pandas-compat | API compatibility for pandas downstream

by pandas-dev Python Version: 0.1.1 License: BSD-3-Clause

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | pandas-compat Summary

pandas-compat is a Python library. pandas-compat has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install pandas-compat' or download it from GitHub, PyPI.

API compatibility for pandas downstream

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Support

pandas-compat has a low active ecosystem.

It has 6 star(s) with 6 fork(s). There are 17 watchers for this library.

It had no major release in the last 12 months.

pandas-compat has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pandas-compat is 0.1.1

Quality

pandas-compat has no bugs reported.

Security

pandas-compat has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

pandas-compat is licensed under the BSD-3-Clause License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pandas-compat releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed pandas-compat and discovered the below as its top functions. This is intended to give you an instant insight into pandas-compat implemented functionality, and help decide if they suit your requirements.

Return a dict containing the command - line tool
Create a ConfigParser from root
Get the project root directory
Extract version information from VCS
Create the versioneer config file
Install versioneer
Scans the setup py file

Get all kandi verified functions for this library.

pandas-compat Key Features

No Key Features are available at this moment for pandas-compat.

pandas-compat Examples and Code Snippets

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Community Discussions

Trending Discussions on pandas-compat

Is there a nullable boolean type I can use in a Pandas dataframe?

apache arrow - adequacy for parallel processing

QUESTION

Is there a nullable boolean type I can use in a Pandas dataframe?

Asked 2021-May-05 at 16:40

In a program I am working on I have to explicitly set the type of a column that contains boolean data. Sometimes all of the values in this column are None. Unless I provide explicit type information Pandas will infer the wrong type information for that column.

Is there a pandas-compatible type that represents a nullable-bool? I want to do something like this, but preserve the Nones:

...

ANSWER

Answered 2021-May-05 at 16:40

boolean dtype should work:

Source https://stackoverflow.com/questions/67402394

QUESTION

apache arrow - adequacy for parallel processing

Asked 2020-Aug-18 at 10:29

I have a huge dataset and am using Apache Spark for data processing.

Using Apache Arrow, we can convert Spark-compatible data-frame to Pandas-compatible data-frame and run operations on it.

By converting the data-frame, will it achieve the performance of parallel processing seen in Spark or will it behave like Pandas?

...

ANSWER

Answered 2020-Aug-18 at 10:29

As you can see on the documentation here

Note that even with Arrow, toPandas() results in the collection of all records in the DataFrame to the driver program and should be done on a small subset of the data

The data will be sent to the driver when the data is moved to the Pandas data frame. That means that you may have performance issues if there is too much data for the driver to deal with. For that reason, if you are decided to use Pandas, try to group the data before calling to toPandas() method.

It won't have the same parallelization once it's converted to a Pandas data frame because Spark executors won't be working on that scenario. The beauty of Arrow is to be able to move from the Spark data frame to Pandas directly, but you have to think on the size of the data

Another possibility would be to use other frameworks like Koalas. It has some of the "beauties" of Pandas but it's integrated into Spark.

Source https://stackoverflow.com/questions/62707855

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

Vulnerabilities

No vulnerabilities reported

Install pandas-compat

You can install using 'pip install pandas-compat' or download it from GitHub, PyPI.
You can use pandas-compat like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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