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Created March 25, 2020 19:14
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2020_03_19a_medicare_hospital_cost_report_data.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mon Mar 23 09:51:19 PDT 2020\r\n"
]
}
],
"source": [
"import numpy as np, matplotlib.pyplot as plt, pandas as pd\n",
"pd.set_option('display.max_rows', 8)\n",
"!date\n",
"\n",
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# For COVID-19 emergency preparedness, it would be good to know about hospital resources\n",
"\n",
"Greg Roth pointed me to the CMS Form 2552-10 as a potential source. It is a bit of a undocumented mess, but I think I know how to tangle with it.\n",
"\n",
"There is something called the \"Hospital 10 Cost Report\" that includes a list of hospitals, with their address, number of beds, number of employees, and number of discharges (among other things). https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Cost-Reports/Hospital-2010-form"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/snfs1/Project/simulation_science/covid/data\n"
]
}
],
"source": [
"%cd /home/j/Project/simulation_science/covid/data/"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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" <td>30-SEP-18</td>\n",
" <td>As Submitted</td>\n",
" <td>MIZELL MEMORIAL HOSPITAL</td>\n",
" <td>702 MAIN STREET</td>\n",
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" <td>1600 JOSEPH DRIVE</td>\n",
" <td>NaN</td>\n",
" <td>BRYAN</td>\n",
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" <td>25-MAY-18</td>\n",
" <td>31-MAY-19</td>\n",
" <td>As Submitted</td>\n",
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" <td>345 S. WATER STREET</td>\n",
" <td>NaN</td>\n",
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"text/plain": [
" PROVIDER_NUMBER FYB FYE STATUS \\\n",
"0 10001 01-OCT-17 30-SEP-18 As Submitted \n",
"1 10005 01-OCT-17 30-SEP-18 As Submitted \n",
"2 10006 01-JUL-18 30-JUN-19 As Submitted \n",
"3 10007 01-OCT-17 30-SEP-18 As Submitted \n",
"... ... ... ... ... \n",
"5700 673064 01-APR-18 31-MAR-19 As Submitted \n",
"5701 673065 01-OCT-17 30-SEP-18 As Submitted \n",
"5702 673066 10-NOV-17 30-SEP-18 As Submitted \n",
"5703 673067 25-MAY-18 31-MAY-19 As Submitted \n",
"\n",
" HOSPITAL_Name \\\n",
"0 SOUTHEAST HEALTH MEDICAL CENTER \n",
"1 MARSHALL MEDICAL CENTER - SOUTH \n",
"2 NORTH ALABAMA MEDICAL CENTER \n",
"3 MIZELL MEMORIAL HOSPITAL \n",
"... ... \n",
"5700 ICARE REHABILITATION HOSPITAL \n",
"5701 CHI ST. JOSEPH HEALTH REHABILITATION \n",
"5702 ENCOMPASS HEALTH REHABILITATION HOSP \n",
"5703 PAM REHAB HOSPITAL CORPUS CHRISTI \n",
"\n",
" Street_Addr \\\n",
"0 1108 ROSS CLARK CIRCLE \n",
"1 2505 U.S. HIGHWAY 431 \n",
"2 1701 VETERANS DRIVE \n",
"3 702 MAIN STREET \n",
"... ... \n",
"5700 3100 PETERS COLONY ROAD \n",
"5701 1600 JOSEPH DRIVE \n",
"5702 2121 BUSINESS CENTER DRIVE \n",
"5703 345 S. WATER STREET \n",
"\n",
" Po_BOx \\\n",
"0 6987 \n",
"1 NaN \n",
"2 818 \n",
"3 429 \n",
"... ... \n",
"5700 NaN \n",
"5701 NaN \n",
"5702 NaN \n",
"5703 NaN \n",
"\n",
" City \\\n",
"0 DOTHAN \n",
"1 BOAZ \n",
"2 FLORENCE \n",
"3 OPP \n",
"... ... \n",
"5700 FLOWER MOUND \n",
"5701 BRYAN \n",
"5702 PEARLAND \n",
"5703 CORPUS CHRIST \n",
"\n",
" State \\\n",
"0 AL \n",
"1 AL \n",
"2 AL \n",
"3 AL \n",
"... ... \n",
"5700 TX \n",
"5701 TX \n",
"5702 TX \n",
"5703 TX \n",
"\n",
" Zip_Code ... \\\n",
"0 36301 ... \n",
"1 35957- ... \n",
"2 35630 ... \n",
"3 36462- ... \n",
"... ... ... \n",
"5700 75022-2949 ... \n",
"5701 77802 ... \n",
"5702 77584 ... \n",
"5703 78401 ... \n",
"\n",
" TOTAL_HOSPITAL_MEDICARE_DISCHARGES TOTAL_HOSPITAL_MEDICAID_DISCHARGES \\\n",
"0 6945.0 3061.0 \n",
"1 3069.0 1765.0 \n",
"2 5017.0 2723.0 \n",
"3 679.0 173.0 \n",
"... ... ... \n",
"5700 221.0 NaN \n",
"5701 1074.0 NaN \n",
"5702 625.0 NaN \n",
"5703 585.0 NaN \n",
"\n",
" TOTAL_HOSPITAL_DISCHARGES MEDICAID_HMO_DISCHARGES \\\n",
"0 19963.0 NaN \n",
"1 9069.0 NaN \n",
"2 12472.0 47.0 \n",
"3 1157.0 NaN \n",
"... ... ... \n",
"5700 293.0 NaN \n",
"5701 1300.0 21.0 \n",
"5702 743.0 4.0 \n",
"5703 660.0 13.0 \n",
"\n",
" MEDICAID_HMO_IPF_SUBPROVIDER MEDICAID_HMO_IRF_SUBPROVIDER \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 1.0 NaN \n",
"... ... ... \n",
"5700 NaN NaN \n",
"5701 NaN NaN \n",
"5702 NaN NaN \n",
"5703 NaN NaN \n",
"\n",
" MEDICAID_IPF_SUBPROVIDER MEDICAID_IRF_SUBPROVIDER \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 31.0 NaN \n",
"... ... ... \n",
"5700 NaN NaN \n",
"5701 NaN NaN \n",
"5702 NaN NaN \n",
"5703 NaN NaN \n",
"\n",
" TOTAL_DISCHARGES_SUBPROVIDER_IPF TOTAL_DISCHARGES_SUBPROVIDER_IRF \n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"3 279.0 NaN \n",
"... ... ... \n",
"5700 NaN NaN \n",
"5701 NaN NaN \n",
"5702 NaN NaN \n",
"5703 NaN NaN \n",
"\n",
"[5704 rows x 38 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_report = pd.read_csv('IME_GME2018.CSV')\n",
