CLS_tests_less_clsloinc.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import ExcelWriter\n",
    "from pandas import ExcelFile\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a.zctestcode, a.name, a.description, b.testableitemname, b.testmethodologydescription, b.qbsspecimendescription, c.aeproductid, c.aespecimen, c.aemethodology from test a left join testtoaetestingdefinitionsuspense b on a.testid = b.testid left join aetestingdefinition c on a.containedbyaetestingdefinitionid = c.aetestingdefinitionid where a.name is not null;\n",
    "ae = pd.read_excel(r'C:\\Users\\jessenw\\Documents\\@Projects\\@Compass 2020\\Covance lab lists\\from_luis\\20200710-luis_garcia_CLS_CT_Test.xlsx', sheet_name = 'CLS Tests')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>zctestcode</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>testableitemname</th>\n",
       "      <th>testmethodologydescription</th>\n",
       "      <th>qbsspecimendescription</th>\n",
       "      <th>aeproductid</th>\n",
       "      <th>aespecimen</th>\n",
       "      <th>aemethodology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACT10</td>\n",
       "      <td>DA 0hr</td>\n",
       "      <td>Dopamine, 0 hr-QT</td>\n",
       "      <td>Dopamine</td>\n",
       "      <td>High performance liquid chromatography-TD</td>\n",
       "      <td>Plasma, EDTA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACT101</td>\n",
       "      <td>H2 3Hr</td>\n",
       "      <td>H2 Breath, 3 Hr-QT</td>\n",
       "      <td>H2 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACT104</td>\n",
       "      <td>CH4 2 Hr</td>\n",
       "      <td>CH4 Breath, 2 Hr-IA</td>\n",
       "      <td>CH4 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACT105</td>\n",
       "      <td>CH4 3 Hr</td>\n",
       "      <td>CH4 Breath, 3 Hr-IA</td>\n",
       "      <td>CH4 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACT11</td>\n",
       "      <td>DA 4hr</td>\n",
       "      <td>Dopamine, 4 hr-QT</td>\n",
       "      <td>Dopamine</td>\n",
       "      <td>High performance liquid chromatography-TD</td>\n",
       "      <td>Plasma, EDTA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zctestcode      name          description testableitemname  \\\n",
       "0      ACT10    DA 0hr    Dopamine, 0 hr-QT         Dopamine   \n",
       "1     ACT101    H2 3Hr   H2 Breath, 3 Hr-QT   H2 Breath Test   \n",
       "2     ACT104  CH4 2 Hr  CH4 Breath, 2 Hr-IA  CH4 Breath Test   \n",
       "3     ACT105  CH4 3 Hr  CH4 Breath, 3 Hr-IA  CH4 Breath Test   \n",
       "4      ACT11    DA 4hr    Dopamine, 4 hr-QT         Dopamine   \n",
       "\n",
       "                  testmethodologydescription qbsspecimendescription  \\\n",
       "0  High performance liquid chromatography-TD           Plasma, EDTA   \n",
       "1           Gas chromatography (absolute)-TD         Expired breath   \n",
       "2           Gas chromatography (absolute)-TD         Expired breath   \n",
       "3           Gas chromatography (absolute)-TD         Expired breath   \n",
       "4  High performance liquid chromatography-TD           Plasma, EDTA   \n",
       "\n",
       "  aeproductid aespecimen aemethodology  \n",
       "0         NaN        NaN           NaN  \n",
       "1         NaN        NaN           NaN  \n",
       "2         NaN        NaN           NaN  \n",
       "3         NaN        NaN           NaN  \n",
       "4         NaN        NaN           NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ae.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 253978 entries, 0 to 253977\n",
      "Data columns (total 9 columns):\n",
      "zctestcode                    253978 non-null object\n",
      "name                          253958 non-null object\n",
      "description                   253975 non-null object\n",
      "testableitemname              200936 non-null object\n",
      "testmethodologydescription    195659 non-null object\n",
      "qbsspecimendescription        200939 non-null object\n",
      "aeproductid                   21734 non-null object\n",
      "aespecimen                    21734 non-null object\n",
      "aemethodology                 21734 non-null object\n",
