Data Viz: Apps & Dashboards

May 20, 2020 — 12 min

As a consultant data scientist, I’m very aware of the importance of summarizing my work into Dashboards and Apps.
This allows to popularise my work and put it into instinctive graphics for better and faster understanding. In this article, we will go through one of the most famous tools and python libraries used in designing dashboards and applications.

Tabe of contents

The summary is as follows:

  1. Dash by Plotly
  2. Streamlite
  3. Bokeh
  4. Kibana
  5. Heroku deployment

Dash by Plotly

Dash is an open-source tool developed by Plotly. It allows to insert multiple types of widgets with the possibility of choosing the disposition and the style since dash’s layout is HTML-based and allow CSS styling.
The setup is done by running the following command line:

pip install dash
pip install dash_html_components
pip install dash_core_components

The layout of a dash application is a hierarchical tree of components, a mixed between HTML elements and graphs built using:

  • dash_html_components library provides classes for all of the HTML tags, and the keyword arguments describe the HTML attributes like style, className, and id. Using this library, we can add HTML elements such as Div, H1, P,... etc. For more details, I advise you to check of its documentation.
  • dash_core_components library generates higher-level components like controls and graphs and uses Plotly’s syntax. For instance, we can insert a Button, Dropdown, Slider,DatePickerRange, ... etc.. For more details, please visit the official website.

A simple Dash’s app can be developed using the following python lines:

import dash 
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash(__name__)
app.title='Hello World'
app.layout = html.Div(children=[

	html.H1(children='Hello Dash!', style={'textAlign':
		'center', 'color': '#7FDBFF'}),
		Dash: A web application framework for Python.
			'data': [
				{'x': [1, 2, 3], 'y': [4, 1, 2], 'type': 'bar',
					'name': 'SF'},
				{'x': [1, 2, 3], 'y': [2, 4, 5], 'type': 'bar',
				'name': u'Montréal'},
			'layout': {
				'title': 'Dash Data Visualization'
if __name__ == '__main__':

The option debug=True allows to take the recent modifications into account without having to re-run the app.
To run the app, we use the following command line:


You get the following app running on :

hello world dash


It is possible to add a CSS file which over-writes the basic style of the app, allowing you to customize your graphics and HTML elements. To do so, the structure of the folder should be as follows:
    |-- style.css
    |-- custom-script.js
    |-- img.png
    |-- background.jpg
    |-- favicon.ico

It is mandatory to put the CSS file and all others inserted images in a folder called assets in order to be read by Dash. Once it’s done, all we have to do is to specify style sheet using the following python lines:

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)


When building a dash-app, I personally prefer to work with Bootstrap. It is a python library for Plotly Dash, containing styled and advanced elements.
The setup is done by using the following command line:

pip install dash-bootstrap-components

It is important to include the bootstrap theme as one of the style sheets:

external_stylesheets=["assets/style.css", dbc.themes.BOOTSTRAP]
app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

Boostrap contains many elements such as DropDownMenu, Navbar, Progress, Button, ... etc. One of the most useful elements is Layout, it allows to structure the dashboard using Rows and Columns which is very useful for the app’s structure.
For further details, don’t hesitate to visit the official documentation.


Like any other app, it is important to enable interactivity between graphics and this is done using callback functions.
Each inserted elements has two important features

  • id: unique appellation of the element
  • properties: differ from an element to another

Linking two elements in an app is done as follows:

callback dash

In this particular case the first element affects the second: the property_1 of the first element selected through its id_1 impacts the property_2 of the second one accessed through its id_2.

from dash.dependencies import Input, Output
    Output(component_id='id_2', component_property='property_2'),
    [Input(component_id='id_1', component_property='property_1')]
def callback_function(input_value):
    What to do

NB: it is possible de take many Inputs in the callback function.


When developing an app, it is very important to have elements in Cache in order to ease the loading and fasten the interactivity, Cache allows to do so.
Dash does not support a specific cache feature, but one way to overcome this problem is using global variables in python.


Tabs in Dash, can be inserted using the dcc.Tab component, where its children are the elements composing the tab.

import dash
import dash_html_components as html
import dash_core_components as dcc

external_stylesheets = ['']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div([
        dcc.Tab(label='1st Tab', children=[
        dcc.Tab(label='2nd Tab', children=[
        dcc.Tab(label='3rd Tab', children=[

Dash has a gallery where you can browse and discover other dashboards, it is a good baseline and a source of inspiration.


