This repo host my answers to the take-home test received from the CTO

This repo contains two sub-folders :

1. web_app_demo contains a demo application to demonstrate my answers to questions one and four
2. Q2and3 as the name implies includes a notebook showcasing my answer to question one

All the dependencies are included in the requirements.txt file

• Install the dependencies

• Create the database by launching the database builder with the following command

  py build_database.py

• Launch the server with

py server.py

• Navigate to the localhost:/5000 for the frontend. You can also visualize the API configuration at localhost:5000/api/ui

This app leverages a REST API configured with swagger.

The REST API accepts and outputs JSON, it enables role-based authentication and sets som rate-limits for certain routes. The pagination has not been implemented but could have been done with Flask_Paginate module. I checked the documentation. It doesn't seem to complicated. However, it was a bit hard to integrate with my JavaScript MVC setup.

The demo app consists mainly of three pages, a home page that lists all materials grouped by family a family page that allows an admin user to create, update or delete a family, a materials page that allows a registered user to do the same for materials.

On any page a double click on a cell will lead you to the view corresponding to the element i.e. the details of a family or of a material.

On the family and material page, a single click on one of the element will retrieve the information from that element and display them in the editor.

On the family page, after having selected a family, clicking on the button calculate will call a function that implements my answer to question one about the count and the sort of materials by family and display the results in the table.

In order to test the functionality above, you will need to log in as an admin, for this you can use the testing below credentials :

• username: admin
• password: Password1

For question two and three, I chosed to used a Notebook.

For the purpose of the question, I created dummy data matching the structure described in the example.

The approach I took was to deserialize the data using the python json and pandas libraries and then to leverage the pandas toolkit for ordering, filtering sorting ...

Here is my process:

1. I read the file and store the content in a variable called data using open
2. I convert the json data to a list of python dictionaries using json.loads()
3. I convert the list of dictionaries into a pandas dataframe using pandas.json_normalize()
4. I do my manipulations over the dataframe using the pandas toolkit
5. I convert back the dataframe to a string in the json format with to_json()

I did not stricly implemented code for Q3 but the process would be the same. Pandas offers functionalities for double grouping/sorting.

I use visual studio code with the following settings :

{
    "python.pythonPath": "C:\\Program Files\\Python37\\python.exe",
    "python.linting.pylintArgs": [

        "--init-hook=import sys; sys.path.append('./web_app_demo')",
        "--generated-members", "flask_sqlalchemy",
        "--ignored-classes=scoped_session"
    ],
}