      "dtypes: object(9)\n",
      "memory usage: 17.4+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['zctestcode',\n",
       " 'name',\n",
       " 'description',\n",
       " 'testableitemname',\n",
       " 'testmethodologydescription',\n",
       " 'qbsspecimendescription',\n",
       " 'aeproductid',\n",
       " 'aespecimen',\n",
       " 'aemethodology']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ae.info();list(ae.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "ae['zctestcode']=ae['zctestcode'].str.strip()\n",
    "ae['name']=ae['name'].str.strip()\n",
    "ae['description']=ae['description'].str.strip()\n",
    "ae['testableitemname']=ae['testableitemname'].str.strip()\n",
    "ae['testmethodologydescription']=ae['testmethodologydescription'].str.strip()\n",
    "ae['qbsspecimendescription']=ae['qbsspecimendescription'].str.strip()\n",
    "ae['aeproductid']=ae['aeproductid'].str.strip()\n",
    "ae['aespecimen']=ae['aespecimen'].str.strip()\n",
    "ae['aemethodology']=ae['aemethodology'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "clsloinc = pd.read_excel(r'C:\\Users\\jessenw\\Documents\\@Projects\\@Compass 2020\\Covance lab lists\\Compass_2020_Covance_new_data_model.xlsx', sheet_name = '2', header=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>test_number</th>\n",
       "      <th>test_name</th>\n",
       "      <th>Unnamed: 2</th>\n",
       "      <th>Unnamed: 3</th>\n",
       "      <th>method</th>\n",
       "      <th>specimen</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>result_loinc</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>...</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>Unnamed: 19</th>\n",
       "      <th>Unnamed: 20</th>\n",
       "      <th>Unnamed: 21</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>Unnamed: 23</th>\n",
       "      <th>loinc_classtype</th>\n",
       "      <th>loinc_class</th>\n",
       "      <th>loinc_long_class</th>\n",
       "      <th>loinc_term_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACT389</td>\n",
       "      <td>Vitamin A-CL-LDT-QT</td>\n",
       "      <td>Vit A</td>\n",
       "      <td>Vitamin A and Vitamin E Panel</td>\n",
       "      <td>High performance liquid chromatography</td>\n",
       "      <td>Serum</td>\n",
       "      <td>14642-3</td>\n",
       "      <td>2053-7</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>ng/mL</td>\n",
       "      <td>...</td>\n",
       "      <td>2.58</td>\n",
       "      <td>Nicolas Martinier</td>\n",
       "      <td>Monica Smoot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Laboratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACT390</td>\n",
       "      <td>Vitamin E-CL-LDT-QT</td>\n",
       "      <td>Vit E</td>\n",
       "      <td>Vitamin A and Vitamin E Panel</td>\n",
       "      <td>High performance liquid chromatography</td>\n",
       "      <td>Serum</td>\n",
       "      <td>29747-3</td>\n",
       "      <td>47791-9</td>\n",
       "      <td>umol/L</td>\n",
       "      <td>ug/mL</td>\n",
       "      <td>...</td>\n",
       "      <td>2.58</td>\n",
       "      <td>Nicolas Martinier</td>\n",
       "      <td>Monica Smoot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Laboratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACT439</td>\n",
       "      <td>Total Testosterone</td>\n",
       "      <td>T. Testo</td>\n",
       "      <td>Testosterone and Dihydrotestosterone Panel</td>\n",
       "      <td>LC-MS/MS</td>\n",
       "      <td>Serum</td>\n",
       "      <td>14913-8</td>\n",
       "      <td>2986-8</td>\n",
       "      <td>nmol/L</td>\n",
       "      <td>ng/dL</td>\n",
       "      <td>...</td>\n",
       "      <td>2.52</td>\n",
       "      <td>Randy Santiano</td>\n",
       "      <td>Nicolas Martinier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Laboratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACT441</td>\n",
       "      <td>Dihydrotestosterone-LDT</td>\n",
       "      <td>DHT</td>\n",
       "      <td>Testosterone and Dihydrotestosterone Panel</td>\n",
       "      <td>LC-MS/MS</td>\n",
       "      <td>Serum</td>\n",
