Skip to main content

Code supporting the computational instruction for the course STAT 89A: Linear Algebra for Data Science at UC Berkeley

Project description

linalg_for_datasci

Code supporting the computational instruction for the course STAT 89A: Linear Algebra for Data Science at UC Berkeley.

Contributing

code style

If the pre-commit Python package is installed, you can set up pre-commit hooks for automatic code formatting via

pre-commit install

You can also invoke the pre-commit hook manually at any time with

pre-commit run

Automatic code formatting has been adopted for linalg_for_datasci to make it unnecessary for contributors to worry about their code style. As long as the code is valid, the pre-commit hook should take care of how the code should look.

There are also plugins to integrate the black code autoformatter into your favorite code editor. This way you can format code automatically.

If you have already committed files before setting up the pre-commit hook with pre-commit install, you can fix everything up using pre-commit run --all-files. You need to make the fixing commit yourself after that.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

linalg_for_datasci-1.0.0.tar.gz (51.7 kB view details)

Uploaded Source

Built Distribution

linalg_for_datasci-1.0.0-py3-none-any.whl (57.6 kB view details)

Uploaded Python 3

File details

Details for the file linalg_for_datasci-1.0.0.tar.gz.

File metadata

  • Download URL: linalg_for_datasci-1.0.0.tar.gz
  • Upload date:
  • Size: 51.7 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1.post20191125 requests-toolbelt/0.9.1 tqdm/4.39.0 CPython/3.8.0

File hashes

Hashes for linalg_for_datasci-1.0.0.tar.gz
Algorithm Hash digest
SHA256 2c00d632be22e35277277b51593c2d39554b94305fe7ded3c027adb98e80f282
MD5 9a9b70ab8f41e519bd3be80c2ee35e80
BLAKE2b-256 aa300e40eafc7e4c7556fb7b827b79f49e7de65279b61ef0d42d1f455f546612

See more details on using hashes here.

File details

Details for the file linalg_for_datasci-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: linalg_for_datasci-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 57.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.1.post20191125 requests-toolbelt/0.9.1 tqdm/4.39.0 CPython/3.8.0

File hashes

Hashes for linalg_for_datasci-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 3e523cac5a1d81783d50d6db8a4bbb56144ac04ccc75c2e27a254597d7cfe9c3
MD5 9d6cc3f94dc9e17c5bc36f198adcd10d
BLAKE2b-256 0223d570b9547edd1c70d21797a43f1b50a68cf45854d3fb92a9275cd31803c2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page