648 Old Dear Lane</td>\n",
       "      <td>Port Jervis</td>\n",
       "      <td>New York</td>\n",
       "      <td>12771.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>JakobCJakobsen@einrot.com+1 (845) 858-7707</td>\n",
       "      <td>8/1/1985</td>\n",
       "      <td>155.8</td>\n",
       "      <td>67</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>220</td>\n",
       "      <td>male</td>\n",
       "      <td>Mỹ</td>\n",
       "      <td>Quynh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/9/1978</td>\n",
       "      <td>237.8</td>\n",
       "      <td>69</td>\n",
       "      <td>35.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>230</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>123 Main Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>johndoe@email.com1234567890</td>\n",
       "      <td>1/1/1975</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>231</td>\n",
       "      <td>female</td>\n",
       "      <td>Elisabeth</td>\n",
       "      <td>Knudsen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9/23/1976</td>\n",
       "      <td>165.9</td>\n",
       "      <td>63</td>\n",
       "      <td>29.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>235</td>\n",
       "      <td>female</td>\n",
       "      <td>Martina</td>\n",
       "      <td>Tománková</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/7/1936</td>\n",
       "      <td>199.5</td>\n",
       "      <td>65</td>\n",
       "      <td>33.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>238</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>123 Main Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>johndoe@email.com1234567890</td>\n",
       "      <td>1/1/1975</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>243</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>O'Brian</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/25/1957</td>\n",
       "      <td>205.3</td>\n",
       "      <td>74</td>\n",
       "      <td>26.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>245</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>123 Main Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>johndoe@email.com1234567890</td>\n",
       "      <td>1/1/1975</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>250</td>\n",
       "      <td>male</td>\n",
       "      <td>Benjamin</td>\n",
       "      <td>Mehler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/30/1951</td>\n",
       "      <td>146.5</td>\n",
       "      <td>69</td>\n",
       "      <td>21.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>252</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>123 Main Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>johndoe@email.com1234567890</td>\n",
       "      <td>1/1/1975</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>258</td>\n",
       "      <td>male</td>\n",
       "      <td>Jin</td>\n",
       "      <td>Kung</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/17/1995</td>\n",
       "      <td>231.7</td>\n",
       "      <td>69</td>\n",
       "      <td>34.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>265</td>\n",
       "      <td>female</td>\n",
       "      <td>Wafiyyah</td>\n",
       "      <td>Asfour</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11/3/1989</td>\n",
       "      <td>158.6</td>\n",
       "      <td>63</td>\n",
       "      <td>28.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>270</td>\n",
       "      <td>female</td>\n",
       "      <td>Flavia</td>\n",
       "      <td>Fiorentino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/9/1937</td>\n",
       "      <td>175.2</td>\n",
       "      <td>61</td>\n",
       "      <td>33.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>278</td>\n",
       "      <td>male</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>123 Main Street</td>\n",
       "      <td>New York</td>\n",
       "      <td>NY</td>\n",
       "      <td>12345.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>johndoe@email.com1234567890</td>\n",
       "      <td>1/1/1975</td>\n",
       "      <td>180.0</td>\n",
       "      <td>72</td>\n",
       "      <td>24.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>279</td>\n",
       "      <td>female</td>\n",
       "      <td>Generosa</td>\n",
       "      <td>Cabán</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/16/1962</td>\n",
       "      <td>124.3</td>\n",
       "      <td>69</td>\n",
       "      <td>18.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>283</td>\n",
       "      <td>female</td>\n",
       "      <td>Sandy</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>2476 Fulton Street</td>\n",
       "      <td>Rainelle</td>\n",
       "      <td>WV</td>\n",
       "      <td>25962.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>304-438-2648SandraCTaylor@dayrep.com</td>\n",
       "      <td>10/23/1960</td>\n",
       "      <td>206.1</td>\n",
       "      <td>64</td>\n",
       "      <td>35.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>287</td>\n",
       "      <td>male</td>\n",
       "      <td>Lewis</td>\n",
       "      <td>Webb</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4/1/1979</td>\n",
       "      <td>155.3</td>\n",
       "      <td>68</td>\n",
       "      <td>23.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>297</td>\n",
       "      <td>female</td>\n",
       "      <td>Chỉ</td>\n",
       "      <td>Lâm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5/14/1990</td>\n",
       "      <td>181.1</td>\n",
       "      <td>63</td>\n",
       "      <td>32.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>503</td>\n",
       "      <td>male</td>\n",
       "      <td>Pat</td>\n",
       "      <td>Gersten</td>\n",
       "      <td>2778 North Avenue</td>\n",
       "      <td>Burr</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>68324.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>PatrickGersten@rhyta.com402-848-4923</td>\n",
       "      <td>5/3/1954</td>\n",
       "      <td>138.2</td>\n",
       "      <td>71</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name     surname             address  \\\n",
       "29           30         male       Jake    Jakobsen   648 Old Dear Lane   \n",
       "219         220         male         Mỹ       Quynh                 NaN   \n",
       "229         230         male       John         Doe     123 Main Street   \n",
       "230         231       female  Elisabeth     Knudsen                 NaN   \n",
       "234         235       female    Martina   Tománková                 NaN   \n",
       "237         238         male       John         Doe     123 Main Street   \n",
       "242         243         male       John     O'Brian                 NaN   \n",
       "244         245         male       John         Doe     123 Main Street   \n",
       "249         250         male   Benjamin      Mehler                 NaN   \n",
       "251         252         male       John         Doe     123 Main Street   \n",
       "257         258         male        Jin        Kung                 NaN   \n",
       "264         265       female   Wafiyyah      Asfour                 NaN   \n",
       "269         270       female     Flavia  Fiorentino                 NaN   \n",
       "277         278         male       John         Doe     123 Main Street   \n",
       "278         279       female   Generosa       Cabán                 NaN   \n",
       "282         283       female      Sandy      Taylor  2476 Fulton Street   \n",
       "286         287         male      Lewis        Webb                 NaN   \n",
       "296         297       female        Chỉ         Lâm                 NaN   \n",
       "502         503         male        Pat     Gersten   2778 North Avenue   \n",
       "\n",
       "            city     state  zip_code        country  \\\n",
       "29   Port Jervis  New York   12771.0  United States   \n",
       "219          NaN       NaN       NaN            NaN   \n",
       "229     New York        NY   12345.0  United States   \n",
       "230          NaN       NaN       NaN            NaN   \n",
       "234          NaN       NaN       NaN            NaN   \n",
       "237     New York        NY   12345.0  United States   \n",
       "242          NaN       NaN       NaN            NaN   \n",
       "244     New York        NY   12345.0  United States   \n",
       "249          NaN       NaN       NaN            NaN   \n",
       "251     New York        NY   12345.0  United States   \n",
       "257          NaN       NaN       NaN            NaN   \n",
       "264          NaN       NaN       NaN            NaN   \n",
       "269          NaN       NaN       NaN            NaN   \n",
       "277     New York        NY   12345.0  United States   \n",
       "278          NaN       NaN       NaN            NaN   \n",
       "282     Rainelle        WV   25962.0  United States   \n",
       "286          NaN       NaN       NaN            NaN   \n",
       "296          NaN       NaN       NaN            NaN   \n",
       "502         Burr  Nebraska   68324.0  United States   \n",
       "\n",
       "                                        contact   birthdate  weight  height  \\\n",
       "29   JakobCJakobsen@einrot.com+1 (845) 858-7707    8/1/1985   155.8      67   \n",
       "219                                         NaN    4/9/1978   237.8      69   \n",
       "229                 johndoe@email.com1234567890    1/1/1975   180.0      72   \n",
       "230                                         NaN   9/23/1976   165.9      63   \n",
       "234                                         NaN    4/7/1936   199.5      65   \n",
       "237                 johndoe@email.com1234567890    1/1/1975   180.0      72   \n",
       "242                                         NaN   2/25/1957   205.3      74   \n",
       "244                 johndoe@email.com1234567890    1/1/1975   180.0      72   \n",
       "249                                         NaN  10/30/1951   146.5      69   \n",
       "251                 johndoe@email.com1234567890    1/1/1975   180.0      72   \n",
       "257                                         NaN   5/17/1995   231.7      69   \n",
       "264                                         NaN   11/3/1989   158.6      63   \n",
       "269                                         NaN   10/9/1937   175.2      61   \n",
       "277                 johndoe@email.com1234567890    1/1/1975   180.0      72   \n",
       "278                                         NaN  12/16/1962   124.3      69   \n",
       "282        304-438-2648SandraCTaylor@dayrep.com  10/23/1960   206.1      64   \n",
       "286                                         NaN    4/1/1979   155.3      68   \n",
       "296                                         NaN   5/14/1990   181.1      63   \n",
       "502        PatrickGersten@rhyta.com402-848-4923    5/3/1954   138.2      71   \n",
       "\n",
       "      bmi  \n",
       "29   24.4  \n",
       "219  35.1  \n",
       "229  24.4  \n",
