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SpeedCrunch VS NumPy

Compare SpeedCrunch VS NumPy and see what are their differences

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SpeedCrunch logo SpeedCrunch

SpeedCrunch. SpeedCrunch is a high-precision scientific calculator featuring a fast, keyboard-driven user interface. It is free and open-source software, licensed under the GPL. Download Documentation Donate .

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • SpeedCrunch Landing page
    Landing page //
    2021-09-19
  • NumPy Landing page
    Landing page //
    2023-05-13

SpeedCrunch features and specs

  • High Precision
    SpeedCrunch utilizes arbitrary-precision arithmetic, which allows calculations to be performed with a very high degree of accuracy.
  • Expression Parsing
    It can handle and parse complex mathematical expressions, making it suitable for advanced scientific and engineering calculations.
  • User-Friendly Interface
    The interface is clean and intuitive, allowing users to easily input and edit mathematical expressions without a steep learning curve.
  • Keyboard Shortcuts
    SpeedCrunch supports extensive keyboard shortcuts, which makes the application very efficient to use for experienced users.
  • Cross-Platform
    The software is available on multiple platforms including Windows, macOS, and Linux, providing flexibility for users on different operating systems.
  • Open Source
    As an open-source project, it allows for community contributions and transparency in its development, enabling users to trust and modify the software if needed.
  • Built-in Functions and Constants
    It includes a comprehensive list of built-in mathematical functions and constants, making it powerful for a wide range of applications.

Possible disadvantages of SpeedCrunch

  • No Graphing Capabilities
    SpeedCrunch does not support graphing of mathematical functions, which can be a limitation for users who need visual representations of data.
  • Limited to Numerical Calculations
    It is primarily focused on numerical calculations and doesn't support symbolic mathematics, limiting its usefulness for some types of algebraic computations.
  • Lack of Documentation
    While it is intuitive for basic use, more advanced features may lack comprehensive documentation, making it harder for new users to fully leverage the software's capabilities.
  • No Mobile Version
    Currently, SpeedCrunch does not have an official mobile version, which could be a drawback for users who need to perform calculations on the go.
  • Customization Limitations
    There are limited options for customizing the user interface or adding new features beyond what is already included, which may be a downside for some 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 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.

SpeedCrunch videos

+ Add

SpeedCrunch

NumPy videos

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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 SpeedCrunch and NumPy)

SpeedCrunch

NumPy

Calculators
100 100%
Calculators
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Advanced Calculator
100 100%
Advanced Calculator
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using SpeedCrunch 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 SpeedCrunch and NumPy

SpeedCrunch Reviews

10 Of The Best Mathway Alternatives
SpeedCrunch allows you to crunch complex sets of math problems at high speeds. The app supports a wide range of mathematical operations, from random number generation to trigonometry.
Source: launchspace.net

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 a lot more popular than SpeedCrunch. While we know about 119 links to NumPy, we've tracked only 6 mentions of SpeedCrunch. 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.

SpeedCrunch mentions (6)

  • Qalculate! - the ultimate desktop calculator
    As well as of https://speedcrunch.org/. Source: about 3 years ago
  • Rewrite KCalc app?
    I would love to see Speedcrunch to become KDE's first choice as a calculator app:. Source: over 3 years ago
  • Does KDE have a native programming calculator?
    Hello, if you are looking for a good scientific calculator you could give a chance to speedcrunch. Source: over 3 years ago
  • AWESOME WINDOWS TOOLS
    SpeedCrunch - The best and only calculator you'll need, completely stripped down of unnecessary UI clutter. - Source: dev.to / about 4 years ago
  • All desktop calculators are wrong, so I had to build my own
    I personally really like using speedcrunch[1] as a desktop calculator, and it’s cross platform. It’s not doing pretty print though. Otherwise it’s wolfram alpha[2], but that needs internet. I never type calculations in any search engines, but that’s way too slow compared to speedcrunch. Maybe I feel similarly to chalk using a web view compared to how electron apps are seen by some. Displaying inaccuracies is neat!... - Source: Hacker News / about 4 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 / 9 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 / 9 months ago
View more

What are some alternatives?

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

Qalculate! - Qalculate! is a multiplatform multi-purpose desktop calculator.

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

Numi App - Numi is a beautiful text calculator for Mac.

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

Soulver - Soulver is a software application that functions as a calculator that allows you type a continuous stream of information rather than having to input data into multiple cells.

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

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