Python dashboard flask

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Python dashboard flask

Hello coder, This article presents a simple admin dashboard generated by the AppSeed platform in Flask Framework. The product is published on Github under the MIT license and can be used for hobby or commercial products.

The app provides a simple set of features, out of the box and might be a good foundation for your next project. App features:. A basic introduction of terms for new commers. In case you are familiar with the information presented in this section, please jump to the next one. Flask is a lightweight WSGI web application framework.

It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja and has become one of the most popular Python web application frameworks. A dashboard is a set of pages that are easy to read and offer information to the user in real-time regarding his business. A dashboard usually consists of graphical representations of the current status and trends within an organization.

Having a well-designed dashboard will give you the possibility to act and make informed decisions based on the data that your business provides - definition provided by Creative-Tim - Free Dashboard Templates. Black Dashboard is a beautiful Bootstrap4 Admin Dashboard with a huge number of components built to fit together and look amazing.

If you are looking for a tool to manage and visualize data about your business, this dashboard is the thing for you. It combines colors that are easy on the eye, spacious cards, beautiful typography, and graphics. As mentioned, the product is built on top of Flaska popular Python Web Framework. To build the app, Python3 should be installed properly in the workstation. If you are not sure if Python is properly installed, please open a terminal and type python --version.

The full-list with dependencies and tools required to build the app:. If you are a little bit unsure how to set up the environment, might be a good idea to spare a few minutes and read the full documentation provided for this app: Flask Dashboard Black Docs. The code-base generated by the AppSeed platform respects the best practices and tries to keep things as simple as possible. Like all other apps provided by the platform, Flask Dashboard Black comes only with a basic set of features, easy to extend by the anyone that has a basic programming knowledge.

The first step is to pull the source code in our workstation. This can be done by download the ZIP archive of the code or using GIT to clone the project locally the recommended way.

Flask python tutorial flask-admin roles and permission

If GIT is properly installed in our system, the dashboard source code is now available in the flask-black-dashboard directory. Once we have the source code, the next step is to install the modules aka dependencies.

Usually, an application requires a set of modules used to implement the dashboard and our app is not an exception. If all goes well, the dependencies listed in the requirements.

The next step is to update the environment where the app runs with a few variables used by Flask Framework. The last stet, is to start the app. This can be done with one single command, using the server provided by the Flask Framework:. By default, the app starts on port In case this port is already in use by another process, just execute the flask command with another value as the argument for the port variable:.Storing the Data in Python list:.

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Exporting the data from the Pandas Dataframe:. Your email address will not be published. A nalyzing your sensor data has always been a daunting task and putting your data in the Dashboard has never been an easy task.

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Here are some of the items that have been used to build the Dashboard. The first step would be create the route for the page load So I would be interested to show the Table containing the first record from the mongo query and two charts which will show the trend of the No. Querying the MongoDB:. We stored all the data in a Python list for each items to be displayed on the Datable i. Converting the data into Pandas Dataframe:. All the list created above is now created and stored in Pandas Dataframe.

ScanStartTimestamp", -1 ]. Template will look like this:. Showing Google Map:. You have to return the lat, long and custom text to be displayed on the map to the template engine:. Map document.

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Exporting the data from the Pandas Dataframe: So Once the Dataframe is ready from the lists then you can use the below piece of code to export the dataframe as csv.

So you have seen how easy it is to create an Analytical Dashboard within a weekend. To improve the look and feel you can add some css and fancy JS and HTML but that was not something I was looking for at this point in time, So I have created a bare minimum dashboard running on Python flask which serves our current need.

Also the Python Pandas helped a lot to play with the data and display it in the required format. Facebook 0 Tweet 0 Pin 0 LinkedIn 0. Leave a Reply Cancel reply Your email address will not be published.Released: Mar 18, View statistics for this project via Libraries.

Author: Patrick Vogel, Bogdan Petre. The Flask Monitoring Dashboard is an extension for Flask applications that offers four main functionalities with little effort from the Flask developer:.

The dashboard is automatically added to your existing Flask application. You can view the results by default using the default endpoint this can be configured to another route :. For more advanced documentation, take a look at the information on this site. To view a live deployment of the Flask-MonitoringDashboard, check this site.

python dashboard flask

In order to improve our Flask-MonitoringDashboard, we would like to hear from you! Therefore, we made a questionnaire with a few questions.

Filling in this form takes less than 3 minutes. You can find the form here. Alternatively, feel free to write to our email-address. For more advanced documentation, see this site If you run into trouble migrating from version 1. X to version 2. The migration from 2. All notable changes to this project will be documented in this file. This project adheres to Semantic Versioning. Please note that the changes before version 1. Mar 18, Feb 28, Nov 29, Oct 1, Sep 26, Sep 25, Apr 1, I spent a good portion of learning JavaScript to create interactive, web-based dashboards for a work project.

I wrapped D3.

python dashboard flask

I missed R. I missed Python. When I found Dash a couple of months ago, I was blown away.

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With Dash, we can create interactive, web-based dashboards with pure Python. All the front-end work, all that dreaded JavaScript, that's not our problem anymore. How easy is Dash to use? Dash is a powerful library that simplifies the development of data-driven applications.

In this post we will explore Dash, discuss how the Model-View-Controller pattern can be used to structure Dash applications, and build a dashboard to display historical soccer results. Dash is a Open Source Python library for creating reactive, Web-based applications. What does this mean?

We can run a Flask app to create a web page with a dashboard. Interaction in the browser can call code to re-render certain parts of our page. We use the provided Python interface to design our application layout and to enable interaction between components. User interaction triggers Python functions; these functions can perform any action before returning a result back to the specified component. We are also able to plug into React's extensive ecosystem through an included toolset that packages React components into Dash-useable components.