       "230  29.4  \n",
       "234  33.2  \n",
       "237  24.4  \n",
       "242  26.4  \n",
       "244  24.4  \n",
       "249  21.6  \n",
       "251  24.4  \n",
       "257  34.2  \n",
       "264  28.1  \n",
       "269  33.1  \n",
       "277  24.4  \n",
       "278  18.4  \n",
       "282  35.4  \n",
       "286  23.6  \n",
       "296  32.1  \n",
       "502  19.3  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients[patients.address.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210     48.8\n",
       "459    102.1\n",
       "335    102.7\n",
       "74     103.2\n",
       "317    106.0\n",
       "171    106.5\n",
       "51     107.1\n",
       "270    108.1\n",
       "198    108.5\n",
       "48     109.1\n",
       "478    109.6\n",
       "141    110.2\n",
       "38     111.8\n",
       "438    112.0\n",
       "14     112.0\n",
       "235    112.2\n",
       "307    112.4\n",
       "191    112.6\n",
       "408    113.1\n",
       "49     113.3\n",
       "326    114.0\n",
       "338    114.1\n",
       "253    117.0\n",
       "321    118.4\n",
       "168    118.8\n",
       "1      118.8\n",
       "350    119.0\n",
       "207    119.2\n",
       "265    120.0\n",
       "341    120.3\n",
       "       ...  \n",
       "332    224.0\n",
       "252    224.2\n",
       "12     224.2\n",
       "222    224.8\n",
       "166    225.3\n",
       "111    225.9\n",
       "101    226.2\n",
       "150    226.6\n",
       "352    227.7\n",
       "428    227.7\n",
       "88     227.7\n",
       "13     228.4\n",
       "339    229.0\n",
       "182    230.3\n",
       "121    230.8\n",
       "257    231.7\n",
       "395    231.9\n",
       "246    232.1\n",
       "219    237.8\n",
       "11     238.7\n",
       "50     238.9\n",
       "441    239.1\n",
       "499    239.6\n",
       "439    242.0\n",
       "487    242.4\n",
       "144    244.9\n",
       "61     244.9\n",
       "283    245.5\n",
       "118    254.5\n",
       "485    255.9\n",
       "Name: weight, Length: 503, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients.weight.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210    19.055827\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weight_lbs = patients[patients.surname == 'Zaitseva'].weight * 2.20462\n",
    "height_in = patients[patients.surname == 'Zaitseva'].height\n",
    "bmi_check = 703 * weight_lbs / (height_in * height_in)\n",
    "bmi_check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "210    19.1\n",
       "Name: bmi, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients[patients.surname == 'Zaitseva'].bmi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(treatments.auralin.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(treatments.novodra.isnull())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quality\n",
    "##### `patients` table\n",
    "- Zip code is a float not a string\n",
    "- Zip code has four digits sometimes\n",
    "- Tim Neudorf height is 27 in instead of 72 in\n",
    "- Full state names sometimes, abbreviations other times\n",
    "- Dsvid Gustafsson\n",
    "- Missing demographic information (address - contact columns) ***(can't clean)***\n",
    "- Erroneous datatypes (assigned sex, state, zip_code, and birthdate columns)\n",
    "- Multiple phone number formats\n",
    "- Default John Doe data\n",
    "- Multiple records for Jakobsen, Gersten, Taylor\n",
    "- kgs instead of lbs for Zaitseva weight\n",
    "\n",
    "##### `treatments` table\n",
    "- Missing HbA1c changes\n",
    "- The letter 'u' in starting and ending doses for Auralin and Novodra\n",
    "- Lowercase given names and surnames\n",
    "- Missing records (280 instead of 350)\n",
    "- Erroneous datatypes (auralin and novodra columns)\n",
    "- Inaccurate HbA1c changes (leading 4s mistaken as 9s)\n",
    "- Nulls represented as dashes (-) in auralin and novodra columns\n",
    "\n",
    "##### `adverse_reactions` table\n",
    "- Lowercase given names and surnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tidiness\n",
    "- Contact column in `patients` table should be split into phone number and email\n",
    "- Three variables in two columns in `treatments` table (treatment, start dose and end dose)\n",
    "- Adverse reaction should be part of the `treatments` table\n",
    "- Given name and surname columns in `patients` table duplicated in `treatments` and `adverse_reactions` tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_clean = patients.copy()\n",
    "treatments_clean = treatments.copy()\n",
    "adverse_reactions_clean = adverse_reactions.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Missing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Complete the following two \"Missing Data\" **Define, Code, and Test** sequences after watching the *\"Address Missing Data First\"* video.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### `treatments`: Missing records (280 instead of 350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "*Your definition here. Note: the missing `treatments` records are stored in a file named `treatments_cut.csv`, which you can see in this Jupyter Notebook's dashboard (click the **jupyter** logo in the top lefthand corner of this Notebook). Hint: [documentation page](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.concat.html) for the function used in the solution.*\n",
    "\n",
    "##### Define\n",
    "Add the missing records to treatments dataframe by using pd.append() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "treatments_cut = pd.read_csv('treatments_cut.csv')\n",
    "#treatments_cut.head()\n",
    "treatments_clean = treatments_clean.append(treatments_cut)  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 350 entries, 0 to 69\n",
      "Data columns (total 7 columns):\n",
      "given_name      350 non-null object\n",
      "surname         350 non-null object\n",
      "auralin         350 non-null object\n",
      "novodra         350 non-null object\n",
      "hba1c_start     350 non-null float64\n",
      "hba1c_end       350 non-null float64\n",
      "hba1c_change    213 non-null float64\n",
      "dtypes: float64(3), object(4)\n",
      "memory usage: 21.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Your testing code here\n",
    "# treatments_complete.shape\n",
    "treatments_clean.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### `treatments`: Missing HbA1c changes and Inaccurate HbA1c changes (leading 4s mistaken as 9s)\n",
    "*Note: the \"Inaccurate HbA1c changes (leading 4s mistaken as 9s)\" observation, which is an accuracy issue and not a completeness issue, is included in this header because it is also fixed by the cleaning operation that fixes the missing \"Missing HbA1c changes\" observation. Multiple observations in one **Define, Code, and Test** header occurs multiple times in this notebook.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here.*\n",
    "recalculate all the HbA1c changes by subtracting hba1c_end with hba1c_start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "treatments_clean['hba1c_change'] = treatments_clean['hba1c_start'] - treatments_clean['hba1c_end']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.43\n",
       "1     0.47\n",
       "2     0.43\n",
       "3     0.35\n",
       "4     0.32\n",
       "5     0.38\n",
       "6     0.38\n",
       "7     0.34\n",
       "8     0.38\n",
       "9     0.39\n",
       "10    0.51\n",
       "11    0.38\n",
       "12    0.40\n",
       "13    0.45\n",
       "14    0.44\n",
       "15    0.44\n",
       "16    0.40\n",
       "17    0.48\n",
       "18    0.42\n",
       "19    0.41\n",
       "20    0.42\n",
       "21    0.32\n",
       "22    0.43\n",
       "23    0.42\n",
       "24    0.39\n",
       "25    0.42\n",
       "26    0.44\n",
       "27    0.47\n",
       "28    0.39\n",
       "29    0.33\n",
       "      ... \n",
       "40    0.28\n",
       "41    0.46\n",
       "42    0.47\n",
       "43    0.40\n",
       "44    0.32\n",
       "45    0.47\n",
       "46    0.29\n",
       "47    0.51\n",
       "48    0.39\n",
       "49    0.38\n",
       "50    0.40\n",
       "51    0.29\n",
       "52    0.43\n",
       "53    0.37\n",
       "54    0.37\n",
       "55    0.39\n",
       "56    0.44\n",
       "57    0.37\n",
       "58    0.45\n",
       "59    0.30\n",
       "60    0.37\n",
       "61    0.36\n",
       "62    0.44\n",
       "63    0.39\n",
       "64    0.41\n",
       "65    0.34\n",
       "66    0.45\n",
       "67    0.30\n",
       "68    0.47\n",
       "69    0.46\n",
       "Name: hba1c_change, Length: 350, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "treatments_clean['hba1c_change']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tidiness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Complete the following four \"Tidiness\" **Define, Code, and Test** sequences after watching the *\"Cleaning for Tidiness\"* video.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Contact column in `patients` table contains two variables: phone number and email"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint 1: use regular expressions with pandas' [`str.extract` method](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.extract.html). Here is an amazing [regex tutorial](https://regexone.com/). Hint 2: [various phone number regex patterns](https://stackoverflow.com/questions/16699007/regular-expression-to-match-standard-10-digit-phone-number). Hint 3: [email address regex pattern](http://emailregex.com/), which you might need to modify to distinguish the email from the phone number.*\n",
    "\n",
    "split contact column in patients table into phone number and email two columns by using regex and drop the contact column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "patients_clean['phone_number'] = patients_clean['contact'].str.extract('((?:\\+\\d{1,2}\\s)?\\(?\\d{3}\\)?[\\s.-]?\\d{3}[\\s.-]?\\d{4})', expand=True)\n",
    "patients_clean['email'] = patients_clean['contact'].str.extract('([a-zA-Z][a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+[a-zA-Z])',expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: axis=1 denotes that we are referring to a column, not a row\n",
    "patients_clean = patients_clean.drop('contact', axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>Zoe</td>\n",
       "      <td>Wellish</td>\n",
       "      <td>576 Brown Bear Drive</td>\n",
       "      <td>Rancho California</td>\n",
       "      <td>California</td>\n",
       "      <td>92390.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>7/10/1976</td>\n",
       "      <td>121.7</td>\n",
       "      <td>66</td>\n",
       "      <td>19.6</td>\n",
       "      <td>951-719-9170</td>\n",
       "      <td>ZoeWellish@superrito.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>Pamela</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2370 University Hill Road</td>\n",
       "      <td>Armstrong</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>61812.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>4/3/1967</td>\n",
       "      <td>118.8</td>\n",