"df_report"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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" <td>202 NORTH DIVISION STREET</td>\n",
" <td>NaN</td>\n",
" <td>AUBURN</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5367</th>\n",
" <td>502001</td>\n",
" <td>01-JUL-18</td>\n",
" <td>30-JUN-19</td>\n",
" <td>As Submitted</td>\n",
" <td>REGIONAL HOSPITAL</td>\n",
" <td>16251 SYLVESTER RD SW</td>\n",
" <td>NaN</td>\n",
" <td>BURIEN</td>\n",
" <td>WA</td>\n",
" <td>98166</td>\n",
" <td>...</td>\n",
" <td>94.0</td>\n",
" <td>44.0</td>\n",
" <td>186.0</td>\n",
" <td>16.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5370</th>\n",
" <td>503300</td>\n",
" <td>01-OCT-17</td>\n",
" <td>30-SEP-18</td>\n",
" <td>Amended</td>\n",
" <td>SEATTLE CHILDRENS HOSPITAL</td>\n",
" <td>4800 SANDPOINT WAY NE</td>\n",
" <td>5371</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>98105-</td>\n",
" <td>...</td>\n",
" <td>112.0</td>\n",
" <td>1763.0</td>\n",
" <td>12145.0</td>\n",
" <td>5191.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5375</th>\n",
" <td>504009</td>\n",
" <td>01-JAN-18</td>\n",
" <td>31-DEC-18</td>\n",
" <td>As Submitted</td>\n",
" <td>NAVOS</td>\n",
" <td>2600 SW HOLDEN STREET</td>\n",
" <td>NaN</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>98126</td>\n",
" <td>...</td>\n",
" <td>103.0</td>\n",
" <td>325.0</td>\n",
" <td>1060.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5376</th>\n",
" <td>504011</td>\n",
" <td>01-JAN-18</td>\n",
" <td>31-DEC-18</td>\n",
" <td>As Submitted</td>\n",
" <td>CASCADE BEHAVIORAL HEALTH</td>\n",
" <td>12844 MILITARY ROAD SOUTH</td>\n",
" <td>NaN</td>\n",
" <td>TUKWILA</td>\n",
" <td>WA</td>\n",
" <td>98168</td>\n",
" <td>...</td>\n",
" <td>817.0</td>\n",
" <td>861.0</td>\n",
" <td>2866.0</td>\n",
" <td>4.0</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20 rows × 38 columns</p>\n",
"</div>"
],
"text/plain": [
" PROVIDER_NUMBER FYB FYE STATUS \\\n",
"5281 500001 01-JUL-18 30-JUN-19 As Submitted \n",
"5284 500005 01-JAN-18 31-DEC-18 Amended \n",
"5286 500011 01-JUL-18 30-JUN-19 As Submitted \n",
"5289 500015 01-JAN-18 31-DEC-18 As Submitted \n",
"... ... ... ... ... \n",
"5367 502001 01-JUL-18 30-JUN-19 As Submitted \n",
"5370 503300 01-OCT-17 30-SEP-18 Amended \n",
"5375 504009 01-JAN-18 31-DEC-18 As Submitted \n",
"5376 504011 01-JAN-18 31-DEC-18 As Submitted \n",
"\n",
" HOSPITAL_Name \\\n",
"5281 UW MEDICINE/NORTHWEST HOSPITAL \n",
"5284 VIRGINIA MASON MEDICAL CENTER \n",
"5286 HIGHLINE MEDICAL CENTER \n",
"5289 MULTICARE AUBURN MEDICAL CENTER \n",
"... ... \n",
"5367 REGIONAL HOSPITAL \n",
"5370 SEATTLE CHILDRENS HOSPITAL \n",
"5375 NAVOS \n",
"5376 CASCADE BEHAVIORAL HEALTH \n",
"\n",
" Street_Addr \\\n",
"5281 1550 NORTH 115TH STREET \n",
"5284 925 SENECA STREET \n",
"5286 16251 SYLVESTER RD. SW \n",
"5289 202 NORTH DIVISION STREET \n",
"... ... \n",
"5367 16251 SYLVESTER RD SW \n",
"5370 4800 SANDPOINT WAY NE \n",
"5375 2600 SW HOLDEN STREET \n",
"5376 12844 MILITARY ROAD SOUTH \n",
"\n",
" Po_BOx \\\n",
"5281 NaN \n",
"5284 1930 \n",
"5286 NaN \n",
"5289 NaN \n",
"... ... \n",
"5367 NaN \n",
"5370 5371 \n",
"5375 NaN \n",
"5376 NaN \n",
"\n",
" City \\\n",
"5281 SEATTLE \n",
"5284 SEATTLE \n",
"5286 SEATTLE \n",
"5289 AUBURN \n",
"... ... \n",
"5367 BURIEN \n",
"5370 SEATTLE \n",
"5375 SEATTLE \n",
"5376 TUKWILA \n",
"\n",
" State \\\n",
"5281 WA \n",
"5284 WA \n",
"5286 WA \n",
"5289 WA \n",
"... ... \n",
"5367 WA \n",
"5370 WA \n",
"5375 WA \n",
"5376 WA \n",
"\n",
" Zip_Code ... \\\n",
"5281 98133 ... \n",
"5284 98111- ... \n",
"5286 98166-0657 ... \n",
"5289 98001 ... \n",
"... ... ... \n",
"5367 98166 ... \n",
"5370 98105- ... \n",
"5375 98126 ... \n",
"5376 98168 ... \n",
"\n",
" TOTAL_HOSPITAL_MEDICARE_DISCHARGES TOTAL_HOSPITAL_MEDICAID_DISCHARGES \\\n",
"5281 3619.0 162.0 \n",
"5284 5594.0 1271.0 \n",
"5286 1668.0 250.0 \n",
"5289 1634.0 378.0 \n",
"... ... ... \n",
"5367 94.0 44.0 \n",
"5370 112.0 1763.0 \n",
"5375 103.0 325.0 \n",
"5376 817.0 861.0 \n",
"\n",
" TOTAL_HOSPITAL_DISCHARGES MEDICAID_HMO_DISCHARGES \\\n",
"5281 10515.0 1378.0 \n",
"5284 12272.0 NaN \n",
"5286 9118.0 1427.0 \n",
"5289 6199.0 2311.0 \n",
"... ... ... \n",
"5367 186.0 16.0 \n",
"5370 12145.0 5191.0 \n",
"5375 1060.0 NaN \n",
"5376 2866.0 4.0 \n",
"\n",
" MEDICAID_HMO_IPF_SUBPROVIDER MEDICAID_HMO_IRF_SUBPROVIDER \\\n",
"5281 10.0 NaN \n",
"5284 NaN NaN \n",
"5286 NaN NaN \n",
"5289 162.0 NaN \n",
"... ... ... \n",
"5367 NaN NaN \n",
"5370 NaN NaN \n",
"5375 NaN NaN \n",
"5376 NaN NaN \n",
"\n",
" MEDICAID_IPF_SUBPROVIDER MEDICAID_IRF_SUBPROVIDER \\\n",
"5281 8.0 NaN \n",
"5284 NaN NaN \n",
"5286 NaN NaN \n",
"5289 19.0 NaN \n",
"... ... ... \n",
"5367 NaN NaN \n",
"5370 NaN NaN \n",
"5375 NaN NaN \n",
"5376 NaN NaN \n",
"\n",
" TOTAL_DISCHARGES_SUBPROVIDER_IPF TOTAL_DISCHARGES_SUBPROVIDER_IRF \n",
"5281 269.0 NaN \n",
"5284 NaN NaN \n",
"5286 NaN NaN \n",
"5289 595.0 NaN \n",
"... ... ... \n",
"5367 NaN NaN \n",
"5370 NaN NaN \n",
"5375 NaN NaN \n",
