notebooks/opioid-prescription-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\n",
    "from project_lib import Project\n",
    "project = Project(project_id='...', project_access_token='...')\n",
    "pc = project.project_context\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use Machine Learning to Predict U.S. Opioid Prescribers with Watson Studio and Scikit Learn\n",
    "\n",
    "This Code Pattern will focus on and guide you through how to use scikit learn and python in Watson Studio to predict opioid prescribers based off of a [2014 kaggle dataset](https://www.kaggle.com/apryor6/us-opiate-prescriptions/data).\n",
    "\n",
    "In this notebook, we explore the dataset `opioids.csv`, `overdoses.csv`, `prescriber-info.csv` and apply:\n",
    "- demographic data visualization\n",
    "- machine learning trainings and model comparisons\n",
    "\n",
    "\n",
    "<a id=\"section0\"></a>\n",
    "### Prerequisites\n",
    "\n",
    "Before you run this notebook complete the following steps:\n",
    "- Insert a project token\n",
    "- Import required packages\n",
    "\n",
    "#### Insert a project token\n",
    "\n",
    "When you import this project from the Watson Studio Gallery, a token should be automatically generated and inserted at the top of this notebook as a code cell such as the one below:\n",
    "\n",
    "```python\n",
    "# @hidden_cell\n",
    "# The project token is an authorization token that is used to access project resources like data sources, connections, and used by platform APIs.\n",
    "from project_lib import Project\n",
    "project = Project(project_id='YOUR_PROJECT_ID', project_access_token='YOUR_PROJECT_TOKEN')\n",
    "pc = project.project_context\n",
    "```\n",
    "\n",
    "If you do not see the cell above, follow these steps to enable the notebook to access the dataset from the project's resources:\n",
    "\n",
    "* Click on `More -> Insert project token` in the top-right menu section\n",
    "\n",
    "![ws-project.mov](https://media.giphy.com/media/jSVxX2spqwWF9unYrs/giphy.gif)\n",
    "\n",
    "* This should insert a cell at the top of this notebook similar to the example given above.\n",
    "\n",
    "  > If an error is displayed indicating that no project token is defined, follow [these instructions](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/token.html?audience=wdp&context=data).\n",
    "\n",
    "* Run the newly inserted cell before proceeding with the notebook execution below\n",
    "\n",
    "#### Import required packages\n",
    "\n",
    "Import and configure the required packages, including StringIO, requests, json, pandas, numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "from io import StringIO\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create method to find filepath based on filename\n",
    "def get_file_handle(fname):\n",
    "    # Project data path for the raw data file\n",
    "    data_path = project.get_file(fname)\n",
    "    data_path.seek(0)\n",
    "    return data_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug Name</th>\n",
       "      <th>Generic Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABSTRAL</td>\n",
       "      <td>FENTANYL CITRATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ACETAMINOPHEN-CODEINE</td>\n",
       "      <td>ACETAMINOPHEN WITH CODEINE</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ACTIQ</td>\n",
       "      <td>FENTANYL CITRATE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ASCOMP WITH CODEINE</td>\n",
       "      <td>CODEINE/BUTALBITAL/ASA/CAFFEIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ASPIRIN-CAFFEINE-DIHYDROCODEIN</td>\n",
       "      <td>DIHYDROCODEINE/ASPIRIN/CAFFEIN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                        Drug Name                    Generic Name\n",
       "0                         ABSTRAL                FENTANYL CITRATE\n",
       "1           ACETAMINOPHEN-CODEINE      ACETAMINOPHEN WITH CODEINE\n",
       "2                           ACTIQ                FENTANYL CITRATE\n",
       "3             ASCOMP WITH CODEINE  CODEINE/BUTALBITAL/ASA/CAFFEIN\n",
       "4  ASPIRIN-CAFFEINE-DIHYDROCODEIN  DIHYDROCODEINE/ASPIRIN/CAFFEIN"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define filename\n",
    "DATA_PATH = 'opioids.csv'\n",
    "\n",
    "# Using pandas to read the data \n",
    "data_path = get_file_handle(DATA_PATH)\n",
    "df_data_1 = pd.read_csv(data_path)\n",
    "# Display the first five rows\n",
    "df_data_1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The opioids.csv dataset has two columns, `Drug Name` and `Generic Name`. Each row represents one medicine with its drug name and generic name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Population</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Abbrev</th>\n",
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       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4,833,722</td>\n",
       "      <td>723</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>735,132</td>\n",
       "      <td>124</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>6,626,624</td>\n",
       "      <td>1,211</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2,959,373</td>\n",
       "      <td>356</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>38,332,521</td>\n",
       "      <td>4,521</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "        State  Population Deaths Abbrev\n",
       "0     Alabama   4,833,722    723     AL\n",
       "1      Alaska     735,132    124     AK\n",
       "2     Arizona   6,626,624  1,211     AZ\n",
       "3    Arkansas   2,959,373    356     AR\n",
       "4  California  38,332,521  4,521     CA"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define filename\n",
    "DATA_PATH = 'overdoses.csv'\n",
    "\n",
    "# Using pandas to read the data \n",
    "data_path = get_file_handle(DATA_PATH)\n",
    "df_data_2 = pd.read_csv(data_path)\n",
    "# Display the first five rows\n",
    "df_data_2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overdoses.csv dateset has four columns, including `State`, `Population`, `Deaths`, and `Abbrev`. Each row represents a state and its populations, number of deaths and state abbreviation names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NPI</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Credentials</th>\n",
       "      <th>Specialty</th>\n",
       "      <th>ABILIFY</th>\n",
       "      <th>ACETAMINOPHEN.CODEINE</th>\n",
       "      <th>ACYCLOVIR</th>\n",
       "      <th>ADVAIR.DISKUS</th>\n",
       "      <th>AGGRENOX</th>\n",
       "      <th>...</th>\n",
       "      <th>VERAPAMIL.ER</th>\n",
       "      <th>VESICARE</th>\n",
       "      <th>VOLTAREN</th>\n",
       "      <th>VYTORIN</th>\n",
       "      <th>WARFARIN.SODIUM</th>\n",
       "      <th>XARELTO</th>\n",
       "      <th>ZETIA</th>\n",
       "      <th>ZIPRASIDONE.HCL</th>\n",
       "      <th>ZOLPIDEM.TARTRATE</th>\n",
       "      <th>Opioid.Prescriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1245278100</td>\n",
       "      <td>F</td>\n",
       "      <td>AL</td>\n",
       "      <td>MD</td>\n",
       "      <td>General Surgery</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1427182161</td>\n",
       "      <td>F</td>\n",
       "      <td>NY</td>\n",
       "      <td>M.D.</td>\n",
       "      <td>General Practice</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1669567541</td>\n",
       "      <td>M</td>\n",
       "      <td>AZ</td>\n",
       "      <td>MD</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1679650949</td>\n",
       "      <td>M</td>\n",
       "      <td>NV</td>\n",
       "      <td>M.D.</td>\n",
       "      <td>Hematology/Oncology</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "<p>5 rows × 256 columns</p>\n",
