crimerates2.py

import random
import json
from datetime import datetime
from collections import namedtuple

f = open("data/crimedata.csv", "r")
data = f.read()

# crime count function
def count_types(nth, data_list):
    count_obj = {}
    for item in data_list:
        if item[nth] in count_obj:
            count_obj[item[nth]] = count_obj[item[nth]] + 1
        else:
            count_obj[item[nth]] = 1
    return count_obj

def data_clean_func(d):
    new_list = []
    crime_with_location = []
    no_location_list = []
    crime_type_list = []
    crime_counts = {}


    rows = d.split("\n")
    last_value = len(rows) - 1

    data_split = rows[1:last_value]
    # data_split = rows[1:2]

    for item in data_split:
        rows_split = item.split(",")

        crime_report_by = rows_split[2]
        crime_long = rows_split[4]
        crime_lat = rows_split[5]
        crime_type = rows_split[9]
        crime_outcome = rows_split[10]
        crime_date = rows_split[1]

        # Uncertainty Setting
        # set uncertainty to < 0.5 if crime_outcome == ""
        if crime_outcome == '':
            rand_num = float(random.uniform(0.1, 0.3))
        else:
            rand_num = float(random.uniform(0.5, 1.0))

        # set uncertainty value
        uncert_val = float(format(rand_num, '.1f'))

        # only float identified numbers
        if crime_long != '':
            crime_long = float(crime_long)
            crime_lat = float(crime_lat)

            crime_with_location.append([item])
        else:
            no_location_list.append([item])


        crime = {'date': crime_date, 'reported_by': crime_report_by, 'long': crime_long, 'latitude': crime_lat,
                 'type': crime_type, 'outcome': crime_outcome, 'uncert': uncert_val}
        new_list.append(crime)

    #crime count start
        crime2 = [crime_date, crime_report_by, crime_long, crime_lat, crime_type, crime_outcome]
        crime_type_list.append(crime2)
    type_data = count_types(4, crime_type_list)
    #crime count end

    return {"data": new_list, "meta": type_data}

cleaned_data = data_clean_func(data)


#convert to list
# print(cleaned_data)

# data cleaning to check for empty spaces, own
# data sanitisation
# data comparison (multiple views)
# merging two data sets

# pipe through and api endpoint
# leaf.js