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NumPy VS Graphmatica

Compare NumPy VS Graphmatica and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Graphmatica logo Graphmatica

Is a powerful, easy-to-use, equation plotter with numerical and calculus features:
  • NumPy Landing page
    Landing page //
    2023-05-13
Not present

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.

Graphmatica features and specs

  • User-Friendly Interface
    Graphmatica offers an intuitive and straightforward user interface that allows users to graph mathematical equations easily without needing extensive prior experience.
  • Versatile Graphing Capabilities
    The software supports a wide range of graph types, including Cartesian, polar, and parametric graphs, which makes it suitable for various mathematical applications.
  • Customizable Settings
    Graphmatica provides numerous customization options, allowing users to modify the appearance of graphs, such as colors, styles, and grid settings, to better suit their needs.
  • In-built Solvers and Calculators
    It includes features like derivative and tangent line calculators, helping users perform advanced mathematical analyses directly within the application.
  • Educational Resource
    Graphmatica can be a valuable educational tool for students and teachers, offering a visual approach to understanding complex mathematical concepts.

Possible disadvantages of Graphmatica

  • Limited 3D Graphing
    Graphmatica primarily focuses on 2D graphing, which may not meet the requirements of users looking to visualize functions in three dimensions.
  • Outdated Interface Design
    The software's interface design is somewhat dated, lacking the modern aesthetic and user experience enhancements found in newer graphing tools.
  • Limited Export Options
    Graphmatica offers limited file format options for exporting graphs, which might impede users needing high-quality exports for presentations or publications.
  • Platform Dependence
    The application is primarily available for Windows, which may limit accessibility for users on other operating systems who are looking for native solutions.
  • Learning Curve for Advanced Features
    While basic functionalities are accessible, mastering more advanced features can be challenging for beginners due to the lack of comprehensive tutorials or guides.

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

Graphmatica videos

+ Add

Working with Graphmatica

More videos:

  • Review - Using Graphmatica for Polar Functions
  • Review - Sinusoidal Regression Using Graphmatica

Category Popularity

0-100% (relative to NumPy and Graphmatica)

NumPy

Graphmatica

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Technical Computing
0 0%
Technical Computing
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Education & Reference
0 0%
Education & Reference
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 Graphmatica

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

Graphmatica Reviews

We have no reviews of Graphmatica yet.
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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.

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 / 3 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 / 8 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

Graphmatica mentions (0)

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

What are some alternatives?

When comparing NumPy and Graphmatica, 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.

GeoGebra CAS Calculator - Free online algebra calculator from GeoGebra: solve equations, expand and factor expressions, find derivatives and integrals

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

Dr. Geo - Dr. Geo, a software to design & manipulate interactive geometric sketches.

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

GnuPlot - Gnuplot is a portable command-line driven interactive data and function plotting utility.

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