       "      <td>66</td>\n",
       "      <td>19.2</td>\n",
       "      <td>+1 (217) 569-3204</td>\n",
       "      <td>PamelaSHill@cuvox.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>Jae</td>\n",
       "      <td>Debord</td>\n",
       "      <td>1493 Poling Farm Road</td>\n",
       "      <td>York</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>68467.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>2/19/1980</td>\n",
       "      <td>177.8</td>\n",
       "      <td>71</td>\n",
       "      <td>24.8</td>\n",
       "      <td>402-363-6804</td>\n",
       "      <td>JaeMDebord@gustr.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>Liêm</td>\n",
       "      <td>Phan</td>\n",
       "      <td>2335 Webster Street</td>\n",
       "      <td>Woodbridge</td>\n",
       "      <td>NJ</td>\n",
       "      <td>7095.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>7/26/1951</td>\n",
       "      <td>220.9</td>\n",
       "      <td>70</td>\n",
       "      <td>31.7</td>\n",
       "      <td>+1 (732) 636-8246</td>\n",
       "      <td>PhanBaLiem@jourrapide.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Neudorf</td>\n",
       "      <td>1428 Turkey Pen Lane</td>\n",
       "      <td>Dothan</td>\n",
       "      <td>AL</td>\n",
       "      <td>36303.0</td>\n",
       "      <td>United States</td>\n",
       "      <td>2/18/1928</td>\n",
       "      <td>192.3</td>\n",
       "      <td>27</td>\n",
       "      <td>26.1</td>\n",
       "      <td>334-515-7487</td>\n",
       "      <td>TimNeudorf@cuvox.de</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id assigned_sex given_name  surname                    address  \\\n",
       "0           1       female        Zoe  Wellish       576 Brown Bear Drive   \n",
       "1           2       female     Pamela     Hill  2370 University Hill Road   \n",
       "2           3         male        Jae   Debord      1493 Poling Farm Road   \n",
       "3           4         male       Liêm     Phan        2335 Webster Street   \n",
       "4           5         male        Tim  Neudorf       1428 Turkey Pen Lane   \n",
       "\n",
       "                city       state  zip_code        country  birthdate  weight  \\\n",
       "0  Rancho California  California   92390.0  United States  7/10/1976   121.7   \n",
       "1          Armstrong    Illinois   61812.0  United States   4/3/1967   118.8   \n",
       "2               York    Nebraska   68467.0  United States  2/19/1980   177.8   \n",
       "3         Woodbridge          NJ    7095.0  United States  7/26/1951   220.9   \n",
       "4             Dothan          AL   36303.0  United States  2/18/1928   192.3   \n",
       "\n",
       "   height   bmi       phone_number                      email  \n",
       "0      66  19.6       951-719-9170   ZoeWellish@superrito.com  \n",
       "1      66  19.2  +1 (217) 569-3204       PamelaSHill@cuvox.de  \n",
       "2      71  24.8       402-363-6804       JaeMDebord@gustr.com  \n",
       "3      70  31.7  +1 (732) 636-8246  PhanBaLiem@jourrapide.com  \n",
       "4      27  26.1       334-515-7487        TimNeudorf@cuvox.de  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         951-719-9170\n",
       "1    +1 (217) 569-3204\n",
       "2         402-363-6804\n",
       "3    +1 (732) 636-8246\n",
       "4         334-515-7487\n",
       "Name: phone_number, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean['phone_number'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498    207-477-0579\n",
       "499    928-284-4492\n",
       "500    816-223-6007\n",
       "501    360 443 2060\n",
       "502    402-848-4923\n",
       "Name: phone_number, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean['phone_number'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     ZoeWellish@superrito.com\n",
       "1         PamelaSHill@cuvox.de\n",
       "2         JaeMDebord@gustr.com\n",
       "3    PhanBaLiem@jourrapide.com\n",
       "4          TimNeudorf@cuvox.de\n",
       "Name: email, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean['email'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "498      MustafaLindstrom@jourrapide.com\n",
       "499               RumanBisliev@gustr.com\n",
       "500            JinkedeKeizer@teleworm.us\n",
       "501    ChidaluOnyekaozulu@jourrapide.com\n",
       "502             PatrickGersten@rhyta.com\n",
       "Name: email, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean['email'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Three variables in two columns in `treatments` table (treatment, start dose and end dose)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: use pandas' [melt function](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.melt.html) and [`str.split()` method](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.split.html). Here is an excellent [`melt` tutorial](https://deparkes.co.uk/2016/10/28/reshape-pandas-data-with-melt/).*\n",
    "\n",
    "split the start_dose and end_dose by \"-\" , start_dose as a column , end_dose as a column.  Melt start_dose and end_dose into 3 columns with pd.melt() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>auralin</th>\n",
       "      <th>novodra</th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>veronika</td>\n",
       "      <td>jindrová</td>\n",
       "      <td>41u - 48u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>elliot</td>\n",
       "      <td>richardson</td>\n",
       "      <td>-</td>\n",
       "      <td>40u - 45u</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.09</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>yukitaka</td>\n",
       "      <td>takenaka</td>\n",
       "      <td>-</td>\n",
       "      <td>39u - 36u</td>\n",
       "      <td>7.68</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skye</td>\n",
       "      <td>gormanston</td>\n",
       "      <td>33u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.97</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alissa</td>\n",
       "      <td>montez</td>\n",
       "      <td>-</td>\n",
       "      <td>33u - 29u</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name     surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0   veronika    jindrová  41u - 48u          -         7.63       7.20   \n",
       "1     elliot  richardson          -  40u - 45u         7.56       7.09   \n",
       "2   yukitaka    takenaka          -  39u - 36u         7.68       7.25   \n",
       "3       skye  gormanston  33u - 36u          -         7.97       7.62   \n",
       "4     alissa      montez          -  33u - 29u         7.78       7.46   \n",
       "\n",
       "   hba1c_change  \n",
       "0          0.43  \n",
       "1          0.47  \n",
       "2          0.43  \n",
       "3          0.35  \n",
       "4          0.32  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your cleaning code here\n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# melt two columns(auralin and novodra) into one(treatment)\n",
    "# all the rest columns can be id_vals, mutiple val ids so put them into a list\n",
    "treatments_clean = pd.melt(treatments_clean, id_vars=['given_name', 'surname', 'hba1c_start', 'hba1c_end', 'hba1c_change'],\n",
    "                           var_name='treatment', value_name='dose')\n",
    "treatments_clean = treatments_clean[treatments_clean.dose != \"-\"]\n",
    "treatments_clean['dose_start'], treatments_clean['dose_end'] = treatments_clean['dose'].str.split(' - ', 1).str\n",
    "treatments_clean = treatments_clean.drop('dose', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "      <th>treatment</th>\n",
       "      <th>dose_start</th>\n",
       "      <th>dose_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>veronika</td>\n",
       "      <td>jindrová</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.43</td>\n",
       "      <td>auralin</td>\n",
       "      <td>41u</td>\n",
       "      <td>48u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>skye</td>\n",
       "      <td>gormanston</td>\n",
       "      <td>7.97</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.35</td>\n",
       "      <td>auralin</td>\n",
       "      <td>33u</td>\n",
       "      <td>36u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sophia</td>\n",
       "      <td>haugen</td>\n",
       "      <td>7.65</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>auralin</td>\n",
       "      <td>37u</td>\n",
       "      <td>42u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>eddie</td>\n",
       "      <td>archer</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "      <td>auralin</td>\n",
       "      <td>31u</td>\n",
       "      <td>38u</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>asia</td>\n",
       "      <td>woźniak</td>\n",
       "      <td>7.76</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>auralin</td>\n",
       "      <td>30u</td>\n",
       "      <td>36u</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name     surname  hba1c_start  hba1c_end  hba1c_change treatment  \\\n",
       "0   veronika    jindrová         7.63       7.20          0.43   auralin   \n",
       "3       skye  gormanston         7.97       7.62          0.35   auralin   \n",
       "6     sophia      haugen         7.65       7.27          0.38   auralin   \n",
       "7      eddie      archer         7.89       7.55          0.34   auralin   \n",
       "9       asia     woźniak         7.76       7.37          0.39   auralin   \n",
       "\n",
       "  dose_start dose_end  \n",
       "0        41u      48u  \n",
       "3        33u      36u  \n",
       "6        37u      42u  \n",
       "7        31u      38u  \n",
       "9        30u      36u  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adverse reaction should be part of the `treatments` table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: [tutorial](https://chrisalbon.com/python/pandas_join_merge_dataframe.html) for the function used in the solution.*\n",
    "\n",
    "Merge the adverse_reaction table into treatment table joining on given name and surname"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "treatments_clean = pd.merge(treatments_clean , adverse_reactions_clean , on= ['surname' ,'given_name' ], how='left')\n",
    "# multiple conditions : use list \n",
    "# based on patient table, so left join "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style>\n",
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "      <th>treatment</th>\n",
       "      <th>dose_start</th>\n",
       "      <th>dose_end</th>\n",
       "      <th>adverse_reaction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>julia</td>\n",
       "      <td>werner</td>\n",
       "      <td>7.58</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.34</td>\n",
       "      <td>auralin</td>\n",
       "      <td>35u</td>\n",
       "      <td>40u</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>alexander</td>\n",
       "      <td>mathiesen</td>\n",
       "      <td>7.96</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>auralin</td>\n",
