Библиотека Python разработчика | Книги по питону
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По всем вопросам @evgenycarter
РКН clck.ru/3Ko7Hq”
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range() defines all integers in a half-open interval. So range(2, 10) means, speaking mathematically, [2, 10). Or, speaking Python, [2, 3, 4, 5, 6, 7, 8, 9].
Despite asymmetry, that is not a mistake nor an accident. It makes perfect sense since it allows you to glue together two adjacent intervals without risk of one-off errors:
[a, c) = [a, b) + [b, c)
Compare to closed intervals that feel more “natural”:
[a, c] = [a, b] + [b+1, c]
This is also a reason for indexing to start from zero: range(0, N) has exactly N elements.
Dijkstra wrote an excellent article on the subject back in 1982.try:
lst = [1, 2, 3, 4, 5]
print(lst[10])
except IndexError:
pass
That will work (without printing anything), but contextlib let you do the same more expressively and semantically correct:
from contextlib import suppress
with suppress(IndexError):
lst = [1, 2, 3, 4, 5]
lst[10][]) by defining __getitem__ magic method. The example is Cycle object that virtually contains an infinite number of repeated elements:
class Cycle:
def __init__(self, lst):
self._lst = lst
def __getitem__(self, index):
return self._lst[index % len(self._lst)]
print(Cycle(['a', 'b', 'c'])[100]) # prints 'b'
The unusual thing here is [] operator supports a unique syntax. It can be used not only like this — [2], but also like this — [2:10], or [2:10:2], or [2::2], or even [:]. The semantic is [start:stop:step] but you can use it any way you want for your custom objects.
But what __getitem__ gets as an index parameter if you call it using that syntax? The slice objects exist precisely for this case.
In : class Inspector:
...: def __getitem__(self, index):
...: print(index)
...:
In : Inspector()[1]
1
In : Inspector()[1:2]
slice(1, 2, None)
In : Inspector()[1:2:3]
slice(1, 2, 3)
In : Inspector()[:]
slice(None, None, None)
You can even combine tuple and slice syntaxes:
In : Inspector()[:, 0, :]
(slice(None, None, None), 0, slice(None, None, None))
slice is not doing anything for you except simply storing start, stop and step attributes.
In : s = slice(1, 2, 3)
In : s.start
Out: 1
In : s.stop
Out: 2
In : s.step
Out: 3(arg=value) rather than just (value).
It may be useful to prevent function calls like this: grep(text, pattern, True, False, True), where True, False, True actually means ignore case, don't invert match, pattern is Perl regexp. It would be nice to force the only reasonable form of this call:
grep(text, pattern,
ignore_case=True,
perl_regexp=True)
To achieve this result you should place the keyword-only arguments after varargs argument (aka *args):
def grep(
text, pattern, *args,
ignore_case=False,
invert_match=False,
perl_regexp=False,
):
pass
If you don't need *args (like in the example), just replace it with a bare asterisk:
def grep(
text, pattern, *,
ignore_case=False,
invert_match=False,
perl_regexp=False,
):
passIndexError and KeyError, you may and should use LookupError, their common ancestor. It proved to be useful while accessing complex nested data:
try:
db_host = config['databases'][0]['hosts'][0]
except LookupError:
db_host = 'localhost'keys, values and items methods of dicts return view objects. They returned lists back in Python 2. The main difference is views don't store all items in memory, but yield them as long as they are requested. It works just fine as long as you are trying to iterate over keys (which you usually are), but you can't access elements by index anymore.
TypeError: 'dict_keys' object does not support indexing
You can argue that you don't really need indexing keys since their order is random, but it's not completely true. First of all, d.keys()[0] can be a proper way to get any key (use next(d.keys()) in Python 3). Second, since Python 3.6 dicts are insertion ordered in CPython and that will be a language feature since Python 3.7.
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