"5376 NaN NaN \n",
"\n",
"[20 rows x 38 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_report[df_report.State.str.startswith('WA') & df_report.County.str.startswith('KING')]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_report['county'] = df_report['County'].str.strip().str.lower()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df_report[df_report.State.str.startswith('WA')].groupby('county').TOTAL_HOSPITAL_BEDS.sum()\\\n",
" .sort_values().plot(kind='barh', figsize=(5,10))\n",
"plt.xlabel('Total Hospital Beds')\n",
"plt.grid();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There is substantial detail beyond this in the Form 2552-10, but to get it requires merging some harder-to-use files. The `hosp10_YYYY_NMRC.csv` file has many values from the form, and the `hosp10_YYYY_RPT.csv` can link these values to the hospital addresses in the IME_GME file."
]
},
{
"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>rpt_rec_num</th>\n",
" <th>wksht_cd</th>\n",
" <th>line_num</th>\n",
" <th>clmn_num</th>\n",
" <th>itm_val_num</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>623132</td>\n",
" <td>A000000</td>\n",
" <td>100</td>\n",
" <td>00200</td>\n",
" <td>33286.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>623132</td>\n",
" <td>A000000</td>\n",
" <td>100</td>\n",
" <td>00300</td>\n",
" <td>33286.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>623132</td>\n",
" <td>A000000</td>\n",
" <td>100</td>\n",
" <td>00500</td>\n",
" <td>33286.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>623132</td>\n",
" <td>A000000</td>\n",
" <td>100</td>\n",
" <td>00700</td>\n",
" <td>33286.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18191388</th>\n",
" <td>662634</td>\n",
" <td>S300001</td>\n",
" <td>2900</td>\n",
" <td>00600</td>\n",
" <td>523.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18191389</th>\n",
" <td>662634</td>\n",
" <td>S300001</td>\n",
" <td>3200</td>\n",
" <td>00700</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18191390</th>\n",
" <td>662634</td>\n",
" <td>S300001</td>\n",
" <td>3200</td>\n",
" <td>00800</td>\n",
" <td>125.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18191391</th>\n",
" <td>662634</td>\n",
" <td>S700000</td>\n",
" <td>20700</td>\n",
" <td>00100</td>\n",
" <td>7566698.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>18191392 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" rpt_rec_num wksht_cd line_num clmn_num itm_val_num\n",
"0 623132 A000000 100 00200 33286.0\n",
"1 623132 A000000 100 00300 33286.0\n",
"2 623132 A000000 100 00500 33286.0\n",
"3 623132 A000000 100 00700 33286.0\n",
"... ... ... ... ... ...\n",
"18191388 662634 S300001 2900 00600 523.0\n",
"18191389 662634 S300001 3200 00700 46.0\n",
"18191390 662634 S300001 3200 00800 125.0\n",
"18191391 662634 S700000 20700 00100 7566698.0\n",
"\n",
"[18191392 rows x 5 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_nmrc = pd.read_csv('hosp10_2018_NMRC.CSV', header=None,\n",
" names=['rpt_rec_num', 'wksht_cd', 'line_num', 'clmn_num', 'itm_val_num'])\n",
"df_nmrc"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <td>07/01/2018</td>\n",
" <td>06/30/2019</td>\n",
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"</table>\n",
"<p>5704 rows × 18 columns</p>\n",
"</div>"
],
"text/plain": [
" rpt_rec_num b PROVIDER_NUMBER npi e f g \\\n",
"0 623132 9 10032 NaN 1 10/01/2017 11/13/2017 \n",
"1 628158 2 250042 NaN 1 11/01/2017 12/31/2017 \n",
"2 628456 2 140147 NaN 1 10/01/2017 12/31/2017 \n",
"3 628833 4 440235 NaN 1 10/11/2017 12/31/2017 \n",
"... ... .. ... ... .. ... ... \n",
"5700 662631 2 530012 NaN 1 07/01/2018 06/30/2019 \n",
"5701 662632 11 530015 NaN 1 07/01/2018 06/30/2019 \n",
"5702 662633 3 531309 NaN 1 07/01/2018 06/30/2019 \n",
"5703 662634 2 531310 NaN 1 07/01/2018 06/30/2019 \n",
"\n",
" h i j k l m n o p q r \n",
"0 04/26/2018 N N K 10001 4 04/19/2018 F NaN NaN 04/16/2018 \n",
"1 06/25/2018 N N L 5901 4 06/21/2018 F NaN NaN 06/01/2018 \n",
"2 06/27/2018 N N L 6101 4 06/25/2018 F NaN NaN 06/01/2018 \n",
"3 07/02/2018 N N L 10001 4 06/28/2018 F NaN NaN 06/04/2018 \n",
"... ... .. .. .. ... .. ... .. ... .. ... \n",
"5700 12/31/2019 N N M 3001 4 12/27/2019 F NaN NaN 11/27/2019 \n",
"5701 12/31/2019 N N M 3001 4 12/30/2019 F NaN NaN 12/04/2019 \n",
"5702 12/31/2019 N N M 3001 4 12/27/2019 F NaN NaN 11/27/2019 \n",
"5703 12/31/2019 N N M 3001 4 12/27/2019 F NaN NaN 11/27/2019 \n",
"\n",
"[5704 rows x 18 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_rpt = pd.read_csv('hosp10_2018_RPT.CSV', header=None,\n",
" names=['rpt_rec_num', 'b', 'PROVIDER_NUMBER',\n",
" 'npi', 'e', 'f', 'g', 'h', 'i', 'j',\n",
" 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r'])\n",
"df_rpt"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(5914, 56)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(df_report, df_rpt, on='PROVIDER_NUMBER').shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looks like everything matches up, although some match up more than once (because of changes in ownership?)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# check if beds in report match or are close to beds in nmrc file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# find ICU beds in each hospital\n",
