Software Alternatives, Accelerators & Startups

Github Profile Visualizer VS Plotly

Compare Github Profile Visualizer VS Plotly and see what are their differences

Github Profile Visualizer logo Github Profile Visualizer

Tool for visualizing GitHub profiles

Plotly logo Plotly

Low-Code Data Apps
  • Github Profile Visualizer Landing page
    Landing page //
    2023-07-27
  • Plotly Landing page
    Landing page //
    2023-07-31

Github Profile Visualizer features and specs

  • Comprehensive Overview
    The GitHub Profile Visualizer provides a comprehensive overview of a user’s GitHub activity, offering a clear summary of contributions, repositories, and activity trends without having to navigate through individual repositories.
  • User-Friendly Interface
    The tool features a user-friendly interface that makes it easy to visualize data, making it accessible even for users who are not very familiar with technical details or GitHub’s native UI.
  • Efficient Data Gathering
    It efficiently gathers information from a user's GitHub profile, presenting it in a consolidated format, which saves users time compared to manually compiling data from various sections of a GitHub profile.

Possible disadvantages of Github Profile Visualizer

  • Privacy Concerns
    Using a third-party tool to access GitHub profiles might raise privacy concerns among users who are wary of sharing their data with external services.
  • Limited Customization
    The tool might not offer extensive customization options, limiting users who want to alter visualizations to better fit their personal branding or analytic needs.
  • Dependency on Tool
    Relying on a third-party visualizer makes users dependent on the continued functionality and availability of the service, posing potential issues if the service is discontinued or faces downtimes.

Plotly features and specs

  • 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.

Possible disadvantages of Plotly

  • Performance
    Rendering very large datasets can sometimes be slow, which may not be suitable for real-time data visualization requirements.
  • Learning curve
    Even though the library is well-documented, the extensive range of features can have a steep learning curve for beginners.
  • Cost for advanced features
    While the basic functionality is free, more advanced features, such as export to certain formats and additional customizable options, require a paid subscription.
  • Dependency management
    Plotly has a number of dependencies that need to be managed properly, which can sometimes complicate the setup process.
  • Complexity
    For simple visualizations, Plotly might be overkill and simpler libraries like Matplotlib or Seaborn could be more appropriate.

Analysis of Plotly

Overall verdict

  • 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.

Why this product is good

  • Plotly is considered good because it offers a comprehensive suite of tools for creating interactive visualizations that can be used in web applications, reports, and dashboards. It supports many different types of plots, is easy to use for both beginners and experienced developers, and integrates well with popular programming languages like Python, R, and JavaScript.

Recommended for

    Plotly is recommended for data scientists, analysts, and developers who need to create interactive and visually appealing data visualizations. It's particularly useful for those who work with Python or R and want the ability to embed their visualizations in web applications or dashboards.

Github Profile Visualizer videos

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Plotly videos

Create Real-time Chart with Javascript | Plotly.js Tutorial

More videos:

  • Review - Introducing plotly.py 3.0
  • Review - Is Plotly The Better Matplotlib?
  • Tutorial - Plotly Tutorial 2021
  • Review - Data Visualization as The First and Last Mile of Data Science Plotly Express and Dash | SciPy 2021

Category Popularity

0-100% (relative to Github Profile Visualizer and Plotly)
Developer Tools
100 100%
0% 0
Data Visualization
0 0%
100% 100
Web App
100 100%
0% 0
Data Dashboard
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Github Profile Visualizer and Plotly

Github Profile Visualizer Reviews

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Plotly Reviews

Best 8 Redash Alternatives in 2023 [In Depth Guide]
Plotly is specifically designed for companies who want to build and deploy analytic applications like dashboards using Python, Julia, or R without needing DevOps or Javascript developers.
Source: www.datapad.io
5 Best Python Libraries For Data Visualization in 2023
Plotly is a web-based data visualization toolkit that comes with unique functionalities such as dendrograms, 3D charts, and also contour plots, which is not very common in other libraries. It has a great API offering scatter plots, line charts, bar charts, error bars, box plots, and other visualizations. Plotly can even be accessed from a Python Notebook.
Top 8 Python Libraries for Data Visualization
Plotly is a free open-source graphing library that can be used to form data visualizations. Plotly (plotly.py) is built on top of the Plotly JavaScript library (plotly.js) and can be used to create web-based data visualizations that can be displayed in Jupyter notebooks or web applications using Dash or saved as individual HTML files. Plotly provides more than 40 unique...
5 top picks for JavaScript chart libraries
Plotly is a graphing library that’s available for various runtime environments, including the browser. It supports many kinds of charts and graphs that we can configure with a variety of options.

Social recommendations and mentions

Based on our record, Plotly seems to be more popular. It has been mentiond 33 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Github Profile Visualizer mentions (0)

We have not tracked any mentions of Github Profile Visualizer yet. Tracking of Github Profile Visualizer recommendations started around Mar 2021.

Plotly mentions (33)

  • Python for Data Visualization: Best Tools and Practices
    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 / 2 months ago
  • Generative AI Powered QnA & Visualization Chatbot
    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 / 4 months ago
  • Build a Stock Dashboard in less than 40 lines of Python code!🤓
    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 / 6 months ago
  • Essential Deep Learning Checklist: Best Practices Unveiled
    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 / 12 months ago
  • Python equivalent to power bi/power query?
    For dashboards: - https://plotly.com/ is probably my favourite, but there are others like streamlit, voila and others... Source: over 1 year ago
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What are some alternatives?

When comparing Github Profile Visualizer and Plotly, you can also consider the following products

GHuser - Better GitHub profiles for everyone

D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.

GitHub Visualizer - Enter user/repo and see the project visually

Chart.js - Easy, object oriented client side graphs for designers and developers.

Beautify Github Profile - This repository helps you to have a more beautiful and attractive github profile, and you can access a set of tools and guides for beautifying your github profile.

RAWGraphs - RAWGraphs is an open source app built with the goal of making the visualization of complex data...