       "</div>"
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      "text/plain": [
       "          NPI Gender State Credentials            Specialty  ABILIFY  \\\n",
       "0  1710982582      M    TX         DDS              Dentist        0   \n",
       "1  1245278100      F    AL          MD      General Surgery        0   \n",
       "2  1427182161      F    NY        M.D.     General Practice        0   \n",
       "3  1669567541      M    AZ          MD    Internal Medicine        0   \n",
       "4  1679650949      M    NV        M.D.  Hematology/Oncology        0   \n",
       "\n",
       "   ACETAMINOPHEN.CODEINE  ACYCLOVIR  ADVAIR.DISKUS  AGGRENOX  ...  \\\n",
       "0                      0          0              0         0  ...   \n",
       "1                      0          0              0         0  ...   \n",
       "2                      0          0              0         0  ...   \n",
       "3                     43          0              0         0  ...   \n",
       "4                      0          0              0         0  ...   \n",
       "\n",
       "   VERAPAMIL.ER  VESICARE  VOLTAREN  VYTORIN  WARFARIN.SODIUM  XARELTO  ZETIA  \\\n",
       "0             0         0         0        0                0        0      0   \n",
       "1             0         0         0        0                0        0      0   \n",
       "2             0         0         0        0                0        0      0   \n",
       "3             0         0         0        0                0        0      0   \n",
       "4             0         0         0        0               17       28      0   \n",
       "\n",
       "   ZIPRASIDONE.HCL  ZOLPIDEM.TARTRATE  Opioid.Prescriber  \n",
       "0                0                  0                  1  \n",
       "1                0                 35                  1  \n",
       "2                0                 25                  0  \n",
       "3                0                  0                  1  \n",
       "4                0                  0                  1  \n",
       "\n",
       "[5 rows x 256 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define filename\n",
    "DATA_PATH = 'prescriber-info.csv'\n",
    "\n",
    "# Using pandas to read the data \n",
    "data_path = get_file_handle(DATA_PATH)\n",
    "df_data_3 = pd.read_csv(data_path)\n",
    "# Display the first five rows\n",
    "df_data_3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The prescriber-info.csv displays number of drugs prescribed by 25000 medical professionals in the United States in 2014. The number of drug includes both opiate and non-opiate drugs. For each row, it represents a medical professional with their NPI number, gender, state, credentials, and speciality. In the paper *Opiate/Opioid prescription analysis using machine learning*, they classify a doctor as opiate prescriber if he/she prescribe opiate drugs more than 10 times in total."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploration and Initial Preprocessing\n",
    "\n",
    "Let's start out by removing the ',' from our numbers in the Deaths and Population columns so that we can use the cells as integers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data_2['Deaths'] = df_data_2['Deaths'].str.replace(',', '')\n",
    "df_data_2['Deaths'] = df_data_2['Deaths'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data_2['Population'] = df_data_2['Population'].str.replace(',', '')\n",
    "df_data_2['Population'] = df_data_2['Population'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add an additional column where we see the deaths per capita per each state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data_2['Deaths/Population'] = (df_data_2['Deaths']/df_data_2['Population'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import pixiedust. Use pixiedust to visualize our initial exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixiedust database opened successfully\n",
      "Table VERSION_TRACKER created successfully\n",
      "Table METRICS_TRACKER created successfully\n",
      "\n",
      "Share anonymous install statistics? (opt-out instructions)\n",
      "\n",
      "PixieDust will record metadata on its environment the next time the package is installed or updated. The data is anonymized and aggregated to help plan for future releases, and records only the following values:\n",
      "\n",
      "{\n",
      "   \"data_sent\": currentDate,\n",
      "   \"runtime\": \"python\",\n",
      "   \"application_version\": currentPixiedustVersion,\n",
      "   \"space_id\": nonIdentifyingUniqueId,\n",
      "   \"config\": {\n",
      "       \"repository_id\": \"https://github.com/ibm-watson-data-lab/pixiedust\",\n",
      "       \"target_runtimes\": [\"Data Science Experience\"],\n",
      "       \"event_id\": \"web\",\n",
      "       \"event_organizer\": \"dev-journeys\"\n",
      "   }\n",
      "}\n",
      "You can opt out by calling pixiedust.optOut() in a new cell.\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mPixiedust runtime updated. Please restart kernel\u001b[0m\n",
      "Table USER_PREFERENCES created successfully\n",
      "Table service_connections created successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>Warning: You are not running the latest version of PixieDust. Current is 1.1.17, Latest is 1.1.18</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                <div>Please copy and run the following command in a new cell to upgrade: <span style=\"background-color:#ececec;font-family:monospace;padding:0 5px\">!pip install --user --upgrade pixiedust</span></div>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>Please restart kernel after upgrading.</div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pixiedust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Curious about how many opioid deaths in each state? Let's plot a graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pixiedust": {
     "displayParams": {
      "aggregation": "SUM",
      "handlerId": "barChart",
      "keyFields": "State",
      "orientation": "horizontal",
      "rendererId": "matplotlib",
      "rowCount": "500",
      "valueFields": "Deaths"
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n",
       "        <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n",
       "            \n",
       "        </div>\n",
       "    <div id=\"chartFiguref98e42bd\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n",
       "            \n",
       "                    \n",
       "                            <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n",
       "                        \n",
       "                    \n",
       "                \n",
       "        </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df_data_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It definitely looks like California has significantly more deaths than any other states. \n",
    "\n",
    "Let's remember, however, California is a huge state with a matching population. Even thtough the total number of deaths is huge, it doesn't imply that people in California have a higher death rate. Because of this we need to take a look at the values of deaths per capita."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pixiedust": {
     "displayParams": {
      "aggregation": "SUM",
      "handlerId": "barChart",
      "keyFields": "State",
      "orientation": "horizontal",
      "rowCount": "500",
      "valueFields": "Deaths/Population"
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n",
       "        <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n",
       "            \n",
       "        </div>\n",
       "    <div id=\"chartFigure4ca8facf\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n",
       "            \n",
       "                    \n",
       "                            <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n",
       "                        \n",
       "                    \n",
       "                \n",
       "        </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What about deaths (% of population) by U.S. State?\n",
    "display(df_data_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "We can see that West Virginia, New Mexico, New Hampshire, Ohio, Kentucky and Delaware stand out. \n",
    "\n",
    "Let's check this out with a map. (I used google maps. To do this, create an API and enable JavaScript and GeoCoding. Then use your API key under 'Options'.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pixiedust": {
     "displayParams": {
      "googlemapapikey": " AIzaSyDi-BV6kv6RzFOB8-F8454uvk1tzYseFVg ",
      "handlerId": "mapView",
      "keyFields": "State",
      "mapboxtoken": "pk.eyJ1IjoibWFwYm94IiwiYSI6ImNpejY4M29iazA2Z2gycXA4N2pmbDZmangifQ.-g_vE53SD2WrJ6tFX7QHmA",
      "rendererId": "google",
      "rowCount": "500",
      "title": "Deaths per Capita by Opioid Overdoses",
      "valueFields": "Deaths/Population"
     }
    }
   },
   "outputs": [],
   "source": [
    "display(df_data_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's move onto exploring our other dataset.\n",
    "\n",
    "We seem to have a great deal of prescriptions as well as physicians' gender, state, speciality, whether they are an opioid prescriber or not and unique ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NPI                               25000\n",
       "Gender                            25000\n",
       "State                             25000\n",
       "Credentials                       24237\n",
       "Specialty                         25000\n",
       "ABILIFY                           25000\n",
       "ACETAMINOPHEN.CODEINE             25000\n",
       "ACYCLOVIR                         25000\n",
       "ADVAIR.DISKUS                     25000\n",
       "AGGRENOX                          25000\n",
       "ALENDRONATE.SODIUM                25000\n",
       "ALLOPURINOL                       25000\n",
       "ALPRAZOLAM                        25000\n",
       "AMIODARONE.HCL                    25000\n",
       "AMITRIPTYLINE.HCL                 25000\n",
       "AMLODIPINE.BESYLATE               25000\n",
       "AMLODIPINE.BESYLATE.BENAZEPRIL    25000\n",
       "AMOXICILLIN                       25000\n",
       "AMOX.TR.POTASSIUM.CLAVULANATE     25000\n",
       "AMPHETAMINE.SALT.COMBO            25000\n",
       "ATENOLOL                          25000\n",
       "ATORVASTATIN.CALCIUM              25000\n",
       "AVODART                           25000\n",
       "AZITHROMYCIN                      25000\n",
       "BACLOFEN                          25000\n",
       "BD.ULTRA.FINE.PEN.NEEDLE          25000\n",
       "BENAZEPRIL.HCL                    25000\n",
       "BENICAR                           25000\n",
       "BENICAR.HCT                       25000\n",
       "BENZTROPINE.MESYLATE              25000\n",
       "                                  ...  \n",
       "TAMSULOSIN.HCL                    25000\n",
       "TEMAZEPAM                         25000\n",
       "TERAZOSIN.HCL                     25000\n",
       "TIMOLOL.MALEATE                   25000\n",
       "TIZANIDINE.HCL                    25000\n",
       "TOLTERODINE.TARTRATE.ER           25000\n",
       "TOPIRAMATE                        25000\n",
       "TOPROL.XL                         25000\n",
       "TORSEMIDE                         25000\n",
       "TRAMADOL.HCL                      25000\n",
       "TRAVATAN.Z                        25000\n",
       "TRAZODONE.HCL                     25000\n",
       "TRIAMCINOLONE.ACETONIDE           25000\n",
       "TRIAMTERENE.HYDROCHLOROTHIAZID    25000\n",
       "VALACYCLOVIR                      25000\n",
       "VALSARTAN                         25000\n",
       "VALSARTAN.HYDROCHLOROTHIAZIDE     25000\n",
       "VENLAFAXINE.HCL                   25000\n",
       "VENLAFAXINE.HCL.ER                25000\n",
       "VENTOLIN.HFA                      25000\n",
       "VERAPAMIL.ER                      25000\n",
       "VESICARE                          25000\n",
       "VOLTAREN                          25000\n",
       "VYTORIN                           25000\n",
       "WARFARIN.SODIUM                   25000\n",
       "XARELTO                           25000\n",
       "ZETIA                             25000\n",
       "ZIPRASIDONE.HCL                   25000\n",
       "ZOLPIDEM.TARTRATE                 25000\n",
       "Opioid.Prescriber                 25000\n",
       "Length: 256, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_data_3.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This table output reflects that there are 25000 unique medical professionals in the United States in year 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "\n",
    "Why are there more than 50 states?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TX', 'AL', 'NY', 'AZ', 'NV', 'PA', 'NH', 'WI', 'PR', 'CO', 'OH',\n",
       "       'MA', 'CT', 'FL', 'MN', 'UT', 'IA', 'IL', 'MT', 'IN', 'VA', 'CA',\n",
       "       'OR', 'NE', 'MI', 'NM', 'TN', 'KS', 'LA', 'MD', 'MO', 'AR', 'NC',\n",
       "       'NJ', 'SC', 'WY', 'ME', 'OK', 'ND', 'KY', 'GA', 'DE', 'WA', 'RI',\n",
       "       'WV', 'AK', 'ID', 'VT', 'HI', 'MS', 'DC', 'SD', 'AE', 'ZZ', 'GU',\n",
       "       'AA', 'VI'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the states.\n",
    "df_data_3.State.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', 'HI',\n",
       "       'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', 'MA', 'MI',\n",
       "       'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', 'NM', 'NY', 'NC',\n",
       "       'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', 'SD', 'TN', 'TX', 'UT',\n",
       "       'VT', 'VA', 'WA', 'WV', 'WI', 'WY'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compare to df_data_2.\n",
    "df_data_2.Abbrev.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare the state list, clean up states to make the dataset state list equal.\n",
    "\n",
    "I checked the list of US state abbreviations and did not recognize PR, AE, ZZ, GU, AA or VI. After checking I learned that PR is Puerto Rico, GU is Guam and VI is Virgin Islands. Though I identified 3 of the 6 unknowns, I'll remove all of them as dataset 2 does not have data regarding PR, GU or VI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data_3 = df_data_3[df_data_3.State != 'AE']\n",
    "df_data_3 = df_data_3[df_data_3.State != 'ZZ']\n",
    "df_data_3 = df_data_3[df_data_3.State != 'AA']\n",
    "df_data_3 = df_data_3[df_data_3.State != 'PR']\n",
    "df_data_3 = df_data_3[df_data_3.State != 'GU']\n",
    "df_data_3 = df_data_3[df_data_3.State != 'VI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['TX', 'AL', 'NY', 'AZ', 'NV', 'PA', 'NH', 'WI', 'CO', 'OH', 'MA',\n",
       "       'CT', 'FL', 'MN', 'UT', 'IA', 'IL', 'MT', 'IN', 'VA', 'CA', 'OR',\n",
       "       'NE', 'MI', 'NM', 'TN', 'KS', 'LA', 'MD', 'MO', 'AR', 'NC', 'NJ',\n",
       "       'SC', 'WY', 'ME', 'OK', 'ND', 'KY', 'GA', 'DE', 'WA', 'RI', 'WV',\n",
       "       'AK', 'ID', 'VT', 'HI', 'MS', 'DC', 'SD'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make sure it worked!\n",
    "df_data_3.State.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['DDS', 'MD', 'M.D.', 'DO', 'RN, MSN, ANP-BC', 'O.D.', nan,\n",
       "       'D.D.S.', 'ACNP', 'DPM', 'PAC', 'A.R.N.P.', 'MSN, APRN, BC',\n",
       "       'D.O.', 'M.D,', 'APRN', 'PA-C', 'CNM', 'RN CNP', 'DDS MS',\n",