       "      <td>47u</td>\n",
       "      <td>58u</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>manuela</td>\n",
       "      <td>cindrić</td>\n",
       "      <td>8.07</td>\n",
       "      <td>7.76</td>\n",
       "      <td>0.31</td>\n",
       "      <td>auralin</td>\n",
       "      <td>55u</td>\n",
       "      <td>66u</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>tosh</td>\n",
       "      <td>jensen</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.24</td>\n",
       "      <td>novodra</td>\n",
       "      <td>51u</td>\n",
       "      <td>48u</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>ilija</td>\n",
       "      <td>horvat</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0.39</td>\n",
       "      <td>auralin</td>\n",
       "      <td>42u</td>\n",
       "      <td>50u</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    given_name    surname  hba1c_start  hba1c_end  hba1c_change treatment  \\\n",
       "36       julia     werner         7.58       7.24          0.34   auralin   \n",
       "16   alexander  mathiesen         7.96       7.55          0.41   auralin   \n",
       "70     manuela    cindrić         8.07       7.76          0.31   auralin   \n",
       "210       tosh     jensen         7.93       7.69          0.24   novodra   \n",
       "126      ilija     horvat         7.77       7.38          0.39   auralin   \n",
       "\n",
       "    dose_start dose_end adverse_reaction  \n",
       "36         35u      40u              NaN  \n",
       "16         47u      58u              NaN  \n",
       "70         55u      66u              NaN  \n",
       "210        51u      48u              NaN  \n",
       "126        42u      50u              NaN  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "treatments_clean.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Given name and surname columns in `patients` table duplicated in `treatments` and `adverse_reactions` tables  and Lowercase given names and surnames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: [tutorial](https://chrisalbon.com/python/pandas_join_merge_dataframe.html) for one function used in the solution and [tutorial](http://erikrood.com/Python_References/dropping_rows_cols_pandas.html) for another function used in the solution.*\n",
    "\n",
    "Replace the given_name and surname columns in treatments_clean with patient_ID \n",
    "1)create a new dataframe \"names_id\" with the surname, given_name and patient ID from patients table, \n",
    "2) change surnames and given_names to lower case \n",
    "3)merge names_id with treatment table on surname and given_name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:3110: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "# Your cleaning code here\n",
    "id_names = patients_clean[['patient_id', 'given_name', 'surname']]\n",
    "id_names.given_name = id_names.given_name.str.lower()\n",
    "id_names.surname = id_names.surname.str.lower()\n",
    "treatments_clean = pd.merge(treatments_clean, id_names, on=['given_name', 'surname'])\n",
    "treatments_clean = treatments_clean.drop(['given_name', 'surname'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
       "      <th>treatment</th>\n",
       "      <th>dose_start</th>\n",
       "      <th>dose_end</th>\n",
       "      <th>adverse_reaction</th>\n",
       "      <th>patient_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.63</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.43</td>\n",
       "      <td>auralin</td>\n",
       "      <td>41u</td>\n",
       "      <td>48u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.97</td>\n",
       "      <td>7.62</td>\n",
       "      <td>0.35</td>\n",
       "      <td>auralin</td>\n",
       "      <td>33u</td>\n",
       "      <td>36u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.65</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.38</td>\n",
       "      <td>auralin</td>\n",
       "      <td>37u</td>\n",
       "      <td>42u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "      <td>auralin</td>\n",
       "      <td>31u</td>\n",
       "      <td>38u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.76</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.39</td>\n",
       "      <td>auralin</td>\n",
       "      <td>30u</td>\n",
       "      <td>36u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.70</td>\n",
       "      <td>7.19</td>\n",
       "      <td>0.51</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>36u</td>\n",
       "      <td>hypoglycemia</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.70</td>\n",
       "      <td>7.19</td>\n",
       "      <td>0.51</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>36u</td>\n",
       "      <td>hypoglycemia</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.54</td>\n",
       "      <td>9.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>38u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.74</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.44</td>\n",
       "      <td>auralin</td>\n",
       "      <td>27u</td>\n",
       "      <td>37u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "      <td>auralin</td>\n",
       "      <td>55u</td>\n",
       "      <td>68u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.53</td>\n",
       "      <td>7.13</td>\n",
       "      <td>0.40</td>\n",
       "      <td>auralin</td>\n",
       "      <td>28u</td>\n",
       "      <td>37u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.61</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.32</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>39u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8.61</td>\n",
       "      <td>8.18</td>\n",
       "      <td>0.43</td>\n",
       "      <td>auralin</td>\n",
       "      <td>53u</td>\n",
       "      <td>60u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9.68</td>\n",
       "      <td>9.29</td>\n",
       "      <td>0.39</td>\n",
       "      <td>auralin</td>\n",
       "      <td>31u</td>\n",
       "      <td>41u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7.79</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.39</td>\n",
       "      <td>auralin</td>\n",
       "      <td>42u</td>\n",
       "      <td>51u</td>\n",
       "      <td>throat irritation</td>\n",
       "      <td>451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7.81</td>\n",
       "      <td>7.48</td>\n",
       "      <td>0.33</td>\n",
       "      <td>auralin</td>\n",
       "      <td>42u</td>\n",
       "      <td>49u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7.70</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0.32</td>\n",
       "      <td>auralin</td>\n",
       "      <td>35u</td>\n",
       "      <td>39u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.96</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.41</td>\n",
       "      <td>auralin</td>\n",
       "      <td>47u</td>\n",
       "      <td>58u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>7.68</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.44</td>\n",
       "      <td>auralin</td>\n",
       "      <td>45u</td>\n",
       "      <td>48u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>7.92</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.45</td>\n",
       "      <td>auralin</td>\n",
       "      <td>24u</td>\n",
       "      <td>37u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7.92</td>\n",
       "      <td>7.57</td>\n",
       "      <td>0.35</td>\n",
       "      <td>auralin</td>\n",
       "      <td>44u</td>\n",
       "      <td>55u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.53</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.38</td>\n",
       "      <td>auralin</td>\n",
       "      <td>37u</td>\n",
       "      <td>43u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.67</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.30</td>\n",
       "      <td>auralin</td>\n",
       "      <td>43u</td>\n",
       "      <td>47u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7.86</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.35</td>\n",
       "      <td>auralin</td>\n",
       "      <td>36u</td>\n",
       "      <td>42u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>9.18</td>\n",
       "      <td>8.64</td>\n",
       "      <td>0.54</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>37u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7.61</td>\n",
       "      <td>7.16</td>\n",
       "      <td>0.45</td>\n",
       "      <td>auralin</td>\n",
       "      <td>57u</td>\n",
       "      <td>64u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7.52</td>\n",
       "      <td>7.11</td>\n",
       "      <td>0.41</td>\n",
       "      <td>auralin</td>\n",
       "      <td>54u</td>\n",
       "      <td>67u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>7.71</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.37</td>\n",
       "      <td>auralin</td>\n",
       "      <td>34u</td>\n",
       "      <td>42u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7.87</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.40</td>\n",
       "      <td>auralin</td>\n",
       "      <td>29u</td>\n",
       "      <td>37u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>7.74</td>\n",
       "      <td>7.32</td>\n",
       "      <td>0.42</td>\n",
       "      <td>auralin</td>\n",
       "      <td>61u</td>\n",
       "      <td>67u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>7.98</td>\n",
       "      <td>7.60</td>\n",
       "      <td>0.38</td>\n",
       "      <td>novodra</td>\n",
       "      <td>37u</td>\n",
       "      <td>38u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>7.61</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.47</td>\n",
       "      <td>novodra</td>\n",
       "      <td>28u</td>\n",
       "      <td>24u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.43</td>\n",
       "      <td>novodra</td>\n",
       "      <td>24u</td>\n",
       "      <td>26u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "      <td>novodra</td>\n",
       "      <td>20u</td>\n",
       "      <td>21u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>novodra</td>\n",
       "      <td>46u</td>\n",
       "      <td>46u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>7.54</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.37</td>\n",
       "      <td>novodra</td>\n",
       "      <td>26u</td>\n",
       "      <td>27u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>7.51</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.43</td>\n",
       "      <td>novodra</td>\n",
       "      <td>34u</td>\n",
       "      <td>33u</td>\n",
       "      <td>injection site discomfort</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "      <td>novodra</td>\n",
       "      <td>30u</td>\n",
       "      <td>35u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.41</td>\n",
       "      <td>novodra</td>\n",
       "      <td>33u</td>\n",
