safe-cmp 0.1.1

Safe comparisons (total ordering) for any object in Python 3

In Python 2, it was possible to compare any object:

>>> None > 2
False
>>> [] < object()
True

But this is no longer true in Python 3:

>>> None > 2
...
TypeError: '>' not supported between instances of 'NoneType' and 'int'
>>> [] < object()
...
TypeError: '<' not supported between instances of 'list' and 'object'

But there are many cases where it is useful to, for example:

• Sort heterogeneous lists (ie, lists that contain many types of object)

• Compare objects to None (for example, to find the max(...) of a list where some items may be None)

• Write generic functions which will have robust, deterministic behaviour on arbitrary input

safe_cmp provides functions for safely sorting and ordering any value in Python 3. In fancy math terms, safe_cmp implements a total ordering of all values in Python 3 [1].

safe_cmp implements Python 2 compatible safe versions of the ordering functions:

• safe_cmp: a Python 2 compatible implementation of cmp for Python 3

• safe_sorted: a safe version of sorted(...)

• safe_min: a safe version of min(...)

• safe_max: a safe version of max(...)

And provides a wrapper - safe_order - which defines a total ordering for any object (for example, heterogeneous_list.sort(key=safe_order)).

Examples

Sorting a heterogeneous list:

>>> from safe_cmp import safe_sorted, safe_order
>>> items = [1, None, "foo", {}, object]
>>> list(safe_sorted(items)) # Using "safe_sorted"
[None, {}, 1, 'foo', object]
>>> items.sort(key=safe_order) # Using "safe_order" with key=
>>> items
[None, {}, 1, 'foo', object]

Finding the max of a list which includes nulls:

>>> from safe_cmp import safe_max
>>> safe_max([1, None, 3, None, 4])
4

The rare situation where Python 2 style cmp is useful:

>>> from safe_cmp import safe_cmp
>>> safe_cmp(None, 1)
-1
>>> safe_cmp(None, None)
0
>>> safe_cmp(1, None)
1

Note: safe_cmp will produce Python 2 compatible results when called with nan:

>>> from safe_cmp import safe_cmp
>>> nan = float("NaN")
>>> safe_cmp(nan, 1)
-1
>>> safe_cmp(1, nan)
1
>>> safe_cmp(nan, nan)
0

As will safe_sorted:

>>> from safe_cmp import safe_sorted
>>> list(safe_sorted([nan, 2, nan, 1]))
[nan, 2, nan, 1]

Performance

Currently safe_cmp methods are currently implemented in Python (in contrast to their unsafe builtin counterparts, which are implemented in C), so performance will notable worse for large comparisons:

In [1]: %timeit safe_max(range(10000000))
2.8 s ± 42 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [2]: %timeit max(range(10000000))
345 ms ± 6.23 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

For smaller comparisons, though, the difference will be negligible:

In [1]: %timeit safe_max(1, 2)
682 ns ± 7.12 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

In [2]: %timeit max(1, 2)
218 ns ± 6.87 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)

If there is interest in performant implementations, however, they will be straight forward to provide.

Additionally, where obvious, performance optimizations have been implemented (for example, caching the result of key= functions).

[1]

More precisely, a total ordering of all values which can be ordered. This excludes NaN, and any other values which are defined as having an undefined ordering.