       "       'DNP, APRN-BC, FNP', 'PHARM D.', 'CRNP', 'ARNP', 'D.D.S', 'DPM MD',\n",
       "       'FNP', 'NP', 'DMD', 'PA', 'MPT', 'D.M.D.', 'APRN BC FNP',\n",
       "       'DMD,FAGD', 'MD,MPH', 'BDS,  DDS', 'D.D.S., F.A.G.D.', 'DDS, MD',\n",
       "       'PMH, CNP/CNS', 'P.A.', 'M.D', 'D.O., MPH & TM', 'M.D., PH.D.',\n",
       "       'RPA-C', 'MD FACOG', 'F.N.P.', 'D.O', 'ANP', 'FNP-C', 'D.M.D',\n",
       "       'D.P.M.', 'OD', 'O. D.', 'CNS', 'MS, APRN, FNP-C', 'MD MPH',\n",
       "       'NP-C', 'PHARM D', 'M.D.,', 'MBBS', 'PHYSICIAN ASSISTANT',\n",
       "       'M.D. FCCP', 'MD.', 'CNP', 'DPT', 'D.D.S, M.D.', 'APRN, BC',\n",
       "       'P.A.-C', 'M.D.P.A.', 'F.N.P.-C', 'M. D.', 'CFNP', 'C.N.P', 'APN',\n",
       "       'D.D.S., A.P.C.', 'NP-C, MSN', 'MD FACP', 'R.P.A.', 'N.P.',\n",
       "       'APRN, CNS', 'D.D.S., M.D.', 'PMHNP', 'MD, PHD, ABFP', 'APNP',\n",
       "       'DMD, MD, PLLC', 'M.D., P.A.', 'MPA, MSED, ATC, PA-C', 'D O',\n",
       "       'M.D., M.H.S.', 'M.S., N.P.-C', 'FNP-BC', 'P.A', 'FPMH-NP', 'NFP',\n",
       "       'ARNP-BC', 'CPNP', 'A.P.R.N.', 'P.A.-C.', 'NURSE PRACTITIONER',\n",
       "       'DMD MS', 'APN, BC', 'M.B.B.S.', 'RN, FNP', 'NP, CNM', 'ANP-BC',\n",
       "       'PA-C, MS', 'MD IN TRAINING', 'RN, CNP', 'CRNP,MSN,BC',\n",
       "       'MSN, FNP-C', 'D.O., D.O.S.', 'D D S  M D', 'DNP, ARNP',\n",
       "       'MSN, ARNP, ACNP-BC', 'OO', 'MD PA', 'RN,FNP', 'D.D.S., M.S.',\n",
       "       'MBBS, MD', 'DNP, APRN', 'D.O .', 'CNP, APRN, CNS-RX', 'M.D. PH.D',\n",
       "       'D.D.S., M.S.D.', 'MB,BS', 'FAMILY NURSE PRACTIT', 'WHNP',\n",
       "       'P.A.C.', 'ND', 'PHARM. D.', 'ANP.', 'RN, APN, FNP', 'PA-C, ATC',\n",
       "       'RPA', 'MD, PHD', 'NP, CNS', 'MD, FACEP, FAAEM', 'ANP,BC',\n",
       "       'WHNP-BC', 'APRN-BC, ANP, FNP', 'ANP-FNP-BC', 'D.D.S, F.A.G.D',\n",
       "       'DDS MD', 'P.A-C', 'M.D.,MBA', 'MSN, APN, CNP, NP-C',\n",
       "       'M.D., M.P.H.', 'R.N.  NPF', 'MSN, FNP-BC', 'PMHHNP', 'FNP,NPP',\n",
       "       'C. - F. N. P.', 'DDS. MS', 'O.D., M.P.H.', 'N.D.', 'N. P.',\n",
       "       'ARNP, BSN, MN', 'MD, MSBS', 'M.D, MPH', 'NP-ADULT', 'MS',\n",
       "       'M.D. P.A.', 'MS,APRN,FNP,BC', 'M.D., PHD', 'DNP, APRN-BC',\n",
       "       'OD PC', 'DNP, APRN, NP-C', 'MD, MS', 'MS,MPH', 'M.S.N., A.P.N.',\n",
       "       'O.D', 'D.D,S.', 'MD, PH.D.', 'MD, MPH', 'PA-C, MPAS',\n",
       "       'D.D.S., F.R.C.D.C.', 'ATC, FNP-C', 'PHD, APN', 'APRN-BC,FNP',\n",
       "       'ARNP, BC-GNP', 'RN', 'M.D., F.A.C.R.', 'PA-C (PHYSICIAN ASSI',\n",
       "       'APRN-CNS', 'D,O.', 'MEDICAL DOCTOR', 'D. O.', 'D.D.S, P.A.',\n",
       "       'MPAS, PA-C', 'OD, PA', 'DDS, MSD', 'A.P.R.N. N.P.', 'CNM, FNP-C',\n",
       "       'B.S., PA-C', 'M.D MRCP', 'MD PHD', 'PCS', 'ACNP-BC', 'C.N.P.',\n",
       "       'MD MBA', 'D.M.D., M.D.', 'RNP', 'PHARMD', 'M.D. ,PH.D.',\n",
       "       'MD, PHD, FACC', 'D.O., M.S.', 'ARNP, CNM', 'M.D. ; M.B.,B.S.',\n",
       "       'MS, FNP-BC', 'MD FACE', 'M.D., PA', 'ARNP-C', 'RN, CFNP',\n",
       "       'ACNP-BC, MSN', 'CANP', 'FNP, PHD', 'MBBS, FRCS', 'PHP MD', 'PHC',\n",
       "       'BOARD CERTIFIED FNP', 'RN, ANP', 'M.D, MSC', 'MNS', 'PMH-NP-BC',\n",
       "       'M.H.S., P.A.', 'DNP, ANP-BC', 'NPC', 'M.S.N.', 'DNP, FNP-BC',\n",
       "       'HERSCHEL ROSS DDS', 'M.D., PH. D.', 'DENTIST', 'M.S. C.N.P.',\n",
       "       'MS, PA-C', 'R.P.A.C.', 'MHS, PA-C', 'RN, NP', 'D.D.S., PC',\n",
       "       'MSPAS, PA-C', 'JAMES KIM', 'DDS PS', 'APRN, FNP-BC',\n",
       "       'BSHS, MSN, FNP-BC', 'ANP-C', 'RPA C', 'MD MPH MS DRPH',\n",
       "       'DDS,CAGS', 'A.P.N.', 'D.D.S., B.S.', 'RPAC', 'N.M.D.', 'DDS,MSD',\n",
       "       'D.D.S.,M.S.', 'CNFP', 'ARNP FNP-BC', 'RN, MSN, CCRN, ANP',\n",
       "       'MS RN NPP', 'APRN-BC', 'APN,FNP', 'M.D. - PH.D', 'PHD, FNP',\n",
       "       'MD P C', 'DDS PLLC', 'RN, CS, FNP', 'CRNP- FAMILY', 'D.C., FNP',\n",
       "       'DOCTOR OF OPTOMETRY', 'D.D.S., F.A.C.D.', 'RN, MSN, PMHNP, BC',\n",
       "       'FNP, PMHNP', 'DNP', 'FLORA LAU', 'DDS,MD', 'MD, FACP',\n",
       "       'APN, BC-FNP', 'DMD, MD', 'MD, PH.D', 'DDS, MAGD', 'D.P.M', 'DDA',\n",
       "       'D.M.D., P.C', 'MD  PA', 'PMHCNS-BC', 'RN MSN FNO-C', 'MD, FACS',\n",
       "       'M.D. (H)', 'ARNP, DNP', 'PHARM.D.', 'MSN ANP', 'FNP - C',\n",
       "       'DNP,PMHCNS-BC', 'D.D.S., PH.D.', 'MD, MBA', 'GNP-BC',\n",
       "       'M.D., M.B.A', 'RN,NP', 'MHSA, PA-C', 'PA C', 'MD FACS',\n",
       "       'APRN, PHD, CNS', 'F.N.P', 'DPM, FACFS', 'M.P.A.S., P.A.C., LP',\n",
       "       'D.O.,', 'RN,CFNP', 'M.D., PHARMD.', 'NP,  APRN BC', 'MD., PH.D.',\n",
       "       'M.D., MPH', 'RN MSN CNM ARNP', 'A.R.N.P.-B.C.', 'R.PH.',\n",
       "       'PMHNP-BC', 'DMD, MSD', 'FNP-BC, NP-C', 'C.N.S.', 'P.D., D.D.S.',\n",
       "       'DNP / FNP', 'CRNP, FNP-BC, RN', 'MD FACC', 'AGNP', 'DP',\n",
       "       'RN, MSN, ACNP-BC', 'RN, CS, MS(N),FNP', 'M,D.', 'ARNP,BC',\n",
       "       'RN, MSN, NP-C', 'N.P', 'M.D.,F.A.C.O.G.', 'MSN,FNP-BC', 'NP-F',\n",
       "       'MSN, APN,C', 'ANNA TAVAKKOLI M.D.', 'MS, ANP-C', 'MSN, ARNP',\n",
       "       'DNP, CRNP, NP-C', 'ANPC', 'M.D., M.S.H.S.', 'M.D.,M.P.H.', 'LPN',\n",
       "       'B.S', 'M.N., ARNP', 'MD RD', 'ANP/CNP', 'M.D.,MPH',\n",
       "       'MD,  FCCP, D-ABSM', 'D.D.S,PC', 'D.O., M.D', 'MD FACP FACC',\n",
       "       'DNP, FNP-C', 'A.N.P.', 'MD, PC', 'D.M.D., F.A.G.D.', 'DMD, MDS',\n",
       "       'FNP, BC', 'M.B.B.S', 'CRNA', 'MSN, CRNP', 'PA-C, L.AC.',\n",
       "       'M.S., NP-C', 'A.T.,C. , C.S.C.S.', 'M.D., F.A.C.P.', 'PHYSICIAN',\n",
       "       'RN, MSN, FNP', 'APN-C', 'MS RN CS', 'M.D., P.C.', 'DDS  PC',\n",
       "       'APRN, PMHNP-BC', 'M.D. MPH', 'A.R.N.P. , PMHNP-BC', 'M.D., MS',\n",
       "       'MSN, CNS, ACNP-BC', 'M.D..', 'PA, O.T.', 'FNP-C, ACNP', 'ACNS-BC',\n",
       "       'R.N.', 'M.S.N., R.N., N.P.', 'MD, MSHS', 'M.B.;B.S.',\n",
       "       'M.D., FACOG', 'D.D.S, M.S', 'C.R.N.P.', 'MBBS (MD)', 'MBBCH',\n",
       "       'MSN, ACNP-BC, CCRN', 'ANP-BC, GNP', 'DDS, MS', 'M.D., M.A.',\n",
       "       'MS ANPC', 'M.D., FACS', 'CNP, CNM', 'DRNP', 'M.B., B.S.',\n",
       "       'MBBS,MS', 'D.M.D., M. S.', 'R.N., N.P.', 'RN NP', 'M.D., FAAFP',\n",
       "       'M.D. PH.D.', 'C-FNP', 'PHARMD, DDS', 'M.D., PH.D', 'ANRP',\n",
       "       'A.P.R.N., B.C.', 'RN, APRN, NP, B.C.', 'PMHNP-BC. NP',\n",
       "       'N.D., LAC', 'DMD MD', 'M.D.PH.D.', 'MD, MS, MBA', 'MB.BS, FRCSC',\n",
       "       'M.D.,P.A.', 'M.D., M.S.', 'RN CFNP', 'M D F A A D',\n",
       "       'A.P.R.N., W.H.N.P.', 'MSN, APRN, FNP-BC', 'D. D. S.',\n",
       "       'D.M.D, M.D.', 'MD, MSPH', 'RPH,BCOP, BCPS', 'DMD, MS',\n",
       "       'MSN, APRN, BC, FNP', 'D.D.S. P.S.', 'RN, PMHNP', 'WHNP, CFNP',\n",
       "       'DPM PC', 'RN,C,FNP', 'ARNP CS', 'DDS, PS', 'C.N.M.,F.N.P.',\n",
       "       'DNP, FNP', 'DMD BA', 'PA (PHYSICIAN ASSIST', 'D.O., RPH',\n",
       "       'CNM, FNP, DNP', 'M.D., F.C.C.P.', 'DO, LACC', 'R PA-C', 'P.A.C/',\n",
       "       'B.D.S.', 'DDS,MS', 'DC, PA-C', 'DMD DR MED DENT', 'RN CNS',\n",
       "       'WHCNP/ANP', 'ARANP', 'MS, RN, FNP-C', 'GNP-C', 'D.O., M.D.',\n",
       "       'PA-C, MSPAC', 'M. D. M.P.H.', 'DO, MPH', 'PH.D., NP-C',\n",
       "       'FNP-APNP', 'OD, FAAO, FOAA', 'BDS, DDS', 'M D', 'MT',\n",
       "       'R.N, M.S.N., G.N.P.', 'MD, M.P.H., A.T.C', 'OD, MS',\n",
       "       'PHD, NP, PMHNP-BC', 'ARNP, CDE', 'RN ACNP', 'M.D.,M.R.C.S.',\n",
       "       'PA-C MPAS', 'RN, MSN, CS, FNP', 'DNP FNP-BC', 'MD, PA', 'NPF',\n",
       "       'PSYD', 'D.D.S., P.A.', 'MSN, NP-C', 'MD, DDS', 'EIN-', 'WHCNP',\n",
       "       'APN, CNP', 'R.N., N.P., C.S.', 'M.D. PHD', 'CCNS, NP',\n",
       "       'DDS. PRACTICE LIMITE', 'ARNP, LP', 'ND, ARNP', 'D.M.D. P.A.',\n",