"\n",
"5.2. Extracting data for ICU; CCU: SICU; BICU; and Other ICU: \n",
"\n",
"The intensive care cost centers that are reported on Worksheets S-3, Part I; G-2; D-1; and D-6 are no longer cost center coded. You extract the actual line number on the form. For example, if you want ICU beds, you should extract Worksheet Code S300001, Line Numbers 00800 through 00899, Column 00200. \n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"a = df_nmrc.query('wksht_cd == \"S300001\" '\n",
" +'and line_num >= 800 and line_num <= 899 '\n",
" +'and clmn_num == \"00200\"'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: top;\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>rpt_rec_num</th>\n",
" <th>wksht_cd</th>\n",
" <th>line_num</th>\n",
" <th>clmn_num_x</th>\n",
" <th>itm_val_num_x</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>628158</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>10.0</td>\n",
" <td>00300</td>\n",
" <td>610.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>628456</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>8.0</td>\n",
" <td>00300</td>\n",
" <td>736.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>631016</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>12.0</td>\n",
" <td>00300</td>\n",
" <td>708.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>631094</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>30.0</td>\n",
" <td>00300</td>\n",
" <td>1770.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3375</th>\n",
" <td>662626</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>15.0</td>\n",
" <td>00300</td>\n",
" <td>5475.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3376</th>\n",
" <td>662628</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>4.0</td>\n",
" <td>00300</td>\n",
" <td>1460.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3377</th>\n",
" <td>662631</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>14.0</td>\n",
" <td>00300</td>\n",
" <td>5110.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3378</th>\n",
" <td>662632</td>\n",
" <td>S300001</td>\n",
" <td>800</td>\n",
" <td>00200</td>\n",
" <td>6.0</td>\n",
" <td>00300</td>\n",
" <td>2190.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3379 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" rpt_rec_num wksht_cd line_num clmn_num_x itm_val_num_x clmn_num_y \\\n",
"0 628158 S300001 800 00200 10.0 00300 \n",
"1 628456 S300001 800 00200 8.0 00300 \n",
"2 631016 S300001 800 00200 12.0 00300 \n",
"3 631094 S300001 800 00200 30.0 00300 \n",
"... ... ... ... ... ... ... \n",
"3375 662626 S300001 800 00200 15.0 00300 \n",
"3376 662628 S300001 800 00200 4.0 00300 \n",
"3377 662631 S300001 800 00200 14.0 00300 \n",
"3378 662632 S300001 800 00200 6.0 00300 \n",
"\n",
" itm_val_num_y \n",
"0 610.0 \n",
"1 736.0 \n",
"2 708.0 \n",
"3 1770.0 \n",
"... ... \n",
"3375 5475.0 \n",
"3376 1460.0 \n",
"3377 5110.0 \n",
"3378 2190.0 \n",
"\n",
"[3379 rows x 7 columns]"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = df_nmrc.query('wksht_cd == \"S300001\" '\n",
" +'and line_num >= 800 and line_num <= 899 '\n",
" +'and clmn_num == \"00300\"'\n",
" )\n",
"c = pd.merge(a, b, on=['rpt_rec_num', 'wksht_cd', 'line_num'])\n",
"c"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x2b6a6a8967b8>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"c['ratio'] = np.round((100 * c.itm_val_num_y / (c.itm_val_num_x * 365)))\n",
"# c.ratio.hist(bins=100)\n",
"c.ratio.value_counts().sort_index().plot()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rpt_rec_num\n",
"649569 1.0\n",
"647619 1.0\n",
"650788 1.0\n",
"659043 1.0\n",
" ... \n",
"654392 300.0\n",
"655215 332.0\n",
"648798 370.0\n",
"653832 506.0\n",
"Name: itm_val_num, Length: 2922, dtype: float64"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# there are some repeated report record numbers here; what does that mean?\n",
"# different types of ICU?\n",
"t = df_nmrc.query('wksht_cd == \"S300001\" '\n",
" +'and line_num >= 800 and line_num <= 899 '\n",
" +'and clmn_num == \"00200\"'\n",
" )\n",
"t = t.groupby('rpt_rec_num').itm_val_num.sum()\n",
"t.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rpt_rec_num\n",
"623132 10032\n",
"628158 250042\n",
"628456 140147\n",
"628833 440235\n",
" ... \n",
"662631 530012\n",
"662632 530015\n",
"662633 531309\n",
"662634 531310\n",
"Name: PROVIDER_NUMBER, Length: 5704, dtype: int64"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# map from rpt_rec_num to provider num\n",
"provider_number_map = df_rpt.set_index('rpt_rec_num').PROVIDER_NUMBER\n",
"provider_number_map"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"rpt_rec_num\n",
"150102 1.0\n",
"171362 1.0\n",
"461308 1.0\n",
