polars-book | Book documentation of the Polars DataFrame library | Learning library

 by   pola-rs HTML Version: Current License: No License

kandi X-RAY | polars-book Summary

kandi X-RAY | polars-book Summary

polars-book is a HTML library typically used in Tutorial, Learning applications. polars-book has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

In this repo one can find the source of the User Guide of the Polars DataFrame library.

            kandi-support Support

              polars-book has a low active ecosystem.
              It has 138 star(s) with 107 fork(s). There are 10 watchers for this library.
              It had no major release in the last 6 months.
              There are 17 open issues and 76 have been closed. On average issues are closed in 227 days. There are 2 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of polars-book is current.

            kandi-Quality Quality

              polars-book has no bugs reported.

            kandi-Security Security

              polars-book has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              polars-book does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              polars-book releases are not available. You will need to build from source code and install.
              Installation instructions are not available. Examples and code snippets are available.

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            polars-book Key Features

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            polars-book Examples and Code Snippets

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            Community Discussions


            Apply function to all columns of a Polars-DataFrame
            Asked 2021-Jun-11 at 09:30

            I know how to apply a function to all columns present in a Pandas-DataFrame. However, I have not figured out yet how to achieve this when using a Polars-DataFrame.

            I checked the section from the Polars User Guide devoted to this topic, but I have not find the answer. Here I attach a code snippet with my unsuccessful attempts.



            Answered 2021-Jun-11 at 09:30

            You can use the expression syntax to select all columns with pl.col("*") and then map the numpy np.log2(..) function over the columns.

            Source https://stackoverflow.com/questions/67834912


            Arrow IPC vs Feather
            Asked 2021-Jun-09 at 20:52

            What is the difference between Arrow IPC and Feather?

            The official documentation says:

            Version 2 (V2), the default version, which is exactly represented as the Arrow IPC file format on disk. V2 files support storing all Arrow data types as well as compression with LZ4 or ZSTD. V2 was first made available in Apache Arrow 0.17.0.

            While vaex, a pandas alternative, has two different functions, one for Arrow IPC and one for Feather. polars, another pandas alternative, indicate that Arrow IPC and Feather are the same.



            Answered 2021-Jun-09 at 20:18

            TL;DR There is no difference between the Arrow IPC file format and Feather V2.

            There's some confusion because of the two versions of Feather, and because of the Arrow IPC file format vs the Arrow IPC stream format.

            For the two versions of Feather, see the FAQ entry:

            What about the “Feather” file format?

            The Feather v1 format was a simplified custom container for writing a subset of the Arrow format to disk prior to the development of the Arrow IPC file format. “Feather version 2” is now exactly the Arrow IPC file format and we have retained the “Feather” name and APIs for backwards compatibility.

            So IPC == Feather(V2). Some places refer to Feather mean Feather(V1) which is different from the IPC file format. However, that doesn't seem to be the issue here: Polars and Vaex appear to use Feather to mean Feather(V2) (though Vaex slightly misleadingly says "Feather is exactly represented as the Arrow IPC file format on disk, but also support compression").

            Vaex exposes both export_arrow and export_feather. This relates to another point of Arrow, as it defines both an IPC stream format and an IPC file format. They differ in that the file format has a magic string (for file identification) and a footer (to support random access reads) (documentation).

            export_feather always writes the IPC file format (==FeatherV2), while export_arrow lets you choose between the IPC file format and the IPC stream format. Looking at where export_feather was added I think the confusion might stem from the PyArrow APIs making it obvious how to enable compression with the Feather API methods (which are a user-friendly convenience) but not with the IPC file writer (which is what export_arrow uses). But ultimately, the format being written is the same.

            Source https://stackoverflow.com/questions/67910612

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


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