{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# U.S. Opiate Prescriptions/Overdoses\n",
"\n",
"Accidental death by fatal drug overdose is a rising trend in the United States. What can you do to help?\n",
"\n",
"This dataset contains:\n",
"- summaries of prescription records for 250 common **opioid** and ***non-opioid*** drugs written by 25,000 unique licensed medical professionals in 2014 in the United States for citizens covered under Class D Medicare\n",
"- Metadata about the doctors themselves. \n",
"- This data here is in a format with 1 row per prescriber and 25,000 unique prescribers down to 25,000 to keep it manageable. \n",
"- The main data is in `prescriber-info.csv`. \n",
"- There is also `opioids.csv` that contains the names of all opioid drugs included in the data \n",
"- There is the file `overdoses.csv` that contains information on opioid related drug overdose fatalities.\n",
"\n",
"The increase in overdose fatalities is a well-known problem, and the search for possible solutions is an ongoing effort. This dataset is can be used to detect sources of significant quantities of opiate prescriptions. \n",
"\n",
"The data consists of the following characteristics for each prescriber\n",
"\n",
"- NPI – unique National Provider Identifier number\n",
"- Gender - (M/F)\n",
"- State - U.S. State by abbreviation\n",
"- Credentials - set of initials indicative of medical degree\n",
"- Specialty - description of type of medicinal practice\n",
"- A long list of drugs with numeric values indicating the total number of prescriptions written for the year by that individual\n",
"- `Opioid.Prescriber` - a boolean label indicating whether or not that individual prescribed opiate drugs more than 10 times in the year.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import our packages and data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\" # so we can see the value of multiple statements at once.\n",
"pd.set_option('display.max_columns', None)\n",
"pd.set_option('display.max_rows', None)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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",
" </tr>\n",
" <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",
"</div>"
],
"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": 2,
"metadata": {},
"output_type": "execute_result"
},
{
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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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>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>0</td>\n",
" <td>0</td>\n",
" <td>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</td>\n",
" <td>182</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24</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>11</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",
" <td>85</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>33</td>\n",
" <td>87</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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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>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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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>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>30</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>32</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",
" <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",
" <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",
" <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",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <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>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>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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</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>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>0</td>\n",
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" <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>14</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",
" <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",
" <td>12</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>47</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>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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
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" <td>48</td>\n",
" <td>81</td>\n",
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" <td>0</td>\n",
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" <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>110</td>\n",
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" <td>12</td>\n",
" <td>12</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>29</td>\n",
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" <td>0</td>\n",
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" <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>0</td>\n",
" <td>25</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <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>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\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",
" 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",
" ALENDRONATE.SODIUM ALLOPURINOL ALPRAZOLAM AMIODARONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 134 0 \n",
"2 0 0 0 0 \n",
"3 21 0 0 0 \n",
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"\n",
" AMITRIPTYLINE.HCL AMLODIPINE.BESYLATE AMLODIPINE.BESYLATE.BENAZEPRIL \\\n",
"0 0 0 0 \n",
"1 0 15 0 \n",
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"3 0 58 0 \n",
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"\n",
" AMOXICILLIN AMOX.TR.POTASSIUM.CLAVULANATE AMPHETAMINE.SALT.COMBO \\\n",
"0 0 0 0 \n",
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"2 0 0 0 \n",
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"\n",
" ATENOLOL ATORVASTATIN.CALCIUM AVODART AZITHROMYCIN BACLOFEN \\\n",
"0 0 0 0 0 0 \n",
"1 15 0 0 0 0 \n",
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"\n",
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"0 0 0 0 0 \n",
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"\n",
" BENZTROPINE.MESYLATE BISOPROLOL.HYDROCHLOROTHIAZIDE BRIMONIDINE.TARTRATE \\\n",
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"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" BUMETANIDE BUPROPION.HCL.SR BUPROPION.XL BUSPIRONE.HCL BYSTOLIC \\\n",
"0 0 0 0 0 0 \n",
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" CARBAMAZEPINE CARBIDOPA.LEVODOPA CARISOPRODOL CARTIA.XT CARVEDILOL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 45 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 29 \n",
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"\n",
" CEFUROXIME CELEBREX CEPHALEXIN CHLORHEXIDINE.GLUCONATE CHLORTHALIDONE \\\n",
"0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
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"\n",
" CILOSTAZOL CIPROFLOXACIN.HCL CITALOPRAM.HBR CLINDAMYCIN.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 21 0 0 \n",
"2 0 0 0 0 \n",
"3 0 15 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOBETASOL.PROPIONATE CLONAZEPAM CLONIDINE.HCL CLOPIDOGREL \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 24 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOTRIMAZOLE.BETAMETHASONE COLCRYS COMBIVENT.RESPIMAT CRESTOR \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 21 \n",
"4 0 0 0 0 \n",
"\n",
" CYCLOBENZAPRINE.HCL DEXILANT DIAZEPAM DICLOFENAC.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 22 0 20 0 \n",
"2 0 0 0 0 \n",
"3 12 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DICYCLOMINE.HCL DIGOX DIGOXIN DILTIAZEM.24HR.CD DILTIAZEM.24HR.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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" DILTIAZEM.ER DILTIAZEM.HCL DIOVAN DIPHENOXYLATE.ATROPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DIVALPROEX.SODIUM DIVALPROEX.SODIUM.ER DONEPEZIL.HCL \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" DORZOLAMIDE.TIMOLOL DOXAZOSIN.MESYLATE DOXEPIN.HCL DOXYCYCLINE.HYCLATE \\\n",
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"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DULOXETINE.HCL ENALAPRIL.MALEATE ESCITALOPRAM.OXALATE ESTRADIOL EXELON \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 22 14 0 \n",
"2 12 0 30 0 0 \n",
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"\n",
" FAMOTIDINE FELODIPINE.ER FENOFIBRATE FENTANYL FINASTERIDE FLOVENT.HFA \\\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 13 0 \n",
"4 0 0 0 22 0 0 \n",
"\n",
" FLUCONAZOLE FLUOXETINE.HCL FLUTICASONE.PROPIONATE FUROSEMIDE \\\n",
"0 0 0 0 0 \n",
"1 0 31 0 0 \n",
"2 0 32 0 0 \n",
"3 0 0 42 48 \n",
"4 0 0 0 0 \n",
"\n",
" GABAPENTIN GEMFIBROZIL GLIMEPIRIDE GLIPIZIDE GLIPIZIDE.ER \\\n",
"0 0 0 0 0 0 \n",
"1 63 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 81 0 0 0 31 \n",
"4 0 0 0 0 0 \n",
"\n",
" GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG HYDRALAZINE.HCL \\\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 0 15 \n",
"4 0 0 0 0 0 \n",
"\n",
" HYDROCHLOROTHIAZIDE HYDROCODONE.ACETAMINOPHEN HYDROCORTISONE \\\n",
"0 0 17 0 \n",
"1 36 182 0 \n",
"2 0 0 0 \n",
"3 34 0 0 \n",
"4 0 22 0 \n",
"\n",
" HYDROMORPHONE.HCL HYDROXYZINE.HCL IBANDRONATE.SODIUM IBUPROFEN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 15 \n",
"4 0 0 0 0 \n",
"\n",
" INSULIN.SYRINGE IPRATROPIUM.BROMIDE IRBESARTAN \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" ISOSORBIDE.MONONITRATE.ER JANTOVEN JANUMET JANUVIA KETOCONAZOLE \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL LACTULOSE \\\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 0 13 \n",
"4 0 0 0 0 0 \n",
"\n",
" LAMOTRIGINE LANSOPRAZOLE LANTUS LANTUS.SOLOSTAR LATANOPROST LEVEMIR \\\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 15 0 0 47 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" LEVEMIR.FLEXPEN LEVETIRACETAM LEVOFLOXACIN LEVOTHYROXINE.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 31 \n",
"2 0 0 0 0 \n",
"3 0 24 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LIDOCAINE LISINOPRIL LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE \\\n",
"0 0 0 0 0 \n",
"1 0 52 45 0 \n",
"2 0 0 0 0 \n",
"3 19 110 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LORAZEPAM LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN \\\n",
"0 12 0 0 0 \n",
"1 27 0 0 11 \n",
"2 19 0 0 0 \n",
"3 0 0 46 0 \n",
"4 0 0 0 0 \n",
"\n",
" LOVAZA LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 13 0 0 39 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 57 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" METFORMIN.HCL.ER METHADONE.HCL METHOCARBAMOL METHOTREXATE \\\n",
"0 0 0 0 0 \n",
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"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
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"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" METOPROLOL.TARTRATE METRONIDAZOLE MIRTAZAPINE MONTELUKAST.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 18 0 0 0 \n",
"2 0 0 0 0 \n",
"3 55 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" MORPHINE.SULFATE MORPHINE.SULFATE.ER MUPIROCIN NABUMETONE NAMENDA \\\n",
"0 0 0 0 0 0 \n",
"1 0 24 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 22 146 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" NAMENDA.XR NAPROXEN NASONEX NEXIUM NIACIN.ER NIFEDICAL.XL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 11 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" NIFEDIPINE.ER NITROFURANTOIN.MONO.MACRO NITROSTAT NORTRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" NOVOLOG NOVOLOG.FLEXPEN NYSTATIN OLANZAPINE OMEPRAZOLE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 85 \n",
"2 0 0 0 0 0 \n",
"3 16 12 12 0 52 \n",
"4 0 0 0 0 0 \n",
"\n",
" ONDANSETRON.HCL ONDANSETRON.ODT ONGLYZA OXCARBAZEPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 23 0 0 0 \n",
"\n",
" OXYBUTYNIN.CHLORIDE OXYBUTYNIN.CHLORIDE.ER OXYCODONE.ACETAMINOPHEN \\\n",
"0 0 0 0 \n",
"1 0 0 33 \n",
"2 0 0 0 \n",
"3 15 0 229 \n",
"4 0 0 0 \n",
"\n",
" OXYCODONE.HCL OXYCONTIN PANTOPRAZOLE.SODIUM PAROXETINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 87 0 0 0 \n",
"2 0 0 0 13 \n",
"3 29 0 0 0 \n",
"4 22 0 0 0 \n",
"\n",
" PHENOBARBITAL PHENYTOIN.SODIUM.EXTENDED PIOGLITAZONE.HCL \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" POLYETHYLENE.GLYCOL.3350 POTASSIUM.CHLORIDE PRADAXA \\\n",
"0 0 0 0 \n",
"1 0 0 13 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" PRAMIPEXOLE.DIHYDROCHLORIDE PRAVASTATIN.SODIUM PREDNISONE PREMARIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 13 0 \n",
"4 0 0 0 0 \n",
"\n",
" PRIMIDONE PROAIR.HFA PROMETHAZINE.HCL PROPRANOLOL.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 23 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 11 0 \n",
"4 0 0 0 0 \n",
"\n",
" PROPRANOLOL.HCL.ER QUETIAPINE.FUMARATE QUINAPRIL.HCL RALOXIFENE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" RAMIPRIL RANEXA RANITIDINE.HCL RESTASIS RISPERIDONE ROPINIROLE.HCL \\\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 25 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SEROQUEL.XR SERTRALINE.HCL SIMVASTATIN SOTALOL SPIRIVA SPIRONOLACTONE \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 29 51 0 0 0 \n",
"2 0 14 0 0 0 0 \n",
"3 0 15 80 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SUCRALFATE SULFAMETHOXAZOLE.TRIMETHOPRIM SUMATRIPTAN.SUCCINATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 14 0 \n",
"\n",
" SYMBICORT SYNTHROID TAMSULOSIN.HCL TEMAZEPAM TERAZOSIN.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 52 18 0 0 \n",
"4 0 0 0 12 0 \n",
"\n",
" TIMOLOL.MALEATE TIZANIDINE.HCL TOLTERODINE.TARTRATE.ER TOPIRAMATE \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" TOPROL.XL TORSEMIDE TRAMADOL.HCL TRAVATAN.Z TRAZODONE.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 36 0 31 \n",
"2 0 0 0 0 12 \n",
"3 0 0 90 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" TRIAMCINOLONE.ACETONIDE TRIAMTERENE.HYDROCHLOROTHIAZID VALACYCLOVIR \\\n",
"0 0 0 0 \n",
"1 0 12 0 \n",
"2 0 0 0 \n",
"3 16 0 0 \n",
"4 0 0 0 \n",
"\n",
" VALSARTAN VALSARTAN.HYDROCHLOROTHIAZIDE VENLAFAXINE.HCL \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VENLAFAXINE.HCL.ER VENTOLIN.HFA VERAPAMIL.ER VESICARE VOLTAREN \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 47 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" VYTORIN WARFARIN.SODIUM XARELTO ZETIA ZIPRASIDONE.HCL \\\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 0 0 \n",
"4 0 17 28 0 0 \n",
"\n",
" ZOLPIDEM.TARTRATE Opioid.Prescriber \n",
"0 0 1 \n",
"1 35 1 \n",
"2 25 0 \n",
"3 0 1 \n",
"4 0 1 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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" <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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <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",
"</div>"
],
"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": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"opioids = pd.read_csv('opioids.csv')\n",
"pinfo = pd.read_csv('prescriber-info.csv')\n",
"overdoses = pd.read_csv('overdoses.csv')\n",
"opioids.head()\n",
"pinfo.head()\n",
"overdoses.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'7231241,2113564,5218996231892,6341,2061572121,7051,1722643321,0777772161,0701,2891,7625173361,0671251255453341,2535472,3001,358432,7447775222,732247701631,2692,60160383980979627853109'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdoses['Deaths'].sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exploration and Initial Preprocessing"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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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",
" <th>Deaths/Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>4833722</td>\n",
" <td>723</td>\n",
" <td>AL</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alaska</td>\n",
" <td>735132</td>\n",
" <td>124</td>\n",
" <td>AK</td>\n",
" <td>0.000169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arizona</td>\n",
" <td>6626624</td>\n",
" <td>1211</td>\n",
" <td>AZ</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Arkansas</td>\n",
" <td>2959373</td>\n",
" <td>356</td>\n",
" <td>AR</td>\n",
" <td>0.000120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>California</td>\n",
" <td>38332521</td>\n",
" <td>4521</td>\n",
" <td>CA</td>\n",
" <td>0.000118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Population Deaths Abbrev Deaths/Population\n",
"0 Alabama 4833722 723 AL 0.000150\n",
"1 Alaska 735132 124 AK 0.000169\n",
"2 Arizona 6626624 1211 AZ 0.000183\n",
"3 Arkansas 2959373 356 AR 0.000120\n",
"4 California 38332521 4521 CA 0.000118"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdoses['Deaths'] = overdoses['Deaths'].str.replace(',', '')\n",
"overdoses['Deaths'] = overdoses['Deaths'].astype(int)\n",
"overdoses['Population'] = overdoses['Population'].str.replace(',', '')\n",
"overdoses['Population'] = overdoses['Population'].astype(int)\n",
"overdoses['Deaths/Population'] = (overdoses['Deaths']/overdoses['Population'])\n",
"overdoses.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Population</th>\n",
" <th>Deaths</th>\n",
" <th>Abbrev</th>\n",
" <th>Deaths/Population</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alabama</th>\n",
" <td>4833722</td>\n",
" <td>723</td>\n",
" <td>AL</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alaska</th>\n",
" <td>735132</td>\n",
" <td>124</td>\n",
" <td>AK</td>\n",
" <td>0.000169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arizona</th>\n",
" <td>6626624</td>\n",
" <td>1211</td>\n",
" <td>AZ</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>2959373</td>\n",
" <td>356</td>\n",
" <td>AR</td>\n",
" <td>0.000120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>California</th>\n",
" <td>38332521</td>\n",
" <td>4521</td>\n",
" <td>CA</td>\n",
" <td>0.000118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Population Deaths Abbrev Deaths/Population\n",
"State \n",
"Alabama 4833722 723 AL 0.000150\n",
"Alaska 735132 124 AK 0.000169\n",
"Arizona 6626624 1211 AZ 0.000183\n",
"Arkansas 2959373 356 AR 0.000120\n",
"California 38332521 4521 CA 0.000118"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdoses_plot = overdoses.copy()\n",
"overdoses_plot.set_index('State', inplace=True)\n",
"overdoses_plot.head()"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"pixiedust": {
"displayParams": {
"aggregation": "SUM",
"handlerId": "barChart",
"keyFields": "State",
"orientation": "horizontal",
"rowCount": "500",
"valueFields": "Deaths/Population"
}
}
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc15748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb78828>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"fig = plt.figure()\n",
"ax = overdoses_plot[['Deaths/Population']].plot(kind='bar', title =\"Death per state\", figsize=(15, 10), fontsize=12)\n",
"ax.set_xlabel(\"State\", fontsize=12)\n",
"ax.set_ylabel(\"Deaths/Population\", fontsize=12)\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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>Deaths/Population</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>0.000338</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Mexico</th>\n",
" <td>0.000262</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Hampshire</th>\n",
" <td>0.000252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kentucky</th>\n",
" <td>0.000245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ohio</th>\n",
" <td>0.000237</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Deaths/Population\n",
"State \n",
"West Virginia 0.000338\n",
"New Mexico 0.000262\n",
"New Hampshire 0.000252\n",
"Kentucky 0.000245\n",
"Ohio 0.000237"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdoses_top_5 = overdoses_plot[['Deaths/Population']].sort_values('Deaths/Population',ascending=False).iloc[0:5]\n",
"overdoses_top_5"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bb17518>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"import matplotlib.pyplot as plt\n",
"ax = overdoses_top_5[['Deaths/Population']].plot(kind='bar', title =\"Death/Population per state\", figsize=(10, 5), fontsize=12)\n",
"ax.set_xlabel(\"State\", fontsize=12)\n",
"ax.set_ylabel(\"Deaths/Population\", fontsize=12)\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Opioid.Prescriber\n",
"0 0.41248\n",
"1 0.58752\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pinfo.groupby('Opioid.Prescriber').size() / pinfo.groupby('Opioid.Prescriber').size().sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11bcedf28>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11158d748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"pd.value_counts(pinfo['Opioid.Prescriber']).plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating our Classifiers to Predict Opioid Prescribers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/marcotavora/Applications/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"from sklearn.cross_validation import train_test_split"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"overdoses.to_csv('overdosesnew.csv')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\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>State</th>\n",
