The station with the highest average ridership per day was Lake/State with an average ridership per day of 13,662

The standard deviation of the Washington/Wabash stop is 3,961. This is a high standard deviation considering that it is around half the daily average. This is a very broad standard deviation, and as we can see in the chart below, most of the data is 1 or 2 standard deviations below the average, suggesting that the Washington/Wabash stop was probably being renovated and had to be shut down during certain times.

Please see the only Python notebook of this repository to see the process for these calculations and also other data wrangling steps that I took. The Python notebook may need to be reloaded several times, as it is long.

I chose a theatre that showcases independent and foreign movies called Landmark's Century Centre Cinema. This theatre currently has a location in 2828 North Clark Street at Diversey. I analyzed the provided L train ridership data to understand transit patterns of the are where the theatre currently has a location.

I also analyzed data from the United States Census Bureau to have a better understanding of the demographics of the zipcode where the zipcode is located and also zipcodes with theatres that are comparable to Landmark's Century Centre Cinema.

Finally, I also looked into data of applications for restaurant licenses, provided The City of Chicago open data portal to have a better idea on how economically vibrant specific zipcoes are.

I did the data wrangling with Python (pandas, numpy, Nominatim and Google Maps Api for geolocation)

To present the data I used pure JavaScript and d3.js. Please find the repository for this in the following link: https://github.com/diegoleonardoro/diegoleonardoro.github.io

For a project like this, I would like all the analysis to be displayed on an interactive website where the client can explore the data in different ways. To have a better idea on how I envision presenting the data to a potential client, please visit the following link to see the d3.js charts that I made:

https://diegoleonardoro.github.io/