drug_trial_wrangling_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "patients_df = pd.read_csv(\"patients.csv\")\n",
    "treatments_df = pd.read_csv(\"treatments.csv\")\n",
    "treatments_other = pd.read_csv('treatments_cut.csv')\n",
    "adverse_reactions_df = pd.read_csv(\"adverse_reactions.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assess Data\n",
    "\n",
    "We'll go through one table at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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": [
    "#contact info has missing values\n",
    "zipcode should be string\n",
    "patients_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    503\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#are there duplicated rows?\n",
    "patients_df.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4      27\n",
       "423    59\n",
       "335    59\n",
       "290    59\n",
       "132    59\n",
       "268    59\n",
       "400    59\n",
       "232    59\n",
       "171    59\n",
       "355    59\n",
       "181    59\n",
       "454    59\n",
       "203    60\n",
       "175    60\n",
       "82     60\n",
       "57     60\n",
       "227    60\n",
       "143    60\n",
       "191    60\n",
       "272    60\n",
       "367    60\n",
       "255    60\n",
       "443    60\n",
       "157    60\n",
       "17     60\n",
       "241    60\n",
       "294    60\n",
       "346    60\n",
       "7      60\n",
       "62     60\n",
       "       ..\n",
       "201    73\n",
       "349    73\n",
       "406    73\n",
       "247    73\n",
       "158    73\n",
       "98     74\n",
       "242    74\n",
       "395    74\n",
       "384    74\n",
       "368    74\n",
       "111    74\n",
       "315    74\n",
       "486    74\n",
       "413    74\n",
       "261    74\n",
       "291    74\n",
       "450    74\n",
       "453    74\n",
       "485    74\n",
       "138    74\n",
       "364    75\n",
       "160    75\n",
       "391    75\n",
       "284    75\n",
       "83     76\n",
       "88     76\n",
       "121    76\n",
       "487    77\n",
       "238    78\n",
       "418    79\n",
       "Name: height, Length: 503, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for strange height values\n",
    "#27 in. doesn't make any sense\n",
    "patients_df.height.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "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": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for strange weight values\n",
    "#48.8 lbs doesn't make any sense. Maybe in kg? \n",
    "patients_df.weight.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48     17.1\n",
       "38     17.5\n",
       "438    17.5\n",
       "265    17.7\n",
       "408    17.7\n",
       "338    17.9\n",
       "321    18.0\n",
       "397    18.2\n",
       "208    18.4\n",
       "278    18.4\n",
       "168    18.6\n",
       "14     18.6\n",
       "307    18.7\n",
       "341    18.8\n",
       "158    18.8\n",
       "317    18.8\n",
       "51     19.0\n",
       "210    19.1\n",
       "31     19.1\n",
       "1      19.2\n",
       "458    19.3\n",
       "459    19.3\n",
       "97     19.3\n",
       "502    19.3\n",
       "306    19.4\n",
       "478    19.4\n",
       "49     19.4\n",
       "213    19.4\n",
       "362    19.5\n",
       "74     19.5\n",
       "       ... \n",
       "13     35.8\n",
       "339    35.9\n",
       "23     36.0\n",
       "157    36.0\n",
       "125    36.0\n",
       "177    36.1\n",
       "61     36.2\n",
       "130    36.2\n",
       "113    36.2\n",
       "10     36.4\n",
       "35     36.4\n",
       "403    36.5\n",
       "236    36.5\n",
       "255    36.5\n",
       "482    36.6\n",
       "479    36.6\n",
       "104    36.7\n",
       "279    36.7\n",
       "376    36.7\n",
       "227    36.7\n",
       "355    36.9\n",
       "52     36.9\n",
       "443    37.3\n",
       "367    37.3\n",
       "283    37.3\n",
       "246    37.5\n",
       "42     37.6\n",
       "203    37.6\n",
       "245    37.7\n",
       "431    37.7\n",
       "Name: bmi, Length: 503, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for strange bmi values\n",
    "patients_df.bmi.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10/9/1965     1\n",
       "5/7/1925      1\n",
       "9/14/1989     1\n",
       "11/17/1985    1\n",
       "10/7/1961     1\n",
       "9/27/1950     1\n",
       "2/20/1996     1\n",
       "2/10/1926     1\n",
       "11/15/1967    1\n",
       "8/4/1943      1\n",
       "6/16/1948     1\n",
       "10/27/1976    1\n",
       "7/24/1922     1\n",
       "9/8/1969      1\n",
       "7/2/1932      1\n",
       "5/23/1990     1\n",
       "9/27/1970     1\n",
       "1/16/1987     1\n",
       "12/14/1926    1\n",
       "4/26/1964     1\n",
       "10/30/1951    1\n",
       "9/12/1923     1\n",
       "2/5/1970      1\n",
       "8/19/1943     1\n",
       "6/18/1980     1\n",
       "12/31/1942    1\n",
       "6/30/1936     1\n",
       "12/25/1935    1\n",
       "4/3/1967      1\n",
       "3/8/1949      1\n",
       "             ..\n",
       "5/1/1985      1\n",
       "4/7/1943      1\n",
       "10/6/1948     1\n",
       "6/23/1967     1\n",
       "9/29/1966     1\n",
       "8/13/1958     1\n",
       "9/18/1937     1\n",
       "2/29/1948     1\n",
       "12/12/1971    1\n",
       "10/26/1976    1\n",
       "10/11/1932    1\n",
       "1/19/1928     1\n",
       "12/3/1930     1\n",
       "11/28/1983    1\n",
       "12/6/1964     1\n",
       "5/2/1922      1\n",
       "3/6/1974      1\n",
       "6/24/1979     1\n",
       "4/7/1936      1\n",
       "1/5/1967      1\n",
       "4/5/1981      1\n",
       "1/21/1985     1\n",
       "2/12/1931     1\n",
       "2/5/1986      1\n",
       "8/1/1985      2\n",
       "3/25/1938     2\n",
       "5/3/1954      2\n",
       "10/23/1960    2\n",
       "4/10/1959     2\n",
       "1/1/1975      6\n",
       "Name: birthdate, Length: 493, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lots of 1/1/1975\n",
    "patients_df.birthdate.value_counts().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "johndoe@email.com1234567890                        6\n",
       "304-438-2648SandraCTaylor@dayrep.com               2\n",
       "PatrickGersten@rhyta.com402-848-4923               2\n",
       "JakobCJakobsen@einrot.com+1 (845) 858-7707         2\n",
       "215-321-9611HuiCheng@rhyta.com                     1\n",
       "863-438-6922FelicijanBubanj@einrot.com             1\n",
       "SaberMenard@rhyta.com+1 (608) 527-1021             1\n",
       "SirkkaPiirainen@teleworm.us+1 (586) 790-0975       1\n",
       "816-223-6007JinkedeKeizer@teleworm.us              1\n",
       "JasmineSykes@jourrapide.com925-283-5425            1\n",
       "201-739-3931KaodilinakachukwuNnonso@gustr.com      1\n",
       "HerczeghCsilla@jourrapide.com1 813 434 8122        1\n",
       "RobertMaslov@fleckens.hu401-535-2675               1\n",
       "601-699-4153StephenFMayberry@jourrapide.com        1\n",
       "SousukeNakahara@jourrapide.com775-848-6970         1\n",
       "MayaIsaksson@jourrapide.com1 608 277 0146          1\n",
       "LovreGalic@gustr.com1 813 355 9476                 1\n",
       "KristiinaHyypia@cuvox.de218-773-9682               1\n",
       "MiloslawWisniewski@rhyta.com618-346-3914           1\n",
       "714-431-2746MeyaSjoberg@cuvox.de                   1\n",
       "724-419-3583JavierMoquin@rhyta.com                 1\n",
       "609-466-3275UrsoArandaSanchez@rhyta.com            1\n",
       "309-671-8852EllaLund@armyspy.com                   1\n",
       "775-533-5933MaryBAdams@einrot.com                  1\n",
       "KyoukoOno@superrito.com1 858 587 9945              1\n",
       "ChidaluOnyekaozulu@jourrapide.com1 360 443 2060    1\n",
       "217-485-5673CumelenMiramontesAcosta@gustr.com      1\n",
       "302-474-8075MarkusSolberg@fleckens.hu              1\n",
       "RobertWagner@jourrapide.com540-986-1369            1\n",
       "573-728-3861ToshCJensen@superrito.com              1\n",
       "                                                  ..\n",
       "870-270-5502ArminaSauve@dayrep.com                 1\n",
       "AlbikaIbragimov@superrito.com716-897-5050          1\n",
       "415-277-2563AngelicoMarchesi@fleckens.hu           1\n",
       "CarolineTShuler@jourrapide.com713-230-1739         1\n",
       "508-454-2027GanimeteScancar@cuvox.de               1\n",
       "MarijaGrubisic@cuvox.de248-944-0932                1\n",
       "AndreaBrodahl@armyspy.com+1 (612) 589-1495         1\n",
       "CsorbaCintia@fleckens.hu916-379-7480               1\n",
       "FinlaySheppard@fleckens.hu+1 (513) 297-6359        1\n",
       "MahmudKadyrov@gustr.com1 701 745 2700              1\n",
       "MakdaAbraham@einrot.com1 508 857 0477              1\n",
       "816-265-9578DavidGustafsson@armyspy.com            1\n",
       "516-740-5280FinleyChandler@dayrep.com              1\n",
       "JaakkoEskelinen@superrito.com+1 (315) 362-5858     1\n",
       "228-378-1355KhalidJohnsrud@teleworm.us             1\n",
       "KrystianaKozlowska@cuvox.de+1 (401) 419-2975       1\n",
       "SophieCabreraIbarra@teleworm.us1 718 795 9124      1\n",
       "985-253-7891LamaraDratchev@teleworm.us             1\n",
       "EvenKnutsen@rhyta.com+1 (605) 440-5492             1\n",
       "408-215-6012SargentFlamand@dayrep.com              1\n",
       "908-871-7009TorfiIrisarson@jourrapide.com          1\n",
       "AnnieJAllen@superrito.com1 508 921 6327            1\n",
       "251-359-2088MgbankwoAfamefuna@einrot.com           1\n",
       "352-453-4601MikaMartinsson@armyspy.com             1\n",
       "EddieAArcher@gustr.com+1 (224) 305-6805            1\n",
       "724-759-0310CsonkaBodor@jourrapide.com             1\n",
       "703-547-0551SunKo@einrot.com                       1\n",
       "706-755-5723MariusHansen@teleworm.us               1\n",
       "219-843-3924AsunaMorita@superrito.com              1\n",
       "KisanetSelassie@gustr.com+1 (916) 453-3601         1\n",
       "Name: contact, Length: 483, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#email and phone are concatenated\n",
    "patients_df.contact.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "United States    491\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_df.country.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12345.0    6\n",
       "10004.0    4\n",
       "30303.0    4\n",
       "11590.0    3\n",
       "10011.0    3\n",
       "90017.0    3\n",
       "1730.0     3\n",
       "70112.0    3\n",
       "60148.0    3\n",
       "35203.0    3\n",
       "11530.0    3\n",
       "15205.0    3\n",
       "98109.0    3\n",
       "92103.0    2\n",
       "2110.0     2\n",
       "92101.0    2\n",
       "60631.0    2\n",
       "19108.0    2\n",
       "13221.0    2\n",
       "23602.0    2\n",
       "33323.0    2\n",
       "95134.0    2\n",
       "39501.0    2\n",
       "12207.0    2\n",
       "94108.0    2\n",
       "2081.0     2\n",
       "34471.0    2\n",
       "7087.0     2\n",
       "97205.0    2\n",
       "25962.0    2\n",
       "          ..\n",
       "95453.0    1\n",
       "53204.0    1\n",
       "55410.0    1\n",
       "73086.0    1\n",
       "48342.0    1\n",
       "59324.0    1\n",
       "92111.0    1\n",
       "19020.0    1\n",
       "22958.0    1\n",
       "46312.0    1\n",
       "13904.0    1\n",
       "2210.0     1\n",
       "2780.0     1\n",
       "75601.0    1\n",
       "73844.0    1\n",
       "64105.0    1\n",
       "36104.0    1\n",
       "64110.0    1\n",
       "35806.0    1\n",
       "83702.0    1\n",
       "27893.0    1\n",
       "10036.0    1\n",
       "79506.0    1\n",
       "39362.0    1\n",
       "94070.0    1\n",
       "94549.0    1\n",
       "19034.0    1\n",
       "11743.0    1\n",
       "38310.0    1\n",
       "1752.0     1\n",
       "Name: zip_code, Length: 430, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lots of 12345 - likely dummy data\n",
    "#note: 4 digit zipcodes are valid\n",
    "patients_df.zip_code.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "California    36\n",
       "TX            32\n",
       "New York      25\n",
       "CA            24\n",
       "NY            22\n",
       "MA            22\n",
       "PA            18\n",
       "GA            15\n",
       "Illinois      14\n",
       "OH            14\n",
       "LA            13\n",
       "MI            13\n",
       "Florida       13\n",
       "OK            13\n",
       "NJ            12\n",
       "VA            11\n",
       "MS            10\n",
       "IL            10\n",
       "WI            10\n",
       "TN             9\n",
       "AL             9\n",
       "MN             9\n",
       "FL             9\n",
       "IN             9\n",
       "KY             8\n",
       "NC             8\n",
       "WA             8\n",
       "MO             7\n",
       "NV             6\n",
       "KS             6\n",
       "ID             6\n",
       "CT             5\n",
       "IA             5\n",
       "SC             5\n",
       "Nebraska       4\n",
       "ND             4\n",
       "AZ             4\n",
       "ME             4\n",
       "CO             4\n",
       "AR             4\n",
       "RI             4\n",
       "MD             3\n",
       "DE             3\n",
       "WV             3\n",
       "OR             3\n",
       "SD             3\n",
       "NE             2\n",
       "MT             2\n",
       "DC             2\n",
       "VT             2\n",
       "NH             1\n",
       "NM             1\n",
       "AK             1\n",
       "WY             1\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#mixed abbreviations and full names\n",
    "patients_df.state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "New York            18\n",
       "San Diego            8\n",
       "Tulsa                7\n",
       "Houston              6\n",
       "Atlanta              6\n",
       "Chicago              6\n",
       "Los Angeles          5\n",
       "Pittsburgh           5\n",
       "Philadelphia         5\n",
       "Portland             4\n",
       "Providence           4\n",
       "New Orleans          4\n",
       "San Antonio          4\n",
       "Sacramento           4\n",
       "Bedford              3\n",
       "San Jose             3\n",
       "Seattle              3\n",
       "Burbank              3\n",
       "Albany               3\n",
       "Boston               3\n",
       "Lombard              3\n",
       "Garden City          3\n",
       "Birmingham           3\n",
       "Newport News         3\n",
       "Manhattan            3\n",
       "Westbury             3\n",
       "Washington           3\n",
       "Hartford             3\n",
       "Tampa                3\n",
       "Oklahoma City        3\n",
       "                    ..\n",
       "Wichita              1\n",
       "Elk Lake             1\n",
       "La Mesa              1\n",
       "El Paso              1\n",
       "Clinton              1\n",
       "Bartlett             1\n",
       "Orlando              1\n",
       "Maitland             1\n",
       "Iowa City            1\n",
       "Stamford             1\n",
       "Omaha                1\n",
       "Oroville             1\n",
       "Franklin             1\n",
       "Yadkinville          1\n",
       "Rockford             1\n",
       "Mountain Home        1\n",
       "Medaryville          1\n",
       "Morgan Hill          1\n",
       "Lynchburg            1\n",
       "Downey               1\n",
       "Wilkes Barre         1\n",
       "Madison              1\n",
       "Belleville           1\n",
       "Meriden              1\n",
       "Wayne                1\n",
       "West Los Angeles     1\n",
       "Pritchardville       1\n",
       "Hopewell  Mercer     1\n",
       "Harrisburg           1\n",
       "Little Rock          1\n",
       "Name: city, Length: 349, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_df.city.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123 Main Street               6\n",
       "2476 Fulton Street            2\n",
       "648 Old Dear Lane             2\n",
       "2778 North Avenue             2\n",
       "1324 Duffy Street             1\n",
       "383 Marion Street             1\n",
       "1403 Clousson Road            1\n",
       "3574 Park Boulevard           1\n",
       "3977 Jail Drive               1\n",
       "3314 Rocket Drive             1\n",
