Python-Dev July 2002

python-dev@python.org

80 participants
155 discussions

RE: [Python-Dev] Those import related syntax errors again...

by Samuele Pedroni

Hi. [Mark Hammond] > The point isn't about my suffering as such. The point is more that > python-dev owns a tiny amount of the code out there, and I don't believe we > should put Python's users through this. > > Sure - I would be happy to "upgrade" all the win32all code, no problem. I > am also happy to live in the bleeding edge and take some pain that will > cause. > > The issue is simply the user base, and giving Python a reputation of not > being able to painlessly upgrade even dot revisions. I agree with all this. [As I imagined explicit syntax did not catch up and would require lot of discussions.] [GvR] > > Another way is to use special rules > > (similar to those for class defs), e.g. having > > > > <frag> > > y=3 > > def f(): > > exec "y=2" > > def g(): > > return y > > return g() > > > > print f() > > </frag> > > > > # print 3. > > > > Is that confusing for users? maybe they will more naturally expect 2 > > as outcome (given nested scopes). > > This seems the best compromise to me. It will lead to the least > broken code, because this is the behavior that we had before nested > scopes! It is also quite easy to implement given the current > implementation, I believe. > > Maybe we could introduce a warning rather than an error for this > situation though, because even if this behavior is clearly documented, > it will still be confusing to some, so it is better if we outlaw it in > some future version. > Yes this can be easy to implement but more confusing situations can arise: <frag> y=3 def f(): y=9 exec "y=2" def g(): return y return y,g() print f() </frag> What should this print? the situation leads not to a canonical solution as class def scopes. or <frag> def f(): from foo import * def g(): return y return g() print f() </frag> [Mark Hammond] > > This probably won't be a very popular suggestion, but how about pulling > > nested scopes (I assume they are at the root of the problem) > > until this can be solved cleanly? > > Agreed. While I think nested scopes are kinda cool, I have lived without > them, and really without missing them, for years. At the moment the cure > appears worse then the symptoms in at least a few cases. If nothing else, > it compromises the elegant simplicity of Python that drew me here in the > first place! > > Assuming that people really _do_ want this feature, IMO the bar should be > raised so there are _zero_ backward compatibility issues. I don't say anything about pulling nested scopes (I don't think my opinion can change things in this respect) but I should insist that without explicit syntax IMO raising the bar has a too high impl cost (both performance and complexity) or creates confusion. [Andrew Kuchling] > >Assuming that people really _do_ want this feature, IMO the bar should be > >raised so there are _zero_ backward compatibility issues. > > Even at the cost of additional implementation complexity? At the cost > of having to learn "scopes are nested, unless you do these two things > in which case they're not"? > > Let's not waffle. If nested scopes are worth doing, they're worth > breaking code. Either leave exec and from..import illegal, or back > out nested scopes, or think of some better solution, but let's not > introduce complicated backward compatibility hacks. IMO breaking code would be ok if we issue warnings today and implement nested scopes issuing errors tomorrow. But this is simply a statement about principles and raised impression. IMO import * in an inner scope should end up being an error, not sure about 'exec's. We will need a final BDFL statement. regards, Samuele Pedroni.

15 years, 7 months

PEP: Support for System Upgrades

by M.-A. Lemburg

PEP: 0??? Title: Support for System Upgrades Version: $Revision: 0.0 $ Author: mal(a)lemburg.com (Marc-Andr? Lemburg) Status: Draft Type: Standards Track Python-Version: 2.3 Created: 19-Jul-2001 Post-History: Abstract This PEP proposes strategies to allow the Python standard library to be upgraded in parts without having to reinstall the complete distribution or having to wait for a new patch level release. Problem Python currently does not allow overriding modules or packages in the standard library per default. Even though this is possible by defining a PYTHONPATH environment variable (the paths defined in this variable are prepended to the Python standard library path), there is no standard way of achieving this without changing the configuration. Since Python's standard library is starting to host packages which are also available separately, e.g. the distutils, email and PyXML packages, which can also be installed independently of the Python distribution, it is desireable to have an option to upgrade these packages without having to wait for a new patch level release of the Python interpreter to bring along the changes. Proposed Solutions This PEP proposes two different but not necessarily conflicting solutions: 1. Adding a new standard search path to sys.path: $stdlibpath/system-packages just before the $stdlibpath entry. This complements the already existing entry for site add-ons $stdlibpath/site-packages which is appended to the sys.path at interpreter startup time. To make use of this new standard location, distutils will need to grow support for installing certain packages in $stdlibpath/system-packages rather than the standard location for third-party packages $stdlibpath/site-packages. 2. Tweaking distutils to install directly into $stdlibpath for the system upgrades rather than into $stdlibpath/site-packages. The first solution has a few advantages over the second: * upgrades can be easily identified (just look in $stdlibpath/system-packages) * upgrades can be deinstalled without affecting the rest of the interpreter installation * modules can be virtually removed from packages; this is due to the way Python imports packages: once it finds the top-level package directory it stay in this directory for all subsequent package submodule imports * the approach has an overall much cleaner design than the hackish install on top of an existing installation approach The only advantages of the second approach are that the Python interpreter does not have to changed and that it works with older Python versions. Both solutions require changes to distutils. These changes can also be implemented by package authors, but it would be better to define a standard way of switching on the proposed behaviour. Scope Solution 1: Python 2.3 and up Solution 2: all Python versions supported by distutils Credits None References None Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil End: -- Marc-Andre Lemburg CEO eGenix.com Software GmbH _______________________________________________________________________ eGenix.com -- Makers of the Python mx Extensions: mxDateTime,mxODBC,... Python Consulting: http://www.egenix.com/ Python Software: http://www.egenix.com/files/python/

