shahpouri_week_03.ipynb

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    "# Week 03 Assignment Covid\n",
    "\n",
    "New types of data and new data science technologies enable new research. These new technologies are technologies such as the ability to combine existing data or the ability to generate synthetic data from existing knowledge. This week casus is based on such research. Data is generated by Synthea's COVID-19 module. The data was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. The Data is stored in separate tables to avoid redundancy, with as a concequence that tables need to be combined and reorganized in dataframes for analysing purpose.\n",
    "\n",
    "Keywords: merge data, subset data, clean data, generate data\n",
    "\n",
    "You will learn about combining data with pandas and numpy and you will learn to visualize with bokeh. Concretely, you will preprocess the partly Synthetic Covid data in an appropiate format in order to conduct statistical and visual analysis. Learning objectives\n",
    "\n",
    "- Combine multiple data sources for analysis\n",
    "- Read, inspect, clean, reshape data\n",
    "- Visualize data using bokeh\n",
    "- Maintain development environment \n",
    "- Apply coding standards and FAIR principles\n",
    "- Reshape the dataset into a format suitable for visual and statistical analysis\n",
    "- Use widgets to make the plot interactive \n",
    "- Use GIS libraries to plot geographical data\n",
    "\n",
    "Tutorials about combining data: https://github.com/fenna/BFVM22PROG1/blob/main/tutorials/tutorial_combine_data.ipynb\n",
    "\n",
    "study case combining data:https://github.com/fenna/BFVM22PROG1/blob/main/study_cases/adults_who_binge_drank_in_hot_towns.ipynb\n",
    "\n",
    "\n",
    "Please add the topics you want to learn about here: https://padlet.com/ffeenstra1/kzh2chaqleq3iovu\n",
    "\n",
    "\n",
    "Your job is to **visualize the lab values taken for COVID-19 patients of survived versus not survived patients**. \n",
    "\n",
    "The assignment consists of 6 parts:\n",
    "\n",
    "- [part 1: load the data](#0)\n",
    "     - [Exercise 1.1](#ex-11)\n",
    "- [part 2: data wrangling](#1)\n",
    "     - [Exercise 2.1](#ex-21)\n",
    "- [part 3: more wrangling](#2)\n",
    "     - [Exercise 3.1](#ex-31)\n",
    "- [part 4: plot the data](#3)\n",
    "     - [Exercise 4.1](#ex-41)\n",
    "- [part 5: plot patient location](#5)\n",
    "     - [Exercise 5.1](#ex-51)\n",
    "\n",
    "\n",
    "Part 1 and 4 are mandatory, part 5 is optional (bonus)\n",
    "Mind you that you cannot copy code without referencing the code. If you copy code you need to be able to explain your code verbally and you will not get the full score. \n",
    "\n",
    "\n",
    "## About the data\n",
    "\n",
    "The data is generated by Synthea's COVID-19 module. The data was constructed using three peer-reviewed publications published in the early stages of the global pandemic, when less was known, along with emerging resources, data, publications, and clinical knowledge. The simulation outputs synthetic Electronic Health Records (EHR), including the daily consumption of Personal Protective Equipment (PPE) and other medical devices and supplies. For this assignment the `conditions`, `patients`, `observations`, `careplans` and `encounters` table will be used. The Data is stored in separate tables to avoid redundancy, with as a concequence that tables need to be combined and reorganized in dataframes for analysing purpose.\n",
    "\n",
    "Source: Walonoski J, Klaus S, Granger E, Hall D, Gregorowicz A, Neyarapally G, Watson A, Eastman J. Synthea™ Novel coronavirus (COVID-19) model and synthetic data set. Intelligence-Based Medicine. 2020 Nov;1:100007. https://doi.org/10.1016/j.ibmed.2020.100007\n",
    "\n",
    "Please <a href = \"https://synthetichealth.github.io/synthea-sample-data/downloads/10k_synthea_covid19_csv.zip\">download</a> the data\n",
    "\n",
    "#### Covid Patients\n",
    "Patients are considered Covid patients if they are identified with `CODE` `840539006`\n",
    "\n",
    "\n",
    "#### Survivors\n",
    "Patients that had covid and where tested negative after isolation have tested code `94531-1`,  SARS-CoV-2 RNA Pnl Resp NAA+probe (covid-sars test) + a value of `Not detected (qualifier value)`. These patients are considered to be survived covid patients. \n",
