Skip to main content

Powerful data manipulation

Project description

Data Manipulation

My data manipulation library includes functions build on top of popular Python libraries such as Pandas, PySpark and more.

What is it?

Data Manipulation is a Python package providing powerful utility functions. It contains many subpackages with utility functions built for popular packages such as Pandas, PySpark and many more.

Where to get it

The source code is currently hosted on GitHub at: https://github.com/shawnngtq/data-manipulation

pip install data-manipulation

Dependencies

The dependencies will be installed automatically along with this package, using metadata in pyproject.toml.

License

BSD 3

Maintainer Release

Release and deployment steps are documented in RELEASE.md.

Getting Help

For usage questions, the best place to go to is StackOverflow.

Discussion and Development

Most development discussion is taking place on github in this repo.

Contributing to data manipulation

All contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcome.

As contributors and maintainers to this project, you are expected to abide by our code of conduct. More information can be found at: Contributor Code of Conduct

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

data_manipulation-0.55.tar.gz (61.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

data_manipulation-0.55-py3-none-any.whl (51.4 kB view details)

Uploaded Python 3

File details

Details for the file data_manipulation-0.55.tar.gz.

File metadata

  • Download URL: data_manipulation-0.55.tar.gz
  • Upload date:
  • Size: 61.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.5

File hashes

Hashes for data_manipulation-0.55.tar.gz
Algorithm Hash digest
SHA256 2f3d64ea3da5e7d1dcfb78d9d9daae476d9b845b8d9c85fb42c2c432ddd20152
MD5 8fd93f8255f3c98d94e61d579aed4f58
BLAKE2b-256 6a1570fefc3f68c4c874be768f8830e0dda35d8dc0d2880a17d7f46e88c7b7c2

See more details on using hashes here.

File details

Details for the file data_manipulation-0.55-py3-none-any.whl.

File metadata

File hashes

Hashes for data_manipulation-0.55-py3-none-any.whl
Algorithm Hash digest
SHA256 8a1d9ef06e98ba2d81e6d1a8bc1dea0609c5426f704b9d5f1be94d53098c7fa8
MD5 599994ee598be171d6d82fad03183ca0
BLAKE2b-256 b2a157dbb29581ba6e13a06993d0fee2257596e3c6dede1f4a0ee163ee86c601

See more details on using hashes here.

Supported by

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