questionary 1.7.0

Python library to build pretty command line user prompts ⭐️

questionary

✨An easy to use python library to build pretty command line user prompts ✨

You need input from a user, e.g. how an output file should be named or if he really wants to execute that dangerous operation? This library will help you make the input prompts easy to read and answer for the user.

Used and Supported by:

Quickstart

questionary can be installed using pip:

$ pip install questionary
✨🎂✨

Satisfaction guaranteed. Let's create a first question:

import questionary

questionary.select(
    "What do you want to do?",
    choices=[
        'Order a pizza',
        'Make a reservation',
        'Ask for opening hours'
    ]).ask()  # returns value of selection

That's all it takes to create a user prompt! There are different types of prompts, you'll find examples for all of them further down.

Alternative: Building from source

questionary uses Poetry for packaging and dependency management. If you want to build it from source, you have to install Poetry first.

This is how it can be done:

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python3

There are several other ways to install Poetry. Please, follow the official guide to see all possible options.

To install dependencies and questionary itself in editable mode execute

make install

Documentation

Different question types

text

A free text input for the user.

questionary.text("What's your first name").ask()

password

A free text input for the user where the input is not shown but replaced with ***.

questionary.password("What's your secret?").ask()

confirm

A yes or no question. The user can either confirm or deny.

questionary.confirm("Are you amazed?").ask()

select

A list of items to select a choice from. The user can pick one option and confirm it.

questionary.select(
    "What do you want to do?",
    choices=[
        "Order a pizza",
        "Make a reservation",
        "Ask for opening hours"
    ]).ask()

rawselect

A list of items to select a choice from. The user can pick one option using shortcuts and confirm it.

questionary.rawselect(
    "What do you want to do?",
    choices=[
        "Order a pizza",
        "Make a reservation",
        "Ask for opening hours"
    ]).ask()

checkbox

A list of items to select multiple choices from. The user can pick none, one or multiple options and confirm the selection.

questionary.checkbox(
    'Select toppings',
    choices=[
        "foo",
        "bar",
        "bazz"
    ]).ask()

autocomplete

Text input with autocomplete help.

questionary.autocomplete(
    'Choose ant specie',
    choices=[
         'Camponotus pennsylvanicus',
         'Linepithema humile',
         'Eciton burchellii',
         "Atta colombica",
         'Polyergus lucidus',
         'Polyergus rufescens',
    ]).ask()

Additional Features

Skipping questions using conditions

Sometimes it is helpful to e.g. provide a command line flag to your app to skip any prompts, to avoid the need for an if around any question you can pass that flag when you create the question:

DISABLED = True

response = questionary.confirm("Are you amazed?").skip_if(DISABLED, default=True).ask()

If the condition (in this case DISABLED) is True, the question will be skipped and the default value gets returned, otherwise the user will be prompted as usual and the default value will be ignored.

Alternative style to create questions using a configuration dictionary

Instead of creating questions using the python functions, you can also create them using a configuration dictionary.

questions = [
    {
        'type': 'text',
        'name': 'phone',
        'message': "What's your phone number",
    },
    {
        'type': 'confirm',
        'message': 'Do you want to continue?',
        'name': 'continue',
        'default': True,
    }
]

answers = prompt(questions)

The returned answers will be a dict containing the responses, e.g. {"phone": "0123123", "continue": False, ""}. The questions will be prompted one after another and prompt will return once all of them are answered.

Each configuration dictionary needs to contain the following keys:

• 'type' - The type of the question.
• 'name' - The name of the question (will be used as key in the answers dictionary)
• 'message' - Message that will be shown to the user

Optional Keys:

• 'qmark' - Question mark to use - defaults to ?.
• 'default' - Preselected value.
• 'choices' - List of choices (applies when 'type': 'select') or function returning a list of choices.
• 'when' - Function checking if this question should be shown or skipped (same functionality than .skip_if()).
• 'validate' - Function or Validator Class performing validation (will be performed in real time as users type).
• filter - Receive the user input and return the filtered value to be used inside the program.

Advanced workflow examples

Questionary allows creating quite complex workflows when combining all of the above concepts.

from questionary import Separator, prompt
questions = [
    {
        'type': 'confirm',
        'name': 'conditional_step',
        'message': 'Would you like the next question?',
        'default': True,
    },
    {
        'type': 'text',
        'name': 'next_question',
        'message': 'Name this library?',
        # Validate if the first question was answered with yes or no
        'when': lambda x: x['conditional_step'],
        # Only accept questionary as answer
        'validate': lambda val: val == 'questionary'
    },
    {
        'type': 'select',
        'name': 'second_question',
        'message': 'Select item',
        'choices': [
            'item1',
            'item2',
            Separator(),
            'other',
        ],
    },
    {
        'type': 'text',
        'name': 'second_question',
        'message': 'Insert free text',
        'when': lambda x: x['second_question'] == 'other'
    },
]
prompt(questions)

The above workflow will show to the user as follows:

1. Yes/No question Would you like the next question?.
2. Name this library? - only shown when the first question is answered with yes
3. A question to select an item from a list.
4. Free text inpt if 'other' is selected in step 3.

Depending on the route the user took, the result will look as follows:

{ 
    'conditional_step': False,
    'second_question': 'Testinput'   # Free form text
}

{ 
    'conditional_step': True,
    'next_question': 'questionary',
    'second_question': 'Testinput'   # Free form text
}

You can test this workflow yourself by running the advanced_workflow.py example.

Styling your prompts with your favorite colors

You can customize all the colors used for the prompts. Every part of the prompt has an identifier, which you can use to style it. Let's create our own custom style:

from prompt_toolkit.styles import Style

custom_style_fancy = Style([
    ('qmark', 'fg:#673ab7 bold'),       # token in front of the question
    ('question', 'bold'),               # question text
    ('answer', 'fg:#f44336 bold'),      # submitted answer text behind the question
    ('pointer', 'fg:#673ab7 bold'),     # pointer used in select and checkbox prompts
    ('highlighted', 'fg:#673ab7 bold'), # pointed-at choice in select and checkbox prompts
    ('selected', 'fg:#cc5454'),         # style for a selected item of a checkbox
    ('separator', 'fg:#cc5454'),        # separator in lists
    ('instruction', ''),                # user instructions for select, rawselect, checkbox
    ('text', ''),                       # plain text
    ('disabled', 'fg:#858585 italic')   # disabled choices for select and checkbox prompts
])

To use our custom style, we need to pass it to the question type:

questionary.text("What's your phone number", style=custom_style_fancy).ask()

It is also possible to use a list of token tuples as a Choice title. This example assumes there is a style token named bold in the custom style you are using:

Choice(
    title=[
        ('class:text', 'plain text '),
        ('class:bold', 'bold text')
    ]
)

As you can see it is possible to use custom style tokens for this purpose as well. Note that Choices with token tuple titles will not be styled by the selected or highlighted tokens. If not provided, the value of the Choice will be the text concatenated ('plain text bold text' in the above example).

How to Contribute

Contributions are highly welcomed and appreciated. Every little help counts, so do not hesitate!

1. Check for open issues or open a fresh issue to start a discussion around a feature idea or a bug. There is a Contributor Friendly tag for issues that should be ideal for people who are not very familiar with the codebase yet.
2. Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
3. Write a test which shows that the bug was fixed or that the feature works as expected.
4. Ensure your code passes running black questionary.
5. Send a pull request and bug the maintainer until it gets merged and published. 🙂

Contributors

questionary is written and maintained by Tom Bocklisch.

It is based on the great work of Oyetoke Toby as well as the work from Mark Fink.

Changelog

1.7.0 (15.10.2020)

• Added support for python 3.9
• Better UX for multiline text input
• Allow passing custom lexer

1.6.0 (04.10.2020)

• Updated black code style formatting and fixed version.
• Fixed colour of answer for some prompts.
• Added py.typed marker file.
• Documented multiline input for devs and users and added tests.
• Accept style tuples in title argument annotation of Choice.
• Added default for select and initial_choice for checkbox prompts.
• Removed check for choices if completer is present.

1.5.2 (16.04.2020)

Bug fix release.

• Added .ask_async support for forms.

1.5.1 (22.01.2020)

Bug fix release.

• Fixed .ask_async for questions on prompt_toolkit==2.*. Added tests for it.

1.5.0 (22.01.2020)

Feature release.

• Added support for prompt_toolkit 3
• All tests will be run against prompt_toolkit 2 and 3
• Removed support for python 3.5 (prompt_toolkit 3 does not support that anymore)

1.4.0 (10.11.2019)

Feature release.

• Added additional question type autocomplete
• Allow pointer and highlight in select question type

1.3.0 (25.08.2019)

Feature release.

• Add additional options to style checkboxes and select prompts https://github.com/tmbo/questionary/pull/14

1.2.1 (19.08.2019)

Bug fix release.

• Fixed compatibility with python 3.5.2 by removing Type annotation (this time for real)

1.2.0 (30.07.2019)

Feature release.

• Allow a user to pass in a validator as an instance https://github.com/tmbo/questionary/pull/10

1.1.1 (21.04.2019)

Bug fix release.

• Fixed compatibility with python 3.5.2 by removing Type annotation

1.1.0 (10.03.2019)

Feature release.

• Added skip_if to questions to allow skipping questions using a flag

1.0.2 (23.01.2019)

Bug fix release.

• Fixed odd behaviour if select is created without providing any choices instead, we will raise a ValueError now. (#6)

1.0.1 (12.01.2019)

Bug fix release, adding some convenience shortcuts.

• Added shortcut keys j (move down^ the list) and k (move up) to the prompts select and checkbox (fixes #2)
• Fixed unclosed file handle in setup.py
• Fixed unnecessary empty lines moving selections to far down (fixes #3)

1.0.0 (14.12.2018)

Initial public release of the library

• Added python interface
• Added dict style question creation
• Improved the documentation
• More tests and automatic travis test execution

Developer Info

Create one of the commandline recordings in the readme

0. Install brew install asciinema and npm install --global asciicast2gif
1. Run asciinema rec
2. Do the thing
3. Convert to giv asciicast2gif -h 7 -w 120 -s 2 <recoding> output.gif

Cutting a new release

0. Update the version number in questionary/version.py AND pyproject.toml
1. Add a new section for the release in the changelog in this readme
2. commit this changes
3. git tag the commit with the release version

Travis will build and push the updated library to pypi.

License

Licensed under the MIT License. Copyright 2020 Tom Bocklisch. Copy of the license.