Python dashboard widget

In this post, I describe another powerful feature of Jupyter Notebooks: The ability to use interactive widgets to build interactive dashboards.

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The included examples are hosted on the Domino data science platform. Why might we want to add interactivity to a notebook? One reason is to use more powerful tools to address traditional business intelligence use cases.

Traditional BI tools work great if you are building a dashboard on top of SQL, but if you want to visualize information that is generated by some more sophisticated logic, they typically fall short. You can think of a widget as having two parts:. The first argument is the function that handles the selected value of the second argument.

The type of second argument will decide the form of the interaction. As you can see: an integer results in a slider.

You can store widgets in variables in your notebook just like any other type of value. As a simple example:. We create two widgets, an input and output. When the value of the input widget changes, we take the new value and update the value of the input widget. You can create much more sophisticated interactions this way. This core flexibility unlocks tremendous potential for using notebooks as dashboards. For example, you can expose widgets to filter, group, or sort data; your Python code can then query data sources, calculate derived data, use pandas and other great packages to do in-memory manipulation — and then render results using any number of great Python visualization packages.

To wrap up, I wanted to combine the concepts in my last post magics, data pipelines with the interactive widgets described above. See below for the code to create this. And so I conclude this blog, but not because this is all there is to tell and show. Please share in the comments what fancy notebook option you discovered while working on your own project.

Data Science. Machine Learning. Practical Techniques. Leaders at Work. Model Management. Building Interactive Dashboards with Jupyter by roos on November 11, Business Intelligence on steroids Why might we want to add interactivity to a notebook?

Basic types of widgets Text input: You can create a text input field by using the widgets. Buttons: The button widget works similar to the text input one. As a simple example: We create two widgets, an input and output. Putting it together To wrap up, I wanted to combine the concepts in my last post magics, data pipelines with the interactive widgets described above. Related posts:. Announcing Enhanced Apache Spark Support.Send us feedback. Input widgets allow you to add parameters to your notebooks and dashboards.

The widget API consists of calls to create various types of input widgets, remove them, and get bound values. You manage widgets through the Databricks Utilities interface. If you add a command to remove a widget, you cannot add a subsequent command to create a widget in the same cell. You must create the widget in another cell.

To see detailed API documentation for each method, use dbutils. The help API is identical in all languages. For example:. You can create a dropdown widget by passing a unique identifying name, default value, and list of default choices, along with an optional label.

Once you create it, a dropdown input widget appears at the top of the notebook. These input widgets are notebook-level entities. If you try to create a widget that already exists, the configuration of the existing widget is overwritten with the new options. The following is an example of creating a text input widget. To specify the selectable values in a dropdown widget in SQL, you can write a sub-query. The first column of the resulting table of the sub-query determines the values.

The default value specified when you create a dropdown widget must be one of the selectable values and must be specified as a string literal. The function is getArgument. On such clusters you can use the syntax shown in Legacy input widgets in SQL. In general, you cannot use widgets to pass arguments between different languages within a notebook.

You can create a widget arg1 in a Python cell and use it in a SQL or Scala cell if you run cell by cell. However, it will not work if you execute all the commands using Run All or run the notebook as a job. To work around this limitation, we recommend that you create a notebook for each language and pass the arguments when you run the notebook. Here is an example:. You can configure the behavior of widgets when a new value is selected and whether the widget panel is always pinned to the top of the notebook.

Click the icon at the right end of the Widget panel. Run Notebook : Every time a new value is selected, the entire notebook is rerun. Run Accessed Commands : Every time a new value is selected, only cells that retrieve the values for that particular widget are rerun. This is the default setting when you create a widget.

You can see a demo of how the Run Accessed Commands setting works in the following notebook. When you change the setting of the year widget tothe DataFrame command reruns, but the SQL command is not rerun. How to import a notebook Get notebook link. When you create a dashboard from a notebook that has input widgets, all the widgets display at the top of the dashboard. In presentation mode, every time you update value of a widget you can click the Update button to re-run the notebook and update your dashboard with new values.

You can also pass in values to widgets. This example runs the specified notebook and passes 10 into widget X and 1 into widget Y. Updated Apr 09, Send us feedback.

Documentation Notebooks Widgets. Widgets Input widgets allow you to add parameters to your notebooks and dashboards.Bokeh has been around since Bokeh : Anaconda.