       "962 George Street             1\n",
       "4064 Marigold Lane            1\n",
       "4945 Jarvis Street            1\n",
       "2338 Virginia Street          1\n",
       "2621 Koontz Lane              1\n",
       "954 Summit Park Avenue        1\n",
       "3945 Simons Hollow Road       1\n",
       "4852 Rose Avenue              1\n",
       "2972 Hillview Street          1\n",
       "2102 Geraldine Lane           1\n",
       "4277 Mutton Town Road         1\n",
       "3595 Stuart Street            1\n",
       "1904 Granville Lane           1\n",
       "1614 Heather Sees Way         1\n",
       "1463 Martha Ellen Drive       1\n",
       "4334 Black Oak Hollow Road    1\n",
       "2915 Lynn Avenue              1\n",
       "3145 Sheila Lane              1\n",
       "2235 Catherine Drive          1\n",
       "3261 Desert Broom Court       1\n",
       "                             ..\n",
       "3180 Still Pastures Drive     1\n",
       "3688 Glenview Drive           1\n",
       "3818 Strother Street          1\n",
       "4932 Goldleaf Lane            1\n",
       "1275 Goldie Lane              1\n",
       "2595 Feathers Hooves Drive    1\n",
       "4476 Center Street            1\n",
       "3730 Seth Street              1\n",
       "2246 Pheasant Ridge Road      1\n",
       "2530 Victoria Court           1\n",
       "3710 Jerry Dove Drive         1\n",
       "373 Fantages Way              1\n",
       "162 Brooklyn Street           1\n",
       "1262 Deans Lane               1\n",
       "631 Isaacs Creek Road         1\n",
       "3958 Liberty Avenue           1\n",
       "4262 Heron Way                1\n",
       "2386 Linda Street             1\n",
       "3423 Burning Memory Lane      1\n",
       "142 Broad Street              1\n",
       "2957 Feathers Hooves Drive    1\n",
       "2881 Harter Street            1\n",
       "1428 Turkey Pen Lane          1\n",
       "430 Canis Heights Drive       1\n",
       "1934 August Lane              1\n",
       "3259 Roy Alley                1\n",
       "4894 Chandler Hollow Road     1\n",
       "1009 Felosa Drive             1\n",
       "2566 Ingram Street            1\n",
       "4291 Patton Lane              1\n",
       "Name: address, Length: 483, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#123 Main likely dummy data\n",
    "patients_df.address.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Doe            6\n",
       "Jakobsen       3\n",
       "Taylor         3\n",
       "Bùi            2\n",
       "Collins        2\n",
       "Silva          2\n",
       "Cindrić        2\n",
       "Lâm            2\n",
       "Johnson        2\n",
       "Liễu           2\n",
       "Souza          2\n",
       "Batukayev      2\n",
       "Correia        2\n",
       "Hueber         2\n",
       "Kowalczyk      2\n",
       "Grímsdóttir    2\n",
       "Woźniak        2\n",
       "Lương          2\n",
       "Tucker         2\n",
       "Kadyrov        2\n",
       "Tạ             2\n",
       "Aranda         2\n",
       "Schiavone      2\n",
       "Cabrera        2\n",
       "Parker         2\n",
       "Gersten        2\n",
       "Nilsen         2\n",
       "Ogochukwu      2\n",
       "Berg           2\n",
       "Dratchev       2\n",
       "              ..\n",
       "Haugen         1\n",
       "Miranda        1\n",
       "Guzman         1\n",
       "Terrazas       1\n",
       "Fomin          1\n",
       "Aličajić       1\n",
       "Uspenskaya     1\n",
       "Lukić          1\n",
       "Štěpánek       1\n",
       "Barsukov       1\n",
       "Piirainen      1\n",
       "Kishiev        1\n",
       "Chung          1\n",
       "Isa            1\n",
       "Grunewald      1\n",
       "Galvez         1\n",
       "Tuma           1\n",
       "Beauvais       1\n",
       "Ek             1\n",
       "Schmitt        1\n",
       "Edwards        1\n",
       "Fakhoury       1\n",
       "Ščančar        1\n",
       "Osman          1\n",
       "Jakšić         1\n",
       "Adamski        1\n",
       "Ibeamaka       1\n",
       "Allan          1\n",
       "Jackson        1\n",
       "Synek          1\n",
       "Name: surname, Length: 466, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lots of Doe, also Taylor and Jakobsen (could be artifact of random name generation used)\n",
    "patients_df.surname.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "John            9\n",
       "Robert          3\n",
       "Søren           3\n",
       "Abel            2\n",
       "Fatimah         2\n",
       "Xiuxiu          2\n",
       "Mustafa         2\n",
       "Yasmin          2\n",
       "Sandy           2\n",
       "Anja            2\n",
       "Sara            2\n",
       "Leo             2\n",
       "David           2\n",
       "Sofia           2\n",
       "Sargent         2\n",
       "Joseph          2\n",
       "Rebecca         2\n",
       "Caroline        2\n",
       "Julia           2\n",
       "Elisabeth       2\n",
       "Alexander       2\n",
       "Stephen         2\n",
       "Muhammad        2\n",
       "Michael         2\n",
       "Rogelio         1\n",
       "Cecilie         1\n",
       "Maya            1\n",
       "Minea           1\n",
       "Lixue           1\n",
       "Hideki          1\n",
       "               ..\n",
       "Enco            1\n",
       "Haruna          1\n",
       "Nina            1\n",
       "Farizah         1\n",
       "Katrine         1\n",
       "Felicijan       1\n",
       "Nora            1\n",
       "Philemon        1\n",
       "Klementyna      1\n",
       "Ming            1\n",
       "Peter           1\n",
       "Jake            1\n",
       "Meya            1\n",
       "Placido         1\n",
       "Nadwah          1\n",
       "Bartosz         1\n",
       "Onyekachukwu    1\n",
       "Even            1\n",
       "Dsvid           1\n",
       "Regolo          1\n",
       "Thể             1\n",
       "Kristiina       1\n",
       "Mùi             1\n",
       "Malik           1\n",
       "Manouck         1\n",
       "Sophie          1\n",
       "Andrea          1\n",
       "Novalie         1\n",
       "Tegan           1\n",
       "Mee             1\n",
       "Name: given_name, Length: 470, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lots of john (e.g. John Doe => Dummy data)\n",
    "patients_df.given_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      253\n",
       "female    250\n",
       "Name: assigned_sex, dtype: int64"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_df.assigned_sex.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    503\n",
       "Name: patient_id, dtype: int64"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#there should be no duplicated Ids\n",
    "patients_df.patient_id.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patients_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  given_name    surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
      "0      jožka  resanovič  22u - 30u          -         7.56       7.22   \n",
      "1  inunnguaq   heilmann  57u - 67u          -         7.85       7.45   \n",
      "2      alwin   svensson  36u - 39u          -         7.78       7.34   \n",
      "3        thể      lương          -  61u - 64u         7.64       7.22   \n",
      "4     amanda    ribeiro  36u - 44u          -         7.85       7.47   \n",
      "\n",
      "   hba1c_change  \n",
      "0          0.34  \n",
      "1           NaN  \n",
      "2           NaN  \n",
      "3          0.92  \n",
      "4          0.38  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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           NaN  \n",
       "1          0.97  \n",
       "2           NaN  \n",
       "3          0.35  \n",
       "4          0.32  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check if treatment_df and treatment_other are the same\n",
    "# they appear to have all the same column names so let's join them now to prevent having to do work on both files seperately\n",
    "print(treatments_other.head())\n",
    "treatments_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    279\n",
      "True       1\n",
      "dtype: int64\n",
      "False    349\n",
      "True       1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#will automatically raise error if conflict of index or duplication\n",
    "treatments_combined = treatments_other.append(treatments_df, ignore_index=True, verify_integrity=True)\n",
    "\n",
    "#check if there are duplicates before reassiging\n",
    "print(treatments_df.duplicated().value_counts())\n",
    "print(treatments_combined.duplicated().value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "treatments_df = treatments_combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>206</th>\n",
       "      <td>joseph</td>\n",
       "      <td>day</td>\n",
       "      <td>29u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7.19</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    given_name surname    auralin novodra  hba1c_start  hba1c_end  \\\n",
       "206     joseph     day  29u - 36u       -          7.7       7.19   \n",
       "\n",
       "     hba1c_change  \n",
       "206           NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we saw there was one duplicate in the original treatment file. let's find it.\n",
    "treatments_df[treatments_df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350 entries, 0 to 349\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: 19.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#looks like hba1c has quite a few value missing, strange considering there are start and end values for all entries\n",
    "treatments_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "joseph       3\n",
       "david        3\n",
       "anja         2\n",
       "søren        2\n",
       "alexander    2\n",
       "sofia        2\n",
       "robert       2\n",
       "mustafa      2\n",
       "rebecca      2\n",
       "yasmin       2\n",
       "mijndert     1\n",
       "merci        1\n",
       "steven       1\n",
       "malik        1\n",
       "kamila       1\n",
       "olof         1\n",
       "annika       1\n",
       "harley       1\n",
       "firenze      1\n",
       "jeramy       1\n",
       "farizah      1\n",
       "habte        1\n",
       "furuta       1\n",
       "john         1\n",
       "tomáš        1\n",
       "muhammad     1\n",
       "avdei        1\n",
       "jackson      1\n",
       "gina         1\n",
       "nile         1\n",
       "            ..\n",
       "mevla        1\n",
       "mgbankwo     1\n",
       "enco         1\n",
       "finley       1\n",
       "finlay       1\n",
       "guðni        1\n",
       "jožka        1\n",
       "leah         1\n",
       "frydryk      1\n",
       "sargent      1\n",
       "sophia       1\n",
       "flavia       1\n",
       "władysław    1\n",
       "nate         1\n",
       "sandy        1\n",
       "camilla      1\n",
       "miłosław     1\n",
       "mette        1\n",
       "mile         1\n",
       "benoît       1\n",
       "chiemela     1\n",
       "mika         1\n",
       "ruman        1\n",
       "melisa       1\n",
       "jaakko       1\n",
       "samúel       1\n",
       "monika       1\n",
       "svanhvít     1\n",
       "cecilie      1\n",
       "mariana      1\n",
       "Name: given_name, Length: 338, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names are lowercase where as `patients` are capital case\n",
    "treatments_df.given_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dratchev          2\n",
       "woźniak           2\n",
       "batukayev         2\n",
       "bùi               2\n",
       "tucker            2\n",
       "jakobsen          2\n",
       "cindrić           2\n",
       "johnson           2\n",
       "berg              2\n",
       "nilsen            2\n",
       "day               2\n",
       "hueber            2\n",
       "aranda            2\n",
       "schiavone         2\n",
       "lâm               2\n",
       "lương             2\n",
       "ogochukwu         2\n",
       "adams             1\n",
       "rushisvili        1\n",
       "madrid            1\n",
       "karlsen           1\n",
       "bubanj            1\n",
       "chung             1\n",
       "dreher            1\n",
       "moore             1\n",
       "halldórsdóttir    1\n",
       "wong              1\n",
       "okoli             1\n",
       "kinfe             1\n",
       "ménard            1\n",
       "                 ..\n",
       "mehari            1\n",
       "balagić           1\n",
       "andreyeva         1\n",
       "sandgreen         1\n",
       "araujo            1\n",
       "johnsrud          1\n",
       "izmailov          1\n",
       "enríquez          1\n",
       "teng              1\n",
       "nyborg            1\n",
       "petersen          1\n",
       "sultygov          1\n",
       "synek             1\n",
       "gustafsson        1\n",
       "grubišić          1\n",
       "koivuniemi        1\n",
       "moquin            1\n",
       "ingason           1\n",
       "selassie          1\n",
       "lund              1\n",
       "sung              1\n",
       "sági              1\n",
       "lundy             1\n",
       "afamefuna         1\n",
       "quynh             1\n",
       "poulsen           1\n",
       "holden            1\n",
       "mai               1\n",
       "zsinkó            1\n",
       "lucchese          1\n",
       "Name: surname, Length: 333, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_df.surname.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-            175\n",
       "30u - 38u      3\n",
       "36u - 44u      3\n",
       "31u - 38u      3\n",
       "29u - 37u      3\n",
       "29u - 36u      2\n",
       "44u - 54u      2\n",
       "38u - 45u      2\n",
       "42u - 49u      2\n",
       "24u - 32u      2\n",
       "37u - 46u      2\n",
       "36u - 39u      2\n",
       "22u - 30u      2\n",
       "37u - 42u      2\n",
       "48u - 56u      2\n",
       "36u - 42u      2\n",
       "55u - 68u      2\n",
       "37u - 43u      2\n",
       "39u - 50u      2\n",
       "32u - 42u      2\n",
       "36u - 46u      2\n",
       "40u - 49u      2\n",
       "33u - 39u      2\n",
       "39u - 46u      2\n",
       "41u - 50u      2\n",
       "51u - 59u      2\n",
       "27u - 37u      2\n",
       "25u - 35u      2\n",
       "34u - 42u      2\n",
       "42u - 50u      2\n",
       "            ... \n",
       "48u - 57u      1\n",
       "28u - 33u      1\n",
       "57u - 64u      1\n",
       "47u - 57u      1\n",
       "54u - 54u      1\n",
       "39u - 43u      1\n",
       "55u - 60u      1\n",
       "58u - 63u      1\n",
       "33u - 36u      1\n",
       "63u - 74u      1\n",
       "31u - 45u      1\n",
       "35u - 39u      1\n",
       "46u - 58u      1\n",
       "46u - 57u      1\n",
       "42u - 51u      1\n",
       "53u - 63u      1\n",
       "31u - 37u      1\n",
       "53u - 57u      1\n",
       "39u - 47u      1\n",
       "39u - 52u      1\n",
       "43u - 50u      1\n",
       "44u - 53u      1\n",
       "41u - 48u      1\n",
       "30u - 35u      1\n",
       "32u - 43u      1\n",
       "37u - 47u      1\n",
       "39u - 44u      1\n",
       "41u - 51u      1\n",
       "25u - 31u      1\n",
       "32u - 37u      1\n",
       "Name: auralin, Length: 142, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#null values are denoted as \"-\"\n",
    "#would be nice to have these values as ints rather than strings\n",
    "treatments_df.auralin.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-            175\n",
       "41u - 39u      4\n",
       "39u - 36u      4\n",
       "40u - 39u      3\n",
       "49u - 46u      3\n",
       "42u - 42u      3\n",
       "44u - 43u      3\n",
       "42u - 41u      3\n",
       "42u - 44u      3\n",
       "26u - 23u      3\n",
       "30u - 32u      3\n",
       "27u - 29u      2\n",
       "40u - 36u      2\n",
       "40u - 45u      2\n",
       "34u - 34u      2\n",
       "43u - 40u      2\n",
       "32u - 27u      2\n",
       "36u - 38u      2\n",
       "38u - 41u      2\n",
       "47u - 48u      2\n",
       "37u - 35u      2\n",
       "28u - 24u      2\n",
       "35u - 29u      2\n",
       "54u - 54u      2\n",
       "51u - 54u      2\n",
       "41u - 38u      2\n",
       "43u - 42u      2\n",
       "26u - 25u      2\n",
