opioid-tn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opioids in Tennessee\n",
    "**An Exploration of the Washington Post Opioid Dataset for Tennessee**\n",
    "\n",
    "**Contact**\n",
    "* [Qiusheng Wu](https://wetlands.io)\n",
    "* [Michael Camponovo](https://geography.utk.edu/about-us/faculty/michael-camponovo/)\n",
    "\n",
    "## Data\n",
    "* [Tennessee Population by County (2019)](http://worldpopulationreview.com/us-counties/tn/)\n",
    "* [Tennessee Opioid Data (2006-2012)](https://www.washingtonpost.com/wp-stat/dea-pain-pill-database/summary/arcos-tn-statewide-itemized.tsv.gz) (zip file: 139 MB; uncompressed: 2.4 GB; 5.75 million rows)\n",
    "\n",
    "\n",
    "## Useful Links\n",
    "\n",
    "* [StoryMaps of Opioids in Tennessee by Michael Camponovo](https://storymaps.arcgis.com/stories/92678e77f9f9467bbad17c06f68ac67b)\n",
    "* [Pandas Basics (Reading Data Files, DataFrames, Data Selection](https://data36.com/pandas-tutorial-1-basics-reading-data-files-dataframes-data-selection/)\n",
    "* [Pandas Tutorial 2: Aggregation and Grouping](https://data36.com/pandas-tutorial-2-aggregation-and-grouping/)\n",
    "* [ipyleaflet: Interactive maps in the Jupyter notebook](https://ipyleaflet.readthedocs.io/en/latest/index.html)\n",
    "* https://spatial.utk.edu/maps/opioids-tn.html\n",
    "\n",
    "## Table of Content\n",
    "* [Loading Opioid Data](#loading-opioid-data)\n",
    "* [Selecting Data Columns](#selecting-data-columns)\n",
    "* [Descriptive Statistics](#descriptive-statistics)\n",
    "* [Pill Count Summary by Year, Month, and Day of Week](#pill-count-summary-by-date)\n",
    "* [Top 10 Distributors, Manufacturers, and Buyers](#top-10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Opioid Data  <a class=\"anchor\" id=\"loading-opioid-data\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import calendar\n",
    "from ipyleaflet import *\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# magic function making plot outputs appear within the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# change the default plot output size\n",
    "plt.rcParams['figure.figsize'] = [18, 8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Tennesee opioid data (2006-2012) can be downloaded via this [direct link](https://www.washingtonpost.com/wp-stat/dea-pain-pill-database/summary/arcos-tn-statewide-itemized.tsv.gz). Note that the downloaded zip file is only 139 MB. When uncompressed, the tab separated values (TSV) file is 2.4 GB, which contains 5.75 millions rows. If you are interested in opioid data for other U.S. states, check this [DEA opioid database](https://www.washingtonpost.com/graphics/2019/investigations/dea-pain-pill-database/) by the Washington Post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set file paths of opioid and population data\n",
    "data_path = \"/media/hdd/Data/Opioids/arcos-tn-statewide-itemized.tsv\"\n",
    "pop_path = '/media/hdd/Data/Opioids/tn-county-pop-2019.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read population data\n",
    "pop = pd.read_csv(pop_path)\n",
    "# read opioid data\n",
    "data = pd.read_csv(data_path, sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 42 columns in this dataset. The **DOSAGE_UNIT** column is the number of pills. More information about each individual column can be found in the [DEA ARCOS's Handbook](https://www.deadiversion.usdoj.gov/arcos/handbook/full.pdf).\n",
    "\n",
    "Some key columns we will be using include:\n",
    "* **REPORTER_NAME**: Distributor\n",
    "* **BUYER_BUS_ACT**: Buyer Type\n",
    "* **BUYER_NAME**: Pharmacies\n",
    "* **BUYER_CITY**: Buyer City\n",
    "* **BUYER_COUNTY**: Buyer County\n",
    "* **BUYER_STATE**: Buyer State\n",
    "* **BUYER_ZIP**: Buyer Zip Code\n",
    "* **DRUG_NAME**: Drug Name\n",
    "* **TRANSACTION_DATE**: Transaction Date\n",
    "* **DOSAGE_UNIT**: Dosage Unit\n",
    "* **Combined_Labeler_Name**: Manufacturers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPORTER_DEA_NO\n",
      "REPORTER_BUS_ACT\n",
      "REPORTER_NAME\n",
      "REPORTER_ADDL_CO_INFO\n",
      "REPORTER_ADDRESS1\n",
      "REPORTER_ADDRESS2\n",
      "REPORTER_CITY\n",
      "REPORTER_STATE\n",
      "REPORTER_ZIP\n",
      "REPORTER_COUNTY\n",
      "BUYER_DEA_NO\n",
      "BUYER_BUS_ACT\n",
      "BUYER_NAME\n",
      "BUYER_ADDL_CO_INFO\n",
      "BUYER_ADDRESS1\n",
      "BUYER_ADDRESS2\n",
      "BUYER_CITY\n",
      "BUYER_STATE\n",
      "BUYER_ZIP\n",
      "BUYER_COUNTY\n",
      "TRANSACTION_CODE\n",
      "DRUG_CODE\n",
      "NDC_NO\n",
      "DRUG_NAME\n",
      "QUANTITY\n",
      "UNIT\n",
      "ACTION_INDICATOR\n",
      "ORDER_FORM_NO\n",
      "CORRECTION_NO\n",
      "STRENGTH\n",
      "TRANSACTION_DATE\n",
      "CALC_BASE_WT_IN_GM\n",
      "DOSAGE_UNIT\n",
      "TRANSACTION_ID\n",
      "Product_Name\n",
      "Ingredient_Name\n",
      "Measure\n",
      "MME_Conversion_Factor\n",
      "Combined_Labeler_Name\n",
      "Revised_Company_Name\n",
      "Reporter_family\n",
      "dos_str\n",
      "\n",
      "Total number of columns: 42\n"
     ]
    }
   ],
   "source": [
    "for col in data.columns:\n",
    "    print(col)\n",
    "    \n",
    "print(f\"\\nTotal number of columns: {len(data.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REPORTER_DEA_NO</th>\n",
       "      <th>REPORTER_BUS_ACT</th>\n",
       "      <th>REPORTER_NAME</th>\n",
       "      <th>REPORTER_ADDL_CO_INFO</th>\n",
       "      <th>REPORTER_ADDRESS1</th>\n",
       "      <th>REPORTER_ADDRESS2</th>\n",
       "      <th>REPORTER_CITY</th>\n",
       "      <th>REPORTER_STATE</th>\n",
       "      <th>REPORTER_ZIP</th>\n",
       "      <th>REPORTER_COUNTY</th>\n",
       "      <th>...</th>\n",
       "      <th>DOSAGE_UNIT</th>\n",
       "      <th>TRANSACTION_ID</th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>Ingredient_Name</th>\n",
       "      <th>Measure</th>\n",
       "      <th>MME_Conversion_Factor</th>\n",
       "      <th>Combined_Labeler_Name</th>\n",
       "      <th>Revised_Company_Name</th>\n",
       "      <th>Reporter_family</th>\n",
       "      <th>dos_str</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PB0034861</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500 NEW HORIZONS BLVD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH AMITYVILLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11701</td>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>...</td>\n",
       "      <td>300.0</td>\n",
       "      <td>729</td>\n",
       "      <td>HYDROCODONE.BIT &amp; ACETA  10MG &amp; 500M</td>\n",
       "      <td>HYDROCODONE BITARTRATE HEMIPENTAHYDRATE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "      <td>Endo Pharmaceuticals, Inc.</td>\n",
