| $ curl -v 'https://ffiec.cfpb.gov/v2/data-browser-api/view/csv?states=ME&years=2018&actions_taken=1,3' | |
| * Trying 3.221.239.145... | |
| * TCP_NODELAY set | |
| * Connected to ffiec.cfpb.gov (3.221.239.145) port 443 (#0) | |
| * ALPN, offering h2 | |
| * ALPN, offering http/1.1 | |
| * successfully set certificate verify locations: | |
| * CAfile: /etc/ssl/cert.pem | |
| CApath: none | |
| * TLSv1.2 (OUT), TLS handshake, Client hello (1): |
| build: | |
| ./node_modules/.bin/esbuild index.js --bundle --outfile=out.js |
This is the story of how it took me about 3 hours to pull a key from a census data index into its own column.
The censusdata library returns dataframes that have an index object:
In [1]: import censusdata
In [2]: df = censusdata.download('acs5', 2015, censusdata.censusgeo([('state', '23'), ('county', '005'), ('block group', '*')]), [('C02003_001E')]).head()
In [3]: df
| package main | |
| import ( | |
| "fmt" | |
| "html/template" | |
| "log" | |
| "net/http" | |
| "os" | |
| ) |
| package main | |
| // An extremely simple web application skeleton | |
| import ( | |
| "fmt" | |
| "log" | |
| "net/http" | |
| "os" | |
| ) |
| #!/usr/bin/env bash | |
| # assume that if an `nb` dir (presumably a virtualenv) exists, then we don't | |
| # need to do any of the time-consuming stuff | |
| if [ ! -d nb ]; then | |
| python -mvenv nb | |
| # # ipykernel is required for bash_kernel \ | |
| nb/bin/pip install \ | |
| bash_kernel \ | |
| ipykernel \ |
| # make a temporary directory and push it on to the stack | |
| tmp=$(mktemp -d); pushd $tmp && \ | |
| # get a US map at 20m resolution and unzip it | |
| curl -s https://www2.census.gov/geo/tiger/GENZ2018/shp/cb_2018_us_region_20m.zip -o us.zip && \ | |
| unzip -q us.zip && \ | |
| # rasterize the map's only layer, using the `ALAND` feature. Restrict the | |
| # bounding box (-te) to the continental US, because the map includes Alaska | |
| # and Hawaii and Guam, and we're not going to bother with those here | |
| # (sorry!). -ts sets the output size to 1200x800, then we give the input and | |
| # output files. |
| import pyglet | |
| class AWindow(pyglet.window.Window): | |
| def __init__(self): | |
| super(AWindow, self).__init__(100, 100) | |
| self.batch = pyglet.graphics.Batch() | |
| self.circle = pyglet.shapes.Circle( |
| import pyglet | |
| class AWindow(pyglet.window.Window): | |
| def __init__(self): | |
| super(AWindow, self).__init__(100, 100) | |
| self.batch = pyglet.graphics.Batch() | |
| self.circle = pyglet.shapes.Circle( |