Software Alternatives & Startups

Matplotlib VS Web Gradients

Compare Matplotlib VS Web Gradients 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
Web Gradients

Curated collection of 180 free fresh background gradients πŸš€

Rating
0 reviews
Pricing
Open source
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 Web Gradients. While we know about 114 links to Matplotlib, we've tracked only 5 mentions of Web Gradients.

social mentions
114 vs 5
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
240+ vs 133

Base details

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

Matplotlib
Web Gradients
Website matplotlib.org webgradients.com
Pricing
Open source
Open source
Listed in

Features and specs

What each product offers, as listed by its team.

Matplotlib 6 features
Web Gradients 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.
  • Variety
    Web Gradients offers a wide selection of gradient presets, making it easy for designers to find the perfect gradient for their needs.
  • Ease of Use
    The website's user interface is straightforward and user-friendly, enabling users to quickly browse and select gradients.
  • Free Access
    All gradients available on Web Gradients are free to use, which is beneficial for designers working with limited budgets.
  • Code Snippets
    Web Gradients provides CSS code snippets for each gradient, making it easy to integrate into web projects.
  • Inspiration
    The curated collection of gradients serves as a great source of inspiration for designers looking to enhance their projects.

Possible disadvantages

  • Limited Customization
    The gradients are predefined, offering limited customization options for those who want to adjust colors or gradients precisely.
  • Static Collection
    The collection does not appear to be updated frequently, which might limit the availability of current design trends.
  • Dependence on External Source
    Relying on an external website for gradient designs might pose an issue if the website goes offline or changes its policies.
  • CSS-Only
    The site provides only CSS code snippets, so users looking for gradient options in other formats (e.g., SVG, PNG) might need additional resources.
  • Bandwidth Usage
    Using multiple gradients can lead to increased CSS file sizes, which may impact the performance of web pages if not managed properly.

Analysis

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

Matplotlib
Web Gradients

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.

Overall verdict

  • Yes, Web Gradients is a good resource for anyone looking to incorporate beautiful and ready-to-use gradients into their web design projects. Its ease of use, variety of options, and compatibility make it a valuable tool for both beginners and experienced web developers.

Why this product is good

  • Web Gradients is a popular resource for designers and developers because it offers a collection of visually appealing gradient backgrounds that can be easily integrated into web projects. The gradients are crafted in a way that ensures compatibility across different devices and browsers, and they provide a modern aesthetic that can enhance the overall look and feel of websites.

Recommended for

    Web Gradients is recommended for web designers, developers, and digital artists who are seeking high-quality gradient backgrounds to elevate the visual design of their websites and applications. It's particularly beneficial for those who want to save time on design work while still achieving professional-looking results.

Videos

Walkthroughs and reviews on video.

Matplotlib 1 video + Add
Web Gradients 0 videos + Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

No Web Gradients videos yet. You could help us improve this page by suggesting one.

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
Web Gradients
0% 0%
100% 100%
100% 100%
0% 0%
0% 0%
100% 100%

User comments

Share your experience with using Matplotlib and Web Gradients. 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
Web Gradients no reviews yet

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Social recommendations and mentions

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

Matplotlib 114 mentions
Web Gradients 5 mentions
  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib β€” 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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  • Top 15 Tools for Frontend Developers: Optimize Your Workflow
    A free collection of 180 linear gradients with CSS3 code is available at WebGradients. These gradients can add depth and visual interest to your web designs. The website provides ready-to-use gradient codes, making it easy to incorporate... - Source: dev.to / over 2 years ago
  • Serious question, is this art good enough to "sell" my game?
    Https://webgradients.com here's a website with super nice gradients and it seems just looking at some of them you can already have a vibe from them. You could just color pick them and use for certain levels. Source: over 3 years ago
  • I created LucaMail - Next Generation Beautiful Featureful E-Mail Client for Desktop
    Totally agree, maybe use softer gradients like those in https://webgradients.com. Source: over 4 years ago

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Alternatives to Matplotlib and Web Gradients

When comparing Matplotlib and Web Gradients, you can also consider the following products.