    "\n",
    "#### Non-Survivors\n",
    "Patients that did not survived Covid have a `DEATHDATE` which is not null. \n",
    "\n",
    "\n",
    "#### Lab values  COVID-19 patients\n",
    "\n",
    "Patients are monitored for blood and heart conditions once they are admitted in Hospital or under treatment. The lab values of interest are as follow: \n",
    "\n",
    "- `48065-7`  Fibrin D-dimer FEU [Mass/volume] in Platelet poor plasma\n",
    "- `26881-3`   Interleukin 6 [Mass/volume] in Serum or Plasma\n",
    "- `2276-4` Ferritin [Mass/volume] in Serum or Plasma\n",
    "- `89579-7` Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method\n",
    "- `731-0` Lymphocytes [#/volume] in Blood by Automated count\n",
    "- `14804-9` Lactate dehydrogenase [Enzymatic activity/volume] in Serum or Plasma by Lactate to pyruvate reaction\n"
   ]
  },
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    "---"
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   "source": [
    "<a name='0'></a>\n",
    "## Part 1: Load the data (20 pt)\n",
    "\n",
    "Instructions: Load the data of the following files. \n",
    "Preferably we read the data not with a hard coded data path but using a config file. See https://fennaf.gitbook.io/bfvm22prog1/data-processing/configuration-files/yaml\n",
    "\n",
    "- conditions.csv\n",
    "- patients.csv\n",
    "- observations.csv\n",
    "- careplans.csv\n",
    "- encounters.csv\n",
    "\n",
    "Get yourself familiar with the data. Create some meaningful overviews. Answer the following questions\n",
    "\n",
    "1. How many patients are there\n",
    "2. How many covid-patients are there\n",
    "3. How many patients do have a 'Hospital admission for isolation' encounter\n",
    "    \n",
    "<details>    \n",
    "<summary>\n",
    "    <font size=\"3\" color=\"darkgreen\"><b>Hints</b></font>\n",
    "</summary>\n",
    "    <ul><li>use a unique dataframe for each file, use a meaningful name</li>\n",
    "    <li>pandas.read_csv() method can be used to read a csv file</li>\n",
    "    <li>pandas.DataFrame.head() method is often used to inspect the dataframe</li>\n",
    "    <li>.unique() returns a list of unique values of a column</li>\n",
    "</ul>\n",
    "</details>\n",
    "\n",
    "<a name='ex-11'></a>\n",
    "### 1.1 Code your solution"
   ]
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   "cell_type": "code",
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       "<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>Id</th>\n",
       "      <th>BIRTHDATE</th>\n",
       "      <th>DEATHDATE</th>\n",
       "      <th>SSN</th>\n",
       "      <th>DRIVERS</th>\n",
       "      <th>PASSPORT</th>\n",
       "      <th>PREFIX</th>\n",
       "      <th>FIRST</th>\n",
       "      <th>LAST</th>\n",
       "      <th>SUFFIX</th>\n",
       "      <th>...</th>\n",
       "      <th>BIRTHPLACE</th>\n",
       "      <th>ADDRESS</th>\n",
       "      <th>CITY</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>ZIP</th>\n",
       "      <th>LAT</th>\n",
       "      <th>LON</th>\n",
       "      <th>HEALTHCARE_EXPENSES</th>\n",
       "      <th>HEALTHCARE_COVERAGE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f0f3bc8d-ef38-49ce-a2bd-dfdda982b271</td>\n",
       "      <td>2017-08-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999-68-6630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jacinto644</td>\n",
       "      <td>Kris249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Beverly  Massachusetts  US</td>\n",
       "      <td>888 Hickle Ferry Suite 38</td>\n",
       "      <td>Springfield</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Hampden County</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>42.151961</td>\n",
       "      <td>-72.598959</td>\n",
       "      <td>8446.49</td>\n",
       "      <td>1499.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>067318a4-db8f-447f-8b6e-f2f61e9baaa5</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999-15-5895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Alva958</td>\n",
       "      <td>Krajcik437</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Boston  Massachusetts  US</td>\n",
       "      <td>1048 Skiles Trailer</td>\n",
       "      <td>Walpole</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Norfolk County</td>\n",
       "      <td>2081.0</td>\n",