17 years, 8 months

PEP 282 Implementation

by Vinay Sajip

I've uploaded my logging module, the proposed implementation for PEP 282, for committer review, to the SourceForge patch manager: http://sourceforge.net/tracker/index.php?func=detail&aid=578494&group_id=547 0&atid=305470 I've assigned it to Mark Hammond as (a) he had posted some comments to Trent Mick's original PEP posting, and (b) Barry Warsaw advised not assigning to PythonLabs people on account of their current workload. The file logging.py is (apart from some test scripts) all that's supposed to go into Python 2.3. The file logging-0.4.6.tar.gz contains the module, an updated version of the PEP (which I mailed to Barry Warsaw on 26th June), numerous test/example scripts, TeX documentation etc. You can also refer to http://www.red-dove.com/python_logging.html Here's hoping for a speedy review :-) Regards, Vinay Sajip

17 years, 11 months

HAVE_CONFIG_H

by Guido van Rossum

I see no references to HAVE_CONFIG_H in the source code (except one #undef in readline.c), yet we #define it on the command line. Is that still necessary? --Guido van Rossum (home page: http://www.python.org/~guido/)

18 years

PEP 293, Codec Error Handling Callbacks

by Walter Dörwald

Here's another new PEP. Bye, Walter Dörwald ---------------------------------------------------------------------- PEP: 293 Title: Codec Error Handling Callbacks Version: $Revision: 1.1 $ Last-Modified: $Date: 2002/06/19 03:22:11 $ Author: Walter Dörwald Status: Draft Type: Standards Track Created: 18-Jun-2002 Python-Version: 2.3 Post-History: Abstract This PEP aims at extending Python's fixed codec error handling schemes with a more flexible callback based approach. Python currently uses a fixed error handling for codec error handlers. This PEP describes a mechanism which allows Python to use function callbacks as error handlers. With these more flexible error handlers it is possible to add new functionality to existing codecs by e.g. providing fallback solutions or different encodings for cases where the standard codec mapping does not apply. Specification Currently the set of codec error handling algorithms is fixed to either "strict", "replace" or "ignore" and the semantics of these algorithms is implemented separately for each codec. The proposed patch will make the set of error handling algorithms extensible through a codec error handler registry which maps handler names to handler functions. This registry consists of the following two C functions: int PyCodec_RegisterError(const char *name, PyObject *error) PyObject *PyCodec_LookupError(const char *name) and their Python counterparts codecs.register_error(name, error) codecs.lookup_error(name) PyCodec_LookupError raises a LookupError if no callback function has been registered under this name. Similar to the encoding name registry there is no way of unregistering callback functions or iterating through the available functions. The callback functions will be used in the following way by the codecs: when the codec encounters an encoding/decoding error, the callback function is looked up by name, the information about the error is stored in an exception object and the callback is called with this object. The callback returns information about how to proceed (or raises an exception). For encoding, the exception object will look like this: class UnicodeEncodeError(UnicodeError): def __init__(self, encoding, object, start, end, reason): UnicodeError.__init__(self, "encoding '%s' can't encode characters " + "in positions %d-%d: %s" % (encoding, start, end-1, reason)) self.encoding = encoding self.object = object self.start = start self.end = end self.reason = reason This type will be implemented in C with the appropriate setter and getter methods for the attributes, which have the following meaning: * encoding: The name of the encoding; * object: The original unicode object for which encode() has been called; * start: The position of the first unencodable character; * end: (The position of the last unencodable character)+1 (or the length of object, if all characters from start to the end of object are unencodable); * reason: The reason why object[start:end] couldn't be encoded. If object has consecutive unencodable characters, the encoder should collect those characters for one call to the callback if those characters can't be encoded for the same reason. The encoder is not required to implement this behaviour but may call the callback for every single character, but it is strongly suggested that the collecting method is implemented. The callback must not modify the exception object. If the callback does not raise an exception (either the one passed in, or a different one), it must return a tuple: (replacement, newpos) replacement