An analysis of Texas Open State data showing legislators who most vote neither yes or no for bills. The Open States data shows which legislators vote yes, no or "other" for each bill. By summarizing graphically the top 10 legislators that have the highest percentage of "other" votes we can ask legislators on why they did not vote at all on bills. Were these strategic abstensions or were they just not bothering to attend the session?
Without further adieu, here is an example plot output from tx_lege_votes:
The conda package ecosystem is utilized heavily by this project. If you don't yet have conda, you can get a barebones installation with Miniconda. Instructions are at https://conda.io/miniconda.html
With a conda installation, create an environment with some prerequisites:
conda create -n openstate1 python=3.6 bokeh pandas fastparquet python-snappy sqlalchemy mysql-connector-python
Not all of our prerequisites are available from the default software channels. We get a few more things from the conda-forge and ioam organizations on anaconda.org:
conda install -n openstate1 -c ioam -c conda-forge notebook holoviews geoviews datashader
Activate this environment, so that the Python environment we've created is the one we'll use to run the bokeh web app:
source activate openstate1
Download the data from:
https://www.dropbox.com/s/xef8tewu9ue6xla/2017-07-01-tx-json.zip?dl=0
Extract it into a folder named data at the same level as main4.py from this
github repo. You don't need to follow this path structure exactly, but if you
don't, you'll need to adjust paths in main4.py.
Bokeh includes a standalone server. For simplicity and self-containment of this repository, that's what we'll demonstrate.
In the folder containing main4.py, run
bokeh serve .
Alternatively, a Jupyter notebook has been provided for running this application To start a notebook server:
jupyter notebook
and then select the TexasLegeVotes.ipny file
dont know yet
This app uses the data from https://openstates.org/