       "       'APRN BC', 'DDS, FAGD', 'C-PA', 'APN, C', 'APN FNP-C', 'MD SC',\n",
       "       'MSN, MPH, CNM, FNP-C', 'D.O.,P.A.', 'D D S', 'GNP-BG',\n",
       "       'MSN, RN, FNP', 'ARNP BC', 'PHD', 'RN, NP-C', 'RN, MSN, FNP-C',\n",
       "       'MD FACG', 'DMD,MD', 'APRN,BC', 'M.D., M.P.H., M.B.A.', 'A.R.N.P',\n",
       "       'DMD, MPH', 'MSN, RN, ACNP-BC', 'M.D., PHD, NEUROLOGY', 'APN-NP',\n",
       "       'BCFNP', 'RN BC ANP', 'CNP-FNP-DNP', 'APN, ACNS-BC', 'GNP/APRN',\n",
       "       'FNPC', 'MMS, PA-C', 'D.M.D., M.S.', 'D.O,', 'MD, FACOG', 'DR',\n",
       "       'FNP-BC, PMHNP-BC', 'C.R.N.P', 'NPP', 'MD, MPH, FACP',\n",
       "       'M.D.,PH.D.', 'DO, FACOOG', 'APN, MSN', 'DMD FAGD', 'D.M.D, M.S.',\n",
       "       'APRN,CS', 'APRN-BC, ND', 'LPN, RN, ANP', 'O.D, F.A.A.O.', 'C- NP',\n",
       "       'CRNP PMH', 'RN, BSN, CRNA, MSN', 'APRN, PHD.', 'C.N.M.',\n",
       "       'D.D.S, M.S.', 'MD/PHD', 'DDS, MS, MSPH', 'R.N.P.', 'MED, LPC',\n",
       "       'PHYSICIAN ASST PA C', 'MD,PA', 'M.D.  MMSC.', 'NP, MSN',\n",
       "       'R.N., C.N.P.', 'PHD APN FNP-C', 'FNP BC', 'M.D.PA', 'MSN',\n",
       "       'RN, CRNP', 'RN-C, FNP', 'DMD PC', 'D.N.P.', 'FNP, ARNP', 'DPM`',\n",
       "       'MFTI', 'PHD PAC', 'APRN-BC, CFNP', 'D.M.D., P.A', 'MD, FACC',\n",
       "       'APRN, NP', 'MB, BCH', 'DC', 'M.D., M.B.A.', 'APRN,PMHNP-BC',\n",
       "       'M.D., P.T., A.T.,C.', 'CRNP-PMH', 'D. P. M.', 'PHARM. D',\n",
       "       'MSN-FNP', 'M. D.,', 'MSN, APRN, PMHNP-BC', 'MD, DVM', 'MS, APRN',\n",
       "       'RN, MA, NP-C,', 'GNP', 'APRN, PMHNP', 'M.D., D.T. M & H',\n",
       "       'M.M.D.', 'RN BSN MSN FNP', 'D.D.S.,M.M.SC.', 'OPTOMETRIST',\n",
       "       'OTRL', 'PHD., ARNP, BC', 'MSN, APRN, FNP-C', 'RN, WHNP, ANP',\n",
       "       'D.M.D.,M.S.', 'DNP, MS, FNP-C', 'CNS/ANP', 'PA-C, MSC',\n",
       "       'D.D.S., M.S., P.C.', 'APRN-FNP', 'D.D.S. M.D.', 'PT', 'MSSW, LSW',\n",
       "       'D.O., P.C.', 'R.N., C.F.N.P.', 'RN,BSN,APN-C', 'APRN, FNP',\n",
       "       'CRNP, NP-C', 'DNP,CRNP', 'D.D.S, M.D', 'RN, CNN, APN', 'RNCS',\n",
       "       'PHYSICIAN ASSISTANTS', 'APRN (N.P)', 'M.D.  MPH', 'PA-C, DMO',\n",
       "       'M.D.FACP', 'DNP-CFNP', 'PA-C, ND, MSOM, MS', 'D.D.S., P.C',\n",
       "       'FNP, CNM', 'RN, FNP-C', 'ANP B-C', 'DPM PA', 'C.F.N.P',\n",
       "       'PA-C, MPA', 'D.D.S.,M.S', 'PA-C, MPH', 'PMHNP, RN', 'MSN, NPC',\n",
       "       'M.D., F.A.C.E', 'RN,CNP', 'NP, PHD', 'DDS, PHD', 'PHD, ARNP',\n",
       "       'D.O., P.A.', 'M.D.F.A.C.C.', 'FNP/C', 'PH. D., PA-C', 'RN, APNP',\n",
       "       'M.D., F.A.C.C.', 'APN, NP-C', 'FNP CRNP', 'D. D. S., M. S.',\n",
       "       'RN,CS,MSN', 'RN, MS, FNP-C', 'M.D.PHD', 'MSM, PA-C',\n",
       "       'FNP-BC, CNM', 'MD DO', 'MS, ARNP, PMHNP-BC', 'D.M.D, P.A.',\n",
       "       'PA-C, PHARM.D.', 'F.N.P.C.', 'F.A.A.O', 'MSN, ACNS-BC, AGNP-C',\n",
       "       'MD, RPVI', 'MS, RN, ANP', 'GREG SAWYER, D.D.S.', 'M,D,', 'DDS PA',\n",
       "       'ARNP RN BSN MSN', 'C-NP', 'M.D., OD', 'ACNP-BC, CCNS',\n",
       "       'DDS, PLLC', 'MSN APRN BC', 'P. A.', 'M.D/', 'MSN, CNS, A.P.R.N.',\n",
       "       'MS, APN, CANP', 'MD, MA', 'M.ED., O.D.', 'RPH.', 'APRN, FNP-C',\n",
       "       'APRN, CSN', 'D.M.D., P.A.', 'MSN, APRN FNP-BC', 'MSN CRNP',\n",
       "       'RN, DDS, MS, PHD', 'O.D, P.A.', 'CRNP-F, APRN-PMH', 'MS,PA-C',\n",
       "       'APRN, CNP, CNNP', 'PA, RN', 'P.T.', 'MSPA, PA-C', 'FNP, DCNP',\n",
       "       'MS PA-C', 'MD, ABIHM', 'NURSE PRACTIONONER', 'MSN, APN, C',\n",
       "       'APRN-CNP', 'M.D., M.P.H', 'MSN, GNP-BC', 'ARNP - FNP-C',\n",
       "       'D.D.S., M.S., PH.D.', 'MD/FACS', 'NP, DNP', 'M.D., M.S',\n",
       "       'MD, CWS, FCCWS', 'M.D. PA', 'PMHNP/ANP', 'NURSE PRACTIONER',\n",
       "       'D.O, P.A.', 'AGACNP', 'DNP, FNP, PMHNP-BC', 'B.S.N., C.R.N.P.',\n",
       "       'RN, FNP-BC', 'MS, MSN, RN, FNP-BC', 'M.D., PH.D., M.SC.',\n",
       "       'RN, MSN, FNP, NP-C', 'PA PHD', 'RN-BC, MSN, CRNP', 'MHS, PAC',\n",
       "       'PHARM.D', 'PH. D. , M.D.', 'ENP', 'M.D,F.A.C.P,F.A.C.G', 'RPH',\n",
       "       'ADULT NP', 'D.D.S.,M.D', 'MB CHB', 'FNP-BC, WHNP-BC',\n",
       "       'M.D., LLC.', 'MD FACEP', 'CRNA, FNP-C', 'ARNP, NP-C', 'RNCNP',\n",
       "       'ARNP    PHD', 'RN,MSN,FNP-C', 'RN, APRN-BC, A', 'APRN-C', 'M.D>',\n",
       "       'D.O., MPH', 'N.P., M.P.H.', 'MD DMD', 'RN MSN APN', 'AGPCNP-BC',\n",
       "       'M.S.N., C.N.S.', '(DMD)', 'B.D.S., M.S.', 'RPA-C MPAS',\n",
       "       'D.D.S, FAGD', 'RN,MSN,CNP', 'MD FACC FACA FCCP', 'MSN, FNP',\n",
       "       'FNP, ACNP', 'DNP, RN, ANP-BC', 'MSARNP', 'APRN-BC, FPMHNP', 'NMD',\n",
       "       'MD, PHD, FACP', 'M.D., J.D.', 'F.N.P.-C.', 'RN, MSN, CANP',\n",
       "       'MD OD', 'D.D.S.,M.P.H', 'DPM, MS', 'R.N.,N.P.,C.N.S.,',\n",
       "       'MD, FACC, NASPE', 'OPHTHALMOLGIST M.D.', 'RN,MS,CNS',\n",
       "       'D.M.D., M.M.SC.', 'MD,MRCPI,FASN', 'M.D. M.P.H.',\n",
       "       'GERALD SULLIVAN', 'M..', 'APRN-RN', 'MD, MSC', 'MD OBGYN',\n",
       "       'DDS, MPH', 'MD`', 'MD, FAAFP', 'M.D./FAMILY PRACTICE', 'APN,C.',\n",
       "       'M.S.N., R.N., A.P.N.', 'RN, MS, FNP', 'N P', 'ACNP/FNP', 'MDPHD',\n",
       "       'RNC-OB', 'CNP, CNS, APRN BC', 'FNP-BC, ARNP', 'APRN-BC, MSN',\n",
       "       'P.A.C', 'RN, ACNP-BC', 'M.D./PH.D.', 'M.D., F.A.A.F.P.',\n",
       "       'M.D., P.C', 'AHCNS', 'RN, ACNS-BC', 'DNP, PMHNP-BC', 'M.D, PHD',\n",
       "       'M.D., PH.D., MRCP', 'M.S.N., N.P.', 'P,A,', 'DR,NP',\n",
       "       'D.D.S., P.C.', 'M.D., PHARM.D.', 'DO, M.S.', 'MHSC, PA-C',\n",
       "       'MSN - FNP-C', 'DDS, PC', 'C.F.N.P.', 'MD FACC FSCAI FSVM',\n",
       "       'DNSC, ARNP, CS', 'NP C', 'M.B.,B.S.', 'PH.D., MSN, PMHNP.',\n",
       "       'CFPMHNP', 'M.D.,  PHD.', 'DDS, MS, PC', 'RN, BSN, APN', 'PA, PT',\n",
       "       'RXN, NP', 'MS,PAC', 'RN, APRN', 'RN, MSN, PMHNP', 'RN,DDS',\n",
       "       'RNCS FNP', 'DDS,MS, DIPLOMATE', 'DDA, PA', 'MSDMD', 'DDS.FAGD',\n",
       "       'RN/PC, PMHCNS-BC', 'PSYNP', 'M.D., FACP', 'MSN APRN',\n",
       "       'M.D.  F.A.C.P.', 'D.M.D., J.D.', 'MD,DC', 'DMD MSD',\n",
       "       'M.D., F.A.P.A.', 'DDS MAGD,PC', 'MD DDS', 'MDFAC',\n",
       "       'R.N. MS. C.U.N.P.', 'ARNP, FNP-BC', 'D.D.S., D.M.D.', 'MSN,  APN',\n",
       "       'FACEP MD', 'D O P C', 'FNP, GNP, RN', 'NP,RN', 'RNC WHNP',\n",
       "       'P.A, A.T.C', 'DNP, PMHNP-C, FNP-C', 'MSN, AGPCNP-BC', 'O D  PS',\n",
       "       'OD, FAAO', 'CNS   FNP-C', 'MSN, APRN', 'MD,PH.D', 'NP, RN',\n",
       "       'MBBS, MRCP', 'RN, MSN, OCN, NP-C', 'M.D.,PHD', 'D.P.M., FACFAS',\n",
       "       'CNM C', 'MS, RPA-C', 'FNP MSN RN', 'BRIAN MASTERSON',\n",
       "       'DO, MBA, MS', 'DDS, PA', 'RN,MSN', 'RN/BSN, MSN ,FNP-BC',\n",
       "       'PH.D., MD', 'MD FAAOS FACS', 'D.O., PH.D.', 'D. M. D.',\n",
       "       'DENTIST DDS', 'MDCM FRCS', 'N.P.-C', 'RNC-ANP', 'APNC',\n",
       "       'APRN, NP-C', 'MD-A PROF CORP.', 'MD MS', 'BA,,DC, DO,JD, LLM',\n",
       "       'M.D., FACC', 'MD, PT', 'P.A.,C.', 'MSN NP', 'PA-C, MSPAS, MPH',\n",
       "       'PHYSICIAN ASSISANT', 'DDS FAGD'], dtype=object)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out how many credentials there are.\n",
    "df_data_3.Credentials.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Dentist', 'General Surgery', 'General Practice',\n",
       "       'Internal Medicine', 'Hematology/Oncology', 'Family Practice',\n",
       "       'Nurse Practitioner', 'Optometry', 'Cardiology',\n",
       "       'Obstetrics/Gynecology', 'Podiatry', 'Physician Assistant',\n",
       "       'Diagnostic Radiology',\n",
       "       'Student in an Organized Health Care Education/Training Program',\n",
       "       'Neurology', 'Certified Nurse Midwife', 'Rheumatology',\n",
