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Which is more popular?
Based on our record, Deno
should be more popular than Matplotlib.
It has been mentioned 201 times since March 2021.
social mentions
114 vs 201
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
239 vs 68
Base details
Website, pricing, platforms and company facts side by side.
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.
Security Deno has a secure-by-default approach, requiring explicit permission for file, network, and environment access, which reduces the risk of malicious code.
Built-in Tooling Deno includes built-in tools like a dependency inspector, a code formatter, and a test runner, reducing the need for additional setup.
Modern JavaScript/TypeScript Deno supports modern JavaScript and has built-in TypeScript support, making it easier to work with contemporary codebases without additional configuration.
Simplified Module Management Deno uses URLs for importing modules, eliminating the need for a package manager like npm and simplifying dependency management.
Compatibility with Web Standards Deno aims to be browser-compatible, adhering closely to web standards like the Fetch API, making it easier to share code between the server and the client.
Possible disadvantages
Ecosystem Maturity Deno's ecosystem is relatively new compared to Node.js, resulting in fewer libraries, tools, and community resources.
Breaking Changes Due to its rapid development, Deno can have breaking changes between versions, potentially requiring more frequent updates and code adjustments.
Performance Deno's performance may not match that of optimized Node.js applications, especially for certain workloads where Node.js has been highly tuned.
Learning Curve Even though Deno is designed to be familiar to JavaScript and TypeScript developers, it introduces new concepts (like secure-by-default) that may require a learning curve.
Limited Enterprise Adoption Being relatively new, Deno has limited enterprise adoption, which might make it less appealing for large-scale or long-term projects that rely on a robust support ecosystem.
Analysis
An editorial look at what each product does well and who it suits.
MatplotlibDeno
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
Deno is a strong option for developers who prioritize security and modern JavaScript/TypeScript features. Its out-of-the-box toolchain can simplify development by reducing dependencies on external libraries and tools.
Why this product is good
Deno is designed to address some of the shortcomings of Node.js. It includes built-in TypeScript support, a secure-by-default runtime, module management using URLs instead of package managers like npm, and built-in utilities for tasks such as linting, formatting, and testing.
Recommended for
Deno is recommended for developers who are starting new projects that can benefit from its modern approach, those who prioritize security, and developers who prefer using TypeScript. However, for large-scale projects that depend heavily on Node.js's extensive package ecosystem, the transition might require additional considerations.
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of...
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data...
Recommendations tracked on public social media and blogs since March 2021.
Matplotlib114 mentionsDeno201 mentions
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
/
8 months ago
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
/
11 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
/
12 months ago
Deno — https://deno.land
Technology: JavaScript/TypeScript runtime.
Backed / sponsored by: Deno Company + open source community.
How to generate revenue: Paid hosting, enterprise support, managed Deno services, training.
Description...
- Source: dev.to
/
about 1 year ago
Benchmarking in Node.js vs Deno: A Comprehensive Comparison
Deno.bench("URL parsing", () => {
new URL("https://deno.land");
});
Deno.bench("Async method", async () => {
await crypto.subtle.digest("SHA-256", new Uint8Array([1, 2, 3]));
});
Deno.bench({
name: "Long form",
fn: () => {
...
- Source: dev.to
/
almost 2 years ago
Deno 2.0 REST API Explained: Faster, Secure JavaScript Development
// Importing the serve function from Deno's standard library
Import { serve } from "https://deno.land/std@0.196.0/http/server.ts";
// Function to handle requests
Async function handler(req: Request): Promise {
const {...
- Source: dev.to
/
about 2 years ago
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
Bun.sh
Node.js
Fresh Framework
Next.js
React
Preact.js
ExpressJS
