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Gleam lets you build interactive web visualizations of data using Python: no knowledge of HTML or JS necessary! You can choose a number of inputs your users can control, then use any Python graphing library to create plots based on those inputs. Gleam puts it all together creates a web interface that lets anyone play with your data in real time. Now it's easier than ever to help others understand and interpret your data. Gleam was inspired by the Shiny package in R.

See here for a live demo! (You can find the code for the demo in examples/


Let's make an interactive visualization of a scatter plot. Here we'll have three inputs that can be controlled by the user:

  • The plot's title
  • What variable goes on the y axis
  • Whether we add a smoothing curve to the data

Start by importing a few packages. Gleam uses the wtforms package to provide form inputs. You can use any Python graphing package you want with Gleam, such as matplotlib, but we recommend the intuitive ggplot.

from wtforms import fields
from ggplot import *

Then import what you'll need from the gleam package:

from gleam import Page, panels


An Inputs panel lets you specify the inputs that the user can control. Here, we add a string input for the title, a multiple choice select field for the Y-axis variable, and a checkbox for the smoother:

class ScatterInput(panels.Inputs):
    title = fields.StringField(label="Title of plot:")
    yvar = fields.SelectField(label="Y axis",
                              choices=[("beef", "Beef"),
                                       ("pork", "Pork")])
    smoother = fields.BooleanField(label="Smoothing Curve")


The output is where your actual plotting goes. It comes in the form of a plot method, which takes an argument inputs containing the inputs from above. Here we use ggplot to make the plot, taking the arguments into consideration. (You could use any Python graphing packages in this function- the sky's the limit, as long as it creates a plot).

class ScatterPlot(panels.Plot):
    name = "Scatter"

    def plot(self, inputs):
        p = ggplot(meat, aes(x='date', y=inputs.yvar))
        if inputs.smoother:
            p = p + stat_smooth(color="blue")
        p = p + geom_point() + ggtitle(inputs.title)
        return p

Tying it together in a Page

Constructing an HTML page to allow this control is as simple as combining the input and the output:

class ScatterPage(Page):
    input = ScatterInput()
    output = ScatterPlot()

Run the app

You can then run the app with:

By default, it will create a local server hosted at you can visit it there to see your cool visualization, which will look something like this:


Try changing the title, the Y axis selector, or try checking the box: you'll see the plot react in real time.

You can add more inputs to the ScatterInput class, and then use them to further customize the plot in the ScatterPlot class.



Creating interactive visualizations with Python




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