" <th>Population</th>\n",
" <th>Deaths</th>\n",
" <th>Abbrev</th>\n",
" <th>Deaths/Population</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Alabama</td>\n",
" <td>4833722</td>\n",
" <td>723</td>\n",
" <td>AL</td>\n",
" <td>0.000150</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Alaska</td>\n",
" <td>735132</td>\n",
" <td>124</td>\n",
" <td>AK</td>\n",
" <td>0.000169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Arizona</td>\n",
" <td>6626624</td>\n",
" <td>1211</td>\n",
" <td>AZ</td>\n",
" <td>0.000183</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Arkansas</td>\n",
" <td>2959373</td>\n",
" <td>356</td>\n",
" <td>AR</td>\n",
" <td>0.000120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>California</td>\n",
" <td>38332521</td>\n",
" <td>4521</td>\n",
" <td>CA</td>\n",
" <td>0.000118</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Population Deaths Abbrev Deaths/Population\n",
"0 Alabama 4833722 723 AL 0.000150\n",
"1 Alaska 735132 124 AK 0.000169\n",
"2 Arizona 6626624 1211 AZ 0.000183\n",
"3 Arkansas 2959373 356 AR 0.000120\n",
"4 California 38332521 4521 CA 0.000118"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"overdoses = pd.read_csv('overdosesnew.csv',index_col=0)\n",
"overdoses.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"opioids = opioids \n",
"name = opioids['Drug Name']\n",
"import re\n",
"new_name = name.apply(lambda x:re.sub(\"\\ |-\",\".\",str(x)))\n",
"columns = pinfo.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": 15,
"metadata": {},
"outputs": [],
"source": [
"df = pinfo[kept_variable]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>NPI</th>\n",
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" <th>State</th>\n",
" <th>Credentials</th>\n",
" <th>Specialty</th>\n",
" <th>ABILIFY</th>\n",
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" <th>AGGRENOX</th>\n",
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" <th>ALLOPURINOL</th>\n",
" <th>ALPRAZOLAM</th>\n",
" <th>AMIODARONE.HCL</th>\n",
" <th>AMITRIPTYLINE.HCL</th>\n",
" <th>AMLODIPINE.BESYLATE</th>\n",
" <th>AMLODIPINE.BESYLATE.BENAZEPRIL</th>\n",
" <th>AMOXICILLIN</th>\n",
" <th>AMOX.TR.POTASSIUM.CLAVULANATE</th>\n",
" <th>AMPHETAMINE.SALT.COMBO</th>\n",
" <th>ATENOLOL</th>\n",
" <th>ATORVASTATIN.CALCIUM</th>\n",
" <th>AVODART</th>\n",
" <th>AZITHROMYCIN</th>\n",
" <th>BACLOFEN</th>\n",
" <th>BD.ULTRA.FINE.PEN.NEEDLE</th>\n",
" <th>BENAZEPRIL.HCL</th>\n",
" <th>BENICAR</th>\n",
" <th>BENICAR.HCT</th>\n",
" <th>BENZTROPINE.MESYLATE</th>\n",
" <th>BISOPROLOL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>BRIMONIDINE.TARTRATE</th>\n",
" <th>BUMETANIDE</th>\n",
" <th>BUPROPION.HCL.SR</th>\n",
" <th>BUPROPION.XL</th>\n",
" <th>BUSPIRONE.HCL</th>\n",
" <th>BYSTOLIC</th>\n",
" <th>CARBAMAZEPINE</th>\n",
" <th>CARBIDOPA.LEVODOPA</th>\n",
" <th>CARISOPRODOL</th>\n",
" <th>CARTIA.XT</th>\n",
" <th>CARVEDILOL</th>\n",
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" <th>CHLORTHALIDONE</th>\n",
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" <th>CITALOPRAM.HBR</th>\n",
" <th>CLINDAMYCIN.HCL</th>\n",
" <th>CLOBETASOL.PROPIONATE</th>\n",
" <th>CLONAZEPAM</th>\n",
" <th>CLONIDINE.HCL</th>\n",
" <th>CLOPIDOGREL</th>\n",
" <th>CLOTRIMAZOLE.BETAMETHASONE</th>\n",
" <th>COLCRYS</th>\n",
" <th>COMBIVENT.RESPIMAT</th>\n",
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" <th>DEXILANT</th>\n",
" <th>DIAZEPAM</th>\n",
" <th>DICLOFENAC.SODIUM</th>\n",
" <th>DICYCLOMINE.HCL</th>\n",
" <th>DIGOX</th>\n",
" <th>DIGOXIN</th>\n",
" <th>DILTIAZEM.24HR.CD</th>\n",
" <th>DILTIAZEM.24HR.ER</th>\n",
" <th>DILTIAZEM.ER</th>\n",
" <th>DILTIAZEM.HCL</th>\n",
" <th>DIOVAN</th>\n",
" <th>DIPHENOXYLATE.ATROPINE</th>\n",
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" <th>DIVALPROEX.SODIUM.ER</th>\n",
" <th>DONEPEZIL.HCL</th>\n",
" <th>DORZOLAMIDE.TIMOLOL</th>\n",
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" <th>DOXEPIN.HCL</th>\n",
" <th>DOXYCYCLINE.HYCLATE</th>\n",
" <th>DULOXETINE.HCL</th>\n",
" <th>ENALAPRIL.MALEATE</th>\n",
" <th>ESCITALOPRAM.OXALATE</th>\n",
" <th>ESTRADIOL</th>\n",
" <th>EXELON</th>\n",
" <th>FAMOTIDINE</th>\n",
" <th>FELODIPINE.ER</th>\n",
" <th>FENOFIBRATE</th>\n",
" <th>FINASTERIDE</th>\n",
" <th>FLOVENT.HFA</th>\n",
" <th>FLUCONAZOLE</th>\n",
" <th>FLUOXETINE.HCL</th>\n",
" <th>FLUTICASONE.PROPIONATE</th>\n",
" <th>FUROSEMIDE</th>\n",
" <th>GABAPENTIN</th>\n",
" <th>GEMFIBROZIL</th>\n",
" <th>GLIMEPIRIDE</th>\n",
" <th>GLIPIZIDE</th>\n",
" <th>GLIPIZIDE.ER</th>\n",
" <th>GLIPIZIDE.XL</th>\n",
" <th>GLYBURIDE</th>\n",
" <th>HALOPERIDOL</th>\n",
" <th>HUMALOG</th>\n",
" <th>HYDRALAZINE.HCL</th>\n",
" <th>HYDROCHLOROTHIAZIDE</th>\n",
" <th>HYDROCORTISONE</th>\n",
" <th>HYDROXYZINE.HCL</th>\n",
" <th>IBANDRONATE.SODIUM</th>\n",
" <th>IBUPROFEN</th>\n",
" <th>INSULIN.SYRINGE</th>\n",
" <th>IPRATROPIUM.BROMIDE</th>\n",
" <th>IRBESARTAN</th>\n",
" <th>ISOSORBIDE.MONONITRATE.ER</th>\n",
" <th>JANTOVEN</th>\n",
" <th>JANUMET</th>\n",
" <th>JANUVIA</th>\n",
" <th>KETOCONAZOLE</th>\n",
" <th>KLOR.CON.10</th>\n",
" <th>KLOR.CON.M10</th>\n",
" <th>KLOR.CON.M20</th>\n",
" <th>LABETALOL.HCL</th>\n",
" <th>LACTULOSE</th>\n",
" <th>LAMOTRIGINE</th>\n",
" <th>LANSOPRAZOLE</th>\n",
" <th>LANTUS</th>\n",
" <th>LANTUS.SOLOSTAR</th>\n",
" <th>LATANOPROST</th>\n",
" <th>LEVEMIR</th>\n",
" <th>LEVEMIR.FLEXPEN</th>\n",
" <th>LEVETIRACETAM</th>\n",
" <th>LEVOFLOXACIN</th>\n",
" <th>LEVOTHYROXINE.SODIUM</th>\n",
" <th>LIDOCAINE</th>\n",
" <th>LISINOPRIL</th>\n",
" <th>LISINOPRIL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LITHIUM.CARBONATE</th>\n",
" <th>LORAZEPAM</th>\n",
" <th>LOSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LOSARTAN.POTASSIUM</th>\n",
" <th>LOVASTATIN</th>\n",
" <th>LOVAZA</th>\n",
" <th>LUMIGAN</th>\n",
" <th>LYRICA</th>\n",
" <th>MECLIZINE.HCL</th>\n",
" <th>MELOXICAM</th>\n",
" <th>METFORMIN.HCL</th>\n",
" <th>METFORMIN.HCL.ER</th>\n",
" <th>METHOCARBAMOL</th>\n",
" <th>METHOTREXATE</th>\n",
" <th>METHYLPREDNISOLONE</th>\n",
" <th>METOCLOPRAMIDE.HCL</th>\n",
" <th>METOLAZONE</th>\n",
" <th>METOPROLOL.SUCCINATE</th>\n",
" <th>METOPROLOL.TARTRATE</th>\n",
" <th>METRONIDAZOLE</th>\n",
" <th>MIRTAZAPINE</th>\n",
" <th>MONTELUKAST.SODIUM</th>\n",
" <th>MUPIROCIN</th>\n",
" <th>NABUMETONE</th>\n",
" <th>NAMENDA</th>\n",
" <th>NAMENDA.XR</th>\n",
" <th>NAPROXEN</th>\n",
" <th>NASONEX</th>\n",
" <th>NEXIUM</th>\n",
" <th>NIACIN.ER</th>\n",
" <th>NIFEDICAL.XL</th>\n",
" <th>NIFEDIPINE.ER</th>\n",
" <th>NITROFURANTOIN.MONO.MACRO</th>\n",
" <th>NITROSTAT</th>\n",
" <th>NORTRIPTYLINE.HCL</th>\n",
" <th>NOVOLOG</th>\n",
" <th>NOVOLOG.FLEXPEN</th>\n",
" <th>NYSTATIN</th>\n",
" <th>OLANZAPINE</th>\n",
" <th>OMEPRAZOLE</th>\n",
" <th>ONDANSETRON.HCL</th>\n",
" <th>ONDANSETRON.ODT</th>\n",
" <th>ONGLYZA</th>\n",
" <th>OXCARBAZEPINE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE.ER</th>\n",
" <th>PANTOPRAZOLE.SODIUM</th>\n",
" <th>PAROXETINE.HCL</th>\n",
" <th>PHENOBARBITAL</th>\n",
" <th>PHENYTOIN.SODIUM.EXTENDED</th>\n",
" <th>PIOGLITAZONE.HCL</th>\n",
" <th>POLYETHYLENE.GLYCOL.3350</th>\n",
" <th>POTASSIUM.CHLORIDE</th>\n",
" <th>PRADAXA</th>\n",
" <th>PRAMIPEXOLE.DIHYDROCHLORIDE</th>\n",
" <th>PRAVASTATIN.SODIUM</th>\n",
" <th>PREDNISONE</th>\n",
" <th>PREMARIN</th>\n",
" <th>PRIMIDONE</th>\n",
" <th>PROAIR.HFA</th>\n",
" <th>PROMETHAZINE.HCL</th>\n",
" <th>PROPRANOLOL.HCL</th>\n",
" <th>PROPRANOLOL.HCL.ER</th>\n",
" <th>QUETIAPINE.FUMARATE</th>\n",
" <th>QUINAPRIL.HCL</th>\n",
" <th>RALOXIFENE.HCL</th>\n",
" <th>RAMIPRIL</th>\n",
" <th>RANEXA</th>\n",
" <th>RANITIDINE.HCL</th>\n",
" <th>RESTASIS</th>\n",
" <th>RISPERIDONE</th>\n",
" <th>ROPINIROLE.HCL</th>\n",
" <th>SEROQUEL.XR</th>\n",
" <th>SERTRALINE.HCL</th>\n",
" <th>SIMVASTATIN</th>\n",
" <th>SOTALOL</th>\n",
" <th>SPIRIVA</th>\n",
" <th>SPIRONOLACTONE</th>\n",
" <th>SUCRALFATE</th>\n",
" <th>SULFAMETHOXAZOLE.TRIMETHOPRIM</th>\n",
" <th>SUMATRIPTAN.SUCCINATE</th>\n",
" <th>SYMBICORT</th>\n",
" <th>SYNTHROID</th>\n",
" <th>TAMSULOSIN.HCL</th>\n",
" <th>TEMAZEPAM</th>\n",
" <th>TERAZOSIN.HCL</th>\n",
" <th>TIMOLOL.MALEATE</th>\n",
" <th>TIZANIDINE.HCL</th>\n",
" <th>TOLTERODINE.TARTRATE.ER</th>\n",
" <th>TOPIRAMATE</th>\n",
" <th>TOPROL.XL</th>\n",
" <th>TORSEMIDE</th>\n",
" <th>TRAVATAN.Z</th>\n",
" <th>TRAZODONE.HCL</th>\n",
" <th>TRIAMCINOLONE.ACETONIDE</th>\n",
" <th>TRIAMTERENE.HYDROCHLOROTHIAZID</th>\n",
" <th>VALACYCLOVIR</th>\n",
" <th>VALSARTAN</th>\n",
" <th>VALSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>VENLAFAXINE.HCL</th>\n",
" <th>VENLAFAXINE.HCL.ER</th>\n",
" <th>VENTOLIN.HFA</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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>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>0</td>\n",
" <td>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</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>11</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",
" <td>85</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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>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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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",
" <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>12</td>\n",
" <td>0</td>\n",
" <td>30</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>32</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",
" <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",
" <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",
" <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",
" <td>0</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",
" <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",
" <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",
" <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",
" <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>13</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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</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",
" <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>12</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>47</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>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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
" <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",
" <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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>48</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>46</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>57</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>55</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",
" <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>16</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
" <td>16</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",
" <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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
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" <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>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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
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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",
" ACYCLOVIR ADVAIR.DISKUS AGGRENOX ALENDRONATE.SODIUM ALLOPURINOL \\\n",
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" AMLODIPINE.BESYLATE.BENAZEPRIL AMOXICILLIN AMOX.TR.POTASSIUM.CLAVULANATE \\\n",
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" AMPHETAMINE.SALT.COMBO ATENOLOL ATORVASTATIN.CALCIUM AVODART \\\n",
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"0 0 0 0 \n",
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"\n",
" BRIMONIDINE.TARTRATE BUMETANIDE BUPROPION.HCL.SR BUPROPION.XL \\\n",
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"2 0 0 19 42 \n",
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"\n",
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"\n",
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"0 0 0 0 0 \n",
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"\n",
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"\n",
" COMBIVENT.RESPIMAT CRESTOR CYCLOBENZAPRINE.HCL DEXILANT DIAZEPAM \\\n",
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"\n",
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"0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
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"\n",
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"0 0 0 0 \n",
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"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" DONEPEZIL.HCL DORZOLAMIDE.TIMOLOL DOXAZOSIN.MESYLATE DOXEPIN.HCL \\\n",
"0 0 0 0 0 \n",
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"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DOXYCYCLINE.HYCLATE DULOXETINE.HCL ENALAPRIL.MALEATE \\\n",
"0 0 0 0 \n",
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"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" ESCITALOPRAM.OXALATE ESTRADIOL EXELON FAMOTIDINE FELODIPINE.ER \\\n",
"0 0 0 0 0 0 \n",
"1 22 14 0 0 0 \n",
"2 30 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FENOFIBRATE FINASTERIDE FLOVENT.HFA FLUCONAZOLE FLUOXETINE.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 31 \n",
"2 0 0 0 0 32 \n",
"3 0 13 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FLUTICASONE.PROPIONATE FUROSEMIDE GABAPENTIN GEMFIBROZIL GLIMEPIRIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 63 0 0 \n",
"2 0 0 0 0 0 \n",
"3 42 48 81 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" GLIPIZIDE GLIPIZIDE.ER GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG \\\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 31 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" HYDRALAZINE.HCL HYDROCHLOROTHIAZIDE HYDROCORTISONE HYDROXYZINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 36 0 0 \n",
"2 0 0 0 0 \n",
"3 15 34 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IBANDRONATE.SODIUM IBUPROFEN INSULIN.SYRINGE IPRATROPIUM.BROMIDE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 15 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IRBESARTAN ISOSORBIDE.MONONITRATE.ER JANTOVEN JANUMET JANUVIA \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" KETOCONAZOLE KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" LACTULOSE LAMOTRIGINE LANSOPRAZOLE LANTUS LANTUS.SOLOSTAR LATANOPROST \\\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 13 0 0 15 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" LEVEMIR LEVEMIR.FLEXPEN LEVETIRACETAM LEVOFLOXACIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 47 0 24 0 \n",
"4 0 0 0 0 \n",
"\n",
" LEVOTHYROXINE.SODIUM LIDOCAINE LISINOPRIL \\\n",
"0 0 0 0 \n",
"1 31 0 52 \n",
"2 0 0 0 \n",
"3 0 19 110 \n",
"4 0 0 0 \n",
"\n",
" LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE LORAZEPAM \\\n",
"0 0 0 12 \n",
"1 45 0 27 \n",
"2 0 0 19 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN LOVAZA \\\n",
"0 0 0 0 0 \n",
"1 0 0 11 0 \n",
"2 0 0 0 0 \n",
"3 0 46 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL METFORMIN.HCL.ER \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 13 0 0 39 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 57 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" METHOCARBAMOL METHOTREXATE METHYLPREDNISOLONE METOCLOPRAMIDE.HCL \\\n",
"0 0 0 0 0 \n",
"1 15 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" METOLAZONE METOPROLOL.SUCCINATE METOPROLOL.TARTRATE METRONIDAZOLE \\\n",
"0 0 0 0 0 \n",
"1 0 0 18 0 \n",
"2 0 0 0 0 \n",
"3 0 0 55 0 \n",
"4 0 0 0 0 \n",
"\n",
" MIRTAZAPINE MONTELUKAST.SODIUM MUPIROCIN NABUMETONE NAMENDA \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" NAMENDA.XR NAPROXEN NASONEX NEXIUM NIACIN.ER NIFEDICAL.XL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 11 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" NIFEDIPINE.ER NITROFURANTOIN.MONO.MACRO NITROSTAT NORTRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" NOVOLOG NOVOLOG.FLEXPEN NYSTATIN OLANZAPINE OMEPRAZOLE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 85 \n",
"2 0 0 0 0 0 \n",
"3 16 12 12 0 52 \n",
"4 0 0 0 0 0 \n",
"\n",
" ONDANSETRON.HCL ONDANSETRON.ODT ONGLYZA OXCARBAZEPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 23 0 0 0 \n",
"\n",
" OXYBUTYNIN.CHLORIDE OXYBUTYNIN.CHLORIDE.ER PANTOPRAZOLE.SODIUM \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 15 0 0 \n",
"4 0 0 0 \n",
"\n",
" PAROXETINE.HCL PHENOBARBITAL PHENYTOIN.SODIUM.EXTENDED PIOGLITAZONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 13 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" POLYETHYLENE.GLYCOL.3350 POTASSIUM.CHLORIDE PRADAXA \\\n",
"0 0 0 0 \n",
"1 0 0 13 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" PRAMIPEXOLE.DIHYDROCHLORIDE PRAVASTATIN.SODIUM PREDNISONE PREMARIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 13 0 \n",
"4 0 0 0 0 \n",
"\n",
" PRIMIDONE PROAIR.HFA PROMETHAZINE.HCL PROPRANOLOL.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 23 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 11 0 \n",
"4 0 0 0 0 \n",
"\n",
" PROPRANOLOL.HCL.ER QUETIAPINE.FUMARATE QUINAPRIL.HCL RALOXIFENE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" RAMIPRIL RANEXA RANITIDINE.HCL RESTASIS RISPERIDONE ROPINIROLE.HCL \\\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 25 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SEROQUEL.XR SERTRALINE.HCL SIMVASTATIN SOTALOL SPIRIVA SPIRONOLACTONE \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 29 51 0 0 0 \n",
"2 0 14 0 0 0 0 \n",
"3 0 15 80 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SUCRALFATE SULFAMETHOXAZOLE.TRIMETHOPRIM SUMATRIPTAN.SUCCINATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 14 0 \n",
"\n",
" SYMBICORT SYNTHROID TAMSULOSIN.HCL TEMAZEPAM TERAZOSIN.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 52 18 0 0 \n",
"4 0 0 0 12 0 \n",
"\n",
" TIMOLOL.MALEATE TIZANIDINE.HCL TOLTERODINE.TARTRATE.ER TOPIRAMATE \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" TOPROL.XL TORSEMIDE TRAVATAN.Z TRAZODONE.HCL TRIAMCINOLONE.ACETONIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 31 0 \n",
"2 0 0 0 12 0 \n",
"3 0 0 0 0 16 \n",
"4 0 0 0 0 0 \n",
"\n",
" TRIAMTERENE.HYDROCHLOROTHIAZID VALACYCLOVIR VALSARTAN \\\n",
"0 0 0 0 \n",
"1 12 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VALSARTAN.HYDROCHLOROTHIAZIDE VENLAFAXINE.HCL VENLAFAXINE.HCL.ER \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 47 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VENTOLIN.HFA VERAPAMIL.ER VESICARE VOLTAREN VYTORIN WARFARIN.SODIUM \\\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 0 0 17 \n",
"\n",
" XARELTO ZETIA ZIPRASIDONE.HCL ZOLPIDEM.TARTRATE Opioid.Prescriber \n",
"0 0 0 0 0 1 \n",
"1 0 0 0 35 1 \n",
"2 0 0 0 25 0 \n",
"3 0 0 0 0 1 \n",
"4 28 0 0 0 1 "
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Gender</th>\n",
" <th>State</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",
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" <th>AMIODARONE.HCL</th>\n",
" <th>AMITRIPTYLINE.HCL</th>\n",
" <th>AMLODIPINE.BESYLATE</th>\n",
" <th>AMLODIPINE.BESYLATE.BENAZEPRIL</th>\n",
" <th>AMOXICILLIN</th>\n",
" <th>AMOX.TR.POTASSIUM.CLAVULANATE</th>\n",
" <th>AMPHETAMINE.SALT.COMBO</th>\n",
" <th>ATENOLOL</th>\n",
" <th>ATORVASTATIN.CALCIUM</th>\n",
" <th>AVODART</th>\n",
" <th>AZITHROMYCIN</th>\n",
" <th>BACLOFEN</th>\n",
" <th>BD.ULTRA.FINE.PEN.NEEDLE</th>\n",
" <th>BENAZEPRIL.HCL</th>\n",
" <th>BENICAR</th>\n",
" <th>BENICAR.HCT</th>\n",
" <th>BENZTROPINE.MESYLATE</th>\n",
" <th>BISOPROLOL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>BRIMONIDINE.TARTRATE</th>\n",
" <th>BUMETANIDE</th>\n",
" <th>BUPROPION.HCL.SR</th>\n",
" <th>BUPROPION.XL</th>\n",
" <th>BUSPIRONE.HCL</th>\n",
" <th>BYSTOLIC</th>\n",
" <th>CARBAMAZEPINE</th>\n",
" <th>CARBIDOPA.LEVODOPA</th>\n",
" <th>CARISOPRODOL</th>\n",
" <th>CARTIA.XT</th>\n",
" <th>CARVEDILOL</th>\n",
" <th>CEFUROXIME</th>\n",
" <th>CELEBREX</th>\n",
" <th>CEPHALEXIN</th>\n",
" <th>CHLORHEXIDINE.GLUCONATE</th>\n",
" <th>CHLORTHALIDONE</th>\n",
" <th>CILOSTAZOL</th>\n",
" <th>CIPROFLOXACIN.HCL</th>\n",
" <th>CITALOPRAM.HBR</th>\n",
" <th>CLINDAMYCIN.HCL</th>\n",
" <th>CLOBETASOL.PROPIONATE</th>\n",
" <th>CLONAZEPAM</th>\n",
" <th>CLONIDINE.HCL</th>\n",
" <th>CLOPIDOGREL</th>\n",
" <th>CLOTRIMAZOLE.BETAMETHASONE</th>\n",
" <th>COLCRYS</th>\n",
" <th>COMBIVENT.RESPIMAT</th>\n",
" <th>CRESTOR</th>\n",
" <th>CYCLOBENZAPRINE.HCL</th>\n",
" <th>DEXILANT</th>\n",
" <th>DIAZEPAM</th>\n",
" <th>DICLOFENAC.SODIUM</th>\n",
" <th>DICYCLOMINE.HCL</th>\n",
" <th>DIGOX</th>\n",
" <th>DIGOXIN</th>\n",
" <th>DILTIAZEM.24HR.CD</th>\n",
" <th>DILTIAZEM.24HR.ER</th>\n",
" <th>DILTIAZEM.ER</th>\n",
" <th>DILTIAZEM.HCL</th>\n",
" <th>DIOVAN</th>\n",
