python-unf

unf contains Python code for Universal Numeric Fingerprints (UNFs).

UNFs are defined at http://guides.dataverse.org/en/latest/developers/unf/index.html.

Usage:

>>> import unf
>>> unf.unf(1.23456789)
'UNF:6:vcKELUSS4s4k1snF4OTB9A=='
>>> unf.unf(1.23456789, 9)
'UNF:6:N9:IKw+l4ywdwsJeDze8dplJA=='

Basic usage

UNFs are calculated according to UNF Version 6 (1).

The number of rounding digits can be specified, but the string truncation length and the hash truncation length are fixed to the specification defaults (128 bytes and 128 bits, respectively).

None can be used to indicate a missing value, so to match the example "Vector (numeric): {1.23456789, <MISSING VALUE>, 0}" (1), use:

>>> unf.unf([1.23456789, None, 0])
'UNF:6:Do5dfAoOOFt4FSj0JcByEw=='

Existing implementations include IQSS/UNF (2) and the R UNF package (3). The data structures supported by these implementations require sequences containing values of a single data type. Although the UNF specification does not explicitly allow or forbid it, this implementation does allow sequences containing multiple basic data types. Compound sequences (sequences containing sequences) are forbidden for native Python data types.

NumPy support

NumPy arrays are supported:

>>> unf.unf(numpy.array([1, 2, 3]))
'UNF:6:AvELPR5QTaBbnq6S22Msow=='

Only numeric NumPy data types are supported.

Note that 2-D NumPy arrays are interpreted as collections of vectors and are subject to the digest-sort-digest rule for "higher-level objects" (1). So:

• numpy.array([[1, 2], [3, 4]]) is treated as a collection of vectors [1, 2] and [3, 4] and will be evaluated the same way as a data frame with two variables. This will therefore evaluate to the same UNF as numpy.array([[3, 4], [1, 2]])), so the UNF is not suitable for matrices.
• numpy.array([[1, 2]]) is interpreted as a collection of one vector and evaluates to the same UNF as the vector itself, numpy.array([1, 2]).
• numpy.array([[1], [2]]) is treated as a collection of two (one-element) vectors.

Primitives (non-arrays) with NumPy data types and arrays of greater than two dimensions are not supported.

Note that NumPy does not have a special indicator for a missing value. If we try to use None as we did in the pure Python example, we find that NumPy translates it to nan:

>>> a = numpy.array([None], dtype=float)
>>> a
array([nan])

which is itself a special value (distinct from a missing value) where UNFs are concerned. NumPy arrays should therefore not be used when data is missing.

pandas support

pandas series and data frames are supported:

>>> unf.unf(pandas.Series([1, 2, 3]))
'UNF:6:AvELPR5QTaBbnq6S22Msow=='
>>> unf.unf(pandas.DataFrame({'a': [1, 2, 3], 'b': [4, 5, 6]}))
'UNF:6:Np0sj111a+rrJBgl6wNF9w=='

Only numeric data types are supported.

Behavior on multi-indexed data frames is undefined.

pandas does have an indicator for missing values (pandas.NA), but its use requires a series of data type object, which is not supported. And like NumPy, pandas translates None to NaN, so there is no unambiguous way to indicate missing data in pandas. pandas objects should therefore not be used when data is missing.

References

1: https://guides.dataverse.org/en/latest/developers/unf/unf-v6.html

2: https://raw.githubusercontent.com/IQSS/UNF/master/doc/unf_examples.txt

3: https://github.com/leeper/UNF

See also

Please see ROUNDING.md for a note about rounding.