Streamlit is an open-source app framework developed to create ML dashboards, mostly, in a very fast and efficient way using python. The setup is very simple and can be carried out using the following command line:

pip install streamlit

Compared to Dash, Streamlit has better handling of Cache, as developed in the next sections, and thus allows faster development iterations using the following process:

Once your file is created, you can type this command line which will run your application on http://localhost:8501

streamlit run

As in Dash, it is possible de choose the mode Always re-run to get the modifications in live.


Streamlit’s widgets gallery contains multiple items which can all be inserted using one line of code: Markdown, SelectBox, Slider, Plot, etc… Note that the widgets can either be inserted in the one-page area or on the sidebar and that the insertion is done following a vertical stacking:

import streamlit as st
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn import datasets
from sklearn.decomposition import PCA
import pandas as pd

iris = datasets.load_iris()
feature_names=['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)']
database=pd.DataFrame(, columns=feature_names)

# plot the first three PCA dimensions
fig = plt.figure(1, figsize=(8, 6))
ax = Axes3D(fig, elev=-150, azim=110)
X_reduced = PCA(n_components=3).fit_transform(
ax.scatter(X_reduced[:, 0], X_reduced[:, 1], X_reduced[:, 2],,
 , edgecolor='k', s=40)
ax.set_title("First three PCA directions")
ax.set_xlabel("1st eigenvector")
ax.set_ylabel("2nd eigenvector")
ax.set_zlabel("3rd eigenvector")

def main():
    ########### Sidebar ##############################
    st.sidebar.markdown("# Hello World")
    st.sidebar.selectbox('Select a tool', ["Dash", "Kibana", "Streamlit", "Bokeh"])
    st.markdown("## Multiselection")
    st.multiselect('Select a tool', ["Dash", "Kibana", "Streamlit", "Bokeh"])

    st.markdown("## Radio buttons")'Select a tool', ["Dash", "Kibana", "Streamlit", "Bokeh"])
    st.markdown("## Slider") 
    st.slider('Select a Number', min_value=1, max_value=4, value=1)

    st.markdown("## Image")
    st.image(img, width=500)

    st.markdown("## DataBase")

    st.markdown("## Plot")

if __name__ == "__main__":

We get the following app:

streamlit example

The function st.write is very important and can be used to insert many python elements. For more details, I advise you to read streamlit’s documentation.

Interactivity: Each element can be assigned to a variable which stands for its current value, it can, next, be used to update another element and hence the interactivity.


Cache is usually used to store data that it is constant and does not change no matter the choices made on the app. We might very often define functions in the app where there is no use for them to be re-run every time the app is refreshed, we can cache them using the following python code:

def function_1():


In many apps, Tabs can be very important: one way to do so in Streamlit is adding a selectbox in the sidebar which choice will affect the structure of the dashboard and that using if statements on the value of the box.

tab=st.sidebar.selectbox('Select a Tab', ["Home", "Documentation", "Partners", "Contact"])
if tab=="Home":
	#Develop the one_pager
elif tab=="Documentation":
elif tab=="Partners":
elif tab=="Contact":

Health app - Streamlit

When I started discovering Streamlit, I have decided to put it into use by developing a simple health app allowing to grasp the nettle.

streamlit gallery

You can visit the app using this url, the script is hosted in this git repository. For more apps, you can visit the official gallery.


Bokeh is a python library which allows to create an interactive dashboard as well. It can be installed using the following command-line:

pip install bokeh

The below app, for instance, can be created using the following python script:

bokeh app
from import show, curdoc
from bokeh.plotting import figure, ColumnDataSource
from bokeh.layouts import row, column
from bokeh.models.widgets import Tabs, Panel
from bokeh.models import Slider, CheckboxGroup, RadioGroup, Button, CustomJS