" <th>DIPHENOXYLATE.ATROPINE</th>\n",
" <th>DIVALPROEX.SODIUM</th>\n",
" <th>DIVALPROEX.SODIUM.ER</th>\n",
" <th>DONEPEZIL.HCL</th>\n",
" <th>DORZOLAMIDE.TIMOLOL</th>\n",
" <th>DOXAZOSIN.MESYLATE</th>\n",
" <th>DOXEPIN.HCL</th>\n",
" <th>DOXYCYCLINE.HYCLATE</th>\n",
" <th>DULOXETINE.HCL</th>\n",
" <th>ENALAPRIL.MALEATE</th>\n",
" <th>ESCITALOPRAM.OXALATE</th>\n",
" <th>ESTRADIOL</th>\n",
" <th>EXELON</th>\n",
" <th>FAMOTIDINE</th>\n",
" <th>FELODIPINE.ER</th>\n",
" <th>FENOFIBRATE</th>\n",
" <th>FINASTERIDE</th>\n",
" <th>FLOVENT.HFA</th>\n",
" <th>FLUCONAZOLE</th>\n",
" <th>FLUOXETINE.HCL</th>\n",
" <th>FLUTICASONE.PROPIONATE</th>\n",
" <th>FUROSEMIDE</th>\n",
" <th>GABAPENTIN</th>\n",
" <th>GEMFIBROZIL</th>\n",
" <th>GLIMEPIRIDE</th>\n",
" <th>GLIPIZIDE</th>\n",
" <th>GLIPIZIDE.ER</th>\n",
" <th>GLIPIZIDE.XL</th>\n",
" <th>GLYBURIDE</th>\n",
" <th>HALOPERIDOL</th>\n",
" <th>HUMALOG</th>\n",
" <th>HYDRALAZINE.HCL</th>\n",
" <th>HYDROCHLOROTHIAZIDE</th>\n",
" <th>HYDROCORTISONE</th>\n",
" <th>HYDROXYZINE.HCL</th>\n",
" <th>IBANDRONATE.SODIUM</th>\n",
" <th>IBUPROFEN</th>\n",
" <th>INSULIN.SYRINGE</th>\n",
" <th>IPRATROPIUM.BROMIDE</th>\n",
" <th>IRBESARTAN</th>\n",
" <th>ISOSORBIDE.MONONITRATE.ER</th>\n",
" <th>JANTOVEN</th>\n",
" <th>JANUMET</th>\n",
" <th>JANUVIA</th>\n",
" <th>KETOCONAZOLE</th>\n",
" <th>KLOR.CON.10</th>\n",
" <th>KLOR.CON.M10</th>\n",
" <th>KLOR.CON.M20</th>\n",
" <th>LABETALOL.HCL</th>\n",
" <th>LACTULOSE</th>\n",
" <th>LAMOTRIGINE</th>\n",
" <th>LANSOPRAZOLE</th>\n",
" <th>LANTUS</th>\n",
" <th>LANTUS.SOLOSTAR</th>\n",
" <th>LATANOPROST</th>\n",
" <th>LEVEMIR</th>\n",
" <th>LEVEMIR.FLEXPEN</th>\n",
" <th>LEVETIRACETAM</th>\n",
" <th>LEVOFLOXACIN</th>\n",
" <th>LEVOTHYROXINE.SODIUM</th>\n",
" <th>LIDOCAINE</th>\n",
" <th>LISINOPRIL</th>\n",
" <th>LISINOPRIL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LITHIUM.CARBONATE</th>\n",
" <th>LORAZEPAM</th>\n",
" <th>LOSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LOSARTAN.POTASSIUM</th>\n",
" <th>LOVASTATIN</th>\n",
" <th>LOVAZA</th>\n",
" <th>LUMIGAN</th>\n",
" <th>LYRICA</th>\n",
" <th>MECLIZINE.HCL</th>\n",
" <th>MELOXICAM</th>\n",
" <th>METFORMIN.HCL</th>\n",
" <th>METFORMIN.HCL.ER</th>\n",
" <th>METHOCARBAMOL</th>\n",
" <th>METHOTREXATE</th>\n",
" <th>METHYLPREDNISOLONE</th>\n",
" <th>METOCLOPRAMIDE.HCL</th>\n",
" <th>METOLAZONE</th>\n",
" <th>METOPROLOL.SUCCINATE</th>\n",
" <th>METOPROLOL.TARTRATE</th>\n",
" <th>METRONIDAZOLE</th>\n",
" <th>MIRTAZAPINE</th>\n",
" <th>MONTELUKAST.SODIUM</th>\n",
" <th>MUPIROCIN</th>\n",
" <th>NABUMETONE</th>\n",
" <th>NAMENDA</th>\n",
" <th>NAMENDA.XR</th>\n",
" <th>NAPROXEN</th>\n",
" <th>NASONEX</th>\n",
" <th>NEXIUM</th>\n",
" <th>NIACIN.ER</th>\n",
" <th>NIFEDICAL.XL</th>\n",
" <th>NIFEDIPINE.ER</th>\n",
" <th>NITROFURANTOIN.MONO.MACRO</th>\n",
" <th>NITROSTAT</th>\n",
" <th>NORTRIPTYLINE.HCL</th>\n",
" <th>NOVOLOG</th>\n",
" <th>NOVOLOG.FLEXPEN</th>\n",
" <th>NYSTATIN</th>\n",
" <th>OLANZAPINE</th>\n",
" <th>OMEPRAZOLE</th>\n",
" <th>ONDANSETRON.HCL</th>\n",
" <th>ONDANSETRON.ODT</th>\n",
" <th>ONGLYZA</th>\n",
" <th>OXCARBAZEPINE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE.ER</th>\n",
" <th>PANTOPRAZOLE.SODIUM</th>\n",
" <th>PAROXETINE.HCL</th>\n",
" <th>PHENOBARBITAL</th>\n",
" <th>PHENYTOIN.SODIUM.EXTENDED</th>\n",
" <th>PIOGLITAZONE.HCL</th>\n",
" <th>POLYETHYLENE.GLYCOL.3350</th>\n",
" <th>POTASSIUM.CHLORIDE</th>\n",
" <th>PRADAXA</th>\n",
" <th>PRAMIPEXOLE.DIHYDROCHLORIDE</th>\n",
" <th>PRAVASTATIN.SODIUM</th>\n",
" <th>PREDNISONE</th>\n",
" <th>PREMARIN</th>\n",
" <th>PRIMIDONE</th>\n",
" <th>PROAIR.HFA</th>\n",
" <th>PROMETHAZINE.HCL</th>\n",
" <th>PROPRANOLOL.HCL</th>\n",
" <th>PROPRANOLOL.HCL.ER</th>\n",
" <th>QUETIAPINE.FUMARATE</th>\n",
" <th>QUINAPRIL.HCL</th>\n",
" <th>RALOXIFENE.HCL</th>\n",
" <th>RAMIPRIL</th>\n",
" <th>RANEXA</th>\n",
" <th>RANITIDINE.HCL</th>\n",
" <th>RESTASIS</th>\n",
" <th>RISPERIDONE</th>\n",
" <th>ROPINIROLE.HCL</th>\n",
" <th>SEROQUEL.XR</th>\n",
" <th>SERTRALINE.HCL</th>\n",
" <th>SIMVASTATIN</th>\n",
" <th>SOTALOL</th>\n",
" <th>SPIRIVA</th>\n",
" <th>SPIRONOLACTONE</th>\n",
" <th>SUCRALFATE</th>\n",
" <th>SULFAMETHOXAZOLE.TRIMETHOPRIM</th>\n",
" <th>SUMATRIPTAN.SUCCINATE</th>\n",
" <th>SYMBICORT</th>\n",
" <th>SYNTHROID</th>\n",
" <th>TAMSULOSIN.HCL</th>\n",
" <th>TEMAZEPAM</th>\n",
" <th>TERAZOSIN.HCL</th>\n",
" <th>TIMOLOL.MALEATE</th>\n",
" <th>TIZANIDINE.HCL</th>\n",
" <th>TOLTERODINE.TARTRATE.ER</th>\n",
" <th>TOPIRAMATE</th>\n",
" <th>TOPROL.XL</th>\n",
" <th>TORSEMIDE</th>\n",
" <th>TRAVATAN.Z</th>\n",
" <th>TRAZODONE.HCL</th>\n",
" <th>TRIAMCINOLONE.ACETONIDE</th>\n",
" <th>TRIAMTERENE.HYDROCHLOROTHIAZID</th>\n",
" <th>VALACYCLOVIR</th>\n",
" <th>VALSARTAN</th>\n",
" <th>VALSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>VENLAFAXINE.HCL</th>\n",
" <th>VENLAFAXINE.HCL.ER</th>\n",
" <th>VENTOLIN.HFA</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>M</td>\n",
" <td>TX</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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>1</th>\n",
" <td>F</td>\n",
" <td>AL</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>0</td>\n",
" <td>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</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>11</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",
" <td>85</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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>F</td>\n",
" <td>NY</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>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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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",
" <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>12</td>\n",
" <td>0</td>\n",
" <td>30</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>32</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",
" <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",
" <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",
" <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",
" <td>0</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",
" <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",
" <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",
" <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",
" <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>13</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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</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",
" <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>12</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>47</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>25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M</td>\n",
" <td>AZ</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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
" <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",
" <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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>48</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>46</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>57</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>55</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",
" <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>16</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
" <td>16</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",
" <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>M</td>\n",
" <td>NV</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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
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"text/plain": [
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" GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG HYDRALAZINE.HCL \\\n",
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" KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL LACTULOSE \\\n",
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" LIDOCAINE LISINOPRIL LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE \\\n",
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"4 0 0 0 0 \n",
"\n",
" LORAZEPAM LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN \\\n",
"0 12 0 0 0 \n",
"1 27 0 0 11 \n",
"2 19 0 0 0 \n",
"3 0 0 46 0 \n",
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"\n",
" LOVAZA LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL \\\n",
"0 0 0 0 0 0 0 \n",
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" METFORMIN.HCL.ER METHOCARBAMOL METHOTREXATE METHYLPREDNISOLONE \\\n",
"0 0 0 0 0 \n",
"1 0 15 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" METOCLOPRAMIDE.HCL METOLAZONE METOPROLOL.SUCCINATE METOPROLOL.TARTRATE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 18 \n",
"2 0 0 0 0 \n",
"3 0 0 0 55 \n",
"4 0 0 0 0 \n",
"\n",
" METRONIDAZOLE MIRTAZAPINE MONTELUKAST.SODIUM MUPIROCIN NABUMETONE \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" NAMENDA NAMENDA.XR NAPROXEN NASONEX NEXIUM NIACIN.ER NIFEDICAL.XL \\\n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 11 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 0 0 0 \n",
"\n",
" NIFEDIPINE.ER NITROFURANTOIN.MONO.MACRO NITROSTAT NORTRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" NOVOLOG NOVOLOG.FLEXPEN NYSTATIN OLANZAPINE OMEPRAZOLE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 85 \n",
"2 0 0 0 0 0 \n",
"3 16 12 12 0 52 \n",
"4 0 0 0 0 0 \n",
"\n",
" ONDANSETRON.HCL ONDANSETRON.ODT ONGLYZA OXCARBAZEPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 23 0 0 0 \n",
"\n",
" OXYBUTYNIN.CHLORIDE OXYBUTYNIN.CHLORIDE.ER PANTOPRAZOLE.SODIUM \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 15 0 0 \n",
"4 0 0 0 \n",
"\n",
" PAROXETINE.HCL PHENOBARBITAL PHENYTOIN.SODIUM.EXTENDED PIOGLITAZONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 13 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" POLYETHYLENE.GLYCOL.3350 POTASSIUM.CHLORIDE PRADAXA \\\n",
"0 0 0 0 \n",
"1 0 0 13 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" PRAMIPEXOLE.DIHYDROCHLORIDE PRAVASTATIN.SODIUM PREDNISONE PREMARIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 13 0 \n",
"4 0 0 0 0 \n",
"\n",
" PRIMIDONE PROAIR.HFA PROMETHAZINE.HCL PROPRANOLOL.HCL \\\n",
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"2 0 0 0 0 \n",
"3 0 0 11 0 \n",
"4 0 0 0 0 \n",
"\n",
" PROPRANOLOL.HCL.ER QUETIAPINE.FUMARATE QUINAPRIL.HCL RALOXIFENE.HCL \\\n",
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"\n",
" RAMIPRIL RANEXA RANITIDINE.HCL RESTASIS RISPERIDONE ROPINIROLE.HCL \\\n",
"0 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 \n",
"3 0 0 25 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SEROQUEL.XR SERTRALINE.HCL SIMVASTATIN SOTALOL SPIRIVA SPIRONOLACTONE \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 29 51 0 0 0 \n",
"2 0 14 0 0 0 0 \n",
"3 0 15 80 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SUCRALFATE SULFAMETHOXAZOLE.TRIMETHOPRIM SUMATRIPTAN.SUCCINATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 14 0 \n",
"\n",
" SYMBICORT SYNTHROID TAMSULOSIN.HCL TEMAZEPAM TERAZOSIN.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 52 18 0 0 \n",
"4 0 0 0 12 0 \n",
"\n",
" TIMOLOL.MALEATE TIZANIDINE.HCL TOLTERODINE.TARTRATE.ER TOPIRAMATE \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" TOPROL.XL TORSEMIDE TRAVATAN.Z TRAZODONE.HCL TRIAMCINOLONE.ACETONIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 31 0 \n",
"2 0 0 0 12 0 \n",
"3 0 0 0 0 16 \n",
"4 0 0 0 0 0 \n",
"\n",
" TRIAMTERENE.HYDROCHLOROTHIAZID VALACYCLOVIR VALSARTAN \\\n",
"0 0 0 0 \n",
"1 12 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VALSARTAN.HYDROCHLOROTHIAZIDE VENLAFAXINE.HCL VENLAFAXINE.HCL.ER \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 47 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VENTOLIN.HFA VERAPAMIL.ER VESICARE VOLTAREN VYTORIN WARFARIN.SODIUM \\\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 0 0 17 \n",
"\n",
" XARELTO ZETIA ZIPRASIDONE.HCL ZOLPIDEM.TARTRATE Opioid.Prescriber \n",
"0 0 0 0 0 1 \n",
"1 0 0 0 35 1 \n",
"2 0 0 0 25 0 \n",
"3 0 0 0 0 1 \n",
"4 28 0 0 0 1 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pinfo[kept_variable]\n",
"df = df.drop(df.columns[[0,3]], axis=1) \n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"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>Specialty_Allergy/Immunology</th>\n",
" <th>Specialty_Anesthesiology</th>\n",
" <th>Specialty_Behavioral Analyst</th>\n",
" <th>Specialty_CRNA</th>\n",
" <th>Specialty_Cardiac Electrophysiology</th>\n",
" <th>Specialty_Cardiac Surgery</th>\n",
" <th>Specialty_Cardiology</th>\n",
" <th>Specialty_Certified Clinical Nurse Specialist</th>\n",
" <th>Specialty_Certified Nurse Midwife</th>\n",
" <th>Specialty_Chiropractic</th>\n",
" <th>Specialty_Clinic/Center</th>\n",
" <th>Specialty_Clinical Pharmacology</th>\n",
" <th>Specialty_Colon & Rectal Surgery</th>\n",
" <th>Specialty_Colorectal Surgery (formerly proctology)</th>\n",
" <th>Specialty_Community Health Worker</th>\n",
" <th>Specialty_Counselor</th>\n",
" <th>Specialty_Critical Care (Intensivists)</th>\n",
" <th>Specialty_Dentist</th>\n",
" <th>Specialty_Dermatology</th>\n",
" <th>Specialty_Diagnostic Radiology</th>\n",
" <th>Specialty_Emergency Medicine</th>\n",
" <th>Specialty_Endocrinology</th>\n",
" <th>Specialty_Family Medicine</th>\n",
" <th>Specialty_Family Practice</th>\n",
" <th>Specialty_Gastroenterology</th>\n",
" <th>Specialty_General Acute Care Hospital</th>\n",
" <th>Specialty_General Practice</th>\n",
" <th>Specialty_General Surgery</th>\n",
" <th>Specialty_Geriatric Medicine</th>\n",
" <th>Specialty_Geriatric Psychiatry</th>\n",
" <th>Specialty_Gynecological/Oncology</th>\n",
" <th>Specialty_Hand Surgery</th>\n",
" <th>Specialty_Health Maintenance Organization</th>\n",
" <th>Specialty_Hematology</th>\n",
" <th>Specialty_Hematology/Oncology</th>\n",
" <th>Specialty_Homeopath</th>\n",
" <th>Specialty_Hospice and Palliative Care</th>\n",
" <th>Specialty_Hospital (Dmercs Only)</th>\n",
" <th>Specialty_Hospitalist</th>\n",
" <th>Specialty_Infectious Disease</th>\n",
" <th>Specialty_Internal Medicine</th>\n",
" <th>Specialty_Interventional Pain Management</th>\n",
" <th>Specialty_Interventional Radiology</th>\n",
" <th>Specialty_Legal Medicine</th>\n",
" <th>Specialty_Licensed Clinical Social Worker</th>\n",
" <th>Specialty_Licensed Practical Nurse</th>\n",
" <th>Specialty_Maxillofacial Surgery</th>\n",
" <th>Specialty_Medical Genetics</th>\n",
" <th>Specialty_Medical Genetics, Ph.D. Medical Genetics</th>\n",
" <th>Specialty_Medical Oncology</th>\n",
" <th>Specialty_Midwife</th>\n",
" <th>Specialty_Military Health Care Provider</th>\n",
" <th>Specialty_Multispecialty Clinic/Group Practice</th>\n",
" <th>Specialty_Naturopath</th>\n",
" <th>Specialty_Nephrology</th>\n",
" <th>Specialty_Neurological Surgery</th>\n",
" <th>Specialty_Neurology</th>\n",
" <th>Specialty_Neuromusculoskeletal Medicine, Sports Medicine</th>\n",
" <th>Specialty_Neuropsychiatry</th>\n",
" <th>Specialty_Neurosurgery</th>\n",
" <th>Specialty_Nuclear Medicine</th>\n",
" <th>Specialty_Nurse Practitioner</th>\n",
" <th>Specialty_Obstetrics/Gynecology</th>\n",
" <th>Specialty_Ophthalmology</th>\n",
" <th>Specialty_Optometry</th>\n",
" <th>Specialty_Oral & Maxillofacial Surgery</th>\n",
" <th>Specialty_Oral Surgery (dentists only)</th>\n",
" <th>Specialty_Orthopaedic Surgery</th>\n",
" <th>Specialty_Orthopedic Surgery</th>\n",
" <th>Specialty_Osteopathic Manipulative Medicine</th>\n",
" <th>Specialty_Otolaryngology</th>\n",
" <th>Specialty_Pain Management</th>\n",
" <th>Specialty_Pathology</th>\n",
" <th>Specialty_Pediatric Medicine</th>\n",
" <th>Specialty_Personal Emergency Response Attendant</th>\n",
" <th>Specialty_Pharmacist</th>\n",
" <th>Specialty_Pharmacy Technician</th>\n",
" <th>Specialty_Physical Medicine and Rehabilitation</th>\n",
" <th>Specialty_Physical Therapist</th>\n",
" <th>Specialty_Physician Assistant</th>\n",
" <th>Specialty_Plastic Surgery</th>\n",
" <th>Specialty_Plastic and Reconstructive Surgery</th>\n",
" <th>Specialty_Podiatry</th>\n",
" <th>Specialty_Preferred Provider Organization</th>\n",
" <th>Specialty_Preventive Medicine</th>\n",
" <th>Specialty_Psychiatry</th>\n",
" <th>Specialty_Psychiatry & Neurology</th>\n",
" <th>Specialty_Psychologist</th>\n",
" <th>Specialty_Psychologist (billing independently)</th>\n",
" <th>Specialty_Pulmonary Disease</th>\n",
" <th>Specialty_Radiation Oncology</th>\n",
" <th>Specialty_Registered Nurse</th>\n",
" <th>Specialty_Rehabilitation Agency</th>\n",
" <th>Specialty_Rheumatology</th>\n",
" <th>Specialty_Sleep Medicine</th>\n",
" <th>Specialty_Slide Preparation Facility</th>\n",
" <th>Specialty_Specialist</th>\n",
" <th>Specialty_Specialist/Technologist</th>\n",
" <th>Specialty_Sports Medicine</th>\n",
" <th>Specialty_Student in an Organized Health Care Education/Training Program</th>\n",
" <th>Specialty_Surgery</th>\n",
" <th>Specialty_Surgical Oncology</th>\n",
" <th>Specialty_Thoracic Surgery</th>\n",
" <th>Specialty_Thoracic Surgery (Cardiothoracic Vascular Surgery)</th>\n",
" <th>Specialty_Unknown Physician Specialty Code</th>\n",
" <th>Specialty_Unknown Supplier/Provider</th>\n",
" <th>Specialty_Urology</th>\n",
" <th>Specialty_Vascular Surgery</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
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" Specialty_Allergy/Immunology Specialty_Anesthesiology \\\n",
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" Specialty_Behavioral Analyst Specialty_CRNA \\\n",
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"3 0 0 \n",
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" Specialty_Cardiac Electrophysiology Specialty_Cardiac Surgery \\\n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Cardiology Specialty_Certified Clinical Nurse Specialist \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Certified Nurse Midwife Specialty_Chiropractic \\\n",
"0 0 0 \n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
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" Specialty_Clinic/Center Specialty_Clinical Pharmacology \\\n",
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" Specialty_Colon & Rectal Surgery \\\n",
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" Specialty_Colorectal Surgery (formerly proctology) \\\n",
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"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Community Health Worker Specialty_Counselor \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
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" Specialty_Critical Care (Intensivists) Specialty_Dentist \\\n",
"0 0 1 \n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Dermatology Specialty_Diagnostic Radiology \\\n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
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" Specialty_Emergency Medicine Specialty_Endocrinology \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
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" Specialty_Family Medicine Specialty_Family Practice \\\n",
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" Specialty_Gastroenterology Specialty_General Acute Care Hospital \\\n",
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" Specialty_General Practice Specialty_General Surgery \\\n",
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"3 0 0 \n",
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" Specialty_Geriatric Medicine Specialty_Geriatric Psychiatry \\\n",
"0 0 0 \n",
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"2 0 0 \n",
"3 0 0 \n",
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" Specialty_Gynecological/Oncology Specialty_Hand Surgery \\\n",
"0 0 0 \n",
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"2 0 0 \n",
"3 0 0 \n",
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" Specialty_Health Maintenance Organization Specialty_Hematology \\\n",
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" Specialty_Hematology/Oncology Specialty_Homeopath \\\n",
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"3 0 0 \n",
"4 1 0 \n",
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" Specialty_Hospice and Palliative Care Specialty_Hospital (Dmercs Only) \\\n",
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" Specialty_Hospitalist Specialty_Infectious Disease \\\n",
"0 0 0 \n",
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"2 0 0 \n",
"3 0 0 \n",
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" Specialty_Internal Medicine Specialty_Interventional Pain Management \\\n",
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"2 0 0 \n",
"3 1 0 \n",
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" Specialty_Interventional Radiology Specialty_Legal Medicine \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Licensed Clinical Social Worker \\\n",
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"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Licensed Practical Nurse Specialty_Maxillofacial Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Medical Genetics \\\n",
