
import os
import math
import requests
import json
import urllib3
import pandas as pd
 
results = [] 
 
url = 'https://data.wprdc.org/api/3/action/datastore_search?offset=0&limit=1&resource_id=1797ead8-8262-41cc-9099-cbc8a161924b'
 
http = urllib3.PoolManager()
row_count = int(requests.get(url).json()['result']['total'])
segments_int = math.floor(row_count / 10000)
segments_dec = row_count / 10000
 
#r = requests.get(f'https://data.wprdc.org/api/3/action/datastore_search?offset={i}&limit=10000&resource_id=1797ead8-8262-41cc-9099-cbc8a161924b').json()['result']['records']

for i in range(segments_int + 1):
    i = i * 10000
    r = requests.get(f'https://data.wprdc.org/api/3/action/datastore_search?offset={i}&limit=10000&resource_id=1797ead8-8262-41cc-9099-cbc8a161924b').json()['result']['records']
    for x in r:
        results.append(x)
        
df = pd.json_normalize(results)
#df

df.to_csv('police_output.csv')


raw_file_in = 'police_output.csv'

rawData = pd.read_csv(raw_file_in, usecols = ['OFFENSES'])
rawData

	OFFENSES
0	3921 Theft by Unlawful Taking or Disposition.
1	3304 Criminal Mischief.
2	9093 Indirect Criminal Contempt
3	9999 9999 - (Misc. Incident):
4	2709(a) Harassment.
...	...
3127	2705 Recklessly Endangering Another Person. / ...
3128	3503 Criminal Trespass
3129	9489 Found Property
3130	9489 Found Property
3131	3503 Criminal Trespass

Get police data from WPRDC and write to a csv¶

Open CSV into dataframe¶