In [1]:
import pandas as pd

In [19]:
assaults = pd.read_csv('assault.csv')

assaults = assaults[["Name","Number of occurrences per 10,000 residents in 2011"]]
assaults = assaults.rename(index=str, columns={"Name": "neighborhood", "Number of occurrences per 10,000 residents in 2011": "crime"})

min_ = float(assaults.crime.min())
max_ = float(assaults.crime.max())

assaults["crime"] = assaults["crime"].apply(lambda x: (float(x) - min_)/(max_-min_))

In [24]:
assaults


Out[24]:
neighborhood crime
0 West Humber-Clairville 0.361266
1 Mount Olive-Silverstone-Jamestown 0.294227
2 Thistletown-Beaumond Heights 0.248045
3 Rexdale-Kipling 0.144879
4 Elms-Old Rexdale 0.235754
5 Kingsview Village-The Westway 0.164618
6 Willowridge-Martingrove-Richview 0.163873
7 Humber Heights-Westmount 0.079702
8 Edenbridge-Humber Valley 0.077840
9 Princess-Rosethorn 0.095345
10 Eringate-Centennial-West Deane 0.049907
11 Markland Woods 0.085289
12 Etobicoke West Mall 0.183985
13 Islington-City Centre West 0.197393
14 Kingsway South 0.085661
15 Stonegate-Queensway 0.132961
16 Mimico 0.216760
17 New Toronto 0.403352
18 Long Branch 0.236872
19 Alderwood 0.088641
20 Humber Summit 0.226071
21 Humbermede 0.242831
22 Pelmo Park-Humberlea 0.203724
23 Black Creek 0.344507
24 Glenfield-Jane Heights 0.289758
25 Downsview-Roding-CFB 0.367225
26 York University Heights 0.313966
27 Rustic 0.258101
28 Maple Leaf 0.070391
29 Brookhaven-Amesbury 0.235009
... ... ...
110 Rockcliffe-Smythe 0.239479
111 Beechborough-Greenbrook 0.600745
112 Weston 0.505028
113 Lambton Baby Point 0.067412
114 Mount Dennis 0.317691
115 Steeles 0.011173
116 L'Amoreaux 0.031285
117 Tam O'Shanter-Sullivan 0.087896
118 Wexford/Maryvale 0.357914
119 Clairlea-Birchmount 0.364246
120 Oakridge 0.401490
121 Birchcliffe-Cliffside 0.290130
122 Cliffcrest 0.245438
123 Kennedy Park 0.425698
124 Ionview 0.166480
125 Dorset Park 0.208566
126 Bendale 0.245438
127 Agincourt South-Malvern West 0.174302
128 Agincourt North 0.026071
129 Milliken 0.043575
130 Rouge 0.072998
131 Malvern 0.198883
132 Centennial Scarborough 0.077467
133 Highland Creek 0.134451
134 Morningside 0.276350
135 West Hill 0.477840
136 Woburn 0.223091
137 Eglinton East 0.324767
138 Scarborough Village 0.442831
139 Guildwood 0.102793

140 rows × 2 columns


In [25]:
import json
demo = {
    'result': assaults.to_dict(orient='record')
}


with open('assaults.json', 'w') as outfile:
    json.dump(demo, outfile)