       "      <td>Bellco Drug Corp</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PB0034861</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500 NEW HORIZONS BLVD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH AMITYVILLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11701</td>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>...</td>\n",
       "      <td>500.0</td>\n",
       "      <td>6798</td>\n",
       "      <td>HYDROCODONE BIT.7.5MG/ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE BITARTRATE HEMIPENTAHYDRATE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "      <td>Allergan, Inc.</td>\n",
       "      <td>Bellco Drug Corp</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PB0034861</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500 NEW HORIZONS BLVD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH AMITYVILLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11701</td>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>...</td>\n",
       "      <td>200.0</td>\n",
       "      <td>1010</td>\n",
       "      <td>ENDOCET TABS - 10MG OXYCODONE.HCL &amp;</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "      <td>Endo Pharmaceuticals, Inc.</td>\n",
       "      <td>Bellco Drug Corp</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PB0034861</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500 NEW HORIZONS BLVD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH AMITYVILLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11701</td>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>...</td>\n",
       "      <td>400.0</td>\n",
       "      <td>3069</td>\n",
       "      <td>HYDROCODONE.BIT./ACET.,10MG &amp; 325MG/</td>\n",
       "      <td>HYDROCODONE BITARTRATE HEMIPENTAHYDRATE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "      <td>Endo Pharmaceuticals, Inc.</td>\n",
       "      <td>Bellco Drug Corp</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PB0034861</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5500 NEW HORIZONS BLVD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NORTH AMITYVILLE</td>\n",
       "      <td>NY</td>\n",
       "      <td>11701</td>\n",
       "      <td>SUFFOLK</td>\n",
       "      <td>...</td>\n",
       "      <td>500.0</td>\n",
       "      <td>10126</td>\n",
       "      <td>HYDROCODONE.BIT. &amp; ACETA  5MG &amp; 500M</td>\n",
       "      <td>HYDROCODONE BITARTRATE HEMIPENTAHYDRATE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "      <td>Endo Pharmaceuticals, Inc.</td>\n",
       "      <td>Bellco Drug Corp</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  REPORTER_DEA_NO REPORTER_BUS_ACT     REPORTER_NAME REPORTER_ADDL_CO_INFO  \\\n",
       "0       PB0034861      DISTRIBUTOR  BELLCO DRUG CORP                   NaN   \n",
       "1       PB0034861      DISTRIBUTOR  BELLCO DRUG CORP                   NaN   \n",
       "2       PB0034861      DISTRIBUTOR  BELLCO DRUG CORP                   NaN   \n",
       "3       PB0034861      DISTRIBUTOR  BELLCO DRUG CORP                   NaN   \n",
       "4       PB0034861      DISTRIBUTOR  BELLCO DRUG CORP                   NaN   \n",
       "\n",
       "        REPORTER_ADDRESS1 REPORTER_ADDRESS2     REPORTER_CITY REPORTER_STATE  \\\n",
       "0  5500 NEW HORIZONS BLVD               NaN  NORTH AMITYVILLE             NY   \n",
       "1  5500 NEW HORIZONS BLVD               NaN  NORTH AMITYVILLE             NY   \n",
       "2  5500 NEW HORIZONS BLVD               NaN  NORTH AMITYVILLE             NY   \n",
       "3  5500 NEW HORIZONS BLVD               NaN  NORTH AMITYVILLE             NY   \n",
       "4  5500 NEW HORIZONS BLVD               NaN  NORTH AMITYVILLE             NY   \n",
       "\n",
       "   REPORTER_ZIP REPORTER_COUNTY  ... DOSAGE_UNIT TRANSACTION_ID  \\\n",
       "0         11701         SUFFOLK  ...       300.0            729   \n",
       "1         11701         SUFFOLK  ...       500.0           6798   \n",
       "2         11701         SUFFOLK  ...       200.0           1010   \n",
       "3         11701         SUFFOLK  ...       400.0           3069   \n",
       "4         11701         SUFFOLK  ...       500.0          10126   \n",
       "\n",
       "                           Product_Name  \\\n",
       "0  HYDROCODONE.BIT & ACETA  10MG & 500M   \n",
       "1   HYDROCODONE BIT.7.5MG/ACETAMINOPHEN   \n",
       "2   ENDOCET TABS - 10MG OXYCODONE.HCL &   \n",
       "3  HYDROCODONE.BIT./ACET.,10MG & 325MG/   \n",
       "4  HYDROCODONE.BIT. & ACETA  5MG & 500M   \n",
       "\n",
       "                           Ingredient_Name Measure MME_Conversion_Factor  \\\n",
       "0  HYDROCODONE BITARTRATE HEMIPENTAHYDRATE     TAB                   1.0   \n",
       "1  HYDROCODONE BITARTRATE HEMIPENTAHYDRATE     TAB                   1.0   \n",
       "2                  OXYCODONE HYDROCHLORIDE     TAB                   1.5   \n",
       "3  HYDROCODONE BITARTRATE HEMIPENTAHYDRATE     TAB                   1.0   \n",
       "4  HYDROCODONE BITARTRATE HEMIPENTAHYDRATE     TAB                   1.0   \n",
       "\n",
       "  Combined_Labeler_Name        Revised_Company_Name   Reporter_family dos_str  \n",
       "0    Par Pharmaceutical  Endo Pharmaceuticals, Inc.  Bellco Drug Corp    10.0  \n",
       "1  Actavis Pharma, Inc.              Allergan, Inc.  Bellco Drug Corp     7.5  \n",
       "2    Par Pharmaceutical  Endo Pharmaceuticals, Inc.  Bellco Drug Corp    10.0  \n",
       "3    Par Pharmaceutical  Endo Pharmaceuticals, Inc.  Bellco Drug Corp    10.0  \n",
       "4    Par Pharmaceutical  Endo Pharmaceuticals, Inc.  Bellco Drug Corp     5.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the first five rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>REPORTER_DEA_NO</th>\n",
       "      <th>REPORTER_BUS_ACT</th>\n",
       "      <th>REPORTER_NAME</th>\n",
       "      <th>REPORTER_ADDL_CO_INFO</th>\n",
       "      <th>REPORTER_ADDRESS1</th>\n",
       "      <th>REPORTER_ADDRESS2</th>\n",
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       "      <th>REPORTER_COUNTY</th>\n",
       "      <th>...</th>\n",
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       "      <th>Product_Name</th>\n",
       "      <th>Ingredient_Name</th>\n",
       "      <th>Measure</th>\n",
       "      <th>MME_Conversion_Factor</th>\n",
       "      <th>Combined_Labeler_Name</th>\n",
       "      <th>Revised_Company_Name</th>\n",
       "      <th>Reporter_family</th>\n",
       "      <th>dos_str</th>\n",
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       "      <th>5744671</th>\n",
       "      <td>RA0287020</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>ANDA PHARMACEUTICALS INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500 ADELAIDE COURT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROVEPORT</td>\n",
       "      <td>OH</td>\n",
