Datapad is a Python library for processing sequence and stream data using a Fluent style API. Data scientists and researchers use it as a lightweight toolset to efficiently explore datasets and to massage data for modeling tasks.
It can be viewed as a combination of syntatic sugar for the Python itertools module and supercharged tooling for working with Structured Sequence data.
pip install datapad
See what you can do with datapad in the examples below.
Count all unique items in a sequence:
>>> import datapad as dp
>>> data = ['a', 'b', 'b', 'c', 'c', 'c']
>>> seq = dp.Sequence(data)
>>> seq.count(distinct=True) \
... .collect()
[('a', 1),
('b', 2),
('c', 3)]Transform individual fields in a sequence:
>>> import datapad as dp
>>> F = datapad.fields
>>> data = [
... {'a': 1, 'b': 2},
... {'a': 4, 'b': 4},
... {'a': 5, 'b': 7}
... ]
>>> seq = dp.Sequence(data)
>>> seq.map(F.apply('a', lambda x: x*2)) \
... .map(F.apply('b', lambda x: x*3)) \
... .collect()
[{'a': 2, 'b': 6},
{'a': 8, 'b': 12},
{'a': 10, 'b': 21}]Chain together multiple transforms for the elements of a sequence:
>>> import datapad as dp
>>> data = ['a', 'b', 'b', 'c', 'c', 'c']
>>> seq = dp.Sequence(data)
>>> seq.distinct() \
... .map(lambda x: x+'z') \
... .map(lambda x: (x, len(x))) \
... .collect()
[('az', 2),
('bz', 2),
('cz', 2)]Check out our documentation below to see what else is possible with Datapad:
Run tests from the root of repo using
pip install pytest
sh test.sh
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