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"2 0 \n",
"3 0 \n",
"4 0 \n",
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" Specialty_Medical Genetics, Ph.D. Medical Genetics \\\n",
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"1 0 \n",
"2 0 \n",
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"4 0 \n",
"\n",
" Specialty_Medical Oncology Specialty_Midwife \\\n",
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"1 0 0 \n",
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"4 0 0 \n",
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" Specialty_Military Health Care Provider \\\n",
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"3 0 \n",
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" Specialty_Multispecialty Clinic/Group Practice Specialty_Naturopath \\\n",
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"4 0 0 \n",
"\n",
" Specialty_Nephrology Specialty_Neurological Surgery Specialty_Neurology \\\n",
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"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Specialty_Neuromusculoskeletal Medicine, Sports Medicine \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Neuropsychiatry Specialty_Neurosurgery \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Nuclear Medicine Specialty_Nurse Practitioner \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Obstetrics/Gynecology Specialty_Ophthalmology \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Optometry Specialty_Oral & Maxillofacial Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Oral Surgery (dentists only) Specialty_Orthopaedic Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Orthopedic Surgery Specialty_Osteopathic Manipulative Medicine \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Otolaryngology Specialty_Pain Management Specialty_Pathology \\\n",
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"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Specialty_Pediatric Medicine \\\n",
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"3 0 \n",
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"\n",
" Specialty_Personal Emergency Response Attendant Specialty_Pharmacist \\\n",
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"3 0 0 \n",
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"\n",
" Specialty_Pharmacy Technician \\\n",
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"3 0 \n",
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"\n",
" Specialty_Physical Medicine and Rehabilitation \\\n",
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"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Physical Therapist Specialty_Physician Assistant \\\n",
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"\n",
" Specialty_Plastic Surgery Specialty_Plastic and Reconstructive Surgery \\\n",
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"3 0 0 \n",
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" Specialty_Podiatry Specialty_Preferred Provider Organization \\\n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Preventive Medicine Specialty_Psychiatry \\\n",
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"2 0 0 \n",
"3 0 0 \n",
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"\n",
" Specialty_Psychiatry & Neurology Specialty_Psychologist \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Psychologist (billing independently) \\\n",
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"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Pulmonary Disease Specialty_Radiation Oncology \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Registered Nurse Specialty_Rehabilitation Agency \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Rheumatology Specialty_Sleep Medicine \\\n",
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"2 0 0 \n",
"3 0 0 \n",
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"\n",
" Specialty_Slide Preparation Facility Specialty_Specialist \\\n",
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"\n",
" Specialty_Specialist/Technologist Specialty_Sports Medicine \\\n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
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" Specialty_Student in an Organized Health Care Education/Training Program \\\n",
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" Specialty_Surgery Specialty_Surgical Oncology Specialty_Thoracic Surgery \\\n",
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"3 0 0 0 \n",
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" Specialty_Thoracic Surgery (Cardiothoracic Vascular Surgery) \\\n",
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"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Unknown Physician Specialty Code \\\n",
"0 0 \n",
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"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Unknown Supplier/Provider Specialty_Urology \\\n",
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"3 0 0 \n",
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"\n",
" Specialty_Vascular Surgery \n",
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]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = ['Specialty']\n",
"dummies = pd.concat([pd.get_dummies(df[cols][col], drop_first = True, prefix= col) for col in df[cols]], axis=1)\n",
"dummies.head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\" # see the value of multiple statements at once.\n",
"from sklearn.model_selection import cross_val_score, StratifiedKFold, train_test_split, GridSearchCV\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier\n",
"from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
"from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfVectorizer, TfidfTransformer\n",
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Specialty_Allergy/Immunology</th>\n",
" <th>Specialty_Anesthesiology</th>\n",
" <th>Specialty_Behavioral Analyst</th>\n",
" <th>Specialty_CRNA</th>\n",
" <th>Specialty_Cardiac Electrophysiology</th>\n",
" <th>Specialty_Cardiac Surgery</th>\n",
" <th>Specialty_Cardiology</th>\n",
" <th>Specialty_Certified Clinical Nurse Specialist</th>\n",
" <th>Specialty_Certified Nurse Midwife</th>\n",
" <th>Specialty_Chiropractic</th>\n",
" <th>Specialty_Clinic/Center</th>\n",
" <th>Specialty_Clinical Pharmacology</th>\n",
" <th>Specialty_Colon & Rectal Surgery</th>\n",
" <th>Specialty_Colorectal Surgery (formerly proctology)</th>\n",
" <th>Specialty_Community Health Worker</th>\n",
" <th>Specialty_Counselor</th>\n",
" <th>Specialty_Critical Care (Intensivists)</th>\n",
" <th>Specialty_Dentist</th>\n",
" <th>Specialty_Dermatology</th>\n",
" <th>Specialty_Diagnostic Radiology</th>\n",
" <th>Specialty_Emergency Medicine</th>\n",
" <th>Specialty_Endocrinology</th>\n",
" <th>Specialty_Family Medicine</th>\n",
" <th>Specialty_Family Practice</th>\n",
" <th>Specialty_Gastroenterology</th>\n",
" <th>Specialty_General Acute Care Hospital</th>\n",
" <th>Specialty_General Practice</th>\n",
" <th>Specialty_General Surgery</th>\n",
" <th>Specialty_Geriatric Medicine</th>\n",
" <th>Specialty_Geriatric Psychiatry</th>\n",
" <th>Specialty_Gynecological/Oncology</th>\n",
" <th>Specialty_Hand Surgery</th>\n",
" <th>Specialty_Health Maintenance Organization</th>\n",
" <th>Specialty_Hematology</th>\n",
" <th>Specialty_Hematology/Oncology</th>\n",
" <th>Specialty_Homeopath</th>\n",
" <th>Specialty_Hospice and Palliative Care</th>\n",
" <th>Specialty_Hospital (Dmercs Only)</th>\n",
" <th>Specialty_Hospitalist</th>\n",
" <th>Specialty_Infectious Disease</th>\n",
" <th>Specialty_Internal Medicine</th>\n",
" <th>Specialty_Interventional Pain Management</th>\n",
" <th>Specialty_Interventional Radiology</th>\n",
" <th>Specialty_Legal Medicine</th>\n",
" <th>Specialty_Licensed Clinical Social Worker</th>\n",
" <th>Specialty_Licensed Practical Nurse</th>\n",
" <th>Specialty_Maxillofacial Surgery</th>\n",
" <th>Specialty_Medical Genetics</th>\n",
" <th>Specialty_Medical Genetics, Ph.D. Medical Genetics</th>\n",
" <th>Specialty_Medical Oncology</th>\n",
" <th>Specialty_Midwife</th>\n",
" <th>Specialty_Military Health Care Provider</th>\n",
" <th>Specialty_Multispecialty Clinic/Group Practice</th>\n",
" <th>Specialty_Naturopath</th>\n",
" <th>Specialty_Nephrology</th>\n",
" <th>Specialty_Neurological Surgery</th>\n",
" <th>Specialty_Neurology</th>\n",
" <th>Specialty_Neuromusculoskeletal Medicine, Sports Medicine</th>\n",
" <th>Specialty_Neuropsychiatry</th>\n",
" <th>Specialty_Neurosurgery</th>\n",
" <th>Specialty_Nuclear Medicine</th>\n",
" <th>Specialty_Nurse Practitioner</th>\n",
" <th>Specialty_Obstetrics/Gynecology</th>\n",
" <th>Specialty_Ophthalmology</th>\n",
" <th>Specialty_Optometry</th>\n",
" <th>Specialty_Oral & Maxillofacial Surgery</th>\n",
" <th>Specialty_Oral Surgery (dentists only)</th>\n",
" <th>Specialty_Orthopaedic Surgery</th>\n",
" <th>Specialty_Orthopedic Surgery</th>\n",
" <th>Specialty_Osteopathic Manipulative Medicine</th>\n",
" <th>Specialty_Otolaryngology</th>\n",
" <th>Specialty_Pain Management</th>\n",
" <th>Specialty_Pathology</th>\n",
" <th>Specialty_Pediatric Medicine</th>\n",
" <th>Specialty_Personal Emergency Response Attendant</th>\n",
" <th>Specialty_Pharmacist</th>\n",
" <th>Specialty_Pharmacy Technician</th>\n",
" <th>Specialty_Physical Medicine and Rehabilitation</th>\n",
" <th>Specialty_Physical Therapist</th>\n",
" <th>Specialty_Physician Assistant</th>\n",
" <th>Specialty_Plastic Surgery</th>\n",
" <th>Specialty_Plastic and Reconstructive Surgery</th>\n",
" <th>Specialty_Podiatry</th>\n",
" <th>Specialty_Preferred Provider Organization</th>\n",
" <th>Specialty_Preventive Medicine</th>\n",
" <th>Specialty_Psychiatry</th>\n",
" <th>Specialty_Psychiatry & Neurology</th>\n",
" <th>Specialty_Psychologist</th>\n",
" <th>Specialty_Psychologist (billing independently)</th>\n",
" <th>Specialty_Pulmonary Disease</th>\n",
" <th>Specialty_Radiation Oncology</th>\n",
" <th>Specialty_Registered Nurse</th>\n",
" <th>Specialty_Rehabilitation Agency</th>\n",
" <th>Specialty_Rheumatology</th>\n",
" <th>Specialty_Sleep Medicine</th>\n",
" <th>Specialty_Slide Preparation Facility</th>\n",
" <th>Specialty_Specialist</th>\n",
" <th>Specialty_Specialist/Technologist</th>\n",
" <th>Specialty_Sports Medicine</th>\n",
" <th>Specialty_Student in an Organized Health Care Education/Training Program</th>\n",
" <th>Specialty_Surgery</th>\n",
" <th>Specialty_Surgical Oncology</th>\n",
" <th>Specialty_Thoracic Surgery</th>\n",
" <th>Specialty_Thoracic Surgery (Cardiothoracic Vascular Surgery)</th>\n",
" <th>Specialty_Unknown Physician Specialty Code</th>\n",
" <th>Specialty_Unknown Supplier/Provider</th>\n",
" <th>Specialty_Urology</th>\n",
" <th>Specialty_Vascular Surgery</th>\n",
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],
"text/plain": [
" Specialty_Allergy/Immunology Specialty_Anesthesiology \\\n",
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"1 0 0 \n",
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"\n",
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" Specialty_Personal Emergency Response Attendant Specialty_Pharmacist \\\n",
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" Specialty_Physical Therapist Specialty_Physician Assistant \\\n",
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"\n",
" Specialty_Preventive Medicine Specialty_Psychiatry \\\n",
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"\n",
" Specialty_Registered Nurse Specialty_Rehabilitation Agency \\\n",
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"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Rheumatology Specialty_Sleep Medicine \\\n",
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"\n",
" Specialty_Slide Preparation Facility Specialty_Specialist \\\n",
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" Specialty_Specialist/Technologist Specialty_Sports Medicine \\\n",
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"3 0 0 \n",
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"\n",
" Specialty_Student in an Organized Health Care Education/Training Program \\\n",
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" Specialty_Surgery Specialty_Surgical Oncology Specialty_Thoracic Surgery \\\n",
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" Specialty_Thoracic Surgery (Cardiothoracic Vascular Surgery) \\\n",
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" Specialty_Unknown Physician Specialty Code \\\n",
"0 0 \n",
"1 0 \n",
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"3 0 \n",
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"\n",
" Specialty_Unknown Supplier/Provider Specialty_Urology \\\n",
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]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = dummies\n",
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Specialty_Geriatric Psychiatry</th>\n",
" <th>Specialty_Gynecological/Oncology</th>\n",
" <th>Specialty_Hand Surgery</th>\n",
" <th>Specialty_Health Maintenance Organization</th>\n",
" <th>Specialty_Hematology</th>\n",
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" <th>Specialty_Obstetrics/Gynecology</th>\n",
" <th>Specialty_Ophthalmology</th>\n",
" <th>Specialty_Optometry</th>\n",
" <th>Specialty_Oral & Maxillofacial Surgery</th>\n",
" <th>Specialty_Oral Surgery (dentists only)</th>\n",
" <th>Specialty_Orthopaedic Surgery</th>\n",
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"text/plain": [
" Specialty_Allergy/Immunology Specialty_Anesthesiology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Behavioral Analyst Specialty_CRNA \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Cardiac Electrophysiology Specialty_Cardiac Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Cardiology Specialty_Certified Clinical Nurse Specialist \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
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"4 0 0 \n",
"\n",
" Specialty_Certified Nurse Midwife Specialty_Chiropractic \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
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"\n",
" Specialty_Clinic/Center Specialty_Clinical Pharmacology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
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"\n",
" Specialty_Colon & Rectal Surgery \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
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"4 0 \n",
"\n",
" Specialty_Colorectal Surgery (formerly proctology) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Community Health Worker Specialty_Counselor \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
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"\n",
" Specialty_Critical Care (Intensivists) Specialty_Dentist \\\n",
"0 0 1 \n",
"1 0 0 \n",
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"4 0 0 \n",
"\n",
" Specialty_Dermatology Specialty_Diagnostic Radiology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Emergency Medicine Specialty_Endocrinology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Family Medicine Specialty_Family Practice \\\n",
"0 0 0 \n",
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"3 0 0 \n",
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"\n",
" Specialty_Gastroenterology Specialty_General Acute Care Hospital \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_General Practice Specialty_General Surgery \\\n",
"0 0 0 \n",
"1 0 1 \n",
"2 1 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Geriatric Medicine Specialty_Geriatric Psychiatry \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Gynecological/Oncology Specialty_Hand Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Health Maintenance Organization Specialty_Hematology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Hematology/Oncology Specialty_Homeopath \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 1 0 \n",
"\n",
" Specialty_Hospice and Palliative Care Specialty_Hospital (Dmercs Only) \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Hospitalist Specialty_Infectious Disease \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Internal Medicine Specialty_Interventional Pain Management \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 1 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Interventional Radiology Specialty_Legal Medicine \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Licensed Clinical Social Worker \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Licensed Practical Nurse Specialty_Maxillofacial Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Medical Genetics \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Medical Genetics, Ph.D. Medical Genetics \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Medical Oncology Specialty_Midwife \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Military Health Care Provider \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Multispecialty Clinic/Group Practice Specialty_Naturopath \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Nephrology Specialty_Neurological Surgery Specialty_Neurology \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Specialty_Neuromusculoskeletal Medicine, Sports Medicine \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Neuropsychiatry Specialty_Neurosurgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Nuclear Medicine Specialty_Nurse Practitioner \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Obstetrics/Gynecology Specialty_Ophthalmology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Optometry Specialty_Oral & Maxillofacial Surgery \\\n",
"0 0 0 \n",
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"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Oral Surgery (dentists only) Specialty_Orthopaedic Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Orthopedic Surgery Specialty_Osteopathic Manipulative Medicine \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Otolaryngology Specialty_Pain Management Specialty_Pathology \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Specialty_Pediatric Medicine \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Personal Emergency Response Attendant Specialty_Pharmacist \\\n",
"0 0 0 \n",
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"3 0 0 \n",
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"\n",
" Specialty_Pharmacy Technician \\\n",
"0 0 \n",
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"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Physical Medicine and Rehabilitation \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Physical Therapist Specialty_Physician Assistant \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Plastic Surgery Specialty_Plastic and Reconstructive Surgery \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Podiatry Specialty_Preferred Provider Organization \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Preventive Medicine Specialty_Psychiatry \\\n",
"0 0 0 \n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Psychiatry & Neurology Specialty_Psychologist \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Psychologist (billing independently) \\\n",
"0 0 \n",
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"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Pulmonary Disease Specialty_Radiation Oncology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Registered Nurse Specialty_Rehabilitation Agency \\\n",
"0 0 0 \n",
"1 0 0 \n",
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"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Rheumatology Specialty_Sleep Medicine \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Slide Preparation Facility Specialty_Specialist \\\n",
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"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Specialist/Technologist Specialty_Sports Medicine \\\n",
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"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Student in an Organized Health Care Education/Training Program \\\n",
"0 0 \n",
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"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Surgery Specialty_Surgical Oncology Specialty_Thoracic Surgery \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" Specialty_Thoracic Surgery (Cardiothoracic Vascular Surgery) \\\n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Unknown Physician Specialty Code \\\n",
"0 0 \n",
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"3 0 \n",
"4 0 \n",
"\n",
" Specialty_Unknown Supplier/Provider Specialty_Urology \\\n",
"0 0 0 \n",
"1 0 0 \n",
"2 0 0 \n",
"3 0 0 \n",
"4 0 0 \n",
"\n",
" Specialty_Vascular Surgery \n",
"0 0 \n",
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"3 0 \n",
"4 0 "
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 1\n",
"1 1\n",
"2 0\n",
"3 1\n",
"4 1\n",
"Name: Opioid.Prescriber, dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = df['Opioid.Prescriber']\n",
"y.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 220 candidates, totalling 1100 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 42 tasks | elapsed: 9.0s\n",
"[Parallel(n_jobs=-1)]: Done 192 tasks | elapsed: 1.4min\n",
"[Parallel(n_jobs=-1)]: Done 442 tasks | elapsed: 4.1min\n",