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Dash : Plotly. Bokeh: BSD 3-Clause permissive license. Dash: MIT permissive license. All these licenses are permissive as well.

Bokeh: D3 frontendTornado backend. Bokeh : supported natively with interactions. Dash : not supported, but Plotly is. Bokeh: Mostly some graph interactions must be written in javascript. Dash : Yes for the core framework. You can also write custom React components or find some on the internet.

Bokeh vs Dash — Which is the Best Dashboard Framework for Python?

These examples show how a selection component can update the graphs. They also show how selecting data on a graph updated the other components. Both dashboards also look very similar. Another point is interaction consistency.

See what happens in the Bokeh example when you first select a category on top, then select data on the scatter plot and then unselect a category. Data in the Bokeh graphs becomes inconsistent. If you come up with an elegant solution to this issue, please let me know. Plotly has always been incredibly intuitive.

Incredibly easy to get live updates and great visualisation. Bokeh was just frustrating when I tried it. The examples are written in Python3. The dataset will be loaded in the following way throughout the examples:.

It makes the code simpler here. When giving lines of code for each example, those previous lines will be excluded. Lines of code for the example: Version of Bokeh in the example: 0. I found it hard to use the Bokeh data sources because I wanted and failed to link them to my pandas dataframes. Configuring graphs to look like I wanted was a hassle, and it took me a lot of time to get around how interactions work. Bokeh has a wider range of interactions than Dash.

For example, you can pan multiple graphs at the same time, which is not possible in Dash right now. Version of Dash in the example: 0. The only difficulty I had was how to use the dcc. Graph object with the regular plotly library.

python dashboard widget

When that was clear for me, development was a breeze. This article was supposed to be a comparison of multiple dashboard frameworks for python. I previously included Pyxley, and Pydashie. However, these projects have either been abandoned or lack proper documentation.Creating an interactive visualization application in Bokeh.

Sometimes I learn a data science technique to solve a specific problem. Data science requires knowledge of many different skills and you never know where that next idea you will use will come from! In the case of Bokeh, several weeks after trying it out, I found a perfect use case in my work as a data science researcher. My research project involves increasing the energy efficiency of commercial buildings using data science, and, for a recent conferencewe needed a way to show off the results of the many techniques we apply.

python dashboard widget

By the time most people at a conference see their third slide deck, they have already stopped paying attention. Skeptical, our team prepared a back-up presentation, but after I showed them some prototypes, they gave it their full support. The final interactive dashboard was a stand-out at the conference and will be adopted by our team for future use:. Along these lines, I started this series to share the capabilities of Bokeha powerful plotting library in Python that allows you to make interactive plots and dashboards.

This third post is a continuation of my Bokeh series, with Part I focused on building a simple graph, and Part II showing how to add interactions to a Bokeh plot. In this post, we will see how to set up a full Bokeh application and run a local Bokeh server accessible in your browser! This article will focus on the structure of a Bokeh application rather than the plot details, but the full code for everything can be found on GitHub.

We will continue to use the NYCFlights13 dataseta real collection of flight information from flights departing 3 NYC airports in There are overflights in the dataset, and for our dashboard, we will focus primarily on exploring the arrival delay information. This will set-up a local Bokeh server and open the application in your browser you can also make Bokeh plots available publicly online, but for now we will stick to local hosting.

Following is a short clip showing how we can interact with the complete dashboard:. At the top we see a number of tabs, each of which contains a different section of the application. The idea of a dashboard is that while each tab can stand on its own, we can join many of them together to enable a complete exploration of the data. The video shows the range of charts we can make with Bokeh, from histograms and density plots, to data tables that we can sort by column, to fully interactive maps.

Besides the range of figures we can create in Bokeh, another benefit of using this library is interactions.

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Each tab has an interactive element which lets users engage with the data and make their own discoveries. From experience, when exploring a dataset, people like to come to insights on their own, which we can allow by letting them select and filter data through various controls. I highly recommend downloading the code for yourself to follow along!

python dashboard widget

This organization will help us keep track of all the elements in our application and assist in debugging when things inevitably go wrong. Also, we can re-use this framework for future projects so our initial investment in the planning stage will pay off down the road. Within this directory, we will have a sub-directory for our data called dataa sub-directory for our scripts scriptsand a main.

Generally, to manage all the code, I have found it best to keep the code for each tab in a separate Python script and call them all from a single main script. Following is the file structure I use for a Bokeh application, adapted from the official documentation. For the flights application, the structure follows the general outline:. There are three main parts: datascriptsand main.

The main. It loads in the data, passes it out to the other scripts, gets back the resulting plots, and organizes them into one single display. This will be the only script I show in its entirety because of how critical it is to the application:.

We start out with the necessary imports including the functions to make the tabs, each of which is stored in a separate script within the scripts directory.

This is a completely blank file that needs to be placed in the directory to allow us to import the appropriate functions using relative statements e.

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Once the data has been read in, the script proceeds to delegation: it passes the appropriate data to each function, the functions each draw and return a tab, and the main script organizes all these tabs in a single layout called tabs.

We covered interactive plots in Part II of this series, and this plot is just an implementation of that idea. The overall structure of the function is:. Once we have the plot set up, the final line returns the entire plot to the main script.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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python dashboard widget

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I have a dataframe with 15 columns and I want to create dropdowns out of those columns populated by dependency on each other.

For example, if my first column is 'country' and if I select 'France' then I want all other 10 dropdowns to change their values accordingly to what is in the dataframe for France.

I have tried everythign I could think of but nothing seems to work and there are no complex examples online where many dropdowns interact. Those are the columns I want to plot against country, year and car columns for example the rest of columns in a dataframe are listed in this dropdown, not their values as in dropdowns above :. So if I select France in a country dropdown I want to see only years in a year dropdown that correspond to France and only cars that correspond to France in year Color dropdown also needs to be updated only for France in and selected type of car.

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I have tried Python Interact widgets and Dash as well but couldn't make it work. Any help would be much appreciated. Learn more. Asked 1 year, 5 months ago. Active 1 year, 5 months ago. Viewed 87 times. Active Oldest Votes. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home?

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Learn Jupyter Notebooks (pt.4) Using Interactive Widgets

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The dark mode beta is finally here. Change your preferences any time. Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. Okay, so I'm just trying to get some clarification on why my code is not working like I thought it would. I have already made a function that updates the Label when the function is called, but of course that is not my problem. My problem stems from me trying to implement a "print one letter at a time" type of label.

While it prints to the terminal in the way I want it to, the label widget only updates after the whole function has finished visually its the same as just printing the whole string instead of printing a letter at a time. So what am I missing, what do I not understand? Can you guys help me? Let me post some code so you guys can see where my error is.

I tried both of these independently and they both game me the same result, which was not what I desired. I'm not positive if I need to post more code, so you guys can understand better, but if thats the case, I will do that.

The UI will update every time the event loop is entered. This is because painting is done via events also known as "idle tasks" because they are done when the UI is otherwise idle. Your problem is this: when you write a loop and do time.

You can solve your problem in at least a couple different ways. That will solve the repainting, but because you are sleeping the UI will be unresponsive during your loop since button and key presses aren't "idle tasks". Another solution is to write a function that takes a string, pulls one character off the string and adds it to the widget. Then it arranges for itself to be called again via the event loop.

For example:. This type of solution guarantees that your UI stays responsive while still running your code in a loop. Only, instead of using an explicit loop you add work to the already running event loop.These days the comparison between React and Angular2 is a very hot topic.

So, I decided to the same thing for my SenseHat and see the result. I should mention that I am not a web developer, I had some experience but for sure I am not an expert. Therefore, I try to do the things easiest way as possible. For configuring and installing it and also getting familiar with flask, I found this beautiful tutorial that explains everything and helped me a lot.

OK so everything is ready to build a nice Dashboard and show some sensor data, wirte some data on LED and also play a little bit with React. Briefly, I explain what skeleton project has:. So what do I want for my Dashboard? First thing I want a page just to show the current value of temperature, Humidity and Pressure. Then I want a page for showing them on the graph but not the historical one, I want real-time Dashboard. The good thing about React is, we just need to write our components widgets in our case and then use them in one view or even use them in multiple view.

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Also, it is OK to copy them from one dashboard to other. In case of the update on any of this widgets, React will update only that widget and nothing. I prepare this with some functions and then inside each component I just import it and use the function that we need to call.

I renamed the helper. Ok, so I start implementing my router and then my navigation. In this way I always have my navigation on top and React is just updating the containers views. I need 3 views, one for monitoring calling Ambientone for controlling Control and one for charts Chart. So I need to have something like this:. Then for navigation I need to modify the navbar file which is already inside component folder.