       "36u - 37u      2\n",
       "32u - 31u      2\n",
       "            ... \n",
       "41u - 43u      1\n",
       "51u - 55u      1\n",
       "23u - 23u      1\n",
       "43u - 38u      1\n",
       "35u - 34u      1\n",
       "26u - 27u      1\n",
       "50u - 50u      1\n",
       "41u - 44u      1\n",
       "49u - 50u      1\n",
       "60u - 60u      1\n",
       "55u - 51u      1\n",
       "50u - 54u      1\n",
       "40u - 31u      1\n",
       "43u - 48u      1\n",
       "41u - 41u      1\n",
       "31u - 36u      1\n",
       "54u - 50u      1\n",
       "38u - 40u      1\n",
       "35u - 39u      1\n",
       "56u - 57u      1\n",
       "42u - 40u      1\n",
       "33u - 31u      1\n",
       "27u - 28u      1\n",
       "37u - 38u      1\n",
       "46u - 43u      1\n",
       "30u - 29u      1\n",
       "26u - 30u      1\n",
       "46u - 46u      1\n",
       "34u - 32u      1\n",
       "32u - 30u      1\n",
       "Name: novodra, Length: 132, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_df.novodra.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.97    10\n",
       "7.66     9\n",
       "7.58     9\n",
       "7.98     8\n",
       "7.85     8\n",
       "7.72     8\n",
       "7.71     8\n",
       "7.77     8\n",
       "7.78     8\n",
       "7.64     8\n",
       "7.59     8\n",
       "7.79     7\n",
       "7.87     7\n",
       "7.88     7\n",
       "7.53     7\n",
       "7.70     7\n",
       "7.93     6\n",
       "7.67     6\n",
       "7.75     6\n",
       "7.73     6\n",
       "7.86     6\n",
       "7.89     6\n",
       "7.56     6\n",
       "7.60     6\n",
       "7.92     6\n",
       "7.90     6\n",
       "7.68     6\n",
       "7.55     6\n",
       "7.76     6\n",
       "7.74     5\n",
       "        ..\n",
       "9.54     1\n",
       "9.64     1\n",
       "9.82     1\n",
       "8.51     1\n",
       "9.13     1\n",
       "8.09     1\n",
       "9.71     1\n",
       "8.02     1\n",
       "9.41     1\n",
       "9.65     1\n",
       "9.35     1\n",
       "9.12     1\n",
       "9.63     1\n",
       "8.29     1\n",
       "9.56     1\n",
       "8.08     1\n",
       "9.87     1\n",
       "8.61     1\n",
       "9.45     1\n",
       "9.95     1\n",
       "9.04     1\n",
       "9.81     1\n",
       "9.62     1\n",
       "9.09     1\n",
       "9.48     1\n",
       "8.19     1\n",
       "8.56     1\n",
       "8.82     1\n",
       "9.27     1\n",
       "9.11     1\n",
       "Name: hba1c_start, Length: 101, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the clustering around 7.97 concerned me at first, but after some research it looks like HbA1c rarely goes above 7 so it makes sense that the distribution would be skewed toward it. Still it's odd that there are so many right below 8 so I'll note it.\n",
    "treatments_df.hba1c_start.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAEdtJREFUeJzt3WuMXHd5x/Hv04SI4KW+EJgu5mKqWmkBl4BHKAUVzdZJ\nFS7CVlVFQaZdUKRtK5pCRV+4fdGqL6qGqqlErUrVCmi3qsk2DUnXCoFiXBaEBClxCDghUHOxIWG9\nC7HjsMEqGD19scdlu97tXHfH85/vR1rNOWf+Z+Z59qx/e/bvMzORmUiSBt/P9LsASVJvGOiSVAgD\nXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQly5kU92zTXX5I4dOzra95lnnmHTpk29LWgA\n2PfwGdbe7Xttx44d+35mPr/ZY21ooO/YsYMHH3ywo31nZ2dpNBq9LWgA2PfwGdbe7XttEXGqlcdy\nykWSCmGgS1IhDHRJKoSBLkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgqxoa8U7ZcdBz7a1f4nb39z\njyqRpPXjGbokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUYmOvQjz9xjnd0eT15P3RzDbzX\nv0tqh2foklSIpoEeEddGxMPLvp6OiPdExLaIOBIRJ6rbrRtRsCRpdU0DPTO/lpnXZeZ1wG7gh8C9\nwAHgaGbuBI5W65KkPml3ymUP8I3MPAXsBaaq7VPAvl4WJklqT7uBfgtwZ7Vcy8y5avk0UOtZVZKk\ntkVmtjYw4irgu8ArMnM+Ip7KzC3L7j+bmZfMo0fEBDABUKvVdk9PT3dU6MKZc8yf72jXru3avrnj\nfY8/ca6r511cXGRkZKTjxxhUw9o3DG/v9r22sbGxY5lZb/ZY7Vy2+Ebgocycr9bnI2I0M+ciYhRY\nWG2nzJwEJgHq9Xo2Go02nvKnDh6a4Y7j/bnK8uT+Rsf7dnOp5cn9DWZnZ+n0ezbIhrVvGN7e7bt7\n7Uy5vI2fTrcAHAbGq+VxYKYnFUmSOtJSoEfEJuBG4J5lm28HboyIE8AN1bokqU9amsPIzGeA563Y\n9iRLV71Iki4DA/PS/37q9iPsJGkj+NJ/SSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBL\nUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKkSrnym6JSLujoivRsRj\nEfErEbEtIo5ExInqdut6FytJWlurZ+jvBz6emb8IvAp4DDgAHM3MncDRal2S1CdNAz0iNgNvAD4I\nkJk/ysyngL3AVDVsCti3XkVKkppr5Qz9ZcD3gH+IiC9GxAciYhNQy8y5asxpoLZeRUqSmovM/P8H\nRNSBzwOvz8wHIuL9wNPAbZm5Zdm4s5l5yTx6REwAEwC1Wm339PR0R4UunDnH/PmOdh1Yu7ZvZnFx\nkZGRkX6XsuGGtW8Y3t7te21jY2PHMrPe7LGubOH5Hgcez8wHqvW7WZovn4+I0cyci4hRYGG1nTNz\nEpgEqNfr2Wg0WnjKSx08NMMdx1sptxwn9zeYnZ2l0+/ZIBvWvmF4e7fv7jWdcsnM08B3IuLaatMe\n4CvAYWC82jYOzPSkIklSR1o95b0NOBQRVwHfBN7J0i+DuyLiVuAUcPP6lChJakVLgZ6ZDwOrzd/s\n6W05kqRO+UpRSSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWp\nEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmFMNAlqRAtfaZoRJwEfgD8BLiQmfWI2Ab8\nC7ADOAncnJln16dMSVIz7Zyhj2XmdZl58cOiDwBHM3MncLRalyT1STdTLnuBqWp5CtjXfTmSpE61\nGugJfDIijkXERLWtlplz1fJpoNbz6iRJLYvMbD4oYntmPhERLwCOALcBhzNzy7IxZzNz6yr7TgAT\nALVabff09HRHhS6cOcf8+Y52HVi7tm9mcXGRkZGRfpey4Ya1bxje3u17bWNjY8eWTXevqaX/FM3M\nJ6rbhYi4F3gtMB8Ro5k5FxGjwMIa+04CkwD1ej0bjUYrT3mJg4dmuON4S+UW4+T+BrOzs3T6PRtk\nw9o3DG/v9t29plMuEbEpIp57cRn4deAR4DAwXg0bB2Z6UpEkqSOtnPLWgHsj4uL4D2fmxyPiC8Bd\nEXErcAq4ef3KlCQ10zTQM/ObwKtW2f4ksGc9ipIktc9XikpSIQx0SSqEgS5JhTDQJakQBrokFcJA\nl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJ\nKoSBLkmFaDnQI+KKiPhiRNxXrW+LiCMRcaK63bp+ZUqSmmnnDP3dwGPL1g8ARzNzJ3C0Wpck9UlL\ngR4RLwLeDHxg2ea9wFS1PAXs621pkqR2RGY2HxRxN/CXwHOBP8rMt0TEU5m5pbo/gLMX11fsOwFM\nANRqtd3T09MdFbpw5hzz5zvadWDt2r6ZxcVFRkZG+l3KhhvWvmF4e7fvtY2NjR3LzHqzx7qy2YCI\neAuwkJnHIqKx2pjMzIhY9TdDZk4CkwD1ej0bjVUfoqmDh2a443jTcotycn+D2dlZOv2eDbJh7RuG\nt3f77l4rCfl64K0R8Sbg2cDPRsQ/A/MRMZqZcxExCiz0pCJJUkeazqFn5h9n5osycwdwC/Afmfl2\n4DAwXg0bB2bWrUpJUlPdXId+O3BjRJwAbqjWJUl90takdGbOArPV8pPAnt6XJEnqhK8UlaRCGOiS\nVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjoklQIA12SCmGgS1IhDHRJKoSBLkmF\nMNAlqRAGuiQVwkCXpEIY6JJUiKaBHhHPjoj/jIgvRcSjEfHn1fZtEXEkIk5Ut1vXv1xJ0lpaOUP/\nb+DXMvNVwHXATRFxPXAAOJqZO4Gj1bokqU+aBnouWaxWn1V9JbAXmKq2TwH71qVCSVJLWppDj4gr\nIuJhYAE4kpkPALXMnKuGnAZq61SjJKkFkZmtD47YAtwL3AZ8NjO3LLvvbGZeMo8eERPABECtVts9\nPT3dUaELZ84xf76jXQfWru2bWVxcZGRkpN+lbLhh7RuGt3f7XtvY2NixzKw3e6wr23nizHwqIj4F\n3ATMR8RoZs5FxChLZ++r7TMJTALU6/VsNBrtPOX/OnhohjuOt1XuwDu5v8Hs7Cydfs8G2bD2DcPb\nu313r5WrXJ5fnZkTEVcDNwJfBQ4D49WwcWCmJxVJkjrSyinvKDAVEVew9Avgrsy8LyI+B9wVEbcC\np4Cb17FOSVITTQM9M78MvHqV7U8Ce9ajKElS+3ylqCQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqE\ngS5JhTDQJakQBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUw0CWpEAa6JBXCQJekQhjo\nklSIpoEeES+OiE9FxFci4tGIeHe1fVtEHImIE9Xt1vUvV5K0llbO0C8A783MlwPXA++KiJcDB4Cj\nmbkTOFqtS5L6pGmgZ+ZcZj5ULf8AeAzYDuwFpqphU8C+9SpSktRcW3PoEbEDeDXwAFDLzLnqrtNA\nraeVSZLaEpnZ2sCIEeDTwF9k5j0R8VRmbll2/9nMvGQePSImgAmAWq22e3p6uqNCF86cY/58R7sO\nrF3bN7O4uMjIyEi/S9lww9o3DG/v9r22sbGxY5lZb/ZYV7byhBHxLOAjwKHMvKfaPB8Ro5k5FxGj\nwMJq+2bmJDAJUK/Xs9FotPKUlzh4aIY7jrdUbjFO7m8wOztLp9+zQTasfcPw9m7f3WvlKpcAPgg8\nlpl/s+yuw8B4tTwOzPSkIklSR1o55X098FvA8Yh4uNr2J8DtwF0RcStwCrh5fUqUJLWiaaBn5meB\nWOPuPb0tR5LUKV8pKkmFMNAlqRAGuiQVwkCXpEIY6JJUCANdkgphoEtSIQx0SSqEgS5JhTDQJakQ\nBrokFcJAl6RCGOiSVAgDXZIKYaBLUiEMdEkqhIEuSYUYrk9dHjA7DnyU9+66wDsOfLTtfU/e/uZ1\nqEjS5cwzdEkqRNNAj4gPRcRCRDyybNu2iDgSESeq263rW6YkqZlWztD/EbhpxbYDwNHM3AkcrdYl\nSX3UNNAz8zPAmRWb9wJT1fIUsK/HdUmS2hSZ2XxQxA7gvsx8ZbX+VGZuqZYDOHtxfZV9J4AJgFqt\ntnt6erqjQhfOnGP+fEe7DrTa1XTU967tm3tfzAZaXFxkZGSk32X0xbD2bt9rGxsbO5aZ9WaP1fVV\nLpmZEbHmb4XMnAQmAer1ejYajY6e5+ChGe44PnwX5bx314WO+j65v9H7YjbQ7Owsnf6sDLph7d2+\nu9fpVS7zETEKUN0u9KQaSVLHOg30w8B4tTwOzPSmHElSp1q5bPFO4HPAtRHxeETcCtwO3BgRJ4Ab\nqnVJUh81nZzNzLetcdeeHtciSeqCrxSVpEIY6JJUCANdkgphoEtSIQx0SSrE8L30UrrM7Fjxfvft\nvAe+73uv5TxDl6RCGOiSVAinXHSJlVMA7XIaYON0c6z6eZxWq7vTj1tsRzc9D8K/C8/QJakQBrok\nFcIpF11WLv5Z28mf3071aNh5hi5JhTDQJakQTrlIPdDtFRCDZlD7HdS6W+UZuiQVwjN0FWNQr8nu\nl9LPVoeRZ+iSVIiuAj0iboqIr0XE1yPiQK+KkiS1r+Mpl4i4Avg74EbgceALEXE4M7/Sq+LUuX7+\nOT2If8oPYs3SSt2cob8W+HpmfjMzfwRMA3t7U5YkqV3dBPp24DvL1h+vtkmS+mDdr3KJiAlgolpd\njIivdfhQ1wDf701Vg+MP7HvoDGvvpfcd71vzrlb6fmkrz9FNoD8BvHjZ+ouqbf9HZk4Ck108DwAR\n8WBm1rt9nEFj38NnWHu37+51M+XyBWBnRLwsIq4CbgEO96IoSVL7Oj5Dz8wLEfH7wL8DVwAfysxH\ne1aZJKktXc2hZ+b9wP09qqWZrqdtBpR9D59h7d2+uxSZ2avHkiT1kS/9l6RCXFaBHhHXRsTDy76e\njoj3rBjTiIhzy8b8ab/q7aWI+MOIeDQiHomIOyPi2Svuj4j42+ptFr4cEa/pV6291ELfpR7vd1c9\nP7ryZ7y6v9Tj3azvYo53RHwoIhYi4pFl27ZFxJGIOFHdbl1j387eViUzL8svlv6j9TTw0hXbG8B9\n/a6vx71uB74FXF2t3wW8Y8WYNwEfAwK4Hnig33VvUN8lHu9XAo8Az2Hp/7E+CfzCEBzvVvou5ngD\nbwBeAzyybNtfAQeq5QPA+1bZ7wrgG8DPA1cBXwJe3spzXlZn6CvsAb6Rmaf6XcgGuRK4OiKuZOkH\n/rsr7t8L/FMu+TywJSJGN7rIddCs7xL9EksB/cPMvAB8GviNFWNKPN6t9F2MzPwMcGbF5r3AVLU8\nBexbZdeO31blcg70W4A717jvddWfoR+LiFdsZFHrITOfAP4a+DYwB5zLzE+sGFbcWy202DcUdrxZ\nOkv91Yh4XkQ8h6Wz8RevGFPc8aa1vqG8471cLTPnquXTQG2VMR0f+8sy0KsXKr0V+NdV7n4IeElm\n/jJwEPi3jaxtPVTzaHuBlwEvBDZFxNv7W9X6a7Hv4o53Zj4GvA/4BPBx4GHgJ30tagO02Hdxx3st\nuTS/0tPLDC/LQAfeCDyUmfMr78jMpzNzsVq+H3hWRFyz0QX22A3AtzLze5n5Y+Ae4HUrxrT0VgsD\npmnfhR5vMvODmbk7M98AnAX+a8WQEo93075LPd7LzF+cOqtuF1YZ0/Gxv1wD/W2sMd0SET8XEVEt\nv5alHp7cwNrWw7eB6yPiOVVve4DHVow5DPx2dfXD9SxNT8ytfKAB07TvQo83EfGC6vYlLM0jf3jF\nkBKPd9O+Sz3eyxwGxqvlcWBmlTEdv63KZfeZohGxiaUPzfidZdt+FyAz/x74TeD3IuICcB64pfrT\nZWBl5gMRcTdLf25eAL4ITK7o+36W5hy/DvwQeGefyu2ZFvsu7nhXPhIRzwN+DLwrM58q/XhXmvVd\nzPGOiDtZumrnmoh4HPgz4Hbgroi4FTgF3FyNfSHwgcx8U3bxtiq+UlSSCnG5TrlIktpkoEtSIQx0\nSSqEgS5JhTDQJakQBrokFcJAl6RCGOiSVIj/ATYetCIM4YjOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111d0bd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "treatments_df.hba1c_start.hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "340    7.50\n",
       "183    7.50\n",
       "196    7.50\n",
       "23     7.51\n",
       "58     7.51\n",
       "123    7.51\n",
       "175    7.51\n",
       "19     7.51\n",
       "124    7.52\n",
       "154    7.52\n",
       "109    7.52\n",
       "47     7.53\n",
       "112    7.53\n",
       "111    7.53\n",
       "254    7.53\n",
       "6      7.53\n",
       "86     7.53\n",
       "276    7.53\n",
       "63     7.54\n",
       "22     7.54\n",
       "174    7.54\n",
       "40     7.54\n",
       "226    7.54\n",
       "193    7.55\n",
       "219    7.55\n",
       "233    7.55\n",
       "297    7.55\n",
       "54     7.55\n",
       "113    7.55\n",
       "259    7.56\n",
       "       ... \n",
       "347    9.04\n",
       "101    9.09\n",
       "289    9.11\n",
       "139    9.12\n",
       "261    9.13\n",
       "121    9.18\n",
       "64     9.21\n",
       "221    9.25\n",
       "322    9.27\n",
       "137    9.35\n",
       "42     9.41\n",
       "265    9.45\n",
       "229    9.47\n",
       "318    9.48\n",
       "302    9.53\n",
       "82     9.54\n",
       "195    9.56\n",
       "321    9.62\n",
       "283    9.63\n",
       "301    9.64\n",
       "303    9.65\n",
       "94     9.68\n",
       "262    9.71\n",
       "194    9.81\n",
       "95     9.82\n",
       "241    9.84\n",
       "151    9.84\n",
       "145    9.87\n",
       "39     9.91\n",
       "236    9.95\n",
       "Name: hba1c_start, Length: 350, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#everyone is above 7, in other words everyone in the study has diabetes\n",
    "treatments_df.hba1c_start.sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.34    12\n",
       "7.30    11\n",
       "7.40    11\n",
       "7.51     9\n",
       "7.29     8\n",
       "7.17     8\n",
       "7.49     8\n",
       "7.42     7\n",
       "7.39     7\n",
       "7.36     7\n",
       "7.56     7\n",
       "7.47     7\n",
       "7.55     7\n",
       "7.27     7\n",
       "7.18     6\n",
       "7.57     6\n",
       "7.53     6\n",
       "7.28     6\n",
       "7.50     6\n",
       "7.46     6\n",
       "7.32     6\n",
       "7.16     6\n",
       "7.48     6\n",
       "7.52     5\n",
       "7.12     5\n",
       "7.26     5\n",
       "7.15     5\n",
       "7.20     5\n",
       "7.59     5\n",
       "7.31     5\n",
       "        ..\n",
       "7.90     1\n",
       "9.10     1\n",
       "9.30     1\n",
       "9.40     1\n",
       "7.01     1\n",
       "9.05     1\n",
       "7.73     1\n",
       "7.94     1\n",
       "9.21     1\n",
       "7.02     1\n",
       "8.62     1\n",
       "9.06     1\n",
       "7.54     1\n",
       "7.67     1\n",
       "9.48     1\n",
       "7.05     1\n",
       "9.52     1\n",
       "8.10     1\n",
       "8.56     1\n",
       "8.08     1\n",
       "8.63     1\n",
       "9.27     1\n",
       "8.27     1\n",
       "9.31     1\n",
       "8.89     1\n",
       "8.39     1\n",
       "7.83     1\n",
       "8.79     1\n",
       "7.10     1\n",
       "7.92     1\n",
       "Name: hba1c_end, Length: 113, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#this is a lot of clustering around .3 and .4\n",
    "treatments_df.hba1c_end.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8g4xZp9Nhampq0mUsmdXUr72O1szMzP7MbPYa1/Mi0WdwLCLWZ+bRiFgPPHamgZm5C9gF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\nuiQV4v8BbCxSZX8OZNoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e825f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# seems to be the center of normal distribution rather than noise\n",
    "treatments_df.hba1c_end.hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.39    23\n",
       "0.35    16\n",
       "0.34    16\n",
       "0.37    14\n",
       "0.33    13\n",
       "0.38    13\n",
       "0.36    12\n",
       "0.97    11\n",
       "0.92    11\n",
       "0.93     9\n",
       "0.30     8\n",
       "0.90     7\n",
       "0.95     7\n",
       "0.91     7\n",
       "0.96     6\n",
       "0.32     6\n",
       "0.31     6\n",
       "0.94     4\n",
       "0.29     4\n",
       "0.99     3\n",
       "0.51     3\n",
       "0.98     3\n",
       "0.27     3\n",
       "0.28     2\n",
       "0.54     1\n",
       "0.53     1\n",
       "0.26     1\n",
       "0.20     1\n",
       "0.24     1\n",
       "0.50     1\n",
       "Name: hba1c_change, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remember there are NaN values to account for\n",
    "treatments_df.hba1c_change.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1145ceeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Appears bimodal, which is interesting - might have effects on later analysis (e.g. regression)\n",
    "#the high incidence of 0.3 - 0.4 values above appears to be the center of a normal distribution rather than noise.\n",
    "treatments_df.hba1c_change.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>350.000000</td>\n",
       "      <td>350.000000</td>\n",
       "      <td>213.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.956343</td>\n",
       "      <td>7.560057</td>\n",
       "      <td>0.540657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.545328</td>\n",
       "      <td>0.545456</td>\n",
       "      <td>0.277417</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>7.650000</td>\n",
       "      <td>7.270000</td>\n",
       "      <td>0.340000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.785000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.950000</td>\n",
       "      <td>7.557500</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   350.000000  350.000000    213.000000\n",
       "mean      7.956343    7.560057      0.540657\n",
       "std       0.545328    0.545456      0.277417\n",
       "min       7.500000    7.010000      0.200000\n",
       "25%       7.650000    7.270000      0.340000\n",
       "50%       7.785000    7.400000      0.380000\n",
       "75%       7.950000    7.557500      0.920000\n",
       "max       9.950000    9.580000      0.990000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>jožka</td>\n",
       "      <td>resanovič</td>\n",
       "      <td>22u - 30u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>-</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>meya</td>\n",
       "      <td>sjöberg</td>\n",
       "      <td>-</td>\n",
       "      <td>42u - 42u</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sara</td>\n",
       "      <td>miles</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.53</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>frydryk</td>\n",
       "      <td>adamski</td>\n",
       "      <td>63u - 74u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.27</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>jane</td>\n",
       "      <td>citizen</td>\n",
       "      <td>-</td>\n",
       "      <td>37u - 38u</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.60</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>angela</td>\n",
       "      <td>lavrentyev</td>\n",
       "      <td>-</td>\n",
       "      <td>28u - 24u</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>edelma</td>\n",