"271327 1.0\n",
" ... \n",
"453304 300.0\n",
"260091 332.0\n",
"100007 370.0\n",
"61336 506.0\n",
"Name: itm_val_num, Length: 2922, dtype: float64"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t.index = t.index.map(provider_number_map)\n",
"t.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"t.name = 'icu_beds'"
]
},
{
"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>icu_beds</th>\n",
" <th>TOTAL_HOSPITAL_BEDS</th>\n",
" <th>HOSPITAL_Name</th>\n",
" <th>Street_Addr</th>\n",
" <th>City</th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>5286</th>\n",
" <td>10.0</td>\n",
" <td>129.0</td>\n",
" <td>HIGHLINE MEDICAL CENTER</td>\n",
" <td>16251 SYLVESTER RD. SW</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5323</th>\n",
" <td>14.0</td>\n",
" <td>124.0</td>\n",
" <td>ST FRANCIS HOSPITAL</td>\n",
" <td>34515 NINTH AVENUE S</td>\n",
" <td>FEDERAL WAY</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5327</th>\n",
" <td>18.0</td>\n",
" <td>126.0</td>\n",
" <td>SWEDISH ISSAQUAH</td>\n",
" <td>751 NE BLAKELY DRIVE</td>\n",
" <td>ISSAQUAH</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5289</th>\n",
" <td>22.0</td>\n",
" <td>73.0</td>\n",
" <td>MULTICARE AUBURN MEDICAL CENTER</td>\n",
" <td>202 NORTH DIVISION STREET</td>\n",
" <td>AUBURN</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5294</th>\n",
" <td>56.0</td>\n",
" <td>181.0</td>\n",
" <td>SWEDISH MEDICAL CENTER CHERRY HILL</td>\n",
" <td>500 - 17TH AVENUE</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5319</th>\n",
" <td>63.0</td>\n",
" <td>304.0</td>\n",
" <td>EVERGREEN HEALTHCARE</td>\n",
" <td>12040 NE 128TH STREET</td>\n",
" <td>KIRKLAND</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5311</th>\n",
" <td>89.0</td>\n",
" <td>321.0</td>\n",
" <td>HARBORVIEW MEDICAL CENTER</td>\n",
" <td>325 9TH AVENUE</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5370</th>\n",
" <td>91.0</td>\n",
" <td>361.0</td>\n",
" <td>SEATTLE CHILDRENS HOSPITAL</td>\n",
" <td>4800 SANDPOINT WAY NE</td>\n",
" <td>SEATTLE</td>\n",
" <td>WA</td>\n",
" <td>KING</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>11 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" icu_beds TOTAL_HOSPITAL_BEDS HOSPITAL_Name \\\n",
"5286 10.0 129.0 HIGHLINE MEDICAL CENTER \n",
"5323 14.0 124.0 ST FRANCIS HOSPITAL \n",
"5327 18.0 126.0 SWEDISH ISSAQUAH \n",
"5289 22.0 73.0 MULTICARE AUBURN MEDICAL CENTER \n",
"... ... ... ... \n",
"5294 56.0 181.0 SWEDISH MEDICAL CENTER CHERRY HILL \n",
"5319 63.0 304.0 EVERGREEN HEALTHCARE \n",
"5311 89.0 321.0 HARBORVIEW MEDICAL CENTER \n",
"5370 91.0 361.0 SEATTLE CHILDRENS HOSPITAL \n",
"\n",
" Street_Addr \\\n",
"5286 16251 SYLVESTER RD. SW \n",
"5323 34515 NINTH AVENUE S \n",
"5327 751 NE BLAKELY DRIVE \n",
"5289 202 NORTH DIVISION STREET \n",
"... ... \n",
"5294 500 - 17TH AVENUE \n",
"5319 12040 NE 128TH STREET \n",
"5311 325 9TH AVENUE \n",
"5370 4800 SANDPOINT WAY NE \n",
"\n",
" City \\\n",
"5286 SEATTLE \n",
"5323 FEDERAL WAY \n",
"5327 ISSAQUAH \n",
"5289 AUBURN \n",
"... ... \n",
"5294 SEATTLE \n",
"5319 KIRKLAND \n",
"5311 SEATTLE \n",
"5370 SEATTLE \n",
"\n",
" State \\\n",
"5286 WA \n",
"5323 WA \n",
"5327 WA \n",
"5289 WA \n",
"... ... \n",
"5294 WA \n",
"5319 WA \n",
"5311 WA \n",
"5370 WA \n",
"\n",
" County \n",
"5286 KING \n",
"5323 KING \n",
"5327 KING \n",
"5289 KING \n",
"... ... \n",
"5294 KING \n",
"5319 KING \n",
"5311 KING \n",
"5370 KING \n",
"\n",
"[11 rows x 7 columns]"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(t, df_report[df_report.State.str.startswith('WA') & df_report.County.str.startswith('KING')],\n",
" left_index=True, right_on='PROVIDER_NUMBER', how='right')\\\n",
" .sort_values('icu_beds').dropna(subset=['icu_beds']).iloc[:, [0,21,5,6,8,9,11]]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>icu_beds</th>\n",
" <th>TOTAL_HOSPITAL_BEDS</th>\n",
" <th>HOSPITAL_Name</th>\n",
" <th>Street_Addr</th>\n",
" <th>City</th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1493</th>\n",
" <td>8.0</td>\n",
" <td>128.0</td>\n",
" <td>HOLY FAMILY MEDICAL CENTER</td>\n",
" <td>100 NORTH RIVER ROAD SECOND FLOOR</td>\n",
" <td>DES PLAINES</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1394</th>\n",
" <td>8.0</td>\n",
" <td>201.0</td>\n",
" <td>JACKSON PARK HOSPITAL</td>\n",
" <td>7531 SOUTH STONEY ISLAND AVENUE</td>\n",
" <td>CHICAGO</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1398</th>\n",
" <td>8.0</td>\n",
" <td>122.0</td>\n",
" <td>SOUTH SHORE HOSPITAL CORPORATION</td>\n",
" <td>8012 SOUTH CRANDON AVENUE</td>\n",
" <td>CHICAGO</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1493</th>\n",
" <td>8.0</td>\n",
" <td>128.0</td>\n",
" <td>HOLY FAMILY MEDICAL CENTER</td>\n",
" <td>100 NORTH RIVER ROAD SECOND FLOOR</td>\n",
" <td>DES PLAINES</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1352</th>\n",
" <td>79.0</td>\n",
" <td>682.0</td>\n",
" <td>UNIVERSITY OF CHICAGO HOSPITALS</td>\n",
" <td>5841 SOUTH MARYLAND AVENUE</td>\n",
