Core Components

In Pyxley, the core component is the UILayout. This component is composed of a list of charts and filters, a single React component from a JavaScript file, and the Flask app.

# Make a UI
from pyxley import UILayout
ui = UILayout(

This will create a UI object that’s based on the FilterChart React component in pyxley.js. It will be bound to an html div element called component_id.

If we wanted to add a filter and a chart we could do so with the following

# Make a Button
cols = [c for c in df.columns if c != "Date"]
btn = SelectButton("Data", cols, "Data", "Steps")

# Make a FilterFrame and add the button to the UI

# Make a Figure, add some settings, make a line plot
fig = Figure("/mgchart/", "mychart")
fig.layout.set_size(width=450, height=200)
fig.layout.set_margin(left=40, right=40)
lc = LineChart(sf, fig, "Date", ["value"], init_params={"Data": "Steps"}, timeseries=True)

Calling the ui.add_chart and ui.add_filter methods simply adds the components we’ve created to the layout.

app = Flask(__name__)
sb = ui.render_layout(app, "./static/layout.js")

Calling ui.render_layout builds the JavaScript file containing everything we’ve created.


Charts are meant to span any visualization of data we wish to construct. This includes line plots, histograms, tables, etc. Several wrappers have been introduced and more will be added over time.


All charts are UIComponents that have the following attributes and methods

  • An endpoint route method. The user may specify one to override the default.
  • A url attribute that the route function is assigned to by the flask app.
  • A chart_id attribute that specifies the element id.
  • A to_json method that formats the json response.


Filters are implemented in nearly the same way that charts are implemented. The only difference is the lack of the to_json method.