       "      <td>villalpando</td>\n",
       "      <td>-</td>\n",
       "      <td>24u - 26u</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gabriela</td>\n",
       "      <td>michalčíková</td>\n",
       "      <td>26u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>8.37</td>\n",
       "      <td>7.97</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>annika</td>\n",
       "      <td>vaara</td>\n",
       "      <td>-</td>\n",
       "      <td>20u - 21u</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bernarda</td>\n",
       "      <td>cindrić</td>\n",
       "      <td>40u - 49u</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>14</th>\n",
       "      <td>chiho</td>\n",
       "      <td>higa</td>\n",
       "      <td>-</td>\n",
       "      <td>46u - 46u</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dani</td>\n",
       "      <td>antoun</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mijael</td>\n",
       "      <td>guerra</td>\n",
       "      <td>37u - 47u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>emma</td>\n",
       "      <td>barlow</td>\n",
       "      <td>48u - 57u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.39</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>flavia</td>\n",
       "      <td>fiorentino</td>\n",
       "      <td>46u - 57u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.60</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>noe</td>\n",
       "      <td>aranda</td>\n",
       "      <td>26u - 34u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>seweryna</td>\n",
       "      <td>czerwinska</td>\n",
       "      <td>39u - 46u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>david</td>\n",
       "      <td>villadsen</td>\n",
       "      <td>26u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.86</td>\n",
       "      <td>7.40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>beatrycze</td>\n",
       "      <td>woźniak</td>\n",
       "      <td>-</td>\n",
       "      <td>26u - 27u</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>miłosław</td>\n",
       "      <td>wiśniewski</td>\n",
       "      <td>-</td>\n",
       "      <td>34u - 33u</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lóa</td>\n",
       "      <td>hrafnsdóttir</td>\n",
       "      <td>22u - 31u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>siebrigje</td>\n",
       "      <td>koldenhof</td>\n",
       "      <td>30u - 38u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.59</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>firenze</td>\n",
       "      <td>fodor</td>\n",
       "      <td>-</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>zoe</td>\n",
       "      <td>wellish</td>\n",
       "      <td>-</td>\n",
       "      <td>33u - 33u</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>una</td>\n",
       "      <td>traustadóttir</td>\n",
       "      <td>-</td>\n",
       "      <td>35u - 34u</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>luboš</td>\n",
       "      <td>pecha</td>\n",
       "      <td>-</td>\n",
       "      <td>30u - 27u</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.34</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>320</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>321</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>322</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>323</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>324</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>325</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>326</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>327</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>328</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>329</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>330</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>331</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>332</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>333</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>334</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>335</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>336</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>337</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>338</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>339</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>340</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>341</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>342</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>343</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>344</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>345</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>346</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>347</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>348</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>349</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>350 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     given_name        surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0         jožka      resanovič  22u - 30u          -         7.56       7.22   \n",
       "1     inunnguaq       heilmann  57u - 67u          -         7.85       7.45   \n",
       "2         alwin       svensson  36u - 39u          -         7.78       7.34   \n",
       "3           thể          lương          -  61u - 64u         7.64       7.22   \n",
       "4        amanda        ribeiro  36u - 44u          -         7.85       7.47   \n",
       "5          meya        sjöberg          -  42u - 42u         7.77       7.42   \n",
       "6          sara          miles  30u - 35u          -         7.53       7.12   \n",
       "7       frydryk        adamski  63u - 74u          -         7.75       7.27   \n",
       "8          jane        citizen          -  37u - 38u         7.98       7.60   \n",
       "9        angela     lavrentyev          -  28u - 24u         7.61       7.14   \n",
       "10       edelma    villalpando          -  24u - 26u         7.99       7.56   \n",
       "11     gabriela   michalčíková  26u - 36u          -         8.37       7.97   \n",
       "12       annika          vaara          -  20u - 21u         7.73       7.34   \n",
       "13     bernarda        cindrić  40u - 49u          -         7.89       7.55   \n",
       "14        chiho           higa          -  46u - 46u         7.71       7.30   \n",
       "15         dani         antoun  36u - 44u          -         7.73       7.34   \n",
       "16       mijael         guerra  37u - 47u          -         7.85       7.46   \n",
       "17         emma         barlow  48u - 57u          -         7.85       7.39   \n",
       "18       flavia     fiorentino  46u - 57u          -         7.95       7.60   \n",
       "19          noe         aranda  26u - 34u          -         7.51       7.17   \n",
       "20     seweryna     czerwinska  39u - 46u          -         7.66       7.30   \n",
       "21        david      villadsen  26u - 39u          -         7.86       7.40   \n",
       "22    beatrycze        woźniak          -  26u - 27u         7.54       7.17   \n",
       "23     miłosław     wiśniewski          -  34u - 33u         7.51       7.08   \n",
       "24          lóa   hrafnsdóttir  22u - 31u          -         7.60       7.09   \n",
       "25    siebrigje      koldenhof  30u - 38u          -         7.90       7.59   \n",
       "26      firenze          fodor          -  30u - 35u         7.89       7.55   \n",
       "27          zoe        wellish          -  33u - 33u         7.71       7.30   \n",
       "28          una  traustadóttir          -  35u - 34u         8.00       7.50   \n",
       "29        luboš          pecha          -  30u - 27u         7.79       7.45   \n",
       "..          ...            ...        ...        ...          ...        ...   \n",
       "320        chen            yao          -  56u - 57u         7.90       7.51   \n",
       "321       aksel    vestergaard          -  42u - 38u         9.62       9.29   \n",
       "322       ellen          luman          -  40u - 39u         9.27       8.77   \n",
       "323      albino      schiavone  35u - 43u          -         7.56       7.15   \n",
       "324        jose          combs          -  39u - 36u         7.89       7.42   \n",
       "325      jia li           teng  48u - 54u          -         7.66       7.32   \n",
       "326       ilija         horvat  42u - 50u          -         7.77       7.38   \n",
       "327    mathilde       nørgaard          -  27u - 28u         8.50       8.10   \n",
       "328      csilla       herczegh          -  43u - 46u         7.71       7.27   \n",
       "329     aaliyah           rice          -  31u - 31u         7.64       7.33   \n",
       "330       david       beauvais          -  26u - 23u         7.87       7.47   \n",
       "331    caroline         shuler          -  50u - 54u         7.63       7.27   \n",
       "332        alex       crawford  51u - 62u          -         7.69       7.30   \n",
       "333     rebecca       jephcott  53u - 63u          -         7.96       7.57   \n",
       "334  chukwumoge      ogochukwu          -  41u - 39u         7.95       7.56   \n",
       "335      fearne       mcgregor          -  27u - 29u         7.83       7.48   \n",
       "336      ursula          freud  42u - 54u          -         7.75       7.46   \n",
       "337        leon         scholz          -  38u - 32u         7.72       7.29   \n",
       "338      yasmin         araujo          -  51u - 54u         7.82       7.36   \n",
       "339      hiromu       horikawa          -  47u - 46u         7.77       7.28   \n",
       "340        mika     martinsson  34u - 43u          -         7.50       7.17   \n",
       "341         leo         vieira          -  30u - 33u         7.74       7.36   \n",
       "342      steven            roy          -  41u - 43u         7.87       7.43   \n",
       "343        kate      wilkinson  36u - 39u          -         7.72       7.20   \n",
       "344        naja        enoksen  43u - 50u          -         7.98       7.59   \n",
       "345      albina       zetticci  45u - 51u          -         7.93       7.73   \n",
       "346        john    teichelmann          -  49u - 49u         7.90       7.58   \n",
       "347      mathea        lillebø  23u - 36u          -         9.04       8.67   \n",
       "348      vallie         prince  31u - 38u          -         7.64       7.28   \n",
       "349      samúel   guðbrandsson  53u - 56u          -         8.00       7.64   \n",
       "\n",
       "     hba1c_change  \n",
       "0            0.34  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3            0.92  \n",
       "4            0.38  \n",
       "5            0.35  \n",
       "6             NaN  \n",
       "7             NaN  \n",
       "8             NaN  \n",
       "9            0.97  \n",
       "10           0.93  \n",
       "11            NaN  \n",
       "12           0.39  \n",
       "13           0.34  \n",
       "14            NaN  \n",
       "15           0.39  \n",
       "16           0.39  \n",
       "17            NaN  \n",
       "18           0.35  \n",
       "19           0.34  \n",
       "20           0.36  \n",
       "21            NaN  \n",
       "22           0.37  \n",
       "23           0.93  \n",
       "24            NaN  \n",
       "25           0.31  \n",
       "26           0.34  \n",
       "27           0.91  \n",
       "28            NaN  \n",
       "29           0.34  \n",
       "..            ...  \n",
       "320          0.39  \n",
       "321           NaN  \n",
       "322          0.50  \n",
       "323           NaN  \n",
       "324           NaN  \n",
       "325          0.34  \n",
       "326          0.39  \n",
       "327          0.90  \n",
       "328           NaN  \n",
       "329          0.31  \n",
       "330           NaN  \n",
       "331           NaN  \n",
       "332          0.39  \n",
       "333          0.39  \n",
       "334          0.39  \n",
       "335          0.35  \n",
       "336          0.29  \n",
       "337          0.93  \n",
       "338          0.96  \n",
       "339           NaN  \n",
       "340          0.33  \n",
       "341           NaN  \n",
       "342          0.94  \n",
       "343           NaN  \n",
       "344           NaN  \n",
       "345          0.20  \n",
       "346           NaN  \n",
       "347          0.37  \n",
       "348          0.36  \n",
       "349          0.36  \n",
       "\n",
       "[350 rows x 7 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looks like the hba1c_change doesn't match the start and end value (.e.g row index 3), especially for high values. This might explain the bi-modality in the data.\n",
    "#let's generate the column directly from the start and end values and redo the above analysis\n",
    "treatments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "treatments_df[\"hba1c_change_calculated\"] = treatments_df.hba1c_start - treatments_df.hba1c_end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hrXi+GhFHq3fbjkOtzBHxsYh4EngA+PVBxo5Am8zQ0e0cETuAjwF/PejYEWiTFzq6jStX\nV6fmvhQR7x5w7LC1yQwDbudWlzSOW0TMsVaS12y0bhecI+81mXk8Ii4DHoqIJzPz65NJ+FqZeS9w\nb0T8AvAnwHUTjrShPpm7up0/A/x+Zv4oIiadpY5+ebu6jb8NvCMzVyLiI8A/ALsmnGkj/TIPtJ27\ncKRe63YDEfEe4G5gT2a+NMjYIWuTl8w8Xn19EbiXtf81G7WBtlO1w7wrIrYNOnaI2mTu8naeARYi\n4hhwE/BXEXFjzbHD1iZvZ7dxZv5nZq5U0w8CF3R9X+6TefDtPOoXCWq8iHA+8CzwTs68iPDu163z\nDuAZ4OpBx3Ys74XA29ZNP8LaTdG6sI1/mjMvOv5stdPFJLbxEDJ3dju/bv3PceaF0k7uy33ydnYb\nAz+5br/4APBvm2BfPlfmgbfzxE+/5DluNxARv1l9/2+AO4CfYO0oAWA1M2fONbareYEea6cKYO0/\n9Ocz88ujzDtA5l8Gfi0i/hc4DfxKru1JE7kdRJvMEdHl7TzQ2K7mpdv78k3Ab0XEKmv7xc2bYF8+\na+Ym+7LvKJWkgnThnLokaUgsdUkqiKUuSQWx1CWpIJa6JBXEUpekgljqklQQS12SCvL/Deg0RNjO\n0REAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114780898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sure enough the larger values were miss calculated\n",
    "treatments_df.hba1c_change_calculated.hist(bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 350 entries, 0 to 349\n",
      "Data columns (total 8 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",
      "hba1c_change_calculated    350 non-null float64\n",
      "dtypes: float64(4), object(4)\n",
      "memory usage: 22.0+ KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\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>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",
       "      <th>hba1c_change_calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jožka</td>\n",
       "      <td>resanovič</td>\n",
       "      <td>22u - 30u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>-</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>meya</td>\n",
       "      <td>sjöberg</td>\n",
       "      <td>-</td>\n",
       "      <td>42u - 42u</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sara</td>\n",
       "      <td>miles</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.53</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>frydryk</td>\n",
       "      <td>adamski</td>\n",
       "      <td>63u - 74u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>jane</td>\n",
       "      <td>citizen</td>\n",
       "      <td>-</td>\n",
       "      <td>37u - 38u</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.60</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>angela</td>\n",
       "      <td>lavrentyev</td>\n",
       "      <td>-</td>\n",
       "      <td>28u - 24u</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>edelma</td>\n",
       "      <td>villalpando</td>\n",
       "      <td>-</td>\n",
       "      <td>24u - 26u</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gabriela</td>\n",
       "      <td>michalčíková</td>\n",
       "      <td>26u - 36u</td>\n",
       "      <td>-</td>\n",
       "      <td>8.37</td>\n",
       "      <td>7.97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>annika</td>\n",
       "      <td>vaara</td>\n",
       "      <td>-</td>\n",
       "      <td>20u - 21u</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bernarda</td>\n",
       "      <td>cindrić</td>\n",
       "      <td>40u - 49u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>chiho</td>\n",
       "      <td>higa</td>\n",
       "      <td>-</td>\n",
       "      <td>46u - 46u</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dani</td>\n",