" <td>CHICAGO</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1381</th>\n",
" <td>112.0</td>\n",
" <td>434.0</td>\n",
" <td>BOARD OF TRUSTEES OF THE UNIVERSITY</td>\n",
" <td>1740 W TAYLOR ST</td>\n",
" <td>CHICAGO</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1315</th>\n",
" <td>115.0</td>\n",
" <td>698.0</td>\n",
" <td>NORTHSHORE UNIVERSITY HEALTHSYSTEM</td>\n",
" <td>2650 RIDGE AVENUE</td>\n",
" <td>EVANSTON</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1410</th>\n",
" <td>182.0</td>\n",
" <td>706.0</td>\n",
" <td>ADVOCATE CHRIST HOSPITAL</td>\n",
" <td>4440 WEST 95TH STREET</td>\n",
" <td>OAK LAWN</td>\n",
" <td>IL</td>\n",
" <td>COOK</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>66 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" icu_beds TOTAL_HOSPITAL_BEDS HOSPITAL_Name \\\n",
"1493 8.0 128.0 HOLY FAMILY MEDICAL CENTER \n",
"1394 8.0 201.0 JACKSON PARK HOSPITAL \n",
"1398 8.0 122.0 SOUTH SHORE HOSPITAL CORPORATION \n",
"1493 8.0 128.0 HOLY FAMILY MEDICAL CENTER \n",
"... ... ... ... \n",
"1352 79.0 682.0 UNIVERSITY OF CHICAGO HOSPITALS \n",
"1381 112.0 434.0 BOARD OF TRUSTEES OF THE UNIVERSITY \n",
"1315 115.0 698.0 NORTHSHORE UNIVERSITY HEALTHSYSTEM \n",
"1410 182.0 706.0 ADVOCATE CHRIST HOSPITAL \n",
"\n",
" Street_Addr \\\n",
"1493 100 NORTH RIVER ROAD SECOND FLOOR \n",
"1394 7531 SOUTH STONEY ISLAND AVENUE \n",
"1398 8012 SOUTH CRANDON AVENUE \n",
"1493 100 NORTH RIVER ROAD SECOND FLOOR \n",
"... ... \n",
"1352 5841 SOUTH MARYLAND AVENUE \n",
"1381 1740 W TAYLOR ST \n",
"1315 2650 RIDGE AVENUE \n",
"1410 4440 WEST 95TH STREET \n",
"\n",
" City \\\n",
"1493 DES PLAINES \n",
"1394 CHICAGO \n",
"1398 CHICAGO \n",
"1493 DES PLAINES \n",
"... ... \n",
"1352 CHICAGO \n",
"1381 CHICAGO \n",
"1315 EVANSTON \n",
"1410 OAK LAWN \n",
"\n",
" State \\\n",
"1493 IL \n",
"1394 IL \n",
"1398 IL \n",
"1493 IL \n",
"... ... \n",
"1352 IL \n",
"1381 IL \n",
"1315 IL \n",
"1410 IL \n",
"\n",
" County \n",
"1493 COOK \n",
"1394 COOK \n",
"1398 COOK \n",
"1493 COOK \n",
"... ... \n",
"1352 COOK \n",
"1381 COOK \n",
"1315 COOK \n",
"1410 COOK \n",
"\n",
"[66 rows x 7 columns]"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(t, df_report[df_report.State.str.startswith('IL') & df_report.County.str.startswith('COOK')],\n",
" left_index=True, right_on='PROVIDER_NUMBER', how='right')\\\n",
" .sort_values('icu_beds').dropna(subset=['icu_beds']).iloc[:, [0,21,5,6,8,9,11]]"
]
},
{
"cell_type": "code",
"execution_count": 56,
"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>icu_beds</th>\n",
" <th>TOTAL_HOSPITAL_BEDS</th>\n",
" <th>HOSPITAL_Name</th>\n",
" <th>Street_Addr</th>\n",
" <th>City</th>\n",
" <th>State</th>\n",
" <th>County</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>571</th>\n",
" <td>4.0</td>\n",
" <td>101.0</td>\n",
" <td>MONTEREY PARK HOSPITAL</td>\n",
" <td>900 S. ATLANTIC BLVD</td>\n",
" <td>MONTEREY PARK</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>640</th>\n",
" <td>4.0</td>\n",
" <td>49.0</td>\n",
" <td>MONROVIA MEMORIAL HOSPITAL</td>\n",
" <td>323 SOUTH HELIOTROPE AVENUE</td>\n",
" <td>MONROVIA</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>376</th>\n",
" <td>4.0</td>\n",
" <td>81.0</td>\n",
" <td>COMMUNITY HOSPITAL OF HUNTINGTON PAR</td>\n",
" <td>2623 E SLAUSON AVENUE</td>\n",
" <td>HUNTINGTON PARK</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>599</th>\n",
" <td>6.0</td>\n",
" <td>31.0</td>\n",
" <td>CASA COLINA HOSPITAL AND CENTERS FOR</td>\n",
" <td>255 E BONITA</td>\n",
" <td>POMONA</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>407</th>\n",
" <td>116.0</td>\n",
" <td>626.0</td>\n",
" <td>KFH - FONTANA</td>\n",
" <td>9961 SIERRA AVENUE</td>\n",
" <td>FONTANA</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>448</th>\n",
" <td>120.0</td>\n",
" <td>445.0</td>\n",
" <td>RONALD REAGAN UCLA</td>\n",
" <td>757 WESTWOOD PLAZA</td>\n",
" <td>LOS ANGELES</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>405</th>\n",
" <td>129.0</td>\n",
" <td>460.0</td>\n",
" <td>KFH - LOS ANGELES</td>\n",
" <td>4867 SUNSET BLVD</td>\n",
" <td>LOS ANGELES</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" <tr>\n",
" <th>483</th>\n",
" <td>168.0</td>\n",
" <td>594.0</td>\n",
" <td>LAC+USC MEDICAL CENTER</td>\n",
" <td>1200 N. STATE STREET</td>\n",
" <td>LOS ANGELES</td>\n",
" <td>CA</td>\n",
" <td>LOS ANGELES</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>68 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" icu_beds TOTAL_HOSPITAL_BEDS HOSPITAL_Name \\\n",
"571 4.0 101.0 MONTEREY PARK HOSPITAL \n",
"640 4.0 49.0 MONROVIA MEMORIAL HOSPITAL \n",
"376 4.0 81.0 COMMUNITY HOSPITAL OF HUNTINGTON PAR \n",
"599 6.0 31.0 CASA COLINA HOSPITAL AND CENTERS FOR \n",
".. ... ... ... \n",
"407 116.0 626.0 KFH - FONTANA \n",
"448 120.0 445.0 RONALD REAGAN UCLA \n",
"405 129.0 460.0 KFH - LOS ANGELES \n",
"483 168.0 594.0 LAC+USC MEDICAL CENTER \n",
"\n",
" Street_Addr \\\n",
"571 900 S. ATLANTIC BLVD \n",
"640 323 SOUTH HELIOTROPE AVENUE \n",