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       "      <td>...</td>\n",
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       "      <td>OXYCODONE HYDROCHLORIDE TABLETS USP</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "      <td>Allergan, Inc.</td>\n",
       "      <td>Anda, Inc</td>\n",
       "      <td>15.0</td>\n",
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       "      <th>5744672</th>\n",
       "      <td>RA0287020</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>ANDA PHARMACEUTICALS INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500 ADELAIDE COURT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROVEPORT</td>\n",
       "      <td>OH</td>\n",
       "      <td>43125</td>\n",
       "      <td>FRANKLIN</td>\n",
       "      <td>...</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>804005220</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE TABLETS USP</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "      <td>Allergan, Inc.</td>\n",
       "      <td>Anda, Inc</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5744673</th>\n",
       "      <td>RA0287020</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>ANDA PHARMACEUTICALS INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500 ADELAIDE COURT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROVEPORT</td>\n",
       "      <td>OH</td>\n",
       "      <td>43125</td>\n",
       "      <td>FRANKLIN</td>\n",
       "      <td>...</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>805003202</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE TABLETS USP</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "      <td>Allergan, Inc.</td>\n",
       "      <td>Anda, Inc</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5744674</th>\n",
       "      <td>RA0287020</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>ANDA PHARMACEUTICALS INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500 ADELAIDE COURT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROVEPORT</td>\n",
       "      <td>OH</td>\n",
       "      <td>43125</td>\n",
       "      <td>FRANKLIN</td>\n",
       "      <td>...</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1005031206</td>\n",
       "      <td>ENDOCET - 7.5MG OXYCODONE.HCL/325MG</td>\n",
       "      <td>OXYCODONE HYDROCHLORIDE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "      <td>Endo Pharmaceuticals, Inc.</td>\n",
       "      <td>Anda, Inc</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5744675</th>\n",
       "      <td>RA0287020</td>\n",
       "      <td>DISTRIBUTOR</td>\n",
       "      <td>ANDA PHARMACEUTICALS INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6500 ADELAIDE COURT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GROVEPORT</td>\n",
       "      <td>OH</td>\n",
       "      <td>43125</td>\n",
       "      <td>FRANKLIN</td>\n",
       "      <td>...</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>1104003769</td>\n",
       "      <td>HYDROCODONE BIT. 10MG/ACETAMINOPHEN</td>\n",
       "      <td>HYDROCODONE BITARTRATE HEMIPENTAHYDRATE</td>\n",
       "      <td>TAB</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "      <td>Allergan, Inc.</td>\n",
       "      <td>Anda, Inc</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        REPORTER_DEA_NO REPORTER_BUS_ACT             REPORTER_NAME  \\\n",
       "5744671       RA0287020      DISTRIBUTOR  ANDA PHARMACEUTICALS INC   \n",
       "5744672       RA0287020      DISTRIBUTOR  ANDA PHARMACEUTICALS INC   \n",
       "5744673       RA0287020      DISTRIBUTOR  ANDA PHARMACEUTICALS INC   \n",
       "5744674       RA0287020      DISTRIBUTOR  ANDA PHARMACEUTICALS INC   \n",
       "5744675       RA0287020      DISTRIBUTOR  ANDA PHARMACEUTICALS INC   \n",
       "\n",
       "        REPORTER_ADDL_CO_INFO    REPORTER_ADDRESS1 REPORTER_ADDRESS2  \\\n",
       "5744671                   NaN  6500 ADELAIDE COURT               NaN   \n",
       "5744672                   NaN  6500 ADELAIDE COURT               NaN   \n",
       "5744673                   NaN  6500 ADELAIDE COURT               NaN   \n",
       "5744674                   NaN  6500 ADELAIDE COURT               NaN   \n",
       "5744675                   NaN  6500 ADELAIDE COURT               NaN   \n",
       "\n",
       "        REPORTER_CITY REPORTER_STATE  REPORTER_ZIP REPORTER_COUNTY  ...  \\\n",
       "5744671     GROVEPORT             OH         43125        FRANKLIN  ...   \n",
       "5744672     GROVEPORT             OH         43125        FRANKLIN  ...   \n",
       "5744673     GROVEPORT             OH         43125        FRANKLIN  ...   \n",
       "5744674     GROVEPORT             OH         43125        FRANKLIN  ...   \n",
       "5744675     GROVEPORT             OH         43125        FRANKLIN  ...   \n",
       "\n",
       "        DOSAGE_UNIT TRANSACTION_ID                         Product_Name  \\\n",
       "5744671      2400.0      709009198  OXYCODONE HYDROCHLORIDE TABLETS USP   \n",
       "5744672      1200.0      804005220  OXYCODONE HYDROCHLORIDE TABLETS USP   \n",
       "5744673      2400.0      805003202  OXYCODONE HYDROCHLORIDE TABLETS USP   \n",
       "5744674       300.0     1005031206  ENDOCET - 7.5MG OXYCODONE.HCL/325MG   \n",
       "5744675      2500.0     1104003769  HYDROCODONE BIT. 10MG/ACETAMINOPHEN   \n",
       "\n",
       "                                 Ingredient_Name Measure  \\\n",
       "5744671                  OXYCODONE HYDROCHLORIDE     TAB   \n",
       "5744672                  OXYCODONE HYDROCHLORIDE     TAB   \n",
       "5744673                  OXYCODONE HYDROCHLORIDE     TAB   \n",
       "5744674                  OXYCODONE HYDROCHLORIDE     TAB   \n",
       "5744675  HYDROCODONE BITARTRATE HEMIPENTAHYDRATE     TAB   \n",
       "\n",
       "        MME_Conversion_Factor Combined_Labeler_Name  \\\n",
       "5744671                   1.5  Actavis Pharma, Inc.   \n",
       "5744672                   1.5  Actavis Pharma, Inc.   \n",
       "5744673                   1.5  Actavis Pharma, Inc.   \n",
       "5744674                   1.5    Par Pharmaceutical   \n",
       "5744675                   1.0  Actavis Pharma, Inc.   \n",
       "\n",
       "               Revised_Company_Name  Reporter_family dos_str  \n",
       "5744671              Allergan, Inc.        Anda, Inc    15.0  \n",
       "5744672              Allergan, Inc.        Anda, Inc    15.0  \n",
       "5744673              Allergan, Inc.        Anda, Inc    15.0  \n",
       "5744674  Endo Pharmaceuticals, Inc.        Anda, Inc     7.5  \n",
       "5744675              Allergan, Inc.        Anda, Inc    10.0  \n",
       "\n",
       "[5 rows x 42 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the last five rows\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selecting Data Columns <a class=\"anchor\" id=\"selecting-data-columns\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "REPORTER_NAME\n",