As I worked my way through the documentationI kept noticing that every Dash application could be divided into the following components:.

Note: I covered MVC in a previous post. I created the following template to help us get started:. In this section, we will create a full-featured Dash application that can be used to view historical soccer data.

There are installation instructions in the Dash Documentation. Alternatively, we can create a virtualenv and pip install the requirements file. Our app will look as follows:. Users will be able to select Division, Season, and Team via Dropdown components. Once we create our layout, we will need to map out the interaction between the various components.

We do this using the provided app. Open a web browser Dash is a Python library that simplifies data-driven web app development. It combines Python's powerful data ecosystem with one of JavaScript's most popular front-end libraries React. In a future post, I will walk through the process of converting a React component from npm into a Dash-useable component. Stay tuned. Toggle navigation. About Talks Archives. Summary Explore Plotly's new Dash library Discuss how to structure Dash apps using MVC Build interactive dashboard to display historical soccer results I spent a good portion of learning JavaScript to create interactive, web-based dashboards for a work project.

Dash applications are written in Python.This is Part Two of a three-part tutorial to build an employee management web app, named Project Dream Team. We created models, migrated the database, and worked on the home and auth blueprints and templates.

By the end of Part One, we had a working app that had a homepage, registration page, login page, and dashboard. We could register a new user, login, and logout.

python dashboard flask

We'll start by creating an admin user through the command line. Flask provides a handy command, flask shellthat allows us to use an interactive Python shell for use with Flask apps. We've just created a user with a username, adminand a password, admin Now that we have an admin user, we need to add a view for an admin dashboard.

We also need to ensure that once the admin user logs in, they are redirected to the admin dashboard and not the one for non-admin users. We will do this in the home blueprint. Next we'll create the admin dashboard template.

If they are, we display the admin menu which will allow them to navigate to the Departments, Roles and Employees pages. Notice that we use for the links in the admin menu. We will update this after we have created the respective views. Now run the app and login as the admin user that we just created. You should see the admin dashboard:. Log out and then log in as a regular user. You should get a Forbidden error. It looks pretty boring now, but don't worry, we'll create custom error pages in Part Three!

Now we'll start working on the admin blueprint, which has the bulk of the functionality in the application. We'll begin by building out CRUD functionality for the departments. The form is pretty simple and has only two fields, name and departmentboth of which are required. Note that we will use the same form for adding and editing departments.

We will call this function in every admin view. If the department name already exists, an error message is displayed. This means that once the admin user creates a new department, they will be redirected to the Departments page. This is the department ID, and will be passed to the view in the template. The view queries the database for a department with the ID specified. If the department doesn't exist, a Not Found error is thrown.

If it does, it is updated with the form data. If it does, it is deleted from the database. Note that we render the same template for adding and editing individual departments: department. We'll use this variable in the department. Inside it, add the departments.Last week I had 3 days to come up with a visualization dashboard. My backend choice was flask we are inseparable however I had to choose the easiest plotting package.

The choices that I had was between plotlydash and bokeh. Looking at the bokeh documentation, I found that it was straight forward. However, one of the cons that I had found was: working with categorical data. While all the other packages work seamlessly with categorical data, one has to do some hacks when it comes to bokeh.

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This made me to look for an alternative. The others were plotly or its dashboard Dash. Now it may seem obvious that one would choose Dash, however I like being in control of the front-end I am not afraid of going head to head with css, js and html.

python dashboard flask

That is why I decided to go with plotly. The structure of the application will be as shown below. The static folder will be used to store all the css, js and image files. If you are using any bootstrap template, this is where you will store the files.

All the html files will be in the template folder. We will then create a html named index. The file will first have a basic code:. We will then change the python file to render the html file. Notice that we had to add render template in the imports. We will the plot a bar graph as shown in the plotly.

We will create a function that will return the plot.

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Now this where I fell in love with this package.Thank you for landing on this page. The article presents a curated list with admin dashboardscoded in Flask Web Framework and released under a permissive license by the publishers. Flask source code is a Python web framework built with a small core and modularity in mind. With a small footprint, well documented and supported by a growing community, Flask can be a good choice to implement on top a nice production-ready Admin Dashboard.

In order to use the boilerplates, we need Python and Flask installed on the workstation. The Python can be downloaded from the official website and Flask can be easily added using PIP command:.

Dashboard features:. If all goes well, this beautiful Flask Dashboard should be visible in your browser. Flask Dashboard Star Admin is crafted on top of Bootstrap and released as an open-source web application. Please visit your browser on port In order to build the dashboard, please access the official Flask Dashboard page and follow the instructions. Thank you! Flask Dashboard Light is crafted on top of Bootstrap and released as an open-source web application.

For more information, please access the official product page: Flask Dashboard Light. Flask Dashboard Argon is built on top of a fully responsive, pixel-perfect design, crafted by Creative-Tim. Does your project require authentication? No worries, Flask Argon Dashboard includes authentication by default. Hello Coder, Thank you for landing on this page.

Data Visualization with Bokeh in Python, Part III: Making a Complete Dashboard

In a rush? All flask dashboards listed in this article can be found on this page, provided by the AppSeed service: Open Source Admin Dashboard s All projects are generated by the AppSeed service using a semi-automated process: Flat HTML themes bootstrap based are parsed and transformed into production-ready Jinja2 templates the native Flask template engine using an HTML Parser The processed design is injected into an existing boilerplate enhanced with SQLite database, SqlAlchemy helpers and basic tooling.

Setup the environment In order to use the boilerplates, we need Python and Flask installed on the workstation. For more information, please access the official product page: Flask Dashboard Light Flask Dashboard Argon Flask Dashboard Argon is built on top of a fully responsive, pixel-perfect design, crafted by Creative-Tim. Follow us on Twitter and Facebook Thank you!


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