       "      <td>42.177370</td>\n",
       "      <td>-71.281353</td>\n",
       "      <td>89893.40</td>\n",
       "      <td>1845.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ae9efba3-ddc4-43f9-a781-f72019388548</td>\n",
       "      <td>1992-06-30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999-27-3385</td>\n",
       "      <td>S99971451</td>\n",
       "      <td>X53218815X</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Jayson808</td>\n",
       "      <td>Fadel536</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Springfield  Massachusetts  US</td>\n",
       "      <td>1056 Harris Lane Suite 70</td>\n",
       "      <td>Chicopee</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Hampden County</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>42.181642</td>\n",
       "      <td>-72.608842</td>\n",
       "      <td>577445.86</td>\n",
       "      <td>3528.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>199c586f-af16-4091-9998-ee4cfc02ee7a</td>\n",
       "      <td>2004-01-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999-73-2461</td>\n",
       "      <td>S99956432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jimmie93</td>\n",
       "      <td>Harris789</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Worcester  Massachusetts  US</td>\n",
       "      <td>201 Mitchell Lodge Unit 67</td>\n",
       "      <td>Pembroke</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Plymouth County</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.075292</td>\n",
       "      <td>-70.757035</td>\n",
       "      <td>336701.72</td>\n",
       "      <td>2705.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>353016ea-a0ff-4154-85bb-1cf8b6cedf20</td>\n",
       "      <td>1996-11-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999-60-7372</td>\n",
       "      <td>S99917327</td>\n",
       "      <td>X58903159X</td>\n",
       "      <td>Mr.</td>\n",
       "      <td>Gregorio366</td>\n",
       "      <td>Auer97</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Patras  Achaea  GR</td>\n",
       "      <td>1050 Lindgren Extension Apt 38</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Suffolk County</td>\n",
       "      <td>2135.0</td>\n",
       "      <td>42.352434</td>\n",
       "      <td>-71.028610</td>\n",
       "      <td>484076.34</td>\n",
       "      <td>3043.04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Id   BIRTHDATE DEATHDATE          SSN  \\\n",
       "0  f0f3bc8d-ef38-49ce-a2bd-dfdda982b271  2017-08-24       NaN  999-68-6630   \n",
       "1  067318a4-db8f-447f-8b6e-f2f61e9baaa5  2016-08-01       NaN  999-15-5895   \n",
       "2  ae9efba3-ddc4-43f9-a781-f72019388548  1992-06-30       NaN  999-27-3385   \n",
       "3  199c586f-af16-4091-9998-ee4cfc02ee7a  2004-01-09       NaN  999-73-2461   \n",
       "4  353016ea-a0ff-4154-85bb-1cf8b6cedf20  1996-11-15       NaN  999-60-7372   \n",
       "\n",
       "     DRIVERS    PASSPORT PREFIX        FIRST        LAST SUFFIX  ...  \\\n",
       "0        NaN         NaN    NaN   Jacinto644     Kris249    NaN  ...   \n",
       "1        NaN         NaN    NaN      Alva958  Krajcik437    NaN  ...   \n",
       "2  S99971451  X53218815X    Mr.    Jayson808    Fadel536    NaN  ...   \n",
       "3  S99956432         NaN    NaN     Jimmie93   Harris789    NaN  ...   \n",
       "4  S99917327  X58903159X    Mr.  Gregorio366      Auer97    NaN  ...   \n",
       "\n",
       "                       BIRTHPLACE                         ADDRESS  \\\n",
       "0      Beverly  Massachusetts  US       888 Hickle Ferry Suite 38   \n",
       "1       Boston  Massachusetts  US             1048 Skiles Trailer   \n",
       "2  Springfield  Massachusetts  US       1056 Harris Lane Suite 70   \n",
       "3    Worcester  Massachusetts  US      201 Mitchell Lodge Unit 67   \n",
       "4              Patras  Achaea  GR  1050 Lindgren Extension Apt 38   \n",
       "\n",
       "          CITY          STATE           COUNTY     ZIP        LAT        LON  \\\n",
       "0  Springfield  Massachusetts   Hampden County  1106.0  42.151961 -72.598959   \n",
       "1      Walpole  Massachusetts   Norfolk County  2081.0  42.177370 -71.281353   \n",
       "2     Chicopee  Massachusetts   Hampden County  1020.0  42.181642 -72.608842   \n",
       "3     Pembroke  Massachusetts  Plymouth County     NaN  42.075292 -70.757035   \n",
