Dash plotly python

Dash plotly python DEFAULT

From my experience here at STATWORX, the best way to learn something is by trying it out yourself – with a little help from a friend! In this article, I will focus on giving you a hands-on guide on how to build a dashboard in Python. As framework, we will be using Dash, and the goal is to create a basic dashboard with a dropdown and two reactive graphs:


Developed as an open-source library by Plotly, the Python framework Dash is built on top of Flask, Plotly.js, and React.js. Dash allows the building of interactive web applications in pure Python and is particularly suited for sharing insights gained from data.

In case you’re interested in interactive charting with Python, I highly recommend my colleague Markus’ blog post Plotly – An Interactive Charting Library. For a general guide about basic visualization techniques, check out this great article by my colleague Vivian on Basic rules for good looking slides and dashboards.

For our purposes, a basic understanding of HTML and CSS can be helpful. Nevertheless, I will provide you with external resources and explain every step thoroughly, so you’ll be able to follow the guide.

Guide structure

The source code can be found on GitHub.


The project comprises a style sheet called , sample stock data and the actual Dash application

Load the Stylesheet

If you want your dashboard to look like the one above, please download the file style.css from our STATWORX GitHub. That is completely optional and won’t affect the functionalities of your app. Our stylesheet is a customized version of the stylesheet used by the Dash Uber Rides Demo. Dash will automatically load any .css-file placed in a folder named .

The documentation on external resources in dash can be found here.

Load the Data

Feel free to use the same data we did (stockdata2.csv), or any pick any data with the following structure:


Getting Started – How to start a Dash app

Back to Guide Structure

After installing Dash (instructions can be found here), we are ready to start with the application. The following statements will load the necessary packages and . Without any layout defined, the app won’t start. An empty will suffice to get the app up and running.

If you have already worked with the WSGI web application framework Flask, the next step will be very familiar to you, as Dash uses Flask under the hood.

How a .css-files changes the layout of an app

The module provides you with several html components, also check out the documentation.

Worth to mention is that the nesting of components is done via the attribute.

The first has one child. Another named , which will contain all our content. The children of are and .

The style for these components come from our .

Now let’s first add some more information to our app, such as a title and a description. For that, we use the Dash Components to render a headline and to generate html paragraphs.

Switch to your terminal and run the app with .


The basics of an app’s layout

Another nice feature of Flask (and hence Dash) is hot-reloading. It makes it possible to update our app on the fly without having to restart the app every time we make a change to our code.

Running our app with also adds a button to the bottom right of our app, which lets us take a look at error messages, as well a . We will come back to the in the last section of the article when we’re done implementing the functionalities of the app.


Charting in Dash – How to display a Plotly-Figure

Back to Guide Structure

With the building blocks for our web app in place, we can now define a plotly-graph. The function from uses the same argument as the plotly package. Dash translates every aspect of a plotly chart to a corresponding key-value pair, which will be used by the underlying JavaScript library Plotly.js.

In the following section, we will need the express version of , as well as the Package Dash Core Components. Both packages are available with the installation of Dash.

Dash Core Components has a collection of useful and easy-to-use components, which add interactivity and functionalities to your dashboard.

Plotly Express is the express-version of , which simplifies the creation of a plotly-graph, with the drawback of having fewer functionalities.

To draw a plot on the right side of our app, add a as a child to the named . The component is used to render any plotly-powered visualization. In this case, it’s will be created by from the Python package . As the express version of Plotly has limited native configurations, we are going to change the layout of our with the method . Here, we use to set the background transparent. Without updating the default background- and paper color, we would have a big white box in the middle of our app. As only renders the figure in the app; we can’t change its appearance once it’s created.

After Dash reload the application, you will end up in something like that: A dashboard with a plotted graph:


Creating a Dropdown Menu

Back to Guide Structure

Another core component is , which is used – you’ve guessed it – to create a dropdown menu. The available options in the dropdown menu are either given as arguments or supplied by a function.
For our dropdown menu, we need a function that returns a list of dictionaries. The list contains dictionaries with two keys, and . These dictionaries provide the available options to the dropdown menu. The value of is displayed in our app. The value of will be exposed for other functions to use, and should not be changed.
If you prefer the full name of a company to be displayed instead of the short name, you can do so by changing the value of the key to . For the sake of simplicity, we will use the same value for the keys and .

Add the following function to your script, before defining the app’s layout.

With a function that returns the names of stocks in our data in key-value pairs, we can now add from the Dash Core Components to our app. Add a as child to the list of children of , with the argument . This has one child, .

We want to be able to select multiple stocks at the same time and a selected default value, so our figure is not empty on startup. Set the argument and chose a default stock for .

The and arguments in will be important in the next section. Every other argument can be changed. If you want to try out different styles for the dropdown menu, follow the link for a list of different dropdown menus.

Working with Callbacks

Back to Guide Structure

How to add interactive functionalities to your app

Callbacks add interactivity to your app. They can take inputs, for example, certain stocks selected via a dropdown menu, pass these inputs to a function and pass the return value of the function to another component. We will write a function that returns a figure based on provided stock names. A callback will pass the selected values from the dropdown to the function and return the figure to a in our app.

At this point, the selected values in the dropdown menu do not change the stocks displayed in our graph. For that to happen, we need to implement a callback. The callback will handle the communication between our dropdown menu and our graph . We can delete the we have previously created, as we won’t need it anymore.

We want two graphs in our app, so we will add another with a different .

  • Remove the argument from
  • Add another with as child to the named .

Callbacks add interactivity to your app. They can take Inputs from components, for example certain stocks selected via a dropdown menu, pass these inputs to a function and pass the returned values from the function back to components.

In our implementation, a callback will be triggered when a user selects a stock. The callback uses the value of the selected items in the dropdown menu (Input) and passes these values to our functions and . The functions will filter the data based on the passed inputs and return a plotly figure from the filtered data. The callback then passes the figure returned from our functions back to the component specified in the output.

A callback is implemented as a decorator for a function. Multiple inputs and outputs are possible, but for now, we will start with a single input and a single output. We need the class and .

Add the following line to your import statements.

and take the id of a component (e.g. in the components id is ) and the property of a component as arguments.

Example Callback:

If we want our to display a time series for one or more specific stocks, we need a function. The of our input is a list of stocks selected from the dropdown menu .

Implementing Callbacks

The function draws the traces of a plotly-figure based on the stocks which were passed as arguments and returns a that can be used by . The inputs for our function are given in the order in which they were set in the callback. Names chosen for the function’s arguments do not impact the way values are assigned.

Update the figure :


  • A will be drawn for each stock. Create an empty for each trace from the plotly figure.


Within the for-loop, a trace for a plotly figure will be drawn with the function .

  • Iterate over the stocks currently selected in our dropdown menu, draw a trace, and append that trace to our list from step 1.



Plotly figures are dictionaries with the keys and . The value of is our flattened list with the traces we have drawn. The is defined with the plotly class go.Layout().

  • Add the trace to our figure
  • Define the layout of our figure

Now we simply repeat the steps above for our second graph. Just change the data for our y-Axis to and slightly adjust the layout.

Update the figure :

Run your app again. You are now able to select one or more stocks from the dropdown. For each selected item, a line plot will be generated in the graph. By default, the dropdown menu has search functionalities, which makes the selection out of many available options an easy task.


Visualize Callbacks – Callback Graph

With the callbacks in place and our app completed, let’s take a quick look at our callback graph. If you are running your app with , a button will appear in the bottom right corner of the app. Here we have access to a callback graph, which is a visual representation of the callbacks which we have implemented in our code. The graph shows that our components and display a based on the value of the component .
If your callbacks don’t work how you expect them to, especially when working on larger and more complex apps, this tool will come in handy.



Let’s recap the most important building blocks of Dash. Getting the App up and running requires just a couple lines of code. A basic understanding of HTML and CSS is enough to create a simple Dash dashboard. You don’t have to worry about creating interactive charts, Plotly already does that for you. Making your dashboard reactive is done via Callbacks, which are functions with the users’ interaction as the input.

If you liked this blog, feel free to contact me via LinkedIn or Email. I am curious to know what you think and always happy to answer any questions about data, my journey to data science, or the exciting things we do here at STATWORX.

Thank you for reading!

Über den Autor

Alexander Blaufuss

I am a data scientist at STATWORX. Fascinated by the world of data and AI, I am interested in Deep Learning, communicating the insights gained from data and the techniques for visualizing data. If you have any questions about my Blog or Data Science in general, you are welcome to contact me via Email or LinkedIn.

is a consulting company for data science, statistics, machine learning and artificial intelligence located in Frankfurt, Zurich and Vienna. Sign up for our NEWSLETTER and receive reads and treats from the world of data science and AI. If you have questions or suggestions, please write us an e-mail addressed to blog(at)statworx.com.  

