Facades and common functions necessary for data science and data engineering workflows
Project description
analytics-mesh
Interfaces and facades that facilitate a common approach to analytics tasks
Getting Started
Please install the requirements:
pip install -r requirements
If you are going to be making modifications to the ipynb
notebooks, then be sure to install the pre-commit hook (see below):
pre-commit install
Tests
Tests are currently split into unit
and integration
tests. As this package integrates with storage systems, the integration tests are typically running against things like Google Cloud Platform.
Tests may be run in the root folder of the repo with:
coverage run -m unittest discover tests && coverage report
If you want to run just the unit tests (and ignore coverage) then,
python -m unittest discover tests/unit
Similarly, for the integration tests.
Pre-Commit Hooks and Notebook Workflow
In this repo we are using the python precommit
package (included in requirements.txt file). In order to leverage it in your development workflow, you need to run the following commands (assuming you have already installed your requirements).
pre-commit install
We follow the convention that a version controlled ipynb
file is converted to a markdown (md
) file to form an ipynb
-md
pair that are both version controlled. This allows us to code review the markdown files whilst keeping the original ipynb file output intact for easy perusal on the repository manager (Gitlab in our case).
Packaging the Code
The contents of the mesh
package are packaged in the build pipeline and submitted to pypi when merged to the master branch. The contents of the demos
folder and tests
is omitted from the package. See the .gitlab-ci.yml
file for the pypi instructions we use, as well as the tests and linting that are performed.
The consequences of this is that you will convert ipynb
notebooks to md
.
Contributions
Contributions are most welcome. Please submit patches or new features for code review by any of the main contributors:
Please be sure to run the tests prior to submission.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
File details
Details for the file analytics_mesh-0.1.11-py3-none-any.whl
.
File metadata
- Download URL: analytics_mesh-0.1.11-py3-none-any.whl
- Upload date:
- Size: 37.8 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.10.5
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 043f4189c788a92dcadabba235033d7200e163c4c3f4c5cda60dc687798acaee |
|
MD5 | cfcaf95ef7bae2e4d57f744c4942b7b9 |
|
BLAKE2b-256 | f697e3450cbe5aaaf83de4dd70db93c4828a9dfd918a7266017ee809751851e6 |