Software Alternatives, Accelerators & Startups
Register | Login

Xmonad VS NumPy

Compare Xmonad VS NumPy and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. sponsored
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.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Xmonad logo Xmonad

xmonad is a dynamically tiling X11 window manager that is written and configured in Haskell.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Xmonad Landing page
    Landing page //
    2022-04-01
  • NumPy Landing page
    Landing page //
    2023-05-13

Xmonad features and specs

  • Highly Customizable
    Xmonad is written in Haskell and allows for extensive customization. Users can write custom configurations and extensions to tailor the window manager to their exact needs.
  • Efficient and Minimalist
    Xmonad is designed to be efficient and lightweight. It uses minimal system resources, making it ideal for older hardware or systems where performance is a priority.
  • Keyboard-Centric
    Xmonad is optimized for keyboard operation, providing a highly efficient and fast way to manage windows without relying on a mouse, which can improve productivity.
  • Tiling Window Manager
    As a tiling window manager, Xmonad automatically arranges windows to use screen space efficiently, reducing the need to manually resize and position windows.
  • Stable and Reliable
    Xmonad is known for its stability and reliability, with a strong track record of stable releases and robust performance.

Possible disadvantages of Xmonad

  • Steep Learning Curve
    New users may find Xmonad difficult to learn due to its reliance on Haskell for customization and a lack of graphical configuration tools.
  • Limited Out-of-the-Box Functionality
    Xmonad comes with a very basic setup by default, requiring significant configuration and customization to fully utilize its capabilities.
  • Haskell Knowledge Required
    Customization of Xmonad requires knowledge of Haskell, which can be a barrier for users unfamiliar with the language.
  • Sparse Community and Documentation
    Compared to more popular window managers, Xmonad has a smaller community and less extensive documentation, which can make troubleshooting and learning more challenging.
  • Not Newbie-Friendly
    Xmonad is not the most user-friendly option for beginners. Its lack of GUI tools and reliance on command-line configuration can be intimidating for new users.

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

Analysis of Xmonad

Overall verdict

  • Xmonad is highly regarded within the Linux and BSD communities, especially among users who prefer or don't mind configuring their environments through coding. It is considered a reliable tool for those who value efficiency and are comfortable with or interested in writing Haskell code for customization. While it has a steep learning curve due to the necessity of understanding Haskell for complex configurations, its performance and flexibility make it a strong choice for the right user.

Why this product is good

  • Xmonad is a dynamically tiling window manager written in Haskell, known for its minimalism, stability, and high customization options. It efficiently manages windows and is ideal for keyboard-driven workflows. Users appreciate its lightweight nature and ability to extend its functionality through Haskell scripts. Being a tiling window manager, it automatically organizes windows to make the best use of screen space, which can significantly enhance productivity for power users.

Recommended for

  • Developers and programmers who appreciate Haskell or are interested in learning more about it.
  • Linux or BSD users seeking a highly customizable and efficient window manager.
  • Power users who prefer or are comfortable with keyboard-driven interfaces and have the willingness to spend time configuring their setup.
  • Users who value system performance and resource efficiency, as Xmonad uses minimal system resources.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Xmonad videos

+ Add

Xmonad Review

More videos:

  • Review - Hacking on Xmonad - GridSelect, ToggleStruts, ToggleBorders
  • Review - Obscure Window Manager Project - Xmonad

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

Category Popularity

0-100% (relative to Xmonad and NumPy)

Xmonad

NumPy

Window Manager
100 100%
Window Manager
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Linux
100 100%
Linux
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Xmonad and NumPy. For example, how are they different and which one is better?
Log in or Post with

Reviews

These are some of the external sources and on-site user reviews we've used to compare Xmonad and NumPy

Xmonad Reviews

Top 13 Best Tiling Window Managers For Linux In 2022
XMonad is a dynamic tiling X11 window manager that allows you to automate window finding and alignment. It may be customised with its own extension library, which includes choices for status bars and window decorations. It’s also simple to set up, stable, and minimal.
Source: www.hubtech.org
13 Best Tiling Window Managers for Linux
spectrwm is a small, dynamic, xmonad, and dwm-inspired reparenting and tiling window manager built for X11 to be fast, compact, and concise. It was created with the aim of solving the issues of xmonad and dwm face.
Source: www.tecmint.com
5 Great Tiling Window Managers for Linux
Xmonad is a tiling window manager written in Haskell. Like most (if not all) window managers, it comes with no frills or window decorations. The keyboard shortcuts are top notch. It works out-of-the-box and is very user friendly. On top of all that, Xmonad sports a fairly big extension library (which can add on even more functionality).
Source: www.maketecheasier.com

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

Social recommendations and mentions

Based on our record, NumPy should be more popular than Xmonad. It has been mentiond 122 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.

Xmonad mentions (15)

  • Rubywm: An X11 window manager in pure Ruby
    If you want tiling, but i3 requires too much manual work, you might like the more managed layouts that are the default in XMonad: https://xmonad.org/ XMonad works fine with multiple monitors. Each monitor displays one of the many virtual desktops. The normal keys for desktops and for windows work pretty intuitively with multiple monitors. - Source: Hacker News / over 1 year ago
  • [Media] shrs: a shell that is configurable and extensible in rust
    Hey everyone 👋 ! I'm currently working on a rust library for building and configuring your own shell! It's inspired by projects like xmonad and penrose where the configuration of the program is done in code. This means that for example, instead of using Bash's arcane syntax for configuring the prompt, it can be configured instead using a rust builder pattern! The project itself is still at a very young stage, so... Source: over 3 years ago
  • What LaTeX setup do you use?
    There are a few other things I could mention, but there are more like side issues, and not relevant to my actual LaTeX setup. First and foremost—and thus perhaps noteworthy after all—is bibliography management with arxiv-citation (see here for more words). This is integrated very well with the XMonad window manager, which makes it even more of a joy to use. Source: over 3 years ago
  • How to map arrows keys to CapsLock+(h,i,j,k) shortcuts in i3
    Another way to do it (and works on Linux and other platforms) is with XMonad, defining Caps Lock as a layer key. Source: about 4 years ago
  • Can ISTP like abstract things and theories?
    I tried it once, it was alright. https://xmonad.org/ But I prefer to build my own. Source: about 4 years ago
View more

NumPy mentions (122)

View more

What are some alternatives?

When comparing Xmonad and NumPy, you can also consider the following products

dwm - dwm is a dynamic window manager for X. It manages windows in tiled, monocle and floating layouts. All of the layouts can be applied dynamically, optimising the environment for the application in use and the task performed.

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

i3 - A dynamic tiling window manager designed for X11, inspired by wmii, and written in C.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

awesome - A dynamic window manager for the X Window System developed in the C and Lua programming languages.

OpenCV - OpenCV is the world's biggest computer vision library

Loading...