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Raymond Hettinger Profile picture
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9 Nov, 5 tweets, 1 min read
#Python tip: Structural pattern matching works with abstract base classes such as: Contains, Hashable, Iterable, Iterator, Reversible, Generator, Sized, Callable, and Collection.

match obj:
case Hashable:
...
Matching a collection ABC is preferable to looking for the required methods directly.

The complication is that those methods may be present but could be set to None.

The ABCs listed above handle the None checks for you.
For more complex ABCs, such as Sequence and Mapping, the presence of the required methods is insufficient.

The object's class needs to either inherit from the ABC or be registered with the ABC.
Note that matches only detect a promise that an API has been implemented. It can't detect whether a call to the API will succeed.

The classic example is that issubclass(tuple, Hashable) returns true, but hashing a tuple will fail if its components are unhashable.
Correction:

match obj:
case Hashable():
...

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More from @raymondh

Raymond Hettinger Profile picture
11 Nov
#Python's structural pattern matching is new, so the best practices aren't yet known.

Just added to my personal list: Add a comment or assertion noting when case ordering is important.

Otherwise, a future maintainer will be bitten by the illusion of case independence.

1 of 4
match x:
case bool():
...
case int():
assert not isinstance(x, bool)
...
case Counter():
...
case dict():
assert not isinstance(x, Counter)
case _:
raise TypeError

2 of 4
match sales_report:
case {'dept': dept, 'holiday': event}:
# Holiday case must precede other cases
...
case {'dept': 'shoes'}:
...
case {'dept': 'hats'}:
...

3 of 4
Read 4 tweets
Raymond Hettinger Profile picture
6 Apr
#Python factlet: The dict.popitem() method is guaranteed to remove key/value pairs in LIFO order.

>>> d = dict(red=1, green=2, blue=3)
>>> d.popitem()
('blue', 3)
>>> d.popitem()
('green', 2)
>>> d.popitem()
('red', 1)

1/
In contrast, OrderedDict.popitem() supports both FIFO and LIFO extraction of key/value pairs.

>>> from collections import OrderedDict
>>> d = OrderedDict(red=1, green=2, blue=3)

>>> d.popitem(last=False) # FIFO
('red', 1)

>>> d.popitem() # LIFO
('blue', 3)

2/
OrderedDict can efficiently move entries to either end without a hash table update.

>>> d = OrderedDict(red=1, green=2, blue=3)

>>> d.move_to_end('green')
>>> list(d)
['red', 'blue', 'green']

>>> d.move_to_end('green', last=False)
>>> list(d)
['green', 'red', 'blue']

3/
Read 4 tweets
Raymond Hettinger Profile picture
3 Jan
#Python factlet: The len() function insists that the corresponding __len__() method return a value x such that:

0 ≤ x.__index__() ≤ sys.maxsize

* 3.0 and '3' don't have an __index__ method.
* -1 is too small.
* sys.maxsize+1 is too big.

1/
You could call __len__() successfully, but the len() function fails:

class A:
def __len__(self):
return -1

>>> a = A()

>>> a.__len__()
-1

>>> len(a)
...
ValueError: __len__() should return >= 0

2/
Classes written in C typically implement __len__() with the mp_length or sq_length slot. That constrains them to sys.maxsize limits:

>>> r = range(10**100)
>>> r.__len__()
Traceback (most recent call last):
...
OverflowError: Python int too large to convert to C ssize_t

3/
Read 4 tweets
Raymond Hettinger Profile picture
2 Jan
@yera_ee Each way has its advantages.

With dataclasses, you get nice attribute access, error checking, a name for the aggregate data, and a more restrictive equality test. All good things.

Dicts are at the core of the language and are interoperable with many other tools: json, **kw, …
@yera_ee Dicts have a rich assortment of methods and operators.
People learn to use dicts on their first day.
Many existing tools accept or return dicts.
pprint() knows how to handle dicts.
Dicts are super fast.
JSON.
Dicts underlie many other tools.
@yera_ee Embrace dataclasses but don't develop an aversion to dicts.

Python is a very dict centric language.

Mentally rejecting dicts would be like developing an allergy to the language itself. It leads to fighting the language rather than working in harmony with it.
Read 4 tweets
Raymond Hettinger Profile picture
27 Dec 20
1/ #Python tip: Override the signature for *args with the __text_signature__ attribute:

def randrange(*args):
'Choose a random value from range(start[, stop[, step]]).'
return random.choice(range(*args))

randrange.__text_signature__ = '(start, stop, step, /)'
2/ The attribute is accessed by the inspect module:

>>> inspect.signature(randrange)
<Signature (start, stop, step, /)>
3/ Tooltips and help() will now be more informative:

>>> help(randrange)
randrange(start, stop, step, /)
Choose a random value from range(start[, stop[, step]]).
Read 4 tweets
Raymond Hettinger Profile picture
25 Oct 20
1/ #Python tip: The functools.cache() decorator is astonishingly fast.

Even an empty function that returns None can be sped-up by caching it. 🤨

docs.python.org/3/library/func…
2/ Here are the timings:

$ python3.9 -m timeit -r11 -s 'def s(x):pass' 's(5)'
5000000 loops, best of 11: 72.1 nsec per loop

$ python3.9 -m timeit -r11 -s 'from functools import cache' -s 'def s(x):pass' -s 'c=cache(s)' 'c(5)'
5000000 loops, best of 11: 60.6 nsec per loop
3/ Behind the scenes, there is a single dictionary lookup and a counter increment.

Some kinds arguments will take longer to hash, but you get the idea, @cache has very little overhead.
Read 5 tweets

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