Overview
What is Plotly Dash?
Plotly headquartered in Montreal creates data visualization and UI tools for ML, data science, engineering, and the sciences with language support for Python, R, Julia, and JS. Plotly's Dash aims to empower teams to build data science and ML apps…
Plotly Dash is an Excellent Platform for Machine Learning
Awesome framework in the dashboarding area
Quick visualization of big data.
Build fully customizable dashboards without web development expertise
Pricing
What is Plotly Dash?
Plotly headquartered in Montreal creates data visualization and UI tools for ML, data science, engineering, and the sciences with language support for Python, R, Julia, and JS. Plotly's Dash aims to empower teams to build data science and ML apps that put Python, R, and Julia in the hands of…
Entry-level set up fee?
- No setup fee
Offerings
- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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Product Details
- About
- Tech Details
What is Plotly Dash?
Dash is available open source, on the Dash Enterprise Cloud ($50,000 per year), or via Dash Enterprise On-Premises.
Plotly Dash Video
Plotly Dash Technical Details
Operating Systems | Unspecified |
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Mobile Application | No |
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Reviews and Ratings
(6)Community Insights
- Business Problems Solved
- Pros
- Cons
- Recommendations
Dash, a powerful data visualization and analytics tool, has been widely used across various industries to solve a range of business problems. Users have found Dash to be particularly useful in analyzing volunteer applications, detecting emergencies, and improving services based on real-time analytics. With its interactive and updatable graphs, Dash enables users to visualize data and make informed decisions. It has also been widely employed for web dashboards, quality control, and business intelligence analytics, saving both time and money in front-end design and software engineering. Furthermore, Dash facilitates the creation of interactive applications for accessing data in a data warehouse and developing new data products quickly.
In addition to these applications, Dash has proven valuable for displaying a dashboard of customer data with reactive callbacks, gathering and transforming data for solving business problems, exploring statistical properties of ECG signals from patients with cardiac arrhythmias, as well as prototyping analytics applications and controlling them with R. Moreover, Dash's capabilities extend to rapid development, slicing and dicing data, visualizing large datasets in the electric power industry, streamlining data analysis workflows, distributing analysis tools across businesses, creating beautiful and interactive web pages easily, and generating comprehensive business reports.
Users appreciate the ease of use that Dash offers and how it enhances their lives. They find it to be an excellent alternative to other libraries due to its greater functionality and user-friendly interface. Plotly Dash, which is built on top of Dash, adds customizable features for real-time interactive plotting. This makes it ideal for online data analysis tools, business intelligence reports, and creating web applications for intranet and outward-facing websites. Overall, Dash's versatility and effectiveness in diverse use cases make it a go-to tool for professionals in various industries seeking reliable data visualization solutions.
Powerful and Intuitive Framework: Users consistently praise Plotly Dash as a powerful and intuitive framework for creating front-end web dashboards in Python. Many reviewers find it easy to pick up and create complex visualizations, thanks to the use of Flask as the back-end and React as the front-end. The versatility of Plotly Dash is highlighted by users who have used it for creating dashboards, data cleaning apps, and even entire products.
Outstanding Documentation: Reviewers appreciate the outstanding quality of Plotly Dash's documentation, which provides many examples to learn from. This comprehensive resource has proven invaluable in assisting users with their interactive and customizable visualization needs.
No Need for JavaScript or Web Development Languages: Users value the ability to develop and deploy web analytics and data science applications using Plotly Dash without having to know JavaScript, CSS, or HTML. This feature is particularly well-received among Python developers focused on data analysis since it allows them to solely rely on their Python expertise when building web apps and user interfaces.
Difficult Learning Curve: Some users have mentioned that Plotly Dash can be challenging to learn, which may make it difficult to convince team members to work on projects with it.
Sparse Documentation: Several users expressed frustration with the documentation for Plotly Dash, stating that it is sparse, unorganized, and lacking in-depth explanations for certain features. They feel that more complete documentation and examples would greatly improve their experience with the software.
Limited Customization Options: Some users have mentioned that the styling of certain components in Plotly Dash is basic and modifying it through CSS can be difficult. They express disappointment with the limited customization options for table experiments in Dash and suggest that having a GUI builder like PsychoPy could make the tool more accessible to users without deep Python skills.
Users have provided the following recommendations based on their experiences with Dash:
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Learn React and Flask to customize and enhance Dash. Users suggest familiarizing oneself with React and Flask in order to fully leverage the customization options and add new features to Dash. This combination of technologies allows users to extend the functionality of Dash beyond its out-of-the-box capabilities.
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Take it slow and utilize tutorials and online help. Users advise taking the time to explore Dash's tutorials and seek assistance through online resources. By carefully going through the provided learning materials, users can gain a comprehensive understanding of Dash's functionality and maximize its potential.
