The goal I set out to do: rewrite the Choropleth d3.js example to work in the IPython notebook. For future work: once we are able to reproduce the Choropleth example, then work to feed the map arbitrary county-level data.
The first thing I did was to make sure I could get
http://bl.ocks.org/mbostock/raw/4060606/
to work by copying the source to
http://mashupguide.net/wwod14/mbostock_4060606.html
and serving us.json
and unemployment.tsv
from my server with CORS enabled for these two files:
With the map working on standalone HTML page, then I turned to embedding the map inside an IPython notebook. That's where it got really interesting!
# print out the version of IPython used
import IPython
IPython.version_info
(3, 0, 0, 'dev')
%%javascript
// https://github.com/mbostock/d3/issues/1693
require.config({
paths: {
d3: "http://d3js.org/d3.v3.min",
queue: "http://d3js.org/queue.v1.min",
topojson: "http://d3js.org/topojson.v1.min"
}
});
require(["d3", "queue", "topojson"], function(d3, queue, topojson) {
console.log(d3.version);
console.log(queue.version);
console.log(topojson.version);
});
%%html
<style type="text/css">
.counties {
fill: none;
}
.states {
fill: none;
stroke: #fff;
stroke-linejoin: round;
}
.q0-9 { fill:rgb(247,251,255); }
.q1-9 { fill:rgb(222,235,247); }
.q2-9 { fill:rgb(198,219,239); }
.q3-9 { fill:rgb(158,202,225); }
.q4-9 { fill:rgb(107,174,214); }
.q5-9 { fill:rgb(66,146,198); }
.q6-9 { fill:rgb(33,113,181); }
.q7-9 { fill:rgb(8,81,156); }
.q8-9 { fill:rgb(8,48,107); }
</style>
%%html
<div id="county_map" style="height:600px; width:100%"></div>
<script>
// https://github.com/mbostock/d3/issues/1693
require.config({
paths: {
d3: "http://d3js.org/d3.v3.min",
queue: "http://d3js.org/queue.v1.min",
topojson: "http://d3js.org/topojson.v1.min"
}
});
require(["d3", "queue", "topojson"], function(d3, queue, topojson) {
console.log(d3.version);
console.log(queue.version);
console.log(topojson.version);
var width = 960,
height = 500;
var rateById = d3.map();
var quantize = d3.scale.quantize()
.domain([0, .15])
.range(d3.range(9).map(function(i) { return "q" + i + "-9"; }));
var path = d3.geo.path();
var svg = d3.select('#county_map').append("svg")
.attr("width", width)
.attr("height", height);
queue()
.defer(d3.json, "files/data/us.json")
.defer(d3.tsv, "files/temp/unemployment.tsv", function(d) { rateById.set(d.id, +d.rate); })
.await(ready);
function ready(error, us) {
svg.append("g")
.attr("class", "counties")
.selectAll("path")
.data(topojson.feature(us, us.objects.counties).features)
.enter().append("path")
.attr("class", function(d) { return quantize(rateById.get(d.id)); })
.attr("d", path);
svg.append("path")
.datum(topojson.mesh(us, us.objects.states, function(a, b) { return a !== b; }))
.attr("class", "states")
.attr("d", path);
}
})
</script>
from census_api_utils import (counties, census_labels, diversity, FINAL_LABELS)
r = list(counties(census_labels()))
from pandas import DataFrame
counties_df = DataFrame(r)
counties_df = diversity(counties_df)
counties_df[FINAL_LABELS].head()
NAME | Total | White | Black | Asian | Hispanic | Other | p_White | p_Black | p_Asian | p_Hispanic | p_Other | entropy5 | entropy4 | entropy_rice | gini_simpson | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | Autauga County | 54571 | 42154 | 9595 | 467 | 1310 | 1045 | 0.772462 | 0.175826 | 0.008558 | 0.024005 | 0.019149 | 0.441816 | 0.453294 | 0.458294 | 0.371372 |
1 | Baldwin County | 182265 | 152200 | 16966 | 1340 | 7992 | 3767 | 0.835048 | 0.093084 | 0.007352 | 0.043848 | 0.020668 | 0.388299 | 0.386196 | 0.392968 | 0.291627 |
2 | Barbour County | 27457 | 12837 | 12820 | 107 | 1387 | 306 | 0.467531 | 0.466912 | 0.003897 | 0.050515 | 0.011145 | 0.580086 | 0.636407 | 0.637309 | 0.560717 |
3 | Bibb County | 22915 | 17191 | 5024 | 22 | 406 | 272 | 0.750207 | 0.219245 | 0.000960 | 0.017718 | 0.011870 | 0.421943 | 0.448712 | 0.451897 | 0.388665 |
4 | Blount County | 57322 | 50952 | 724 | 115 | 4626 | 905 | 0.888873 | 0.012630 | 0.002006 | 0.080702 | 0.015788 | 0.274015 | 0.263741 | 0.270876 | 0.202978 |
5 rows × 16 columns
counties_df['fips_for_map'] = counties_df.apply(lambda s: str(int(s['state']))+s['county'], axis=1)
df = DataFrame()
df[['id','diversity']] = counties_df[['fips_for_map', 'entropy5']]
df.to_csv('temp/entropy5.tsv', sep="\t", index=False)
!head temp/entropy5.tsv
id diversity 1001 0.4418158563234724 1003 0.3882988721091073 1005 0.5800861120053159 1007 0.42194274556423633 1009 0.27401484623038297 1011 0.5101470309826847 1013 0.5120159003143947 1015 0.48169478052525105 1017 0.5139170933218179
%%html
<div id="diversity_map" style="height:600px; width:100%"></div>
<script>
// https://github.com/mbostock/d3/issues/1693
require.config({
paths: {
d3: "http://d3js.org/d3.v3.min",
queue: "http://d3js.org/queue.v1.min",
topojson: "http://d3js.org/topojson.v1.min"
}
});
require(["d3", "queue", "topojson"], function(d3, queue, topojson) {
console.log(d3.version);
console.log(queue.version);
console.log(topojson.version);
var width = 960,
height = 500;
var rateById = d3.map();
var quantize = d3.scale.quantize()
.domain([0, 1.0])
.range(d3.range(9).map(function(i) { return "q" + i + "-9"; }));
var path = d3.geo.path();
var svg = d3.select('#diversity_map').append("svg")
.attr("width", width)
.attr("height", height);
queue()
.defer(d3.json, "files/data/us.json")
.defer(d3.tsv, "files/temp/entropy5.tsv", function(d) { rateById.set(d.id, +d.diversity); })
.await(ready);
function ready(error, us) {
svg.append("g")
.attr("class", "counties")
.selectAll("path")
.data(topojson.feature(us, us.objects.counties).features)
.enter().append("path")
.attr("class", function(d) { return quantize(rateById.get(d.id)); })
.attr("d", path);
svg.append("path")
.datum(topojson.mesh(us, us.objects.states, function(a, b) { return a !== b; }))
.attr("class", "states")
.attr("d", path);
}
})
</script>