Data-linter

- Dropping git repo references for pypi release

- Updated github repo dependencies in `pyproject.toml`

- Created Pandas Validator (now replaces frictionless as the default validator) ([issue 120](https://github.com/moj-analytical-services/data_linter/issues/120), [issue #98](https://github.com/moj-analytical-services/data_linter/issues/98))
- Enabled parallelisation of validators ([122](https://github.com/moj-analytical-services/data_linter/issues/122))
- Migrated to the new metadata schemas ([140](https://github.com/moj-analytical-services/data_linter/issues/140))
- Single process validator can run locally ([121](https://github.com/moj-analytical-services/data_linter/issues/121))
- Full log now writes to it's own folder ([130](https://github.com/moj-analytical-services/data_linter/issues/130))
- Renamed default branch of repo from master -> main


[ALL CLOSED ISSUES]
- issue 140
- issue 139
- issue 133
- issue 132
- issue 131
- issue 130
- issue 129
- issue 128
- issue 125
- issue 122
- issue 121
- issue 120
- issue 110
- issue 100
- issue 98
- issue 87
- issue 70

- Add pandas-kwargs to table params to pass through to Great Expectations (112)
- Update dependencies

- Split out the codebase to make it easier to add new validators. These have to conform to the validator base class `data_linter/validators/base.py`. (101)
- Added a great expectations validator. (103)
- Suprise! Reverted back to frictionless (from the previous revert in v3 to goodtables) (102)
- Improved logging now log up to level INFO is written to standard out and level DEBUG to S3 log.
- Added ability for user to define how data is written to S3. Specifically if you want it to be partitioned by timestamp or not.
- Dropping the `v` from our releases.

- Revert back to goodtables