       "      <td>antoun</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mijael</td>\n",
       "      <td>guerra</td>\n",
       "      <td>37u - 47u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>emma</td>\n",
       "      <td>barlow</td>\n",
       "      <td>48u - 57u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>flavia</td>\n",
       "      <td>fiorentino</td>\n",
       "      <td>46u - 57u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.60</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>noe</td>\n",
       "      <td>aranda</td>\n",
       "      <td>26u - 34u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>seweryna</td>\n",
       "      <td>czerwinska</td>\n",
       "      <td>39u - 46u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>david</td>\n",
       "      <td>villadsen</td>\n",
       "      <td>26u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.86</td>\n",
       "      <td>7.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>beatrycze</td>\n",
       "      <td>woźniak</td>\n",
       "      <td>-</td>\n",
       "      <td>26u - 27u</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>miłosław</td>\n",
       "      <td>wiśniewski</td>\n",
       "      <td>-</td>\n",
       "      <td>34u - 33u</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lóa</td>\n",
       "      <td>hrafnsdóttir</td>\n",
       "      <td>22u - 31u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>siebrigje</td>\n",
       "      <td>koldenhof</td>\n",
       "      <td>30u - 38u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.59</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>firenze</td>\n",
       "      <td>fodor</td>\n",
       "      <td>-</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>zoe</td>\n",
       "      <td>wellish</td>\n",
       "      <td>-</td>\n",
       "      <td>33u - 33u</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>una</td>\n",
       "      <td>traustadóttir</td>\n",
       "      <td>-</td>\n",
       "      <td>35u - 34u</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>luboš</td>\n",
       "      <td>pecha</td>\n",
       "      <td>-</td>\n",
       "      <td>30u - 27u</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.34</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>320</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</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",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</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",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</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",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</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",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</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",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</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",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</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",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</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",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</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",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</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",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</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",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</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",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</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",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</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",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</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",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</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",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</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",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</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",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</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",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</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",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</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",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</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",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</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",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</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",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</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",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>350 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     given_name        surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0         jožka      resanovič  22u - 30u          -         7.56       7.22   \n",
       "1     inunnguaq       heilmann  57u - 67u          -         7.85       7.45   \n",
       "2         alwin       svensson  36u - 39u          -         7.78       7.34   \n",
       "3           thể          lương          -  61u - 64u         7.64       7.22   \n",
       "4        amanda        ribeiro  36u - 44u          -         7.85       7.47   \n",
       "5          meya        sjöberg          -  42u - 42u         7.77       7.42   \n",
       "6          sara          miles  30u - 35u          -         7.53       7.12   \n",
       "7       frydryk        adamski  63u - 74u          -         7.75       7.27   \n",
       "8          jane        citizen          -  37u - 38u         7.98       7.60   \n",
       "9        angela     lavrentyev          -  28u - 24u         7.61       7.14   \n",
       "10       edelma    villalpando          -  24u - 26u         7.99       7.56   \n",
       "11     gabriela   michalčíková  26u - 36u          -         8.37       7.97   \n",
       "12       annika          vaara          -  20u - 21u         7.73       7.34   \n",
       "13     bernarda        cindrić  40u - 49u          -         7.89       7.55   \n",
       "14        chiho           higa          -  46u - 46u         7.71       7.30   \n",
       "15         dani         antoun  36u - 44u          -         7.73       7.34   \n",
       "16       mijael         guerra  37u - 47u          -         7.85       7.46   \n",
       "17         emma         barlow  48u - 57u          -         7.85       7.39   \n",
       "18       flavia     fiorentino  46u - 57u          -         7.95       7.60   \n",
       "19          noe         aranda  26u - 34u          -         7.51       7.17   \n",
       "20     seweryna     czerwinska  39u - 46u          -         7.66       7.30   \n",
       "21        david      villadsen  26u - 39u          -         7.86       7.40   \n",
       "22    beatrycze        woźniak          -  26u - 27u         7.54       7.17   \n",
       "23     miłosław     wiśniewski          -  34u - 33u         7.51       7.08   \n",
       "24          lóa   hrafnsdóttir  22u - 31u          -         7.60       7.09   \n",
       "25    siebrigje      koldenhof  30u - 38u          -         7.90       7.59   \n",
       "26      firenze          fodor          -  30u - 35u         7.89       7.55   \n",
       "27          zoe        wellish          -  33u - 33u         7.71       7.30   \n",
       "28          una  traustadóttir          -  35u - 34u         8.00       7.50   \n",
       "29        luboš          pecha          -  30u - 27u         7.79       7.45   \n",
       "..          ...            ...        ...        ...          ...        ...   \n",
       "320        chen            yao          -  56u - 57u         7.90       7.51   \n",
       "321       aksel    vestergaard          -  42u - 38u         9.62       9.29   \n",
       "322       ellen          luman          -  40u - 39u         9.27       8.77   \n",
       "323      albino      schiavone  35u - 43u          -         7.56       7.15   \n",
       "324        jose          combs          -  39u - 36u         7.89       7.42   \n",
       "325      jia li           teng  48u - 54u          -         7.66       7.32   \n",
       "326       ilija         horvat  42u - 50u          -         7.77       7.38   \n",
       "327    mathilde       nørgaard          -  27u - 28u         8.50       8.10   \n",
       "328      csilla       herczegh          -  43u - 46u         7.71       7.27   \n",
       "329     aaliyah           rice          -  31u - 31u         7.64       7.33   \n",
       "330       david       beauvais          -  26u - 23u         7.87       7.47   \n",
       "331    caroline         shuler          -  50u - 54u         7.63       7.27   \n",
       "332        alex       crawford  51u - 62u          -         7.69       7.30   \n",
       "333     rebecca       jephcott  53u - 63u          -         7.96       7.57   \n",
       "334  chukwumoge      ogochukwu          -  41u - 39u         7.95       7.56   \n",
       "335      fearne       mcgregor          -  27u - 29u         7.83       7.48   \n",
       "336      ursula          freud  42u - 54u          -         7.75       7.46   \n",
       "337        leon         scholz          -  38u - 32u         7.72       7.29   \n",
       "338      yasmin         araujo          -  51u - 54u         7.82       7.36   \n",
       "339      hiromu       horikawa          -  47u - 46u         7.77       7.28   \n",
       "340        mika     martinsson  34u - 43u          -         7.50       7.17   \n",
       "341         leo         vieira          -  30u - 33u         7.74       7.36   \n",
       "342      steven            roy          -  41u - 43u         7.87       7.43   \n",
       "343        kate      wilkinson  36u - 39u          -         7.72       7.20   \n",
       "344        naja        enoksen  43u - 50u          -         7.98       7.59   \n",
       "345      albina       zetticci  45u - 51u          -         7.93       7.73   \n",
       "346        john    teichelmann          -  49u - 49u         7.90       7.58   \n",
       "347      mathea        lillebø  23u - 36u          -         9.04       8.67   \n",
       "348      vallie         prince  31u - 38u          -         7.64       7.28   \n",
       "349      samúel   guðbrandsson  53u - 56u          -         8.00       7.64   \n",
       "\n",
       "     hba1c_change  hba1c_change_calculated  \n",
       "0            0.34                     0.34  \n",
       "1             NaN                     0.40  \n",
       "2             NaN                     0.44  \n",
       "3            0.92                     0.42  \n",
       "4            0.38                     0.38  \n",
       "5            0.35                     0.35  \n",
       "6             NaN                     0.41  \n",
       "7             NaN                     0.48  \n",
       "8             NaN                     0.38  \n",
       "9            0.97                     0.47  \n",
       "10           0.93                     0.43  \n",
       "11            NaN                     0.40  \n",
       "12           0.39                     0.39  \n",
       "13           0.34                     0.34  \n",
       "14            NaN                     0.41  \n",
       "15           0.39                     0.39  \n",
       "16           0.39                     0.39  \n",
       "17            NaN                     0.46  \n",
       "18           0.35                     0.35  \n",
       "19           0.34                     0.34  \n",
       "20           0.36                     0.36  \n",
       "21            NaN                     0.46  \n",
       "22           0.37                     0.37  \n",
       "23           0.93                     0.43  \n",
       "24            NaN                     0.51  \n",
       "25           0.31                     0.31  \n",
       "26           0.34                     0.34  \n",
       "27           0.91                     0.41  \n",
       "28            NaN                     0.50  \n",
       "29           0.34                     0.34  \n",
       "..            ...                      ...  \n",
       "320          0.39                     0.39  \n",
       "321           NaN                     0.33  \n",
       "322          0.50                     0.50  \n",
       "323           NaN                     0.41  \n",
       "324           NaN                     0.47  \n",
       "325          0.34                     0.34  \n",
       "326          0.39                     0.39  \n",
       "327          0.90                     0.40  \n",
       "328           NaN                     0.44  \n",
       "329          0.31                     0.31  \n",
       "330           NaN                     0.40  \n",
       "331           NaN                     0.36  \n",
       "332          0.39                     0.39  \n",
       "333          0.39                     0.39  \n",
       "334          0.39                     0.39  \n",
       "335          0.35                     0.35  \n",
       "336          0.29                     0.29  \n",
       "337          0.93                     0.43  \n",
       "338          0.96                     0.46  \n",
       "339           NaN                     0.49  \n",
       "340          0.33                     0.33  \n",
       "341           NaN                     0.38  \n",
       "342          0.94                     0.44  \n",
       "343           NaN                     0.52  \n",
       "344           NaN                     0.39  \n",
       "345          0.20                     0.20  \n",
       "346           NaN                     0.32  \n",
       "347          0.37                     0.37  \n",
       "348          0.36                     0.36  \n",
       "349          0.36                     0.36  \n",
       "\n",
       "[350 rows x 8 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looks like re-calculating the change also filled in it's missing values\n",
    "print(treatments_df.info())\n",
    "treatments_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "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": [
    "# looks adverse reactions also doesn't have `patient_id`, but has `given_name` and `surname`\n",
    "# small number of adverse reactions \n",
    "# no null values\n",
    "adverse_reactions_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    34\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#no duplicates\n",
    "adverse_reactions_df.duplicated().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "elliot         1\n",
       "miłosław       1\n",
       "alex           1\n",
       "albinca        1\n",
       "lixue          1\n",
       "jia li         1\n",
       "merci          1\n",
       "steven         1\n",
       "clinton        1\n",
       "kang           1\n",
       "abdul-nur      1\n",
       "manouck        1\n",
       "gabriele       1\n",
       "tegan          1\n",
       "jasmine        1\n",
       "noe            1\n",
       "monika         1\n",
       "idalia         1\n",
       "cecilie        1\n",
       "krisztina      1\n",
       "flavia         1\n",
       "leon           1\n",
       "anenechi       1\n",
       "abel           1\n",
       "berta          1\n",
       "christopher    1\n",
       "louise         1\n",
       "xiuxiu         1\n",
       "finley         1\n",
       "sofia          1\n",
       "joseph         1\n",
       "ole            1\n",
       "lena           1\n",
       "jakob          1\n",
       "Name: given_name, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#names in lowercase like everything by patients\n",
    "adverse_reactions_df.given_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "johnson       2\n",
       "wiśniewski    1\n",
       "leroux        1\n",
       "petersen      1\n",
       "magyar        1\n",
       "teng          1\n",
       "mai           1\n",
       "richardson    1\n",
       "day           1\n",
       "scholz        1\n",
       "hermansen     1\n",
       "isa           1\n",