"[Parallel(n_jobs=-1)]: Done 792 tasks | elapsed: 9.3min\n",
"[Parallel(n_jobs=-1)]: Done 1100 out of 1100 | elapsed: 15.5min finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearch results\n",
"The best parameters on the training data are:\n",
" {'max_depth': None, 'n_estimators': 100}\n",
"best max_depth: None\n",
"best n_estimators: 100\n",
"best score is: 0.76\n",
"Is the prediction smaller (S) or larger (L) than the median:\n",
"\n",
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'L', 'S', 'S', 'L', 'L', 'L', 'S', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'S', 'L', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'L', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'L', 'L', 'L', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'L', 'L', 'L', 'L', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'S', 'L', 'S', 'L', 'L', 'S', 'S', 'L', 'L', 'L', 'L', 'S', 'L', 'S', 'L', 'L', 'L', 'L', 'S', 'S', 'L', 'L']\n",
"\n",
"What were the probabilities of the each result above:\n",
"\n",
"Probabilities that the number of comments is smaller than the media for each observation are:\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
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('S', 0.23), ('S', 0.23), ('S', 0.28), ('S', 0.47), ('S', 0.76), ('S', 0.14), ('S', 0.23), ('S', 0.99), ('S', 0.77), ('S', 0.12), ('S', 0.47), ('S', 0.32), ('S', 0.62), ('S', 0.32), ('S', 0.87), ('S', 0.28), ('S', 0.77), ('S', 0.12), ('S', 0.23), ('S', 0.62), ('S', 0.11), ('S', 0.23), ('S', 0.38), ('S', 0.47), ('S', 0.99), ('S', 0.11), ('S', 0.42), ('S', 0.62), ('S', 0.62), ('S', 0.82), ('S', 0.11), ('S', 0.53), ('S', 0.47), ('S', 0.62), ('S', 0.32), ('S', 0.32), ('S', 0.77), ('S', 0.62), ('S', 0.22), ('S', 0.77), ('S', 0.02), ('S', 0.32), ('S', 0.56), ('S', 0.62), ('S', 0.32), ('S', 0.62), ('S', 0.42), ('S', 0.12), ('S', 0.04), ('S', 0.12), ('S', 0.22), ('S', 0.12), ('S', 0.62), ('S', 0.04), ('S', 0.12), ('S', 0.89), ('S', 0.47), ('S', 0.22), ('S', 0.38), ('S', 0.41), ('S', 0.87), ('S', 0.75), ('S', 0.23), ('S', 0.53), ('S', 0.04), ('S', 0.12), ('S', 0.82), ('S', 0.89), ('S', 0.04), ('S', 0.47), ('S', 0.08), ('S', 0.23), ('S', 0.32), ('S', 0.47), ('S', 0.04), ('S', 0.62), ('S', 0.02), ('S', 0.32), ('S', 0.75), ('S', 0.62), ('S', 0.32), ('S', 0.87), ('S', 0.53), ('S', 0.38), ('S', 0.77), ('S', 0.81), ('S', 0.32), ('S', 0.62), ('S', 0.47), ('S', 0.32), ('S', 0.87), ('S', 0.82), ('S', 0.11), ('S', 0.22), ('S', 0.12), ('S', 0.02), ('S', 0.62), ('S', 0.32), ('S', 0.75), ('S', 0.23), ('S', 0.04), ('S', 0.32), ('S', 0.04), ('S', 0.62), ('S', 0.82), ('S', 0.23), ('S', 0.32)]\n",
"\n",
"Confusion Matrix:\n",
"\n",
"Predicted Values 0 1\n",
"Actual Values \n",
"0 1981 1132\n",
"1 690 3697\n",
"Features and their importance:\n",
"\n",
"Specialty_Psychiatry & Neurology 0.037951\n",
"Specialty_Cardiology 0.041783\n",
"Specialty_Orthopedic Surgery 0.043174\n",
"Specialty_Internal Medicine 0.051702\n",
"Specialty_Ophthalmology 0.054285\n",
"Specialty_Dentist 0.057214\n",
"Specialty_Emergency Medicine 0.074261\n",
"Specialty_Psychiatry 0.079047\n",
"Specialty_Optometry 0.099274\n",
"Specialty_Family Practice 0.123578\n",
"dtype: float64\n",
"AxesSubplot(0.125,0.125;0.775x0.755)\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc06358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"n_estimators = list(range(20,220,10))\n",
"max_depth = list(range(2, 22, 2)) + [None]\n",
"\n",
"\n",
"target = 'Opioid.Prescriber'\n",
"\n",
"def rfscore2(X,y,test_size,n_estimators,max_depth):\n",
"\n",
" X_train, X_test, y_train, y_test = train_test_split(X, \n",
" y, test_size = test_size, random_state=42) \n",
" rf_params = {\n",
" 'n_estimators':n_estimators,\n",
" 'max_depth':max_depth} # parameters for grid search\n",
" rf_gs = GridSearchCV(RandomForestClassifier(), rf_params, cv=5, verbose=1, n_jobs=-1)\n",
" rf_gs.fit(X_train,y_train) # training the random forest with all possible parameters\n",
" print('GridSearch results')\n",
" print('The best parameters on the training data are:\\n',rf_gs.best_params_) # printing the best parameters\n",
" max_depth_best = rf_gs.best_params_['max_depth'] # getting the best max_depth\n",
" n_estimators_best = rf_gs.best_params_['n_estimators'] # getting the best n_estimators\n",
" print(\"best max_depth:\",max_depth_best)\n",
" print(\"best n_estimators:\",n_estimators_best)\n",
" best_rf_gs = RandomForestClassifier(max_depth=max_depth_best,n_estimators=n_estimators_best) # instantiate the best model\n",
" best_rf_gs.fit(X_train,y_train) # fitting the best model\n",
" best_rf_score = best_rf_gs.score(X_test,y_test) \n",
" print (\"best score is:\",round(best_rf_score,2))\n",
" print(\"Is the prediction smaller (S) or larger (L) than the median:\\n\")\n",
" preds = best_rf_gs.predict(X_test)\n",
" print(['S' if p == 0 else 'L' for p in best_rf_gs.predict(X_test)])\n",
" print(\"\")\n",
" print(\"What were the probabilities of the each result above:\\n\")\n",
" print(\"Probabilities that the number of comments is smaller than the media for each observation are:\\n\")\n",
" print([('S',round(p[0],2)) if p[0] > p[1] else ('S',round(p[0],2)) for p in best_rf_gs.predict_proba(X_test)])\n",
" print(\"\")\n",
" print(\"Confusion Matrix:\\n\")\n",
" print(pd.crosstab(pd.concat([X_test,y_test],axis=1)[target], preds, rownames=['Actual Values'], colnames=['Predicted Values']))\n",
" print('Features and their importance:\\n')\n",
" feature_importances = pd.Series(best_rf_gs.feature_importances_, index=X.columns).sort_values().tail(10)\n",
" print(feature_importances)\n",
" print(feature_importances.plot(kind=\"barh\", figsize=(6,6)))\n",
" return\n",
"\n",
"\n",
"rfscore2(X,y,0.3,n_estimators,max_depth)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"def cv_score(X,y,cv,n_estimators,max_depth):\n",
" rf = RandomForestClassifier(n_estimators=n_estimators_best,\n",
" max_depth=max_depth_best)\n",
" s = cross_val_score(rf, X, y, cv=cv, n_jobs=-1)\n",
" return(\"{} Score is :{:0.3} ± {:0.3}\".format(\"Random Forest\", s.mean().round(3), s.std().round(3)))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Random Forest Score is :0.756 ± 0.006'"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dict_best = {'max_depth': None, 'n_estimators': 20}\n",
"n_estimators_best = dict_best['n_estimators']\n",
"max_depth_best = dict_best['max_depth']\n",
"cv_score(X,y,5,n_estimators_best,max_depth_best)"
]
},
{
"cell_type": "code",
"execution_count": 69,
"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>ALLOPURINOL</th>\n",
" <th>ALPRAZOLAM</th>\n",
" <th>AMIODARONE.HCL</th>\n",
" <th>AMITRIPTYLINE.HCL</th>\n",
" <th>AMLODIPINE.BESYLATE</th>\n",
" <th>AMLODIPINE.BESYLATE.BENAZEPRIL</th>\n",
" <th>AMOXICILLIN</th>\n",
" <th>AMOX.TR.POTASSIUM.CLAVULANATE</th>\n",
" <th>AMPHETAMINE.SALT.COMBO</th>\n",
" <th>ATENOLOL</th>\n",
" <th>ATORVASTATIN.CALCIUM</th>\n",
" <th>AVODART</th>\n",
" <th>AZITHROMYCIN</th>\n",
" <th>BACLOFEN</th>\n",
" <th>BD.ULTRA.FINE.PEN.NEEDLE</th>\n",
" <th>BENAZEPRIL.HCL</th>\n",
" <th>BENICAR</th>\n",
" <th>BENICAR.HCT</th>\n",
" <th>BENZTROPINE.MESYLATE</th>\n",
" <th>BISOPROLOL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>BRIMONIDINE.TARTRATE</th>\n",
" <th>BUMETANIDE</th>\n",
" <th>BUPROPION.HCL.SR</th>\n",
" <th>BUPROPION.XL</th>\n",
" <th>BUSPIRONE.HCL</th>\n",
" <th>BYSTOLIC</th>\n",
" <th>CARBAMAZEPINE</th>\n",
" <th>CARBIDOPA.LEVODOPA</th>\n",
" <th>CARISOPRODOL</th>\n",
" <th>CARTIA.XT</th>\n",
" <th>CARVEDILOL</th>\n",
" <th>CEFUROXIME</th>\n",
" <th>CELEBREX</th>\n",
" <th>CEPHALEXIN</th>\n",
" <th>CHLORHEXIDINE.GLUCONATE</th>\n",
" <th>CHLORTHALIDONE</th>\n",
" <th>CILOSTAZOL</th>\n",
" <th>CIPROFLOXACIN.HCL</th>\n",
" <th>CITALOPRAM.HBR</th>\n",
" <th>CLINDAMYCIN.HCL</th>\n",
" <th>CLOBETASOL.PROPIONATE</th>\n",
" <th>CLONAZEPAM</th>\n",
" <th>CLONIDINE.HCL</th>\n",
" <th>CLOPIDOGREL</th>\n",
" <th>CLOTRIMAZOLE.BETAMETHASONE</th>\n",
" <th>COLCRYS</th>\n",
" <th>COMBIVENT.RESPIMAT</th>\n",
" <th>CRESTOR</th>\n",
" <th>CYCLOBENZAPRINE.HCL</th>\n",
" <th>DEXILANT</th>\n",
" <th>DIAZEPAM</th>\n",
" <th>DICLOFENAC.SODIUM</th>\n",
" <th>DICYCLOMINE.HCL</th>\n",
" <th>DIGOX</th>\n",
" <th>DIGOXIN</th>\n",
" <th>DILTIAZEM.24HR.CD</th>\n",
" <th>DILTIAZEM.24HR.ER</th>\n",
" <th>DILTIAZEM.ER</th>\n",
" <th>DILTIAZEM.HCL</th>\n",
" <th>DIOVAN</th>\n",
" <th>DIPHENOXYLATE.ATROPINE</th>\n",
" <th>DIVALPROEX.SODIUM</th>\n",
" <th>DIVALPROEX.SODIUM.ER</th>\n",
" <th>DONEPEZIL.HCL</th>\n",
" <th>DORZOLAMIDE.TIMOLOL</th>\n",
" <th>DOXAZOSIN.MESYLATE</th>\n",
" <th>DOXEPIN.HCL</th>\n",
" <th>DOXYCYCLINE.HYCLATE</th>\n",
" <th>DULOXETINE.HCL</th>\n",
" <th>ENALAPRIL.MALEATE</th>\n",
" <th>ESCITALOPRAM.OXALATE</th>\n",
" <th>ESTRADIOL</th>\n",
" <th>EXELON</th>\n",
" <th>FAMOTIDINE</th>\n",
" <th>FELODIPINE.ER</th>\n",
" <th>FENOFIBRATE</th>\n",
" <th>FINASTERIDE</th>\n",
" <th>FLOVENT.HFA</th>\n",
" <th>FLUCONAZOLE</th>\n",
" <th>FLUOXETINE.HCL</th>\n",
" <th>FLUTICASONE.PROPIONATE</th>\n",
" <th>FUROSEMIDE</th>\n",
" <th>GABAPENTIN</th>\n",
" <th>GEMFIBROZIL</th>\n",
" <th>GLIMEPIRIDE</th>\n",
" <th>GLIPIZIDE</th>\n",
" <th>GLIPIZIDE.ER</th>\n",
" <th>GLIPIZIDE.XL</th>\n",
" <th>GLYBURIDE</th>\n",
" <th>HALOPERIDOL</th>\n",
" <th>HUMALOG</th>\n",
" <th>HYDRALAZINE.HCL</th>\n",
" <th>HYDROCHLOROTHIAZIDE</th>\n",
" <th>HYDROCORTISONE</th>\n",
" <th>HYDROXYZINE.HCL</th>\n",
" <th>IBANDRONATE.SODIUM</th>\n",
" <th>IBUPROFEN</th>\n",
" <th>INSULIN.SYRINGE</th>\n",
" <th>IPRATROPIUM.BROMIDE</th>\n",
" <th>IRBESARTAN</th>\n",
" <th>ISOSORBIDE.MONONITRATE.ER</th>\n",
" <th>JANTOVEN</th>\n",
" <th>JANUMET</th>\n",
" <th>JANUVIA</th>\n",
" <th>KETOCONAZOLE</th>\n",
" <th>KLOR.CON.10</th>\n",
" <th>KLOR.CON.M10</th>\n",
" <th>KLOR.CON.M20</th>\n",
" <th>LABETALOL.HCL</th>\n",
" <th>LACTULOSE</th>\n",
" <th>LAMOTRIGINE</th>\n",
" <th>LANSOPRAZOLE</th>\n",
" <th>LANTUS</th>\n",
" <th>LANTUS.SOLOSTAR</th>\n",
" <th>LATANOPROST</th>\n",
" <th>LEVEMIR</th>\n",
" <th>LEVEMIR.FLEXPEN</th>\n",
" <th>LEVETIRACETAM</th>\n",
" <th>LEVOFLOXACIN</th>\n",
" <th>LEVOTHYROXINE.SODIUM</th>\n",
" <th>LIDOCAINE</th>\n",
" <th>LISINOPRIL</th>\n",
" <th>LISINOPRIL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LITHIUM.CARBONATE</th>\n",
" <th>LORAZEPAM</th>\n",
" <th>LOSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LOSARTAN.POTASSIUM</th>\n",
" <th>LOVASTATIN</th>\n",
" <th>LOVAZA</th>\n",
" <th>LUMIGAN</th>\n",
" <th>LYRICA</th>\n",
" <th>MECLIZINE.HCL</th>\n",
" <th>MELOXICAM</th>\n",
" <th>METFORMIN.HCL</th>\n",
" <th>METFORMIN.HCL.ER</th>\n",
" <th>METHOCARBAMOL</th>\n",
" <th>METHOTREXATE</th>\n",
" <th>METHYLPREDNISOLONE</th>\n",
" <th>METOCLOPRAMIDE.HCL</th>\n",
" <th>METOLAZONE</th>\n",
" <th>METOPROLOL.SUCCINATE</th>\n",
" <th>METOPROLOL.TARTRATE</th>\n",
" <th>METRONIDAZOLE</th>\n",
" <th>MIRTAZAPINE</th>\n",
" <th>MONTELUKAST.SODIUM</th>\n",
" <th>MUPIROCIN</th>\n",
" <th>NABUMETONE</th>\n",
" <th>NAMENDA</th>\n",
" <th>NAMENDA.XR</th>\n",
" <th>NAPROXEN</th>\n",
" <th>NASONEX</th>\n",
" <th>NEXIUM</th>\n",
" <th>NIACIN.ER</th>\n",
" <th>NIFEDICAL.XL</th>\n",
" <th>NIFEDIPINE.ER</th>\n",
" <th>NITROFURANTOIN.MONO.MACRO</th>\n",
" <th>NITROSTAT</th>\n",
" <th>NORTRIPTYLINE.HCL</th>\n",
" <th>NOVOLOG</th>\n",
" <th>NOVOLOG.FLEXPEN</th>\n",
" <th>NYSTATIN</th>\n",
" <th>OLANZAPINE</th>\n",
" <th>OMEPRAZOLE</th>\n",
" <th>ONDANSETRON.HCL</th>\n",
" <th>ONDANSETRON.ODT</th>\n",
" <th>ONGLYZA</th>\n",
" <th>OXCARBAZEPINE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE.ER</th>\n",
" <th>PANTOPRAZOLE.SODIUM</th>\n",
" <th>PAROXETINE.HCL</th>\n",
" <th>PHENOBARBITAL</th>\n",
" <th>PHENYTOIN.SODIUM.EXTENDED</th>\n",
" <th>PIOGLITAZONE.HCL</th>\n",
" <th>POLYETHYLENE.GLYCOL.3350</th>\n",
" <th>POTASSIUM.CHLORIDE</th>\n",
" <th>PRADAXA</th>\n",
" <th>PRAMIPEXOLE.DIHYDROCHLORIDE</th>\n",
" <th>PRAVASTATIN.SODIUM</th>\n",
" <th>PREDNISONE</th>\n",
" <th>PREMARIN</th>\n",
" <th>PRIMIDONE</th>\n",
" <th>PROAIR.HFA</th>\n",
" <th>PROMETHAZINE.HCL</th>\n",
" <th>PROPRANOLOL.HCL</th>\n",
" <th>PROPRANOLOL.HCL.ER</th>\n",
" <th>QUETIAPINE.FUMARATE</th>\n",
" <th>QUINAPRIL.HCL</th>\n",
" <th>RALOXIFENE.HCL</th>\n",
" <th>RAMIPRIL</th>\n",
" <th>RANEXA</th>\n",
" <th>RANITIDINE.HCL</th>\n",
" <th>RESTASIS</th>\n",
" <th>RISPERIDONE</th>\n",
" <th>ROPINIROLE.HCL</th>\n",
" <th>SEROQUEL.XR</th>\n",
" <th>SERTRALINE.HCL</th>\n",
" <th>SIMVASTATIN</th>\n",
" <th>SOTALOL</th>\n",
" <th>SPIRIVA</th>\n",
" <th>SPIRONOLACTONE</th>\n",
" <th>SUCRALFATE</th>\n",
" <th>SULFAMETHOXAZOLE.TRIMETHOPRIM</th>\n",
" <th>SUMATRIPTAN.SUCCINATE</th>\n",
" <th>SYMBICORT</th>\n",
" <th>SYNTHROID</th>\n",
" <th>TAMSULOSIN.HCL</th>\n",
" <th>TEMAZEPAM</th>\n",
" <th>TERAZOSIN.HCL</th>\n",
" <th>TIMOLOL.MALEATE</th>\n",
" <th>TIZANIDINE.HCL</th>\n",
" <th>TOLTERODINE.TARTRATE.ER</th>\n",
" <th>TOPIRAMATE</th>\n",
" <th>TOPROL.XL</th>\n",
" <th>TORSEMIDE</th>\n",
" <th>TRAVATAN.Z</th>\n",
" <th>TRAZODONE.HCL</th>\n",
" <th>TRIAMCINOLONE.ACETONIDE</th>\n",
" <th>TRIAMTERENE.HYDROCHLOROTHIAZID</th>\n",
" <th>VALACYCLOVIR</th>\n",
" <th>VALSARTAN</th>\n",
" <th>VALSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>VENLAFAXINE.HCL</th>\n",
" <th>VENLAFAXINE.HCL.ER</th>\n",
" <th>VENTOLIN.HFA</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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>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>0</td>\n",
" <td>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
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" <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>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>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</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>11</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",
" <td>85</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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>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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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",
" <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>12</td>\n",
" <td>0</td>\n",
" <td>30</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>32</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",
" <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",
" <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",
" <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",
" <td>0</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",
" <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",
" <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",
" <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",
" <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>13</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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</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",
" <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>12</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>47</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>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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
" <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",
" <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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>48</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>46</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>57</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>55</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",
" <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>16</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
" <td>16</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",
" <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>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",
" <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",
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" </tr>\n",
" </tbody>\n",
"</table>\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 ALLOPURINOL \\\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",
" ALPRAZOLAM AMIODARONE.HCL AMITRIPTYLINE.HCL AMLODIPINE.BESYLATE \\\n",
"0 0 0 0 0 \n",
"1 134 0 0 15 \n",
"2 0 0 0 0 \n",
"3 0 0 0 58 \n",
"4 0 0 19 0 \n",
"\n",
" AMLODIPINE.BESYLATE.BENAZEPRIL AMOXICILLIN AMOX.TR.POTASSIUM.CLAVULANATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" AMPHETAMINE.SALT.COMBO ATENOLOL ATORVASTATIN.CALCIUM AVODART \\\n",
"0 0 0 0 0 \n",
"1 0 15 0 0 \n",
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"3 0 0 13 0 \n",
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"\n",
" AZITHROMYCIN BACLOFEN BD.ULTRA.FINE.PEN.NEEDLE BENAZEPRIL.HCL BENICAR \\\n",
"0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 \n",
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"\n",
" BENICAR.HCT BENZTROPINE.MESYLATE BISOPROLOL.HYDROCHLOROTHIAZIDE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" BRIMONIDINE.TARTRATE BUMETANIDE BUPROPION.HCL.SR BUPROPION.XL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 19 42 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" BUSPIRONE.HCL BYSTOLIC CARBAMAZEPINE CARBIDOPA.LEVODOPA CARISOPRODOL \\\n",
"0 0 0 0 0 0 \n",
"1 15 0 0 0 45 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" CARTIA.XT CARVEDILOL CEFUROXIME CELEBREX CEPHALEXIN \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 11 19 \n",
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"3 0 29 0 0 0 \n",
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"\n",
" CHLORHEXIDINE.GLUCONATE CHLORTHALIDONE CILOSTAZOL CIPROFLOXACIN.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 21 \n",
"2 0 0 0 0 \n",
"3 0 0 0 15 \n",
"4 0 0 0 0 \n",
"\n",
" CITALOPRAM.HBR CLINDAMYCIN.HCL CLOBETASOL.PROPIONATE CLONAZEPAM \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 16 \n",
"2 0 0 0 24 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLONIDINE.HCL CLOPIDOGREL CLOTRIMAZOLE.BETAMETHASONE COLCRYS \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" COMBIVENT.RESPIMAT CRESTOR CYCLOBENZAPRINE.HCL DEXILANT DIAZEPAM \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 22 0 20 \n",
"2 0 0 0 0 0 \n",
"3 0 21 12 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" DICLOFENAC.SODIUM DICYCLOMINE.HCL DIGOX DIGOXIN DILTIAZEM.24HR.CD \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" DILTIAZEM.24HR.ER DILTIAZEM.ER DILTIAZEM.HCL DIOVAN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DIPHENOXYLATE.ATROPINE DIVALPROEX.SODIUM DIVALPROEX.SODIUM.ER \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" DONEPEZIL.HCL DORZOLAMIDE.TIMOLOL DOXAZOSIN.MESYLATE DOXEPIN.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DOXYCYCLINE.HYCLATE DULOXETINE.HCL ENALAPRIL.MALEATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 12 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" ESCITALOPRAM.OXALATE ESTRADIOL EXELON FAMOTIDINE FELODIPINE.ER \\\n",
"0 0 0 0 0 0 \n",
"1 22 14 0 0 0 \n",
"2 30 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FENOFIBRATE FINASTERIDE FLOVENT.HFA FLUCONAZOLE FLUOXETINE.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 31 \n",
"2 0 0 0 0 32 \n",
"3 0 13 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FLUTICASONE.PROPIONATE FUROSEMIDE GABAPENTIN GEMFIBROZIL GLIMEPIRIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 63 0 0 \n",
"2 0 0 0 0 0 \n",
"3 42 48 81 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" GLIPIZIDE GLIPIZIDE.ER GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG \\\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 31 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" HYDRALAZINE.HCL HYDROCHLOROTHIAZIDE HYDROCORTISONE HYDROXYZINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 36 0 0 \n",
"2 0 0 0 0 \n",
"3 15 34 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IBANDRONATE.SODIUM IBUPROFEN INSULIN.SYRINGE IPRATROPIUM.BROMIDE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 15 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IRBESARTAN ISOSORBIDE.MONONITRATE.ER JANTOVEN JANUMET JANUVIA \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" KETOCONAZOLE KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" LACTULOSE LAMOTRIGINE LANSOPRAZOLE LANTUS LANTUS.SOLOSTAR LATANOPROST \\\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 13 0 0 15 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" LEVEMIR LEVEMIR.FLEXPEN LEVETIRACETAM LEVOFLOXACIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 47 0 24 0 \n",
"4 0 0 0 0 \n",
"\n",
" LEVOTHYROXINE.SODIUM LIDOCAINE LISINOPRIL \\\n",
"0 0 0 0 \n",
"1 31 0 52 \n",
"2 0 0 0 \n",
"3 0 19 110 \n",
"4 0 0 0 \n",
"\n",
" LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE LORAZEPAM \\\n",
"0 0 0 12 \n",
"1 45 0 27 \n",
"2 0 0 19 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN LOVAZA \\\n",
"0 0 0 0 0 \n",
"1 0 0 11 0 \n",
"2 0 0 0 0 \n",
"3 0 46 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL METFORMIN.HCL.ER \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 13 0 0 39 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 57 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" METHOCARBAMOL METHOTREXATE METHYLPREDNISOLONE METOCLOPRAMIDE.HCL \\\n",
