Dash Learn about its potential

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Contents

Learning Agility: Understanding Its Potential in the Workplace

HR managers are key resources in spotting and developing learning agility in any organisation. Research from many respected human resources authorities has shown that the ability to learn from experience is one of the key characteristics of people with high potential. High potential employees usually have learning agility but not always. Those who respond flexibly to new processes and institutional changes perform even more effectively than high potential employees.

What Is Learning Agility?

Learning agility is the ability to incorporate new material quickly, and the concept developed in the business world where it was found that the ability to learn quickly and use that information in business was the strongest predictor of success. Those with agility show strong leadership qualities. Only about one-third of high-potential employees live up to their promise, but those with various dimensions of learning agility perform at the highest rate. These skills can be developed and improved. Those with various agilities learn quickly from information and experience, take risks, strive for growth and exhibit resiliency. These people absorb information through books and classes, peer learning, direct experience and reflections on past performances. Even failure can prove valuable to those with agility because they grow from their unsuccessful experiences.

The Five Dimensions of Learning Agility

Learning agility isn’t a single skill but a broad area that includes at least five aspects of learning. Each person is likely to have relative skills and shortcomings in different categories, so it’s critical to determine how each employee ranks in five categories. These categories, which were identified by Korn/Ferry Lominger after extensive research, include:

1. Mental Agility

Mental agility, despite common misinterpretation, doesn’t mean intelligence and book smarts but is closer in meaning to street smarts. Those with mental agility are curious and work quickly to identify the salient practicalities in new information and work processes. These people cut through extraneous information, quickly find the most relevant insights and use that data to improve business practices and perform their duties at the highest level.

2. People Agility

People agility consists of people skills and leadership qualities. Those with people agility connect with others on an emotional level and display true empathy. Others look to them when changes occur or a crisis develops. These individuals work through conflict, value diversity and obtain actionable insights from different perspectives.

3. Change Agility

People with change agility thrive on new challenges and first-time endeavours. Unlike people who prefer highly structured and predictable work situations, these workers prefer to challenge the status quo, try new methods and improve business operations and/or their work environments. These individuals aren’t afraid to fail because they learn something even when they don’t succeed the first time.

4. Results Agility

Results agility is similar to change agility, but those with strong results agility strive to succeed the first time. These people consistently deliver the best results when undertaking new challenges.

5. Self-Awareness

Self-awareness is a critical element of career success. People who know their own strengths and weaknesses perform better on average than those with any of the other skills. A Cornell study found that accurate self-awareness was the greatest factor in predicting leadership qualities and career success.

Smart managers and HR staff encourage development in each area while using relative skills and weaknesses to allocate resources more effectively. For example, those with strong people skills might become managers, front-office staff or salespeople. Those with self-awareness know their limitations and try to get projects within their respective wheelhouses. Results-oriented performers show others how to succeed in new projects, and those with change agility find new and better ways to accomplish their duties.

Why Learning Agility Is Important?

Today’s complex and competitive business environment has generated many changes in standard operating procedures including more flexible and agile responses to change. Leaders can’t rely on traditional practices where employees follow well-established and inflexible rules. Savvy leaders respond proactively to change and industry trends, and they do so by inspiring and cultivating learning agility in their teams. Fostering a learning ecosystem gives employees the tools to access educational resources, advance their careers and establish themselves favourably in their industries and among their peer-to-peer contacts.

Learning Agility’s Relevance in Developing Leadership Qualities

Leadership qualities often occur naturally in certain individuals, but these qualities can be developed by cultivating learning agility. Today’s rapidly evolving business trends require new skills and a willingness to adapt. Developing agility helps to foster the right leadership qualities that statistics show are increasingly essential. Research conducted by Forrester and the Business Marketing Association found the following relevant insights:

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  • Leadership qualities are essential because 88 percent of workers turn to their peers for data and insights.
  • About 34 percent of people feel overwhelmed by change, so learning agility becomes increasingly critical.
  • 60 percent of HR departments actively recruit younger people because they are more comfortable with digital technologies and innovations.
  • 21 percent of workers feel that their skills have already become obsolete.

Predictors of Learning Agility

There are many benchmarks for predicting learning agility. The best forecasters are based on core competencies, which is why it’s critical to analyse work performance to identify strengths and weaknesses. Managers can strengthen the analyses by monitoring performance carefully, challenging teams to develop new skills and encouraging team members to identify and reflect on their strengths and weaknesses. Some of the best predictors of people who have learning agility include:

  • Deals with ambiguity
  • Shows strong problem-solving skills
  • Learns on the fly
  • Manages conflict well
  • Listens actively
  • Demonstrates perspective
  • Sizes up people quickly
  • Deals well with paradoxes
  • Works well alone
  • Pursues personal learning
  • Demonstrates patience
  • Grasps process management imperatives
  • Thinks creatively
  • Understands others through strong empathy
  • Retains composure in difficult situations
  • Motivates others
  • Organises effectively
  • Shows accurate self-knowledge
  • Has command skills and leadership qualities
  • Recognises political issues and deals with them perceptively

How to Cultivate Learning Agility

HR staff and managers can cultivate learning agility by creating supportive environments for taking risks and learning new skills. Creating a conducive environment for learning involves designing workspaces that are free-flowing instead of trapping people in small cubicles. The best learning environments go beyond the physical to include the entire ecosystem of the organisation. That includes workspace design, managerial and executive support for innovation and building a learning culture. Identify, recognise and encourage people to develop leadership qualities.

It’s critical to give people enough space to experiment through trial-and-error. Encourage workers to take educated risks without delivering recriminations for failure. It’s important to develop a mindset of openness and receptivity to new ideas and different ways of doing things. Managers can reward workers based on how quickly they learn new skills. Motivating people to learn is a direct benefit of developing managers who have strong leadership qualities. Best practices for fostering workplace agility include:

  • Discussing inspirational stories and case studies of creativity and innovation
  • Initiating group discussions where people build pro-and-con arguments for specific initiatives
  • Solving problems with a logical and systematic approach
  • Carefully considering each team member’s viewpoint, beliefs and values
  • Incorporating different perspectives in action plans
  • Evaluating arguments critically to encourage rational thinking
  • Offering developmental, educational and microlearning resources
  • Promoting people who quickly absorb new skills
  • Using rewards, recognition and gamification to encourage developing leadership qualities
  • Providing mentors, coaches and peer-to-peer assistance for learning new skills

Learning agility is perhaps the single most critical benchmark that determines business success, employee potential and leadership qualities. HR managers face enormous challenges for succession planning, retaining top talent and troubleshooting change management. Leaders must constantly adapt to succeed and remain competitive. Fostering greater learning agility provides the critical blueprints for developing talent in-house and fostering staff loyalty and career satisfaction.

Should Dash be a Flask Extension? #38

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mylbp2ps3 commented Jun 23, 2020

Extensions such as flask_sqlalchemy or whatever?

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hclent commented Jun 28, 2020

Somewhat related, is it possible to use Dash from inside my Flask app?

I’ve been wanting to use the Python dendrogram+heatmap visualization in my Flask app for a long time. But sadly there is no Javascript dendrogram+heatmap, so I’ve had no choice but to use something else :'(

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mylbp2ps3 commented Jun 28, 2020

Yeah I’ve been thinking about learning a little bit of Javascript to use one of it’s libraries for making graphs. But Dash sounds like it would work fantastic with what I want to do, if it was more like a Flask extension.

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chriddyp commented Jun 28, 2020

I’ve been wanting to use the Python dendrogram+heatmap visualization in my Flask app for a long time.

For this example, you can render these dendrograms as a Graph object since they use the same underlying graphing library:

All of the plotly.figure_factory functions return a figure object that can be used directly as the figure attribute in the dcc.Graph

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chriddyp commented Jun 28, 2020

Could folks explain a little bit more why they would like Dash as a flask extension? Some leading questions:

  • Is it just to use Dash in apps that they have already written?
  • Does embedding a Dash app as an in an existing app work instead?
  • How do you want to embed Dash in your existing application? As a separate page or as a Dash app as part of a particular page?
  • You can pass in your own server instance into the Dash constructor. In which ways would making Dash a flask extension improve the functionality over passing in your own Flask instance? Here’s how it currently works:

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mylbp2ps3 commented Jun 28, 2020

It just seems a bit too limiting to have Dash stand on its own. It would create more consistent to make it an extension.
Embedding it in an just sounds needlessly complicated.
I’d want to embed a Dash app in an existing page, a template for Flask, and I’d like to be able to just pass the graph into Flask’s render_template function.
Passing the flask instance into Dash, doesn’t give Dash the same functionality as Flask, it just leaves me with two different objects to render a page with. Although I haven’t seen any real examples on what passing the Flask instance into Dash and using them both together would look like.

Forgive me if any of my points are wrong, this is the way I understand Dash to be working from reading a bit into it and experimenting with it a bit.

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hclent commented Jun 28, 2020 •

@chriddyp Firstly, thank you for your reply to my post! I’m very excited to try this out! My remaining concern is, I’m not sure how I will pass data from my Flask app to the Dash app? (More on that below).

Secondly to answer your questions,

  1. Yes, I would like Dash as a Flask extension so I can use Dash in an app I’ve already written. (For the purpose of getting that dendrogram+heatmap!!)
  2. Perhaps, embedding a Dash app as an is doable in theory, but I think this would really only work if you’re not providing buttons/options for a user to update or change the vis. I’m not sure how I could get that Dash app iframe to “talk” to my Flask app, when there’s a request to update a visualization per user specifications. (Currently I use Jinja to pass data from my Flask app into the Plotly Javascript vis). I’m not sure what a solution is here, that could be robust enough for a Flask app in production.
  3. Right now my Plotly Javascript visualizations live in s, and I pass data with Jinja to update the visualizations on demand. So I would want to do the same thing, but again. how to pass the data?
  4. I’m interested to try how it currently works. If I would need to change a lot of things about my current Flask app to get Dash to work, it wouldn’t be worth it (since I’ve already built the Flask app). I will give it a try and report back!

Edit – Silly me! I don’t use Django. I use Jinja!

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havok2063 commented Jun 29, 2020

I would second most of what has been said here. I think it would be great to have Dash integrable into existing Flask apps. My biggest problem with using things like Plotly or Bokeh was that it always seemed like they had to be used as standalone products.

I would like to able to integrate Dash into Flask apps I’ve already written and are quite developed. I want to be able to use the Plotly and Dash functionality without relying on its internal system. I like Dash instead of standard Plotly because it makes building callbacks and interactivity into the visualizations quite easy.

iframes no longer follow best web practices for modern development. AJAX was designed to replace iframes and iframes makes debugging quite difficult. I don’t think iframes fits into the MVC framework of Flask apps either.

Primarily I’d like to add it into existing pages. I have existing pages that I don’t want to completely convert. I’d like to drop in a new Dash app that is a mini-vis or two. For new pages, I would prefer to build them with Jinja2 templating for consistency, and standardization, and just push any Dash apps through the render_template Flask function. I can see either pushing data through to the front-end, pushing just the dcc.Graphs into an already built div, or building the complete dash app on the backend and pushing the final html div to the front.

What does pushing the server instance into Dash actually do? I couldn’t find any documentation that explains that functionality. This is the only help I can find. https://plot.ly/dash/

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fcollman commented Nov 6, 2020

I think what I’m saying is covered by the more general case statements that others have made in this issue, but sometimes simpler more concrete examples make the point more strongly.

I have written a number of flask apps that have complex navigation and information display to get to help the user navigate to a particular dataset. I would like to have a dash app that lets me display that particular dataset to the user after they navigate to it, and give the user a fixed URL to get that dataset back at a later date, or email as a link. In such a case I need the flask endpoint to be able to convey information to the dash app in order to load the appropriate dataset and generate the interactive visualization. I could redo the whole dataset navigation in dash, but it seems overly complex in many instances as the layout’s for selection procedures often vary dramatically from level to level, and the dataset selection parameters wouldn’t be stored in the URL. Having different flask endpoints point to dash apps would then let you easily parameterize dash visualizations. I understand that ‘snapshots’ is an enterprise feature now for capturing the state of the visualization, but I’m talking about parameters that naturally live outside the UI visualization loop.

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yk-tanigawa commented Feb 12, 2020

I would also love to have the dash-app as a flask extension so that we can pass dataset (path to the data file on the server) as an argument to Flask’s render_template to generate interactive plots. I am thinking to create separate pages without using iframe . I am thinking bioinformatics/genomics as an application area, where we have roughly 20,000 datasets for different genes. Although it is possible to have input box (like drop-down) to select a particular dataset, it would be great if we can assign a fixed URL to an interactive plot for a particular dataset.

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sladkovm commented Feb 12, 2020

@yk-tanigawa flask extension would be awesome, but until we’ve gotten there here is how I implement the functionality you describe:

  1. Add location element to the app layout
  1. Use the value of the location element (referenced by ) in the callback
  1. The router function does. well. routing, e.g. based on the value of location it renders the page you want – it will get static url with unique id.
  1. To navigate to these urls use:

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Aso1977 commented Feb 25, 2020 •

Dear @sladkovm
I am trying to embed a dash plot into a route in flask using your above code. I linked the two as:

I want to generate the dash layout based on variables defined as flask config parameter, e.g. app.Config[‘plot_type’] in the dash app:

And I use the following callback in the flask app:

I have a template ‘plot.html’ with jinja2 div of ‘plot’, which I want to be replaced by the dash graph > I don’t know how to embed the fig. If I replace ‘plot’ container in the template with the generated dash layout, it doesn’t get rendered.
It would be great if you point me to the right direction. Thanks, Aso

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sladkovm commented Feb 26, 2020

I wrote you a very long answer, but than I’ve noticed that you might have a different problem.

I have a template ‘plot.html’ with jinja2 div of ‘plot’, which I want to be replaced by the dash graph > I don’t know how to embed the fig. If I replace ‘plot’ container in the template with the generated dash layout, it doesn’t get rendered.

For your particular case, apart from the fact that you are populating the non-existent children property of the dcc.Graph (see below), the solution must be in defining dcc.Location element outside of the template scope. but I’ve never tried it and, in general, I’m not sure how jinja templates should be working with Dash. I would guess these two approaches are fundamentally incompatible.

I would not be able to answer all your questions since some of them (jinja2 etc.) are something I’ve never worked with, but let me guide you through the thinking process.

  1. You define in the app.layout the element, which you want to populate dynamically based on the value of the location. In your case, this element is dcc.Graph and you refer to it by . Location you read from the element dcc.Location and you refer to this element by it’s

In your code this step is:

  1. The actual logic behind dynamically populating the dcc.Graph(id=’fig’) is handled by the callback:

What it says in plain english is something like this: “find the element with , read it’s property-value ‘pathname’ and pass it to the element with to the property . in your case it is ‘children’, but. the dcc.Graph element does not have property ‘children’. What it has instead is a property ‘figure’.

Your code should look like this:

  1. What should the router() function return? Easy – the figure object. Example:

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