       "moore         1\n",
       "lončar        1\n",
       "sykes         1\n",
       "yonatan       1\n",
       "miller        1\n",
       "wubbels       1\n",
       "roy           1\n",
       "jakobsen      1\n",
       "chandler      1\n",
       "fiorentino    1\n",
       "saenger       1\n",
       "aranda        1\n",
       "chidi         1\n",
       "napolitani    1\n",
       "baer          1\n",
       "hsueh         1\n",
       "komavec       1\n",
       "crawford      1\n",
       "chang         1\n",
       "woodward      1\n",
       "nilsen        1\n",
       "Name: surname, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adverse_reactions_df.surname.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hypoglycemia                 19\n",
       "injection site discomfort     6\n",
       "headache                      3\n",
       "nausea                        2\n",
       "throat irritation             2\n",
       "cough                         2\n",
       "Name: adverse_reaction, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#given that this is a trial for diabetics it is hypoglycemia is an interesting side effect - something to note in an exploratory analysis later.\n",
    "adverse_reactions_df.adverse_reaction.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adverse_reactions_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality\n",
    "#### Patients Data\n",
    "- [ ] Wrong types\n",
    "    - zipcode should be str\n",
    "    - should patient_id be str?\n",
    "- [ ] Null values for address, city, state, zip_code\n",
    "- [ ] names are capital case when everything else is lowercase\n",
    "- [x] Patient 4 has height of 27 inches (too low? check if realistic)\n",
    "- [x] Patient 210 has weight of 48.8 (too low? check if realistic)\n",
    "- [ ] 6 entries of birthday = 1/1/1975 (too high? Especially given the 1/1)\n",
    "    - is there a cause in the way the input was designed?\n",
    "- [ ] Phone and Email are merged in `contact`\n",
    "- [ ] 6 occurences of contact johndoe@email.com1234567890\n",
    "- [ ] 6 occurence of zip_code 12345.0 \n",
    "    - same as johndoe entries?\n",
    "- [ ] 6 occurences of address 123 Main Street\n",
    "- [ ] 6 occurrences of surname Doe\n",
    "- [ ] 6 occurreneces of `given_name` John\n",
    "- [ ] Some `state` are abbreviations others are full name\n",
    "- [ ] 3 Occurrences of `surname` Jackobsen and Taylor\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treatments Data\n",
    "\n",
    "- [x] there are mutiple treatment files\n",
    "- [x] `auralin` and `novodra` column names could be more descriptive (add \"\\_dose\"?)\n",
    "- [x] `hba1c_change` has >100 null values \n",
    "- [x] replace erroneous `hba1c_change` with the `hba1c_change_calculated`\n",
    "- [x] has duplicate patient joseph day\n",
    "- [x] `auralin` and `novodra` have null values denoted by \"-\"\n",
    "    - [x] would be nice to have this be a number to test for dose dependence\n",
    "    - are doses determined by weight or are their different dose groups not annotated?\n",
    "- [x] `hbalc_change` has erroneous values\n",
    "- [ ] no patient id in the table - would be better than names for anonymity and resilience\n",
    "- [ ] there are only 208 entries with data vs. 503 contacts - why? control group?\n",
    "- [ ] Check for control group size with people who have doses of neither auralin nor novodra\n",
    "\n",
    "Notes: \n",
    "- John Doe names not included\n",
    "- Seems to be a threshold effect for `hab1c_start` at 7.97\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adverse Reactions Data\n",
    "- [ ] uses name as foreign key instead of patient ID\n",
    "\n",
    "Note:\n",
    "- would be nice to compare adverse reactions with dosages in a later EDA\n",
    "   - tempting to consolidate this with the treatment data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning\n",
    "\n",
    "All in all this data is fairly clean. I'd like to be able to tell if the drugs worked so I'll focus on cleaning the treatments data. Patient data shouldn't effect this analysis so I won't do much with it, except for a few things that would screw up someone else's analysis if they picked this data up (like outliers). Taking from the above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#copy to prevent direct mutation\n",
    "patients_clean = patients_df.copy()\n",
    "treatments_clean = treatments_df.copy()\n",
    "adverse_reactions_clean = adverse_reactions_df.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `patients` - Patient 4 has height of 27 inches "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                                    5\n",
       "assigned_sex                               male\n",
       "given_name                                  Tim\n",
       "surname                                 Neudorf\n",
       "address                    1428 Turkey Pen Lane\n",
       "city                                     Dothan\n",
       "state                                        AL\n",
       "zip_code                                  36303\n",
       "country                           United States\n",
       "contact         334-515-7487TimNeudorf@cuvox.de\n",
       "birthdate                             2/18/1928\n",
       "weight                                    192.3\n",
       "height                                       27\n",
       "bmi                                        26.1\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm guess 27 might be 72?\n",
    "patients_clean.iloc[4,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.1\n",
      "26.0777199074\n"
     ]
    }
   ],
   "source": [
    "#looks like that's the case. Let's change it below.\n",
    "tim_n = patients_clean.iloc[4,:]\n",
    "test_bmi = tim_n.weight / (72 ** 2) * 703\n",
    "print(tim_n.bmi)\n",
    "print(test_bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "patients_clean.loc[4,\"height\"] = 72"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                                    5\n",
       "assigned_sex                               male\n",
       "given_name                                  Tim\n",
       "surname                                 Neudorf\n",
       "address                    1428 Turkey Pen Lane\n",
       "city                                     Dothan\n",
       "state                                        AL\n",
       "zip_code                                  36303\n",
       "country                           United States\n",
       "contact         334-515-7487TimNeudorf@cuvox.de\n",
       "birthdate                             2/18/1928\n",
       "weight                                    192.3\n",
       "height                                       72\n",
       "bmi                                        26.1\n",
       "Name: 4, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm change\n",
    "patients_clean.iloc[4,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `patients` - Patient 210 has weight of 48.8 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                                            211\n",
       "assigned_sex                                       female\n",
       "given_name                                        Camilla\n",
       "surname                                          Zaitseva\n",
       "address                               4689 Briarhill Lane\n",
       "city                                              Wooster\n",
       "state                                                  OH\n",
       "zip_code                                            44691\n",
       "country                                     United States\n",
       "contact         330-202-2145CamillaZaitseva@superrito.com\n",
       "birthdate                                      11/26/1938\n",
       "weight                                               48.8\n",
       "height                                                 63\n",
       "bmi                                                  19.1\n",
       "Name: 210, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm guessing weight is in kilograms instead of pounds\n",
    "patients_clean.loc[210,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19.1\n",
      "19.0558265477\n"
     ]
    }
   ],
   "source": [
    "#looks right\n",
    "camilla_z = patients_clean.loc[210,:]\n",
    "weight_lbs = camilla_z.weight * 2.20462\n",
    "test_bmi = weight_lbs / (camilla_z.height ** 2) * 703\n",
    "print(camilla_z.bmi)\n",
    "print(test_bmi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "patients_clean.loc[210,\"weight\"] = weight_lbs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patient_id                                            211\n",
       "assigned_sex                                       female\n",
       "given_name                                        Camilla\n",
       "surname                                          Zaitseva\n",
       "address                               4689 Briarhill Lane\n",
       "city                                              Wooster\n",
       "state                                                  OH\n",
       "zip_code                                            44691\n",
       "country                                     United States\n",
       "contact         330-202-2145CamillaZaitseva@superrito.com\n",
       "birthdate                                      11/26/1938\n",
       "weight                                            107.585\n",
       "height                                                 63\n",
       "bmi                                                  19.1\n",
       "Name: 210, dtype: object"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#confirm change. ~100 lbs is realistic for someone 5'3\"\n",
    "patients_clean.loc[210,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treatments` - has duplicate patient joseph day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>hba1c_change_calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>joseph</td>\n",
       "      <td>tucker</td>\n",
       "      <td>48u - 56u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.67</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</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",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</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",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    given_name surname    auralin novodra  hba1c_start  hba1c_end  \\\n",
       "57      joseph  tucker  48u - 56u       -         7.67       7.30   \n",
       "80      joseph     day  29u - 36u       -         7.70       7.19   \n",
       "206     joseph     day  29u - 36u       -         7.70       7.19   \n",
       "\n",
       "     hba1c_change  hba1c_change_calculated  \n",
       "57           0.37                     0.37  \n",
       "80            NaN                     0.51  \n",
       "206           NaN                     0.51  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean[treatments_clean.given_name == \"joseph\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False    349\n",
      "dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "      <th>hba1c_change_calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>joseph</td>\n",
       "      <td>tucker</td>\n",
       "      <td>48u - 56u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.67</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</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",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   given_name surname    auralin novodra  hba1c_start  hba1c_end  \\\n",
       "57     joseph  tucker  48u - 56u       -         7.67       7.30   \n",
       "80     joseph     day  29u - 36u       -         7.70       7.19   \n",
       "\n",
       "    hba1c_change  hba1c_change_calculated  \n",
       "57          0.37                     0.37  \n",
       "80           NaN                     0.51  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean = treatments_clean.drop_duplicates()\n",
    "\n",
    "#confirm change\n",
    "print(treatments_clean.duplicated().value_counts())\n",
    "treatments_clean[treatments_clean.given_name == \"joseph\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treatments` - replace erroneous `hba1c_change` with the `hba1c_change_calculated` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</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>jožka</td>\n",
       "      <td>resanovič</td>\n",
       "      <td>22u - 30u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>-</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>-</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name    surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0      jožka  resanovič  22u - 30u          -         7.56       7.22   \n",
       "1  inunnguaq   heilmann  57u - 67u          -         7.85       7.45   \n",
       "2      alwin   svensson  36u - 39u          -         7.78       7.34   \n",
       "3        thể      lương          -  61u - 64u         7.64       7.22   \n",
       "4     amanda    ribeiro  36u - 44u          -         7.85       7.47   \n",
       "\n",
       "   hba1c_change  \n",
       "0          0.34  \n",
       "1          0.40  \n",
       "2          0.44  \n",
       "3          0.42  \n",
       "4          0.38  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean.drop(columns=['hba1c_change'], inplace=True)\n",
    "treatments_clean.rename(columns={\"hba1c_change_calculated\":\"hba1c_change\"}, inplace=True)\n",
    "\n",
    "#check work\n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treatments` - `auralin` and `novodra` have null values denoted by \"-\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <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>jožka</td>\n",
       "      <td>resanovič</td>\n",
       "      <td>22u - 30u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name    surname    auralin    novodra  hba1c_start  hba1c_end  \\\n",
       "0      jožka  resanovič  22u - 30u        NaN         7.56       7.22   \n",
       "1  inunnguaq   heilmann  57u - 67u        NaN         7.85       7.45   \n",
       "2      alwin   svensson  36u - 39u        NaN         7.78       7.34   \n",
       "3        thể      lương        NaN  61u - 64u         7.64       7.22   \n",
       "4     amanda    ribeiro  36u - 44u        NaN         7.85       7.47   \n",
       "\n",
       "   hba1c_change  \n",
       "0          0.34  \n",
       "1          0.40  \n",
       "2          0.44  \n",
       "3          0.42  \n",
       "4          0.38  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replace \"-\" with NaN\n",
    "treatments_clean[['auralin', 'novodra']] = treatments_clean[['auralin', 'novodra']].replace(\"-\",np.nan)\n",
    "\n",
    "#confirm change\n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treatments` - would be nice to have dose average for each patient for future analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#pass in each row, extract numbers, and return average\n",
    "import re\n",
    "def average_dosage(row, drug_name):\n",
    "    try:\n",
    "        min_max = re.findall(\"\\d\\d\", row[drug_name])\n",
    "        return np.mean([int(x) for x in min_max])\n",
    "    except:\n",
    "        return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>auralin</th>\n",
       "      <th>auralin_avg_dose</th>\n",
       "      <th>novodra</th>\n",
       "      <th>novodra_avg_dose</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>jožka</td>\n",
       "      <td>resanovič</td>\n",
       "      <td>22u - 30u</td>\n",
       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>62.5</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name    surname    auralin  auralin_avg_dose    novodra  \\\n",
       "0      jožka  resanovič  22u - 30u              26.0        NaN   \n",
       "1  inunnguaq   heilmann  57u - 67u              62.0        NaN   \n",
       "2      alwin   svensson  36u - 39u              37.5        NaN   \n",
       "3        thể      lương        NaN               NaN  61u - 64u   \n",
       "4     amanda    ribeiro  36u - 44u              40.0        NaN   \n",
       "\n",
       "   novodra_avg_dose  hba1c_start  hba1c_end  hba1c_change  \n",
       "0               NaN         7.56       7.22          0.34  \n",
       "1               NaN         7.85       7.45          0.40  \n",
