In [3]:

    

import pandas as pd
import numpy as np

df = pd.read_csv('/home/keer/DSSG/data-challenges/BuildingInspections/data/Building_Violations_sample_50000.csv')

import re

def spaces_to_snake(column_name):
    """
    converts a string that has spaces into snake_case
    Example:
        print camel_to_snake("KENNY BROUGHT HIS WIFE")
        > KENNY_BROUGHT_HIS_WIFE
    To see how to apply this to camel case, see:
        http://stackoverflow.com/questions/1175208/elegant-python-function-to-convert-camelcase-to-camel-case
    """
    s = re.sub(r"\s+", '_', column_name)
    return s.lower()

df.columns = [spaces_to_snake(col) for col in df.columns]


df.columns


    

    
Out[3]:





Index([u'unnamed:_0', u'id', u'violation_last_modified_date', u'violation_date', u'violation_code', u'violation_status', u'violation_status_date', u'violation_description', u'violation_location', u'violation_inspector_comments', u'violation_ordinance', u'inspector_id', u'inspection_number', u'inspection_status', u'inspection_waived', u'inspection_category', u'department_bureau', u'address', u'property_group', u'ssa', u'latitude', u'longitude', u'location'], dtype='object')

In [8]:

    

inspection_frequency=df[['location','id']].groupby('location').count()


    

In [14]:

    

violation_frequency=df[['location','id']][df['inspection_status']=='FAILED'].groupby(['location']).count()


    

In [83]:

    

dummy=df[['location','violation_code','id']][df['inspection_status']=='FAILED'].groupby(['location','violation_code']).count()
dummy['id']=1
violation_type_count=dummy.reset_index()[['location','id']].groupby('location').count().reset_index()
violation_type_count.columns=['locations','unique_violation_codes']
violation_type_count.head(25)


    

    
Out[83]:






  

    

      

      
locations
      
unique_violation_codes
    
  
  

    

      
0 
      
  (41.64692911211202,-87.61447966111751)
      
 1
    
    

      
1 
      
 (41.647795874157346,-87.54018148507018)
      
 2
    
    

      
2 
      
  (41.64781346219875,-87.61421661741555)
      
 1
    
    

      
3 
      
   (41.6478148942479,-87.61645925569275)
      
 1
    
    

      
4 
      
 (41.648032636313566,-87.61392876189757)
      
 2
    
    

      
5 
      
 (41.648033312120475,-87.61449814827196)
      
 1
    
    

      
6 
      
  (41.64803352865298,-87.61558610924808)
      
 1
    
    

      
7 
      
  (41.64831133789253,-87.54450422674195)
      
 1
    
    

      
8 
      
  (41.64868870411254,-87.54488161972836)
      
 1
    
    

      
9 
      
  (41.64892413385787,-87.54628496618952)
      
 1
    
    

      
10
      
 (41.649007538483055,-87.55916462709274)
      
 1
    
    

      
11
      
  (41.64919842993987,-87.54578457882039)
      
 1
    
    

      
12
      
  (41.649496000274205,-87.5385111493897)
      
 1
    
    

      
13
      
    (41.64954707623299,-87.547037252503)
      
 1
    
    

      
14
      
  (41.64968175814829,-87.54578298947494)
      
 1
    
    

      
15
      
  (41.64988698229695,-87.54607483071594)
      
 1
    
    

      
16
      
  (41.64993012181813,-87.54701738845729)
      
 2
    
    

      
17
      
 (41.650343384158205,-87.54607282205943)
      
 1
    
    

      
18
      
   (41.65054094697297,-87.5482607057881)
      
 1
    
    

      
19
      
  (41.65062179173726,-87.54095925939012)
      
 1
    
    

      
20
      
   (41.6507152932624,-87.54824755540024)
      
 1
    
    

      
21
      
  (41.65074076438346,-87.54607322537252)
      
 1
    
    

      
22
      
  (41.65079720427416,-87.54578041438954)
      
 1
    
    

      
23
      
  (41.65091265199324,-87.54701434883746)
      
 1
    
    

      
24
      
  (41.65096740821523,-87.54578010931255)
      
 1
    
  

In [103]:

    

%matplotlib inline

violation_type_dist=violation_type_count.copy()
violation_type_dist['locations']=1
violation_type_dist=violation_type_dist.groupby('unique_violation_codes').count().reset_index()
violation_type_dist.columns=['unique_violation_codes','number_of_buildings']
violation_type_dist.head()


    

    
Out[103]:






  

    

      

      
unique_violation_codes
      
number_of_buildings
    
  
  

    

      
0
      
 1
      
 20145
    
    

      
1
      
 2
      
  4921
    
    

      
2
      
 3
      
  1347
    
    

      
3
      
 4
      
   482
    
    

      
4
      
 5
      
   181
    
  

In [ ]:

    




    

In [ ]: