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PyPOTS VS Libraries.io

Compare PyPOTS VS Libraries.io and see what are their differences

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PyPOTS logo PyPOTS

a Python lib for data mining on PartiallyObserved TimeSeries

Libraries.io logo Libraries.io

:books: The Open Source Discovery Service. Contribute to librariesio/libraries.io development by creating an account on GitHub.
  • PyPOTS Landing page
    Landing page //
    2023-09-15
  • Libraries.io Landing page
    Landing page //
    2023-08-29

PyPOTS features and specs

No features have been listed yet.

Libraries.io features and specs

  • Comprehensive Package Tracking
    Libraries.io provides detailed tracking for a wide range of programming languages and package managers, offering developers a centralized location to manage dependencies across projects.
  • Open Source
    Being open source, Libraries.io allows developers to contribute to its development, suggest improvements, and customize the tool to fit specific needs.
  • Dependency Insights
    The platform offers insights into project dependencies and provides notifications about releases, security vulnerabilities, and licensing changes.
  • Integration Capabilities
    Libraries.io integrates well with other development tools, providing seamless workflows for maintaining up-to-date project dependencies.
  • Community Contribution
    Combining data from thousands of projects, Libraries.io benefits from community contributions that enhance the accuracy and depth of its datasets.

Possible disadvantages of Libraries.io

  • Scalability Concerns
    As Libraries.io grows in the number of packages and users, there might be potential concerns regarding its ability to scale and maintain performance.
  • Dependency on External Sources
    The tool relies on data from external sources like package managers, which means any issues with these sources could affect Libraries.io's accuracy and uptime.
  • Maintenance Requirements
    As an open-source project, it depends on community involvement for maintenance, which might lead to slower updates and bug fixes if interest wanes.
  • Complexity for Beginners
    The extensive features and data available can be overwhelming for new users, leading to a steeper learning curve when first using the platform.

Category Popularity

0-100% (relative to PyPOTS and Libraries.io)

PyPOTS

Libraries.io

Productivity
100 100%
Productivity
0% 0
Software Development
0 0%
Software Development
100% 100
Open Source
100 100%
Open Source
0% 0
Security
0 0%
Security
100% 100

User comments

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Social recommendations and mentions

Based on our record, PyPOTS seems to be more popular. 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

Libraries.io mentions (0)

We have not tracked any mentions of Libraries.io yet. Tracking of Libraries.io recommendations started around Mar 2021.

What are some alternatives?

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

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Awesome Python - Your go-to Python Toolbox. A curated list of awesome Python frameworks, packages, software and resources. 1303 projects organized into 177 categories.

WhiteSource Renovate - Automate your dependency updates

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

Quick License Manager - Quick License Manager (QLM) is a license protection framework that creates professional and secure license keys to protect software against piracy.

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