       "2               NaN         7.78       7.34          0.44  \n",
       "3              62.5         7.64       7.22          0.42  \n",
       "4               NaN         7.85       7.47          0.38  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs = ['auralin', 'novodra']\n",
    "for d in drugs:\n",
    "    index = treatments_clean.columns.get_loc(d) + 1\n",
    "    avg_dose = treatments_clean.apply(lambda row: average_dosage(row, d), axis=1)\n",
    "    treatments_clean.insert(index, d + \"_avg_dose\", avg_dose)\n",
    "\n",
    "#confirm change. Looks sane.    \n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `treatments` - `auralin` and `novodra` column names could be more descriptive"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>given_name</th>\n",
       "      <th>surname</th>\n",
       "      <th>auralin_dose_range</th>\n",
       "      <th>auralin_avg_dose</th>\n",
       "      <th>novodra_dose_range</th>\n",
       "      <th>novodra_avg_dose</th>\n",
       "      <th>hba1c_start</th>\n",
       "      <th>hba1c_end</th>\n",
       "      <th>hba1c_change</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>jožka</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
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       "      <td>57u - 67u</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>62.5</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  given_name    surname auralin_dose_range  auralin_avg_dose  \\\n",
       "0      jožka  resanovič          22u - 30u              26.0   \n",
       "1  inunnguaq   heilmann          57u - 67u              62.0   \n",
       "2      alwin   svensson          36u - 39u              37.5   \n",
       "3        thể      lương                NaN               NaN   \n",
       "4     amanda    ribeiro          36u - 44u              40.0   \n",
       "\n",
       "  novodra_dose_range  novodra_avg_dose  hba1c_start  hba1c_end  hba1c_change  \n",
       "0                NaN               NaN         7.56       7.22          0.34  \n",
       "1                NaN               NaN         7.85       7.45          0.40  \n",
       "2                NaN               NaN         7.78       7.34          0.44  \n",
       "3          61u - 64u              62.5         7.64       7.22          0.42  \n",
       "4                NaN               NaN         7.85       7.47          0.38  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean.rename(columns={\"auralin\":\"auralin_dose_range\", \"novodra\":\"novodra_dose_range\"}, inplace=True)\n",
    "\n",
    "#confirm change\n",
    "treatments_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Finish Up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>novodra_dose_range</th>\n",
       "      <th>novodra_avg_dose</th>\n",
       "      <th>hba1c_start</th>\n",
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       "      <td>26.0</td>\n",
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       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>inunnguaq</td>\n",
       "      <td>heilmann</td>\n",
       "      <td>57u - 67u</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>alwin</td>\n",
       "      <td>svensson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.78</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thể</td>\n",
       "      <td>lương</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61u - 64u</td>\n",
       "      <td>62.5</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.22</td>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>amanda</td>\n",
       "      <td>ribeiro</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>meya</td>\n",
       "      <td>sjöberg</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42u - 42u</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sara</td>\n",
       "      <td>miles</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>32.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.53</td>\n",
       "      <td>7.12</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>frydryk</td>\n",
       "      <td>adamski</td>\n",
       "      <td>63u - 74u</td>\n",
       "      <td>68.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>jane</td>\n",
       "      <td>citizen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37u - 38u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.60</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>angela</td>\n",
       "      <td>lavrentyev</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28u - 24u</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.61</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>edelma</td>\n",
       "      <td>villalpando</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24u - 26u</td>\n",
       "      <td>25.0</td>\n",
       "      <td>7.99</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gabriela</td>\n",
       "      <td>michalčíková</td>\n",
       "      <td>26u - 36u</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.37</td>\n",
       "      <td>7.97</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>annika</td>\n",
       "      <td>vaara</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20u - 21u</td>\n",
       "      <td>20.5</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>bernarda</td>\n",
       "      <td>cindrić</td>\n",
       "      <td>40u - 49u</td>\n",
       "      <td>44.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>chiho</td>\n",
       "      <td>higa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46u - 46u</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dani</td>\n",
       "      <td>antoun</td>\n",
       "      <td>36u - 44u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.73</td>\n",
       "      <td>7.34</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>mijael</td>\n",
       "      <td>guerra</td>\n",
       "      <td>37u - 47u</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>emma</td>\n",
       "      <td>barlow</td>\n",
       "      <td>48u - 57u</td>\n",
       "      <td>52.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.85</td>\n",
       "      <td>7.39</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>flavia</td>\n",
       "      <td>fiorentino</td>\n",
       "      <td>46u - 57u</td>\n",
       "      <td>51.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.60</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>noe</td>\n",
       "      <td>aranda</td>\n",
       "      <td>26u - 34u</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>seweryna</td>\n",
       "      <td>czerwinska</td>\n",
       "      <td>39u - 46u</td>\n",
       "      <td>42.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>david</td>\n",
       "      <td>villadsen</td>\n",
       "      <td>26u - 39u</td>\n",
       "      <td>32.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.86</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>beatrycze</td>\n",
       "      <td>woźniak</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26u - 27u</td>\n",
       "      <td>26.5</td>\n",
       "      <td>7.54</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>miłosław</td>\n",
       "      <td>wiśniewski</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34u - 33u</td>\n",
       "      <td>33.5</td>\n",
       "      <td>7.51</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>lóa</td>\n",
       "      <td>hrafnsdóttir</td>\n",
       "      <td>22u - 31u</td>\n",
       "      <td>26.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.60</td>\n",
       "      <td>7.09</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>siebrigje</td>\n",
       "      <td>koldenhof</td>\n",
       "      <td>30u - 38u</td>\n",
       "      <td>34.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.59</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>firenze</td>\n",
       "      <td>fodor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30u - 35u</td>\n",
       "      <td>32.5</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.55</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>zoe</td>\n",
       "      <td>wellish</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33u - 33u</td>\n",
       "      <td>33.0</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>una</td>\n",
       "      <td>traustadóttir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35u - 34u</td>\n",
       "      <td>34.5</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.50</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>luboš</td>\n",
       "      <td>pecha</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30u - 27u</td>\n",
       "      <td>28.5</td>\n",
       "      <td>7.79</td>\n",
       "      <td>7.45</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>chen</td>\n",
       "      <td>yao</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56u - 57u</td>\n",
       "      <td>56.5</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.51</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>aksel</td>\n",
       "      <td>vestergaard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42u - 38u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>9.62</td>\n",
       "      <td>9.29</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>ellen</td>\n",
       "      <td>luman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40u - 39u</td>\n",
       "      <td>39.5</td>\n",
       "      <td>9.27</td>\n",
       "      <td>8.77</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>albino</td>\n",
       "      <td>schiavone</td>\n",
       "      <td>35u - 43u</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.56</td>\n",
       "      <td>7.15</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>jose</td>\n",
       "      <td>combs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39u - 36u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>7.89</td>\n",
       "      <td>7.42</td>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>jia li</td>\n",
       "      <td>teng</td>\n",
       "      <td>48u - 54u</td>\n",
       "      <td>51.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.66</td>\n",
       "      <td>7.32</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>ilija</td>\n",
       "      <td>horvat</td>\n",
       "      <td>42u - 50u</td>\n",
       "      <td>46.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.38</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>mathilde</td>\n",
       "      <td>nørgaard</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27u - 28u</td>\n",
       "      <td>27.5</td>\n",
       "      <td>8.50</td>\n",
       "      <td>8.10</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>csilla</td>\n",
       "      <td>herczegh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43u - 46u</td>\n",
       "      <td>44.5</td>\n",
       "      <td>7.71</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>aaliyah</td>\n",
       "      <td>rice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31u - 31u</td>\n",
       "      <td>31.0</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.33</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>david</td>\n",
       "      <td>beauvais</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26u - 23u</td>\n",
       "      <td>24.5</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.47</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>caroline</td>\n",
       "      <td>shuler</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50u - 54u</td>\n",
       "      <td>52.0</td>\n",
       "      <td>7.63</td>\n",
       "      <td>7.27</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>alex</td>\n",
       "      <td>crawford</td>\n",
       "      <td>51u - 62u</td>\n",
       "      <td>56.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.69</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>rebecca</td>\n",
       "      <td>jephcott</td>\n",
       "      <td>53u - 63u</td>\n",
       "      <td>58.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.96</td>\n",
       "      <td>7.57</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>chukwumoge</td>\n",
       "      <td>ogochukwu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41u - 39u</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.95</td>\n",
       "      <td>7.56</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>fearne</td>\n",
       "      <td>mcgregor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27u - 29u</td>\n",
       "      <td>28.0</td>\n",
       "      <td>7.83</td>\n",
       "      <td>7.48</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>ursula</td>\n",
       "      <td>freud</td>\n",
       "      <td>42u - 54u</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.75</td>\n",
       "      <td>7.46</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>leon</td>\n",
       "      <td>scholz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38u - 32u</td>\n",
       "      <td>35.0</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>yasmin</td>\n",
       "      <td>araujo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51u - 54u</td>\n",
       "      <td>52.5</td>\n",
       "      <td>7.82</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>hiromu</td>\n",
       "      <td>horikawa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47u - 46u</td>\n",
       "      <td>46.5</td>\n",
       "      <td>7.77</td>\n",
       "      <td>7.28</td>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>mika</td>\n",
       "      <td>martinsson</td>\n",
       "      <td>34u - 43u</td>\n",
       "      <td>38.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.50</td>\n",
       "      <td>7.17</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>leo</td>\n",
       "      <td>vieira</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30u - 33u</td>\n",
       "      <td>31.5</td>\n",
       "      <td>7.74</td>\n",
       "      <td>7.36</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>steven</td>\n",
       "      <td>roy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41u - 43u</td>\n",
       "      <td>42.0</td>\n",
       "      <td>7.87</td>\n",
       "      <td>7.43</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>kate</td>\n",
       "      <td>wilkinson</td>\n",
       "      <td>36u - 39u</td>\n",
       "      <td>37.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.72</td>\n",
       "      <td>7.20</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>naja</td>\n",
       "      <td>enoksen</td>\n",
       "      <td>43u - 50u</td>\n",
       "      <td>46.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.98</td>\n",
       "      <td>7.59</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>albina</td>\n",
       "      <td>zetticci</td>\n",
       "      <td>45u - 51u</td>\n",
       "      <td>48.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.93</td>\n",
       "      <td>7.73</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>john</td>\n",
       "      <td>teichelmann</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49u - 49u</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.90</td>\n",
       "      <td>7.58</td>\n",
       "      <td>0.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>mathea</td>\n",
       "      <td>lillebø</td>\n",
       "      <td>23u - 36u</td>\n",
       "      <td>29.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.04</td>\n",
       "      <td>8.67</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>vallie</td>\n",
       "      <td>prince</td>\n",
       "      <td>31u - 38u</td>\n",
       "      <td>34.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.64</td>\n",
       "      <td>7.28</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>samúel</td>\n",
       "      <td>guðbrandsson</td>\n",
       "      <td>53u - 56u</td>\n",
       "      <td>54.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.00</td>\n",
       "      <td>7.64</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>349 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     given_name        surname auralin_dose_range  auralin_avg_dose  \\\n",
       "0         jožka      resanovič          22u - 30u              26.0   \n",
       "1     inunnguaq       heilmann          57u - 67u              62.0   \n",
       "2         alwin       svensson          36u - 39u              37.5   \n",
       "3           thể          lương                NaN               NaN   \n",