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Create prototypes and refer to Dash's documentation. Users recommend creating prototypes while using Dash and find that referring to its documentation can be quite helpful. They consider the documentation to be well-written, providing valuable insights into how to effectively use the framework. This approach ensures a solid foundation for building interactive web applications.
In general, users find Dash easy to use for individuals of all ages. While they acknowledge that there may be a steep learning curve compared to other data analytics tools, they appreciate the freedom it offers for data analysis and creating dashboards. Additionally, they emphasize exploring available support and resources to further enhance their implementation of Dash.
Reviews
(1-4 of 4)- Data visualisation
- Low code dev
- Based on python so easy to implement
- Would be good if Dashboard Engine was included in the Enterprise VPC plan
- Would love to see ready made fintech apps
- Open Source
- Generous allowance for 5 devs and unlimited business users
- Perfect building block that brings our machine learning applications to life for the business
- Connect to Multiple Data Sources
- 90%9.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 80%8.0
- MDM Integration
- 90%9.0
- Visualization
- 100%10.0
- Interactive Data Analysis
- 100%10.0
- Interactive Data Cleaning and Enrichment
- 10%1.0
- Data Transformations
- 100%10.0
- Data Encryption
- 10%1.0
- Built-in Processors
- 80%8.0
- Multiple Model Development Languages and Tools
- 100%10.0
- Automated Machine Learning
- N/AN/A
- Single platform for multiple model development
- 100%10.0
- Self-Service Model Delivery
- 100%10.0
- Flexible Model Publishing Options
- 90%9.0
- Security, Governance, and Cost Controls
- 100%10.0
- Reduce product monetization lead time
- Real time performance monitoring
- Deep learning to allow better marketing segmentation models
Awesome framework in the dashboarding area
- Intuitive interface
- Detialed user guide
- Can still get by with knowledge of only python
- A bit of a learning curve
- While the interface in intuitive, it takes getting used to at first
- Difficult to create more complex application layouts
- Major cloud provider support
- Can be supported on any server that supports Flask apps
- The deployment server comes with it
- Connect to Multiple Data Sources
- 90%9.0
- Extend Existing Data Sources
- 90%9.0
- Automatic Data Format Detection
- 100%10.0
- MDM Integration
- 100%10.0
- Visualization
- 70%7.0
- Interactive Data Analysis
- 70%7.0
- Interactive Data Cleaning and Enrichment
- 80%8.0
- Data Transformations
- 70%7.0
- Data Encryption
- 70%7.0
- Built-in Processors
- 80%8.0
- Multiple Model Development Languages and Tools
- 80%8.0
- Automated Machine Learning
- 70%7.0
- Single platform for multiple model development
- 80%8.0
- Self-Service Model Delivery
- 70%7.0
- Flexible Model Publishing Options
- 100%10.0
- Security, Governance, and Cost Controls
- 100%10.0
- Graphs always help!
- The integration capabilities speed things up
- The robust API gives a lot of flexibility
Quick visualization of big data.
- Simple codes to create quick graphs.
- Nice exportable figures.
- Good for the initial exploratory analysis of the data.
- Difficult to set up.
- Steep learning curve.
- lot of codes to write.
- Options for advanced data analytics.
- It is customizable but I have not tried that feature.
- Nice features.
- Connect to Multiple Data Sources
- N/AN/A
- Extend Existing Data Sources
- N/AN/A
- Automatic Data Format Detection
- N/AN/A
- MDM Integration
- N/AN/A
- Visualization
- 90%9.0
- Interactive Data Analysis
- 90%9.0
- Interactive Data Cleaning and Enrichment
- N/AN/A
- Data Transformations
- N/AN/A
- Data Encryption
- N/AN/A
- Built-in Processors
- N/AN/A
- Multiple Model Development Languages and Tools
- N/AN/A
- Automated Machine Learning
- N/AN/A
- Single platform for multiple model development
- N/AN/A
- Self-Service Model Delivery
- N/AN/A
- Flexible Model Publishing Options
- N/AN/A
- Security, Governance, and Cost Controls
- N/AN/A
- N/A
- Powerful visualization options.
- Ability to create in-browser interactive visualization apps.
- Ability to create hosted apps.
- Allows you to develop web-based reporting applications without requiring web application development expertise.
- React JSX syntax support can be added/improved.
- Built-in UI components can be improved.
- The API used for AJAX calls can be made more understandable and simpler.
- Requires only Python expertise.
- Ease of maintenance.
- App-based API.
- Connect to Multiple Data Sources
- 70%7.0
- Extend Existing Data Sources
- 100%10.0
- Automatic Data Format Detection
- 70%7.0
- MDM Integration
- N/AN/A
- Visualization
- 100%10.0
- Interactive Data Analysis
- 100%10.0
- A no-cost option as it is open sourced.