As an Abstract, this is a Streamlit dashboard that makes requests to an API and shows some charts. The requested API get the info from a MongoDB Database with some datasets from Barcelona. Every one of those communication elements were performed on this project.
The origin data was collected from this Barcelona dataset from Kaggle and later, the infomation has been enriched by expanding it with more data, by collecting it directly from the Open Data BCN API, the Ajuntament de Barcelona's open data service.
This datasets where reviewed and cleaned to being uploaded into a MongoDB Database in a cluster on cloud. This task was carried out with Jupyter Notebooks.
There are 2 ways to make requests to the API:
- Sending query parameters in the request.
- Using some already created ENDPOINTS.
- Query Parameters: The request must be directed to the API url + "/read/" + "Query Parameters" where the string must be one from the database ("population","unemployed"), and the "query parameters" must match the titles of the columns of the same collection:
Example: url = "http:// localhost:port/read/population?Year=2018&District.Name=Ciutat Vella"
- ENDPOINTS : There are some Endpoints already implemented in the API like the "/read/" one. Those are:
- /bcn-population-by-year/ gives all the population by the selected year
- /bcn-population-by-district/ gives all the population by the selected district
- /bcn-population-by-neighborhood/ gives all the population by the selected Neighborhood
- /bcn-unemployment-by-year/ gives all the registered unemployees by the selected year
- /bcn-unemployed-by-district/ gives all the registered unemployees by the selected district
- /bcn-unemployed-by-neighborhood/ gives all the registered unemployees by the selected Neighborhood
Finally, it was performed an Streamlit dashboard to make the requests and representing some charts about the information.
- flask
- streamlit
- pandas
- plotly
- mongoDB