       "4        amanda        ribeiro          36u - 44u              40.0   \n",
       "5          meya        sjöberg                NaN               NaN   \n",
       "6          sara          miles          30u - 35u              32.5   \n",
       "7       frydryk        adamski          63u - 74u              68.5   \n",
       "8          jane        citizen                NaN               NaN   \n",
       "9        angela     lavrentyev                NaN               NaN   \n",
       "10       edelma    villalpando                NaN               NaN   \n",
       "11     gabriela   michalčíková          26u - 36u              31.0   \n",
       "12       annika          vaara                NaN               NaN   \n",
       "13     bernarda        cindrić          40u - 49u              44.5   \n",
       "14        chiho           higa                NaN               NaN   \n",
       "15         dani         antoun          36u - 44u              40.0   \n",
       "16       mijael         guerra          37u - 47u              42.0   \n",
       "17         emma         barlow          48u - 57u              52.5   \n",
       "18       flavia     fiorentino          46u - 57u              51.5   \n",
       "19          noe         aranda          26u - 34u              30.0   \n",
       "20     seweryna     czerwinska          39u - 46u              42.5   \n",
       "21        david      villadsen          26u - 39u              32.5   \n",
       "22    beatrycze        woźniak                NaN               NaN   \n",
       "23     miłosław     wiśniewski                NaN               NaN   \n",
       "24          lóa   hrafnsdóttir          22u - 31u              26.5   \n",
       "25    siebrigje      koldenhof          30u - 38u              34.0   \n",
       "26      firenze          fodor                NaN               NaN   \n",
       "27          zoe        wellish                NaN               NaN   \n",
       "28          una  traustadóttir                NaN               NaN   \n",
       "29        luboš          pecha                NaN               NaN   \n",
       "..          ...            ...                ...               ...   \n",
       "320        chen            yao                NaN               NaN   \n",
       "321       aksel    vestergaard                NaN               NaN   \n",
       "322       ellen          luman                NaN               NaN   \n",
       "323      albino      schiavone          35u - 43u              39.0   \n",
       "324        jose          combs                NaN               NaN   \n",
       "325      jia li           teng          48u - 54u              51.0   \n",
       "326       ilija         horvat          42u - 50u              46.0   \n",
       "327    mathilde       nørgaard                NaN               NaN   \n",
       "328      csilla       herczegh                NaN               NaN   \n",
       "329     aaliyah           rice                NaN               NaN   \n",
       "330       david       beauvais                NaN               NaN   \n",
       "331    caroline         shuler                NaN               NaN   \n",
       "332        alex       crawford          51u - 62u              56.5   \n",
       "333     rebecca       jephcott          53u - 63u              58.0   \n",
       "334  chukwumoge      ogochukwu                NaN               NaN   \n",
       "335      fearne       mcgregor                NaN               NaN   \n",
       "336      ursula          freud          42u - 54u              48.0   \n",
       "337        leon         scholz                NaN               NaN   \n",
       "338      yasmin         araujo                NaN               NaN   \n",
       "339      hiromu       horikawa                NaN               NaN   \n",
       "340        mika     martinsson          34u - 43u              38.5   \n",
       "341         leo         vieira                NaN               NaN   \n",
       "342      steven            roy                NaN               NaN   \n",
       "343        kate      wilkinson          36u - 39u              37.5   \n",
       "344        naja        enoksen          43u - 50u              46.5   \n",
       "345      albina       zetticci          45u - 51u              48.0   \n",
       "346        john    teichelmann                NaN               NaN   \n",
       "347      mathea        lillebø          23u - 36u              29.5   \n",
       "348      vallie         prince          31u - 38u              34.5   \n",
       "349      samúel   guðbrandsson          53u - 56u              54.5   \n",
       "\n",
       "    novodra_dose_range  novodra_avg_dose  hba1c_start  hba1c_end  hba1c_change  \n",
       "0                  NaN               NaN         7.56       7.22          0.34  \n",
       "1                  NaN               NaN         7.85       7.45          0.40  \n",
       "2                  NaN               NaN         7.78       7.34          0.44  \n",
       "3            61u - 64u              62.5         7.64       7.22          0.42  \n",
       "4                  NaN               NaN         7.85       7.47          0.38  \n",
       "5            42u - 42u              42.0         7.77       7.42          0.35  \n",
       "6                  NaN               NaN         7.53       7.12          0.41  \n",
       "7                  NaN               NaN         7.75       7.27          0.48  \n",
       "8            37u - 38u              37.5         7.98       7.60          0.38  \n",
       "9            28u - 24u              26.0         7.61       7.14          0.47  \n",
       "10           24u - 26u              25.0         7.99       7.56          0.43  \n",
       "11                 NaN               NaN         8.37       7.97          0.40  \n",
       "12           20u - 21u              20.5         7.73       7.34          0.39  \n",
       "13                 NaN               NaN         7.89       7.55          0.34  \n",
       "14           46u - 46u              46.0         7.71       7.30          0.41  \n",
       "15                 NaN               NaN         7.73       7.34          0.39  \n",
       "16                 NaN               NaN         7.85       7.46          0.39  \n",
       "17                 NaN               NaN         7.85       7.39          0.46  \n",
       "18                 NaN               NaN         7.95       7.60          0.35  \n",
       "19                 NaN               NaN         7.51       7.17          0.34  \n",
       "20                 NaN               NaN         7.66       7.30          0.36  \n",
       "21                 NaN               NaN         7.86       7.40          0.46  \n",
       "22           26u - 27u              26.5         7.54       7.17          0.37  \n",
       "23           34u - 33u              33.5         7.51       7.08          0.43  \n",
       "24                 NaN               NaN         7.60       7.09          0.51  \n",
       "25                 NaN               NaN         7.90       7.59          0.31  \n",
       "26           30u - 35u              32.5         7.89       7.55          0.34  \n",
       "27           33u - 33u              33.0         7.71       7.30          0.41  \n",
       "28           35u - 34u              34.5         8.00       7.50          0.50  \n",
       "29           30u - 27u              28.5         7.79       7.45          0.34  \n",
       "..                 ...               ...          ...        ...           ...  \n",
       "320          56u - 57u              56.5         7.90       7.51          0.39  \n",
       "321          42u - 38u              40.0         9.62       9.29          0.33  \n",
       "322          40u - 39u              39.5         9.27       8.77          0.50  \n",
       "323                NaN               NaN         7.56       7.15          0.41  \n",
       "324          39u - 36u              37.5         7.89       7.42          0.47  \n",
       "325                NaN               NaN         7.66       7.32          0.34  \n",
       "326                NaN               NaN         7.77       7.38          0.39  \n",
       "327          27u - 28u              27.5         8.50       8.10          0.40  \n",
       "328          43u - 46u              44.5         7.71       7.27          0.44  \n",
       "329          31u - 31u              31.0         7.64       7.33          0.31  \n",
       "330          26u - 23u              24.5         7.87       7.47          0.40  \n",
       "331          50u - 54u              52.0         7.63       7.27          0.36  \n",
       "332                NaN               NaN         7.69       7.30          0.39  \n",
       "333                NaN               NaN         7.96       7.57          0.39  \n",
       "334          41u - 39u              40.0         7.95       7.56          0.39  \n",
       "335          27u - 29u              28.0         7.83       7.48          0.35  \n",
       "336                NaN               NaN         7.75       7.46          0.29  \n",
       "337          38u - 32u              35.0         7.72       7.29          0.43  \n",
       "338          51u - 54u              52.5         7.82       7.36          0.46  \n",
       "339          47u - 46u              46.5         7.77       7.28          0.49  \n",
       "340                NaN               NaN         7.50       7.17          0.33  \n",
       "341          30u - 33u              31.5         7.74       7.36          0.38  \n",
       "342          41u - 43u              42.0         7.87       7.43          0.44  \n",
       "343                NaN               NaN         7.72       7.20          0.52  \n",
       "344                NaN               NaN         7.98       7.59          0.39  \n",
       "345                NaN               NaN         7.93       7.73          0.20  \n",
       "346          49u - 49u              49.0         7.90       7.58          0.32  \n",
       "347                NaN               NaN         9.04       8.67          0.37  \n",
       "348                NaN               NaN         7.64       7.28          0.36  \n",
       "349                NaN               NaN         8.00       7.64          0.36  \n",
       "\n",
       "[349 rows x 9 columns]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#one last check to make sure everything looks sane\n",
    "treatments_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auralin_avg_dose</th>\n",
       "      <th>novodra_avg_dose</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>174.000000</td>\n",
       "      <td>175.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>349.000000</td>\n",
       "      <td>349.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>43.247126</td>\n",
       "      <td>38.931429</td>\n",
       "      <td>7.957077</td>\n",
       "      <td>7.561117</td>\n",
       "      <td>0.395960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.780502</td>\n",
       "      <td>8.914239</td>\n",
       "      <td>0.545938</td>\n",
       "      <td>0.545878</td>\n",
       "      <td>0.059054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>20.500000</td>\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>35.625000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>7.270000</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.500000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>7.790000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.500000</td>\n",
       "      <td>44.500000</td>\n",
       "      <td>7.950000</td>\n",
       "      <td>7.560000</td>\n",
       "      <td>0.440000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>68.500000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>9.950000</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.540000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       auralin_avg_dose  novodra_avg_dose  hba1c_start   hba1c_end  \\\n",
       "count        174.000000        175.000000   349.000000  349.000000   \n",
       "mean          43.247126         38.931429     7.957077    7.561117   \n",
       "std            9.780502          8.914239     0.545938    0.545878   \n",
       "min           26.000000         20.500000     7.500000    7.010000   \n",
       "25%           35.625000         32.000000     7.650000    7.270000   \n",
       "50%           41.500000         39.000000     7.790000    7.400000   \n",
       "75%           49.500000         44.500000     7.950000    7.560000   \n",
       "max           68.500000         62.500000     9.950000    9.580000   \n",
       "\n",
       "       hba1c_change  \n",
       "count    349.000000  \n",
       "mean       0.395960  \n",
       "std        0.059054  \n",
       "min        0.200000  \n",
       "25%        0.350000  \n",
       "50%        0.390000  \n",
       "75%        0.440000  \n",
       "max        0.540000  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treatments_clean.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#export\n",
    "treatments_clean.to_csv('treatments_merged_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>72</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      72  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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sanity check\n",
    "patients_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "patients_clean.to_csv('patients_partially_clean.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#no changes to adverse_reactions, so no need to save"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bonus : do these drugs actually work?\n",
    "\n",
    "Doing this cleaning got me curious, so let's label the data and do a quick hypothesis test.\n",
    "\n",
    "I'm guessing that the drug worked from a first peak at the data so the mean hba1c of the treament group should be lower than the control group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Step 1 label the data so we can seperate treatment and control groups\n",
    "\n",
    "def label_treatment_group(row):\n",
    "    if row[\"auralin_avg_dose\"] > 0:\n",
    "        return \"auralin\"\n",
    "    elif row[\"novodra_avg_dose\"]> 0:\n",
    "        return \"novodra\"\n",
    "    else:\n",
    "        return \"control\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "novodra    175\n",
       "auralin    174\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#well that's a problem. Whoever simulated this data didn't include a control group!\n",
    "groups = treatments_clean.apply(lambda row: label_treatment_group(row), axis=1)\n",
    "groups.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "treatment_grouped = treatments_clean.groupby(groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>auralin_avg_dose</th>\n",
       "      <th>novodra_avg_dose</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>auralin</th>\n",
       "      <td>43.247126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.960632</td>\n",
       "      <td>7.573678</td>\n",
       "      <td>0.386954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>novodra</th>\n",
       "      <td>NaN</td>\n",
       "      <td>38.931429</td>\n",
       "      <td>7.953543</td>\n",
       "      <td>7.548629</td>\n",
       "      <td>0.404914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         auralin_avg_dose  novodra_avg_dose  hba1c_start  hba1c_end  \\\n",
       "auralin         43.247126               NaN     7.960632   7.573678   \n",
       "novodra               NaN         38.931429     7.953543   7.548629   \n",
       "\n",
       "         hba1c_change  \n",
       "auralin      0.386954  \n",
       "novodra      0.404914  "
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#it looks like hba1c did go down under both drug conditions, but because there is no control, it's hard to draw any conclusions. \n",
    "#ordinarily I would do a paired-ttest on this kind of data, but I guess that will have to wait until I contact udacity and ask if they have simulated a control group.\n",
    "treatment_grouped.mean()"
   ]
  },
  {
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   "source": [
    "## Areas for Further Exploration\n",
    "\n",
    "- Check if adverse effects are asssociated with dosage"
   ]
  }
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