       "      <td>33u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>8.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.50</td>\n",
       "      <td>novodra</td>\n",
       "      <td>35u</td>\n",
       "      <td>34u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>7.79</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>novodra</td>\n",
       "      <td>30u</td>\n",
       "      <td>27u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>7.70</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.34</td>\n",
       "      <td>novodra</td>\n",
       "      <td>37u</td>\n",
       "      <td>41u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>7.84</td>\n",
       "      <td>7.37</td>\n",
       "      <td>0.47</td>\n",
       "      <td>novodra</td>\n",
       "      <td>43u</td>\n",
       "      <td>40u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>8.90</td>\n",
       "      <td>8.57</td>\n",
       "      <td>0.33</td>\n",
       "      <td>novodra</td>\n",
       "      <td>34u</td>\n",
       "      <td>34u</td>\n",
       "      <td>injection site discomfort</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>7.64</td>\n",
       "      <td>7.24</td>\n",
       "      <td>0.40</td>\n",
       "      <td>novodra</td>\n",
       "      <td>40u</td>\n",
       "      <td>36u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>7.54</td>\n",
       "      <td>7.26</td>\n",
       "      <td>0.28</td>\n",
       "      <td>novodra</td>\n",
       "      <td>37u</td>\n",
       "      <td>40u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>7.67</td>\n",
       "      <td>7.21</td>\n",
       "      <td>0.46</td>\n",
       "      <td>novodra</td>\n",
       "      <td>26u</td>\n",
       "      <td>23u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>9.41</td>\n",
       "      <td>8.94</td>\n",
       "      <td>0.47</td>\n",
       "      <td>novodra</td>\n",
       "      <td>40u</td>\n",
       "      <td>45u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>7.68</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.32</td>\n",
       "      <td>novodra</td>\n",
       "      <td>52u</td>\n",
       "      <td>52u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>7.78</td>\n",
       "      <td>7.39</td>\n",
       "      <td>0.39</td>\n",
       "      <td>novodra</td>\n",
       "      <td>37u</td>\n",
       "      <td>39u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>7.63</td>\n",
       "      <td>7.25</td>\n",
       "      <td>0.38</td>\n",
       "      <td>novodra</td>\n",
       "      <td>42u</td>\n",
       "      <td>42u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>7.92</td>\n",
       "      <td>7.52</td>\n",
       "      <td>0.40</td>\n",
       "      <td>novodra</td>\n",
       "      <td>26u</td>\n",
       "      <td>25u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>7.99</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.29</td>\n",
       "      <td>novodra</td>\n",
       "      <td>36u</td>\n",
       "      <td>33u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>7.55</td>\n",
       "      <td>7.18</td>\n",
       "      <td>0.37</td>\n",
       "      <td>novodra</td>\n",
       "      <td>42u</td>\n",
       "      <td>41u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>7.65</td>\n",
       "      <td>7.26</td>\n",
       "      <td>0.39</td>\n",
       "      <td>novodra</td>\n",
       "      <td>32u</td>\n",
       "      <td>33u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>7.51</td>\n",
       "      <td>7.06</td>\n",
       "      <td>0.45</td>\n",
       "      <td>novodra</td>\n",
       "      <td>55u</td>\n",
       "      <td>51u</td>\n",
       "      <td>nausea</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>7.67</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.37</td>\n",
       "      <td>novodra</td>\n",
       "      <td>26u</td>\n",
       "      <td>23u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>9.21</td>\n",
       "      <td>8.80</td>\n",
       "      <td>0.41</td>\n",
       "      <td>novodra</td>\n",
       "      <td>22u</td>\n",
       "      <td>23u</td>\n",
       "      <td>injection site discomfort</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>7.96</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.45</td>\n",
       "      <td>novodra</td>\n",
       "      <td>28u</td>\n",
       "      <td>26u</td>\n",
       "      <td>hypoglycemia</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>7.68</td>\n",
       "      <td>7.21</td>\n",
       "      <td>0.47</td>\n",
       "      <td>novodra</td>\n",
       "      <td>42u</td>\n",
       "      <td>44u</td>\n",
       "      <td>injection site discomfort</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     hba1c_start  hba1c_end  hba1c_change treatment dose_start dose_end  \\\n",
       "0           7.63       7.20          0.43   auralin        41u      48u   \n",
       "1           7.97       7.62          0.35   auralin        33u      36u   \n",
       "2           7.65       7.27          0.38   auralin        37u      42u   \n",
       "3           7.89       7.55          0.34   auralin        31u      38u   \n",
       "4           7.76       7.37          0.39   auralin        30u      36u   \n",
       "5           7.70       7.19          0.51   auralin        29u      36u   \n",
       "6           7.70       7.19          0.51   auralin        29u      36u   \n",
       "7           9.54       9.14          0.40   auralin        29u      38u   \n",
       "8           7.74       7.30          0.44   auralin        27u      37u   \n",
       "9           7.78       7.34          0.44   auralin        55u      68u   \n",
       "10          7.53       7.13          0.40   auralin        28u      37u   \n",
       "11          7.61       7.29          0.32   auralin        29u      39u   \n",
       "12          8.61       8.18          0.43   auralin        53u      60u   \n",
       "13          9.68       9.29          0.39   auralin        31u      41u   \n",
       "14          7.79       7.40          0.39   auralin        42u      51u   \n",
       "15          7.81       7.48          0.33   auralin        42u      49u   \n",
       "16          7.70       7.38          0.32   auralin        35u      39u   \n",
       "17          7.96       7.55          0.41   auralin        47u      58u   \n",
       "18          7.68       7.24          0.44   auralin        45u      48u   \n",
       "19          7.92       7.47          0.45   auralin        24u      37u   \n",
       "20          7.92       7.57          0.35   auralin        44u      55u   \n",
       "21          7.53       7.15          0.38   auralin        37u      43u   \n",
       "22          7.67       7.37          0.30   auralin        43u      47u   \n",
       "23          7.86       7.51          0.35   auralin        36u      42u   \n",
       "24          9.18       8.64          0.54   auralin        29u      37u   \n",
       "25          7.61       7.16          0.45   auralin        57u      64u   \n",
       "26          7.52       7.11          0.41   auralin        54u      67u   \n",
       "27          7.71       7.34          0.37   auralin        34u      42u   \n",
       "28          7.87       7.47          0.40   auralin        29u      37u   \n",
       "29          7.74       7.32          0.42   auralin        61u      67u   \n",
       "..           ...        ...           ...       ...        ...      ...   \n",
       "319         7.98       7.60          0.38   novodra        37u      38u   \n",
       "320         7.61       7.14          0.47   novodra        28u      24u   \n",
       "321         7.99       7.56          0.43   novodra        24u      26u   \n",
       "322         7.73       7.34          0.39   novodra        20u      21u   \n",
       "323         7.71       7.30          0.41   novodra        46u      46u   \n",
       "324         7.54       7.17          0.37   novodra        26u      27u   \n",
       "325         7.51       7.08          0.43   novodra        34u      33u   \n",
       "326         7.89       7.55          0.34   novodra        30u      35u   \n",
       "327         7.71       7.30          0.41   novodra        33u      33u   \n",
       "328         8.00       7.50          0.50   novodra        35u      34u   \n",
       "329         7.79       7.45          0.34   novodra        30u      27u   \n",
       "330         7.70       7.36          0.34   novodra        37u      41u   \n",
       "331         7.84       7.37          0.47   novodra        43u      40u   \n",
       "332         8.90       8.57          0.33   novodra        34u      34u   \n",
       "333         7.64       7.24          0.40   novodra        40u      36u   \n",
       "334         7.54       7.26          0.28   novodra        37u      40u   \n",
       "335         7.67       7.21          0.46   novodra        26u      23u   \n",
       "336         9.41       8.94          0.47   novodra        40u      45u   \n",
       "337         7.68       7.36          0.32   novodra        52u      52u   \n",
       "338         7.78       7.39          0.39   novodra        37u      39u   \n",
       "339         7.63       7.25          0.38   novodra        42u      42u   \n",
       "340         7.92       7.52          0.40   novodra        26u      25u   \n",
       "341         7.99       7.70          0.29   novodra        36u      33u   \n",
       "342         7.55       7.18          0.37   novodra        42u      41u   \n",
       "343         7.65       7.26          0.39   novodra        32u      33u   \n",
       "344         7.51       7.06          0.45   novodra        55u      51u   \n",
       "345         7.67       7.30          0.37   novodra        26u      23u   \n",
       "346         9.21       8.80          0.41   novodra        22u      23u   \n",
       "347         7.96       7.51          0.45   novodra        28u      26u   \n",
       "348         7.68       7.21          0.47   novodra        42u      44u   \n",
       "\n",
       "              adverse_reaction  patient_id  \n",
       "0                          NaN         225  \n",
       "1                          NaN         242  \n",
       "2                          NaN         345  \n",
       "3                          NaN         276  \n",
       "4                          NaN          15  \n",
       "5                 hypoglycemia          70  \n",
       "6                 hypoglycemia          70  \n",
       "7                          NaN          18  \n",
       "8                          NaN         424  \n",
       "9                          NaN         292  \n",
       "10                         NaN         211  \n",
       "11                         NaN         133  \n",
       "12                         NaN         316  \n",
       "13                         NaN         101  \n",
       "14           throat irritation         451  \n",
       "15                         NaN         335  \n",
