In [1]:

    

from __future__ import division, print_function, unicode_literals

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline


    

In [2]:

    

housing_owners = pd.read_csv('fema_data/chi_housing_assistance_owners.csv')
housing_owners['zipCode'] = housing_owners['zipCode'].fillna(0).astype(int).astype(str)
housing_renters = pd.read_csv('fema_data/chi_housing_assistance_renters.csv')
housing_renters['zipCode'] = housing_renters['zipCode'].fillna(0).astype(int).astype(str)
combined_fema_df = pd.concat([housing_owners[['zipCode', 'approvedForFemaAssistance', 'totalApprovedIhpAmount']],
                              housing_renters[['zipCode', 'approvedForFemaAssistance', 'totalApprovedIhpAmount']]])
combined_fema_df.head()


    

    
Out[2]:






  

    

      

      
zipCode
      
approvedForFemaAssistance
      
totalApprovedIhpAmount
    
  
  

    

      
0
      
60637
      
0.0
      
0.0
    
    

      
1
      
60641
      
0.0
      
0.0
    
    

      
2
      
60628
      
1.0
      
1888.0
    
    

      
3
      
60468
      
0.0
      
0.0
    
    

      
4
      
60613
      
3.0
      
20496.0
    
  

In [3]:

    

combined_fema_zips = pd.DataFrame(combined_fema_df.groupby(['zipCode']).agg({'approvedForFemaAssistance': 'sum',
                                                                             'totalApprovedIhpAmount': 'sum'})).reset_index()
combined_fema_zips = combined_fema_zips.dropna()
combined_fema_zips.head()


    

    
Out[3]:






  

    

      

      
zipCode
      
totalApprovedIhpAmount
      
approvedForFemaAssistance
    
  
  

    

      
1
      
60020
      
1532.0
      
1.0
    
    

      
2
      
60104
      
4953.0
      
2.0
    
    

      
3
      
60131
      
0.0
      
0.0
    
    

      
4
      
60155
      
0.0
      
0.0
    
    

      
5
      
60290
      
6769.0
      
4.0
    
  

In [4]:

    

wib_07_df = pd.read_csv('311_data/wib_calls_311_zip.csv')
wib_07_df['Created Date'] = pd.to_datetime(wib_07_df['Created Date'])
wib_07_df = wib_07_df.set_index(wib_07_df['Created Date'])
wib_07_df = wib_07_df['2007-01-01':][wib_07_df.columns.values[2:]]
wib_07_stack = pd.DataFrame(wib_07_df.stack()).reset_index()
wib_07_stack = wib_07_stack.rename(columns={'level_0':'Created Date','level_1':'Zip Code',0:'Count Calls'})
wib_07_sum = wib_07_stack.groupby(['Zip Code'])['Count Calls'].sum()
wib_07_sum = wib_07_sum.reset_index()
wib_07_sum = wib_07_sum.sort_values(by='Count Calls',ascending=False)
wib_07_sum.head()


    

    
Out[4]:






  

    

      

      
Zip Code
      
Count Calls
    
  
  

    

      
26
      
60628
      
6893
    
    

      
18
      
60619
      
6575
    
    

      
27
      
60629
      
6215
    
    

      
16
      
60617
      
5771
    
    

      
19
      
60620
      
5518
    
  

In [5]:

    

wib_07_zips = wib_07_sum['Zip Code'].unique()
fema_zip_df = combined_fema_zips.loc[combined_fema_zips['zipCode'].isin(wib_07_zips)].copy()
fema_zip_df.head()


    

    
Out[5]:






  

    

      

      
zipCode
      
totalApprovedIhpAmount
      
approvedForFemaAssistance
    
  
  

    

      
11
      
60601
      
0.0
      
0.0
    
    

      
12
      
60604
      
4079.0
      
2.0
    
    

      
13
      
60605
      
150.0
      
1.0
    
    

      
14
      
60606
      
2829.0
      
1.0
    
    

      
15
      
60607
      
164883.0
      
90.0
    
  

In [6]:

    

plt.rcParams["figure.figsize"] = [15, 5]
fema_wib_zip = pd.DataFrame()
fema_wib_zip[['Zip Code', 'FEMA Approved', 'FEMA Claims Total']] = fema_zip_df.copy()
fema_wib_zip = fema_wib_zip.merge(wib_07_sum, on='Zip Code')
fema_wib_zip.plot(title='FEMA Totals and FOIA Scatter Plot', x='Count Calls', y='FEMA Claims Total', kind='scatter')


    

    
Out[6]:





<matplotlib.axes._subplots.AxesSubplot at 0x9c3f7f0>






    

In [7]:

    

# Total dollar amount of FEMA claims generally seems to line up with the number of approved claims by zip
fema_wib_zip.plot(title='FEMA and FOIA Data Scatter Plot', x='FEMA Approved', y='FEMA Claims Total', kind='scatter')


    

    
Out[7]:





<matplotlib.axes._subplots.AxesSubplot at 0x9bf05c0>






    

In [8]:

    

fema_zips_by_total = fema_wib_zip.sort_values(by='FEMA Claims Total', ascending=False)[:20].copy()
fema_zips_by_total.plot(kind='bar', x='Zip Code', y='FEMA Claims Total')


    

    
Out[8]:





<matplotlib.axes._subplots.AxesSubplot at 0xa31bc88>






    

In [9]:

    

fema_zips_by_approved = fema_wib_zip.sort_values(by='FEMA Approved', ascending=False)[:20].copy()
fema_zips_by_approved.plot(kind='bar', x='Zip Code', y='FEMA Approved')


    

    
Out[9]:





<matplotlib.axes._subplots.AxesSubplot at 0xa3bc710>






    

In [ ]: