I just spent a few minutes staring at a bug caused by a missing comma
-- I got a mysterious argument count error because instead of foo('a',
'b') I had written foo('a' 'b').
This is a fairly common mistake, and IIRC at Google we even had a lint
rule against this (there was also a Python dialect used for some
specific purpose where this was explicitly forbidden).
Now, with modern compiler technology, we can (and in fact do) evaluate
compile-time string literal concatenation with the '+' operator, so
there's really no reason to support 'a' 'b' any more. (The reason was
always rather flimsy; I copied it from C but the reason why it's
needed there doesn't really apply to Python, as it is mostly useful
inside macros.)
Would it be reasonable to start deprecating this and eventually remove
it from the language?
--
--Guido van Rossum (python.org/~guido)
Dear all,
I guess this is so obvious that someone must have suggested it before:
in list comprehensions you can currently exclude items based on the if
conditional, e.g.:
[n for n in range(1,1000) if n % 4 == 0]
Why not extend this filtering by allowing a while statement in addition to
if, as in:
[n for n in range(1,1000) while n < 400]
Trivial effect, I agree, in this example since you could achieve the same by
using range(1,400), but I hope you get the point.
This intuitively understandable extension would provide a big speed-up for
sorted lists where processing all the input is unnecessary.
Consider this:
some_names=["Adam", "Andrew", "Arthur", "Bob", "Caroline","Lancelot"] #
a sorted list of names
[n for n in some_names if n.startswith("A")]
# certainly gives a list of all names starting with A, but .
[n for n in some_names while n.startswith("A")]
# would have saved two comparisons
Best,
Wolfgang
Wouldn't it be nice if this
>>> import ast
>>> print repr(ast.parse("(1 + 1)").body[0].value)
<_ast.BinOp object at 0x0000000001E94B38>
printed something more useful?
>>> print repr(ast.parse("(1 + 1)").body[0].value)
BinOp(left=Num(n=1), op=Add(), right=Num(n=1))
I've been doing some work on macropy <https://github.com/lihaoyi/macropy>,
which uses the ast.* classes extensively, and it's annoying that we have to
resort to dirty-tricks like monkey-patching the AST classes (for CPython
2.7) or even monkey-patching __builtin__.repr (to get it working on PyPy)
just to get
eval(repr(my_ast)) == my_ast
to hold true. And a perfectly good solution already exists in the
ast.dump() method, too! (It would also be nice if "==" did a structural
comparison on the ast.* classes too, but that's a different issue).
-Haoyi
Gzip files can contains an extra field [1] and some applications use
this for extending gzip format. The current GzipFile implementation
ignores this field on input and doesn't allow to create a new file with
an extra field.
ZIP file entries also can contains an extra field [2]. Currently it just
saved as bytes in the `extra` attribute of ZipInfo.
I propose to save an extra field for gzip file and provide structural
access to subfields.
f = gzip.GzipFile('somefile.gz', 'rb')
f.extra_bytes # A raw extra field as bytes
# iterating over all subfields
for xid, data in f.extra_map.items():
...
# get Apollo file type information
f.extra_map[b'AP'] # (or f.extra_map['AP']?)
# creating gzip file with extra field
f = gzip.GzipFile('somefile.gz', 'wb', extra=extrabytes)
f = gzip.GzipFile('somefile.gz', 'wb', extra=[(b'AP', apollodata)])
f = gzip.GzipFile('somefile.gz', 'wb', extra={b'AP': apollodata})
# change Apollo file type information
f.extra_map[b'AP'] = ...
Issue #17681 [3] has preliminary patches. There is some open doubt about
interface. Is not it over-engineered?
Currently GzipFile supports seamless reading a sequence of separately
compressed gzip files. Every such chunk can have own extra field (this
is used in dictzip for example). It would be desirable to be able to
read only until the end of current chunk in order not to miss an extra
field.
[1] http://www.gzip.org/format.txt
[2] http://www.pkware.com/documents/casestudies/APPNOTE.TXT
[3] http://bugs.python.org/issue17681
FWIW, I am +1 on for the ability to read YAML based configs Python
without dependencies, but waiting for several years is hard.
Maybe try an alternative data driven development process as opposed
to traditional PEP based all-or-nothing style to speed up the process?
It is possible to make users happy incrementally and keep development
fun without sacrificing too much on the Zen side. If open source is
about scratching your own itches, then the most effective way to
implement a spec would be to allow people add support for their own
flavor without disrupting works of the others.
For some reason I think most people don't need full YAML
speccy, especially if final implementation will be slow and heavy.
So instead of:
import yaml
I'd start with something more generic and primitive:
from datatrans import yamlish
Where `datatrans` is data transformation framework taking care
of usual text parsing (data partitioning), partition mapping (structure
transformation) and conversion (binary to string etc.) trying to be as fast
and lightweight as possible, bringing vast field for future optimizations on
algorithmic level. `yamlish` is an implementation which is not vastly
optimized (datatrans to yamlish is like RPython to PyPy) and can be
easily extended to cover more YAML (hopefully). Therefore the name -
it doesn't pretend to parse YAML - it parses some supported subset,
which is improved over time by different parties (if datatrans is done right
to provide readable (maintainable + extensible) code for implementation).
There is an exisiting package called `yamlish` on PyPI - I am not talking
about it - it is PyYAML based, which is not an option for now as I see it.