       "16                         NaN         389  \n",
       "17                         NaN          71  \n",
       "18                         NaN         297  \n",
       "19                         NaN         188  \n",
       "20                         NaN         282  \n",
       "21                         NaN         174  \n",
       "22                         NaN         146  \n",
       "23                         NaN          35  \n",
       "24                         NaN         350  \n",
       "25                         NaN         220  \n",
       "26                         NaN         102  \n",
       "27                         NaN         181  \n",
       "28                         NaN         466  \n",
       "29                         NaN         205  \n",
       "..                         ...         ...  \n",
       "319                        NaN         187  \n",
       "320                        NaN         234  \n",
       "321                        NaN         479  \n",
       "322                        NaN          49  \n",
       "323                        NaN         356  \n",
       "324                        NaN         208  \n",
       "325  injection site discomfort         373  \n",
       "326                        NaN          63  \n",
       "327                        NaN           1  \n",
       "328                        NaN         291  \n",
       "329                        NaN         363  \n",
       "330                        NaN         465  \n",
       "331                        NaN         421  \n",
       "332  injection site discomfort         376  \n",
       "333                        NaN         463  \n",
       "334                        NaN          81  \n",
       "335                        NaN         322  \n",
       "336                        NaN         392  \n",
       "337                        NaN         262  \n",
       "338                        NaN          68  \n",
       "339                        NaN         152  \n",
       "340                        NaN         431  \n",
       "341                        NaN         450  \n",
       "342                        NaN         194  \n",
       "343                        NaN           7  \n",
       "344                     nausea         153  \n",
       "345                        NaN         420  \n",
       "346  injection site discomfort         336  \n",
       "347               hypoglycemia          25  \n",
       "348  injection site discomfort         477  \n",
       "\n",
       "[349 rows x 8 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "treatments_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22    patient_id\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Patient ID should be the only duplicate column\n",
    "all_columns = pd.Series(list(patients_clean) + list(treatments_clean))\n",
    "all_columns[all_columns.duplicated()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='red'>Complete the remaining \"Quality\" **Define, Code, and Test** sequences after watching the *\"Cleaning for Quality\"* video.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Zip code is a float not a string and Zip code has four digits sometimes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: see the \"Data Cleaning Process\" page.*\n",
    "* convert float to string using\n",
    "* remove .0\n",
    "* pad leading 0 to make sure each str zip_code is 5 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(patients['zip_code'].isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "patients['zip_code'] = patients['zip_code'].astype(str).str[:-2] #.str.rjust(5,'0') # str[:-2] to get rid of decimal\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "# memthod 1:\n",
    "#for i in range(len(patients['zip_code'])): # len(patients['zip_code'] refers to all the rows under this column\n",
    "     #print(patients['zip_code'][i])\n",
    "#    if len(patients['zip_code'][i])== 4:\n",
    "#        patients['zip_code'][i] = '0'+ patients['zip_code'][i]\n",
    "        \n",
    "# method 2: this method is better coz there may be 3-digit zip code cases\n",
    "patients_clean['zip_code'] = patients_clean['zip_code'].astype(str).str[:-2].str.rjust(5,'0') \n",
    "# str[:-2] to get rid of decimal\n",
    "# rjust(5,'0') to pad 0 at the left side\n",
    "# nan should be condsider too\n",
    "patients_clean['zip_code'] = patients_clean['zip_code'].replace('0000n', np.nan)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    92390\n",
       "1    61812\n",
       "2    68467\n",
       "3    07095\n",
       "4    36303\n",
       "Name: zip_code, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean['zip_code'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Tim Neudorf height is 27 in instead of 72 in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here.*\n",
    "replace the value 27 in height column with 72"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "patients_clean.height = patients_clean.height.replace(27,72)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>Zoe</td>\n",
       "      <td>Wellish</td>\n",
       "      <td>576 Brown Bear Drive</td>\n",
       "      <td>Rancho California</td>\n",
       "      <td>California</td>\n",
       "      <td>92390</td>\n",
       "      <td>United States</td>\n",
       "      <td>7/10/1976</td>\n",
       "      <td>121.7</td>\n",
       "      <td>66</td>\n",
       "      <td>19.6</td>\n",
       "      <td>951-719-9170</td>\n",
       "      <td>ZoeWellish@superrito.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>female</td>\n",
       "      <td>Pamela</td>\n",
       "      <td>Hill</td>\n",
       "      <td>2370 University Hill Road</td>\n",
       "      <td>Armstrong</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>61812</td>\n",
       "      <td>United States</td>\n",
       "      <td>4/3/1967</td>\n",
       "      <td>118.8</td>\n",
       "      <td>66</td>\n",
       "      <td>19.2</td>\n",
       "      <td>+1 (217) 569-3204</td>\n",
       "      <td>PamelaSHill@cuvox.de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>Jae</td>\n",
       "      <td>Debord</td>\n",
       "      <td>1493 Poling Farm Road</td>\n",
       "      <td>York</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>68467</td>\n",
       "      <td>United States</td>\n",
       "      <td>2/19/1980</td>\n",
       "      <td>177.8</td>\n",
       "      <td>71</td>\n",
       "      <td>24.8</td>\n",
       "      <td>402-363-6804</td>\n",
       "      <td>JaeMDebord@gustr.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>Liêm</td>\n",
       "      <td>Phan</td>\n",
       "      <td>2335 Webster Street</td>\n",
       "      <td>Woodbridge</td>\n",
       "      <td>NJ</td>\n",
       "      <td>07095</td>\n",
       "      <td>United States</td>\n",
       "      <td>7/26/1951</td>\n",
       "      <td>220.9</td>\n",
       "      <td>70</td>\n",
       "      <td>31.7</td>\n",
       "      <td>+1 (732) 636-8246</td>\n",
       "      <td>PhanBaLiem@jourrapide.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>Tim</td>\n",
       "      <td>Neudorf</td>\n",
       "      <td>1428 Turkey Pen Lane</td>\n",
       "      <td>Dothan</td>\n",
       "      <td>AL</td>\n",
       "      <td>36303</td>\n",
       "      <td>United States</td>\n",
       "      <td>2/18/1928</td>\n",
       "      <td>192.3</td>\n",
       "      <td>72</td>\n",
       "      <td>26.1</td>\n",
       "      <td>334-515-7487</td>\n",
       "      <td>TimNeudorf@cuvox.de</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id assigned_sex given_name  surname                    address  \\\n",
       "0           1       female        Zoe  Wellish       576 Brown Bear Drive   \n",
       "1           2       female     Pamela     Hill  2370 University Hill Road   \n",
       "2           3         male        Jae   Debord      1493 Poling Farm Road   \n",
       "3           4         male       Liêm     Phan        2335 Webster Street   \n",
       "4           5         male        Tim  Neudorf       1428 Turkey Pen Lane   \n",
       "\n",
       "                city       state zip_code        country  birthdate  weight  \\\n",
       "0  Rancho California  California    92390  United States  7/10/1976   121.7   \n",
       "1          Armstrong    Illinois    61812  United States   4/3/1967   118.8   \n",
       "2               York    Nebraska    68467  United States  2/19/1980   177.8   \n",
       "3         Woodbridge          NJ    07095  United States  7/26/1951   220.9   \n",
       "4             Dothan          AL    36303  United States  2/18/1928   192.3   \n",
       "\n",
       "   height   bmi       phone_number                      email  \n",
       "0      66  19.6       951-719-9170   ZoeWellish@superrito.com  \n",
       "1      66  19.2  +1 (217) 569-3204       PamelaSHill@cuvox.de  \n",
       "2      71  24.8       402-363-6804       JaeMDebord@gustr.com  \n",
       "3      70  31.7  +1 (732) 636-8246  PhanBaLiem@jourrapide.com  \n",
       "4      72  26.1       334-515-7487        TimNeudorf@cuvox.de  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full state names sometimes, abbreviations other times"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: [tutorial](https://chrisalbon.com/python/pandas_apply_operations_to_dataframes.html) for method used in solution.*\n",
    "* create a function to change all the full name states to abbreviation with lambda\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "us_state_abbrev = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}\n",
    "\n",
    "# the parameter in the function should be the same level with the varible to be used later\n",
    "# patients is the same level with patients_clean \n",
    "def abbrevi(patients):\n",
    "    if patients['state'] in us_state_abbrev.keys():\n",
    "        abbre = us_state_abbrev[patients['state']]\n",
    "        return abbre\n",
    "    #else:\n",
    "    if patients['state'] in us_state_abbrev.values():\n",
    "        return patients['state']\n",
    "        \n",
    "patients_clean['state'] = patients_clean.apply(abbrevi,axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      CA\n",
       "1      IL\n",
       "2      NE\n",
       "3      NJ\n",
       "4      AL\n",
       "5      FL\n",
       "6      NV\n",
       "7      CA\n",
       "8      MO\n",
       "9      NY\n",
       "10     MI\n",
       "11     TN\n",
       "12     VA\n",
       "13     AL\n",
       "14     OK\n",
       "15     VA\n",
       "16     CA\n",
       "17     GA\n",
       "18     MT\n",
       "19     CA\n",
       "20     MA\n",
       "21     NJ\n",
       "22     NY\n",
       "23     FL\n",
       "24     NY\n",
       "25     NM\n",
       "26     IL\n",
       "27     LA\n",
       "28     PA\n",
       "29     NY\n",
       "       ..\n",
       "473    MA\n",
       "474    GA\n",
       "475    CA\n",
       "476    PA\n",
       "477    SC\n",
       "478    CA\n",
       "479    CA\n",
       "480    TX\n",
       "481    OK\n",
       "482    TX\n",