      "BUYER_BUS_ACT\n",
      "BUYER_NAME\n",
      "BUYER_CITY\n",
      "BUYER_COUNTY\n",
      "BUYER_STATE\n",
      "BUYER_ZIP\n",
      "DRUG_NAME\n",
      "TRANSACTION_DATE\n",
      "DOSAGE_UNIT\n",
      "Combined_Labeler_Name\n"
     ]
    }
   ],
   "source": [
    "# Select 11 columns for further investigation\n",
    "sel_cols = ['REPORTER_NAME', 'BUYER_BUS_ACT', 'BUYER_NAME', 'BUYER_CITY', 'BUYER_COUNTY', 'BUYER_STATE', \n",
    "            'BUYER_ZIP', 'DRUG_NAME', 'TRANSACTION_DATE', 'DOSAGE_UNIT', 'Combined_Labeler_Name']\n",
    "data = data[sel_cols]\n",
    "for col in data.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>REPORTER_NAME</th>\n",
       "      <th>BUYER_BUS_ACT</th>\n",
       "      <th>BUYER_NAME</th>\n",
       "      <th>BUYER_CITY</th>\n",
       "      <th>BUYER_COUNTY</th>\n",
       "      <th>BUYER_STATE</th>\n",
       "      <th>BUYER_ZIP</th>\n",
       "      <th>DRUG_NAME</th>\n",
       "      <th>TRANSACTION_DATE</th>\n",
       "      <th>DOSAGE_UNIT</th>\n",
       "      <th>Combined_Labeler_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>ADVANCED PHARMACY SERVICES</td>\n",
       "      <td>LASCASSAS</td>\n",
       "      <td>RUTHERFORD</td>\n",
       "      <td>TN</td>\n",
       "      <td>37085</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>1042006</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>ADVANCED PHARMACY SERVICES</td>\n",
       "      <td>LASCASSAS</td>\n",
       "      <td>RUTHERFORD</td>\n",
       "      <td>TN</td>\n",
       "      <td>37085</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>6262006</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>CLENS PHARMACY II</td>\n",
       "      <td>KNOXVILLE</td>\n",
       "      <td>KNOX</td>\n",
       "      <td>TN</td>\n",
       "      <td>37924</td>\n",
       "      <td>OXYCODONE</td>\n",
       "      <td>3022007</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>HOMETOWNE PHARMACY</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>COCKE</td>\n",
       "      <td>TN</td>\n",
       "      <td>37821</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>10102006</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>HOMETOWNE PHARMACY</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>COCKE</td>\n",
       "      <td>TN</td>\n",
       "      <td>37821</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>4302007</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      REPORTER_NAME    BUYER_BUS_ACT                  BUYER_NAME BUYER_CITY  \\\n",
       "0  BELLCO DRUG CORP  RETAIL PHARMACY  ADVANCED PHARMACY SERVICES  LASCASSAS   \n",
       "1  BELLCO DRUG CORP  RETAIL PHARMACY  ADVANCED PHARMACY SERVICES  LASCASSAS   \n",
       "2  BELLCO DRUG CORP  RETAIL PHARMACY           CLENS PHARMACY II  KNOXVILLE   \n",
       "3  BELLCO DRUG CORP  RETAIL PHARMACY          HOMETOWNE PHARMACY    NEWPORT   \n",
       "4  BELLCO DRUG CORP  RETAIL PHARMACY          HOMETOWNE PHARMACY    NEWPORT   \n",
       "\n",
       "  BUYER_COUNTY BUYER_STATE  BUYER_ZIP    DRUG_NAME  TRANSACTION_DATE  \\\n",
       "0   RUTHERFORD          TN      37085  HYDROCODONE           1042006   \n",
       "1   RUTHERFORD          TN      37085  HYDROCODONE           6262006   \n",
       "2         KNOX          TN      37924    OXYCODONE           3022007   \n",
       "3        COCKE          TN      37821  HYDROCODONE          10102006   \n",
       "4        COCKE          TN      37821  HYDROCODONE           4302007   \n",
       "\n",
       "   DOSAGE_UNIT Combined_Labeler_Name  \n",
       "0        300.0    Par Pharmaceutical  \n",
       "1        500.0  Actavis Pharma, Inc.  \n",
       "2        200.0    Par Pharmaceutical  \n",
       "3        400.0    Par Pharmaceutical  \n",
       "4        500.0    Par Pharmaceutical  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of rows: 5,744,676\n"
     ]
    }
   ],
   "source": [
    "print(f\"Total number of rows: {len(data.index):,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1042006\n",
       "1     6262006\n",
       "2     3022007\n",
       "3    10102006\n",
       "4     4302007\n",
       "Name: TRANSACTION_DATE, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the transaction date format. Note that it is in the MMDDYYYY format, but we need to pad zeros \n",
    "data['TRANSACTION_DATE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert TRANSACTION_DATE from String to DateTime and pad zeros\n",
    "data['TRANSACTION_DATE'] = pd.to_datetime(data['TRANSACTION_DATE'].astype(str).str.zfill(8), format='%m%d%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2006-01-04\n",
       "1   2006-06-26\n",
       "2   2007-03-02\n",
       "3   2006-10-10\n",
       "4   2007-04-30\n",
       "Name: TRANSACTION_DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the revised TRANSACTION_DATE\n",
    "data['TRANSACTION_DATE'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "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>REPORTER_NAME</th>\n",
       "      <th>BUYER_BUS_ACT</th>\n",
       "      <th>BUYER_NAME</th>\n",
       "      <th>BUYER_CITY</th>\n",
       "      <th>BUYER_COUNTY</th>\n",
       "      <th>BUYER_STATE</th>\n",
       "      <th>BUYER_ZIP</th>\n",
       "      <th>DRUG_NAME</th>\n",
       "      <th>TRANSACTION_DATE</th>\n",
       "      <th>DOSAGE_UNIT</th>\n",
       "      <th>Combined_Labeler_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>ADVANCED PHARMACY SERVICES</td>\n",
       "      <td>LASCASSAS</td>\n",
       "      <td>RUTHERFORD</td>\n",
       "      <td>TN</td>\n",
       "      <td>37085</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>2006-01-04</td>\n",
       "      <td>300.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>ADVANCED PHARMACY SERVICES</td>\n",
       "      <td>LASCASSAS</td>\n",
       "      <td>RUTHERFORD</td>\n",
       "      <td>TN</td>\n",
       "      <td>37085</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>2006-06-26</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Actavis Pharma, Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>CLENS PHARMACY II</td>\n",
       "      <td>KNOXVILLE</td>\n",
       "      <td>KNOX</td>\n",
       "      <td>TN</td>\n",
       "      <td>37924</td>\n",
       "      <td>OXYCODONE</td>\n",
       "      <td>2007-03-02</td>\n",
       "      <td>200.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>HOMETOWNE PHARMACY</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>COCKE</td>\n",
       "      <td>TN</td>\n",
       "      <td>37821</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>2006-10-10</td>\n",
       "      <td>400.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BELLCO DRUG CORP</td>\n",
