Generate Widgets from Parameters#

This guide addresses how to generate UIs from Parameterized classes without writing any GUI related code.


  1. The Param User Guide provides the conceptual foundation for use of Parameterized objects.

Parameters are Python attributes extended using the Param library to support types, ranges, and documentation, which turns out to be just the information you need to automatically create widgets for each parameter.

Declaring and displaying parameters#

Internally parameters have a mapping that generates widgets appropriate for each type. This means that by declaring a Parameterized class we can automatically generate a full UI. Let us declare a BaseClass:

import param
import panel as pn
import pandas as pd
import datetime as dt


class BaseClass(param.Parameterized):
    x                       = param.Parameter(default=3.14, doc="X position")
    y                       = param.Parameter(default="Not editable", constant=True)
    string_value            = param.String(default="str", doc="A string")
    num_int                 = param.Integer(default=50000, bounds=(-200, 100000))
    unbounded_int           = param.Integer(default=23)
    float_with_hard_bounds  = param.Number(default=8.2, bounds=(7.5, 10))
    float_with_soft_bounds  = param.Number(default=0.5, bounds=(0, None), softbounds=(0,2))
    unbounded_float         = param.Number(default=30.01, precedence=0)
    hidden_parameter        = param.Number(default=2.718, precedence=-1)
    integer_range           = param.Range(default=(3, 7), bounds=(0, 10))
    float_range             = param.Range(default=(0, 1.57), bounds=(0, 3.145))
    dictionary              = param.Dict(default={"a": 2, "b": 9})

and then render the resulting UI using Panel:


By changing a widget and re-running the following outputs, we can see that changes in the widgets are automatically reflected in Python:


The reverse is also true; editing a parameter from Python will automatically update any widgets that were generated from the parameter:

BaseClass.num_int = 1

Passing the .param object renders the full set of widgets, while passing a single parameter will display just one widget. In this way we can easily declare exactly which parameters to display:

pn.Row(BaseClass.param.float_range, BaseClass.param.num_int)

Advanced parameters#

The BaseClass primarily declared simple parameters, however there are a wide range of parameter types covering many use cases. Below we define a subclass with some of these additional parameter types.

class Example(BaseClass):
    """An example Parameterized class"""

    timestamps = []

    boolean                 = param.Boolean(default=True, doc="A sample Boolean parameter")
    color                   = param.Color(default='#FFFFFF')
    date                    = param.Date(default=dt.datetime(2017, 1, 1),
                                         bounds=(dt.datetime(2017, 1, 1), dt.datetime(2017, 2, 1)))
    dataframe               = param.DataFrame(default=pd._testing.makeDataFrame().iloc[:3])
    select_string           = param.ObjectSelector(default="yellow", objects=["red", "yellow", "green"])
    select_fn               = param.ObjectSelector(default=list,objects=[list, set, dict])
    int_list                = param.ListSelector(default=[3, 5], objects=[1, 3, 5, 7, 9], precedence=0.5)
    single_file             = param.FileSelector(path='../../*/*.py*', precedence=0.5)
    multiple_files          = param.MultiFileSelector(path='../../*/*.py?', precedence=0.5)
    record_timestamp        = param.Action(default=lambda x: x.timestamps.append(dt.datetime.utcnow()),
                                           doc="""Record timestamp.""", precedence=0.7)

example = Example()


For example, the Example.timestamps Parameter records the timestamps from every “record timestamp” button press above. Rerun the code block below after clicking the button in order to see the output in the docs.