       "483    TN\n",
       "484    NC\n",
       "485    FL\n",
       "486    TX\n",
       "487    KS\n",
       "488    FL\n",
       "489    CA\n",
       "490    NY\n",
       "491    TX\n",
       "492    CA\n",
       "493    ID\n",
       "494    MI\n",
       "495    CA\n",
       "496    CA\n",
       "497    OK\n",
       "498    ME\n",
       "499    AZ\n",
       "500    MO\n",
       "501    WA\n",
       "502    NE\n",
       "Name: state, Length: 503, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dsvid Gustafsson"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "patients_clean.given_name = patients_clean.given_name.replace('Dsvid' , 'David')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>male</td>\n",
       "      <td>David</td>\n",
       "      <td>Gustafsson</td>\n",
       "      <td>1790 Nutter Street</td>\n",
       "      <td>Kansas City</td>\n",
       "      <td>MO</td>\n",
       "      <td>64105</td>\n",
       "      <td>United States</td>\n",
       "      <td>3/6/1937</td>\n",
       "      <td>163.9</td>\n",
       "      <td>66</td>\n",
       "      <td>26.5</td>\n",
       "      <td>816-265-9578</td>\n",
       "      <td>DavidGustafsson@armyspy.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient_id assigned_sex given_name     surname             address  \\\n",
       "8           9         male      David  Gustafsson  1790 Nutter Street   \n",
       "\n",
       "          city state zip_code        country birthdate  weight  height   bmi  \\\n",
       "8  Kansas City    MO    64105  United States  3/6/1937   163.9      66  26.5   \n",
       "\n",
       "   phone_number                        email  \n",
       "8  816-265-9578  DavidGustafsson@armyspy.com  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean[patients_clean.surname=='Gustafsson']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Erroneous datatypes (assigned sex, state, zip_code, and birthdate columns) and Erroneous datatypes (auralin and novodra columns) and The letter 'u' in starting and ending doses for Auralin and Novodra"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: [documentation page](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html) for one method used in solution, [documentation page](http://pandas.pydata.org/pandas-docs/version/0.20/generated/pandas.to_datetime.html) for one function used in the solution, and [documentation page](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.strip.html) for another method used in the solution.*\n",
    "\n",
    "* change the data types of assigned sex and state from object to category\n",
    "* change the data types of birthdate from object to date_time\n",
    "* two columns dose_start and dose_end should be integer\n",
    "* replace \"u\" with \"\" for starting and ending doses columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "#patients_clean.dtypes\n",
    "#treatments.dtypes\n",
    "patients_clean[['assigned_sex','state','zip_code']] = patients_clean[['assigned_sex','state','zip_code']].apply(lambda x: x.astype('category'))\n",
    "patients_clean['birthdate'] = pd.to_datetime( patients_clean['birthdate'] )\n",
    "\n",
    "# to use strip(), need to convert to string first\n",
    "treatments_clean['dose_start'] = treatments_clean['dose_start'].str.strip('u').astype(int)\n",
    "treatments_clean['dose_end' ] = treatments_clean['dose_end' ].str.strip('u').astype(int)\n",
    "\n",
    "# to use replace, need to be data frame \n",
    "#treatments_clean['dose_start'] = treatments_clean['dose_start'].replace('u','').astype(int)\n",
    "#treatments_clean['dose_start'] = treatments_clean['dose_start'].replace('u','').astype(int)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id               int64\n",
       "assigned_sex          category\n",
       "given_name              object\n",
       "surname                 object\n",
       "address                 object\n",
       "city                    object\n",
       "state                 category\n",
       "zip_code              category\n",
       "country                 object\n",
       "birthdate       datetime64[ns]\n",
       "weight                 float64\n",
       "height                   int64\n",
       "bmi                    float64\n",
       "phone_number            object\n",
       "email                   object\n",
       "dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dose_start</th>\n",
       "      <th>dose_end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>41</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>31</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>29</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>29</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>55</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>53</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>31</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>35</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>47</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>44</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>37</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>43</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>57</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>54</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>34</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>61</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>37</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>28</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>24</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>26</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>35</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>30</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>37</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>40</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>40</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>52</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>37</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>26</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>36</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>42</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>55</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>26</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>22</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>28</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>42</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     dose_start  dose_end\n",
       "0            41        48\n",
       "1            33        36\n",
       "2            37        42\n",
       "3            31        38\n",
       "4            30        36\n",
       "5            29        36\n",
       "6            29        36\n",
       "7            29        38\n",
       "8            27        37\n",
       "9            55        68\n",
       "10           28        37\n",
       "11           29        39\n",
       "12           53        60\n",
       "13           31        41\n",
       "14           42        51\n",
       "15           42        49\n",
       "16           35        39\n",
       "17           47        58\n",
       "18           45        48\n",
       "19           24        37\n",
       "20           44        55\n",
       "21           37        43\n",
       "22           43        47\n",
       "23           36        42\n",
       "24           29        37\n",
       "25           57        64\n",
       "26           54        67\n",
       "27           34        42\n",
       "28           29        37\n",
       "29           61        67\n",
       "..          ...       ...\n",
       "319          37        38\n",
       "320          28        24\n",
       "321          24        26\n",
       "322          20        21\n",
       "323          46        46\n",
       "324          26        27\n",
       "325          34        33\n",
       "326          30        35\n",
       "327          33        33\n",
       "328          35        34\n",
       "329          30        27\n",
       "330          37        41\n",
       "331          43        40\n",
       "332          34        34\n",
       "333          40        36\n",
       "334          37        40\n",
       "335          26        23\n",
       "336          40        45\n",
       "337          52        52\n",
       "338          37        39\n",
       "339          42        42\n",
       "340          26        25\n",
       "341          36        33\n",
       "342          42        41\n",
       "343          32        33\n",
       "344          55        51\n",
       "345          26        23\n",
       "346          22        23\n",
       "347          28        26\n",
       "348          42        44\n",
       "\n",
       "[349 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean[['dose_start' ,'dose_end' ]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple phone number formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Hint: helpful [Stack Overflow answer](https://stackoverflow.com/a/123681).*\n",
    "* Strip all \" \", \"-\", \"(\", \")\", and \"+\" and store each number without any formatting.\n",
    "* Pad the phone number with a 1 if the length of the number is 10 digits (we want country code)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "patients_clean.phone_number = patients_clean.phone_number.str.replace(r'\\D+', '').str.pad(11, fillchar='1')\n",
    "# '\\D+' stands for all string except digits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      19517199170\n",
       "1      12175693204\n",
       "2      14023636804\n",
       "3      17326368246\n",
       "4      13345157487\n",
       "5      13863345237\n",
       "6      17755335933\n",
       "7      14087783236\n",
       "8      18162659578\n",
       "9      17187959124\n",
       "10     19064788949\n",
       "11     19312070839\n",
       "12     14345092614\n",
       "13     12054178095\n",
       "14     19187123469\n",
       "15     12762251955\n",
       "16     15597657836\n",
       "17     16788298578\n",
       "18     14067752696\n",
       "19     15305328397\n",
       "20     17814471763\n",
       "21     19734455341\n",
       "22     19147456108\n",
       "23     18133559476\n",
       "24     18458587707\n",
       "25     15058284955\n",
       "26     13096718852\n",
       "27     19858147603\n",
       "28     12678957462\n",
       "29     18458587707\n",
       "          ...     \n",
       "473    15089052371\n",
       "474    16782633564\n",
       "475    16509627179\n",
       "476    12679723749\n",
       "477    18432126421\n",
       "478    14157556435\n",
       "479    19094582515\n",
       "480    12103265509\n",
       "481    19187062776\n",
       "482    13616934960\n",
       "483    17315770292\n",
       "484    13366972005\n",
       "485    18506590417\n",
       "486    12546814504\n",
       "487    17852291188\n",
       "488    13524534601\n",
       "489    19252835425\n",
       "490    19146369304\n",
