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NumPy VS Email This

Compare NumPy VS Email This and see what are their differences

Topolog
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

Email This logo Email This

Save ad-free articles and web pages and articles to your email inbox for reading later.
  • NumPy Landing page
    Landing page //
    2023-05-13
  • Email This Landing page
    Landing page //
    2021-10-19

Found a great article but donโ€™t have time to read it now? Save the complete web page to your email and read it later.

Email This removes ads, distractions and crufty sidebars from a web page and sends a cleaned-up, readable view of the page to your email inbox. You can then open up your email inbox and read your saved articles whenever you want.

Email This is a simpler alternative to bookmarking and "read later" tools like Pocket, Instapaper, and Pinboard. There is no need to signup for a new service or install any additional applications to read your saved bookmarks. You can even access your saved bookmarks offline on your mobile phones and tablets.

Benefits & features

  • Save any web page or article with one-click
  • Save the current page with a keyboard shortcut
  • Browser extensions available for all browsers and devices - Chrome, Firefox, Edge, Opera, Safari.
  • Also works from iOS and Android
  • [NEW] Add notes and keywords to your saved pages. This helps you search for your content faster.
  • [NEW] Include PDF snapshot of all web pages
  • [NEW] PDF files, images, DOCX, PPTs and Excel sheets will be automatically downloaded and sent as email attachments.
  • Right-click and save links without opening them.
  • Completely free to use.

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.

Email This features and specs

  • Easy to Set-up and use
    Browser extensions available for all major browsers
  • One Click Save
    Save any web page with a single click
  • Works everywhere
    You control the content you save. No lock-ins

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.

Analysis of Email This

Overall verdict

  • Email This is a reliable and effective tool for users who want to focus on content without interruptions from ads or unnecessary elements. It can be particularly useful for those who prefer reading content offline or need to access their saved articles across multiple devices.

Why this product is good

  • Email This is considered good because it provides a simple and efficient way to save web pages for later reading, minus the ads and distractions. It allows users to save content to their email with a single click, making it accessible across all devices and platforms.

Recommended for

  • Users who prefer email as a primary organizational tool
  • Individuals seeking a distraction-free reading experience
  • Professionals and students who need to save articles for research or later reference
  • Users who want a cross-platform solution for saving and reading web content

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

Email This videos

+ Add

Getting started with EmailThis

Category Popularity

0-100% (relative to NumPy and Email This)

NumPy

Email This

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Bookmark Manager
0 0%
Bookmark Manager
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
Bookmarks
0 0%
Bookmarks
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 Email This

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

Email This Reviews

  1. Guest
    Nifty little app

    EmailThis is a great app that replaces Pocket and Instapaper.

    ๐Ÿ Competitors: Pocket, Instapaper, Reader Mode
11 Pocket Alternatives You Must Try Out!
EmailThis.me, unlike other apps and sites, helps you save stuff by simply sending you an email. So now you can actually read it later by going to your inbox anytime!
Source: blog.elink.io

Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 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 (122)

View more

Email This mentions (0)

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

What are some alternatives?

When comparing NumPy and Email This, 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.

Instapaper - Instapaper is a simple tool to save web pages for reading later.

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

wallabag - Save the web, freely.

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

Raindrop.io - All your articles, photos, video & content from web & apps in one place.

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