Data were munged here.
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>"))
df = pd.read_csv('../../data/processed/complaints-3-29-scrape.csv')
In [2]:
df[['outcome','incident_date']][(df['facility_id']=='70M226') &
(df['incident_date']<'2013-08-01') &
(df['incident_date']>'2013-05-01') &
(df['public']=='offline') &
(df['outcome'].str.contains('Property'))].sort_values('incident_date').count()
Out[2]:
In [3]:
df[['abuse_number','outcome_notes']][(df['facility_id']=='70M226') &
(df['incident_date']<'2013-08-01') &
(df['incident_date']>'2013-05-01') &
(df['public']=='offline') &
(df['outcome'].str.contains('Property'))]
Out[3]:
The case below lists two thefts. That's why our total in the paragraph is 11.
In [4]:
df['outcome_notes'][df['abuse_number']=='ES133150']
Out[4]:
In [5]:
df[['outcome_notes','abuse_number']][(df['facility_id']=='70A299') &
(df['outcome']=='Loss of Resident Property') &
(df['public']=='online') &
(df['year']==2013)]
Out[5]:
Upon review, use my own judgement to determine that cases ES134746 and ES133151 have a similar severity to the ten cases at Woodside.
In [6]:
df[['abuse_number','outcome_notes']][df['abuse_number'].isin(['ES133151','ES134746'])]
Out[6]: