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Exercise

Loading data to a Postgres database

After data has been extracted from a source system and transformed to align with analytics or reporting use cases, it's time to load the data to a final storage medium. Storing cleaned data in a SQL database makes it simple for data consumers to access and run queries against. In this example, you'll practice loading cleaned data to a Postgres database.

sqlalchemy has been imported, and pandas is available as pd. The first few rows of the cleaned_testing_scores DataFrame are shown below:

             street_address       city  math_score  ... best_score
01M539  111 Columbia Street  Manhattan       657.0      Math
02M545     350 Grand Street  Manhattan       613.0      Math
01M292     220 Henry Street  Manhattan       410.0      Math

Instructions

100 XP
  • Update the connection string to write to the schools database and create a connection object using sqlalchemy.
  • Use pandas to write the cleaned_testing_scores DataFrame to the scores table in the schools database.
  • If the table is already populated with data, make sure to replace the values with the current DataFrame.