In [11]:

    

# This gets the most recent crime, but might have to query in batches of 1000


    

In [2]:

    

from urllib2 import urlopen
import json
from pandas.io.json import json_normalize
import pandas as pd


    

In [3]:

    

# Access Chicago Data Portal
# The json request url comes from Chicago Website: https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2
request = urlopen('https://data.cityofchicago.org/resource/6zsd-86xi.json')
#output = []
output = json.load(request)


    

In [4]:

    

# Format output into pandas
data = json_normalize(output)


    

In [10]:

    

data.head()


    

    
Out[10]:






  

    

      

      
arrest
      
beat
      
block
      
case_number
      
community_area
      
date
      
description
      
district
      
domestic
      
fbi_code
      
...
      
location.coordinates
      
location.type
      
location_description
      
longitude
      
primary_type
      
updated_on
      
ward
      
x_coordinate
      
y_coordinate
      
year
    
  
  

    

      
0
      
True
      
1232
      
007XX W MADISON ST
      
HY243642
      
28
      
2015-05-01T22:55:00.000
      
CALL OPERATION
      
012
      
False
      
16
      
...
      
[-87.64697, 41.881676]
      
Point
      
HOTEL/MOTEL
      
-87.64697006
      
PROSTITUTION
      
2015-05-09T12:51:58.000
      
27
      
1171158
      
1900220
      
2015
    
    

      
1
      
False
      
0714
      
062XX S HONORE ST
      
G186972
      
NaN
      
2001-04-01T08:00:00.000
      
TO PROPERTY
      
NaN
      
False
      
14
      
...
      
[-87.670136, 41.77986]
      
Point
      
RESIDENCE
      
-87.670135735
      
CRIMINAL DAMAGE
      
2006-03-31T22:03:38.000
      
NaN
      
1165136
      
1863069
      
2001
    
    

      
2
      
False
      
0513
      
003XX W 110TH ST
      
HY191041
      
49
      
2014-11-02T05:57:00.000
      
HARASSMENT BY TELEPHONE
      
005
      
False
      
26
      
...
      
[-87.631029, 41.694202]
      
Point
      
RESIDENCE
      
-87.631029227
      
OTHER OFFENSE
      
2015-03-22T16:07:18.000
      
34
      
1176057
      
1831941
      
2014
    
    

      
3
      
True
      
1023
      
014XX S TALMAN AVE
      
HY194551
      
29
      
2015-03-22T11:00:00.000
      
TO VEHICLE
      
010
      
False
      
14
      
...
      
[-87.691879, 41.861639]
      
Point
      
STREET
      
-87.691879227
      
CRIMINAL DAMAGE
      
2015-05-09T12:51:58.000
      
28
      
1158983
      
1892824
      
2015
    
    

      
4
      
True
      
1511
      
056XX W CHICAGO AVE
      
HY196277
      
25
      
2015-03-24T08:11:00.000
      
ATTEMPT ARSON
      
015
      
False
      
09
      
...
      
[-87.765976, 41.894735]
      
Point
      
VACANT LOT/LAND
      
-87.765976491
      
ARSON
      
2015-05-11T12:38:40.000
      
29
      
1138719
      
1904742
      
2015
    
  

5 rows × 23 columns

In [6]:

    

data.tail()


    

    
Out[6]:






  

    

      

      
arrest
      
beat
      
block
      
case_number
      
community_area
      
date
      
description
      
district
      
domestic
      
fbi_code
      
...
      
location.coordinates
      
location.type
      
location_description
      
longitude
      
primary_type
      
updated_on
      
ward
      
x_coordinate
      
y_coordinate
      
year
    
  
  

    

      
995
      
False
      
0214
      
040XX S ELLIS AVE
      
HX220675
      
36
      
2014-04-11T20:30:00.000
      
TO VEHICLE
      
002
      
False
      
14
      
...
      
[-87.603363, 41.820965]
      
Point
      
STREET
      
-87.603363192
      
CRIMINAL DAMAGE
      
2014-04-15T00:40:31.000
      
4
      
1183221
      
1878195
      
2014
    
    

      
996
      
True
      
0915
      
021XX W 51ST ST
      
HJ108928
      
61
      
2003-01-05T18:39:00.000
      
POSS: CRACK
      
NaN
      
False
      
18
      
...
      
NaN
      
NaN
      
SIDEWALK
      
NaN
      
NARCOTICS
      
2006-03-22T21:58:07.000
      
16
      
NaN
      
NaN
      
2003
    
    

      
997
      
True
      
1222
      
012XX W 18TH ST
      
HJ107566
      
31
      
2003-01-04T22:55:00.000
      
SOLICIT ON PUBLIC WAY
      
NaN
      
False
      
16
      
...
      
NaN
      
NaN
      
STREET
      
NaN
      
PROSTITUTION
      
2006-03-22T21:58:07.000
      
25
      
NaN
      
NaN
      
2003
    
    

      
998
      
False
      
0712
      
060XX S MAY ST
      
HJ124431
      
68
      
2003-01-13T18:00:00.000
      
STRONGARM - NO WEAPON
      
NaN
      
False
      
03
      
...
      
NaN
      
NaN
      
APARTMENT
      
NaN
      
ROBBERY
      
2006-03-22T21:58:07.000
      
16
      
NaN
      
NaN
      
2003
    
    

      
999
      
False
      
0711
      
004XX W 61ST PL
      
HJ121444
      
68
      
2003-01-12T05:50:00.000
      
TO PROPERTY
      
NaN
      
False
      
14
      
...
      
NaN
      
NaN
      
RESIDENCE
      
NaN
      
CRIMINAL DAMAGE
      
2006-03-22T21:58:07.000
      
20
      
NaN
      
NaN
      
2003
    
  

5 rows × 23 columns

In [17]:

    

#data.describe()


    

In [18]:

    

# Assign variable names for each column
arrest = data['arrest']
primary_type = data['primary_type']
beat = data['beat']
block = data['block']
case_number = data['case_number']
area_num = data['community_area']
date = data['date']
description = data['description']
district  = data['district']
fbi_code = data['fbi_code']
location_coordinates = data['location.coordinates']
location_type = data['location.type']
location_description = data['location_description']
longitude = data['longitude']
updated_on = data['updated_on']


    

In [16]:

    

#print crime


    

In [ ]: