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SafeUtils VS NumPy

Compare SafeUtils VS NumPy and see what are their differences

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SafeUtils logo SafeUtils

SafeUtils: Native MacOS, Linux and Windows desktop application with 110+ carefully crafted tools for yours and your teams everyday work with sensitive data in various formats.

NumPy logo NumPy

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

SafeUtils

Website
safeutils.com
Pricing URL
Official SafeUtils Pricing
$ Details
paid $19.0 / One-off
Platforms
MacOS Windows Linux
Release Date
2024 June
Startup details
Country
Poland
City
Warsaw
Founder(s)
Wiktor Plaga
Employees
1 - 9
Edit details

NumPy

Website
numpy.org
Pricing URL
-
$ Details
Platforms
-
Release Date
-
Edit details

SafeUtils features and specs

  • Converters
    JSON to YAML, CSV, TOML, XML; ASCII Text to Binary, Decimal, Octal, Hex
  • Generators
    Lorem Ipsum, Random
  • Decoders
    Base64, URL
  • Encoders
    Base64, URL
  • Previews
    HTML, Markdown, URL
  • Formatters
    HTML, JSON, CSV, TOML, XML, YAML, Markdown

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.

SafeUtils videos

No SafeUtils videos yet. You could help us improve this page by suggesting one.

Add video

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 SafeUtils and NumPy)

SafeUtils

NumPy

Productivity
100 100%
Productivity
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Developer Tools
100 100%
Developer Tools
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

Questions and Answers

As answered by people managing SafeUtils and NumPy.

Which are the primary technologies used for building your product?

SafeUtils's answer

Tauri, Rust, React.js, TypeScript, Vite

How would you describe your primary audience?

SafeUtils's answer

Software Engineers, Programmers, Data Analysts

Why should a person choose your product over its competitors?

SafeUtils's answer

Much more tools than any competition & supports all mayor platforms.

What makes your product unique?

SafeUtils's answer

More tools, all mayor platforms, performance, beautiful UI, developer experience.

What's the story behind your product?

SafeUtils's answer

Hey, it's Wiktor 👋. I built and now happily maintain the SafeUtils app for two reasons:

  1. It felt like I'm about to go to jail every time I pasted my data on the Internet.
  2. It was WAY too many bookmarks, and they didn't even cover half of my needs.

I decided to equip other developers with a missing solution to their everyday operations.

User comments

Share your experience with using SafeUtils and NumPy. For example, how are they different and which one is better?
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Reviews

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

SafeUtils Reviews

We have no reviews of SafeUtils yet.
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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 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.

SafeUtils mentions (0)

We have not tracked any mentions of SafeUtils yet. Tracking of SafeUtils recommendations started around Jun 2024.

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 / 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 SafeUtils and NumPy, you can also consider the following products

DevToys - A collection of converters, formaters, encoders, generators and other tools for your Windows desktop.

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

DevToys for Mac - DevToys For mac. Contribute to ObuchiYuki/DevToysMac development by creating an account on GitHub.

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

DevKnife.app - Your all-in-one macOS solution for dev tasks. Simplify workflows and boost efficiency for developers.

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

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