is a unicode object that the encoder will encode and emit instead of the unencodable object[start:end] part, newpos specifies a new position within object, where (after encoding the replacement) the encoder will continue encoding. If the replacement string itself contains an unencodable character the encoder raises the exception object (but may set a different reason string before raising). Should further encoding errors occur, the encoder is allowed to reuse the exception object for the next call to the callback. Furthermore the encoder is allowed to cache the result of codecs.lookup_error. If the callback does not know how to handle the exception, it must raise a TypeError. Decoding works similar to encoding with the following differences: The exception class is named UnicodeDecodeError and the attribute object is the original 8bit string that the decoder is currently decoding. The decoder will call the callback with those bytes that constitute one undecodable sequence, even if there is more than one undecodable sequence that is undecodable for the same reason directly after the first one. E.g. for the "unicode-escape" encoding, when decoding the illegal string "\\u00\\u01x", the callback will be called twice (once for "\\u00" and once for "\\u01"). This is done to be able to generate the correct number of replacement characters. The replacement returned from the callback is a unicode object that will be emitted by the decoder as-is without further processing instead of the undecodable object[start:end] part. There is a third API that uses the old strict/ignore/replace error handling scheme: PyUnicode_TranslateCharmap/unicode.translate The proposed patch will enhance PyUnicode_TranslateCharmap, so that it also supports the callback registry. This has the additional side effect that PyUnicode_TranslateCharmap will support multi-character replacement strings (see SF feature request #403100 [1]). For PyUnicode_TranslateCharmap the exception class will be named UnicodeTranslateError. PyUnicode_TranslateCharmap will collect all consecutive untranslatable characters (i.e. those that map to None) and call the callback with them. The replacement returned from the callback is a unicode object that will be put in the translated result as-is, without further processing. All encoders and decoders are allowed to implement the callback functionality themselves, if they recognize the callback name (i.e. if it is a system callback like "strict", "replace" and "ignore"). The proposed patch will add two additional system callback names: "backslashreplace" and "xmlcharrefreplace", which can be used for encoding and translating and which will also be implemented in-place for all encoders and PyUnicode_TranslateCharmap. The Python equivalent of these five callbacks will look like this: def strict(exc): raise exc def ignore(exc): if isinstance(exc, UnicodeError): return (u"", exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def replace(exc): if isinstance(exc, UnicodeEncodeError): return ((exc.end-exc.start)*u"?", exc.end) elif isinstance(exc, UnicodeDecodeError): return (u"\\ufffd", exc.end) elif isinstance(exc, UnicodeTranslateError): return ((exc.end-exc.start)*u"\\ufffd", exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def backslashreplace(exc): if isinstance(exc, (UnicodeEncodeError, UnicodeTranslateError)): s = u"" for c in exc.object[exc.start:exc.end]: if ord(c)<=0xff: s += u"\\x%02x" % ord(c) elif ord(c)<=0xffff: s += u"\\u%04x" % ord(c) else: s += u"\\U%08x" % ord(c) return (s, exc.end) else: raise TypeError("can't handle %s" % exc.__name__) def xmlcharrefreplace(exc): if isinstance(exc, (UnicodeEncodeError, UnicodeTranslateError)): s = u"" for c in exc.object[exc.start:exc.end]: s += u"&#%d;" % ord(c) return (s, exc.end) else: raise TypeError("can't handle %s" % exc.__name__) These five callback handlers will also be accessible to Python as codecs.strict_error, codecs.ignore_error, codecs.replace_error, codecs.backslashreplace_error and codecs.xmlcharrefreplace_error. Rationale Most legacy encoding do not support the full range of Unicode characters. For these cases many high level protocols support a way of escaping a Unicode character (e.g. Python itself supports the \x, \u and \U convention, XML supports character references via &#xxx; etc.). When implementing such an encoding algorithm, a problem with the current implementation of the encode method of Unicode objects becomes apparent: For determining which characters are unencodable by a certain encoding, every single character has to be tried, because encode does not provide any information about the location of the error(s), so # (1) us = u"xxx" s = us.encode(encoding) has to be replaced by # (2) us = u"xxx" v = [] for c in us: try: v.append(c.encode(encoding)) except UnicodeError: v.append("&#%d;" % ord(c)) s = "".join(v) This slows down encoding dramatically as now the loop through the string is done in Python code and no longer in C code. Furthermore this solution poses problems with stateful encodings. For example UTF-16 uses a Byte Order Mark at the start of the encoded byte string to specify the byte order. Using (2) with UTF-16, results in an 8 bit string with a BOM between every character. To work around this problem, a stream writer - which keeps state between calls to the encoding function - has to be used: # (3) us = u"xxx" import codecs, cStringIO as StringIO writer = codecs.getwriter(encoding) v = StringIO.StringIO() uv = writer(v) for c in us: try: uv.write(c) except UnicodeError: uv.write(u"&#%d;" % ord(c)) s = v.getvalue() To compare the speed of (1) and (3) the following test script has been used: # (4) import time us = u"äa"*1000000 encoding = "ascii" import codecs, cStringIO as StringIO t1 = time.time() s1 = us.encode(encoding, "replace") t2 = time.time() writer = codecs.getwriter(encoding) v = StringIO.StringIO() uv = writer(v) for c in us: try: uv.write(c) except UnicodeError: uv.write(u"?") s2 = v.getvalue() t3 = time.time() assert(s1==s2) print "1:", t2-t1 print "2:", t3-t2 print "factor:", (t3-t2)/(t2-t1) On Linux this gives the following output (with Python 2.3a0): 1: 0.274321913719 2: 51.1284689903 factor: 186.381278466 i.e. (3) is 180 times slower than (1). Codecs must be stateless, because as soon as a callback is registered it is available globally and can be called by multiple encode() calls. To be able to use stateful callbacks, the errors parameter for encode/decode/translate would have to be changed from char * to PyObject *, so that the callback could be used directly, without the need to register the callback globally. As this requires changes to lots of C prototypes, this approach was rejected. Currently all encoding/decoding functions have arguments const Py_UNICODE *p, int size or const char *p, int size to specify the unicode characters/8bit characters to be encoded/decoded. So in case of an error the codec has to create a new unicode or str object from these parameters and store it in the exception object. The callers of these encoding/decoding functions extract these parameters from str/unicode objects themselves most of the time, so it could speed up error handling if these object were passed directly. As this again requires changes to many C functions, this approach has been rejected. Implementation Notes A sample implementation is available as SourceForge patch #432401 [2]. The current version of this patch differs from the specification in the following way: * The error information is passed from the codec to the callback not as an exception object, but as a tuple, which has an additional entry state, which can be used for additional information the codec might want to pass to the callback. * There are two separate registries (one for encoding/translating and one for decoding) The class codecs.StreamReaderWriter uses the errors parameter for both reading and writing. To be more flexible this should probably be changed to two separate parameters for reading and writing. The errors parameter of PyUnicode_TranslateCharmap is not availably to Python, which makes testing of the new functionality of PyUnicode_TranslateCharmap impossible with Python scripts. The patch should add an optional argument errors to unicode.translate to expose the functionality and make testing possible. Codecs that do something different than encoding/decoding from/to unicode and want to use the new machinery can define their own exception classes and the strict handlers will automatically work with it. The other predefined error handlers are unicode specific and expect to get a Unicode(Encode|Decode|Translate)Error exception object so they won't work. Backwards Compatibility The semantics of unicode.encode with errors="replace" has changed: The old version always stored a ? character in the output string even if no character was mapped to ? in the mapping. With the proposed patch, the replacement string from the callback callback will again be looked up in the mapping dictionary. But as all supported encodings are ASCII based, and thus map ? to ?, this should not be a problem in practice. References [1] SF feature request #403100 "Multicharacter replacements in PyUnicode_TranslateCharmap" http://www.python.org/sf/403100 [2] SF patch #432401 "unicode encoding error callbacks" http://www.python.org/sf/432401 Copyright This document has been placed in the public domain. Local Variables: mode: indented-text indent-tabs-mode: nil sentence-end-double-space: t fill-column: 70 End:

