Table of contents
Interactivity
Plotly offers highly interactive plots that allow users to pan, zoom, and hover over data points for more information. This enhances the user experience and provides deeper insights.
High-quality visualizations
It provides aesthetically pleasing and highly customizable charts, making it suitable for publication-quality visuals.
Versatility
Plotly supports multiple chart types including line charts, scatter plots, bar charts, and 3D plots, making it suitable for a wide range of applications.
Python integration
Plotly is well-integrated with Python and works seamlessly with other popular data science libraries like Pandas, NumPy, and Scikit-learn.
Web-based
The plots can be easily embedded in web applications or dashboards, making it ideal for sharing insights over the internet.
Open-source
Plotly offers an open-source version, which allows users to create and share visualizations without any cost.
Overall, Plotly is a strong choice for those looking to create dynamic and interactive data visualizations, thanks to its range of features and ease of integration with web technologies.
We have collected here some useful links to help you find out if Plotly is good.
Check the traffic stats of Plotly on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Plotly on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Plotly's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Plotly on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about Plotly on Reddit. This can help you find out how popualr the product is and what people think about it.
Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 4 months ago
Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago
Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago
In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / over 1 year ago
How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago
For dashboards: - https://plotly.com/ is probably my favourite, but there are others like streamlit, voila and others... Source: over 2 years ago
If your CEO wants you to solo build an alternative to Tableau, PowerBi, or even Plotly then consider him/her delusional. Source: about 3 years ago
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 3 years ago
I use plotly and like it a lot. It is slower though. Noticeable if you want to batch-generate a bunch of images and dump them into a folder. But that probably isn't the case most times. Source: over 3 years ago
Plotly Dash is a great framework for developing interactive data dashboards using Python, R, and Javascript. It works alongside Plotly to bring your beautiful visualizations to the masses. - Source: dev.to / almost 4 years ago
Data helps organizations make better decisions. With a programming language like Python to analyze your data and transform data into visual representations, you can effortlessly tell the story of your business. One way to create customized visuals from your data would be to use data visualization libraries in Python like Matplotlib, Seaborn, Ggplot2, Plotly, or Pandas. When you want to accomplish this task with... - Source: dev.to / about 4 years ago
If you are using template engine, you can use Plotly on the backend to generate a chart, convert it to an HTML div to plugged into the rendered template. Then introduce plotly.js to allow the browser to render the generated chart on the frontend. See an example. Source: about 4 years ago
Consider Plotly, it's built on top of d3js and provides a nice abstraction layer that you can use from JavaScript, Python, R, etc. Source: about 4 years ago
To plot some sensor data I use Plotly or Matplotlib. Source: about 4 years ago
Wouldn't plotly and nbviewer work for your use case? Source: over 4 years ago
Python package manager (pip) consists of over 300,000 packages. You can use libraries like โโSciPy, which contains various modules for optimization, linear algebra, integration, and statistics, or Plotly, which you can use for scientific-quality graphing. The scope of problems that Python libraries address is vast. Rarely will you struggle to find the right library that will help you solve the problem. - Source: dev.to / over 4 years ago
Check out Plotly. It's very easy to make interactive plots. Source: over 4 years ago
Now, it's time to populate these plots with data. Luckily, these plots are not specific to Anvil, but are good old Plotly graphs. So, we can simply use the go functions from the plotly.graph_objects module. - Source: dev.to / over 4 years ago
A while ago, I built a data-intensive application that collects, analyzes, and visualizes data using Django, Plotly, and Django templating language. However, an upgrade was recently requested which made me tend to re-develop the application from the ground up. A pivotal aspect of the app is Authentication and Authorization system since the data are confidential and only authorized personnel should be allowed... - Source: dev.to / over 4 years ago
Plotly The Plotly library is an online platform for data visualization and it can be used in making interactive plots that are not possible using other Python libraries. Few such plots include dendrograms, contour plots, and 3D charts. Other than these graphics, some basic visualization graphs such as area charts, bar charts, box plots, histograms, polar charts, and bubble charts can also be created using the... - Source: dev.to / over 4 years ago
Plotly | Full-time | REMOTE, (CA or US) | https://plotly.com/ Looking for a senior backend engineer. We're building Dash Enterprise, a web app that lets data scientists quickly and easily spin up and scale data science and ML apps. We do this using Kubernetes. Cool, right? * You won't work overtime. - Source: Hacker News / over 4 years ago
Plotly, an advanced data visualization library and platform, has garnered significant attention in the software industry as a versatile tool for developing interactive analytics and web-based visualizations. Built on top of the Plotly JavaScript library (plotly.js), it supports usage in Python, R, and Julia, making it an attractive option for data scientists and developers looking to create interactive plotting applications without deep expertise in DevOps or JavaScript.
Diverse Application and Features
Plotly stands out due to its ability to render a broad spectrum of chart types. With support for dendrograms, 3D charts, and contour plotsโvisuals not commonly found in other librariesโPlotly serves the needs of specialized data representation. The library's extensive API covers simpler visualizations like scatter plots, line charts, and bar charts, up to more complex layouts like pie charts and error bars. This range makes Plotly particularly appealing for creating dynamic dashboards in environments like Python Notebooks and Dash applications.
Integration and Extensibility
Its ability to integrate with platforms like Jupyter Notebooks, Dash, and even React applications highlights Plotly's flexibility. It complements backend systems by generating charts that can be rendered in web templates, ensuring smooth integration in web applications. Moreover, Plotly's use of JSON for graph serialization enhances its adaptability, as the visualizations can easily be imported into different software environments like R and MATLAB.
Popularity in Data Science and Analytics
In data science, Plotly has been praised for its interactive visualization capabilities. These features enable users to create responsive visual content that supports chart interaction through hovers, clicks, and zooms, significantly boosting the user experience in data exploration and storytelling. Among Python's visualization toolkit, Plotly is frequently mentioned alongside Matplotlib and Seaborn for its capability to generate insightful visualizations efficiently.
Challenges and Areas for Improvement
Despite its advantages, some users note that Plotly can be slower than alternatives when producing a significant number of static chart images due to its comprehensive interactive functionalities. Although being open-source and extensive in functionality, this performance issue might limit its feasibility for certain high-volume tasks, requiring users to weigh the trade-offs between interactivity and speed.
Comparison with Competitors
When stacked against competitors like D3.js, Chart.js, and Tableau, Plotly's interactivity and ease of use without demanding in-depth front-end development knowledge stand out. D3.js, known for its customizable and robust visualizations, often demands a steeper learning curve. Meanwhile, platforms like Tableau and Microsoft Power BI, although user-friendly with drag-and-drop interfaces, are proprietary, potentially limiting customization options compared to Plotly's open-source nature.
In summary, Plotly is celebrated for its versatility and depth, offering a comprehensive set of tools for developing interactive, web-based visualizations. Its integration capabilities and wide-ranging visualization options make it a valuable asset in the toolkit of data-driven organizations, especially those favoring open-source solutions. However, its slower performance with static image batch processing can be a consideration for users needing faster execution for less interactive outputs.
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