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Things VS NumPy

Compare Things VS NumPy and see what are their differences

PrivacyNotes
Zero-knowledge encrypted notes, tasks, journals, files, and passwords in one app. Your keys never leave your device. One-time price, no subscription. Hosted in Switzerland. featured
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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

Things logo Things

Things is an easy to use task manager.

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python
  • Things Landing page
    Landing page //
    2023-01-17
  • NumPy Landing page
    Landing page //
    2023-05-13

Things features and specs

  • User Experience
    Things is known for its clean, intuitive, and beautifully designed user interface, making it easy to use.
  • Integration with Apple Ecosystem
    Seamlessly integrates with macOS and iOS devices, offering features like Handoff and deep Apple Calendar integration.
  • Powerful Task Management
    Supports projects, areas, headings, and tags, providing a robust system for managing complex tasks and workflows.
  • Quick Entry
    Provides a quick entry function allowing users to capture tasks efficiently, which can later be categorized and detailed.
  • Updates and Support
    Regularly updated with new features and enhancements, backed by reliable customer support.
  • Keyboard Shortcuts
    Offers extensive keyboard shortcuts for power users to navigate and manage tasks quickly.
  • Natural Language Processing
    Allows users to input tasks using natural language, which is then intelligently parsed and scheduled.

Possible disadvantages of Things

  • Cost
    Things requires a one-time purchase for each platform (macOS, iOS), making it relatively expensive compared to some subscription-based competitors.
  • Platform Limitation
    Only available on Apple devices (macOS and iOS), making it inaccessible for users on Windows, Android, or other platforms.
  • No Collaboration Features
    Lacks built-in collaboration tools, which can be a drawback for teams looking to share and manage tasks collectively.
  • Learning Curve for Advanced Features
    While the basic interface is user-friendly, fully utilizing advanced features can require time and a deeper understanding.
  • Limited Automation
    Offers fewer automation options and integrations compared to some competitors like Todoist or Microsoft To Do.

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 Things

Overall verdict

  • Things is widely regarded as an excellent productivity tool, especially for Apple ecosystem users. It combines elegance with functionality, making it a top choice for those who prefer a minimalist but powerful task manager.

Why this product is good

  • Things by Cultured Code is highly acclaimed for its clean, intuitive design and effective task management features. It provides a seamless user experience with its natural language input, powerful integration with macOS and iOS, and features like projects, areas, deadlines, and reminders that help users organize their tasks efficiently. The app is particularly praised for its focus on simplicity and ease of use, which allows users to focus on their tasks without being overwhelmed by features.

Recommended for

    Things is ideal for individuals who are deeply integrated into the Apple ecosystem and appreciate a minimalist design approach. It's perfect for users who prefer a straightforward, no-frills task management system that emphasizes ease of use, efficiency, and aesthetic appeal.

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.

Things videos

+ Add

Brandon's Cult Movie Reviews: THINGS

More videos:

  • Review - Things 3: Full Review (2019)
  • Review - OmniFocus vs. Things 3 review: which is best for you?

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 Things and NumPy)

Things

NumPy

Task Management
100 100%
Task Management
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Project Management
100 100%
Project Management
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using Things 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 Things and NumPy

Things Reviews

11 Ayanza Alternatives
Things 3 is a multi-award-winning personal task manager that assists you in keeping track of your tasks. The environment of the application is attractive with a fresh new look, delightful integrations, and powerful features. It has been completely effective to boost efficiency with easy to use and is attractive to the eye. The themes are a creative and powerful feature that...
Source: www.justalternativeto.com
Five of the Best To-Do Apps for iOS
Things 3 is one of the few to-do apps that's not subscription based, and it costs $9.99 to purchase. Things 3 is also available for Mac and iPad, though each app must be purchased individually.
Source: www.macrumors.com

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 should be more popular than Things. 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.

Things mentions (58)

  • We don't need startups, we need Digital-Mittelstand
    Correct: https://culturedcode.com/things/ Looks like the different apps (desktop, mobile, iPad) have different prices, but all are one-time payments of $10-$50. - Source: Hacker News / over 1 year ago
  • Essential Software for Mac Users: Three Recommended Efficient Tools
    Things 3is an award-winning task management application known for its clean, elegant interface and intuitive usability. It employs a minimalist design style, allowing users to easily add, organize, and view tasks, helping individuals efficiently manage daily affairs. While Things 3 does not support team collaboration features, it provides a smooth user experience on macOS as a personal task management tool. - Source: dev.to / over 1 year ago
  • Show HN: I built a task manager that separates "Do" & "Due" dates
    How badly do Twos want to SEO rank on searches for Things? https://culturedcode.com/things/. - Source: Hacker News / over 1 year ago
  • Ask HN: What macOS apps/programs do you use daily and recommend?
    Alfred - Productivity App for macOS [1] iTerm2 - macOS Terminal Replacement [2] Dropshare App - upload anything anywhere on macOS [3] Mimestream - A native macOS email client for Gmail [4] Things - To-Do List for Mac & iOS [5] [1] https://www.alfredapp.com [2] https://iterm2.com [3] https://dropshare.app [4] https://mimestream.com [5] https://culturedcode.com/things. - Source: Hacker News / about 2 years ago
  • Ready to advance from Evernote, looking at Obsidian
    Currently, I use Things (https://culturedcode.com/things/) for tasks and Evernote for notes, and experimented with Freeform (I love the visual aspect and simplicity). At work, I've used Notion, Mural, Miro, LucidChart, Quip, and many other collaboration-based knowledge systems. I never researched the best of personal knowledge systems until now. Source: almost 3 years ago
View more

NumPy mentions (122)

View more

What are some alternatives?

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

Todoist - Todoist is a to-do list that helps you get organized, at work and in life.

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

TickTick - TickTickis a cross-platform to-do list app & task manager helps you to get all things done and make life well organized.

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

Remember The Milk - Remember The Milk is a task and time management application for mobile devices.

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

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