#data source
x = [x*0.005 for x in range(0, 200)]
y = x
source = ColumnDataSource(data=dict(x=x, y=y))
# %%
plot_1 = figure(plot_width=400, plot_height=400)
plot_1.line('x', 'y', source=source, line_width=3, line_alpha=0.6)

plot_2=figure(tools='box_zoom, lasso_select')[1,2,3,4,5], y=[2,3,1,1,2])
plot_2.background_fill_color = 'black'

plot_3=figure(tools='box_zoom, lasso_select')[1,2,3,4,5], y=[2,3,1,1,2])

slider = Slider(start=0.1, end=4, value=1, step=.1, title="power")
button = Button(label='Click')
checkbox = CheckboxGroup(labels=['Kibana', 'Bokeh', 'Streamlit', 'Dash']) 
radio = RadioGroup(labels=['Kibana', 'Bokeh', 'Streamlit', 'Dash'])

callback = CustomJS(args=dict(source=source), code="""
        var data =;
        var f = cb_obj.value
        var x = data['x']
        var y = data['y']
        for (var i = 0; i < x.length; i++) {
            y[i] = Math.pow(x[i], f)
slider.js_on_change('value', callback)

#Plots zoom-linking

first=Panel(child=row(column(slider, button, checkbox, radio), plot_1), title='first')
second=Panel(child=row(plot_2,plot_3), title='second')
tabs=Tabs(tabs=[first, second])


Note that once we define the figure plot, we use the syntax plot.graph to assign the desired graph to it. The data is handled using ColumnDataSource and layout using row, column. We customize the added widgets, Slider, CheckboxGroup, RadioGroup, Button, and link them to graphs using the JS callback CustomJS. To run your app, on http://localhost:5006/myapp, you can use the following command-line:

bokeh serve --show

As all the others tools, Bokeh has a very rich gallery to start with.

Elastic stack


Elasticsearch is a special type of tables’ indexation which gives a faster search engine. We can transform a pandas database into an Elasticbase and then visualize it using Kibana which is the visualization tool of the stack Elastic. You, first, need to download Kibana & Elasticsearch depending on your OS.
Once installed, we will want to properly index our pandas dataframe using elasticsearch. To do so, we first need to launch both of services elasticsearch and kibana using the command-lines:


Once Kibana is launched, we can use this python script to sent data to the Elasticsearch services

import pandas as pd
from elasticsearch import Elasticsearch

# Database loading and service openning
es_client = Elasticsearch(http_compress=True)

#Elasticsearch does not accept NAN values

INDEX="laposte" #Its name in Elasticsearch (laposte for example)
TYPE= "record"

def rec_to_actions(df):
    import json
    for record in df.to_dict(orient="records"):
        yield ('{ "index" : { "_index" : "%s", "_type" : "%s" }}'% (INDEX, TYPE))
        yield (json.dumps(record, default=int))

e = Elasticsearch() 
r = e.bulk(rec_to_actions(df))

#Verify if everything went fine
print(not r["errors"])


The servie kibana is launched in the local host on the port 5601, we can visit it in a browser using the address localhost:5601. Once on the main page, we can click on Dashboard and Create dashboard as follows:

create kibana

We can now add widgets through these steps:

insert kibana

The inserted graph can be positioned using the cursor which makes the layout easy to customize. Note the presence of a searching bar where the data is indexed using Elasticsearch, this makes the querying less time consuming and adds more interactivity to the dashboard, specially when dealing with large databases. The interactivity is automatically handled in kibana.

result kibana


Tabs in Kibana can be created using embedded links in markdowns widgets. You first create all the tabs each one in a different dashboard and then add in each one the embedded links to the others.

Do not hesitate to visit kibana’s official gallery.

Heroku deployment

Once your app is developed, you can host it online so it can be accessed by anyone using an Url. One way to do so it to use Heroku which offer this free service with some limitations.
You first need to sign up and then create a heroku app online which will be linked to your git repository. The repo should have the following structure:

├── requirements.txt
└── Procfile

You can check my git repository for more information. On your local git folder run the following command-lines:

heroku create
git push heroku master
heroku ps:scale web=1
heroku open

Your app will be on constant standby to every push you make in your git in order to take the latest changes into account.


Apps and dashboards are a very important and decisive phase of each project, they allow to summarize our work and make it more accessible to the user through an instinctive interface. The use of each technology mainly depends on your delivery and your deadlines: some tools allow more flexibility, others are faster to develop:


In my personal point of view, if all the conditions are met, Dash can be the best option to go with.