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Finicky VS NumPy

Compare Finicky VS NumPy and see what are their differences

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Finicky logo Finicky

A MacOS app for creating rules that decide which browser is opened for every link that would open...

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Finicky Landing page
    Landing page //
    2022-11-04
  • NumPy Landing page
    Landing page //
    2023-05-13

Finicky features and specs

  • Custom URL Handling
    Finicky allows users to customize which browser opens certain URLs, providing a tailored browsing experience.
  • Open Source
    Being open-source, Finicky allows users to inspect, modify, and contribute to the code, enhancing transparency and community involvement.
  • Scriptable
    Users can write scripts to finely control how URLs are routed, providing a high degree of flexibility and customization.
  • Lightweight
    Finicky is designed to be a lightweight application, minimizing system resource usage compared to heavier browser manager alternatives.

Possible disadvantages of Finicky

  • Complex Configuration
    For users unfamiliar with scripting, setting up Finicky could be complex and intimidating, requiring a learning curve.
  • Limited to macOS
    Finicky is only available for macOS, meaning users on other platforms cannot take advantage of its features.
  • Community Support
    As an open-source project, the level of support and documentation may not match commercial software, possibly leading to challenges in troubleshooting.

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.

Finicky videos

+ Add

Finicky Eater Diago TruDog Dog Food Review

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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 Finicky and NumPy)

Finicky

NumPy

Project Management
100 100%
Project Management
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Website Testing
100 100%
Website Testing
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 Finicky and NumPy

Finicky Reviews

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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 Finicky. 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.

Finicky mentions (21)

  • Show HN: I built a small utility that handles multiple browser instances for you
    Just curious, did you explore finicky(https://github.com/johnste/finicky)? - Source: Hacker News / about 1 year ago
  • Use Finicky to direct urls to multiple Chrome profiles at workplace
    Finicky is a macOS utility that allows you to configure rules for directing URLs to different web browsers or browser profiles. Using it to direct URLs to multiple Chrome profiles at the workplace can be useful for separating work-related browsing from personal activities or for managing different projects. With this optimal way, you can gain the benefit of not wasting your time to re-do actions to make it works... - Source: dev.to / about 1 year ago
  • Outlook now ignores Windows' Default Browser and opens links in Edge by default
    I'm currently experimenting with "link eye" from FDroid on Android. There's also [finicky](https://github.com/johnste/finicky) for MacOS. - Source: Hacker News / almost 2 years ago
  • Is there a way to open certain links on chrome rather than on default browser.
    Here's an open source solution. Set it up, forget about it. https://github.com/johnste/finicky. Source: almost 2 years ago
  • PSA: If you removed the old Discover button with --disable-features and it returned as the Bing logo, the feature name has changed
    The workaround to disable Bing/Discover (msEdgeSidebarV2) on MacOS is to install finicky and set it as the default browser so it can launch Edge for you with customized arguments. Source: about 2 years ago
View more

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 / 4 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 / 8 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 Finicky and NumPy, you can also consider the following products

Choosy - Choosy opens links in different browsers as specified, according to rules, set by the user.

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

Browser Select - Browser Select is a utility to dynamically select the browser you want instead of just having one...

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

Browser Tamer - Makes correct URLs open in browsers you want instead of the system defaults.

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

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