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wikiHow VS NumPy

Compare wikiHow VS NumPy and see what are their differences

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wikiHow logo wikiHow

wikiHow is a wiki based collaboration to build the world’s largest, highest quality how to manual.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • wikiHow Landing page
    Landing page //
    2023-10-14
  • NumPy Landing page
    Landing page //
    2023-05-13

wikiHow features and specs

  • Comprehensive Coverage
    wikiHow offers a wide range of articles on various topics, providing users with instructions for many different tasks and subjects.
  • Step-by-Step Instructions
    Articles typically provide detailed, step-by-step guidance that helps users understand and follow the instructions easily.
  • Free Access
    The platform is free to use, allowing anyone with internet access to learn how to do things without paying a fee.
  • Community Participation
    Users can contribute to the content, ensuring the information is kept up-to-date and frequently enriched by a variety of perspectives.
  • Visual Aids
    Many articles come with images, illustrations, and videos that enhance understanding of the instructions.

Possible disadvantages of wikiHow

  • Variable Quality
    The quality of articles can vary, as they are often written by volunteers, leading to inconsistencies in the detail or clarity provided.
  • Reliability of Information
    Some articles may not be reviewed by experts, which can raise concerns about the accuracy or reliability of the information presented.
  • Ads and Promotions
    The presence of advertisements can distract from the content and may affect the user experience negatively.
  • General Guidance
    Instructions may sometimes be too general and not account for specific individual requirements or regional variations.
  • Limited Depth
    While articles provide basic guidance, they might lack depth, especially for more complex topics or tasks that require specialized knowledge.

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 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.

wikiHow videos

+ Add

The Disturbing Side Of WikiHow

More videos:

  • Review - Deeply Troubling WikiHow Articles (w/ Ryan Trahan)
  • Review - The WORST Smash Bros Content Ever Made (WikiHow)

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 wikiHow and NumPy)

wikiHow

NumPy

Communication
100 100%
Communication
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Knowledge Sharing
100 100%
Knowledge Sharing
0% 0
Data Science Tools
0 0%
Data Science Tools
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 wikiHow and NumPy

wikiHow Reviews

25+ Medium Alternative Platforms for Publishing Articles
Wikihow is another medium alternative platform that provides step-by-step instructions format articles. If you love to write articles in step format, this platform is going to become the best for you.
Source: forgefusion.io

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 seems to be more popular. It has been mentiond 119 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.

wikiHow mentions (0)

We have not tracked any mentions of wikiHow yet. Tracking of wikiHow recommendations started around Mar 2021.

NumPy mentions (119)

  • Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
    The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis. - Source: dev.to / 5 months ago
  • F1 FollowLine + HSV filter + PID Controller
    This library provides functions for working in domain of linear algebra, fourier transform, matrices and arrays. - Source: dev.to / 9 months ago
  • Intro to Ray on GKE
    The Python Library components of Ray could be considered analogous to solutions like numpy, scipy, and pandas (which is most analogous to the Ray Data library specifically). As a framework and distributed computing solution, Ray could be used in place of a tool like Apache Spark or Python Dask. It’s also worthwhile to note that Ray Clusters can be used as a distributed computing solution within Kubernetes, as... - Source: dev.to / 9 months ago
  • Streamlit 101: The fundamentals of a Python data app
    It's compatible with a wide range of data libraries, including Pandas, NumPy, and Altair. Streamlit integrates with all the latest tools in generative AI, such as any LLM, vector database, or various AI frameworks like LangChain, LlamaIndex, or Weights & Biases. Streamlit’s chat elements make it especially easy to interact with AI so you can build chatbots that “talk to your data.”. - Source: dev.to / 10 months ago
  • A simple way to extract all detected objects from image and save them as separate images using YOLOv8.2 and OpenCV
    The OpenCV image is a regular NumPy array. You can see it shape:. - Source: dev.to / 10 months ago
View more

What are some alternatives?

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

The Answer Bank - The Answer Bank is the UK’s leading question-and-answer site.

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

Blurtit - Blurtit is a Q&A website. You can ask a question or answer on any question.

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

Ask Me Help Desk - Ask Me Help Desk is a question-and-answer site.

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

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