Forwarded from Находки в опенсорсе
1. sphinx-version-warning: allows you to add a custom warning banner at the top of your documentation pages to communicate some important about this documentation: https://sphinx-version-warning.readthedocs.io
2. sphinx-notfound-page: is great to create a "Not found" (or 404) page to show when the reader hit a not found page: https://sphinx-notfound-page.readthedocs.io
3. sphinx-hoverxref: adds amazing tooltips on your cross-references that points to another page/section of the documentation including its content on the tooltip: https://sphinx-hoverxref.readthedocs.io
#python
2. sphinx-notfound-page: is great to create a "Not found" (or 404) page to show when the reader hit a not found page: https://sphinx-notfound-page.readthedocs.io
3. sphinx-hoverxref: adds amazing tooltips on your cross-references that points to another page/section of the documentation including its content on the tooltip: https://sphinx-hoverxref.readthedocs.io
#python
Forwarded from Находки в опенсорсе
Great Expectations: Always know what to expect from your data.
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.
Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams.
See Down with Pipeline Debt! for an introduction to the philosophy of pipeline testing: https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a
Key features:
- Expectations or assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues
- Batteries-included data validation
- Tests are docs and docs are tests: many data teams struggle to maintain up-to-date data documentation. Great Expectations solves this problem by rendering Expectations directly into clean, human-readable documentation
- Automated data profiling: wouldn't it be great if your tests could write themselves? Run your data through one of Great Expectations' data profilers and it will automatically generate Expectations and data documentation
- Pluggable and extensible
https://github.com/great-expectations/great_expectations
#python #ds #docops
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling.
Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams.
See Down with Pipeline Debt! for an introduction to the philosophy of pipeline testing: https://medium.com/@expectgreatdata/down-with-pipeline-debt-introducing-great-expectations-862ddc46782a
Key features:
- Expectations or assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues
- Batteries-included data validation
- Tests are docs and docs are tests: many data teams struggle to maintain up-to-date data documentation. Great Expectations solves this problem by rendering Expectations directly into clean, human-readable documentation
- Automated data profiling: wouldn't it be great if your tests could write themselves? Run your data through one of Great Expectations' data profilers and it will automatically generate Expectations and data documentation
- Pluggable and extensible
https://github.com/great-expectations/great_expectations
#python #ds #docops