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ward year streets lighting parks schools cameras misc arts trees other libraries
1 2012 451883 161961 605000 100155 0 0 0 0 0 0
1 2013 719642 1550 450000 23808 0 125000 0 0 0 0
1 2014 939539 5460 350000 25000 0 0 0 0 0 0
1 2015 590760 194240 535000 0 0 0 0 0 0 0
1 2016 1257477.99 8500 0 0 54022 0 0 0 0 0
1 2017 1138100.3 64888 0 9300 0 0 20000 50000 37711 0
1 2018 698099.99 0 150000 470100 1800 0 0 0 0 0
2 2012 867896 451500 0 0 0 0 0 0 0 0
2 2013 989909 311250 0 0 0 18841 0 0 0 0
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@pjsier
pjsier / zips-centroids.csv
Created June 25, 2020 15:35
ZIP Centroids
We can't make this file beautiful and searchable because it's too large.
lat,lon,zip
40.27794,-121.00042,00012
32.28632,-113.11597,00014
32.38239,-110.6671,00015
35.51737,-118.36611,00016
37.10494,-118.84675,00017
34.59578,-118.98752,00018
40.63259,-120.18884,00019
37.59397,-119.22124,00020
39.46131,-123.62041,00022
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import json
import os
import boto3
# We don't need to manually specify the credentials if they have these names, but
# including for clarity
client = boto3.client(
"s3",
aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),