A Basic Pyxley App

Here we will go through building a basic web-application using Pyxley and Flask.

I recommend visiting the Real Python blog for a great intro to a basic app.

|   package.json
|   .bowerrc
|   bower.json
    |   app.py
    |   templates
        |   css
        |   js

Some notes about the above structure

  • This assumes that you are running the app from the project folder.
  • Any JavaScript created by the app should go in the js folder.


Node & NPM

At the highest level, Node is our biggest JavaScript dependency. All of the JavaScript dependencies are managed via NPM. This document won’t show you how to get Node or NPM, but for Mac OS X users, you can get it through homebrew.

Note: Prior to version 0.0.9, Bower was used to manage dependencies. In an effort to simplify the massive amount of dependencies, NPM will be used as the primary package manager and Bower is completely optional. In addition, PyReact has been deprecated and will no longer be used to transpile the jsx code.


HTML templates used by flask are stored in the templates folder. For our purposes, we only need a really basic template that has a single div element.

<div id="component_id"></div>

Everything in our app will be tied to this single component.


We store any additional CSS we need in the static\CSS folder.


Courtesy of the Flask website, “Hello, World!” in Flask looks like the code below.

from flask import Flask
app = Flask(__name__)

def hello_world():
    return 'Hello World!'

if __name__ == '__main__':

We simply need to build upon this.

Adding Some Pyxley

Let’s start by importing some simple things and building upon the example above. We will import some pyxley components, create a UI, and load a data frame.

# Import flask and pandas
from flask import Flask, render_template
import pandas as pd

# Import pyxley stuff
from pyxley import UILayout
from pyxley.filters import SelectButton
from pyxley.charts.mg import LineChart, Figure

# Read in the data and stack it, so that we can filter on columns
df = pd.read_csv("fitbit_data.csv")
sf = df.set_index("Date").stack().reset_index()
sf = sf.rename(columns={"level_1": "Data", 0: "value"})

# Make a UI
ui = UILayout(

# Create the flask app
app = Flask(__name__)

At this point we now have some data and a layout to build upon. Adding the code below will add a dropdown select button and a line plot.

# 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)

Now that our ui object is full of filters and charts, we need to write out the JavaScript and transpile the jsx code. Previously, we used PyReact, but unfortunately that has been deprecated. Instead, we rely on webpack. We have written a wrapper for webpack that does the bundling for us.


sb = ui.render_layout(app, ”./project/static/layout.js”)

# Create a webpack file and bundle our javascript from pyxley.utils import Webpack wp = Webpack(”.”) wp.create_webpack_config(

“layout.js”, ”./project/static/”, “bundle”, ”./project/static/”

) wp.run()

@app.route(‘/’, methods=[“GET”]) @app.route(‘/index’, methods=[“GET”]) def index():

_scripts = [”./bundle.js”] css = [”./css/main.css”] return render_template(‘index.html’,

title=TITLE, base_scripts=[], page_scripts=_scripts, css=css)
if __name__ == ‘__main__’:

wp.run() will transpile ”./project/static/layout.js” with the necessary dependencies and produce “bundle.js”. If you had further dependencies not managed by NPM, you could include them in the base_scripts keyword argument.

Now when you run app.py from the project folder, accessing your localhost on port 5000 will lead to a simple plot. This example was adapted from the metricsgraphics example in the Pyxley repository.