This is a dataset of Assisted Living, Nursing and Residential Care facilities in Oregon, open as of January, 2017. For each, we have:

facility_id: Unique ID used to join to complaints
fac_ccmunumber: Unique ID used to join to ownership history
facility_type: NF - Nursing Facility; RCF - Residential Care Facility; ALF - Assisted Living Facility
fac_capacity: Number of beds facility is licensed to have. Not necessarily the number of beds facility does have.
offline: created in munging notebook, a count of complaints that DO NOT appear when facility is searched on state's complaint search website (https://apps.state.or.us/cf2/spd/facility_complaints/).
online: created in munging notebook, a count of complaints that DO appear when facility is searched on state's complaint search website (https://apps.state.or.us/cf2/spd/facility_complaints/).



In [1]:

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



In [2]:

    
df = pd.read_csv('/Users/fzarkhin/OneDrive - Advance Central Services, Inc/fproj/github/database-story/data/processed/facilities.csv')

How many facilities have accurate records online?

Those that have no offline records.



In [3]:

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









    Out[3]:





57

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

Those that have offline records.



In [4]:

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









    Out[4]:





585

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



In [5]:

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









    Out[5]:





357

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



In [6]:

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









    Out[6]:





59

How many facilities have complaints and are accurate online?



In [7]:

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









    Out[7]:





14

How many facilities have complaints?



In [8]:

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









    Out[8]:





599

What percent of facilities have accurate records online?



In [9]:

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









    Out[9]:





8.8785046728971952

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



In [10]:

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









    Out[10]:





35238.0



In [11]:

    
df[df['fac_capacity'].isnull()]









    Out[11]:






  
    
      
      facility_id
      fac_ccmunumber
      facility_type
      fac_capacity
      facility_name
      offline
      online



In [12]:

    
#df#['fac_capacity'].sum()



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