       "      <td>RETAIL PHARMACY</td>\n",
       "      <td>HOMETOWNE PHARMACY</td>\n",
       "      <td>NEWPORT</td>\n",
       "      <td>COCKE</td>\n",
       "      <td>TN</td>\n",
       "      <td>37821</td>\n",
       "      <td>HYDROCODONE</td>\n",
       "      <td>2007-04-30</td>\n",
       "      <td>500.0</td>\n",
       "      <td>Par Pharmaceutical</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      REPORTER_NAME    BUYER_BUS_ACT                  BUYER_NAME BUYER_CITY  \\\n",
       "0  BELLCO DRUG CORP  RETAIL PHARMACY  ADVANCED PHARMACY SERVICES  LASCASSAS   \n",
       "1  BELLCO DRUG CORP  RETAIL PHARMACY  ADVANCED PHARMACY SERVICES  LASCASSAS   \n",
       "2  BELLCO DRUG CORP  RETAIL PHARMACY           CLENS PHARMACY II  KNOXVILLE   \n",
       "3  BELLCO DRUG CORP  RETAIL PHARMACY          HOMETOWNE PHARMACY    NEWPORT   \n",
       "4  BELLCO DRUG CORP  RETAIL PHARMACY          HOMETOWNE PHARMACY    NEWPORT   \n",
       "\n",
       "  BUYER_COUNTY BUYER_STATE  BUYER_ZIP    DRUG_NAME TRANSACTION_DATE  \\\n",
       "0   RUTHERFORD          TN      37085  HYDROCODONE       2006-01-04   \n",
       "1   RUTHERFORD          TN      37085  HYDROCODONE       2006-06-26   \n",
       "2         KNOX          TN      37924    OXYCODONE       2007-03-02   \n",
       "3        COCKE          TN      37821  HYDROCODONE       2006-10-10   \n",
       "4        COCKE          TN      37821  HYDROCODONE       2007-04-30   \n",
       "\n",
       "   DOSAGE_UNIT Combined_Labeler_Name  \n",
       "0        300.0    Par Pharmaceutical  \n",
       "1        500.0  Actavis Pharma, Inc.  \n",
       "2        200.0    Par Pharmaceutical  \n",
       "3        400.0    Par Pharmaceutical  \n",
       "4        500.0    Par Pharmaceutical  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take a look at the first five rows again with revised TRANSACTION_DATE\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Statistics <a class=\"anchor\" id=\"descriptive-statistics\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of pills distributed to Tennessee (2006-2012):  2,519,779,624\n"
     ]
    }
   ],
   "source": [
    "total_pills = int(data.DOSAGE_UNIT.sum())\n",
    "print(f\"Total number of pills distributed to Tennessee (2006-2012): {total_pills: ,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total population in Tennessee: 6,770,010\n"
     ]
    }
   ],
   "source": [
    "total_pop = pop['Pop'].sum()\n",
    "print(f\"Total population in Tennessee: {total_pop:,}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average number of pills per person: 372\n"
     ]
    }
   ],
   "source": [
    "avg_pills = int(total_pills / total_pop)\n",
    "print(f\"Average number of pills per person: {avg_pills}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ANDERSON', 'BEDFORD', 'BENTON', 'BLEDSOE', 'BLOUNT', 'BRADLEY', 'CAMPBELL', 'CANNON', 'CARROLL', 'CARTER', 'CHEATHAM', 'CHESTER', 'CLAIBORNE', 'CLAY', 'COCKE', 'COFFEE', 'CROCKETT', 'CUMBERLAND', 'DAVIDSON', 'DECATUR', 'DEKALB', 'DICKSON', 'DYER', 'FAYETTE', 'FENTRESS', 'FRANKLIN', 'GIBSON', 'GILES', 'GRAINGER', 'GREENE', 'GRUNDY', 'HAMBLEN', 'HAMILTON', 'HANCOCK', 'HARDEMAN', 'HARDIN', 'HAWKINS', 'HAYWOOD', 'HENDERSON', 'HENRY', 'HICKMAN', 'HOUSTON', 'HUMPHREYS', 'JACKSON', 'JEFFERSON', 'JOHNSON', 'KNOX', 'LAKE', 'LAUDERDALE', 'LAWRENCE', 'LEWIS', 'LINCOLN', 'LOUDON', 'MACON', 'MADISON', 'MARION', 'MARSHALL', 'MAURY', 'MCMINN', 'MCNAIRY', 'MEIGS', 'MONROE', 'MONTGOMERY', 'MOORE', 'MORGAN', 'OBION', 'OVERTON', 'PERRY', 'PICKETT', 'POLK', 'PUTNAM', 'RHEA', 'ROANE', 'ROBERTSON', 'RUTHERFORD', 'SCOTT', 'SEQUATCHIE', 'SEVIER', 'SHELBY', 'SMITH', 'STEWART', 'SULLIVAN', 'SUMNER', 'TIPTON', 'TROUSDALE', 'UNICOI', 'UNION', 'VAN BUREN', 'WARREN', 'WASHINGTON', 'WAYNE', 'WEAKLEY', 'WHITE', 'WILLIAMSON', 'WILSON']\n",
      "\n",
      "Total number of counties: 95\n"
     ]
    }
   ],
   "source": [
    "# Check TN counties\n",
    "counties = data['BUYER_COUNTY']\n",
    "print(sorted(set(counties)))\n",
    "print(f\"\\nTotal number of counties: {len(set(counties))}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of cities: 315\n"
     ]
    }
   ],
   "source": [
    "# Check TN cities\n",
    "cities = data['BUYER_CITY']\n",
    "# print(sorted(set(cities)))\n",
    "print(f\"Total number of cities: {len(set(cities))}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pill Count Summary by Year, Month, and Day of Week <a class=\"anchor\" id=\"pill-count-summary-by-date\"></a>\n",
    "Let's summarize the dataset by year to see how the number of opioids were distributed to Tennessee over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRANSACTION_DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2.674714e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>2.991512e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3.281947e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>3.665679e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4.005066e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>4.337140e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>4.241738e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   DOSAGE_UNIT\n",
       "TRANSACTION_DATE              \n",
       "2006              2.674714e+08\n",
       "2007              2.991512e+08\n",
       "2008              3.281947e+08\n",
       "2009              3.665679e+08\n",
       "2010              4.005066e+08\n",
       "2011              4.337140e+08\n",
       "2012              4.241738e+08"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pills_by_year = data[['TRANSACTION_DATE', 'DOSAGE_UNIT']].groupby(data.TRANSACTION_DATE.dt.year).sum()\n",
    "pills_by_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '300,000,000'),\n",
       " Text(0, 0, '400,000,000'),\n",
       " Text(0, 0, '500,000,000')]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by year\n",
    "ax = pills_by_year.plot.bar(title=\"Pill Count Summary by Year\", rot=0)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's summarize the dataset by month to see how the number of opioids were distributed to Tennessee over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Jan</th>\n",
       "      <td>1.915765e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feb</th>\n",
       "      <td>1.873105e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mar</th>\n",
       "      <td>2.087190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr</th>\n",
       "      <td>2.076771e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>May</th>\n",
       "      <td>2.136185e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jun</th>\n",
       "      <td>2.162458e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jul</th>\n",
       "      <td>2.154702e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aug</th>\n",