So I stole its name. Sorry. This PyPI package was used to parse TAP
format, which is again, a subset. Subset..
It appears that YAML is good for humans for its subsets. It leaves an
impression (maybe it's just an illusion) that development work for subset
support can also be partitioned. If `datatrans` "done right" is possible, it
will allow incremental addition of new YAML features as the need for
them arises (new data examples are added). Or it can help to build
parsers for YAML subsets that are intentionally limited to make them
performance efficient.
Because `datatrans` is a package isolating parsing, mapping and
conversion parts of the process to make it modular and extensible, it can
serve as a reference point for various kinds of (scientific) papers
including
the ones that prove that such data transformation framework is impossible.
As for `yamlish` submodule, the first important paper covering it will be a
reference table matrix of supported features.
While it all sounds too complicated, driving development by data and real
short term user needs (as opposed to designing everything upfront) will
make the process more attractive. In data driven development, there are not
many things that can break - you either continue parsing previous data or
not. The output from the parsing process may change over time, but it may
be controlled by configuring the last step of data transformation phase.
`Parsing AppEngine config file` or `reading package meta data`
are good starting points. Once package meta data subset is parsed, it is
done and won't break. The implementation for meta data parsing may mature
in distutils package, for AppEngine in its SDK, and merged in either of
those,
sending patches for `datastrans` to stdlib. The question is only to design
output format for the parse stage. I am not sure everything should be
convertible into Python objects using the "best fit" technique.
I will be pretty comfortable if target format will not be native Python
objects
at all. More than that - I will even insist to avoid converting to native
Python
object from the start. The ideal output for the first version should be
generic
tree structure with defined names for YAML elements. The tree that can be
represented as XML where these names are tags. The tree can be
therefore traversed and selected using XPath/JQuery syntax.
It will take several years for implementation to mature and in the end there
will be a plenty of backward compatibility matters with the API, formatting
and serializing. So the first thing I'd do is [abandon serialization]. From
the
point of view of my self-proclaimed data transformation theory, the input
and
output formats are data. If output format is not human readable - as some
linked Python data structures in memory - it wastes time and hinders
development. Serializing Python is a problem of different level, which is an
example of binary, abstract, memory-only output format - a lot of properties
that you don't want to deal with while working with data.
To summarize:
1. full spec support is no goal
2. data driven (using real world examples/stories)
3. incremental (one example/story at a time)
4. research based (beautiful ideas vs ugly real world limitations)
5. maintainable (code is easy to read and understand the structure)
6. extensible (easy to find out the place to be modified)
7. core "generic tree" data structure as an intermediate format and
"yaml tree" data structure as a final format from parsing process
P.S. I am willing to wok on this "data transformation theory" stuff and
prototype implementation, because it is generally useful in many
areas. But I need support.
--
anatoly t.
FILE = os.path.abspath(__file__)
DIR = os.path.abspath(os.path.dirname(__file__))
?
Repeated pattern for referencing resources relative to your scripts. Ideas
about alternative names / locations are welcome.
In PHP these are __FILE__ and __DIR__. For Python 3 adding __dir__ is
impossible, because the name clashes with __dir__ method (which is not
implemented for module object, but should be [ ] for consistency). Also
current __file__ is rarely absolute path, because it is never normalized [
].
So it will be nice to see normalization of Python file name after the
import to reduce mess and make its behaviour predictable -
http://stackoverflow.com/questions/7116889/python-file-attribute-absolute...
----[ possible spec. draft for a beautiful internal structure ]--
The Python interpreter should provide run-time information about:
1. order of import sequence
2. names of imported modules
3. unique location for each imported module which unambiguously identifies
it
4. run-time import dependency tree (not sure about this, but it can help
with debugging)
5. information about sys.path entry where this module was imported from
6. information about who and when added this sys.path entry
--
anatoly t.
Currently, several strategies exist for exhausting an iterable when
one does not care about what the iterable returns (such as when one
merely wants a side effect of the iteration process).
One can either use an empty for loop:
for x in side_effect_iterable:
pass
A throwaway list comprehension:
[x for x in side_effect_iterable]
A try/except and a while:
next = side_effect_iterable.next
try:
while True:
next()
except StopIteration:
pass
Or a number of other methods.
The question is, which one is the fastest? Which one is the most
memory efficient?
Though these are all obvious methods, none of them are both the
fastest and the most memory efficient (though the for/pass method
comes close).
As it turns out, the fastest and most efficient method available in
the standard library is collections.deque's __init__ and extend
methods.
from collections import deque
exhaust_iterable = deque(maxlen=0).extend
exhaust_iterable(side_effect_iterable)
When a deque object is initialized with a max length of zero or less,
a special function, consume_iterator, is used instead of the regular
element insertion calls.
This function, found at
http://hg.python.org/cpython/file/tip/Modules/_collectionsmodule.c#l278,
merely iterates through the iterator, without doing any work
allocating the object to the deque's internal structure.
I would like to propose that this function, or one very similar to it,
be added to the standard library, either in the itertools module, or
the standard namespace.
If nothing else, doing so would at least give a single *obvious* way
to exhaust an iterator, instead of the several miscellaneous methods
available.
--
"Evil begins when you begin to treat people as things." - Terry Pratchett
What are you think about using pprint.pprint() to output the result of
evaluating an expression entered in an interactive Python session (and
in IDLE)?
