Jupyter Notebook forms using ipywidgets
Project description
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 JSON-formatted config file to set up the Notebook
class. The default path that Notebook
checks is ./nbforms_config.json
, although you can pass a custom path to the Notebook
constructor. The structure of the config file is very specific; it contains the information that the notebook needs to create widgets and send requests. The structure of this file is:
{
"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.json
. Each nbforms-server comes with a page that will generate a config file for you. The config generator for the demo server can be found here.
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.json
) 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.
Notebook.ask
can accept multiple questions; for example, form.ask("q1", "q3")
would display a widget with q1
and q3
as its tabs. Passing no arguments to Notebook.ask
will display all of the questions.
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.
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