       "      <td>15057-3</td>\n",
       "      <td>1848-1</td>\n",
       "      <td>nmol/L</td>\n",
       "      <td>ng/dL</td>\n",
       "      <td>...</td>\n",
       "      <td>2.52</td>\n",
       "      <td>Randy Santiano</td>\n",
       "      <td>Nicolas Martinier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Laboratory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACT503</td>\n",
       "      <td>Sensitive Estrone-LDT</td>\n",
       "      <td>E1</td>\n",
       "      <td>Estrone</td>\n",
       "      <td>LC-MS/MS</td>\n",
       "      <td>Serum</td>\n",
       "      <td>22663-9</td>\n",
       "      <td>2258-2</td>\n",
       "      <td>pmol/L</td>\n",
       "      <td>pg/mL</td>\n",
       "      <td>...</td>\n",
       "      <td>2.52</td>\n",
       "      <td>Randy Santiano</td>\n",
       "      <td>Nicolas Martinier</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-12-31</td>\n",
       "      <td>2019-12-31 00:00:00</td>\n",
       "      <td>CHEM</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Chemistry</td>\n",
       "      <td>Laboratory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  test_number                test_name Unnamed: 2  \\\n",
       "0      ACT389      Vitamin A-CL-LDT-QT      Vit A   \n",
       "1      ACT390      Vitamin E-CL-LDT-QT      Vit E   \n",
       "2      ACT439       Total Testosterone   T. Testo   \n",
       "3      ACT441  Dihydrotestosterone-LDT        DHT   \n",
       "4      ACT503    Sensitive Estrone-LDT         E1   \n",
       "\n",
       "                                   Unnamed: 3  \\\n",
       "0               Vitamin A and Vitamin E Panel   \n",
       "1               Vitamin A and Vitamin E Panel   \n",
       "2  Testosterone and Dihydrotestosterone Panel   \n",
       "3  Testosterone and Dihydrotestosterone Panel   \n",
       "4                                     Estrone   \n",
       "\n",
       "                                   method specimen Unnamed: 6 result_loinc  \\\n",
       "0  High performance liquid chromatography    Serum    14642-3       2053-7   \n",
       "1  High performance liquid chromatography    Serum    29747-3      47791-9   \n",
       "2                                LC-MS/MS    Serum    14913-8       2986-8   \n",
       "3                                LC-MS/MS    Serum    15057-3       1848-1   \n",
       "4                                LC-MS/MS    Serum    22663-9       2258-2   \n",
       "\n",
       "  Unnamed: 8 Unnamed: 9  ... Unnamed: 18        Unnamed: 19  \\\n",
       "0     umol/L      ng/mL  ...        2.58  Nicolas Martinier   \n",
       "1     umol/L      ug/mL  ...        2.58  Nicolas Martinier   \n",
       "2     nmol/L      ng/dL  ...        2.52     Randy Santiano   \n",
       "3     nmol/L      ng/dL  ...        2.52     Randy Santiano   \n",
       "4     pmol/L      pg/mL  ...        2.52     Randy Santiano   \n",
       "\n",
       "         Unnamed: 20 Unnamed: 21 Unnamed: 22          Unnamed: 23  \\\n",
       "0       Monica Smoot         NaN  2019-12-31  2019-12-31 00:00:00   \n",
       "1       Monica Smoot         NaN  2019-12-31  2019-12-31 00:00:00   \n",
       "2  Nicolas Martinier         NaN  2019-12-31  2019-12-31 00:00:00   \n",
       "3  Nicolas Martinier         NaN  2019-12-31  2019-12-31 00:00:00   \n",
       "4  Nicolas Martinier         NaN  2019-12-31  2019-12-31 00:00:00   \n",
       "\n",
       "  loinc_classtype loinc_class  loinc_long_class loinc_term_class  \n",
       "0            CHEM         1.0         Chemistry       Laboratory  \n",
       "1            CHEM         1.0         Chemistry       Laboratory  \n",
       "2            CHEM         1.0         Chemistry       Laboratory  \n",
       "3            CHEM         1.0         Chemistry       Laboratory  \n",
       "4            CHEM         1.0         Chemistry       Laboratory  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clsloinc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1002 entries, 0 to 1001\n",
      "Data columns (total 28 columns):\n",
      "test_number               1002 non-null object\n",
      "test_name                 1002 non-null object\n",
