Software Alternatives, Accelerators & Startups
Register | Login

PyPOTS VS Awesome Python

Compare PyPOTS VS Awesome Python and see what are their differences

ASocks
Clear, Fast & Unlimited. Residential & Mobile Proxies For Best Price. featured
Contents:
  1. » Base Details
  2. » Reviews
  3. » Alternatives

PyPOTS logo PyPOTS

a Python lib for data mining on PartiallyObserved TimeSeries

Awesome Python logo Awesome Python

Your go-to Python Toolbox. A curated list of awesome Python frameworks, packages, software and resources. 1303 projects organized into 177 categories.
  • PyPOTS Landing page
    Landing page //
    2023-09-15
  • Awesome Python Landing page
    Landing page //
    2023-01-12

PyPOTS features and specs

No features have been listed yet.

Awesome Python features and specs

  • Comprehensive Resource
    Awesome Python offers a wide array of libraries and frameworks, making it a comprehensive resource for Python developers seeking tools across different categories.
  • Community Driven
    The repository is community-driven, with users contributing and curating the list, ensuring that it stays up-to-date with the latest and most popular tools.
  • Categorized Listings
    Resources are organized into categories, allowing users to quickly find tools relevant to their specific project needs.
  • Brief Descriptions
    Each library and framework comes with a brief description, helping users quickly understand the purpose and function of each tool.
  • Popularity Indicators
    Includes indicators such as stars and forks on GitHub, providing a sense of how widely used or trusted a particular library is within the community.

Possible disadvantages of Awesome Python

  • Quality Variation
    Since anyone can contribute, there is a variation in quality and maturity among the listed projects, which could lead to unreliable tools being included.
  • Overwhelming for Beginners
    The sheer volume of listed resources might be overwhelming for beginners who may struggle to identify which tools best fit their needs.
  • Lack of Deep Reviews
    Descriptions are generally brief, providing limited insight into the pros and cons of using each tool, which might require additional research from users.
  • Inconsistency in Updates
    Despite community efforts, some entries might lag in updates, potentially listing outdated or deprecated libraries.
  • No Direct Support
    As a curated list, it does not offer direct support or guidance on implementing the tools, leaving users to seek other sources for help.

Category Popularity

0-100% (relative to PyPOTS and Awesome Python)

PyPOTS

Awesome Python

Developer Tools
22 22%
Developer Tools
78% 78
Productivity
22 22%
Productivity
78% 78
Open Source
23 23%
Open Source
77% 77
Web Scraping
100 100%
Web Scraping
0% 0

User comments

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

Social recommendations and mentions

Based on our record, PyPOTS should be more popular than Awesome Python. It has been mentiond 3 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.

PyPOTS mentions (3)

  • [R] SAITS: Self-Attention-based Imputation for Time Series. Expert Systems with Applications, 219:119619, 2023.
    Absolutely my pleasure! Please pay a visit to the toolbox PyPOTS https://pypots.com if you're interested in modelling partially-observed time series (POTS). It deserves your attention ;-). Source: almost 2 years ago
  • Missing values in time series collected from the real world are common to see and very pesky. A new state-of-the-art and fast neural network called SAITS is proposed to impute missing data in partially-observed multivariate time series. The code is open source on GitHub.
    If your research lies in time-series modeling, you may also be interested in the work PyPOTS: a Python toolbox for data mining on Partially-Observed Time Series https://pypots.com/. Its full paper is available on arXiv as well https://arxiv.org/abs/2305.18811, which has been peer-reviewed and accepted by the 9th SIGKDD international workshop Mining and Learning from Time Series (MiLeTS'23). Source: almost 2 years ago
  • We built PyPOTS: an open-source toolbox for data mining on partially-observed time series
    Due to all kinds of reasons like failure of collection sensors, communication error, and unexpected malfunction, missing values are common to see in time series from the real-world environment. This makes partially-observed time series (POTS) a pervasive problem in open-world modelling and prevents advanced data analysis. Although this problem is important, the area of data mining on POTS still lacks a dedicated... Source: almost 2 years ago

Awesome Python mentions (1)

What are some alternatives?

When comparing PyPOTS and Awesome Python, you can also consider the following products

Apify Python SDK - Build and manage web scraping Actors in the cloud.

My Good First Issue - mygoodfirstissue helps you find open source projects with a codebase you are comfortable with.

Google Workspace - Google's encompassing suite of cloud-based business apps.

Request for maintainers - Find any OSS project calling for collaborators

Learning Django Web Development - From idea to prototype, a learner guide for Django web dev

Serverless - Toolkit for building serverless applications

Loading...