Docker Image 
The city now has a pretty good site for vizualizing the 311 (and other open data) so you should check it out here: https://explore.nola.gov/
Also, you can submit a 311 ticket online now! https://www.nola.gov/311/
This purpose of this project will now focus on working with the 311 data for data analysis use cases.
Ensure you have Docker b/c that makes the database installation easier.
You will also need to install Python and Jupiter Notebooks to work with the data. We recommend downloading Anaconda to do this.
You probably want to download the Python 3 version b/c Python 2 will become unsupported soon
For local development, you can use Docker to setup the database
docker-compose up -d db
Then you can run the setup.sh script to download the data and load it into the database.
./setup.sh
If you are loading the data into a remote database, use environment variables to tell the script where to load:
NOLA311_DB_USER=nola311 \
NOLA311_DB_NAME=nola311 \
NOLA311_DB_HOST=c2rp0kujqp.us-east-1.rds.amazonaws.com \
NOLA311_DB_PORT=5432 \
./setup.sh
Once jupiter is installed, you can start the notebook server and open the "Getting Started" notebook
jupyter notebook Getting_Started.ipynb
Login to the db with psql psql -h localhost -U nola311 and run some queries:
-- what are the top issues that people call about?
select issue_type, count(*) as num_calls
from nola311.calls
group by issue_type
order by num_calls desc;
-- which council district has the most calls?
select council_district, count(*) as num_calls
from nola311.calls
group by council_district
order by num_calls desc;
-- how many pothole calls have been opened and closed this year?
select ticket_status, count(*) as total
from nola311.calls
where issue_type = 'Pothole/Roadway Surface Repair'
and ticket_created_date_time >= '2018-01-01'::date
group by ticket_status;
--- dont forget to checkout the views
select *
from open_tickets_stats
where issue_type = 'Catch Basin Maintenance'
and year_created = '2018';