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The target table

In the previous exercises, you've calculated a DataFrame called recommendations. It contains pairs of user_id's' and course_id's, with a rating that represents the average rating of this course. The assumption is the highest rated course, which is eligible for a user would be best to recommend.

It's time to put this table into a database so that it can be used by several products like a recommendation engine or an emailing system.

Since it's a pandas.DataFrame object, you can use the .to_sql() method. Of course, you'll have to connect to the database using the connection URI first. The recommendations table is available in your environment.

This exercise is part of the course

Introduction to Data Engineering

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Exercise instructions

  • Fill in the connection URI for the Postgres database on host localhost with port 5432. You can connect with user repl and password password. The database name is dwh.
  • Complete the load_to_dwh() function. It should write to the "recommendations" table and replace the table if it already exists.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

connection_uri = "____://____:____@____:____/____"
db_engine = sqlalchemy.create_engine(connection_uri)

def load_to_dwh(recommendations):
    recommendations.____("____", ____, ____="____")
Edit and Run Code