      "Unnamed: 2                974 non-null object\n",
      "Unnamed: 3                974 non-null object\n",
      "method                    973 non-null object\n",
      "specimen                  974 non-null object\n",
      "Unnamed: 6                952 non-null object\n",
      "result_loinc              956 non-null object\n",
      "Unnamed: 8                721 non-null object\n",
      "Unnamed: 9                722 non-null object\n",
      "loinc_long_common_name    954 non-null object\n",
      "Unnamed: 11               944 non-null object\n",
      "loinc_component           946 non-null object\n",
      "Unnamed: 13               944 non-null object\n",
      "Unnamed: 14               944 non-null object\n",
      "Unnamed: 15               946 non-null object\n",
      "Unnamed: 16               946 non-null object\n",
      "Unnamed: 17               409 non-null object\n",
      "Unnamed: 18               964 non-null float64\n",
      "Unnamed: 19               1002 non-null object\n",
      "Unnamed: 20               1002 non-null object\n",
      "Unnamed: 21               32 non-null object\n",
      "Unnamed: 22               787 non-null datetime64[ns]\n",
      "Unnamed: 23               820 non-null object\n",
      "loinc_classtype           945 non-null object\n",
      "loinc_class               945 non-null float64\n",
      "loinc_long_class          945 non-null object\n",
      "loinc_term_class          945 non-null object\n",
      "dtypes: datetime64[ns](1), float64(2), object(25)\n",
      "memory usage: 219.3+ KB\n"
     ]
    }
   ],
   "source": [
    "clsloinc.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_number',\n",
       " 'test_name',\n",
       " 'Unnamed: 2',\n",
       " 'Unnamed: 3',\n",
       " 'method',\n",
       " 'specimen',\n",
       " 'Unnamed: 6',\n",
       " 'result_loinc',\n",
       " 'Unnamed: 8',\n",
       " 'Unnamed: 9',\n",
       " 'loinc_long_common_name',\n",
       " 'Unnamed: 11',\n",
       " 'loinc_component',\n",
       " 'Unnamed: 13',\n",
       " 'Unnamed: 14',\n",
       " 'Unnamed: 15',\n",
       " 'Unnamed: 16',\n",
       " 'Unnamed: 17',\n",
       " 'Unnamed: 18',\n",
       " 'Unnamed: 19',\n",
       " 'Unnamed: 20',\n",
       " 'Unnamed: 21',\n",
       " 'Unnamed: 22',\n",
       " 'Unnamed: 23',\n",
       " 'loinc_classtype',\n",
       " 'loinc_class',\n",
       " 'loinc_long_class',\n",
       " 'loinc_term_class']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(clsloinc.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clsloinc['test_number']=clsloinc['test_number'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = clsloinc.drop(clsloinc.columns[1:28],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1002 entries, 0 to 1001\n",
      "Data columns (total 1 columns):\n",
      "test_number    1002 non-null object\n",
      "dtypes: object(1)\n",
      "memory usage: 8.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['test_number']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls.info();list(cls.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cls_ae = ae.merge(cls, how='outer',indicator=True, left_on='zctestcode',right_on='test_number').query('_merge==\"left_only\"')\n",
    "cls_less_loinc = pd.merge(ae,cls,how='left',left_on='zctestcode',right_on='test_number',indicator=True).query('_merge == \"left_only\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 252977 entries, 0 to 253977\n",
      "Data columns (total 11 columns):\n",
      "zctestcode                    252977 non-null object\n",
      "name                          252904 non-null object\n",
      "description                   252974 non-null object\n",
      "testableitemname              200477 non-null object\n",
      "testmethodologydescription    153238 non-null object\n",
      "qbsspecimendescription        153247 non-null object\n",
      "aeproductid                   21045 non-null object\n",
      "aespecimen                    21042 non-null object\n",
      "aemethodology                 21042 non-null object\n",
      "test_number                   0 non-null object\n",
      "_merge                        252977 non-null category\n",
