Software Alternatives, Accelerators & Startups

GHuser VS Plotly

Compare GHuser VS Plotly and see what are their differences

GHuser logo GHuser

Better GitHub profiles for everyone

Plotly logo Plotly

Low-Code Data Apps
  • GHuser Landing page
    Landing page //
    2023-06-12
  • Plotly Landing page
    Landing page //
    2023-07-31

GHuser features and specs

  • Enhanced Profile Visualization
    GHuser provides a more visually appealing and detailed overview of GitHub user profiles compared to the standard GitHub interface. This makes it easier to understand the contributions and activities of a user at a glance.
  • Comprehensive Contribution Insights
    The platform offers more comprehensive insights into a user's contributions, including statistics and visual representations that are not available on GitHub's default profile page.
  • User-Friendly Interface
    GHuser comes with an intuitive interface that makes navigation and profile exploration more user-friendly, especially for non-technical users or those new to GitHub.
  • Resource for Recruiters
    For recruiters and hiring managers, GHuser provides a streamlined view of potential candidates' GitHub activities, making it easier to assess a developer's skills and contributions.

Possible disadvantages of GHuser

  • Limited Integration
    GHuser might not be as seamlessly integrated with GitHub as some users might hope, potentially leading to discrepancies or incomplete data transfers.
  • Dependence on GitHub
    Being an external tool, GHuser relies heavily on GitHub APIs. Changes or limitations in these APIs can affect GHuser’s functionality and accuracy.
  • Privacy Concerns
    Some users might have concerns about privacy, as GHuser introduces another platform that accesses and displays their GitHub data. Users need to trust that their data is handled responsibly.
  • Additional Learning Curve
    Although designed to be user-friendly, there is still an initial learning curve for new users who must get accustomed to navigating the platform and understanding its unique features.

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.

GHuser 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 GHuser 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 GHuser and Plotly

GHuser 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.

GHuser mentions (0)

We have not tracked any mentions of GHuser yet. Tracking of GHuser 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 / 3 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 / 5 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 / about 1 year 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
View more

What are some alternatives?

When comparing GHuser and Plotly, you can also consider the following products

Github Profile Visualizer - Tool for visualizing GitHub profiles

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.

Refined GitHub - Browser extension that makes GitHub cleaner & more powerful

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