Effective-Python | 59个编写高质量Python代码的中文翻译版。

by guoruibiao CSS Version: Current License: No License

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kandi X-RAY | Effective-Python Summary

Effective-Python is a CSS library. Effective-Python has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

59个编写高质量Python代码的中文翻译版。

Support

Quality

Security

License

Reuse

Support

Effective-Python has a low active ecosystem.

It has 211 star(s) with 32 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 2 have been closed. On average issues are closed in 45 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Effective-Python is current.

Quality

Effective-Python has no bugs reported.

Security

Effective-Python has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

Effective-Python 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.

Reuse

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

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Effective-Python Key Features

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Effective-Python Examples and Code Snippets

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Community Discussions

Trending Discussions on Effective-Python

How to install optional components (anaconda, jupyter) in custom dataproc image

QUESTION

How to install optional components (anaconda, jupyter) in custom dataproc image

Asked 2021-May-03 at 20:41

To speed up my cluster instantiation time, I've created a custom image with all the additional dependencies installed using miniconda3 available for dataproc image 1.5.34-debian10. (I followed the steps here: GCP Dataproc custom image Python environment to ensure I used the correct python environment).

However, when I start my cluster with --optional-components ANACONDA,JUPYTER my custom dependencies are removed and I'm left with a base installation of anaconda and jupyter. I assume the anaconda installation is overwriting my custom dependencies. Is there any way to ensure my dependencies aren't overwritten? If not, is it possible to install anaconda and jupyter as part of my custom dataproc image instead?

I've used the following command to create the custom image:

...

ANSWER

Answered 2021-May-03 at 20:41

The customize_conda.sh script is the recommended way of customizing Conda env for custom images.

If you need more than the script does, you can read the code and create your own script, but anyway you want to use the absolute path e.g., /opt/conda/anaconda/bin/conda, /opt/conda/anaconda/bin/pip, /opt/conda/miniconda3/bin/conda, /opt/conda/miniconda3/bin/pip to install/uninstall packages for the Anaconda/Miniconda env.

Source https://stackoverflow.com/questions/67357670

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

Vulnerabilities

No vulnerabilities reported

Install Effective-Python

You can download it from GitHub.

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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