Assignment

As mentioned before, HP Norton has been using a mix of different file formats to keep track of their business. One of those formats is called comma-separated file which can be distinguished by the .csv extension.

This week, you have been assigned to work on a prototype migration of product data from a sample csv file into MongoDB. You will use the MongoDB API while exploiting mongo’s ability to allow the Python module, not the database, to specify the schema for the data to be stored.

You implementation should address the following requirements:

  1. As a HP Norton customer I want to see a list of all products available for rent so that I can make a rental choice.

  2. As a HP Norton salesperson I want to see a list of all of the different products, showing product ID, description, product type and quantity available.

  3. As a HP Norton salesperson I want to see a list of the names and contact details (address, phone number and email) of all customers who have rented a certain product.

Here is what you need to do:

  1. Create a product database with attributes that reflect the contents of the csv file.

  2. Import all data in the csv files into your MongoDB implementation.

  3. Write queries to retrieve the product data.

  4. Write a query to integrate customer and product data.

Other requirements:

  • Your code should not trigger any warnings or errors from Pylint.

  • You can have a specific field to indicate if a product is available, however, a quantity_available of 0 is understood as “not available”.

Testing

In order for your code to be evaluated, you need to create a file called database.py with the following functions:

  • import_data(directory_name, product_file, customer_file, rentals_file): This function takes a directory name three csv files as input, one with product data, one with customer data and the third one with rentals data and creates and populates a new MongoDB database with these data. It returns 2 tuples: the first with a record count of the number of

products, customers and rentals added (in that order), the second with a count of any errors that occurred, in the same order.

  • show_available_products(): Returns a Python dictionary of products listed as available with the following fields:

    • product_id.

    • description.

    • product_type.

    • quantity_available.

    For example:

    {‘prd001’:{‘description’:‘60-inch TV stand’,’product_type’:’livingroom’,’quantity_available’:‘3’},’prd002’:{‘description’:’L-shaped sofa’,’product_type’:’livingroom’,’quantity_available’:‘1’}}

  • show_rentals(product_id): Returns a Python dictionary with the following user information from users that have rented products matching product_id:

    • user_id.

    • name.

    • address.

    • phone_number.

    • email.

    For example:

    {‘user001’:{‘name’:’Elisa Miles’,’address’:‘4490 Union Street’,’phone_number’:‘206-922-0882’,’email’:’elisa.miles@yahoo.com’},’user002’:{‘name’:’Maya Data’,’address’:‘4936 Elliot Avenue’,’phone_number’:‘206-777-1927’,’email’:’mdata@uw.edu’}}

Submission

Your submission should include all files necessary to create and access your database, as well as test_database.py in order to test your submission.