"0 0 0 0 0 \n",
"1 15 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" METOLAZONE METOPROLOL.SUCCINATE METOPROLOL.TARTRATE METRONIDAZOLE \\\n",
"0 0 0 0 0 \n",
"1 0 0 18 0 \n",
"2 0 0 0 0 \n",
"3 0 0 55 0 \n",
"4 0 0 0 0 \n",
"\n",
" MIRTAZAPINE MONTELUKAST.SODIUM MUPIROCIN NABUMETONE NAMENDA \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" NAMENDA.XR NAPROXEN NASONEX NEXIUM NIACIN.ER NIFEDICAL.XL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 0 11 0 0 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" NIFEDIPINE.ER NITROFURANTOIN.MONO.MACRO NITROSTAT NORTRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" NOVOLOG NOVOLOG.FLEXPEN NYSTATIN OLANZAPINE OMEPRAZOLE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 85 \n",
"2 0 0 0 0 0 \n",
"3 16 12 12 0 52 \n",
"4 0 0 0 0 0 \n",
"\n",
" ONDANSETRON.HCL ONDANSETRON.ODT ONGLYZA OXCARBAZEPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 23 0 0 0 \n",
"\n",
" OXYBUTYNIN.CHLORIDE OXYBUTYNIN.CHLORIDE.ER PANTOPRAZOLE.SODIUM \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 15 0 0 \n",
"4 0 0 0 \n",
"\n",
" PAROXETINE.HCL PHENOBARBITAL PHENYTOIN.SODIUM.EXTENDED PIOGLITAZONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 13 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" POLYETHYLENE.GLYCOL.3350 POTASSIUM.CHLORIDE PRADAXA \\\n",
"0 0 0 0 \n",
"1 0 0 13 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" PRAMIPEXOLE.DIHYDROCHLORIDE PRAVASTATIN.SODIUM PREDNISONE PREMARIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 13 0 \n",
"4 0 0 0 0 \n",
"\n",
" PRIMIDONE PROAIR.HFA PROMETHAZINE.HCL PROPRANOLOL.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 23 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 11 0 \n",
"4 0 0 0 0 \n",
"\n",
" PROPRANOLOL.HCL.ER QUETIAPINE.FUMARATE QUINAPRIL.HCL RALOXIFENE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" RAMIPRIL RANEXA RANITIDINE.HCL RESTASIS RISPERIDONE ROPINIROLE.HCL \\\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 25 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SEROQUEL.XR SERTRALINE.HCL SIMVASTATIN SOTALOL SPIRIVA SPIRONOLACTONE \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 29 51 0 0 0 \n",
"2 0 14 0 0 0 0 \n",
"3 0 15 80 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SUCRALFATE SULFAMETHOXAZOLE.TRIMETHOPRIM SUMATRIPTAN.SUCCINATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 14 0 \n",
"\n",
" SYMBICORT SYNTHROID TAMSULOSIN.HCL TEMAZEPAM TERAZOSIN.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 52 18 0 0 \n",
"4 0 0 0 12 0 \n",
"\n",
" TIMOLOL.MALEATE TIZANIDINE.HCL TOLTERODINE.TARTRATE.ER TOPIRAMATE \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" TOPROL.XL TORSEMIDE TRAVATAN.Z TRAZODONE.HCL TRIAMCINOLONE.ACETONIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 31 0 \n",
"2 0 0 0 12 0 \n",
"3 0 0 0 0 16 \n",
"4 0 0 0 0 0 \n",
"\n",
" TRIAMTERENE.HYDROCHLOROTHIAZID VALACYCLOVIR VALSARTAN \\\n",
"0 0 0 0 \n",
"1 12 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VALSARTAN.HYDROCHLOROTHIAZIDE VENLAFAXINE.HCL VENLAFAXINE.HCL.ER \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 47 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VENTOLIN.HFA VERAPAMIL.ER VESICARE VOLTAREN VYTORIN WARFARIN.SODIUM \\\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 0 0 17 \n",
"\n",
" XARELTO ZETIA ZIPRASIDONE.HCL ZOLPIDEM.TARTRATE Opioid.Prescriber \n",
"0 0 0 0 0 1 \n",
"1 0 0 0 35 1 \n",
"2 0 0 0 25 0 \n",
"3 0 0 0 0 1 \n",
"4 28 0 0 0 1 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pinfo[kept_variable]\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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>ALLOPURINOL</th>\n",
" <th>ALPRAZOLAM</th>\n",
" <th>AMIODARONE.HCL</th>\n",
" <th>AMITRIPTYLINE.HCL</th>\n",
" <th>AMLODIPINE.BESYLATE</th>\n",
" <th>AMLODIPINE.BESYLATE.BENAZEPRIL</th>\n",
" <th>AMOXICILLIN</th>\n",
" <th>AMOX.TR.POTASSIUM.CLAVULANATE</th>\n",
" <th>AMPHETAMINE.SALT.COMBO</th>\n",
" <th>ATENOLOL</th>\n",
" <th>ATORVASTATIN.CALCIUM</th>\n",
" <th>AVODART</th>\n",
" <th>AZITHROMYCIN</th>\n",
" <th>BACLOFEN</th>\n",
" <th>BD.ULTRA.FINE.PEN.NEEDLE</th>\n",
" <th>BENAZEPRIL.HCL</th>\n",
" <th>BENICAR</th>\n",
" <th>BENICAR.HCT</th>\n",
" <th>BENZTROPINE.MESYLATE</th>\n",
" <th>BISOPROLOL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>BRIMONIDINE.TARTRATE</th>\n",
" <th>BUMETANIDE</th>\n",
" <th>BUPROPION.HCL.SR</th>\n",
" <th>BUPROPION.XL</th>\n",
" <th>BUSPIRONE.HCL</th>\n",
" <th>BYSTOLIC</th>\n",
" <th>CARBAMAZEPINE</th>\n",
" <th>CARBIDOPA.LEVODOPA</th>\n",
" <th>CARISOPRODOL</th>\n",
" <th>CARTIA.XT</th>\n",
" <th>CARVEDILOL</th>\n",
" <th>CEFUROXIME</th>\n",
" <th>CELEBREX</th>\n",
" <th>CEPHALEXIN</th>\n",
" <th>CHLORHEXIDINE.GLUCONATE</th>\n",
" <th>CHLORTHALIDONE</th>\n",
" <th>CILOSTAZOL</th>\n",
" <th>CIPROFLOXACIN.HCL</th>\n",
" <th>CITALOPRAM.HBR</th>\n",
" <th>CLINDAMYCIN.HCL</th>\n",
" <th>CLOBETASOL.PROPIONATE</th>\n",
" <th>CLONAZEPAM</th>\n",
" <th>CLONIDINE.HCL</th>\n",
" <th>CLOPIDOGREL</th>\n",
" <th>CLOTRIMAZOLE.BETAMETHASONE</th>\n",
" <th>COLCRYS</th>\n",
" <th>COMBIVENT.RESPIMAT</th>\n",
" <th>CRESTOR</th>\n",
" <th>CYCLOBENZAPRINE.HCL</th>\n",
" <th>DEXILANT</th>\n",
" <th>DIAZEPAM</th>\n",
" <th>DICLOFENAC.SODIUM</th>\n",
" <th>DICYCLOMINE.HCL</th>\n",
" <th>DIGOX</th>\n",
" <th>DIGOXIN</th>\n",
" <th>DILTIAZEM.24HR.CD</th>\n",
" <th>DILTIAZEM.24HR.ER</th>\n",
" <th>DILTIAZEM.ER</th>\n",
" <th>DILTIAZEM.HCL</th>\n",
" <th>DIOVAN</th>\n",
" <th>DIPHENOXYLATE.ATROPINE</th>\n",
" <th>DIVALPROEX.SODIUM</th>\n",
" <th>DIVALPROEX.SODIUM.ER</th>\n",
" <th>DONEPEZIL.HCL</th>\n",
" <th>DORZOLAMIDE.TIMOLOL</th>\n",
" <th>DOXAZOSIN.MESYLATE</th>\n",
" <th>DOXEPIN.HCL</th>\n",
" <th>DOXYCYCLINE.HYCLATE</th>\n",
" <th>DULOXETINE.HCL</th>\n",
" <th>ENALAPRIL.MALEATE</th>\n",
" <th>ESCITALOPRAM.OXALATE</th>\n",
" <th>ESTRADIOL</th>\n",
" <th>EXELON</th>\n",
" <th>FAMOTIDINE</th>\n",
" <th>FELODIPINE.ER</th>\n",
" <th>FENOFIBRATE</th>\n",
" <th>FINASTERIDE</th>\n",
" <th>FLOVENT.HFA</th>\n",
" <th>FLUCONAZOLE</th>\n",
" <th>FLUOXETINE.HCL</th>\n",
" <th>FLUTICASONE.PROPIONATE</th>\n",
" <th>FUROSEMIDE</th>\n",
" <th>GABAPENTIN</th>\n",
" <th>GEMFIBROZIL</th>\n",
" <th>GLIMEPIRIDE</th>\n",
" <th>GLIPIZIDE</th>\n",
" <th>GLIPIZIDE.ER</th>\n",
" <th>GLIPIZIDE.XL</th>\n",
" <th>GLYBURIDE</th>\n",
" <th>HALOPERIDOL</th>\n",
" <th>HUMALOG</th>\n",
" <th>HYDRALAZINE.HCL</th>\n",
" <th>HYDROCHLOROTHIAZIDE</th>\n",
" <th>HYDROCORTISONE</th>\n",
" <th>HYDROXYZINE.HCL</th>\n",
" <th>IBANDRONATE.SODIUM</th>\n",
" <th>IBUPROFEN</th>\n",
" <th>INSULIN.SYRINGE</th>\n",
" <th>IPRATROPIUM.BROMIDE</th>\n",
" <th>IRBESARTAN</th>\n",
" <th>ISOSORBIDE.MONONITRATE.ER</th>\n",
" <th>JANTOVEN</th>\n",
" <th>JANUMET</th>\n",
" <th>JANUVIA</th>\n",
" <th>KETOCONAZOLE</th>\n",
" <th>KLOR.CON.10</th>\n",
" <th>KLOR.CON.M10</th>\n",
" <th>KLOR.CON.M20</th>\n",
" <th>LABETALOL.HCL</th>\n",
" <th>LACTULOSE</th>\n",
" <th>LAMOTRIGINE</th>\n",
" <th>LANSOPRAZOLE</th>\n",
" <th>LANTUS</th>\n",
" <th>LANTUS.SOLOSTAR</th>\n",
" <th>LATANOPROST</th>\n",
" <th>LEVEMIR</th>\n",
" <th>LEVEMIR.FLEXPEN</th>\n",
" <th>LEVETIRACETAM</th>\n",
" <th>LEVOFLOXACIN</th>\n",
" <th>LEVOTHYROXINE.SODIUM</th>\n",
" <th>LIDOCAINE</th>\n",
" <th>LISINOPRIL</th>\n",
" <th>LISINOPRIL.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LITHIUM.CARBONATE</th>\n",
" <th>LORAZEPAM</th>\n",
" <th>LOSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>LOSARTAN.POTASSIUM</th>\n",
" <th>LOVASTATIN</th>\n",
" <th>LOVAZA</th>\n",
" <th>LUMIGAN</th>\n",
" <th>LYRICA</th>\n",
" <th>MECLIZINE.HCL</th>\n",
" <th>MELOXICAM</th>\n",
" <th>METFORMIN.HCL</th>\n",
" <th>METFORMIN.HCL.ER</th>\n",
" <th>METHOCARBAMOL</th>\n",
" <th>METHOTREXATE</th>\n",
" <th>METHYLPREDNISOLONE</th>\n",
" <th>METOCLOPRAMIDE.HCL</th>\n",
" <th>METOLAZONE</th>\n",
" <th>METOPROLOL.SUCCINATE</th>\n",
" <th>METOPROLOL.TARTRATE</th>\n",
" <th>METRONIDAZOLE</th>\n",
" <th>MIRTAZAPINE</th>\n",
" <th>MONTELUKAST.SODIUM</th>\n",
" <th>MUPIROCIN</th>\n",
" <th>NABUMETONE</th>\n",
" <th>NAMENDA</th>\n",
" <th>NAMENDA.XR</th>\n",
" <th>NAPROXEN</th>\n",
" <th>NASONEX</th>\n",
" <th>NEXIUM</th>\n",
" <th>NIACIN.ER</th>\n",
" <th>NIFEDICAL.XL</th>\n",
" <th>NIFEDIPINE.ER</th>\n",
" <th>NITROFURANTOIN.MONO.MACRO</th>\n",
" <th>NITROSTAT</th>\n",
" <th>NORTRIPTYLINE.HCL</th>\n",
" <th>NOVOLOG</th>\n",
" <th>NOVOLOG.FLEXPEN</th>\n",
" <th>NYSTATIN</th>\n",
" <th>OLANZAPINE</th>\n",
" <th>OMEPRAZOLE</th>\n",
" <th>ONDANSETRON.HCL</th>\n",
" <th>ONDANSETRON.ODT</th>\n",
" <th>ONGLYZA</th>\n",
" <th>OXCARBAZEPINE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE</th>\n",
" <th>OXYBUTYNIN.CHLORIDE.ER</th>\n",
" <th>PANTOPRAZOLE.SODIUM</th>\n",
" <th>PAROXETINE.HCL</th>\n",
" <th>PHENOBARBITAL</th>\n",
" <th>PHENYTOIN.SODIUM.EXTENDED</th>\n",
" <th>PIOGLITAZONE.HCL</th>\n",
" <th>POLYETHYLENE.GLYCOL.3350</th>\n",
" <th>POTASSIUM.CHLORIDE</th>\n",
" <th>PRADAXA</th>\n",
" <th>PRAMIPEXOLE.DIHYDROCHLORIDE</th>\n",
" <th>PRAVASTATIN.SODIUM</th>\n",
" <th>PREDNISONE</th>\n",
" <th>PREMARIN</th>\n",
" <th>PRIMIDONE</th>\n",
" <th>PROAIR.HFA</th>\n",
" <th>PROMETHAZINE.HCL</th>\n",
" <th>PROPRANOLOL.HCL</th>\n",
" <th>PROPRANOLOL.HCL.ER</th>\n",
" <th>QUETIAPINE.FUMARATE</th>\n",
" <th>QUINAPRIL.HCL</th>\n",
" <th>RALOXIFENE.HCL</th>\n",
" <th>RAMIPRIL</th>\n",
" <th>RANEXA</th>\n",
" <th>RANITIDINE.HCL</th>\n",
" <th>RESTASIS</th>\n",
" <th>RISPERIDONE</th>\n",
" <th>ROPINIROLE.HCL</th>\n",
" <th>SEROQUEL.XR</th>\n",
" <th>SERTRALINE.HCL</th>\n",
" <th>SIMVASTATIN</th>\n",
" <th>SOTALOL</th>\n",
" <th>SPIRIVA</th>\n",
" <th>SPIRONOLACTONE</th>\n",
" <th>SUCRALFATE</th>\n",
" <th>SULFAMETHOXAZOLE.TRIMETHOPRIM</th>\n",
" <th>SUMATRIPTAN.SUCCINATE</th>\n",
" <th>SYMBICORT</th>\n",
" <th>SYNTHROID</th>\n",
" <th>TAMSULOSIN.HCL</th>\n",
" <th>TEMAZEPAM</th>\n",
" <th>TERAZOSIN.HCL</th>\n",
" <th>TIMOLOL.MALEATE</th>\n",
" <th>TIZANIDINE.HCL</th>\n",
" <th>TOLTERODINE.TARTRATE.ER</th>\n",
" <th>TOPIRAMATE</th>\n",
" <th>TOPROL.XL</th>\n",
" <th>TORSEMIDE</th>\n",
" <th>TRAVATAN.Z</th>\n",
" <th>TRAZODONE.HCL</th>\n",
" <th>TRIAMCINOLONE.ACETONIDE</th>\n",
" <th>TRIAMTERENE.HYDROCHLOROTHIAZID</th>\n",
" <th>VALACYCLOVIR</th>\n",
" <th>VALSARTAN</th>\n",
" <th>VALSARTAN.HYDROCHLOROTHIAZIDE</th>\n",
" <th>VENLAFAXINE.HCL</th>\n",
" <th>VENLAFAXINE.HCL.ER</th>\n",
" <th>VENTOLIN.HFA</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",
" </tr>\n",
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" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</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>11</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",
" <td>85</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\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>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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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",
" <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>12</td>\n",
" <td>0</td>\n",
" <td>30</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>32</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",
" <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",
" <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",
" <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",
" <td>0</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",
" <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",
" <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",
" <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",
" <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>13</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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</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",
" <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>12</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>47</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>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
" <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",
" <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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>48</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>46</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>57</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>55</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",
" <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>16</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
" <td>16</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",
" <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>4</th>\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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
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" <td>0</td>\n",
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" <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>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",
" <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>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <td>0</td>\n",
" <td>17</td>\n",
" <td>28</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Specialty ABILIFY ACYCLOVIR ADVAIR.DISKUS AGGRENOX \\\n",
"0 Dentist 0 0 0 0 \n",
"1 General Surgery 0 0 0 0 \n",
"2 General Practice 0 0 0 0 \n",
"3 Internal Medicine 0 0 0 0 \n",
"4 Hematology/Oncology 0 0 0 0 \n",
"\n",
" ALENDRONATE.SODIUM ALLOPURINOL ALPRAZOLAM AMIODARONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 134 0 \n",
"2 0 0 0 0 \n",
"3 21 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" AMITRIPTYLINE.HCL AMLODIPINE.BESYLATE AMLODIPINE.BESYLATE.BENAZEPRIL \\\n",
"0 0 0 0 \n",
"1 0 15 0 \n",
"2 0 0 0 \n",
"3 0 58 0 \n",
"4 19 0 0 \n",
"\n",
" AMOXICILLIN AMOX.TR.POTASSIUM.CLAVULANATE AMPHETAMINE.SALT.COMBO \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" ATENOLOL ATORVASTATIN.CALCIUM AVODART AZITHROMYCIN BACLOFEN \\\n",
"0 0 0 0 0 0 \n",
"1 15 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 13 0 0 27 \n",
"4 0 0 0 0 0 \n",
"\n",
" BD.ULTRA.FINE.PEN.NEEDLE BENAZEPRIL.HCL BENICAR BENICAR.HCT \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" BENZTROPINE.MESYLATE BISOPROLOL.HYDROCHLOROTHIAZIDE BRIMONIDINE.TARTRATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" BUMETANIDE BUPROPION.HCL.SR BUPROPION.XL BUSPIRONE.HCL BYSTOLIC \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 15 0 \n",
"2 0 19 42 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" CARBAMAZEPINE CARBIDOPA.LEVODOPA CARISOPRODOL CARTIA.XT CARVEDILOL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 45 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 29 \n",
"4 0 0 0 0 0 \n",
"\n",
" CEFUROXIME CELEBREX CEPHALEXIN CHLORHEXIDINE.GLUCONATE CHLORTHALIDONE \\\n",
"0 0 0 0 0 0 \n",
"1 0 11 19 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" CILOSTAZOL CIPROFLOXACIN.HCL CITALOPRAM.HBR CLINDAMYCIN.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 21 0 0 \n",
"2 0 0 0 0 \n",
"3 0 15 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOBETASOL.PROPIONATE CLONAZEPAM CLONIDINE.HCL CLOPIDOGREL \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 24 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOTRIMAZOLE.BETAMETHASONE COLCRYS COMBIVENT.RESPIMAT CRESTOR \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 21 \n",
"4 0 0 0 0 \n",
"\n",
" CYCLOBENZAPRINE.HCL DEXILANT DIAZEPAM DICLOFENAC.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 22 0 20 0 \n",
"2 0 0 0 0 \n",
"3 12 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DICYCLOMINE.HCL DIGOX DIGOXIN DILTIAZEM.24HR.CD DILTIAZEM.24HR.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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" DILTIAZEM.ER DILTIAZEM.HCL DIOVAN DIPHENOXYLATE.ATROPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DIVALPROEX.SODIUM DIVALPROEX.SODIUM.ER DONEPEZIL.HCL \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" DORZOLAMIDE.TIMOLOL DOXAZOSIN.MESYLATE DOXEPIN.HCL DOXYCYCLINE.HYCLATE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DULOXETINE.HCL ENALAPRIL.MALEATE ESCITALOPRAM.OXALATE ESTRADIOL EXELON \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 22 14 0 \n",
"2 12 0 30 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FAMOTIDINE FELODIPINE.ER FENOFIBRATE FINASTERIDE FLOVENT.HFA \\\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 13 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FLUCONAZOLE FLUOXETINE.HCL FLUTICASONE.PROPIONATE FUROSEMIDE \\\n",
"0 0 0 0 0 \n",
"1 0 31 0 0 \n",
"2 0 32 0 0 \n",
"3 0 0 42 48 \n",
"4 0 0 0 0 \n",
"\n",
" GABAPENTIN GEMFIBROZIL GLIMEPIRIDE GLIPIZIDE GLIPIZIDE.ER \\\n",
"0 0 0 0 0 0 \n",
"1 63 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 81 0 0 0 31 \n",
"4 0 0 0 0 0 \n",
"\n",
" GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG HYDRALAZINE.HCL \\\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 0 15 \n",
"4 0 0 0 0 0 \n",
"\n",
" HYDROCHLOROTHIAZIDE HYDROCORTISONE HYDROXYZINE.HCL IBANDRONATE.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 36 0 0 0 \n",
"2 0 0 0 0 \n",
"3 34 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IBUPROFEN INSULIN.SYRINGE IPRATROPIUM.BROMIDE IRBESARTAN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 15 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" ISOSORBIDE.MONONITRATE.ER JANTOVEN JANUMET JANUVIA KETOCONAZOLE \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL LACTULOSE \\\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 0 13 \n",
"4 0 0 0 0 0 \n",
"\n",
" LAMOTRIGINE LANSOPRAZOLE LANTUS LANTUS.SOLOSTAR LATANOPROST LEVEMIR \\\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 15 0 0 47 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" LEVEMIR.FLEXPEN LEVETIRACETAM LEVOFLOXACIN LEVOTHYROXINE.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 31 \n",
"2 0 0 0 0 \n",
"3 0 24 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LIDOCAINE LISINOPRIL LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE \\\n",
"0 0 0 0 0 \n",
"1 0 52 45 0 \n",
"2 0 0 0 0 \n",
"3 19 110 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LORAZEPAM LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN \\\n",
"0 12 0 0 0 \n",
"1 27 0 0 11 \n",
"2 19 0 0 0 \n",
"3 0 0 46 0 \n",
"4 0 0 0 0 \n",
"\n",
" LOVAZA LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 13 0 0 39 \n",
"2 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 57 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" METFORMIN.HCL.ER METHOCARBAMOL METHOTREXATE METHYLPREDNISOLONE \\\n",
"0 0 0 0 0 \n",
"1 0 15 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" METOCLOPRAMIDE.HCL METOLAZONE METOPROLOL.SUCCINATE METOPROLOL.TARTRATE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 18 \n",
"2 0 0 0 0 \n",
"3 0 0 0 55 \n",
"4 0 0 0 0 \n",
"\n",
" METRONIDAZOLE MIRTAZAPINE MONTELUKAST.SODIUM MUPIROCIN NABUMETONE \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" NAMENDA NAMENDA.XR NAPROXEN NASONEX NEXIUM NIACIN.ER NIFEDICAL.XL \\\n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 11 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 0 0 0 \n",
"\n",
" NIFEDIPINE.ER NITROFURANTOIN.MONO.MACRO NITROSTAT NORTRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" NOVOLOG NOVOLOG.FLEXPEN NYSTATIN OLANZAPINE OMEPRAZOLE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 85 \n",
"2 0 0 0 0 0 \n",
"3 16 12 12 0 52 \n",
"4 0 0 0 0 0 \n",
"\n",
" ONDANSETRON.HCL ONDANSETRON.ODT ONGLYZA OXCARBAZEPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 23 0 0 0 \n",
"\n",
" OXYBUTYNIN.CHLORIDE OXYBUTYNIN.CHLORIDE.ER PANTOPRAZOLE.SODIUM \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 15 0 0 \n",
"4 0 0 0 \n",
"\n",
" PAROXETINE.HCL PHENOBARBITAL PHENYTOIN.SODIUM.EXTENDED PIOGLITAZONE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 13 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" POLYETHYLENE.GLYCOL.3350 POTASSIUM.CHLORIDE PRADAXA \\\n",
"0 0 0 0 \n",
"1 0 0 13 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" PRAMIPEXOLE.DIHYDROCHLORIDE PRAVASTATIN.SODIUM PREDNISONE PREMARIN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 13 0 \n",
"4 0 0 0 0 \n",
"\n",
" PRIMIDONE PROAIR.HFA PROMETHAZINE.HCL PROPRANOLOL.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 23 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 11 0 \n",
"4 0 0 0 0 \n",
"\n",
" PROPRANOLOL.HCL.ER QUETIAPINE.FUMARATE QUINAPRIL.HCL RALOXIFENE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" RAMIPRIL RANEXA RANITIDINE.HCL RESTASIS RISPERIDONE ROPINIROLE.HCL \\\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 25 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SEROQUEL.XR SERTRALINE.HCL SIMVASTATIN SOTALOL SPIRIVA SPIRONOLACTONE \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 29 51 0 0 0 \n",
"2 0 14 0 0 0 0 \n",
"3 0 15 80 0 0 0 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" SUCRALFATE SULFAMETHOXAZOLE.TRIMETHOPRIM SUMATRIPTAN.SUCCINATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 14 0 \n",
"\n",
