Analyzing Toronto Business Licenses Data.

New Resturant licenses by number in the city of Toronto for 2014.

So, someone came with a request asking us to show a heat map of restaurants licenses granted in the city of Toronto in 2014 to compare it with 2015 (maybe to see the effect of certain urban changes on restaurants opening )

Why restaurants  ?, I think they do represent quite a good indicator of extra spending and the health of the economy of certain areas. just look at the difference between 1994 when the city issued 569 licenses to 2014 where the number shot up to 1519

So in this post we will discuss the steps I went through to transfer the CSV data available from the city into a heat map of city licenses in the city.

Downloading the Code

The Code is available on GitHub 

https://github.com/c0dingarchit3ct/Open_TO_BusinessLicenses

Data Acquisition

OK This is the easiest part, just go to the City of Toronto Portal and you can easily Acquire the file which is called 'Business Licenses.csv'

City of Toronto Business Licensing and Permits

Another file we will need is a file that contains all Canadian Postal Codes and their Latitude and Longitude (I contemplated using google maps API's but the sheer number of Postal code calls made me realize I will run my 2400 calls per day quota in no time).

The Geocoder site

The Postal Code to Coordinates CSV file 

Once you Acquire the data , you can use the Code in PostalCodeBuilder.ipynb notebook to isolate the 'ontario.csv' from all of Canada which will make searches 3 times faster (and believe me you will need it !).

If you pull the whole project from github you can also find the ontario.csv included.

This is the code involved

all_postals = pd.read_table('canada.csv', sep=',') 

on_postals = all_postals[all_postals['prov']=='ON']
on_postals.to_csv('ontario.csv')

Munging the Data

Focus on the Study Subset

Reporting and Visualization