       "       'Pharmacist', 'Urology', 'Cardiac Electrophysiology',\n",
       "       'Dermatology', 'Emergency Medicine', 'Psychiatry & Neurology',\n",
       "       'Infectious Disease', 'Psychiatry', 'Gastroenterology',\n",
       "       'Ophthalmology', 'Thoracic Surgery',\n",
       "       'Oral Surgery (dentists only)', 'Anesthesiology',\n",
       "       'Orthopedic Surgery', 'Otolaryngology', 'Pulmonary Disease',\n",
       "       'Neuropsychiatry', 'Physical Therapist', 'Pediatric Medicine',\n",
       "       'Physical Medicine and Rehabilitation', 'Maxillofacial Surgery',\n",
       "       'Certified Clinical Nurse Specialist', 'Preventive Medicine',\n",
       "       'Allergy/Immunology', 'Hand Surgery',\n",
       "       'Hospice and Palliative Care', 'Medical Oncology', 'Nephrology',\n",
       "       'Plastic and Reconstructive Surgery', 'Colon & Rectal Surgery',\n",
       "       'Hospitalist', 'Colorectal Surgery (formerly proctology)',\n",
       "       'Specialist', 'Geriatric Medicine', 'Gynecological/Oncology',\n",
       "       'Neurosurgery', 'Clinic/Center', 'Interventional Pain Management',\n",
       "       'Orthopaedic Surgery', 'Radiation Oncology', 'Endocrinology',\n",
       "       'Critical Care (Intensivists)', 'Vascular Surgery',\n",
       "       'Family Medicine', 'Registered Nurse', 'Pain Management',\n",
       "       'Sports Medicine', 'Unknown Physician Specialty Code',\n",
       "       'Osteopathic Manipulative Medicine', 'Interventional Radiology',\n",
       "       'Naturopath', 'Hematology', 'Preferred Provider Organization',\n",
       "       'Cardiac Surgery', 'Geriatric Psychiatry',\n",
       "       'Medical Genetics, Ph.D. Medical Genetics', 'Surgical Oncology',\n",
       "       'Surgery', 'Legal Medicine',\n",
       "       'Neuromusculoskeletal Medicine, Sports Medicine',\n",
       "       'Health Maintenance Organization',\n",
       "       'Psychologist (billing independently)', 'Neurological Surgery',\n",
       "       'Plastic Surgery', 'Homeopath', 'Licensed Practical Nurse', 'CRNA',\n",
       "       'Addiction Medicine', 'Specialist/Technologist',\n",
       "       'Nuclear Medicine', 'Unknown Supplier/Provider',\n",
       "       'Medical Genetics', 'Oral & Maxillofacial Surgery',\n",
       "       'Clinical Pharmacology', 'Sleep Medicine', 'Pathology',\n",
       "       'Counselor', 'Slide Preparation Facility', 'Rehabilitation Agency',\n",
       "       'Psychologist', 'Military Health Care Provider',\n",
       "       'General Acute Care Hospital',\n",
       "       'Multispecialty Clinic/Group Practice',\n",
       "       'Thoracic Surgery (Cardiothoracic Vascular Surgery)', 'Midwife',\n",
       "       'Chiropractic', 'Licensed Clinical Social Worker',\n",
       "       'Community Health Worker', 'Hospital (Dmercs Only)',\n",
       "       'Pharmacy Technician', 'Behavioral Analyst'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check out the specialties.\n",
    "df_data_3.Specialty.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    0.378166\n",
       "M    0.621834\n",
       "dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How much of the dataset is male vs female?\n",
    "df_data_3.groupby('Gender').size() / df_data_3.groupby('Gender').size().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Opioid.Prescriber\n",
       "0    0.41282\n",
       "1    0.58718\n",
       "dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How many prescribers in our dataset prescribe opioid drugs vs do not?\n",
    "df_data_3.groupby('Opioid.Prescriber').size() / df_data_3.groupby('Opioid.Prescriber').size().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f93bb0a3278>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the opioid prescriber count vs non opioid prescriber count.\n",
    "# The dataset has a slightly higher number of opioid prescribers.\n",
    "pd.value_counts(df_data_3['Opioid.Prescriber']).plot.bar()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combination of Approaches: Kaggle and Indiana University"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - \"Quick and Dirty\" approach from Kaggle (https://www.kaggle.com/jiashenliu/quick-and-dirty-attempt-on-voting-classifier)\n",
    " - \"Detecting Frequent Opioid Prescription\" (https://inclass.kaggle.com/apryor6/detecting-frequent-opioid-prescription)\n",
    " - Indiana University: \"Opiate prescription analysis using machine learning\" (https://github.com/anirudhkm/opiate-prescription-analysis/blob/master/AML_report.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24759, 256)\n"
     ]
    }
   ],
   "source": [
    "# Find the shape of our data frame so that we know how to set our classifiers up.\n",
    "print(df_data_3.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Mark opioid vs non opiod drugs in df_data_3 with use of df_data_1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "opioids = df_data_1 \n",
    "name = opioids['Drug Name']\n",
    "import re\n",
    "new_name = name.apply(lambda x:re.sub(\"\\ |-\",\".\",str(x)))\n",
    "columns = df_data_3.columns\n",
    "Abandoned_variables = set(columns).intersection(set(new_name))\n",
    "Kept_variable = []\n",
    "for each in columns:\n",
    "    if each in Abandoned_variables:\n",
    "        pass\n",
    "    else:\n",
    "        Kept_variable.append(each)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(24759, 245)\n"
     ]
    }
   ],
   "source": [
    "# Look at our new shape.\n",
    "df = df_data_3[Kept_variable]\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>NPI</th>\n",
       "      <th>Gender</th>\n",
       "      <th>State</th>\n",
       "      <th>Credentials</th>\n",
       "      <th>Specialty</th>\n",
       "      <th>ABILIFY</th>\n",
       "      <th>ACYCLOVIR</th>\n",
       "      <th>ADVAIR.DISKUS</th>\n",
       "      <th>AGGRENOX</th>\n",
       "      <th>ALENDRONATE.SODIUM</th>\n",
       "      <th>...</th>\n",
       "      <th>VERAPAMIL.ER</th>\n",
       "      <th>VESICARE</th>\n",
       "      <th>VOLTAREN</th>\n",
       "      <th>VYTORIN</th>\n",
       "      <th>WARFARIN.SODIUM</th>\n",
       "      <th>XARELTO</th>\n",
       "      <th>ZETIA</th>\n",
       "      <th>ZIPRASIDONE.HCL</th>\n",
       "      <th>ZOLPIDEM.TARTRATE</th>\n",
       "      <th>Opioid.Prescriber</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1710982582</td>\n",
       "      <td>M</td>\n",
       "      <td>TX</td>\n",
       "      <td>DDS</td>\n",
       "      <td>Dentist</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1245278100</td>\n",
       "      <td>F</td>\n",
       "      <td>AL</td>\n",
       "      <td>MD</td>\n",
       "      <td>General Surgery</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1427182161</td>\n",
       "      <td>F</td>\n",
       "      <td>NY</td>\n",
       "      <td>M.D.</td>\n",
       "      <td>General Practice</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1669567541</td>\n",
       "      <td>M</td>\n",
       "      <td>AZ</td>\n",
       "      <td>MD</td>\n",
       "      <td>Internal Medicine</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1679650949</td>\n",
       "      <td>M</td>\n",
       "      <td>NV</td>\n",
       "      <td>M.D.</td>\n",
       "      <td>Hematology/Oncology</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 245 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          NPI Gender State Credentials            Specialty  ABILIFY  \\\n",
       "0  1710982582      M    TX         DDS              Dentist        0   \n",
       "1  1245278100      F    AL          MD      General Surgery        0   \n",
       "2  1427182161      F    NY        M.D.     General Practice        0   \n",
