nada-data

Python library to enable tabular data operations with nada-dsl.

Purpose

Installation and Usage

This library is available as a package on PyPI:

python -m pip install nada-data

The library can be imported in the usual way:

import nada_data
from nada_data import *

The library provides two basic types: NadaArray and NadaTable. The NadaArray type is analogous to Python's builtin list type, and can be created as follows:

from nada_dsl import SecretInteger, Input, Party
from nada_data import NadaArray

party = Party(name="me")

# can be instantiated as comma-separated list of values
arr = NadaArray(
    SecretInteger(Input(name="int1", party=party)),
    SecretInteger(Input(name="int2", party=party))
)

# OR from list
values = [SecretInteger(Input(name="int1", party=party)), SecretInteger(Input(name="int2", party=party))]
arr = NadaArray(values)

# OR as list comp from list
arr = NadaArray(v for v in values)

>>> arr
NadaArray | len=2 | parties=['me']

Utility functions analogous to those available to the list type are provided:

from nada_data import sum_nada_array, filter_nada_array, nada_gt

# sum a NadaArray, outputting a single SecretInteger
s = sum_nada_array(arr)

new_party = Party(name="new_party")
# filter a NadaArray according to which values are greater than 'int3'
f = filter_nada_array(arr, nada_gt, SecretInteger(Input(name="int3", party=new_party)))

# display metadata about this NadaArray
>>> f
NadaArray | len=2 | parties=['me', 'new_party']

The NadaTable type is a tabular data structure that enables relational workflows over arrays of SecretInteger instances. Accordingly, it is instantiated from an array of NadaArray objects and a comma-separated list of column names:

from nada_data import NadaTable

rows = [
    NadaArray(SecretInteger(Input(name="int1", party=party)), SecretInteger(Input(name="int2", party=party))),
    NadaArray(SecretInteger(Input(name="int3", party=new_party)), SecretInteger(Input(name="int4", party=new_party)))
]

tbl = NadaTable(
    "a", "b", rows=rows
)
>>> tbl
NadaTable | cols=['a','b'] | rows=2 | parties=['me','new_party']

The NadaTable type provides relational-style functions that one would expect from something like a SQL table:

# construct a new table from one or more columns in an existing one
col = tbl.select("a")
>>> col
NadaTable | cols=['a'] | rows=2 | parties=['me', 'new_party']

# sum column 'b' ordered over column 'a'
tbl.aggregate_sum(key_col="a", agg_col="b")
>>> tbl
NadaTable | cols=['a','b'] | rows=2 | parties=['me','new_party']

Development

All installation and development dependencies are fully specified in pyproject.toml. The project.optional-dependencies object is used to specify optional requirements for various development tasks. This makes it possible to specify additional options (such as docs, lint, and so on) when performing installation using pip:

python -m pip install .[docs,lint]

Documentation

The documentation can be generated automatically from the source files using Sphinx:

python -m pip install .[docs]
cd docs
sphinx-apidoc -f -E --templatedir=_templates -o _source .. && make html

Testing and Conventions

All unit tests are executed using a combination of doctest and unittest. Both can be run simultaneously via the following:

python -m pip install .[test]
python -m unittest discover -s tests

Coverage can be measured while the tests are run using coverage:

coverage run -m unittest discover -s tests
coverage report

Style conventions are enforced using Pylint:

python -m pip install .[lint]
python -m pylint src/nada_data

Contributions

In order to contribute to the source code, open an issue or submit a pull request on the GitHub page for this library.

Versioning

The version number format for this library and the changes to the library associated with version number increments conform with Semantic Versioning 2.0.0.

Publishing

This library can be published as a package on PyPI by a package maintainer. First, install the dependencies required for packaging and publishing:

python -m pip install .[publish]

Ensure that the correct version number appears in pyproject.toml, and that any links in this README document to the Read the Docs documentation of this package (or its dependencies) have appropriate version numbers. Also ensure that the Read the Docs project for this library has an automation rule that activates and sets as the default all tagged versions. Create and push a tag for this version (replacing ?.?.? with the version number):

git tag ?.?.?
git push origin ?.?.?

Remove any old build/distribution files. Then, package the source into a distribution archive:

rm -rf build dist src/*.egg-info
python -m build --sdist --wheel .

Finally, upload the package distribution archive to PyPI:

python -m twine upload dist/*