Sours: https://www.statworx.com/en/blog/how-to-build-a-dashboard-in-python-plotly-dash-step-by-step-tutorial/

Dash for Beginners : Python Dashboards

Python interactive dashboards using plotly dash

Want to learn plotly Dash ? Read the following article to get a basic understanding of plotly Dash. Get an in-depth understanding by either following the official documentation or check out my course on Udemy where you will learn how to create full fledged interactive dashboard. Use the following link to get upto 60% off : https://bit.ly/3iFGJTQ

Dash is a python framework created by plotly for creating interactive web applications. Dash is written on the top of Flask, Plotly.js and React.js. With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python. Dash is open source and the application build using this framework are viewed on the web browser.

With Dash, you don’t have to learn HTML, CSS and Javascript in order to create interactive dashboards, you only need python.

Dash applications are made up of 2 building blocks :

  1. Layout
  2. Callbacks

Layout describes the look and feel of the app, it defines the elements such as graphs, dropdowns etc and the placement, size, color etc of these elements. Dash contains Dash HTML components using which we can create and style HTML content such as headings, paragraph, images etc using python. Elements such as graphs, dropdowns, sliders are created using Dash Core components.

Callbacks are used to bring interactivity to the dash applications. These are the functions using which, for example, we can define the activity that would happen on clicking a button or a dropdown.

Now, let’s have a look at how we can create web based layouts using plotly Dash. Before starting with the layout, let’s install some required packages.

Now, we will import dash package, dash_html_components for HTML classes, dash_core_components for elements such as graph, dropdown etc, and plotly packages for creating plots and reading stock prices dataset.

(In the below code) We are initializing our dash app using dash package. Then, we are reading the stock prices data for different companies from 2018 to 2019. We are creating stock_prices function that returns the line chart for Google’s stock prices.

(In the above code) At line 16, we are setting our layout using html Div component, which is a kind of wrapper within which the elements(heading, graph) of the layout will be created. The Div component contains arguments such as id (a unique identifier of the element), style (for setting the width, height, color etc) and children (equal to square bracket within which elements of the layout are initialised).

Inside the children component (of html.Div) we are creating html H1 heading at line 17 using H1 function. Inside the function, we are setting the unique id of the function (id = ‘H1’), children property using which we set the text of the heading and style property as a dictionary within which we are setting styling such as center aligning the text, setting top and bottom margin to 40 pixel. At line 21, we are using dash core component (dcc) to create graph , where we are setting the id of the graph and the figure argument, which is equal to the function call (stock_pricest()) that returns the plotly figure object.

In order to view our application, we need to run our web server just like in Flask. Remember Dash is built on top of Flask.

On running the app, you will see that the app is running on , which is your local server. Copy this url and paste it in your browser and you will see the below vizualisation.

Now, we will create a dropdown using dash core components. Using the dropdown, we will be able to select the stocks of Google, Apple or Amazon.

Dropdowns are created using the Dropdown() function, which has the following arguments -

  1. id — Unique identifier of the dropdown.
  2. options — Sets the ‘label’ (the text visible in the dropdown) and ‘value’ (used by dash to communicate with callbacks) as key value pair.
  3. value — default selection for the dropdown.

Now, let’s have a look at how we can create the callback that will connect the dropdown and the stock prices line chart.

A callback is initialised using @app.callback(), which is followed by a function definition. Within this function, we define what happens on changing the value of the dropdown.

Let’s have a look at the arguments within the callback function :

  1. Output: This is used to define the components within the layout which will be updated when the function below the callback (graph_update()) return some object. Output function takes 2 arguments — 1) component_id defines the id of the component we want to update with our function graph_update. We want to update stock prices chart within the dcc.Graph, so we will set the component id to ‘line_plot’, which is id of our graph component. 2) Component property defines the property of the component that will be updated, which is figure property of dcc.Graph in our layout.
  2. Input: This is used to define the components, the change in whose value will trigger the callback. Input function also takes component_id and component_property as argument. We want the callback to get triggered based on change in the value of our dropdown, so we set the component_property to ‘value’ property of dropdown. Please note that Input is defined within a list.

The component property of Input function, which is ‘value’ of the dropdown, goes as an argument within the function graph_update. Inside the function we are creating scatter plot and returning the figure object fig, which is passed to the figure property of dcc.Graph using the Output function of the callback.

Now let’s combine the layout, the dropdown and the callback in the below code :

The image below shows how the change in the value of dropdown updates our stock prices line chart.

So, we looked at how we can create interactive dashboards using plotly Dash. Firstly, we created the layout using html components and dash core components. Then, we added dropdown and connected it with the stock prices line chart using callback.

Want to learn more about plotly Dash ? Either you can follow the official documentation or check out my course on Udemy where you will learn how to create full fledged interactive dashboard. Use the following link to get upto 60% off : https://bit.ly/3iFGJTQ

Sours: https://towardsdatascience.com/dash-for-beginners-create-interactive-python-dashboards-338bfcb6ffa4
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Dash is the most downloaded, trusted Python framework for building ML & data science web apps.

