datarocks

## Ward_69.json

      
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                datarocks
                / Ward_69.json
            
            
              Last active
              July 30, 2019 01:36
            
              
                South Madison Voters Wards
              
          
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## hashtag_stack.py
import os, codecs

import tablib
from itp import itp
from copy import deepcopy

original_file = 'C_000.CSV'


# This is to remove the pointless BOM (byte order mark) that windows helpfully puts at the beginning of many UTF8 files

## town_hall_map.json
{
	"features": [{
		"geometry": {
			"coordinates": [-112.073896, 33.56109],
			"type": "Point"
		},
		"properties": {
			"address": "8525 N Central Ave, Phoenix, AZ 85020",
			"date": "Saturday, February 4, 2017",
			"district": "AZ-9\n",

## hashtag_stack.py
import tablib
from copy import deepcopy

# pull the data in, using tablib, a really great library for messing around with tabular data
# more details about tablib here: http://docs.python-tablib.org/en/latest/).
# For serious data analysis, you are going to need to mess around with pandas http://pandas.pydata.org/
data = tablib.Dataset().load(open('picodash_instagram_nra_blog_2016-12-14.csv').read())

# make the headers for the new dataset, there has to be a more elegant way to do this, but this works
headers = data.headers
	import os, codecs

	import tablib
	from itp import itp
	from copy import deepcopy

	original_file = 'C_000.CSV'


	# This is to remove the pointless BOM (byte order mark) that windows helpfully puts at the beginning of many UTF8 files
	{
	"features": [{
	"geometry": {
	"coordinates": [-112.073896, 33.56109],
	"type": "Point"
	},
	"properties": {
	"address": "8525 N Central Ave, Phoenix, AZ 85020",
	"date": "Saturday, February 4, 2017",
	"district": "AZ-9\n",
	import tablib
	from copy import deepcopy

	# pull the data in, using tablib, a really great library for messing around with tabular data
	# more details about tablib here: http://docs.python-tablib.org/en/latest/).
	# For serious data analysis, you are going to need to mess around with pandas http://pandas.pydata.org/
	data = tablib.Dataset().load(open('picodash_instagram_nra_blog_2016-12-14.csv').read())

	# make the headers for the new dataset, there has to be a more elegant way to do this, but this works
	headers = data.headers