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NumPy VS pikaur

Compare NumPy VS pikaur and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

pikaur logo pikaur

AUR helper with minimal dependencies. Review PKGBUILDs all in once, next build them all without user interaction.Inspired by pacaur, yaourt and yay.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • pikaur Landing page
    Landing page //
    2023-08-18

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.

pikaur features and specs

  • AUR Helper
    Pikaur is an Arch User Repository (AUR) helper, which simplifies the process of installing and managing AUR packages on Arch Linux systems.
  • Interactive Search
    It provides an interactive search feature that allows users to easily find and select packages using a command-line interface.
  • Dependency Management
    Automatically resolves and manages package dependencies, making installation and updates easier for users.
  • User-friendly Interface
    Offers a user-friendly interface that improves the overall experience of managing packages compared to using standard pacman commands.
  • Sudo Privilege Management
    Manages sudo privileges efficiently, requiring fewer password prompts during package operations.

Possible disadvantages of pikaur

  • Limited to Arch-based Systems
    Pikaur is specifically designed for Arch Linux and its derivatives, limiting its use to those systems.
  • Dependency on Python
    Requires Python, meaning users need to ensure Python is installed and properly configured on their system.
  • Potential for AUR Package Issues
    Since AUR packages are user-generated, there can be inconsistencies or issues with package scripts that might affect installations.
  • Security Risks
    As with other AUR helpers, users may inadvertently install potentially harmful or insecure software from the AUR.
  • Learning Curve
    New users may face a learning curve when first using Pikaur compared to more graphical or traditional package managers.

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.

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

pikaur videos

+ Add

Pikaur et Wish, deux successeurs potentiels ร  Pacaur ?

Category Popularity

0-100% (relative to NumPy and pikaur)

NumPy

pikaur

Data Science And Machine Learning
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Data Science And Machine Learning
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Work Music
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Work Music
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Focus Music
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Focus Music
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 NumPy and pikaur

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

pikaur Reviews

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Social recommendations and mentions

Based on our record, NumPy seems to be a lot more popular than pikaur. While we know about 122 links to NumPy, we've tracked only 4 mentions of pikaur. 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.

NumPy mentions (122)

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pikaur mentions (4)

  • Using pikaur, how would I disable asking me "Do you want to edit PKGBUILD for <package_name> package? [Y/n]"
    Have a look here. Did you not search for the answer? That's part of the Arch(based) ethos. We tend to like to learn by reading whatever is required. :). Source: about 3 years ago
  • Nala v0.10.0 - Nala's A Legible Apt
    I was also looking for something nicer for Arch, but haven't found anything as nice as Nala. For now, I switched to pikaur, which at least displays updates in a much clearer way. Source: almost 4 years ago
  • I created a tool to install AUR packages in 1 click from the website: Aurin
    Nice, but this definately needs a dependency resolver, otherwise it can only install a fraction of the available AUR packages. Since you're already using python, you may adapt your whole code on top a another python-based AUR helper like pikaur. You maybe also could take at the dep resolver of my ABS project. It's python, too, maybe not as clean as pikaur's code but simpler and not too integrated. Source: over 4 years ago
  • Which AUR-helper is recommended?
    I've been using pikaur ever since pacaur became abandonware and I'm very happy with it, can't recommend it enough. Sure, it's not implemented in Rust or Go so it's certainly not as cool as yay or paru but that doesn't really matter much to me, being an end user. I don't really care as long as it does its job, as advertised. Source: about 5 years ago

What are some alternatives?

When comparing NumPy and pikaur, 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.

Yay - Yay is an AUR helper written in go, based on the design of yaourt, apacman and pacaur.

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

paru - An AUR helper written in Rust and based on the design of yay. It aims to be your standard pacman wrapping AUR helper with minimal interaction.

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

Trizen - Trizen AUR Package Manager: A lightweight wrapper for AUR.

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