Software Alternatives, Accelerators & Startups
Register | Login

NumPy VS Dillinger

Compare NumPy VS Dillinger and see what are their differences

DocRaptor
As the only API powered by the Prince HTML-to-PDF engine, DocRaptor provides the best support for complex PDFs with powerful support for headers, page breaks, page numbers, flexbox, watermarks, accessible PDFs, and much more featured
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Dillinger logo Dillinger

joemccann has 95 repositories available. Follow their code on GitHub.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Dillinger Landing page
    Landing page //
    2024-10-09

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.

Dillinger features and specs

  • Real-time Markdown Rendering
    Dillinger provides live rendering of Markdown text, allowing users to see a side-by-side preview of their formatted text.
  • Cloud Integration
    It offers integration with cloud services like Dropbox, Google Drive, OneDrive, and GitHub, making it easy to save and manage documents.
  • User-friendly Interface
    The platform boasts an intuitive and clean interface, which makes it easy for both beginners and experienced users to navigate and use effectively.
  • Export Options
    Dillinger supports exporting documents in multiple formats, including Markdown, HTML, and PDF, providing flexibility in how users can use their content.
  • Open Source
    As an open-source platform, Dillinger allows developers to contribute to the project or customize the tool for their specific needs.

Possible disadvantages of Dillinger

  • Limited Offline Support
    Dillinger is primarily a web-based application and requires an internet connection for full functionality, limiting its usability offline.
  • Basic Markdown Features
    While it covers the basics well, advanced Markdown features or plugins might be missing compared to more comprehensive editors.
  • Dependency on External Services
    Heavy reliance on third-party cloud services may be a drawback for users who prefer to keep their data localized or have privacy concerns.
  • No Native Desktop Application
    Dillinger does not offer a native desktop application, which might be a disadvantage for users who prefer or require desktop-based tools.
  • Limited Customization
    While the interface is user-friendly, it offers limited customization options in terms of themes and editor settings compared to some other Markdown editors.

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

Dillinger videos

+ Add

The Dillinger Escape Plan - Dissociation ALBUM REVIEW

More videos:

  • Review - The Dillinger Escape Plan - One Of Us Is The Killer ALBUM REVIEW
  • Review - DILLINGER ESCAPE PLAN Dissociation Album Review | Overkill Reviews

Category Popularity

0-100% (relative to NumPy and Dillinger)

NumPy

Dillinger

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Markdown Editor
0 0%
Markdown Editor
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Text Editors
0 0%
Text Editors
100% 100

User comments

Share your experience with using NumPy and Dillinger. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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

Dillinger Reviews

We have no reviews of Dillinger yet.
Be the first one to post

Social recommendations and mentions

Based on our record, NumPy should be more popular than Dillinger. 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 / 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 / 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

Dillinger mentions (26)

  • Markdown Syntax & Features: A Comprehensive 2025 Guide
    Dillinger - A cloud-enabled, mobile-ready, offline-storage, AngularJS-powered, HTML5 Markdown editor. - Source: dev.to / 4 months ago
  • 100+ Must-Have Web Development Resources
    Dillinger: An online editor that offers cloud storage and supports various export formats like HTML5 and PDF. - Source: dev.to / 7 months ago
  • Converting Markdown to PDF
    Simply access https://dillinger.io and paste your markdown code there. It has the option to export to PDF, as well as some other formats. - Source: dev.to / 10 months ago
  • Building a simple but scalable blog using Astro
    I have used Markdown before (https://dillinger.io/) so wouldn't have a problem with using it again as long as on page SEO isn't any extra effort. I am not sure how I would use Markdown and then add the content to the blog to be deployed and if that is going to be much harder than a headless CMS, I would go for the headless. Source: over 1 year ago
  • Getting Started with Git and GitHub: A simple roadmap
    Useful rescources for this are: Markdown Cheatsheet and Markdown Editor. - Source: dev.to / almost 2 years ago
View more

What are some alternatives?

When comparing NumPy and Dillinger, 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.

Typora - A minimal Markdown reading & writing app.

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

StackEdit - Full-featured, open-source Markdown editor based on PageDown, the Markdown library used by Stack Overflow and the other Stack Exchange sites.

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

Markdown by DaringFireball - Text-to-HTML conversion tool/syntax for web writers, by John Gruber

Loading...