       "4       Boston  Massachusetts   Suffolk County  2135.0  42.352434 -71.028610   \n",
       "\n",
       "  HEALTHCARE_EXPENSES HEALTHCARE_COVERAGE  \n",
       "0             8446.49             1499.08  \n",
       "1            89893.40             1845.72  \n",
       "2           577445.86             3528.84  \n",
       "3           336701.72             2705.64  \n",
       "4           484076.34             3043.04  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "import yaml\n",
    "\n",
    "#open files with a config file\n",
    "with open(\"config.yml\", 'r') as reader:\n",
    "    config = yaml.safe_load(reader)\n",
    "\n",
    "file = config['condition_path']\n",
    "df_condition = pd.read_csv(file)\n",
    "\n",
    "file = config[\"careplans_path\"]\n",
    "df_careplan = pd.read_csv(file)\n",
    "\n",
    "file = config[\"observations_path\"]\n",
    "df_observation = pd.read_csv(file)\n",
    "\n",
    "file = config[\"encounters_path\"]\n",
    "df_encounter = pd.read_csv(file)\n",
    "\n",
    "file = config['patients_path']\n",
    "df_patient = pd.read_csv(file)\n",
    "\n",
    "#Total number of patients\n",
    "num_pat = len(pd.DataFrame(df_patient.groupby('Id')))\n",
    "# num_pat=len(patient_df['Id'])\n",
    "\n",
    "# Covid patients number with code: 840539006\n",
    "num_cov = len(df_condition[df_condition['CODE']==840539006])\n",
    "# num_cov=len(condition_df.loc[condition_df['CODE']==840539006]['PATIENT'].unique())\n",
    "\n",
    "# total number of isolated people.\n",
    "num_admitted = df_encounter['DESCRIPTION'].str.contains('Hospital admission for isolation').sum()\n",
    "# num_admitted=len(encounter_df.loc[encounter_df['DESCRIPTION']=='Hospital admission for isolation (procedure)']['PATIENT'].unique())\n",
    "\n",
    "# Number of death patients.\n",
    "num_died = df_patient['DEATHDATE'].notnull().sum()\n",
    "#num_died=patient_df['DEATHDATE'].count()\n",
    "df_patient.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Test your solution\n",
    "The following function needs to be called. You can use this as a test. There are however more meaningful overviews \n",
    "you can create. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 12352 patients in total.\n",
      "There are 8820 covid patients.\n",
      "There are 1867 admitted patients.\n",
      "Unfortunately, we missed 2352 patients, because of Covid!\n"
     ]
    }
   ],
   "source": [
    "def general_information(num_pat, num_cov, num_admitted, num_died):\n",
    "    print(f'There are {num_pat} patients in total.')\n",
    "    print(f'There are {num_cov} covid patients.')\n",
    "    print(f'There are {num_admitted} admitted patients.')\n",
    "    print(f'Unfortunately, we missed {num_died} patients, because of Covid!')\n",
    "\n",
    "general_information(num_pat, num_cov, num_admitted, num_died)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expected outcome"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are 12352 patients in total\n",
    "There are 8820 covid patients\n",
    "There are 1867 admitted patients\n",
    "2352 patients died"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='1'></a>\n",
    "## Part 2: Data Wrangling: set up the dataframe (30 pt)\n",
    "\n",
    "In this part we are going to combine data to create a dataframe with values of interest for the lab values analysis. \n",
    "\n",
    "We would like a dataframe containing the following information per record (only Covid patients!!!)\n",
    "\n",
    "- `PATIENT` - the ID of the covid patient\n",
    "- `days` - the number of days the patient is under observation\n",
    "- `CODE-Y` - the code of the observation  \n",
    "- `VALUE` - the lab value of the observation\n",
    "\n",
    "where only the following observation codes needs to be selected:\n",
    "\n",
    "- `48065-7`  Fibrin D-dimer FEU [Mass/volume] in Platelet poor plasma\n",
    "- `26881-3`   Interleukin 6 [Mass/volume] in Serum or Plasma\n",
    "- `2276-4` Ferritin [Mass/volume] in Serum or Plasma\n",
    "- `89579-7` Troponin I.cardiac [Mass/volume] in Serum or Plasma by High sensitivity method\n",
    "- `731-0` Lymphocytes [#/volume] in Blood by Automated count\n",
    "- `14804-9` Lactate dehydrogenase [Enzymatic activity/volume] in Serum or Plasma by Lactate to pyruvate reaction\n",
    "\n",
