Quantile_Normalize | function implements quantile normalization | Data Visualization library

by ShawnLYU Python Version: Current License: No License

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kandi X-RAY | Quantile_Normalize Summary

Quantile_Normalize is a Python library typically used in Analytics, Data Visualization, Numpy, Pandas applications. Quantile_Normalize has no bugs, it has no vulnerabilities and it has low support. However Quantile_Normalize build file is not available. You can download it from GitHub.

This function implements quantile normalization in python matrix

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Quantile_Normalize has a low active ecosystem.

It has 15 star(s) with 11 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 2 have been closed. On average issues are closed in 456 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Quantile_Normalize is current.

Quality

Quantile_Normalize has 0 bugs and 0 code smells.

Security

Quantile_Normalize has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Quantile_Normalize code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Quantile_Normalize 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.

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Quantile_Normalize releases are not available. You will need to build from source code and install.

Quantile_Normalize has no build file. You will be need to create the build yourself to build the component from source.

Quantile_Normalize saves you 4 person hours of effort in developing the same functionality from scratch.

It has 13 lines of code, 1 functions and 2 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Performs quantiles normalization .

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Quantile_Normalize Key Features

No Key Features are available at this moment for Quantile_Normalize.

Quantile_Normalize Examples and Code Snippets

No Code Snippets are available at this moment for Quantile_Normalize.

Community Discussions

Trending Discussions on Quantile_Normalize

mypy overload function with numpy ndarray and pandas dataframe (signature parameter type(s) are the same or broader)

QUESTION

mypy overload function with numpy ndarray and pandas dataframe (signature parameter type(s) are the same or broader)

Asked 2020-Aug-02 at 13:52

I have a function that does some arithmetic stuff (quantile normalization) over either a numpy array or pandas dataframe. When you put in a ndarray, you should get back a ndarray, and when you put in a pandas dataframe you should get back a dataframe:

...

ANSWER

Answered 2020-Aug-02 at 13:52

When a library is missing type hints, every import will resolve to Any. Both numpy and pandas aren't PEP 526 conform (not offering any type hints) and have no stubs in typeshed, so both pd.DataFrame and np.ndarray will resolve to Any, thus both overloads resolve to def quantile_normalize(data: Any) -> Any: .... To fix the issue, add stubs for numpy and pandas.

Either use existing type stubs - I use data-science-types (PyPI, GitHub) which offer stubs for numpy, pandas and matplotlib:

Source https://stackoverflow.com/questions/63214123

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

Vulnerabilities

No vulnerabilities reported

Install Quantile_Normalize

You can download it from GitHub.
You can use Quantile_Normalize 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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