       "3  1669567541      M    AZ          MD    Internal Medicine        0   \n",
       "4  1679650949      M    NV        M.D.  Hematology/Oncology        0   \n",
       "\n",
       "   ACYCLOVIR  ADVAIR.DISKUS  AGGRENOX  ALENDRONATE.SODIUM  ...  VERAPAMIL.ER  \\\n",
       "0          0              0         0                   0  ...             0   \n",
       "1          0              0         0                   0  ...             0   \n",
       "2          0              0         0                   0  ...             0   \n",
       "3          0              0         0                  21  ...             0   \n",
       "4          0              0         0                   0  ...             0   \n",
       "\n",
       "   VESICARE  VOLTAREN  VYTORIN  WARFARIN.SODIUM  XARELTO  ZETIA  \\\n",
       "0         0         0        0                0        0      0   \n",
       "1         0         0        0                0        0      0   \n",
       "2         0         0        0                0        0      0   \n",
       "3         0         0        0                0        0      0   \n",
       "4         0         0        0               17       28      0   \n",
       "\n",
       "   ZIPRASIDONE.HCL  ZOLPIDEM.TARTRATE  Opioid.Prescriber  \n",
       "0                0                  0                  1  \n",
       "1                0                 35                  1  \n",
       "2                0                 25                  0  \n",
       "3                0                  0                  1  \n",
       "4                0                  0                  1  \n",
       "\n",
       "[5 rows x 245 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's remove the credentials column so that we can use the speciality column instead.\n",
    "\n",
    "We will also remove the NPI column in order to trim our features down."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p>5 rows × 243 columns</p>\n",
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      ],
      "text/plain": [
       "  Gender State            Specialty  ABILIFY  ACYCLOVIR  ADVAIR.DISKUS  \\\n",
       "0      M    TX              Dentist        0          0              0   \n",
       "1      F    AL      General Surgery        0          0              0   \n",
       "2      F    NY     General Practice        0          0              0   \n",
       "3      M    AZ    Internal Medicine        0          0              0   \n",
       "4      M    NV  Hematology/Oncology        0          0              0   \n",
       "\n",
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       "3         0                  21            0           0  ...             0   \n",
       "4         0                   0            0           0  ...             0   \n",
       "\n",
       "   VESICARE  VOLTAREN  VYTORIN  WARFARIN.SODIUM  XARELTO  ZETIA  \\\n",
       "0         0         0        0                0        0      0   \n",
       "1         0         0        0                0        0      0   \n",
       "2         0         0        0                0        0      0   \n",
       "3         0         0        0                0        0      0   \n",
       "4         0         0        0               17       28      0   \n",
       "\n",
       "   ZIPRASIDONE.HCL  ZOLPIDEM.TARTRATE  Opioid.Prescriber  \n",
       "0                0                  0                  1  \n",
       "1                0                 35                  1  \n",
       "2                0                 25                  0  \n",
       "3                0                  0                  1  \n",
       "4                0                  0                  1  \n",
       "\n",
       "[5 rows x 243 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(df.columns[[0, 3]], axis = 1) \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19807, 243)\n",
      "(4952, 243)\n"
     ]
    }
   ],
   "source": [
    "# Let's now create our training and test data.\n",
    "train,test = train_test_split(df,test_size = 0.2,random_state = 42)\n",
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "# Now we convert our categorical columns.\n",
    "Categorical_columns = ['Gender','State','Specialty']\n",
    "\n",
    "for col in Categorical_columns:\n",
    "    train[col] = pd.factorize(train[col], sort = True)[0]\n",
    "    test[col] = pd.factorize(test[col],sort = True)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12366</th>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 241 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State  Specialty  ABILIFY  ACYCLOVIR  ADVAIR.DISKUS  AGGRENOX  \\\n",
       "11299     34          1        0          0             13         0   \n",
       "17247     14         38        0          0              0         0   \n",
       "14337      9         52        0          0              0         0   \n",
       "14452     35         85        0          0             17         0   \n",
       "12366      9         66        0          0              0         0   \n",
       "\n",
       "       ALENDRONATE.SODIUM  ALLOPURINOL  ALPRAZOLAM  AMIODARONE.HCL  ...  \\\n",
       "11299                   0            0           0               0  ...   \n",
       "17247                   0            0           0               0  ...   \n",
       "14337                   0           61           0               0  ...   \n",
       "14452                   0            0           0               0  ...   \n",
       "12366                   0            0           0               0  ...   \n",
       "\n",
       "       VENTOLIN.HFA  VERAPAMIL.ER  VESICARE  VOLTAREN  VYTORIN  \\\n",
       "11299             0             0         0         0        0   \n",
       "17247             0             0         0         0        0   \n",
       "14337             0             0         0         0        0   \n",
       "14452             0             0         0         0        0   \n",
       "12366             0             0         0        19        0   \n",
       "\n",
       "       WARFARIN.SODIUM  XARELTO  ZETIA  ZIPRASIDONE.HCL  ZOLPIDEM.TARTRATE  \n",
       "11299                0        0      0                0                  0  \n",
       "17247                0        0      0                0                  0  \n",
       "14337                0        0      0                0                  0  \n",
       "14452                0        0      0                0                  0  \n",
       "12366                0        0      0                0                  0  \n",
       "\n",
       "[5 rows x 241 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set our features.\n",
    "features = train.iloc[:,1:242] #make sure we only use the columns that we want as our features\n",
    "features.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating our Classifiers to Predict Opioid Prescribers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train our models. Let's use several classifiers so that we can check out which has the highest accuracy.\n",
    "\n",
    "Added bagging classifier to check for overfitting (along with cross validation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.607866 of model Logistic Regression\n",
      "Accuracy: 0.612056 of model Naive Bayes\n",
      "Accuracy: 0.833039 of model Random Forest\n",
      "Accuracy: 0.824607 of model Gradient Boosting\n",
      "Accuracy: 0.778513 of model KNN\n",
      "Accuracy: 0.779321 of model Decision Tree\n",
      "Accuracy: 0.716665 of model LDA\n",
      "Accuracy: 0.810926 of model Bagging Classifier\n",
      "Accuracy: 0.834452 of model Ensemble\n"
     ]
    }
   ],
   "source": [
    "# With 'Gender' included.\n",
    "features = train.iloc[:,0:242] \n",
