Software Alternatives & Startups

Matplotlib VS Superset

Compare Matplotlib VS Superset and see what are their differences

Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...

Rating
0 reviews
Pricing
Open source
Superset

Superset is a platform that enables colleges to automate campus placements end-to-end, helps employers hire young talent from across colleges in the country, and empowers students to access opportunities democratically.

Rating
0 reviews
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Which is more popular?

Based on our record, Matplotlib seems to be a lot more popular than Superset. While we know about 114 links to Matplotlib, we've tracked only 1 mention of Superset.

social mentions
114 vs 1
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
240+ vs 151

Base details

Website, pricing, platforms and company facts side by side.

Matplotlib
Superset
Website matplotlib.org joinsuperset.com
Pricing
Open source
—
Listed in

Features and specs

What each product offers, as listed by its team.

Matplotlib 6 features
Superset 5 features
  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.
  • User-Friendly Interface
    Superset provides a user-friendly and intuitive interface, which makes it easy for users to create and share dashboards without the need for extensive technical expertise.
  • Open Source
    As an open-source platform, Superset allows customization and integration with other tools and systems, providing flexibility and the ability to tailor the tool to specific organizational needs.
  • Extensive Visualization Options
    Superset offers a wide range of visualization options, including bar charts, line graphs, pie charts, and more, enabling users to effectively present data in various formats.
  • Scalability
    Designed to handle large volumes of data, Superset can scale effectively, making it a suitable choice for organizations with growing data analysis needs.
  • SQL Editor
    The included SQL editor allows for direct querying of databases, providing experienced users the ability to perform complex data analyses and transformations.

Possible disadvantages

  • Steep Learning Curve
    Despite its user-friendly design, new users may face a steep learning curve, particularly if they are unfamiliar with SQL or data visualization principles.
  • Performance Issues
    Some users have reported performance issues, especially with very large datasets or complex queries, which can hinder effective data analysis.
  • Limited Advanced Analytics
    While Superset is great for basic visualizations and dashboarding, it lacks more advanced analytics features that other BI tools might offer, such as predictive analytics or machine learning integrations.
  • Requires Technical Expertise
    Setting up and maintaining Superset can require significant technical expertise, particularly for installation, configuration, and integrating with data sources.
  • Community Support
    Being an open-source project, support is reliant predominantly on community forums and documentation, which might not be as comprehensive or timely as commercial support services.

Analysis

An editorial look at what each product does well and who it suits.

Matplotlib
Superset

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

No analysis of Superset yet.

Videos

Walkthroughs and reviews on video.

Matplotlib 1 video + Add
Superset 3 videos + Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

SUPERSET PRE WORKOUT REVIEW | BEYOND YOURSELF

More videos

  • - Open Source Data Visualization: What is Apache Superset? Part 2/2 - Why / when to use Superset
  • - FITNESS CREATORS NEED TO CHECK THIS OUT! - SuperSet Review

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Matplotlib
Superset
0% 0%
100% 100%
100% 100%
0% 0%
0% 0%
AI
100% 100%

User comments

Share your experience with using Matplotlib and Superset. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Matplotlib no reviews yet
Superset no reviews yet

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We have no reviews of Superset yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Matplotlib 114 mentions
Superset 1 mention
  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib — the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review.... - Source: dev.to / 7 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes it’s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw... - Source: dev.to / 10 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 11 months ago

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  • Revolutionize Your Campus Recruitment with Superset's Advanced Software Solutions
    Superset's virtual campus placement and campus hiring software redefines the landscape of campus recruitment. By embracing automation, optimizing processes, and fostering meaningful connections, Superset empowers colleges and employers... Source: almost 3 years ago

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