jaydebeapi VS cinemagoer

Loading Pandas DataFrames into SQL databases of all names is a common task between all developers working on building data pipelines for their environments or trying to automate ETL jobs generally. And for that, Pandas DataFrame class has the built-in method pandas.DataFrame.to_sql that allows to do so very quickly, for SQLite and all the databases supported by SQLAlchemy library, and when it comes to those who doesn’t have a good support by it ( in my case was IBM DB2 ), developers find themselves forced to think twice for some work around in order to get the job done. Jaydebeapi introduces himself as a good alternative, and it’s particularly seen thus by all developers coming from a Java background and having some familiarities of working with JDBC API to access the database. Let’s start first by creating the database connection. for that reason I will be creating a simple function that takes in params all the informations required and it will give a connection to DB2 as a return.

I am in the early stages of retrieving IMDB data via the Python package IMDBPy (their site) (project github). I have been referring to this nice IMDBpy implementation for help with aspects of what I'm doing here. Several of the queries that I'm using generate datasets that come in "nested dictionary" form, e.g. 'movie_1':{'title':'One Flew Over the Cuckoo's Nest', 'director': 'Milos Forman'...}. My early version of the title retrieval takes this form in VIEWS.PY: def index(request): imdb = IMDb() test_movies = {} for i in range(1100000, 1100015): test_movies[f'movie_{i}'] = imdb.get_movie(f'{i}') context = { 'test_movies':test_movies, } return render(request, 'index.html', context)