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Matplotlib VS Ninja Build

Compare Matplotlib VS Ninja Build and see what are their differences

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Matplotlib logo Matplotlib

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

Ninja Build logo Ninja Build

Ninja is a small build system with a focus on speed.
  • Matplotlib Landing page
    Landing page //
    2023-06-14
  • Ninja Build Landing page
    Landing page //
    2021-09-14

Matplotlib features and specs

  • 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 of Matplotlib

  • 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.

Ninja Build features and specs

  • Speed
    Ninja is designed for high performance, making it one of the fastest build systems available. It minimizes the time spent on tasks such as parsing, dependency resolution, and build command execution.
  • Simplicity
    Ninjaโ€™s configuration syntax is straightforward and concise, reducing the complexity involved in setting up builds and allowing for a clear overview of build rules.
  • Parallelism
    Ninja excels at handling parallel builds, leveraging multiple cores effectively to decrease overall build times.
  • Incremental Builds
    Ninja efficiently handles incremental builds by only recompiling what is necessary, which significantly speeds up iterative development processes.
  • Integration
    Ninja is often used as the backend for more complex build systems (e.g., CMake), making it a versatile tool within a larger toolchain.

Possible disadvantages of Ninja Build

  • Limited Features
    Ninja is deliberately minimalist, lacking many of the features found in other build systems, such as built-in support for complex dependency management and custom build steps.
  • Learning Curve
    While Ninja itself has a simple syntax, the learning curve can be steep for those unfamiliar with how build systems work or for those coming from more feature-rich environments.
  • Dependency on Generators
    Ninja often requires an external generator (like CMake) to create its build files, which can add to the setup complexity and introduce dependencies on other tools.
  • Limited Scripting Capabilities
    Unlike some build systems that offer extensive scripting support (e.g., Python in SCons), Ninja's functionality is largely limited to what its syntax and predefined rules allow.
  • Less Flexibility
    Due to its minimalist nature, Ninja may not be as flexible as other build systems, potentially limiting its use in more complex or unusual build scenarios.

Analysis of Matplotlib

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.

Analysis of Ninja Build

Overall verdict

  • Ninja Build is considered a strong choice for users seeking a fast, reliable, and efficient build system. Its simplicity and focus on performance make it appealing to developers working on projects where build speed is critical.

Why this product is good

  • Ninja Build is a high-performance build system designed to handle complex build processes efficiently. It is known for its minimalistic yet powerful design, which allows for faster build times compared to traditional build systems like Make. Its approach to dependency tracking and parallelism is optimized for modern build environments, making it suitable for large codebases and incremental builds.

Recommended for

    Ninja Build is recommended for developers working on large-scale projects with complex build processes, particularly in environments where build speed and efficiency are prioritized. It is especially beneficial for projects that are continuously integrated or require frequent incremental builds.

Matplotlib videos

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Ninja Build videos

FORTNITE STW: HERE IS THE BEST NINJA BUILD (AFTER MONTHS OF TESTING)

Category Popularity

0-100% (relative to Matplotlib and Ninja Build)
Data Science And Machine Learning
Front End Package Manager
Technical Computing
100 100%
0% 0
JS Build Tools
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 Matplotlib and Ninja Build

Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
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 code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
15 data science tools to consider using in 2021
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 visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...

Ninja Build Reviews

We have no reviews of Ninja Build yet.
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Social recommendations and mentions

Based on our record, Matplotlib should be more popular than Ninja Build. It has been mentiond 114 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.

Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™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 numbers into clear charts. - Source: dev.to / 7 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 / 8 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 10 months ago
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Ninja Build mentions (23)

  • CMake Made Simple: A Reusable Template for Your First C++ Project
    On Windows, download the binaries from the cmake and Ninja websites. After that, add the executables to your PATH. - Source: dev.to / 11 months ago
  • TypeScript's Successor is Waiting, and You'll Never Want to Turn Back
    Under the hood, Rescript uses a build system called Ninja. Ninja is similar to Make, but cross-platform and more minimal/performant. - Source: dev.to / over 2 years ago
  • Using Make โ€“ writing less Makefile
    Ninja was super easy to pick up even after using make for some time (10+ years). GN is just a ninja generator that is optional. https://gn.googlesource.com/gn/+/main/docs/quick_start.md https://ninja-build.org/. - Source: Hacker News / over 2 years ago
  • Ask HN: What outdated tech are you still using and are perfectly happy with?
    Really? I thought most new projects were switching to ninja[^1] and have never used it. [^1]: https://ninja-build.org/. - Source: Hacker News / almost 3 years ago
  • What was used to build C++ programs before Cmake?
    Ninja showed real promise for a while, but then CMake grew up and people stopped seeing a reason to leave it behind. Source: about 3 years ago
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What are some alternatives?

When comparing Matplotlib and Ninja Build, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

GNU Make - GNU Make is a tool which controls the generation of executables and other non-source files of a program from the program's source files.

NumPy - NumPy is the fundamental package for scientific computing with Python

SCons - SCons is an Open Source software construction toolโ€”that is, a next-generation build tool.

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

npm - npm is a package manager for Node.