    "The days information is not primarely available and needs to be calculated by substracting observation DATE - START. \n",
    "\n",
    "An example of such a dataframe is given below:"
   ]
  },
  {
   "cell_type": "raw",
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   "source": [
    "                                      days   CODE-Y  VALUE    UNITS\n",
    "PATIENT                                                            \n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   0.0  14804-9  241.8      U/L\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   0.0   2276-4  489.5     ug/L\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   0.0  48065-7    0.4    ug/mL\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   0.0    731-0    1.1  10*3/uL\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   0.0  89579-7    3.4    pg/mL\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   1.0    731-0    1.1  10*3/uL\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   2.0  14804-9  233.7      U/L\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   2.0   2276-4  497.3     ug/L\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   2.0  48065-7    0.3    ug/mL\n",
    "00079a57-24a8-430f-b4f8-a1cf34f90060   2.0    731-0    1.0  10*3/uL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Combine conditions table (only covid patients) with the patient table into a covid_patient table\n",
    "#select the only the relevant lab observations from the observations table into a lab_obs table\n",
    "#merge the covid_patient table with the lab_obs table into a covid_patients_obs table\n",
    "#clean the covid_patients_obs table (rename columns, select only relevant columns, sort, typecast, add days column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details>    \n",
    "<summary>\n",
    "    <font size=\"3\" color=\"darkgreen\"><b>Hints</b></font>\n",
    "</summary>\n",
    "    <ul><li>you can use pandas.DataFrame.merge() to merge dataframes</li>\n",
    "    <li>df = df[(df.CODE == condition1 | df.CODE == condition1 )] selects rows with CODE of 2 conditional values</li>\n",
    "    <li>df.DATE - df.START return days if DATE and START are datetime format</li>\n",
    "    <li>pd.to_datetime() can be used to typecast to datetime</li>\n",
    "</ul>\n",
    "</details>\n",
    "\n",
    "<a name='ex-21'></a>\n",
    "### 2.1 Code your solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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</th>\n",
       "      <th>DAY</th>\n",
       "      <th>CODE-Y</th>\n",
       "      <th>VALUE</th>\n",
       "      <th>UNITS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>973902</th>\n",
       "      <td>00079a57-24a8-430f-b4f8-a1cf34f90060</td>\n",
       "      <td>0</td>\n",
       "      <td>14804-9</td>\n",
       "      <td>241.8</td>\n",
       "      <td>U/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973901</th>\n",
       "      <td>00079a57-24a8-430f-b4f8-a1cf34f90060</td>\n",
       "      <td>0</td>\n",
       "      <td>89579-7</td>\n",
       "      <td>3.4</td>\n",
       "      <td>pg/mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973900</th>\n",
       "      <td>00079a57-24a8-430f-b4f8-a1cf34f90060</td>\n",
       "      <td>0</td>\n",
       "      <td>2276-4</td>\n",
       "      <td>489.5</td>\n",
       "      <td>ug/L</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973899</th>\n",
       "      <td>00079a57-24a8-430f-b4f8-a1cf34f90060</td>\n",
       "      <td>0</td>\n",
       "      <td>48065-7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>ug/mL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973880</th>\n",
       "      <td>00079a57-24a8-430f-b4f8-a1cf34f90060</td>\n",
       "      <td>0</td>\n",
       "      <td>731-0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>10*3/uL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     PATIENT  DAY   CODE-Y  VALUE    UNITS\n",
       "973902  00079a57-24a8-430f-b4f8-a1cf34f90060    0  14804-9  241.8      U/L\n",
       "973901  00079a57-24a8-430f-b4f8-a1cf34f90060    0  89579-7    3.4    pg/mL\n",
       "973900  00079a57-24a8-430f-b4f8-a1cf34f90060    0   2276-4  489.5     ug/L\n",
       "973899  00079a57-24a8-430f-b4f8-a1cf34f90060    0  48065-7    0.4    ug/mL\n",
       "973880  00079a57-24a8-430f-b4f8-a1cf34f90060    0    731-0    1.1  10*3/uL"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All patients with covid, siged by code : 840539006\n",