"376 2623 E SLAUSON AVENUE \n",
"599 255 E BONITA \n",
".. ... \n",
"407 9961 SIERRA AVENUE \n",
"448 757 WESTWOOD PLAZA \n",
"405 4867 SUNSET BLVD \n",
"483 1200 N. STATE STREET \n",
"\n",
" City \\\n",
"571 MONTEREY PARK \n",
"640 MONROVIA \n",
"376 HUNTINGTON PARK \n",
"599 POMONA \n",
".. ... \n",
"407 FONTANA \n",
"448 LOS ANGELES \n",
"405 LOS ANGELES \n",
"483 LOS ANGELES \n",
"\n",
" State \\\n",
"571 CA \n",
"640 CA \n",
"376 CA \n",
"599 CA \n",
".. ... \n",
"407 CA \n",
"448 CA \n",
"405 CA \n",
"483 CA \n",
"\n",
" County \n",
"571 LOS ANGELES \n",
"640 LOS ANGELES \n",
"376 LOS ANGELES \n",
"599 LOS ANGELES \n",
".. ... \n",
"407 LOS ANGELES \n",
"448 LOS ANGELES \n",
"405 LOS ANGELES \n",
"483 LOS ANGELES \n",
"\n",
"[68 rows x 7 columns]"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.merge(t, df_report[df_report.State.str.startswith('CA') & df_report.County.str.startswith('LOS ANGELES')],\n",
" left_index=True, right_on='PROVIDER_NUMBER', how='right')\\\n",
" .sort_values('icu_beds').dropna(subset=['icu_beds']).iloc[:, [0,21,5,6,8,9,11]]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"# looks like I can get a count of hospital beds and ICU beds by county or state\n",
"# cool!"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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>name</th>\n",
" <th>address</th>\n",
" <th>city</th>\n",
" <th>state</th>\n",
" <th>county</th>\n",
" <th>total_beds</th>\n",
" <th>icu_beds</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>113</th>\n",
" <td>PROVIDENCE ALASKA MEDICAL CENTER</td>\n",
" <td>3200 PROVIDENCE DRIVE</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>AK</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>391.0</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td>ALASKA NATIVE MEDICAL CENTER</td>\n",
" <td>4315 DIPLOMACY DRIVE</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>AK</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>174.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td>ALASKA SPECIALTY HOSPITAL LLC</td>\n",
" <td>4800 CORDOVA STREET</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>AK</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>59.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>136</th>\n",
" <td>ALASKA PSYCHIATRIC INSTITUTE</td>\n",
" <td>3700 PIPER ST</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>AK</td>\n",
" <td>ANCHORAGE</td>\n",
" <td>80.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5586</th>\n",
" <td>ST. JOHNS HOSPITAL</td>\n",
" <td>625 E. BROADWAY</td>\n",
" <td>JACKSON</td>\n",
" <td>WY</td>\n",
" <td>TETON</td>\n",
" <td>48.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5589</th>\n",
" <td>EVANSTON REGIONAL</td>\n",
" <td>190 ARROWHEAD DR</td>\n",
" <td>EVANSTON</td>\n",
" <td>WY</td>\n",
" <td>UINTA</td>\n",
" <td>32.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5594</th>\n",
" <td>WASHAKIE MEDICAL CENTER</td>\n",
" <td>400 SOUTH 19TH STREET</td>\n",
" <td>WORLAND</td>\n",
" <td>WY</td>\n",
" <td>WASHAKIE</td>\n",
" <td>18.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5603</th>\n",
" <td>WYOMING BEHAVIORAL INSTITUTE</td>\n",
" <td>2521 EAST 15TH STREET</td>\n",
" <td>CASPER</td>\n",
" <td>WY</td>\n",
" <td>NaN</td>\n",
" <td>81.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5836 rows × 7 columns</p>\n",
"</div>"
],
"text/plain": [
" name \\\n",
"113 PROVIDENCE ALASKA MEDICAL CENTER \n",
"120 ALASKA NATIVE MEDICAL CENTER \n",
"134 ALASKA SPECIALTY HOSPITAL LLC \n",
"136 ALASKA PSYCHIATRIC INSTITUTE \n",
"... ... \n",
"5586 ST. JOHNS HOSPITAL \n",
"5589 EVANSTON REGIONAL \n",
"5594 WASHAKIE MEDICAL CENTER \n",
"5603 WYOMING BEHAVIORAL INSTITUTE \n",
"\n",
" address \\\n",
"113 3200 PROVIDENCE DRIVE \n",
"120 4315 DIPLOMACY DRIVE \n",
"134 4800 CORDOVA STREET \n",
"136 3700 PIPER ST \n",
"... ... \n",
"5586 625 E. BROADWAY \n",
"5589 190 ARROWHEAD DR \n",
"5594 400 SOUTH 19TH STREET \n",
"5603 2521 EAST 15TH STREET \n",
"\n",
" city \\\n",
"113 ANCHORAGE \n",
"120 ANCHORAGE \n",
"134 ANCHORAGE \n",
"136 ANCHORAGE \n",
"... ... \n",
"5586 JACKSON \n",
"5589 EVANSTON \n",
"5594 WORLAND \n",
"5603 CASPER \n",
"\n",
" state \\\n",
"113 AK \n",
"120 AK \n",
"134 AK \n",
"136 AK \n",
"... ... \n",
"5586 WY \n",
"5589 WY \n",
"5594 WY \n",
"5603 WY \n",
"\n",
" county total_beds icu_beds \n",
"113 ANCHORAGE 391.0 46.0 \n",
"120 ANCHORAGE 174.0 NaN \n",
"134 ANCHORAGE 59.0 NaN \n",
"136 ANCHORAGE 80.0 NaN \n",
"... ... ... ... \n",
"5586 TETON 48.0 6.0 \n",
"5589 UINTA 32.0 6.0 \n",
"5594 WASHAKIE 18.0 NaN \n",
"5603 NaN 81.0 NaN \n",
"\n",
"[5836 rows x 7 columns]"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tt = pd.merge(t, df_report,\n",
" left_index=True, right_on='PROVIDER_NUMBER', how='right')\\\n",
" .sort_values(['State', 'County', 'City']).iloc[:, [5,6,8,9,11,21,0]]\n",
"tt.columns = ['name', 'address', 'city', 'state', 'county', 'total_beds', 'icu_beds']\n",