       "      <td>2.170487e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sep</th>\n",
       "      <td>2.080198e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oct</th>\n",
       "      <td>2.185815e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nov</th>\n",
       "      <td>2.163975e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dec</th>\n",
       "      <td>2.191145e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        DOSAGE_UNIT\n",
       "Month              \n",
       "Jan    1.915765e+08\n",
       "Feb    1.873105e+08\n",
       "Mar    2.087190e+08\n",
       "Apr    2.076771e+08\n",
       "May    2.136185e+08\n",
       "Jun    2.162458e+08\n",
       "Jul    2.154702e+08\n",
       "Aug    2.170487e+08\n",
       "Sep    2.080198e+08\n",
       "Oct    2.185815e+08\n",
       "Nov    2.163975e+08\n",
       "Dec    2.191145e+08"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pills_by_month = data[['TRANSACTION_DATE', 'DOSAGE_UNIT']].groupby(data.TRANSACTION_DATE.dt.month).sum()\n",
    "pills_by_month['Month'] = pills_by_month.index\n",
    "pills_by_month.index = pills_by_month['Month'].apply(lambda x: calendar.month_abbr[x])\n",
    "pills_by_month = pills_by_month[['DOSAGE_UNIT']]\n",
    "pills_by_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '50,000,000'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '150,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '250,000,000')]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by month\n",
    "ax = pills_by_month.plot.bar(title=\"Pill Count Summary by Month\", rot=0)\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's summarize the dataset by day to see how the number of opioids were distributed to Tennessee over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '40,000,000'),\n",
       " Text(0, 0, '50,000,000'),\n",
       " Text(0, 0, '60,000,000'),\n",
       " Text(0, 0, '70,000,000'),\n",
       " Text(0, 0, '80,000,000'),\n",
       " Text(0, 0, '90,000,000'),\n",
       " Text(0, 0, '100,000,000')]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pills_by_day = data[['TRANSACTION_DATE', 'DOSAGE_UNIT']].groupby(data.TRANSACTION_DATE.dt.day).sum()\n",
    "pills_by_day\n",
    "# Plot the trend by day\n",
    "ax = pills_by_day.plot(title=\"Pill Count Summary by Day\", rot=0, style='-', marker='o')\n",
    "ax.set_xlabel(\"Day\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's summarize the dataset by day of week to see how the number of opioids were distributed to Tennessee over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DayofWeek</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mon</th>\n",
       "      <td>4.754779e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tue</th>\n",
       "      <td>4.987488e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wed</th>\n",
       "      <td>4.591238e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Thu</th>\n",
       "      <td>5.431192e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fri</th>\n",
       "      <td>4.410413e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sat</th>\n",
       "      <td>2.803524e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun</th>\n",
       "      <td>7.423334e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            DOSAGE_UNIT\n",
       "DayofWeek              \n",
       "Mon        4.754779e+08\n",
       "Tue        4.987488e+08\n",
       "Wed        4.591238e+08\n",
       "Thu        5.431192e+08\n",
       "Fri        4.410413e+08\n",
       "Sat        2.803524e+07\n",
       "Sun        7.423334e+07"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pills_by_dayofweek = data[['TRANSACTION_DATE', 'DOSAGE_UNIT']].groupby(data.TRANSACTION_DATE.dt.dayofweek).sum()\n",
    "pills_by_dayofweek['DayofWeek'] = pills_by_dayofweek.index\n",
    "pills_by_dayofweek.index = pills_by_dayofweek['DayofWeek'].apply(lambda x: calendar.day_abbr[x])\n",
    "pills_by_dayofweek = pills_by_dayofweek[['DOSAGE_UNIT']]\n",
    "pills_by_dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '300,000,000'),\n",
       " Text(0, 0, '400,000,000'),\n",
       " Text(0, 0, '500,000,000'),\n",
       " Text(0, 0, '600,000,000')]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = pills_by_dayofweek.plot.bar(title=\"Pill Count Summary by Day of Week\", rot=0)\n",
    "ax.set_xlabel(\"Day of Week\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the total pills per month by year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '15,000,000'),\n",
       " Text(0, 0, '17,500,000'),\n",
       " Text(0, 0, '20,000,000'),\n",
       " Text(0, 0, '22,500,000'),\n",
       " Text(0, 0, '25,000,000'),\n",
       " Text(0, 0, '27,500,000'),\n",
       " Text(0, 0, '30,000,000'),\n",
       " Text(0, 0, '32,500,000'),\n",
       " Text(0, 0, '35,000,000'),\n",
       " Text(0, 0, '37,500,000'),\n",
       " Text(0, 0, '40,000,000')]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pills_by_year_month = data[['TRANSACTION_DATE', 'DOSAGE_UNIT']].groupby([data.TRANSACTION_DATE.dt.month, \n",
    "                                                                        data.TRANSACTION_DATE.dt.year]).sum()\n",
    "# pills_by_year_month.index\n",
    "# pills_by_month['Month'] = pills_by_month.index\n",
    "# pills_by_month.index = pills_by_month['Month'].apply(lambda x: calendar.month_abbr[x])\n",
    "# pills_by_month = pills_by_month[['DOSAGE_UNIT']]\n",
    "ax = pills_by_year_month['DOSAGE_UNIT'].unstack().plot(style='-', marker='o')\n",
    "ax.set_xlabel(\"Month\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 Distributors, Manufacturers, and Buyers<a class=\"anchor\" id=\"top-10\"></a>\n",
    "\n",
    "Let's take a look at the top 10 distributors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPORTER_NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AMERISOURCEBERGEN DRUG CORP</th>\n",
       "      <td>422382170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALGREEN CO</th>\n",
       "      <td>398849480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDINAL HEALTH</th>\n",
       "      <td>346617844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCKESSON CORPORATION</th>\n",
       "      <td>250733060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMITH DRUG COMPANY</th>\n",
       "      <td>182987750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVS TN DISTRIBUTION, LLC</th>\n",
       "      <td>141681000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAL-MART PHARM WAREHOUSE #1</th>\n",
       "      <td>107693400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WALGREEN CO.</th>\n",
       "      <td>105143560.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KROGER LIMITED PARTNERSHIP I</th>\n",
       "      <td>93372360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORRIS &amp; DICKSON CO</th>\n",
