Overview

This is the individual component of the back-end component of your team’s project. For this component, you need to create a database with at least one table with a portion of your team’s data and use psycopg2 to access some of the data. Much of this code won’t end up in your team’s repository because it will be duplicate. Instead, the goal is to get you to practice and start thinking about the back-end individually.

Collaboration

You should work individually on this component. You shouldn’t coordinate with your team, since the goal is that you get practice individually, and so it’s fine if there is some duplication. You can get conceptual help from your team and others, however what you submit must be all your own work.

Due Date

The individual deliverable for this iteration is due Friday May 2nd at 10pm on the ID3 git repository.

Your Task

I recommend that you work on this component on the stearns server since it has all the necessary things installed already.

You should:

  • Copy some of your team’s data into your ID3 repository in the Data folder (should only be a couple of columns max)
  • Edit the Data/createtable.sql file so that it creates a table with some of your team’s data
  • Add the necessary \copy command to your README.md file (For example \copy earthquakes FROM 'Data/earthquakeData.csv' DELIMITER ',' CSV)
  • Expand on the datasource.py file by adding a method that uses the psycopg2 module to connect to the database, execute a query, and return the result (note that I’ve provided the connect method already, so you should use the instance variable self.connection)
  • Edit the app.py file so that it calls your DataSource method and prints the result

The grader will grade your assignment by:

  • Pulling down your submission for this deliverable
  • Use psql -f Data/createtable.sql to recreate your table in their database
  • Use the copy command that you specify to import your data into the table
  • Create the psql_config.py file with their information and the database name webapp
  • Run your app.py file
  • Look through your code and database structure

Turning it in

You should submit the files with your individual database to your ID3 repository.

Evaluation

The criteria for “Proficiency” and “Exemplary” are below.

Proficiency

  • Functionality:
    • Table is created with createtable.sql
    • README specifies the correct copy command with a relative file path
    • Code to be run is named app.py
    • Executes the expected query correctly
    • Data types match the types from the original dataset and/or are appropriate for the modified data from the dataset
  • Design:
    • All style checks at 5 or higher
    • Sufficient design of individual methods, with most best practices (parameters, return values, responsibilities) followed.
    • Column names somewhat signify the data they contain
    • Docstrings exist for all methods

Exemplary

  • All the Proficiency criteria are met
  • Functionality:
    • Data is well-curated from the original dataset, with no extraneous data that isn’t used by the query
    • Data types are well-chosen for the dataset
  • Design:
    • All style checks are at a 10
    • Function/method docstrings all provide the required information (i.e. functionality, arguments if applicable)
    • Function/method signatures match the SQL queries (i.e. get_events_by_type() correspond to a SQL query that selects all events of that type)
    • Column names strongly signify the data they contain (e.g. clear names, no obscure acronyms)