" SYMBICORT SYNTHROID TAMSULOSIN.HCL TEMAZEPAM TERAZOSIN.HCL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 52 18 0 0 \n",
"4 0 0 0 12 0 \n",
"\n",
" TIMOLOL.MALEATE TIZANIDINE.HCL TOLTERODINE.TARTRATE.ER TOPIRAMATE \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" TOPROL.XL TORSEMIDE TRAVATAN.Z TRAZODONE.HCL TRIAMCINOLONE.ACETONIDE \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 31 0 \n",
"2 0 0 0 12 0 \n",
"3 0 0 0 0 16 \n",
"4 0 0 0 0 0 \n",
"\n",
" TRIAMTERENE.HYDROCHLOROTHIAZID VALACYCLOVIR VALSARTAN \\\n",
"0 0 0 0 \n",
"1 12 0 0 \n",
"2 0 0 0 \n",
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"\n",
" VALSARTAN.HYDROCHLOROTHIAZIDE VENLAFAXINE.HCL VENLAFAXINE.HCL.ER \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 47 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" VENTOLIN.HFA VERAPAMIL.ER VESICARE VOLTAREN VYTORIN WARFARIN.SODIUM \\\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 0 0 17 \n",
"\n",
" XARELTO ZETIA ZIPRASIDONE.HCL ZOLPIDEM.TARTRATE \n",
"0 0 0 0 0 \n",
"1 0 0 0 35 \n",
"2 0 0 0 25 \n",
"3 0 0 0 0 \n",
"4 28 0 0 0 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c = ['NPI','Gender','State','Credentials','Opioid.Prescriber']\n",
"df3 = df.drop(c,axis=1)\n",
"df3.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 12\n",
"1 1056\n",
"2 289\n",
"3 1198\n",
"4 113\n",
"dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df4 = df3.drop('Specialty',axis=1)\n",
"all_ops = df4[df4.columns.tolist()].sum(axis=1)\n",
"all_ops.head()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" total_opioids Specialty\n",
"0 12 Dentist\n",
"1 1056 General Surgery\n",
"2 289 General Practice\n",
"3 1198 Internal Medicine\n",
"4 113 Hematology/Oncology"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df6 = df3[['Specialty']]\n",
"df5 = pd.concat([all_ops, df6], axis = 1)\n",
"df5 = df5.rename(columns={0: 'total_opioids'})\n",
"df5.head()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" total_opioids\n",
"total_opioids \n",
"0 0\n",
"11 7788\n",
"12 7464\n",
"13 4940\n",
"14 4732"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df5_new = df5[['Specialty','total_opioids']].groupby(df5['total_opioids']).sum()\n",
"df5_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
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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>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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>1</th>\n",
" <td>AL</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>134</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>19</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</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>22</td>\n",
" <td>0</td>\n",
" <td>20</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",
" <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>22</td>\n",
" <td>14</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>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>63</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>36</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",
" <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",
" <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>31</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>45</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>39</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>18</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>11</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",
" <td>85</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" <td>51</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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>NY</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",
" <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",
" <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>19</td>\n",
" <td>42</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",
" <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>24</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",
" <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",
" <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>12</td>\n",
" <td>0</td>\n",
" <td>30</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>32</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",
" <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",
" <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",
" <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",
" <td>0</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",
" <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",
" <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",
" <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",
" <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>13</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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14</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",
" <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>12</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>47</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>25</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AZ</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>58</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</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",
" <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>29</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>15</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>21</td>\n",
" <td>12</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",
" <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",
" <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>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>48</td>\n",
" <td>81</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>34</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" <td>110</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>46</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>57</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>55</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",
" <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>16</td>\n",
" <td>12</td>\n",
" <td>12</td>\n",
" <td>0</td>\n",
" <td>52</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</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",
" <td>0</td>\n",
" <td>13</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>11</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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>80</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>52</td>\n",
" <td>18</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",
" <td>16</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",
" <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>NV</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>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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <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",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23</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",
" <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",
" <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",
" <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>14</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>12</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",
" <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",
" <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",
"</div>"
],
"text/plain": [
" State ABILIFY ACYCLOVIR ADVAIR.DISKUS AGGRENOX ALENDRONATE.SODIUM \\\n",
"0 TX 0 0 0 0 0 \n",
"1 AL 0 0 0 0 0 \n",
"2 NY 0 0 0 0 0 \n",
"3 AZ 0 0 0 0 21 \n",
"4 NV 0 0 0 0 0 \n",
"\n",
" ALLOPURINOL ALPRAZOLAM AMIODARONE.HCL AMITRIPTYLINE.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 134 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 19 \n",
"\n",
" AMLODIPINE.BESYLATE AMLODIPINE.BESYLATE.BENAZEPRIL AMOXICILLIN \\\n",
"0 0 0 0 \n",
"1 15 0 0 \n",
"2 0 0 0 \n",
"3 58 0 0 \n",
"4 0 0 0 \n",
"\n",
" AMOX.TR.POTASSIUM.CLAVULANATE AMPHETAMINE.SALT.COMBO ATENOLOL \\\n",
"0 0 0 0 \n",
"1 0 0 15 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" ATORVASTATIN.CALCIUM AVODART AZITHROMYCIN BACLOFEN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 13 0 0 27 \n",
"4 0 0 0 0 \n",
"\n",
" BD.ULTRA.FINE.PEN.NEEDLE BENAZEPRIL.HCL BENICAR BENICAR.HCT \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" BENZTROPINE.MESYLATE BISOPROLOL.HYDROCHLOROTHIAZIDE BRIMONIDINE.TARTRATE \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" BUMETANIDE BUPROPION.HCL.SR BUPROPION.XL BUSPIRONE.HCL BYSTOLIC \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 0 15 0 \n",
"2 0 19 42 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" CARBAMAZEPINE CARBIDOPA.LEVODOPA CARISOPRODOL CARTIA.XT CARVEDILOL \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 45 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 29 \n",
"4 0 0 0 0 0 \n",
"\n",
" CEFUROXIME CELEBREX CEPHALEXIN CHLORHEXIDINE.GLUCONATE CHLORTHALIDONE \\\n",
"0 0 0 0 0 0 \n",
"1 0 11 19 0 0 \n",
"2 0 0 0 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" CILOSTAZOL CIPROFLOXACIN.HCL CITALOPRAM.HBR CLINDAMYCIN.HCL \\\n",
"0 0 0 0 0 \n",
"1 0 21 0 0 \n",
"2 0 0 0 0 \n",
"3 0 15 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOBETASOL.PROPIONATE CLONAZEPAM CLONIDINE.HCL CLOPIDOGREL \\\n",
"0 0 0 0 0 \n",
"1 0 16 0 0 \n",
"2 0 24 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" CLOTRIMAZOLE.BETAMETHASONE COLCRYS COMBIVENT.RESPIMAT CRESTOR \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 21 \n",
"4 0 0 0 0 \n",
"\n",
" CYCLOBENZAPRINE.HCL DEXILANT DIAZEPAM DICLOFENAC.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 22 0 20 0 \n",
"2 0 0 0 0 \n",
"3 12 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DICYCLOMINE.HCL DIGOX DIGOXIN DILTIAZEM.24HR.CD DILTIAZEM.24HR.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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" DILTIAZEM.ER DILTIAZEM.HCL DIOVAN DIPHENOXYLATE.ATROPINE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DIVALPROEX.SODIUM DIVALPROEX.SODIUM.ER DONEPEZIL.HCL \\\n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"2 0 0 0 \n",
"3 0 0 0 \n",
"4 0 0 0 \n",
"\n",
" DORZOLAMIDE.TIMOLOL DOXAZOSIN.MESYLATE DOXEPIN.HCL DOXYCYCLINE.HYCLATE \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 0 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" DULOXETINE.HCL ENALAPRIL.MALEATE ESCITALOPRAM.OXALATE ESTRADIOL EXELON \\\n",
"0 0 0 0 0 0 \n",
"1 0 0 22 14 0 \n",
"2 12 0 30 0 0 \n",
"3 0 0 0 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FAMOTIDINE FELODIPINE.ER FENOFIBRATE FINASTERIDE FLOVENT.HFA \\\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 13 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" FLUCONAZOLE FLUOXETINE.HCL FLUTICASONE.PROPIONATE FUROSEMIDE \\\n",
"0 0 0 0 0 \n",
"1 0 31 0 0 \n",
"2 0 32 0 0 \n",
"3 0 0 42 48 \n",
"4 0 0 0 0 \n",
"\n",
" GABAPENTIN GEMFIBROZIL GLIMEPIRIDE GLIPIZIDE GLIPIZIDE.ER \\\n",
"0 0 0 0 0 0 \n",
"1 63 0 0 0 0 \n",
"2 0 0 0 0 0 \n",
"3 81 0 0 0 31 \n",
"4 0 0 0 0 0 \n",
"\n",
" GLIPIZIDE.XL GLYBURIDE HALOPERIDOL HUMALOG HYDRALAZINE.HCL \\\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 0 15 \n",
"4 0 0 0 0 0 \n",
"\n",
" HYDROCHLOROTHIAZIDE HYDROCORTISONE HYDROXYZINE.HCL IBANDRONATE.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 36 0 0 0 \n",
"2 0 0 0 0 \n",
"3 34 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" IBUPROFEN INSULIN.SYRINGE IPRATROPIUM.BROMIDE IRBESARTAN \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"2 0 0 0 0 \n",
"3 15 0 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" ISOSORBIDE.MONONITRATE.ER JANTOVEN JANUMET JANUVIA KETOCONAZOLE \\\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 0 0 \n",
"4 0 0 0 0 0 \n",
"\n",
" KLOR.CON.10 KLOR.CON.M10 KLOR.CON.M20 LABETALOL.HCL LACTULOSE \\\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 0 13 \n",
"4 0 0 0 0 0 \n",
"\n",
" LAMOTRIGINE LANSOPRAZOLE LANTUS LANTUS.SOLOSTAR LATANOPROST LEVEMIR \\\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 15 0 0 47 \n",
"4 0 0 0 0 0 0 \n",
"\n",
" LEVEMIR.FLEXPEN LEVETIRACETAM LEVOFLOXACIN LEVOTHYROXINE.SODIUM \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 31 \n",
"2 0 0 0 0 \n",
"3 0 24 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LIDOCAINE LISINOPRIL LISINOPRIL.HYDROCHLOROTHIAZIDE LITHIUM.CARBONATE \\\n",
"0 0 0 0 0 \n",
"1 0 52 45 0 \n",
"2 0 0 0 0 \n",
"3 19 110 0 0 \n",
"4 0 0 0 0 \n",
"\n",
" LORAZEPAM LOSARTAN.HYDROCHLOROTHIAZIDE LOSARTAN.POTASSIUM LOVASTATIN \\\n",
"0 12 0 0 0 \n",
"1 27 0 0 11 \n",
"2 19 0 0 0 \n",
"3 0 0 46 0 \n",
"4 0 0 0 0 \n",
"\n",
" LOVAZA LUMIGAN LYRICA MECLIZINE.HCL MELOXICAM METFORMIN.HCL \\\n",
"0 0 0 0 0 0 0 \n",
"1 0 0 13 0 0 39 \n",
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]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols_to_drop = ['Specialty','NPI','Gender','Credentials']\n",
"df = df.drop(cols_to_drop,axis=1)\n",
"df.head()\n",
"#dummies = pd.concat([pd.get_dummies(X_train[cols][col], drop_first = True, prefix= col) for col in X_train[cols]], axis=1)\n",
"#train_wo_dummies = X_train.drop(cols,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0 12\n",
"1 1056\n",
"2 289\n",
"3 1198\n",
"4 113\n",
"dtype: int64"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cols = ['Opioid.Prescriber','State']\n",
"aux = df.drop(cols,axis=1)\n",
"all_ops = aux[aux.columns.tolist()].sum(axis=1)\n",
"all_ops.head()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>State</th>\n",
" <th>Opioid.Prescriber</th>\n",
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"text/plain": [
" State Opioid.Prescriber\n",
"0 TX 1\n",
"1 AL 1\n",
"2 NY 0\n",
"3 AZ 1\n",
"4 NV 1"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1 = df[['State','Opioid.Prescriber']]\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
" 0 State Opioid.Prescriber\n",
"0 12 TX 1\n",
"1 1056 AL 1\n",
"2 289 NY 0\n",
"3 1198 AZ 1\n",
"4 113 NV 1"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = pd.concat([all_ops, df1], axis = 1)\n",
"#X_train = X_train.rename(columns={0: 'total_opioids'})\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
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],
"text/plain": [
" total_opioids State Opioid.Prescriber\n",
"0 12 TX 1\n",
"1 1056 AL 1\n",
"2 289 NY 0\n",
"3 1198 AZ 1\n",
"4 113 NV 1"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#df2.set_index('State', inplace=True)\n",
"df2 = df2.rename(columns={0: 'total_opioids'})\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" State total_opioids\n",
"0 TX 12\n",
"1 AL 1056\n",
"2 NY 289\n",
"3 AZ 1198\n",
"4 NV 113"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2 = df2[['State','total_opioids']]\n",
"df2.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"CA 2562\n",
"NY 1956\n",
"FL 1570\n",
"TX 1500\n",
"PA 1211\n",
"Name: State, dtype: int64"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2['State'].value_counts().head()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
" total_opioids\n",
"State \n",
"AA 16\n",
"AE 1314\n",
"AK 14754\n",
"AL 849871\n",
"AR 402372"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2_new = df2[['total_opioids']].groupby(df2['State']).sum()\n",
"df2_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" State total_opioids\n",
"0 AA 16\n",
"1 AE 1314\n",
"2 AK 14754\n",
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"4 AR 402372"
]
},
"execution_count": 44,
"metadata": {},
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],
"source": [
"df2_new.reset_index(level=0, inplace=True)\n",
"df2_new.head()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['State', 'total_opioids'], dtype='object')"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2.columns"
]
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"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
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" <td>43419</td>\n",
" <td>GA</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_opioids State\n",
"21326 54958 TN\n",
"16707 50628 MA\n",
"14630 44138 AL\n",
"14387 43702 LA\n",
"22092 43419 GA"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ops_top_5 = df2[['total_opioids','State']].sort_values('total_opioids',ascending=False).iloc[0:5]\n",
"ops_top_5"
]
},
{
"cell_type": "code",
"execution_count": 47,
"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>total_opioids</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>TN</th>\n",
" <td>54958</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MA</th>\n",
" <td>50628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AL</th>\n",
" <td>44138</td>\n",
" </tr>\n",
" <tr>\n",
" <th>LA</th>\n",
" <td>43702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GA</th>\n",
" <td>43419</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" total_opioids\n",
"State \n",
"TN 54958\n",
"MA 50628\n",
"AL 44138\n",
"LA 43702\n",
"GA 43419"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ops_top_5.set_index('State', inplace=True)\n",
"ops_top_5.head()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
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JUmtMSOhLsmmSA5JMTTIlyauBvYFvAZ8DXprkBUk2AU4HLm5mBZcBFwOnJ9kkyZ7Ay4HPNLsezVhJkqTWmKiZvg2A9wGLgDuBNwKvqKpfNcunHE8nwP2Rzjl3J3aNPRHYqGn7PHDCwJIroxkrSZLUJhNyIUdVLQJ2H6b9QuDCIdruBl4xHmMlSZLawtuwSZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILTOl3AZLUL9ud/I1+lzCubj7zoH6XIGkScaZPkiSpBZzpkyStldblmVpnaTUenOmTJElqAWf6JEnShFqXZ2lh8s7UOtMnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUmPPQl2THJ8iSf7do2N8nCJMuSfDXJ5l1tmye5pGlbmGTuoP2t8VhJkqS26MdM3znAzwZeJNkZ+DhwBLAlcD9w7qD+DzVtrwY+2owZ1VhJkqQ2mTKRb5bkMGAxcAXw583mVwP/UVWXNX3eDVyXZDqwEjgE2KWqlgKXJ/kanZB38ijHSpIktcaEzfQlmQGcDrxtUNPOwPyBF1V1E53Zuac3jxVVdUNX//nNmNGOHVzfcUnmJZm3aNGi1f+AkiRJk9hEHt49A/hkVf1+0PZpwJJB25YA00doG+3Yx6iq86pqTlXNmTVr1ggfRZIkae0yIYd3k+wG/DXwnFU0LwVmDNo2A7iPziHaodpGO1aSJKk1Juqcvn2B7YDfJYHOLNz6SZ4FXArMHuiYZAdgQ+AGOsFtSpIdq+rGpstsYEHzfMEoxkqSJLXGRIW+84AvdL1+O50QeALwJODHSV4AXEnnvL+Lq+o+gCQXA6cnORbYDXg5sEezn8+NYqwkSVJrTMg5fVV1f1XdPvCgc1h2eVUtqqoFwPF0Atwf6Zxzd2LX8BOBjZq2zwMnNGMYzVhJkqQ2mdAlWwZU1XsGvb4QuHCIvncDrxhmX2s8VpIkqS28DZskSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWqBnkJfkicm2ah5niSHJ/nb8S1NkiRJY6XXmb5LgWc1z88A3gu8J8kHxqUqSZIkjaleQ99OwC+a50cCLwb2Bl4zHkVJkiRpbPV6792VwJQkOwLLqurmJAGmj19pkiRJGiu9hr5vA58FZgFfbLbtBNw2HkVJkiRpbPUa+l4HHAs8DHyy2bYV8L7xKEqSJEljq6fQV1X3A/86aNv3xqUiSZIkjbkhQ1+S83rZQVUdN3blSJIkaTwMd/XuXV2PlcCrgZnAUmBzYC6wYrwLlCRJ0ugNOdNXVe8ceJ7kG8BLq+r7Xdv2A94+vuVJkiRpLPS6Tt9ewGWDtl0OvGBsy5EkSdJ46DX0/ZLOHTieAND8PLXZLkmSpEmu19D3WuBAYHGShcA9wP9qtkuSJGmS63XJlpuA5yZ5OvAU4LaqumFcK5MkSdKY6XVxZgCaoGfYkyRJWssMt07f3VW1efP8YaAGdwGqqp4wjvVJkiRpDAw30/e8rue7jHchkiRJGj/DrdN3Y9fzXw08T7JZVd0z3oVJkiRp7PR09W6STZKcl2QZcGeSpUk+nmTaONcnSZKkMdDrki0foXPV7vPp3IJtD+DJzXZJkiRNcr1evXsQsGNVLW1eX53kNcCNw4yRJEnSJNHrTN9DwKaDtm0KPDy25UiSJGk89DrTdwHwrSQfBBYC2wJvB/59nOqSJEnSGOo19L0XuAM4FtgKuBX4aPOQJEnSJNfrbdhWAuc2D0mSJK1lhrsjx6FVdVHzfO5Q/arqwvEoTJIkSWNnuJm+44GLmudvHKJPAYY+SZKkSW64O3K8qOv58yemHEmSJI2HXi/koLn7xgE8eiHHt6vqvvEqTJIkSWOn19uwvYDOUi2nAvsD7wZubrb3JMlnk9yW5N4kNyQ5tqvtRUmuT3J/kh8k2barbcMk5zfjbk/y1kH7XeOxkiRJbdHr4swfBf6+qmZX1cuqajfgzcDHVuO93g9sV1UzgJcB70vy3CRbABfTCZKbA/OAL3aNew+wI521AfcDTkryEoDRjJUkSWqTXkPfU3n8BRufB7bu9Y2qakFVPTjwsnk8DXglsKCqLqqq5XSC2uwkOzV9jwTOqKp7quo64BPAUU3baMZKkiS1Rq+h7/N0Fmbu9jpW88rdJOcmuR+4HrgN+E9gZ2D+QJ+qWgbcBOycZDM65xDO79rN/GYMoxw7uLbjksxLMm/RokWr87EkSZImvV5D347A2UluSvLDJDcBZwM7Jvn2wGOknVTVicB04AV0Dss+CEwDlgzquqTpN63r9eA2Rjl2cG3nVdWcqpoza9askT6KJEnSWqXXq3e/1DxGrapWAJcneQ1wArAUmDGo2wzgvqZt4PXyQW2McqwkSVJr9Hobto+P03s/DVgAvHZgY5JNBrZX1T1JbgNmA99pusxuxjDKsZIkSa0x4uHdJNOSnJzke0nmNz/fmWTwDNtw+3hSksOafa2f5ADgcOD7wCXALkkOSTKVzrIwV1fV9c3wTwOnJNmsuUDj9cAFTdtoxkqSJLXGsKEvyZOBK4FjgJ8B5zc/jwKubNp7UXQO5d4C3AOcRWcJmP9XVYuAQ4B/bNqeBxzWNfY0OhdnLAR+CHywqi4FGM1YSZKkNhnp8O4/Az8FjqyqlQMbk7yLzizaB+gsizKsJpztM0z7d4Gdhmh7kE7oPGasx0qSJLXFSKHvQGDX7sAHUFUrk5zEY5dDkSRJ0iQ10jl9GwN3D9F2J7DR2JYjSZKk8TBS6LsOeOkQbS+js8iyJEmSJrmRDu/+E3B+c8HGl6vqjiRbAocCZ9C5K4ckSZImuWFDX1VdkmRj4IPAvyYpIMAdwJur6uIJqFGSJEmjNOLizFX1uSRfAHYBtqBzLt81zZ01JEmStBbo9Y4cK/BKXUmSpLXWiHfkkCRJ0trP0CdJktQChj5JkqQWGPKcviRb9bKDqrp17MqRJEnSeBjuQo5bgIElWoZSwPpjWpEkSZLG3HChz1usSZIkrSOGDH1V9eBEFiJJkqTx09M6fUnWA44F9qGzQPOfDvlW1YvHpzRJkiSNlV6v3j0LeDtwNbAn8D1gB+Cn41SXJEmSxlCvoe9vgQOq6p+BFc3PlwN7jFtlkiRJGjO9hr5pVfXb5vkDSTaqqgXAnHGqS5IkSWOop3P6gOuTPLeqfg5cCbwryRLgtvErTZIkSWOl19D3Vh69eONtwCeAacAJ41GUJEmSxlavoe+6qroHoKquA/YCSLLpeBUmSZKksdPrOX0Lh9j+m7EqRJIkSeOn19D3uFuxJdkEWDm25UiSJGk8DHt4N8mNdO6vu1GSGwY1Pwn42ngVJkmSpLEz0jl9b6Azy3cx8Mau7QXcUVXzx6swSZIkjZ1hQ19VfQsgydZVdffElCRJkqSx1us5fUuSvDPJtUmWND/fmaTXq38lSZLUR72GtvcD+wIn07mSd1vgXcDmwD+MS2WSJEkaM72GvsOA51bVoub1/CQ/oXN3DkOfJEnSJNfr4d0pwCODtj0CrD+25UiSJGk89Br6LgG+mmSfJNsn2Rf4Cp2reiVJkjTJ9Rr63gb8D/A54EbgM8A84O3jVJckSZLG0LChL8nhAFW1vKpOqqqtq2pKVT21qv6hqh6YmDIlSZI0GiPN9H18QqqQJEnSuBop9D3unruSJEla+4y0ZMv6SfZjmPBXVd8f25IkSZI01kYKfRsCn2To0FfADmNakSRJksbcSId3l1XVDlW1/RCPngJfkg2TfDLJwiT3JflFkgO72l+U5Pok9yf5QZJtB409P8m9SW5P8tZB+17jsZIkSW3R65ItozUF+D2wD/BE4N3Al5Jsl2QLOuv9vZvObd