The main_.py file uses this endpoint to extract the most Recent Played Tracks out of the spotify API. After performing the extraction, I performed a basic clean-up of the data extracted as well as creating a unique identifier for the load-up of my dataframe to a PostgreSQL database, for which I used SQL.
The data was extracted using the spotify API mentioned up above to get the most recent 20 played tracks in spotify by sending a request to the API. The result of this is a .json response stored in the response variable, this dictionary was used to extract specific values out of our response to create a dictionary with all of our data and then, appending it to a list to be later converted to a DataFrame using pandas.
The transformation of my dataframe consisted of some basic checks here and there, starting by converting my list to a dataframe, re-ordering the dataframe columns and changing the dataype of datetime, date & time.
Now that we are done with our basic checks using pandas, we use the psycopg2 library to create a connection to an existing database in postgreSQL, starting by creating a table with a unique_identifier along with our key values according to what we've got in our DataFrame. Last but no least, in order to load the Dataframe to our table called spotify, we have to create an engine using the sqlalchemy libray to append my existing dataframe to such table.
