oldest-supported-numpy | package providing the oldest supported Numpy | Data Manipulation library

by scipy Python Version: 2023.12.21 License: BSD-2-Clause

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

kandi X-RAY | oldest-supported-numpy Summary

oldest-supported-numpy is a Python library typically used in Utilities, Data Manipulation, Numpy applications. oldest-supported-numpy 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 oldest-supported-numpy' or download it from GitHub, PyPI.

Meta-package providing the oldest supported Numpy for a given Python version and platform

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Quality

Security

License

Reuse

Support

oldest-supported-numpy has a low active ecosystem.

It has 38 star(s) with 24 fork(s). There are 27 watchers for this library.

There were 3 major release(s) in the last 6 months.

There are 3 open issues and 25 have been closed. On average issues are closed in 34 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of oldest-supported-numpy is 2023.12.21

Quality

oldest-supported-numpy has 0 bugs and 0 code smells.

Security

oldest-supported-numpy has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

oldest-supported-numpy code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

oldest-supported-numpy is licensed under the BSD-2-Clause License. This license is Permissive.

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

Reuse

oldest-supported-numpy 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.

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oldest-supported-numpy Key Features

No Key Features are available at this moment for oldest-supported-numpy.

oldest-supported-numpy Examples and Code Snippets

No Code Snippets are available at this moment for oldest-supported-numpy.

Community Discussions

Trending Discussions on oldest-supported-numpy

Trying to import scikit-learn to PyCharm on Mac M1

QUESTION

Trying to import scikit-learn to PyCharm on Mac M1

Asked 2022-Feb-07 at 15:42

I wanted to install the sklearn package in PyCharm. However, I always got the same error (below is an extract):

...

ANSWER

Answered 2022-Feb-07 at 12:38

I think the easiest approach is to create a conda environment from PyCharm. Go to the python interpreter settings and create a new conda environment from there. Then, install packages with conda inside that environment, all from inside PyCharm.

Technically speaking, you're never installing anything into PyCharm, but into a python installation, which can again either be a virtualenv or a conda env (env as in environment). If you manage to point PyCharm to the correct python executable, you should be good. On the other hand if you're conda/pip installing into some other environment in the terminal, you'll just be confused. Since I don't have an M1 Mac I can't say exactly where you went wrong, though.

Source https://stackoverflow.com/questions/71018623

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

Vulnerabilities

No vulnerabilities reported

Install oldest-supported-numpy

You can install using 'pip install oldest-supported-numpy' or download it from GitHub, PyPI.
You can use oldest-supported-numpy 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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