nbforms 0.1.3

Jupyter Notebook forms using ipywidgets

nbforms

nbforms is a Python package designed to allow forms to be submitted by users such that the data they submit is immediately available for use in the notebook by the entire group. This is accomplished using ipywidgets and a Heroku-deployable Sinatra webapp, nbforms-server.

Installation

To install the Python package, use pip:

pip install nbforms

Deployment

Setup: Server

Before using nbforms in a notebook, you must deploy a webapp to Heroku which will collect and organize the responses. If you plan to have multiple notebooks, you only need one server, as you can provide a notebook identifier in the config files that will distinguish responses.

To deploy the webapp, click the deploy button in the nbforms-server README.

Setup: Config File

nbforms requires a config file to set up the Notebook class. The default path that Notebook checks is ./nbforms_config.py, although you can pass a custom path to the Notebook constructor. The structure of the config file is very specific; you must assign the variable nbforms_config to a dict containing the information that the notebook needs to create widgets and send requests. The structure of this file is:

nbforms_config = {
		"server_url": "",        		  # URL to your Heroku app

		"notebook": "",  		 		  # an ID to collect responses

		"questions": [{					  # questions to ask, a list of dicts

			"identifier": "",   		  # a question identifer, should be unique within
										  # this notebook
			"type": "",					  # question type; can be one of:
										  # 	multiplechoice, checkbox, text, paragraph

			"question": "",				  # the question text

			"options": [				  # options from which to choose if type is 
				...						  # multiplechoice or checkbox
			],
			"placeholder": ""			  # placeholder for textbox if type is text or
										  # paragraph
		}, 
		...								  # more question dictionaries
	]
}

The server_url key should be the URL to your Heroku-deployed nbforms-server, e.g. https://my-nbforms-server.herokuapp.com. The notebook key should be some string or number to identify the notebook that you're deploying. This is used to keep the notebook responses distinguished on the server. Finally, the questions key should be a list of dictionaries that define the information for your questions.

Questions can have one of four types: multiplechoice, checkbox, text, or paragraph. The type key in the question is used to create the widget. If you have a multiplechoice or checkbox, you must provide a list of options as the options key. For text and paragraph responses, you can provide an optional placeholder key which will replace the default placeholder.

There is a sample config file at demo/nbforms_config.py.

In-Notebook: Import and Instantiate

To use the nbforms, you must first import it and create a Notebook instance. This will load the config file (defaulting to look at ./nbforms_config.py) and ask the user to input a username and a password. If the username already exists on the server, the password will be checked and an API key will be generated, to be stored in the Notebook class. If it does not exist, a new user will be created, and an API key generated. If the user does exist but an incorrect password is provided, the cell will error.

import nbforms
form = nbforms.Notebook()

In-Notebook: Collecting Responses

To collect the responses for a question, insert a cell that calls the Notebook.ask function on the identifier of the question. For example, if I had a question q1, I would call

form.ask("q1")

This will output the widget and a "Submit" button that, when clicked, will send an HTTP POST request to your nbforms server with the student's username hash, notebook ID, question identifier, and response to be stored on the server.

In-Notebook: Retrieving Data

nbforms allows you to get your data from the server and collect it into either a datascience Table or a pandas DataFrame. To retrieve the responses from the server, use Notebook.to_table or Notebook.to_df; the optional user_hashes argument (default False) indicates whether or not to include a column with the hashes username.

# datascience Table
form.to_table("q1", "q2", ...)

# pandas DataFrame
form.to_df("q1", "q3", ..., user_hashes=True)

Database Maintenance

There is not much database maintance that can be done, but you can optionally delete all responses on the server by running rake clear on your Heroku app.