       "      <td>52357760.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DOSAGE_UNIT\n",
       "REPORTER_NAME                            \n",
       "AMERISOURCEBERGEN DRUG CORP   422382170.0\n",
       "WALGREEN CO                   398849480.0\n",
       "CARDINAL HEALTH               346617844.0\n",
       "MCKESSON CORPORATION          250733060.0\n",
       "SMITH DRUG COMPANY            182987750.0\n",
       "CVS TN DISTRIBUTION, LLC      141681000.0\n",
       "WAL-MART PHARM WAREHOUSE #1   107693400.0\n",
       "WALGREEN CO.                  105143560.0\n",
       "KROGER LIMITED PARTNERSHIP I   93372360.0\n",
       "MORRIS & DICKSON CO            52357760.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distributors = data[['REPORTER_NAME', 'DOSAGE_UNIT']].groupby('REPORTER_NAME').sum()\n",
    "distributors_top10 = distributors.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "distributors_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The table above shows that the 2nd distributor (**WALGREEN CO**) and 8th distributor (**WALGREEN CO.**) are treated as different distirbutors. We need to remove the extra period from the 8th distributor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REPORTER_NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WALGREEN CO</th>\n",
       "      <td>503993040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AMERISOURCEBERGEN DRUG CORP</th>\n",
       "      <td>422382170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CARDINAL HEALTH</th>\n",
       "      <td>346617844.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MCKESSON CORPORATION</th>\n",
       "      <td>250733060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMITH DRUG COMPANY</th>\n",
       "      <td>182987750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CVS TN DISTRIBUTION, LLC</th>\n",
       "      <td>141681000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAL-MART PHARM WAREHOUSE #1</th>\n",
       "      <td>107693400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KROGER LIMITED PARTNERSHIP I</th>\n",
       "      <td>93372360.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORRIS &amp; DICKSON CO</th>\n",
       "      <td>52357760.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MURFREESBORO PHARMACEUTICAL</th>\n",
       "      <td>47027606.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              DOSAGE_UNIT\n",
       "REPORTER_NAME                            \n",
       "WALGREEN CO                   503993040.0\n",
       "AMERISOURCEBERGEN DRUG CORP   422382170.0\n",
       "CARDINAL HEALTH               346617844.0\n",
       "MCKESSON CORPORATION          250733060.0\n",
       "SMITH DRUG COMPANY            182987750.0\n",
       "CVS TN DISTRIBUTION, LLC      141681000.0\n",
       "WAL-MART PHARM WAREHOUSE #1   107693400.0\n",
       "KROGER LIMITED PARTNERSHIP I   93372360.0\n",
       "MORRIS & DICKSON CO            52357760.0\n",
       "MURFREESBORO PHARMACEUTICAL    47027606.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['REPORTER_NAME'] = data['REPORTER_NAME'].str.replace('.', '', regex=False)\n",
    "distributors = data[['REPORTER_NAME', 'DOSAGE_UNIT']].groupby('REPORTER_NAME').sum()\n",
    "distributors_top10 = distributors.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "distributors_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '300,000,000'),\n",
       " Text(0, 0, '400,000,000'),\n",
       " Text(0, 0, '500,000,000'),\n",
       " Text(0, 0, '600,000,000')]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distributors_top10.index = distributors_top10.index.str[:15]\n",
    "ax = distributors_top10.plot.bar(title=\"Pill Count Summary by Distributor\", rot=0)\n",
    "ax.set_xlabel(\"Distributor\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the top 10 manufacturers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combined_Labeler_Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actavis Pharma, Inc.</th>\n",
       "      <td>1.113634e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SpecGx LLC</th>\n",
       "      <td>6.530802e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Par Pharmaceutical</th>\n",
       "      <td>4.796291e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Purdue Pharma LP</th>\n",
       "      <td>7.493952e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amneal Pharmaceuticals LLC</th>\n",
       "      <td>6.000654e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KVK-Tech, Inc.</th>\n",
       "      <td>1.406460e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teva Pharmaceuticals USA, Inc.</th>\n",
       "      <td>1.327000e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UCB, Inc.</th>\n",
       "      <td>9.361457e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ethex Corporation</th>\n",
       "      <td>7.713100e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sun Pharmaceutical Industries, Inc.</th>\n",
       "      <td>5.962985e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      DOSAGE_UNIT\n",
       "Combined_Labeler_Name                            \n",
       "Actavis Pharma, Inc.                 1.113634e+09\n",
       "SpecGx LLC                           6.530802e+08\n",
       "Par Pharmaceutical                   4.796291e+08\n",
       "Purdue Pharma LP                     7.493952e+07\n",
       "Amneal Pharmaceuticals LLC           6.000654e+07\n",
       "KVK-Tech, Inc.                       1.406460e+07\n",
       "Teva Pharmaceuticals USA, Inc.       1.327000e+07\n",
       "UCB, Inc.                            9.361457e+06\n",
       "Ethex Corporation                    7.713100e+06\n",
       "Sun Pharmaceutical Industries, Inc.  5.962985e+06"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "manufacturers = data[['Combined_Labeler_Name', 'DOSAGE_UNIT']].groupby('Combined_Labeler_Name').sum()\n",
    "manufacturers_top10 = manufacturers.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "manufacturers_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '400,000,000'),\n",
       " Text(0, 0, '600,000,000'),\n",
       " Text(0, 0, '800,000,000'),\n",
       " Text(0, 0, '1,000,000,000'),\n",
       " Text(0, 0, '1,200,000,000')]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "manufacturers_top10.index = manufacturers_top10.index.str[:15]\n",
    "ax = manufacturers_top10.plot.bar(title=\"Pill Count Summary by Manufacturer\", rot=0)\n",
    "ax.set_xlabel(\"Manufacturer\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the top 10 buyers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUYER_NAME</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>WALGREEN CO.</th>\n",
       "      <td>513226530.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TENNESSEE CVS PHARMACY, L.L.C.</th>\n",