    "df_covid = df_condition[df_condition['CODE']==840539006]\n",
    "#df_covid=df_condition.loc[df_condition['CODE']==840539006]\n",
    "\n",
    "# Merge two tables together, colunm 'PATIENT' is common.\n",
    "df_merged = pd.merge(\n",
    "    df_covid, df_observation, on='PATIENT', how=\"inner\"\n",
    "    )\n",
    "#df_merged = df_covid.merge(df_observation, how='inner', on = 'PATIENT')\n",
    "\n",
    "# Calculate the total days of hospitalized.\n",
    "df_merged['DAY'] = (\n",
    "    pd.to_datetime(df_merged['DATE'])).dt.dayofyear - (\n",
    "        pd.to_datetime(df_merged['START'])\n",
    "        ).dt.dayofyear\n",
    "\n",
    "# These code asked.\n",
    "filter_list = ['48065-7','26881-3','2276-4','89579-7','731-0','14804-9']\n",
    "\n",
    "#Correct the name.\n",
    "df_merged.rename(\n",
    "    columns={\"CODE_y\":\"CODE-Y\"} ,inplace=True\n",
    "    )\n",
    "\n",
    "# Exctract information related to the desire codes.\n",
    "df_conditional=df_merged[df_merged['CODE-Y'].isin(filter_list)]\n",
    "\n",
    "#Sort by DAY of bedridden.\n",
    "df_conditional=df_conditional.sort_values(['DAY'])\n",
    "\n",
    "#Creat a new data frame with desire columns.\n",
    "df_final=pd.DataFrame(\n",
    "    df_conditional.loc[:,['PATIENT','DAY', 'CODE-Y','VALUE','UNITS']])\n",
    "\n",
    "#Show one of the patient's information.\n",
    "df_final[df_final['PATIENT']=='00079a57-24a8-430f-b4f8-a1cf34f90060'].head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='2'></a>\n",
    "## Part 3: Data Wrangling, split into survived and not survived (10 pt)\n",
    "\n",
    "Now we have the required data we would like to split the data into survived and not survived. First we fetch all the ids of the survived and deceased patients. We can use these ids to select the records of the survived patients and the patients that did not survived"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your job is to split the data into survived and not survived records. There are multiple ways to do this. One way is the  `.isin()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the following code is given, RUN THIS CELL\n",
    "#get survived and deceased ids\n",
    "import numpy as np\n",
    "completed_isolation_patients = df_careplan[(df_careplan.CODE == 736376001) & (df_careplan.STOP.notna()) \\\n",
    "                                          & (df_careplan.REASONCODE == 840539006)].PATIENT\n",
    "negative_covid_patient_ids = df_observation[(df_observation.CODE == '94531-1') \\\n",
    "                                          & (df_observation.VALUE == 'Not detected (qualifier value)')].PATIENT.unique()\n",
    "survivor_ids = np.union1d(completed_isolation_patients, negative_covid_patient_ids)\n",
    "deceased_ids = df_patient[df_patient.DEATHDATE.notna()].Id\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-31'></a>\n",
    "### 3.1 Code your solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find survived poeple by ID.\n",
    "df_survived=df_final[df_final['PATIENT'].isin(survivor_ids)]\n",
    "\n",
    "#find expired patients by ID.\n",
    "df_died=df_final[df_final['PATIENT'].isin(deceased_ids)]     \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Test your solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "patients records survived: 57303, patients records deceased 16793\n"
     ]
    }
   ],
   "source": [
    "def test3(survived, died):\n",
    "    print(f'patients records survived: {len(df_survived)}, patients records deceased {len(df_died)}')\n",
    "#call the test3\n",
    "test3(df_survived, df_died)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Expected outcome"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "patients records survived: 57303, patients records deceased 16793"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='3'></a>\n",
    "## Part 4: Plot the data (20 pt)\n",
    "\n",
    "Create plots with the lab data, for each code one plot. Separate the survivors and the deceased by color. An example of such a plot is given below. You can create 6 plots in one grid (for each code one plot) or use a widget (for instance a drop down menu widget) to select a lab CODE. Plot on the x-axis the days, on the y-axis the VALUE. Use proper labels, titles and legends.\n",
    "\n",
    "<img src=\"../images/week3_plot.png\" width=\"500\" height=\"500\"/>\n",