3mAV/sGvseYEc6t37bDzgpyUsARjNWkiSpTUY6vDsmF3JU1TI6AWzA15P8FnguMBNYUFUXASR5D3Bnkp2q6nrgSODoqroHuCfJJ4CjgEuBV45irCRJUmsMO9NXVdPH402TbAk8HVgA7AzM73rPZcBNwM5JNgO26m5vnu/cPB/N2ME1HZdkXpJ5ixYtWlUXSZKktdZEHd79kyQb0Lmzx6ea2bhpwJJB3ZYA05s2BrUPtDHKsY9RVedV1ZyqmjNr1qzeP5AkSdJaYEJDX5L16NzC7SHgDc3mpcCMQV1nAPc1bQxqH2gb7VhJkqTWmLDQlyR0ln/ZEjikqh5umhYAs7v6bQI8jc65evcAt3W3N88XjMFYSZKk1pjImb6PAs8EXjronr2XALskOSTJVOBU4Orm0C/Ap4FTkmyWZCfg9cAFYzBWkiSpNSYk9DVr5/0dsBtwe5KlzePVVbUIOAT4R+Ae4HnAYV3DT6NzccZC4IfAB6vqUoDRjJUkSWqTkZZsGRNVtZDhb+X2XWCnIdoeBI5pHmM6VpIkqS0m/OpdSZIkTTxDnyRJUgsY+iRJklrA0CdJktQChj5JkqQWMPRJkiS1gKFPkiSpBQx9kiRJLWDokyRJagFDnyRJUgsY+iRJklrA0CdJktQChj5JkqQWMPRJkiS1gKFPkiSpBQx9kiRJLWDokyRJagFDnyRJUgsY+iRJklrA0CdJktQChj5JkqQWMPRJkiS1gKFPkiSpBQx9kiRJLWDokyRJagFDnyRJUgsY+iRJklrA0CdJktQChj5JkqQWMPRJkiS1gKFPkiSpBQx9kiRJLWDokyRJagFDnyRJUgsY+iRJklrA0CdJktQChj5JkqQWmLDQl+QNSeYleTDJBYPaXpTk+iT3J/lBkm272jZMcn6Se5PcnuStYzVWkiSpLSZypu9W4H3A+d0bk2wBXAy8G9gcmAd8savLe4AdgW2B/YCTkrxktGMlSZLaZMJCX1VdXFVfBe4a1PRKYEFVXVRVy+kEtdlJdmrajwTOqKp7quo64BPAUWMwVpIkqTUmwzl9OwPzB15U1TLgJmDnJJsBW3W3N893HoOxj5HkuObw87xFixaN+kNJkiRNJpMh9E0DlgzatgSY3rQxqH2gbbRjH6OqzquqOVU1Z9asWav1ASRJkia7yRD6lgIzBm2bAdzXtDGofaBttGMlSZJaYzKEvgXA7IEXSTYBnkbnXL17gNu625vnC8ZgrCRJUmtM5JItU5JMBdYH1k8yNckU4BJglySHNO2nAldX1fXN0E8DpyTZrLlA4/XABU3baMZKkiS1xkTO9J0CPACcDLymeX5KVS0CDgH+EbgHeB5wWNe40+hcnLEQ+CHwwaq6FGA0YyVJktpkykS9UVW9h86SKqtq+y6w0xBtDwLHNI8xHStJktQWk+GcPkmSJI0zQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFjD0SZIktYChT5IkqQUMfZIkSS1g6JMkSWoBQ58kSVILGPokSZJawNAnSZLUAoY+SZKkFljnQ1+SzZNckmRZkoVJ5va7JkmSpIk2pd8FTIBzgIeALYHdgG8kmV9VC/pbliRJ0sRZp2f6kmwCHAK8u6qWVtXlwNeAI/pbmSRJ0sRKVfW7hnGT5DnAFVW1Ude2twP7VNVLB/U9DjiuefkM4FcTVujE2wK4s99FaI343a3d/P7WXn53a7d1/fvbtqpmjdRpXT+8Ow1YMmjbEmD64I5VdR5w3kQU1W9J5lXVnH7XodXnd7d28/tbe/ndrd38/jrW6cO7wFJgxqBtM4D7+lCLJElS36zroe8GYEqSHbu2zQa8iEOSJLXKOh36qmoZcDFwepJfTPOQAAAHRUlEQVRNkuwJvBz4TH8r67tWHMZeR/ndrd38/tZefndrN78/1vELOaCzTh9wPrA/cBdwclVd2N+qJEmSJtY6H/okSZK0jh/elSRJUoehT5IkqQXW9XX6WivJNiP1qarfTUQtkiSp/zynbx2VZCVQQAY1DXzhVVWG/rVYkmdV1bX9rkO9S7IB8L2q2rvftUht1Pa/mx7eXXdtADyh+TnwmA6cDNwNfKt/pWlNJZmZ5A1J5gG/6Hc9Wm3rAXv2uwituSQ7J/lAv+tQ7/y7+ShD3zqqqlYMPJpNxwLXAQcCL6+qg/pXnVZHkilJXpHkEuBW4CPAd4Dt+lqY1BJJtkjypiY0zAee1e+aNDz/bq6ah3fXYUkCHAGcBiwCTqmq7/a3KvUqyRzgtcDhzaYvAxcCXwJ2rao/9qs2rZkkGwL3V9X6/a5Fw2sOxb8UOJLOP5Z/D2wF7FVVV/azNg3Nv5vD85yudVSSQ4DTgYeAN1fV1/tcklbfT+ksKP4m4EtV9QhAEv+lNoklOX2YZv/mrgWSnA0cBjwMXATsU1U/SXIbcEtfi9NI/Ls5DGf61lHNhRx30Tl3b+Wq+lTVkRNalFZLkvcArwGeAvwH8Dngm8DvgN3a/i/WySrJv4/QZX3/25vckqygc+7zKcAXqmpJs/02YLb/7U1e/t0cnqFvHZXktJH6VNV7J6IWjU6SvekcYjqUzszDNOCgqvpeXwvTakmyK53vcW5VbdXvejS0JNvR+a6OBP4M+E86hwjPBZ7d9uCwNvDv5qoZ+tZRSQ6vqs/3uw6NnSQbAa+kc77KC4FfVNXu/a1Kw0kyC5hL5zubDfw3cE5VXdTXwtSzJC/g0fAwA/gk8C9VtaCvhaknXX83jwReRMv/bhr61lFJ7q2qGf2uQ2tuhAW2t6ZzQrlLR0wyzQUALwOOAg4Afg18HngLsJOzRGunJFPphIdjgBdU1YZ9LklDSLJBVT3cPN+LR1cq2Rp4WlWd0bfi+szQt45Kcl9VTe93HVpzXQtsw2MX2R5YdLu8CnTySXI3nfNoLwAuHLjS0/PB1g1N+Lu/qlzybBJKcgKwR1Ud0by+H7iTzt/MjYF3VNW/9bHEvvJKsnXX+kn24/F35PiTqvr+BNaj1Xc1MBX4FPBZOmtNafK7GtgLeB5wY5LfVtU9fa5JY6d49B9jmnyOBI7vev1gVW0DkGQ34KNAa0OfM33rqObqs4UMHfqqqnaYwJK0BpLsQud8sL8Frgc+DVxcVQ/0tTANK8m2PHohwDbAt4F9gGdW1R/6WZtGx7UWJ7ckt1fVk7te/6iq9myeB7i9qrbsW4F9ZuhbR3lO37olyXrA/nTOEzsQeKELxK4dmnOKjqQT3B8Bzq+qk/pblYaT5IXDND8B+Iahb3JKshTYsqqWraJtE+COqpo28ZVNDh7eldYOO9KZKXo+nXtHerhwLVFVlwOXJ3kTcDCdAKjJ7ZMjtP9uQqrQmrgGeDFwySraXgK0+qprQ9+6a8hz+bR2SLI5nVsJvRaYDnwG2Luq/B/OWqiqltO5itellCa5qtq+3zVojf0LcG5zB46vVdXK5kjJy4Gzgbf2tbo+8/CuNEklWQ78lk7Y+8mq+ngxjiQ9VpK3Ae+lcyj+TmAL4EHg9Kr6YD9r6zdDnzRJJbmZ4a8S9GIcSVqFJDPonA6zBZ1bkv544HZ6bWbokyRJagEXl5QkSWoBQ58kSVILGPokSZJawNAnSaspyV5JrkiyJMndSX6UZPckRyW5fDX2s12SSuLyWZLGnX9oJGk1NFcFfh04AfgSnWUhXkBnSQhJmrSc6ZOk1fN0gKr6fFWtqKoHqurbwMPAx4DnJ1maZDFAkoOS/CLJvUl+n+Q9Xfu6rPm5uBnz/GbMMUmuS3JPkm819/KVpFEx9EnS6rkBWJHkU0kOTLIZQFVdBxxPZz2waVW1adN/GZ1br20KHASckOQVTdvezc9NmzE/btreBbwSmAX8N97FQ9IYMPRJ0mqoqnuBvegsnP0JYFGSryXZcoj+/1VVv6yqlVV1NZ0At88wb/F3wPur6rqqegT4J2A3Z/skjZahT5JWUxPIjqqqrYFdgK3o3PPzcZI8L8kPkixKsoTObOAWw+x+W+AjSRY3h4jvpnMv7T8b208hqW0MfZI0ClV1PXABnfC3qlscXQh8DXhqVT2Rznl/GRi+iv6/B/6uqjbtemxUVVeMffWS2sTQJ0mrIclOSd6WZOvm9VOBw4GfAHcAWyd5QteQ6cDdVbU8yV8Cc7vaFgErge57KH8MeGeSnZv9PzHJoeP3iSS1haFPklbPfcDzgP9JsoxO2LsGeBvwfWABcHuSO5v+JwKnJ7kPOJXOMi8AVNX9wD8CP2oO5/5VVV0C/DPwhST3Nvs+cGI+mqR1WapWdXRBkiRJ6xJn+iRJklrA0CdJktQChj5JkqQWMPRJkiS1gKFPkiSpBQx9kiRJLWDokyRJagFDnyRJUgsY+iRJklrg/wPHRBOZy6C6xgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x114b4d208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"ax = ops_top_5.plot(kind='bar', title =\"Total Opioids per state\", figsize=(10, 5), fontsize=12)\n",
"ax.set_xlabel(\"State\", fontsize=12)\n",
"ax.set_ylabel(\"Total Opioids\", fontsize=12)\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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>State</th>\n",
" <th>Income</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Mississippi</td>\n",
" <td>40593.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Arkansas</td>\n",
" <td>41995.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>West Virginia</td>\n",
" <td>42019.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Alabama</td>\n",
" <td>44765.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Kentucky</td>\n",
" <td>45215.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" State Income\n",
"0 Mississippi 40593.0\n",
"1 Arkansas 41995.0\n",
"2 West Virginia 42019.0\n",
"3 Alabama 44765.0\n",
"4 Kentucky 45215.0"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"income = pd.read_csv('mhincome.csv')\n",
"income.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"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>Income</th>\n",
" <th>State</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>45382.0</td>\n",
" <td>New Mexico</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>45215.0</td>\n",
" <td>Kentucky</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>44765.0</td>\n",
" <td>Alabama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>42019.0</td>\n",
" <td>West Virginia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>41995.0</td>\n",
" <td>Arkansas</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Income State\n",
"5 45382.0 New Mexico\n",
"4 45215.0 Kentucky\n",
"3 44765.0 Alabama\n",
"2 42019.0 West Virginia\n",
"1 41995.0 Arkansas"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"income_top_5 = income[['Income','State']].sort_values('Income',ascending=False).iloc[0:5]\n",
"income_top_5"
]
},
{
"cell_type": "code",
"execution_count": 51,
"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>Income</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>New Mexico</th>\n",
" <td>45382.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Kentucky</th>\n",
" <td>45215.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Alabama</th>\n",
" <td>44765.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>42019.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>41995.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Income\n",
"State \n",
"New Mexico 45382.0\n",
"Kentucky 45215.0\n",
"Alabama 44765.0\n",
"West Virginia 42019.0\n",
"Arkansas 41995.0"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"income_top_5.set_index('State', inplace=True)\n",
"income_top_5"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bc650b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"ax = income_top_5.plot(kind='bar', title =\"Median Household Income\", figsize=(10, 5), fontsize=12)\n",
"ax.set_xlabel(\"State\", fontsize=12)\n",
"ax.set_ylabel(\"Median Household Income\", fontsize=12)\n",
"plt.show();"
]
},
{
"cell_type": "code",
"execution_count": 53,
"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>Median Age</th>\n",
" <th>State</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>44.5</td>\n",
" <td>Maine</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>43.1</td>\n",
" <td>Vermont</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>42.7</td>\n",
" <td>New Hampshire</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>42.3</td>\n",
" <td>West Virginia</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>42.1</td>\n",
" <td>Florida</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Median Age State\n",
"0 44.5 Maine\n",
"1 43.1 Vermont\n",
"2 42.7 New Hampshire\n",
"3 42.3 West Virginia\n",
"4 42.1 Florida"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = {'State': ['Maine','Vermont','New Hampshire','West Virginia','Florida'], 'Median Age': [44.5,43.1,42.7,42.3,42.1]}\n",
"ages = pd.DataFrame(data=d)\n",
"ages.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"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>Median Age</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Maine</th>\n",
" <td>44.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Vermont</th>\n",
" <td>43.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>New Hampshire</th>\n",
" <td>42.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>42.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Florida</th>\n",
" <td>42.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Median Age\n",
"State \n",
"Maine 44.5\n",
"Vermont 43.1\n",
"New Hampshire 42.7\n",
"West Virginia 42.3\n",
"Florida 42.1"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ages.set_index('State', inplace=True)\n",
"ages.head()"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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2TnJQknWTrJbk9cDBwMXA14HtkhyQZC3gOOAaFwNIkqRBNFE9Z0UzhDkXuBv4FPD+qvpmVc0DDgBO7F3bGThoguqSJEnqlOXaSiPJZsCmVXXl8ryuF8B2H+H694Btluc9JUmSVkWj6jlLsnmSH9JsgfG9Xtubk3xhPIuTJEkaNKMd1vw8cAHN9haP9tq+C7xuPIqSJEkaVKMd1nwlsE9VPZGkAKpqUZJnj19pkiRJg2e0PWd3AVv1NyR5CXDrmFckSZI0wEYbzj4FnJ/kHcDqSQ6mOZz8H8atMkmSpAE0qmHNqjotye+BI4HbgLcBx1bVN8azOEmSpEEz6q00ekHMMCZJkjSORhXOkhyxlEsP02wse2VVPTxmVUmSJA2o0facvRXYhWZhwFyaQ8s3AWYDMwCSvLGqZo9DjZIkSQNjtAsCrgM+VFWbV9Wrqmpz4APAz2iC2r8D/zZONUqSJA2M0YazQ4DPDGv7d+DQqirgk8BLxrIwSZKkQbQ8+5ztN6xtH+B3vcdr8eTJAZIkSVpBo51z9hfAOUmupdlKYzNgO+DA3vWdcVhTkiRppY12n7OLkjwf2Bt4HvBt4IKqWjB0Hbho3KqUJEkaEMuzz9kC4PSh50m2TfJXVfXhcalMkiRpAI12zhkASTZK8hdJZgPX4CIASZKkMbXMnrMka9AsBngrsBfNnLPnATtV1U/HtzxJkqTBMmLPWZLPAHcAnwVuBXavqq2ARTSb0UqSJGkMLavn7N3A74HjgbOratG4VyRJkjTAljXn7AU0W2R8CLgzyblJDhjF6yRJkrQCRgxZVTWnqk7oDWXuSdOL9h/ANODEJC4IkCRJGkOj7gGrqsur6s+B5wCH0mxE+/PxKkySJGkQLffwZFU9VFVnVdUbgBljX5IkSdLgWqm5Y1V1+1gVIkmSJCf2S5IkdYrhTJIkqUMMZ5IkSR0yqoPPk2wAfBDYHli3/1pV7TYOdUmSJA2kUYUz4EzgmcDXgAfGrxxJkqTBNtpw9ipgWlU9PJ7FSJIkDbrRzjm7Bpg+noVIkiRp9D1nFwMXJvkicGf/hao6bcyrkiRJGlCjDWevBuYCrxvWXoDhTJIkaYyMKpxV1R+OdyGSJEkafc/ZYkkCZOh5VT0xphVJkiQNsFEtCEiyaZKvJ1kAPAY82vclSZKkMTLa1ZonA48ArwXuA14O/DfwrnGqS5IkaSAtzz5nm1fV/Umqqq5O8mfAj4BTx688SZKkwTLanrPHaYYzARYmmQbcD2w6LlVJkiQNqNGGsx8De/cefwf4KnAeMHs8ipIkSRpUox3WPJwng9z7aQ5BXxc4aTyKkiRJGlSj3edsYd/jB4GPj1tFkiRJA2yp4SzJR6vqxN7jE5Z2X1UdNx6FSZIkDaKRes76DzrfbLwLkSRJ0gjhrKre3ff4HRNTjiRJ0mAbaVjz+aN5g6r6zdiVI0mSNNhGGta8ESiaczSrr33489XGoS5JkqSBtNR9zqrqGVW1WlU9A3gncDawDbBW759nAn82IVVKkiQNiNHuc/Zx4IW9bTQAfp3kKOBXwH+OR2GSJEmDaLQnBDwDmDGsbQtGOaSZ5JlJ/iPJLUnuTfKzJHv1XX9tkhuSPJDkkiRbjLIuSZKkVcpoe84+DVyc5IvAbTRba7y91z7az7kN2B24leYoqK8leSlwH81RUO8EvkXTS/dVYNYo31uSJGmVMdoTAj6Z5BfAgcAOwB3AEVV14Shffz9wfF/T+UluBl4BbAhcV1XnACQ5HpifZJuqumG034gkSdKqYLQ9Z/SC2KjC2LIk2QR4EXAd8G7g6r7PuT/JTcC2wA3DXnckcCTA5ptvPhalSJIkdcqo5pz15oydmOQ3SRb12vZM8t7l/cAkawBfAb7U6xlbF1g07LZFwHrDX1tVp1TVjlW147Rp05b3oyVJkjpvtAsCPg1sBxzKk3ucDfV6jVqSZwCnA48AQ8HuPmDKsFunAPcuz3tLkiStCkY7rPnHwFa9IccnAKrqt0k2He0HJQnwH8AmwN5V9Wjv0nXA2/ruexbwgl67JEnSQBltz9kjDAtySaYBC5bjs/4deDGwX99+aQBfB7ZLckCStYDjgGtcDCBJkgbRaMPZOcCXkmwJkOS5wGdoTg1Ypt6+ZUcB2wN3Jrmv93VoVc0DDgBOBO4GdgYOWr5vQ5IkadUw2mHNjwD/CPwCWAf4NXAq8DejeXFV3UJzJufSrn+P5kgoSZKkgTbafc4eAd4PvL83nDm/qmoZL5MkSdJyGjGcJVnaZmKbNfP7oapuHeuiJEmSBtWyes7m8OTWGUsalixGeb6mJEmSlm1ZCwKuoZlfdgzNQedrDPtac1yrkyRJGjAjhrOq2h54M7AB8APg2zQrKdesqser6vHxL1GSJGlwLHMrjaq6tqo+BGwJ/DOwL3BHkpePd3GSJEmDZrT7nAG8ENgd2AX4Gc2eZJIkSRpDy1qtuQFwMM3xSuvRnIu5mys0JUmSxseyVmveDtxME8qu7LVtlWSroRuq6uJxqk2SJGngLCuc3QmsBfx572u4Ap4/1kVJkiQNqhHDWVXNmKA6JEmSxPItCJAkSdI4M5xJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUocYziRJkjrEcCZJktQhhjNJkqQOMZxJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUocYziRJkjrEcCZJktQhhjNJkqQOMZxJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUocYziRJkjrEcCZJktQhhjNJkqQOMZxJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUocYziRJkjrEcCZJktQhhjNJkqQOMZxJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6pAJC2dJ3ptkdpKHk/znsGuvTXJDkgeSXJJki4mqS5IkqUsmsufsduATwGn9jUk2As4DjgU2AGYDX53AuiRJkjpj9Yn6oKo6DyDJjsD0vkt/AlxXVef0rh8PzE+yTVXdMFH1SZIkdUEX5pxtC1w99KSq7gdu6rU/RZIje0Ojs+fNmzeBJUqSJE2MLoSzdYFFw9oWAesNv7GqTqmqHatqx2nTpk1IcZIkSROpC+HsPmDKsLYpwL0t1CJJktSqLoSz64CXDT1J8izgBb12SZKkgTKRW2msnmQtYDVgtSRrJVkd+DqwXZIDetePA65xMYAkSRpEE9lzdgzwIPDXwGG9x8dU1TzgAOBE4G5gZ+CgCaxLkiSpMyZyK43jgeOXcu17wDYTVYskSVJXdWHOmSRJknoMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdYjhTJIkqUMMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdYjhTJIkqUMMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdYjhTJIkqUMMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdYjhTJIkqUMMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdYjhTJIkqUMMZ5IkSR1iOJMkSeoQw5kkSVKHGM4kSZI6xHAmSZLUIYYzSZKkDjGcSZIkdUhnwlmSDZJ8Pcn9SW5JckjbNUmSJE201dsuoM9ngUeATYDtgQuSXF1V17VbliRJ0sTpRM9ZkmcBBwDHVtV9VfUD4L+Bw9utTJIkaWKlqtqugSQ7AD+qqrX72j4I7F5V+/W1HQkc2Xu6NfDLCS10Ym0EzG+7CK0wf36Tlz+7yc2f3+S2Kv/8tqiqaaO5sSvDmusCi4a1LQLW62+oqlOAUyaqqDYlmV1VO7Zdh1aMP7/Jy5/d5ObPb3Lz59foxLAmcB8wZVjbFODeFmqRJElqTVfC2a+A1ZO8sK/tZYCLASRJ0kDpRDirqvuB84ATkjwryR8AbwROb7eyVg3E8O0qzJ/f5OXPbnLz5ze5+fOjIwsCoNnnDDgNeB2wAPjrqjqz3aokSZImVmfCmSRJkjoyrClJkqSG4UySJKlDurLPmSRJyy3JmjSbkm8EZKi9qi5urShpJdlzJq2kJN9cSvt5E12Lll+SZyY5Mclvkizqte2Z5L1t16aRJdkVuAX4PvBd4L+A7wBfaLMuaWUZzjomyYZJDk/y4d7z5yWZ3nZdGtEfLqV9j4ksQivs08B2wKHA0Aqp64B3t1aRRuvTwD9W1QbAvb1/fhz4XLtlSSvH1ZodkmR34FxgNvAHVbVer+2D/WeMqhuSnNB7+GHgH4ddfj6wbVXtMLFVaXkluQPYqqruT/L73l/wJFlYVVNbLk8j6PV0rl9VTyS5u6rW7w1z3lxVm7Zdn5YtyerAe4DdefrQ9G5t1dU2e8665STgLVX1BuCxXtuPgVe2V5JGsFnv6xl9jzcDpgO3AQe2V5qWwyMMm3+bZBrNfovqtkU8efTfHUleAqxPc16zJodPA0cBlwGvoOmg2BgY6DmD9px1yNBvfr3Hv6+qDZI8A5hXVRu2XJ6WIsmfV9WpbdehFZPkU8BWwF8CVwHb0vyidGNVfbTN2jSyJCcBP6mqM5N8gKYX+1Hgwqp6Z7vVaTSS/BbYpapuHeqtTrIN8Pmq2r3t+tpiOOuQJD8ETqiq7/SFsz2Bj1TVHi2XpxEkeTbNirGn/MbuirHu6w2D/SPwTmAd4AHgVOCvquqRNmvT8knyapr/B79TVU+0XY+WLcndwAZVVb0pBi+oqgeS3FNVU5b1+lWV4axDkswCzgcuAP4U+DKwH/DGqvrfNmvT0iV5O/BZ4D6av9iHVFU9v5WiNCq9nuk9gB9W1cO94cz55R+M0oRI8iPg/VX1kyTfAq4H7gEOraoXt1tdewxnHZNkU5pVY1vQzFs6o6rmtluVRtLrln9nVf1P27Vo+SW5t6rWa7sOjU6SC3vzcklyOU+usH2KQZ5MPpkk2Ql4vKp+muSFwL8D69EshLu83eraYziTVlKSu4DnVdXjbdei5ZfkAuDjVXVl27Vo2ZIcUlVn9h6/bWn3VdWXJq4qaWwZzjokyQbAB4HtefrcJX8L7Kgk/5fmN72PO89l8knyOeBg4Js0vdWL/1CsquPaqktaVSV5zWjuG+Q5u4azDklyIfBM4Gs8de6SvwV2WJLbgOfQbMnwlO0XqmrzVorSqCX54tKuVdU7JrIWLb/eoqkl/UJrsO6oJDcPa9qU5peiBcCGNHudzR3kObuerdktrwKmVdXDbRei5XJY2wVoxRnAJq8kn6FZPHUJw36hVXdV1ZZDj5N8hCaQHdtbpbkOcAIDvs+gPWcdkuQHwNuq6qa2a5FWZUlmVNWc3uOl/nZeVb+ZsKK03JIsALavqtvarkUrJsk8mjm7j/a1rQHcXlXT2qusXfacdcvFwIW9YZY7+y9U1WntlKRl6f1BcgxwOPA84HbgdOBE98nqrF/QzBMEuJFmSCXD7ilgtYksSsttAbCw7SK0Uu6nOQXnh31tOzHgPaH2nHVIkkuWcqmqalQTKDXxknya5g+XvwFuodkG5VhgdlX9ZZu1SauyJEcB+wB/B9zVf81ez8khyeE0B9V/i2ZBzmbAvsD/qarT26ytTYYzaSUlmQu8rKoW9LVtBFzt4cvS+EmytNXRVVX2ek4SvTNRD6AZebgD+K+q+n/tVtUuhzVbliRDu5H3ditfIrdo6LThw2HLaleHJNkSOJElr/hztW2HVdVS/8zU5NELYgMdxoYznLVvETB0fthjPH236+Dcl647B/hWkr8BbqUZ1jyGZksUdd+ZwE3ABxjweS7SREhySlUd2Xt8Oks/5eGtE1pYhxjO2rdt3+Mtl3qXuuzDNGHsszy5IOAs4BNtFqVR2xb4A3unJwePb1ol9O9zdmNrVXSYc84kDbQk5wMfq6qr2q5Fy+bxTauOJKsBbwPOrKqH2q6nSwxnHZNkf2B3YCP65iwNcvfuZJBkBjCTp89ZOrONejSyJCf0Pd0AOAg4j6dvYeMu89I4SrKwqqa2XUfXOKzZIUk+BrwLOBs4EPg8cAjw1Tbr0siSHA0cB1wHPNh3qWjmM6ncOhOSAAAOuElEQVR7Nhv2/FvAGktoV4clOWIplx4G5gJXeuJK530ryX5V9a22C+kSe846JMktwD5Vde3QbxNJXgkcU1X7t12flizJfGC3QV/6LU20JJcCu9DscTYXmA5sAswGZvRue2NVzW6jPi1bknOA/YEraPY5WxxKBnnEyJ6zbplaVdf2Hj+SZI2q+kmS3VutSsuyAJjTdhFaMb09lhZU1V1J1gU+BDwOfKqqXL3ZbdcB51XVvw41JHkvsA2wK/BR4N9oApy66drel/rYc9YhSX4KHF5V1yW5GPgGcDfw8aqa0WpxWqokewGHAicBv+u/VlW3tlKURi3Jz4G3VNUvk5wMbA08BMyvqsPbrU4jSXI3sGH/StveJPP5VbV+kmcCv6uqZ7dWpLQC7DnrlmOADXuPjwa+QjPB/D2tVaTRWBPYk2Z+YD/3p5scZvSCWYA/ptla40Geutxf3XQXsB/wzb62fXjyl6S1gEeHv0jdkuQPac4m3hT4LXBGVV3cblXtMpx1QJKhXciv7Xt+B+B5mpPD54CP0CzkeHAZ96p7Hk6yHvAS4Laqmp9kdZq/2NVtfwGck+RanjyXcTuaBVUAO9MMa6qjkrwT+FvgC8CPgc2BM5McW1WntlpcixzW7IDe+XBDP4j+I3+q99xz4josyV3A86rq8bZr0fLrHVy/K7Ae8Jmq+kxvIc6pVfWydqvTsiTZENibJ89lvKD/nFt1W5JfAQdW1dV9bTOBc6vqhe1V1i7DWQf05rysBXwJOINmh/mn8C/+7kryIZqhzb8t/4ealJLsCTxaVZf0nu8ITBn0oZUu680t+xXwErfLmLySLACeU1WP9rU9E7i9qjZc+itXbYazjkiyHc1OyX8K3AB8mWYVksNkHZfkNuA5wCM0KzcX8+DsySPJpvSO36qq37Zdj5at1+uyU1UtarsWrZgk36Q5k/ivquqBJM8C/g7Ysqr2a7e69hjOOibJM4DXAW8H9gJeU1U/bbUojWikrU6q6vsTWYuWX2+O51eAWTSrozcArgQOrapb2qxNI0vyHuCNNHOW5vLUPbJ+01ZdGr0kz6WZr/sq4Pc0///9CDi4qp42ijQoDGcdk2Rrmh60Q2hWix1RVa4a66je0MppwJEOrUxOSS4BrgY+WlX39/Y6+ziwQ1Xt0WpxGlFvvu6SOE93kkmyGfBcmp7ruW3X0zbDWQck2QA4mCaUrQecTrOU2D2yJoEkdwCb98+Z0OSR5B6avbL657ysSbMx7XrtVSatmnojRMvUv3/doHErjW64naaX7HSa4RSArZJsNXSDE5M77dPA3yT5mAFtUroSeCXww762HWmOk5E09h6jbwh6CcKA7xNpz1kHJJnDyP+hVlU9f4LK0XLqWxDwODCPp857cUFAxyX5d5ppBBfw5F5Ze9McWj9/6L6qOq6VAvUUSS6sqjf0Hl/OUv7srKrdJrQwjVqSLegtvhnpvkGe82nPWQd4NNOkd1jbBWilrAWc13u8MfAw8HVgbZqgBiP/8qSJ9eW+x19orQqtsKq6JckvqmrKUFuS86rqT9qsq0vsOZMkTSpJXlJV/6/tOrTiktzbP6czye+raoM2a+oSe86kldTbMPE4mkUdG1bVs3ubmr6oqj7TbnUajSTrAFvRnGW7WFX9qJ2KtAw/S3IN8J/A2Z4IMCnZMzQCe86klZTkczQH9v498D9VNbW3oelFVbVtu9VpWZK8FfgMzSbC/Zs+l3MGu6l3ZNPBNIdlvwz4H5qgdkFVPdZiaRqlJA/QHFI/dGThN2j2rFt8hOEgL4QznEkrqbeVxla9PbIWd80nWVhVU1suT8uQ5E7g8Kr6btu1aPn19oY8HDiUpufzbODLVfW/rRamEbkQbmSGM2klJbkFmFlVi4bCWZJpwJVV9YK269PIktwKvMBtUCa/JK8DTqHZd3Bgt2HQ5DeqjeAkjegc4EtJtoTFx5F8huY3eHXfscA/J9mo7UK0YpLM6k0vOAt4Ajih5ZKklWI4k1ZQ3y7XHwHmAL8ApgK/ptm/52/aqUzL6VfA/sBdSR7vfT2R5PG2C9PSJdkiyTFJfglcCKwJ/HFVvaCq/H9Pk5rDmtIK6s01O51mfsu1vbZpwPzyf6xJI8mNND0uX+WpCwKoqptaKUojSvJ9YBfgEuBLwNer6sGRXyVNHoYzaQUleSPNBrT7AtfTrBY7s6rmj/Q6dUuSu4ENDNSTR5K/Bk6vqt+2XYs0Hgxn0kpKMhV4C82KsR2Bi2h+m/9vJ5l3X5J/Bn5eVV9e5s2SNAEMZ9IY6i0KOBx4J7BOVTnJvOOS/IDm4PObgbv6r3k+o6Q2eEKANEZ6JwXsBOwMbAK4u/zkcGrvS5I6wZ4zaSUl2RV4K/CnwO94cpHALa0WJq3ikjynqu4cbbs0WRjOpBWU5HiaIcwN6O11VlU/bLUorZAkm9AMbW7EU4+POa21orRMSe6pqilLaPcQbU1qDmtKK24W8FHgG1X1UNvFaMUkeRNwBs3+dNsC1wHbAT8ADGfdlqc1JFNoNqKVJi3DmbSCquoNbdegMfEJ4B1VdU6Su6tqhyTvoAlq6qAkt9Gcy7h27/itfhvS7FsnTVoOa0oaaP1DY71wtn7v9Ic7q2rjlsvTEiTZnabX7NvAXn2XCrirqn7ZSmHSGLHnTNKg+12STarqLmBOkl2A+YAHZ3dUVX0fIMlGVfVA2/VIY82zNSUNulOBXXuPP01zJNDVwOdaq0ij9a4k28Piw89vTfKbJK9quzBpZTisKUl9kmwOPKuqrm+7Fo2sN/dsu6palOQS4JvAvcCRVbVzu9VJK85wJkmalIbmCyZZD7gFmFZVjydZWFVT265PWlHOOZM0kJJcTjOBfKk8vqnzbusNYW4LXNYLZlOAx1uuS1ophjNJg+oLfY8DfBZ4T0u1aMV8CPgv4BHggF7bvsBPWqtIGgMOa0oS7iq/qkiyBkBVPdp2LdKKsudMkjRpJXkx8GZgk6p6L/ACYE3gmlYLk1aCW2lIkialJAcClwGbAm/tNa8L/HNrRUljwGFNSQMpyWuGNX0DeCNPPfj84gktSsslyfXAwVX1877THdYAbq+qaW3XJ60ow5mkgZTk5mXcUlX1/AkpRiskyQJgo6qqoTmDSVanCWcevaVJyzlnkgZSVW3Zdg1aaVcBhwNf7ms7CFdrapKz50ySNCkl2Qa4CLgZmAVcCrwI2LOqft1iadJKMZxJkiatJOvQ7G22BXAbcH5V3dduVdLKMZxJkiaVJH9KcyLAnW3XIo0Hw5kkaVJJ8iua/cxuotlK4/s0Ye2WVguTxojhTNJAS/IXwPer6uq2a9HoJdkE2K339WpgO+C39MJaVX1hhJdLnWY4kzTQkpxO8xf8esDlNL0w3wd+VlVPtFmbRi/JVOBI4P8C06pqtZZLklaY4UySgCQzgN17X38CUFVTWyxJI0gSYHue7D17FXA78EPg8qo6p8XypJXiPmeSBl6SrWlC2R7AHwC/ouk9UwclOR94OfBL4AfAKcDbq+reVguTxojhTNJAS3IXcC/wXzSbmR7lX/KdtzXwMM3+ZjcBN/oz06rEYU1JAy3JF4Bde0+H5ptdVlVz26tKy7KEBQEb0RvSBH5QVT9vsTxppRjOJImn/GW/O3AYML+qtmq3Ko2WCwK0KnFYU9LAS7IDTSj7Q5pemPvxfMZOW8KCgF2BqcBs4LQWS5NWmj1nkgZakruBRTy5men3q+rGdqvSSJJcQLM6c03gx/SGooErquqhNmuTxoLhTNJASzKjqua0XYdGL8lf04Sx/62qR9uuRxprhjNJAy/Ji4E3A5tU1XuTbAOsWVXXtFyapAH0jLYLkKQ2JTmQphdmU+CtveZ1gX9urShJA82eM0kDLcn1wMFV9fMkd1fV+knWAG6vqmlt1ydp8NhzJmnQbQwMHXpeff/0N1dJrTCcSRp0VwGHD2s7CLfSkNQShzUlDbTe5P+LaI4CmgVcCrwI2LOqft1iaZIGlOFM0sBLsg6wL7AFcBtwflXd125VkgaV4UySJKlDPL5J0kBKcgkjT/qvqnrtRNUjSUMMZ5IG1RlLad8U+AtgnQmsRZIWc1hTkoAkGwJHA38OfBU4oarmtluVpEHkVhqSBlqSKUk+DtwIbAK8vKqONJhJaovhTNJASrJ2kqOB3wAvBnatqsOr6qaWS5M04BzWlDSQktwJrAZ8Epi9pHuq6uIJLUqSMJxJGlBJ5rDs1ZrPn6ByJGkxw5kkSVKHOOdMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUocYziStkpLsmuRHSRYl+X2SHybZKcnbk/xgOd5nRpJK4nF3kiaEf9hIWuUkmQKcD7wb+BqwJvBq4OE265Kk0bDnTNKq6EUAVXVWVT1eVQ9W1UXAo8DJwC5J7kuyECDJPkl+luSeJLclOb7vvS7r/XNh7zW79F5zRJLrk9yd5DtJtpi4b0/SqsxwJmlV9Cvg8SRfSrJXkvUBqup64F3AFVW1blVN7d1/P/BWYCqwD/DuJG/qXdut98+pvddc0bv2EeBPgGnA5cBZE/KdSVrlGc4krXKq6h5gV5oTAE4F5iX57ySbLOX+S6vqF1X1RFVdQxO0dh/hI44C/q6qrq+qx4C/Bba390zSWDCcSVol9YLT26tqOrAd8DzgpCXdm2TnJJckmZdkEU3v2kYjvP0WwL8kWdgbGv09EGDTsf0uJA0iw5mkVV5V3QD8J01IW9KZdWcC/w1sVlXPppmXlqGXL+H+24Cjqmpq39faVfWjsa9e0qAxnEla5STZJskHkkzvPd8MOBi4ErgLmJ5kzb6XrAf8vqoeSvJK4JC+a/OAJ4D+Q9BPBo5Osm3v/Z+d5MDx+44kDRLDmaRV0b3AzsCPk9xPE8quBT4AXAxcB9yZZH7v/vcAJyS5FziOZvsNAKrqAeBE4Ie9YcxZVfV14B+As5Pc03vvvSbmW5O0qkvVknrsJUmS1AZ7ziRJkjrEcCZJktQhhjNJkqQOMZxJkiR1iOFMkiSpQwxnkiRJHWI4kyRJ6hDDmSRJUof8f/uO8rNn5OgoAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11bbf54e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from matplotlib import pyplot\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"ax = ages.plot(kind='bar', title =\"Median Age\", figsize=(10, 5), fontsize=12)\n",
"ax.set_xlabel(\"State\", fontsize=12)\n",
"ax.set_ylabel(\"Median Age\", fontsize=12)\n",
"plt.show();"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"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": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 1
}