       "      <td>215691864.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ECKERD CORPORATION</th>\n",
       "      <td>51414225.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>REEVES-SAIN EXTENDED CARE</th>\n",
       "      <td>45925980.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RITE AID OF TENNESSEE, INC.</th>\n",
       "      <td>39738680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRED'S PHARMACY</th>\n",
       "      <td>29194750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOOD CITY PHARMACY #674</th>\n",
       "      <td>21555400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PHARMERICA</th>\n",
       "      <td>13256670.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P &amp; S PHARMACY</th>\n",
       "      <td>12662200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>THE WELLNESS PHARMACY INC</th>\n",
       "      <td>11849020.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                DOSAGE_UNIT\n",
       "BUYER_NAME                                 \n",
       "WALGREEN CO.                    513226530.0\n",
       "TENNESSEE CVS PHARMACY, L.L.C.  215691864.0\n",
       "ECKERD CORPORATION               51414225.0\n",
       "REEVES-SAIN EXTENDED CARE        45925980.0\n",
       "RITE AID OF TENNESSEE, INC.      39738680.0\n",
       "FRED'S PHARMACY                  29194750.0\n",
       "FOOD CITY PHARMACY #674          21555400.0\n",
       "PHARMERICA                       13256670.0\n",
       "P & S PHARMACY                   12662200.0\n",
       "THE WELLNESS PHARMACY INC        11849020.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buyers = data[['BUYER_NAME', 'DOSAGE_UNIT']].groupby('BUYER_NAME').sum()\n",
    "buyers_top10 = buyers.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "buyers_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '300,000,000'),\n",
       " Text(0, 0, '400,000,000'),\n",
       " Text(0, 0, '500,000,000'),\n",
       " Text(0, 0, '600,000,000')]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "buyers_top10.index = buyers_top10.index.str[:15]\n",
    "ax = buyers_top10.plot.bar(title=\"Pill Count Summary by Buyer\", rot=0)\n",
    "ax.set_xlabel(\"Buyer\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUYER_COUNTY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNOX</th>\n",
       "      <td>2.126212e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DAVIDSON</th>\n",
       "      <td>2.108839e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SHELBY</th>\n",
       "      <td>1.574319e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAMILTON</th>\n",
       "      <td>1.419497e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUTHERFORD</th>\n",
       "      <td>1.177333e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SULLIVAN</th>\n",
       "      <td>1.080344e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAMBLEN</th>\n",
       "      <td>5.985344e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WASHINGTON</th>\n",
       "      <td>5.919680e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BRADLEY</th>\n",
       "      <td>5.408902e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUMNER</th>\n",
       "      <td>4.949380e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DOSAGE_UNIT\n",
       "BUYER_COUNTY              \n",
       "KNOX          2.126212e+08\n",
       "DAVIDSON      2.108839e+08\n",
       "SHELBY        1.574319e+08\n",
       "HAMILTON      1.419497e+08\n",
       "RUTHERFORD    1.177333e+08\n",
       "SULLIVAN      1.080344e+08\n",
       "HAMBLEN       5.985344e+07\n",
       "WASHINGTON    5.919680e+07\n",
       "BRADLEY       5.408902e+07\n",
       "SUMNER        4.949380e+07"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counties = data[['BUYER_COUNTY', 'DOSAGE_UNIT']].groupby('BUYER_COUNTY').sum()\n",
    "counties_top10 = counties.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "counties_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '25,000,000'),\n",
       " Text(0, 0, '50,000,000'),\n",
       " Text(0, 0, '75,000,000'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '125,000,000'),\n",
       " Text(0, 0, '150,000,000'),\n",
       " Text(0, 0, '175,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '225,000,000')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = counties_top10.plot.bar(title=\"Pill Count Summary by County\", rot=0)\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>DOSAGE_UNIT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BUYER_CITY</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>KNOXVILLE</th>\n",
       "      <td>1.783076e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASHVILLE</th>\n",
       "      <td>1.349968e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMPHIS</th>\n",
       "      <td>1.007746e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MURFREESBORO</th>\n",
       "      <td>8.780814e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHATTANOOGA</th>\n",
       "      <td>8.401485e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KINGSPORT</th>\n",
       "      <td>7.619618e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MORRISTOWN</th>\n",
       "      <td>5.984324e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLEVELAND</th>\n",
       "      <td>5.074759e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JOHNSON CITY</th>\n",
       "      <td>4.428983e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CLARKSVILLE</th>\n",
       "      <td>4.201488e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               DOSAGE_UNIT\n",
       "BUYER_CITY                \n",
       "KNOXVILLE     1.783076e+08\n",
       "NASHVILLE     1.349968e+08\n",
       "MEMPHIS       1.007746e+08\n",
       "MURFREESBORO  8.780814e+07\n",
       "CHATTANOOGA   8.401485e+07\n",
       "KINGSPORT     7.619618e+07\n",
       "MORRISTOWN    5.984324e+07\n",
       "CLEVELAND     5.074759e+07\n",
       "JOHNSON CITY  4.428983e+07\n",
       "CLARKSVILLE   4.201488e+07"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = data[['BUYER_CITY', 'DOSAGE_UNIT']].groupby('BUYER_CITY').sum()\n",
    "cities_top10 = cities.sort_values('DOSAGE_UNIT', ascending=False).head(10)\n",
    "cities_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '25,000,000'),\n",
       " Text(0, 0, '50,000,000'),\n",
       " Text(0, 0, '75,000,000'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '125,000,000'),\n",
       " Text(0, 0, '150,000,000'),\n",
       " Text(0, 0, '175,000,000'),\n",
       " Text(0, 0, '200,000,000')]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = cities_top10.plot.bar(title=\"Pill Count Summary by City\", rot=0)\n",
    "ax.set_xlabel(\"City\")\n",
    "ax.set_ylabel(\"Pill Count\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}