    "\n",
    "<a name='ex-41'></a>\n",
    "### 4.1 Code your solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
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the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = 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else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    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      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "967c96289688450ab264d0a7c01f1751",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BokehModel(combine_events=True, render_bundle={'docs_json': {'ede0cc74-afcd-43de-a291-596ec611ebe9': {'defs': …"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import panel as pn\n",
    "from bokeh.plotting import show,figure\n",
    "\n",
    "pn.extension()\n",
    "def interpolated_plot(Lab_CODE): \n",
    "    \n",
    "    #Select survived patients based on specific CODE-Y\n",
    "    survived_selected=df_survived[df_survived['CODE-Y']==Lab_CODE]\n",
    "    x1 = survived_selected['DAY']\n",
    "    Y1 = survived_selected['VALUE']\n",
    "\n",
    "    #Select died patients based on specific CODE-Y\n",
    "    died_selected=df_died[df_died['CODE-Y']==Lab_CODE]     \n",
    "    x2 = died_selected['DAY']\n",
    "    Y2 = died_selected['VALUE']\n",
    "    y_label = died_selected['UNITS'].values[0]\n",
    "\n",
    "\n",
    "    #Set a title for the plot.\n",
    "    p = figure(title = 'Different Medicine perscription')\n",
    "\n",
    "    #scatter plot for survived petients with green color\n",
    "    p.scatter(x = x1, y = Y1, color = 'green', legend_label = 'Survived')\n",
    "\n",
    "    #scatter plot for died petients with red color\n",
    "    p.scatter(x = x2, y = Y2, color = 'red', legend_label = 'Decreased')\n",
    "\n",
    "    # label the axis.\n",
    "    p.xaxis.axis_label = 'Day'\n",
    "    p.yaxis.axis_label = y_label\n",
    "\n",
    "    return p\n",
    "\n",
    "filter_list=['48065-7','26881-3','2276-4','89579-7','731-0','14804-9'] \n",
    "\n",
    "# create an interactive user interface object \n",
    "plot = pn.interact(interpolated_plot, Lab_CODE=filter_list)\n",
    "plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='4'></a>\n",
    "## Part 5: Plot the location of the patients (10 pt)\n",
    "\n",
    "This is a bonus part. Can you plot the patients location on a map? See also \n",
    "https://docs.bokeh.org/en/latest/docs/user_guide/geo.html \n",
    "\n",
    "You can use either package folium or geopandas. You need the Latitude and Longitude information from the patient tabel\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='ex-51'></a>\n",
    "### 5.1 Code your solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "Int64Index: 73918 entries, 0 to 73917\n",
      "Series name: LON\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "73918 non-null  float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 1.1 MB\n"
     ]
    }
   ],
   "source": [
    "#rename PATIENT column ti Id for merge two datasets.\n",
    "df_final.rename(\n",
    "    columns={\"PATIENT\":\"Id\"} ,inplace=True\n",
    "    )\n",
    "\n",
    "#Merge the covid patients data set to patient information\n",
    "#  for getting the location of each of them.\n",
    "df_location = pd.merge(df_final, df_patient, on='Id', how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x5000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import geopandas\n",
    "import matplotlib.pyplot as plt \n",
    "\n",
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "\n",
    "# Creating axes and plotting world map\n",
    "fig, ax = plt.subplots(figsize=(10, 50))\n",
    "world.plot(color=\"palegreen\", ax=ax)\n",
    "\n",
    "# Plotting our data with a color map\n",
    "x = df_patient['LON']\n",
    "y = df_patient['LAT']\n",
    "plt.scatter(x, y, alpha=0.5)\n",
    "\n",
    "\n",
    "# Creating axis limits and title\n",
    "plt.xlim([-180, 180])\n",
    "plt.ylim([-90, 90])\n",
    "\n",
    "plt.xlabel(\"Longitude\")\n",
    "plt.ylabel(\"Latitude\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.0 64-bit",
   "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": "3.11.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "108095e740c160118120ad5e22811b4aca9f414a6f3c55c969835bc2c10848d1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}