Software Alternatives, Accelerators & Startups

Things VS Pandas

Compare Things VS Pandas and see what are their differences

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Things logo Things

Things is an easy to use task manager.

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • Things Landing page
    Landing page //
    2023-01-17
  • Pandas Landing page
    Landing page //
    2023-05-12

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.

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

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 Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

Things videos

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?

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Category Popularity

0-100% (relative to Things and Pandas)
Task Management
100 100%
0% 0
Data Science And Machine Learning
Project Management
100 100%
0% 0
Data Science Tools
0 0%
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 Things and Pandas

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...
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.

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas should be more popular than Things. It has been mentiond 231 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
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Pandas mentions (231)

  • MLOps Lifecycle: Stages, Workflow, and Best Practices
    Feature transformations should be deterministic: The same input should produce the same output when the same feature definition and configuration are applied. This is what allows training, backtesting, and live inference to remain aligned. Tools such as Pandas, Spark, or feature platforms such as Feast can be used to implement that logic. - Source: dev.to / about 2 months ago
  • What Training Exists for Security Professionals Learning AI and Data Science?
    For early-career security practitioners (0-3 years). Start with Python literacy if you do not have it. The free Python Crash Course book and the pandas getting-started guide are enough to bootstrap. Then a hands-on applied course: GTK Cyber's Applied Data Science & AI for Cybersecurity and SANS SEC595 are both reasonable starting points. The goal at this stage is to be able to load a Zeek conn.log into a pandas... - Source: dev.to / about 2 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Evaluate the Options
    Python and data engineering for security data. Pandas for ingesting Zeek, Sysmon, EDR, and SIEM exports. Timestamp normalization to UTC, join keys across heterogeneous sources, feature extraction from raw logs. Without this layer, the ML content downstream is theater. - Source: dev.to / 2 months ago
  • Best AI Cybersecurity Training for Security Teams: How to Pick
    Pre-configured environment. A working VM or container with Jupyter, pandas, scikit-learn, and transformers already installed. Realistic security datasets loaded. GTK Cyber students work in the Centaur VM, a free Apache 2.0 portable lab. If the first hour of training is fighting CUDA installs, the course is not ready. - Source: dev.to / 2 months ago
  • Introduction to Python for Data Analysis: A Beginnerโ€™s Guide
    Pandas url is the most widely used library for data manipulation. - Source: dev.to / 2 months ago
View more

What are some alternatives?

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

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

NumPy - NumPy is the fundamental package for scientific computing with 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