       "491    12102228684\n",
       "492    17144962264\n",
       "493    12083881065\n",
       "494    15867900975\n",
       "495    16505704896\n",
       "496    12097622320\n",
       "497    19189849171\n",
       "498    12074770579\n",
       "499    19282844492\n",
       "500    18162236007\n",
       "501    13604432060\n",
       "502    14028484923\n",
       "Name: phone_number, Length: 503, dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean.phone_number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Default John Doe data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here. Recall that it is assumed that the data that this John Doe data displaced is not recoverable.*\n",
    "* Remove the non-recoverable John Doe records from the patients table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "patients_clean = patients_clean[patients_clean.surname != 'Doe']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jakobsen        3\n",
       "Taylor          3\n",
       "Batukayev       2\n",
       "Silva           2\n",
       "Lâm             2\n",
       "Souza           2\n",
       "Collins         2\n",
       "Correia         2\n",
       "Lương           2\n",
       "Tucker          2\n",
       "Bùi             2\n",
       "Gersten         2\n",
       "Tạ              2\n",
       "Woźniak         2\n",
       "Grímsdóttir     2\n",
       "Ogochukwu       2\n",
       "Schiavone       2\n",
       "Berg            2\n",
       "Cindrić         2\n",
       "Kadyrov         2\n",
       "Liễu            2\n",
       "Lund            2\n",
       "Hueber          2\n",
       "Dratchev        2\n",
       "Nilsen          2\n",
       "Aranda          2\n",
       "Cabrera         2\n",
       "Johnson         2\n",
       "Parker          2\n",
       "Kowalczyk       2\n",
       "               ..\n",
       "Rice            1\n",
       "Hervinsson      1\n",
       "Mikkelsen       1\n",
       "Mathiesen       1\n",
       "Thạch           1\n",
       "Day             1\n",
       "Mata            1\n",
       "Štěpánek        1\n",
       "Aličajić        1\n",
       "Wiśniewska      1\n",
       "Rokavc          1\n",
       "Kašná           1\n",
       "Azevedo         1\n",
       "Ščančar         1\n",
       "Efrem           1\n",
       "Richardson      1\n",
       "Nnonso          1\n",
       "Adams           1\n",
       "Jakšić          1\n",
       "Stanić          1\n",
       "House           1\n",
       "Frederiksen     1\n",
       "Guðbrandsson    1\n",
       "Reynoso         1\n",
       "Sokołowska      1\n",
       "Rushisvili      1\n",
       "Yuan            1\n",
       "Tomaszewski     1\n",
       "Pak             1\n",
       "Lorenzo         1\n",
       "Name: surname, Length: 465, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean.surname.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple records for Jakobsen, Gersten, Taylor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Your definition here.*\n",
    "* remove Jake Jakobsen, Pat Gersten, and Sandy Taylor rows from the patients table. They are all nicknames, second occurence of \n",
    "duplicate. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(patients_clean.address.duplicated())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "482"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(patients_clean.address.notnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here   duplicated but not null values \n",
    "patients_clean = patients_clean[~((patients_clean.address.duplicated()) & patients_clean.address.notnull())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>male</td>\n",
       "      <td>Jakob</td>\n",
       "      <td>Jakobsen</td>\n",
       "      <td>648 Old Dear Lane</td>\n",
       "      <td>Port Jervis</td>\n",
       "      <td>NY</td>\n",
       "      <td>12771</td>\n",
       "      <td>United States</td>\n",
       "      <td>1985-08-01</td>\n",
       "      <td>155.8</td>\n",
       "      <td>67</td>\n",
       "      <td>24.4</td>\n",
       "      <td>18458587707</td>\n",
       "      <td>JakobCJakobsen@einrot.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>433</td>\n",
       "      <td>female</td>\n",
       "      <td>Karen</td>\n",
       "      <td>Jakobsen</td>\n",
       "      <td>1690 Fannie Street</td>\n",
       "      <td>Houston</td>\n",
       "      <td>TX</td>\n",
       "      <td>77020</td>\n",
       "      <td>United States</td>\n",
       "      <td>1962-11-25</td>\n",
       "      <td>185.2</td>\n",
       "      <td>67</td>\n",
       "      <td>29.0</td>\n",
       "      <td>19792030438</td>\n",
       "      <td>KarenJakobsen@jourrapide.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name   surname             address  \\\n",
       "24           25         male      Jakob  Jakobsen   648 Old Dear Lane   \n",
       "432         433       female      Karen  Jakobsen  1690 Fannie Street   \n",
       "\n",
       "            city state zip_code        country  birthdate  weight  height  \\\n",
       "24   Port Jervis    NY    12771  United States 1985-08-01   155.8      67   \n",
       "432      Houston    TX    77020  United States 1962-11-25   185.2      67   \n",
       "\n",
       "      bmi phone_number                         email  \n",
       "24   24.4  18458587707     JakobCJakobsen@einrot.com  \n",
       "432  29.0  19792030438  KarenJakobsen@jourrapide.com  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean[ patients_clean.surname == 'Jakobsen' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>98</td>\n",
       "      <td>male</td>\n",
       "      <td>Patrick</td>\n",
       "      <td>Gersten</td>\n",
       "      <td>2778 North Avenue</td>\n",
       "      <td>Burr</td>\n",
       "      <td>NE</td>\n",
       "      <td>68324</td>\n",
       "      <td>United States</td>\n",
       "      <td>1954-05-03</td>\n",
       "      <td>138.2</td>\n",
       "      <td>71</td>\n",
       "      <td>19.3</td>\n",
       "      <td>14028484923</td>\n",
       "      <td>PatrickGersten@rhyta.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient_id assigned_sex given_name  surname            address  city  \\\n",
       "97          98         male    Patrick  Gersten  2778 North Avenue  Burr   \n",
       "\n",
       "   state zip_code        country  birthdate  weight  height   bmi  \\\n",
       "97    NE    68324  United States 1954-05-03   138.2      71  19.3   \n",
       "\n",
       "   phone_number                     email  \n",
       "97  14028484923  PatrickGersten@rhyta.com  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean[ patients_clean.surname == 'Gersten' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>132</td>\n",
       "      <td>female</td>\n",
       "      <td>Sandra</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>2476 Fulton Street</td>\n",
       "      <td>Rainelle</td>\n",
       "      <td>WV</td>\n",
       "      <td>25962</td>\n",
       "      <td>United States</td>\n",
       "      <td>1960-10-23</td>\n",
       "      <td>206.1</td>\n",
       "      <td>64</td>\n",
       "      <td>35.4</td>\n",
       "      <td>13044382648</td>\n",
       "      <td>SandraCTaylor@dayrep.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>427</td>\n",
       "      <td>male</td>\n",
       "      <td>Rogelio</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>4064 Marigold Lane</td>\n",
       "      <td>Miami</td>\n",
       "      <td>FL</td>\n",
       "      <td>33179</td>\n",
       "      <td>United States</td>\n",
       "      <td>1992-09-02</td>\n",
       "      <td>186.6</td>\n",
       "      <td>69</td>\n",
       "      <td>27.6</td>\n",
       "      <td>13054346299</td>\n",
       "      <td>RogelioJTaylor@teleworm.us</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name surname             address      city  \\\n",
       "131         132       female     Sandra  Taylor  2476 Fulton Street  Rainelle   \n",
       "426         427         male    Rogelio  Taylor  4064 Marigold Lane     Miami   \n",
       "\n",
       "    state zip_code        country  birthdate  weight  height   bmi  \\\n",
       "131    WV    25962  United States 1960-10-23   206.1      64  35.4   \n",
       "426    FL    33179  United States 1992-09-02   186.6      69  27.6   \n",
       "\n",
       "    phone_number                       email  \n",
       "131  13044382648    SandraCTaylor@dayrep.com  \n",
       "426  13054346299  RogelioJTaylor@teleworm.us  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_clean[ patients_clean.surname == 'Taylor' ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### kgs instead of lbs for Zaitseva weight"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Define\n",
    "*Use advanced indexing to isolate the row where the surname is Zaitseva and convert the entry in its weight field from kg to lbs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your cleaning code here\n",
    "weight_kg = patients_clean.weight.min()\n",
    "mask = patients_clean.surname == 'Zaitseva'\n",
    "column_name = 'weight'\n",
    "patients_clean.loc[mask, column_name] = weight_kg * 2.20462\n",
    "\n",
    "#  To replace multiples columns convert to numpy array using .values:\n",
    "#  df.loc[df.A==0, ['B', 'C']] = df.loc[df.A==0, ['B', 'C']].values / 2    \n",
    "# df.A==0 is condition , once meet the condition,  column B , C values /2\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>assigned_sex</th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>country</th>\n",
       "      <th>birthdate</th>\n",
       "      <th>weight</th>\n",
       "      <th>height</th>\n",
       "      <th>bmi</th>\n",
       "      <th>phone_number</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>211</td>\n",
       "      <td>female</td>\n",
       "      <td>Camilla</td>\n",
       "      <td>Zaitseva</td>\n",
       "      <td>4689 Briarhill Lane</td>\n",
       "      <td>Wooster</td>\n",
       "      <td>OH</td>\n",
       "      <td>44691</td>\n",
       "      <td>United States</td>\n",
       "      <td>1938-11-26</td>\n",
       "      <td>107.585456</td>\n",
       "      <td>63</td>\n",
       "      <td>19.1</td>\n",
       "      <td>13302022145</td>\n",
       "      <td>CamillaZaitseva@superrito.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     patient_id assigned_sex given_name   surname              address  \\\n",
       "210         211       female    Camilla  Zaitseva  4689 Briarhill Lane   \n",
       "\n",
       "        city state zip_code        country  birthdate      weight  height  \\\n",
       "210  Wooster    OH    44691  United States 1938-11-26  107.585456      63   \n",
       "\n",
       "      bmi phone_number                          email  \n",
       "210  19.1  13302022145  CamillaZaitseva@superrito.com  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your testing code here\n",
    "patients_clean[patients_clean.surname == 'Zaitseva']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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