      "dtypes: category(1), object(10)\n",
      "memory usage: 21.5+ MB\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>zctestcode</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>testableitemname</th>\n",
       "      <th>testmethodologydescription</th>\n",
       "      <th>qbsspecimendescription</th>\n",
       "      <th>aeproductid</th>\n",
       "      <th>aespecimen</th>\n",
       "      <th>aemethodology</th>\n",
       "      <th>test_number</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ACT10</td>\n",
       "      <td>DA 0hr</td>\n",
       "      <td>Dopamine, 0 hr-QT</td>\n",
       "      <td>Dopamine</td>\n",
       "      <td>High performance liquid chromatography-TD</td>\n",
       "      <td>Plasma, EDTA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACT101</td>\n",
       "      <td>H2 3Hr</td>\n",
       "      <td>H2 Breath, 3 Hr-QT</td>\n",
       "      <td>H2 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACT104</td>\n",
       "      <td>CH4 2 Hr</td>\n",
       "      <td>CH4 Breath, 2 Hr-IA</td>\n",
       "      <td>CH4 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ACT105</td>\n",
       "      <td>CH4 3 Hr</td>\n",
       "      <td>CH4 Breath, 3 Hr-IA</td>\n",
       "      <td>CH4 Breath Test</td>\n",
       "      <td>Gas chromatography (absolute)-TD</td>\n",
       "      <td>Expired breath</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ACT11</td>\n",
       "      <td>DA 4hr</td>\n",
       "      <td>Dopamine, 4 hr-QT</td>\n",
       "      <td>Dopamine</td>\n",
       "      <td>High performance liquid chromatography-TD</td>\n",
       "      <td>Plasma, EDTA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  zctestcode      name          description testableitemname  \\\n",
       "0      ACT10    DA 0hr    Dopamine, 0 hr-QT         Dopamine   \n",
       "1     ACT101    H2 3Hr   H2 Breath, 3 Hr-QT   H2 Breath Test   \n",
       "2     ACT104  CH4 2 Hr  CH4 Breath, 2 Hr-IA  CH4 Breath Test   \n",
       "3     ACT105  CH4 3 Hr  CH4 Breath, 3 Hr-IA  CH4 Breath Test   \n",
       "4      ACT11    DA 4hr    Dopamine, 4 hr-QT         Dopamine   \n",
       "\n",
       "                  testmethodologydescription qbsspecimendescription  \\\n",
       "0  High performance liquid chromatography-TD           Plasma, EDTA   \n",
       "1           Gas chromatography (absolute)-TD         Expired breath   \n",
       "2           Gas chromatography (absolute)-TD         Expired breath   \n",
       "3           Gas chromatography (absolute)-TD         Expired breath   \n",
       "4  High performance liquid chromatography-TD           Plasma, EDTA   \n",
       "\n",
       "  aeproductid aespecimen aemethodology test_number     _merge  \n",
       "0         NaN        NaN           NaN         NaN  left_only  \n",
       "1         NaN        NaN           NaN         NaN  left_only  \n",
       "2         NaN        NaN           NaN         NaN  left_only  \n",
       "3         NaN        NaN           NaN         NaN  left_only  \n",
       "4         NaN        NaN           NaN         NaN  left_only  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_less_loinc.info();cls_less_loinc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>zctestcode</th>\n",
       "      <th>name</th>\n",
       "      <th>description</th>\n",
       "      <th>testableitemname</th>\n",
       "      <th>testmethodologydescription</th>\n",
       "      <th>qbsspecimendescription</th>\n",
       "      <th>aeproductid</th>\n",
       "      <th>aespecimen</th>\n",
       "      <th>aemethodology</th>\n",
       "      <th>test_number</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>253973</th>\n",
       "      <td>TCT10971</td>\n",
       "      <td>Vial #3</td>\n",
       "      <td>CD138- Vial #3-QT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253974</th>\n",
       "      <td>ODT20058</td>\n",
       "      <td>%115s-B 6h</td>\n",
       "      <td>%CD115[surf]-(Bls)6hrRUO412-QT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253975</th>\n",
       "      <td>SRT8311</td>\n",
       "      <td>HA</td>\n",
       "      <td>Hyaluronic Acid-291</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HYALURONIC ACID</td>\n",
