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This is a dataset of Assisted Living, Nursing and Residential Care facilities in Oregon, open as of September, 2016. For each, we have:

Data were munged here.

  1. facility_id: Unique ID used to join to complaints
  2. fac_ccmunumber: Unique ID used to join to ownership history
  3. facility_type: NF - Nursing Facility; RCF - Residential Care Facility; ALF - Assisted Living Facility
  4. fac_capacity: Number of beds facility is licensed to have. Not necessarily the number of beds facility does have.
  5. facility_name: Facility name at time of September extract.
  6. offline: created in munging notebook, a count of complaints that DO NOT appear when facility is searched on state's complaint search website.
  7. online: created in munging notebook, a count of complaints that DO appear when facility is searched on state's complaint search website.




In [91]:



    


import pandas as pd
import numpy as np
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))



    


















    























In [92]:



    


df = pd.read_csv('../../data/processed/facilities-3-29-scrape.csv')

How many facilities are there?





In [93]:



    


df.count()[0]



    


















    
Out[93]:








642

How many facilities have accurate records online?

Those that have no offline records.





In [94]:



    


df[(df['offline'].isnull())].count()[0]



    


















    
Out[94]:








59

How many facilities have inaccurate records online?<h/3>

Those that have offline records.





In [95]:



    


df[(df['offline'].notnull())].count()[0]



    


















    
Out[95]:








583

How many facilities had more than double the number of complaints shown online?





In [96]:



    


df[(df['offline']>df['online']) & (df['online'].notnull())].count()[0]



    


















    
Out[96]:








358

How many facilities show zero complaints online but have complaints offline?





In [97]:



    


df[(df['online'].isnull()) & (df['offline'].notnull())].count()[0]



    


















    
Out[97]:








59

How many facilities have complaints and are accurate online?





In [98]:



    


df[(df['online'].notnull()) & (df['offline'].isnull())].count()[0]



    


















    
Out[98]:








16

How many facilities have complaints?





In [99]:



    


df[(df['online'].notnull()) | df['offline'].notnull()].count()[0]



    


















    
Out[99]:








599

What percent of facilities have accurate records online?





In [100]:



    


df[(df['offline'].isnull())].count()[0]/df.count()[0]*100



    


















    
Out[100]:








9.1900311526479754

What is the total capacity of all facilities with inaccurate records?





In [101]:



    


df[df['offline'].notnull()].sum()['fac_capacity']



    


















    
Out[101]:








35129.0

How many facilities appear to have no complaints, whether or not they do?





In [102]:



    


df[df['online'].isnull()].count()[0]



    


















    
Out[102]:








102

What are the ten facilities with >50 complaints that have the highest disparities?

For graphics





In [114]:



    


over_50 = df[((df['offline']+df['online'])>50)]



    











In [115]:



    


over_50['total'] = over_50['online']+over_50['offline']



    


















    






/Users/fzarkhin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

















In [116]:



    


over_50['pct_offline'] = over_50['offline']/over_50['total']*100



    


















    






/Users/fzarkhin/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  if __name__ == '__main__':

















In [117]:



    


over_50[over_50['facility_name']=='Avamere Health Services of Rogue Valley']



    


















    
Out[117]:










  
    

      
      facility_id
      fac_ccmunumber
      facility_type
      fac_capacity
      facility_name
      offline
      online
      total
      pct_offline
    

  
  
    

      4
      385024
      385024
      NF
      91.0
      Avamere Health Services of Rogue Valley
      67.0
      27.0
      94.0
      71.276596
    

  




















In [118]:



    


over_50.sort_values('pct_offline',ascending = False).head(10)



    


















    
Out[118]:










  
    

      
      facility_id
      fac_ccmunumber
      facility_type
      fac_capacity
      facility_name
      offline
      online
      total
      pct_offline
    

  
  
    

      50
      385166
      385166
      NF
      165.0
      Maryville Nursing Home
      53.0
      12.0
      65.0
      81.538462
    

    

      78
      385219
      385219
      NF
      93.0
      Care Center East Health & Specialty Care Center
      63.0
      16.0
      79.0
      79.746835
    

    

      45
      385157
      385157
      NF
      114.0
      Life Care Center Of Coos Bay
      74.0
      21.0
      95.0
      77.894737
    

    

      63
      385190
      385190
      NF
      78.0
      Prestige Post-Acute and Rehabilitation Center-...
      50.0
      15.0
      65.0
      76.923077
    

    

      34
      385143
      385143
      NF
      118.0
      Umpqua Valley Nursing & Rehabilitation Center
      55.0
      17.0
      72.0
      76.388889
    

    

      144
      50A263
      50A263
      RCF
      59.0
      Brookdale Bend
      40.0
      13.0
      53.0
      75.471698
    

    

      23
      385120
      385120
      NF
      121.0
      Valley West Health Care Center
      55.0
      20.0
      75.0
      73.333333
    

    

      113
      385270
      385270
      NF
      96.0
      Prestige Post-Acute and Rehabilitation Center ...
      50.0
      19.0
      69.0
      72.463768
    

    

      4
      385024
      385024
      NF
      91.0
      Avamere Health Services of Rogue Valley
      67.0
      27.0
      94.0
      71.276596
    

    

      27
      385132
      385132
      NF
      148.0
      Avamere Rehabilitation of King City
      36.0
      15.0
      51.0
      70.588235
    

  




















In [ ]:

Content source: TheOregonian/long-term-care-db

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