Built on top of Plotly.js, React and Flask, Dash ties modern UI elements like dropdowns, sliders, and graphs directly to your analytical Python code. Read our tutorial (proudly crafted with Dash itself).

  • Docs: Create your first Dash app in under 5 minutes

  • dash.gallery: Dash app gallery with Python & R code

Dash App Examples

To learn more about Dash, read the extensive announcement letter or jump in with the user guide.

Dash OSS & Dash Enterprise

With Dash Open Source, Dash apps run on your local laptop or workstation, but cannot be easily accessed by others in your organization.

Scale up with Dash Enterprise when your Dash app is ready for department or company-wide consumption. Or, launch your initiative with Dash Enterprise from the start to unlock developer productivity gains and hands-on acceleration from Plotly's team.

ML Ops Features: A one-stop shop for ML Ops: Horizontally scalable hosting, deployment, and authentication for your Dash apps. No IT or DevOps required.

  • App manager Deploy & manage Dash apps without needing IT or a DevOps team. App Manager gives you point & click control over all aspects of your Dash deployments.
  • Kubernetes scaling Ensure high availability of Dash apps and scale horizontally with Dash Enterprise’s Kubernetes architecture. No IT or Helm required.
  • No code auth Control Dash app access in a few clicks. Dash Enterprise supports LDAP, AD, PKI, Okta, SAML, OpenID Connect, OAuth, SSO, and simple email authentication.
  • Job Queue The Job Queue is the key to building scalable Dash apps. Move heavy computation from synchronous Dash callbacks to the Job Queue for asynchronous background processing.

Low-Code Features: Low-code Dash app capabilities that supercharge developer productivity.

  • Design Kit Design like a pro without writing a line of CSS. Easily arrange, style, brand, and customize your Dash apps.
  • Snapshot Engine Save & share Dash app views as links or PDFs. Or, run a Python job through Dash and have Snapshot Engine email a report when the job is done.
  • Dashboard Toolkit Drag & drop layouts, chart editing, and crossfilter for your Dash apps.
  • Embedding Natively embed Dash apps in an existing web application or website without the use of IFrames.

Enterprise AI Features: Everything that your data science team needs to rapidly deliver AI/ML research and business initiatives.

  • AI App Marketplace Dash Enterprise ships with dozens of Dash app templates for business problems where AI/ML is having the greatest impact.
  • Big Data for Pything Connect to Python's most popular big data back ends: Dask, Databricks, NVIDIA RAPIDS, Snowflake, Postgres, Vaex, and more.
  • GPU & Dask Acceleration Dash Enterprise puts Python’s most popular HPC stack for GPU and parallel CPU computing in the hands of business users.
  • Data Science Workspaces Be productive from Day 1. Write and execute Python, R, & Julia code from Dash Enterprise's onboard code editor.

See https://plotly.com/contact-us/ to get in touch.


Sours: https://github.com/plotly/dash
Live de Python #161 - Plotly Dash

Introduction to Dash

Downloaded 600,000 times per month, Dash is the original low-code framework
for rapidly building data apps in Python, R, Julia, and F#.

Written on top of Plotly.js and React.js,
Dash is ideal for building and deploying data apps
with customized user interfaces in pure Python, R, Julia, or F#.
It’s particularly suited for anyone who works with data.

Through a couple of simple patterns, Dash abstracts away all of the
technologies and protocols that are required to build a full-stack
web app with interactive data visualization.

Dash is simple enough that you can bind a user interface
to your Python, R, Julia, or F# code in less than 10 minutes.

Dash apps are rendered in the web browser. You can deploy your apps
to VMs or Kubernetes clusters and then share them through URLs.
Since Dash apps are viewed in the web browser, Dash is inherently
cross-platform and mobile ready.

There is a lot behind the framework. To learn more about how it is built
and what motivated Dash, read our post
Dash is React for Python.

Dash is an open source library released under the permissive MIT license.
Plotly develops Dash and also offers a platform for writing and deploying Dash
apps in an enterprise environment.
If you’re interested, please get in touch.

These docs are a Dash app running on Dash Enterprise on Azure Kubernetes Service.

Write, deploy, and scale Dash apps on a Dash Enterprise Kubernetes cluster.

Learn more | Pricing | Dash Enterprise Demo | Dash Enterprise Overview

Sours: https://dash.plotly.com/introduction

Plotly python dash

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Como criar um Dashboard em Python com o Dash

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