       "      <td>SERUM</td>\n",
       "      <td>ENZYME-LINKED IMMUNOSORBENT ASSAY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253976</th>\n",
       "      <td>SRT844</td>\n",
       "      <td>PK 24h</td>\n",
       "      <td>PK AG1343, 24hr-75</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253977</th>\n",
       "      <td>SCT1297</td>\n",
       "      <td>Ur Creat</td>\n",
       "      <td>Urine Creatinine (CONV:g/L)-QT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       zctestcode        name                     description  \\\n",
       "253973   TCT10971     Vial #3               CD138- Vial #3-QT   \n",
       "253974   ODT20058  %115s-B 6h  %CD115[surf]-(Bls)6hrRUO412-QT   \n",
       "253975    SRT8311          HA             Hyaluronic Acid-291   \n",
       "253976     SRT844      PK 24h              PK AG1343, 24hr-75   \n",
       "253977    SCT1297    Ur Creat  Urine Creatinine (CONV:g/L)-QT   \n",
       "\n",
       "       testableitemname testmethodologydescription qbsspecimendescription  \\\n",
       "253973              NaN                        NaN                    NaN   \n",
       "253974              NaN                        NaN                    NaN   \n",
       "253975              NaN                        NaN                    NaN   \n",
       "253976              NaN                        NaN                    NaN   \n",
       "253977              NaN                        NaN                    NaN   \n",
       "\n",
       "            aeproductid aespecimen                      aemethodology  \\\n",
       "253973              NaN        NaN                                NaN   \n",
       "253974              NaN        NaN                                NaN   \n",
       "253975  HYALURONIC ACID      SERUM  ENZYME-LINKED IMMUNOSORBENT ASSAY   \n",
       "253976              NaN        NaN                                NaN   \n",
       "253977              NaN        NaN                                NaN   \n",
       "\n",
       "       test_number     _merge  \n",
       "253973         NaN  left_only  \n",
       "253974         NaN  left_only  \n",
       "253975         NaN  left_only  \n",
       "253976         NaN  left_only  \n",
       "253977         NaN  left_only  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_less_loinc.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_less_loinc_ae = cls_less_loinc[cls_less_loinc.aeproductid.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21045 entries, 119 to 253975\n",
      "Data columns (total 11 columns):\n",
      "zctestcode                    21045 non-null object\n",
      "name                          21045 non-null object\n",
      "description                   21045 non-null object\n",
      "testableitemname              5021 non-null object\n",
      "testmethodologydescription    5016 non-null object\n",
      "qbsspecimendescription        5018 non-null object\n",
      "aeproductid                   21045 non-null object\n",
      "aespecimen                    21042 non-null object\n",
      "aemethodology                 21042 non-null object\n",
      "test_number                   0 non-null object\n",
      "_merge                        21045 non-null category\n",
      "dtypes: category(1), object(10)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "cls_less_loinc_ae.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create excel writer\n",
    "writer = ExcelWriter('cls_ae_less_loinc.xlsx')\n",
    "# write dataframes\n",
    "cls_less_loinc_ae.to_excel(writer,'CLSAE_less_CLSLOINC_only_AE',index=False)\n",
    "cls_less_loinc.to_excel(writer,'CLSAE_less_CLSLOINC',index=False)\n",
    "ae.to_excel(writer, 'CLSAE',index=False)\n",
    "clsloinc.to_excel(writer,'CLSLOINC',index=False)\n",
    "writer.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib_venn import venn3_unweighted\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "venn3_unweighted([set(cls['test_number']), set(ae['zctestcode']), set(cls_less_loinc_ae['zctestcode'])], set_labels=('CLSLOINC','AE','CLS less LOINC plus AE'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}