    "# Make sure to remove Opioid.Prescriber (our target)!\n",
    "target = train['Opioid.Prescriber']\n",
    "Name = []\n",
    "Accuracy = []\n",
    "model1 = LogisticRegression(random_state = 22,C = 0.000000001,solver = 'liblinear',max_iter = 200)\n",
    "model2 = GaussianNB()\n",
    "model3 = RandomForestClassifier(n_estimators = 200,random_state = 22)\n",
    "model4 = GradientBoostingClassifier(n_estimators = 200)\n",
    "model5 = KNeighborsClassifier()\n",
    "model6 = DecisionTreeClassifier()\n",
    "model7 = LinearDiscriminantAnalysis()\n",
    "model8 = BaggingClassifier()\n",
    "Ensembled_model = VotingClassifier(estimators = [('lr', model1), ('gn', model2), ('rf', model3),('gb',model4),('kn',model5),('dt',model6),('lda',model7), ('bc',model8)], voting = 'hard')\n",
    "for model, label in zip([model1, model2, model3, model4,model5,model6,model7,model8,Ensembled_model], ['Logistic Regression','Naive Bayes','Random Forest', 'Gradient Boosting','KNN','Decision Tree','LDA', 'Bagging Classifier', 'Ensemble']):\n",
    "    scores = cross_val_score(model, features, target, cv = 5, scoring = 'accuracy')\n",
    "    Accuracy.append(scores.mean())\n",
    "    Name.append(model.__class__.__name__)\n",
    "    print(\"Accuracy: %f of model %s\" % (scores.mean(),label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.607866 of model Logistic Regression\n",
      "Accuracy: 0.612157 of model Naive Bayes\n",
      "Accuracy: 0.835109 of model Random Forest\n",
      "Accuracy: 0.824406 of model Gradient Boosting\n",
      "Accuracy: 0.778058 of model KNN\n",
      "Accuracy: 0.782400 of model Decision Tree\n",
      "Accuracy: 0.712727 of model LDA\n",
      "Accuracy: 0.809159 of model Bagging Classifier\n",
      "Accuracy: 0.834806 of model Ensemble\n"
     ]
    }
   ],
   "source": [
    "# Gender not included.\n",
    "features=train.iloc[:,1:242] \n",
    "#Make sure to remove Opioid.Prescriber (our target)!\n",
    "target = train['Opioid.Prescriber']\n",
    "Name = []\n",
    "Accuracy = []\n",
    "model1 = LogisticRegression(random_state = 22,C = 0.000000001,solver = 'liblinear',max_iter = 200)\n",
    "model2 = GaussianNB()\n",
    "model3 = RandomForestClassifier(n_estimators = 200,random_state = 22)\n",
    "model4 = GradientBoostingClassifier(n_estimators = 200)\n",
    "model5 = KNeighborsClassifier()\n",
    "model6 = DecisionTreeClassifier()\n",
    "model7 = LinearDiscriminantAnalysis()\n",
    "model8 = BaggingClassifier()\n",
    "Ensembled_model = VotingClassifier(estimators = [('lr', model1), ('gn', model2), ('rf', model3),('gb',model4),('kn',model5),('dt',model6),('lda',model7), ('bc',model8)], voting = 'hard')\n",
    "for model, label in zip([model1, model2, model3, model4,model5,model6,model7,model8,Ensembled_model], ['Logistic Regression','Naive Bayes','Random Forest', 'Gradient Boosting','KNN','Decision Tree','LDA', 'Bagging Classifier', 'Ensemble']):\n",
    "    scores = cross_val_score(model, features, target, cv = 5, scoring = 'accuracy')\n",
    "    Accuracy.append(scores.mean())\n",
    "    Name.append(model.__class__.__name__)\n",
    "    print(\"Accuracy: %f of model %s\" % (scores.mean(),label))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Looks like our highest accuracy score is 83.5% with Random Forest, followed by 83.3% with the Ensemble."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Overall, it seems our models are less accurate when 'Gender' is included, with the exception of our Ensemble which gets a fairly high accuracy at 83.4% accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.835109 of model Random Forest\n",
      "Accuracy: 0.824456 of model Gradient Boosting\n",
      "Accuracy: 0.836573 of model Ensemble\n",
      "Accuracy: 0.811430 of model Bagging Classifier\n"
     ]
    }
   ],
   "source": [
    "# Let's check out our best models from our run without 'Gender'.\n",
    "Name_2 = []\n",
    "Accuracy_2 = []\n",
    "Ensembled_model_3 = VotingClassifier(estimators = [('rf', model3),('em',Ensembled_model)], voting = 'hard')\n",
    "for model, label in zip([model3, model4,Ensembled_model_3, model8], ['Random Forest', 'Gradient Boosting', 'Ensemble', 'Bagging Classifier']):\n",
    "    scores = cross_val_score(model, features, target, cv = 5, scoring = 'accuracy')\n",
    "    Accuracy_2.append(scores.mean())\n",
    "    Name_2.append(model.__class__.__name__)\n",
    "    print(\"Accuracy: %f of model %s\" % (scores.mean(),label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "classifers = [model3,model4,model8]\n",
    "out_sample_accuracy = []\n",
    "Name_2 = []\n",
    "for each in classifers:\n",
    "    fit = each.fit(features,target)\n",
    "    pred = fit.predict(test.iloc[:,1:242])\n",
    "    accuracy = accuracy_score(test['Opioid.Prescriber'],pred)\n",
    "    Name_2.append(each.__class__.__name__)\n",
    "    out_sample_accuracy.append(accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1255,  796],\n",
       "       [ 533, 2368]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "y_actu = test['Opioid.Prescriber']\n",
    "confusion_matrix(y_actu, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.58582391, 0.74841972, 1.        ]),\n",
       " array([1.        , 0.81627025, 0.        ]),\n",
       " array([0, 1]))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Precision-Recall Curve\n",
    "sklearn.metrics.precision_recall_curve(y_actu, pred, pos_label = None, sample_weight = None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision-recall score: 0.72\n"
     ]
    }
   ],
   "source": [
    "# Precision-Recall Score\n",
    "from sklearn.metrics import average_precision_score\n",
    "average_precision = average_precision_score(y_actu, pred)\n",
    "\n",
    "print('Average precision-recall score: {0:0.2f}'.format(\n",
    "      average_precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2-class Precision-Recall curve: AP=0.72')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precision-Recall Curve Plot\n",
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "precision, recall, _ = precision_recall_curve(y_actu, pred)\n",
    "\n",
    "plt.step(recall, precision, color = 'b', alpha = 0.2,\n",
    "         where = 'post')\n",
    "plt.fill_between(recall, precision, step = 'post', alpha = 0.2,\n",
    "                 color = 'b')\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n",
    "          average_precision))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "After running various classifiers, we find that Random Forest, Gradient Boosting and our Ensemble models had the best performance comparatively. This means that if we were to build a larger project, we could focus on these particular classifiers, building upon them to help predict opioid prescribers (given more years of data). \n",
    "\n",
    "The precision-recall curve can help us determine if we were successful enough. For the unfamiliar precision-recall scores represent a balance between high recall and high precision relating to a low false positive rate and a low false negative rate respectively. When evaluating, you have four outcomes: true positive, true negative, false positive and false negative. Depending on the project, you would aim for different balances, but ideally you want everything to be as accurate, or true, as possible. In the graph above, the y axis is the precision and the x axis is recall. If the graph went straight across the middle, that would be a random-like output. Below it would be poor performance and above it would be a more accurate and better quality performance. If it were 100 it would be a perfect classifier. Because the classifier above is at 0.84 we can feel confident that our precision was good. Now I challenge you to go improve it!"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}