"tt"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"tt.to_csv('/home/j/Project/simulation_science/covid/data/hosp_beds.csv', index=False)"
]
},
{
"attachments": {
"image.png": {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"https://www.sccm.org/Blog/March-2020/United-States-Resource-Availability-for-COVID-19\n",
"\n",
"![image.png](attachment:image.png)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"75425.0"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t.sum() # icu beds from 2018 CMS data"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2917"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(t[t>1])"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"State\n",
"AK 24\n",
"AL 113\n",
"AR 93\n",
"AZ 102\n",
" ... \n",
"WA 97\n",
"WI 143\n",
"WV 60\n",
"WY 23\n",
"Name: PROVIDER_NUMBER, Length: 55, dtype: int64"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# how does cms data compare to kff data from https://www.kff.org/state-category/providers-service-use/ ?\n",
"\n",
"df_report.groupby('State').PROVIDER_NUMBER.count() # close but different"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about comparing CMS to https://www.sccm.org/Communications/Critical-Care-Statistics ?"
]
},
{
"cell_type": "code",
"execution_count": 71,
"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>GM1_name</th>\n",
" <th>GM1_type</th>\n",
" <th>GM1_lat</th>\n",
" <th>GM1_long</th>\n",
" <th>GM1_buffer</th>\n",
" <th>GM2_name</th>\n",
" <th>GM2_type</th>\n",
" <th>GM2_lat</th>\n",
" <th>GM2_long</th>\n",
" <th>GM2_buffer</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3200 Providence Dr, Anchorage, AK 99508, USA</td>\n",
" <td>street_address</td>\n",
" <td>61.187905</td>\n",
" <td>-149.816914</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4315 Diplomacy Dr, Anchorage, AK 99508, USA</td>\n",
" <td>premise</td>\n",
" <td>61.182742</td>\n",
" <td>-149.800663</td>\n",
" <td>0.233846</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>4800 Cordova St, Anchorage, AK 99503, USA</td>\n",
" <td>premise</td>\n",
" <td>61.176427</td>\n",
" <td>-149.880229</td>\n",
" <td>0.110496</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3700 Piper St, Anchorage, AK 99508, USA</td>\n",
" <td>premise</td>\n",
" <td>61.185266</td>\n",
" <td>-149.829269</td>\n",
" <td>0.211896</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5832</th>\n",
" <td>625 E Broadway, Hopewell, VA 23860, USA</td>\n",
" <td>street_address</td>\n",
" <td>37.307780</td>\n",
" <td>-77.279769</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5833</th>\n",
" <td>190 Arrowhead Dr, Kansas City, MO 64129, USA</td>\n",
" <td>street_address</td>\n",
" <td>39.046949</td>\n",
" <td>-94.484155</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5834</th>\n",
" <td>400 S 19th St, Philadelphia, PA 19146, USA</td>\n",
" <td>street_address</td>\n",
" <td>39.946553</td>\n",
" <td>-75.173195</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5835</th>\n",
" <td>2521 E 15th St, Casper, WY 82609, USA</td>\n",
" <td>establishment;health;hospital;point_of_interest</td>\n",
" <td>42.834843</td>\n",
" <td>-106.294330</td>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5836 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" GM1_name \\\n",
"0 3200 Providence Dr, Anchorage, AK 99508, USA \n",
"1 4315 Diplomacy Dr, Anchorage, AK 99508, USA \n",
"2 4800 Cordova St, Anchorage, AK 99503, USA \n",
"3 3700 Piper St, Anchorage, AK 99508, USA \n",
"... ... \n",
"5832 625 E Broadway, Hopewell, VA 23860, USA \n",
"5833 190 Arrowhead Dr, Kansas City, MO 64129, USA \n",
"5834 400 S 19th St, Philadelphia, PA 19146, USA \n",
"5835 2521 E 15th St, Casper, WY 82609, USA \n",
"\n",
" GM1_type GM1_lat GM1_long \\\n",
"0 street_address 61.187905 -149.816914 \n",
"1 premise 61.182742 -149.800663 \n",
"2 premise 61.176427 -149.880229 \n",
"3 premise 61.185266 -149.829269 \n",
"... ... ... ... \n",
"5832 street_address 37.307780 -77.279769 \n",
"5833 street_address 39.046949 -94.484155 \n",
"5834 street_address 39.946553 -75.173195 \n",
"5835 establishment;health;hospital;point_of_interest 42.834843 -106.294330 \n",
"\n",
" GM1_buffer GM2_name GM2_type GM2_lat GM2_long GM2_buffer \n",
"0 0.000000 NaN NaN NaN NaN NaN \n",
"1 0.233846 NaN NaN NaN NaN NaN \n",
"2 0.110496 NaN NaN NaN NaN NaN \n",
"3 0.211896 NaN NaN NaN NaN NaN \n",
"... ... ... ... ... ... ... \n",
"5832 0.000000 NaN NaN NaN NaN NaN \n",
"5833 0.000000 NaN NaN NaN NaN NaN \n",
"5834 0.000000 NaN NaN NaN NaN NaN \n",
"5835 0.000000 NaN NaN NaN NaN NaN \n",
"\n",
"[5836 rows x 10 columns]"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Paulina geomatched these facilities\n",
"pd.read_csv('/home/j/Project/simulation_science/covid/data/hosp_beds_geomatched.csv').iloc[:,-10:]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "dismod_mr",
"language": "python",
"name": "dismod_mr"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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