Python module for use within Jupyterlab notebooks, specifically aimed for Statistics Norway's data platform called Dapla
Project description
dapla-toolbelt
Python module for use within Jupyterlab notebooks, specifically aimed for Statistics Norway's data platform called
Dapla
. It contains support for authenticated access to Google Services such as Google Cloud Storage (GCS) and custom
Dapla services such as Maskinporten Guardian. The
authentication process is based on the TokenExchangeAuthenticator
for Jupyterhub.
These operations are supported:
- List contents of a bucket
- Open a file in GCS
- Copy a file from GCS into local
- Load a file (CSV, JSON or XML) from GCS into a pandas dataframe
- Save contents of a data frame into a file (CSV, JSON, XML) in GCS
When the user gives the path to a resource, they do not need to give the GCS uri, only the path. This just means users don't have to prefix a path with "gs://". It is implicitly understood that all resources accessed with this tool are located in GCS, with the first level of the path being a GCS bucket name.
Installation
pip install dapla-toolbelt
Usage Examples
from dapla import FileClient
from dapla import GuardianClient
import pandas as pd
# Load data using the Maskinporten Guardian client
response = GuardianClient.call_api("https://data.udir.no/api/kag", "88ace991-7871-4ccc-aaec-8fb6d78ed04e", "udir:datatilssb")
data_json = response.json()
raw_data_df = pd.DataFrame(data_json) # create pandas data frame from json
raw_data_df.head() # show first rows of data frame
FileClient.ls("bucket-name/folder") # list contents of given folder
# Save data into different formats
path_base = "bucket-name/folder/raw_data"
FileClient.save_pandas_to_json(raw_data_df, f"{path_base}.json") # generate json from data frame, and save to given path
FileClient.save_pandas_to_csv(raw_data_df, f"{path_base}.csv") # generate csv from data frame, and save to given path
FileClient.save_pandas_to_xml(raw_data_df, f"{path_base}.xml") # generate xml from data frame, and save to given path
FileClient.cat(f"{path_base}.json") # print contents of file
# Load data from different formats
# All these data frames should contain the same data:
df = FileClient.load_json_to_pandas(f"{path_base}.json") # read json from path and load into pandas data frame
df.head() # show first rows of data frame
df = FileClient.load_csv_to_pandas(f"{path_base}.csv") # read csv from path and load into pandas data frame
df.head() # show first rows of data frame
df = FileClient.load_xml_to_pandas(f"{path_base}.xml") # read xml from path and load into pandas data frame
df.head() # show first rows of data frame
Development
Prerequisites
- Python >3.8 (3.10 is preferred)
- Poetry, install via
curl -sSL https://install.python-poetry.org | python3 -
You can also execute make
in the project folder to see available make
commands.
Dependency Management
Poetry is used for dependency management. Poe the Poet is used for running poe tasks within poetry's virtualenv. Upon cloning this project first install necessary dependencies, then run the tests to verify everything is working.
Install all dependencies
poetry install
Add dependencies
Main
poetry add <python package name>
Dev
poetry add --group dev <python package name>
Run tests
poetry run poe test
Run project locally in Jupyter
To run the project locally in Jupyter run:
poetry run poe jupyter
A Jupyter instance should open in your browser. Open and run the cells in the demo.ipynb
file.
Bumping version
Use make
to bump the patch, minor version or major version before creating a pull request to the main
GIT branch.
Or run a poe task like this:
poetry run poe bump-patch-version
You can use either bump-version-patch
, bump-version-minor
, or bump-version-major
.
Bumping must be done with a clean git working space, and automatically commits with the new version number.
Then just run git push origin --tags
to push the changes and trigger the release process.
Building and releasing
Before merging your changes into the main
branch, make sure you have bumped the version like outlined above.
An automatic release process will build dapla-toolbelt upon pull request-creation, merges, and direct commits to the
main
GIT branch. It will also release a new version of the package to pypi.org automatically when a commit is
tagged, for example by a GitHub release.
Building and releasing manually
Run make build
to build a wheel and a source distribution.
Run make release-validate
to do all that AND validate it for release.
Run this (replacing with your current version number) to check the contents of your wheel:
tar tzf dist/dapla-toolbelt-<SEMVER>.tar.gz
Test release
You have to bump the version of the package (see documentation on "Bumping version" above) before releasing, because even test.pypi.org does not allow re-releases of a previously released version.
Run the following command in order to build, validate, and test package publication by uploading to TestPyPI:
make release-test
You will have to manually enter a username and password for a user registered to test.pypi.org in order for this to work.
Production release
NB: A manual production release should only be done as a last resort, if the regular CI/CD pipeline does not work, and it's necessary to release regardless.
You have to bump the version of the package (see documentation on "Bumping version" above) to something different from the last release before releasing.
In order to publish a new version of the package to PyPI for real, run make release
.
Authenticate by manually entering your pypi.org username and password.
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 Distribution
Built Distribution
Hashes for dapla_toolbelt-1.4.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b82460d1853246fb577d08f76ad569d7f73b46ec15c94d00e3e9eaf053664e44 |
|
MD5 | 6718f9ad1ffbe40a21022625eafb3ea4 |
|
BLAKE2b-256 | 45c2d6db51a60eb210614997f99953f5b49c99a57f8e5d706bbb3cf7b59bd963 |