dapla-toolbelt 1.2.2

Python module for use within Jupyterlab notebooks, specifically aimed for Statistics Norway's data platform called Dapla

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

Dependencies

External package dependencies are specified in the setup.py file, at install_requires=.

Bumping version

Use make to bump the patch, minor version or major version before creating a pull request to main.

The version number is located in bumpversion.cfg, dapla-toolbelt/dapla-toolbelt/__init__.py and in dapla-toolbelt/setup.py. To bump the version everywhere at once, run make bump-version-patch, make bump-version-minor, or make bump-version-major. Bumping must be done with a clean git working space, and automatically commits with the new version number.

Building and/or releasing automatically

Azure pipelines will build automatically on pull request creation, merge, and commit to main, and will release the new version of the package to pypi.org automatically upon tagging a given commit.

Building locally for development

Run make build to build wheel, source distribution, and check the distribution with twine.

Run this to check the contents of your wheel: tar tzf dist/dapla-toolbelt-<SEMVER>.tar.gz replacing with your current version number

Manually releasing

Test

Run this to test package publication by uploading to TestPyPI: twine upload --repository-url https://test.pypi.org/legacy/ dist/* You will have to manually enter a username and password for a user registered to test.pypi.org in order for this to work.

Prod

NB: This should only be done as a last resort, if the regular CI/CD pipeline (azure pipeline) does not work and it's necessary to release regardless.

In order to publish a new version of the package to PyPI for real, run this: twine upload dist/* Authenticate with your regular pypi.org username and password.