numpy_ext | extension library for NumPy that implements common array | Storage library

by 3jane Python Version: v0.9.8 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | numpy_ext Summary

numpy_ext is a Python library typically used in Storage, Numpy applications. numpy_ext has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. However numpy_ext has 1 bugs. You can install using 'pip install numpy_ext' or download it from GitHub, PyPI.

An extension library for NumPy that implements common array operations not present in NumPy

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Support

numpy_ext has a low active ecosystem.

It has 33 star(s) with 11 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 2 open issues and 5 have been closed. On average issues are closed in 97 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of numpy_ext is v0.9.8

Quality

numpy_ext has 1 bugs (0 blocker, 0 critical, 1 major, 0 minor) and 1 code smells.

Security

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

numpy_ext code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

numpy_ext is licensed under the MIT License. This license is Permissive.

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

Reuse

numpy_ext releases are available to install and integrate.

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

It has 14114 lines of code, 37 functions and 61 files.

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

Top functions reviewed by kandi - BETA

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

Implementation of expstep .
Apply a function to a list of arrays .
Apply a function to a rolling window .
Create a rolling window of an array .
Expand an array .
Create nans .
Prepend nans to the given array .
Apply a function to an array .
Fill missing values with given value .
Fill missing values .

Get all kandi verified functions for this library.

numpy_ext Key Features

No Key Features are available at this moment for numpy_ext.

numpy_ext Examples and Code Snippets

No Code Snippets are available at this moment for numpy_ext.

Community Discussions

Trending Discussions on numpy_ext

Pandas' expanding with apply function on multiple columns

QUESTION

Pandas' expanding with apply function on multiple columns

Asked 2021-Apr-29 at 22:59

Is it possible to use panda's expanding function to calculate the coefficient of a polynomial regression using several columns of the window object?

I have a data frame which has two columns, a predictor and a response. I want to use pandas' expanding() function to calculate the corresponding coefficients of a second order polynomial regression for each expanding pair of series. For each row I would like to get the updated coefficients from the regression applied to all previous rows.

...

ANSWER

Answered 2021-Apr-22 at 16:35

I suspect what you are looking for is the new df.expanding(..., method='table') in the upcoming pandas=1.3 (see "Other enhancements").

In the meantime, you can do it "by hand", using a loop (sorry):

Source https://stackoverflow.com/questions/67138720

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

Vulnerabilities

No vulnerabilities reported

Install numpy_ext

You can install using 'pip install numpy_ext' or download it from GitHub, PyPI.
You can use numpy_ext 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.