18 years, 1 month

Priority queue (binary heap) python code

by Kevin O'Connor

I often find myself needing priority queues in python, and I've finally broken down and written a simple implementation. Previously I've used sorted lists (via bisect) to get the job done, but the heap code significantly improves performance. There are C based implementations, but the effort of compiling in an extension often isn't worth the effort. I'm including the code here for everyone's amusement. Any chance something like this could make it into the standard python library? It would save a lot of time for lazy people like myself. :-) Cheers, -Kevin def heappush(heap, item): pos = len(heap) heap.append(None) while pos: parentpos = (pos - 1) / 2 parent = heap[parentpos] if item <= parent: break heap[pos] = parent pos = parentpos heap[pos] = item def heappop(heap): endpos = len(heap) - 1 if endpos <= 0: return heap.pop() returnitem = heap[0] item = heap.pop() pos = 0 while 1: child2pos = (pos + 1) * 2 child1pos = child2pos - 1 if child2pos < endpos: child1 = heap[child1pos] child2 = heap[child2pos] if item >= child1 and item >= child2: break if child1 > child2: heap[pos] = child1 pos = child1pos continue heap[pos] = child2 pos = child2pos continue if child1pos < endpos: child1 = heap[child1pos] if child1 > item: heap[pos] = child1 pos = child1pos break heap[pos] = item return returnitem -- ------------------------------------------------------------------------ | Kevin O'Connor "BTW, IMHO we need a FAQ for | | kevin(a)koconnor.net 'IMHO', 'FAQ', 'BTW', etc. !" | ------------------------------------------------------------------------

18 years, 1 month

RE: companies data for sorting comparisons

by Tim Peters

Kevin Altis kindly forwarded a 1.5MB XML database with about 6600 company records. A record looks like this after running his script to turn them into Python dicts: {'Address': '395 Page Mill Road\nPalo Alto, CA 94306', 'Company': 'Agilent Technologies Inc.', 'Exchange': 'NYSE', 'NumberOfEmployees': '41,000', 'Phone': '(650) 752-5000', 'Profile': 'http://biz.yahoo.com/p/a/a.html', 'Symbol': 'A', 'Web': 'http://www.agilent.com'} It appears to me that the XML file is maintained by hand, in order of ticker symbol. But people make mistakes when alphabetizing by hand, and there are 37 indices i such that data[i]['Symbol'] > data[i+1]['Symbol'] So it's "almost sorted" by that measure, with a few dozen glitches -- and msort should be able to exploit this! I think this is an important case of real-life behavior. The proper order of Yahoo profile URLs is also strongly correlated with ticker symbol, while both the company name and web address look weakly correlated, so there's hope that msort can get some benefit on those too. Here are runs sorting on all field names, building a DSU tuple list to sort via values = [(x.get(fieldname), x) for x in data] Each field sort was run 5 times under sort, and under msort. So 5 times are reported for each sort, reported in milliseconds, and listed from quickest to slowest: Sorting on field 'Address' -- 6589 records via sort: 43.03 43.35 43.37 43.54 44.14 via msort: 45.15 45.16 45.25 45.26 45.30 Sorting on field 'Company' -- 6635 records via sort: 40.41 40.55 40.61 42.36 42.63 via msort: 30.68 30.80 30.87 30.99 31.10 Sorting on field 'Exchange' -- 6579 records via sort: 565.28 565.49 566.70 567.12 567.45 via msort: 573.29 573.61 574.55 575.34 576.46 Sorting on field 'NumberOfEmployees' -- 6531 records via sort: 120.15 120.24 120.26 120.31 122.58 via msort: 134.25 134.29 134.50 134.74 135.09 Sorting on field 'Phone' -- 6589 records via sort: 53.76 53.80 53.81 53.82 56.03 via msort: 56.05 56.10 56.19 56.21 56.86 Sorting on field 'Profile' -- 6635 records via sort: 58.66 58.71 58.84 59.02 59.50 via msort: 8.74 8.81 8.98 8.99 8.99 Sorting on field 'Symbol' -- 6635 records via sort: 39.92 40.11 40.19 40.38 40.62 via msort: 6.49 6.52 6.53 6.72 6.73 Sorting on field 'Web' -- 6632 records via sort: 47.23 47.29 47.36 47.45 47.45 via msort: 37.12 37.27 37.33 37.42 37.89 So the hopes are realized: msort gets huge benefit from the nearly-sorted Symbol field, also huge benefit from the correlated Profile field, and highly significant benefit from the weakly correlated Company and Web fields. K00L! The Exchange field sort is so bloody slow because there are few distinct Exchange values, and whenever there's a tie on those the tuple comparison routine tries to break it by comparing the dicts. Note that I warned about this kind of thing a week or two ago, in the context of trying to implement priority queues by storing and comparing (priority, object) tuples -- it can be a timing disaster if priorities are ever equal. The other fields (Phone, etc) are in essentially random order, and msort is systematically a bit slower on all of those. Note that these are all string comparisons. I don't think it's a coincidence that msort went from a major speedup on the Python-Dev task, to a significant slowdown, when I removed all duplicate lines and shuffled the corpus first. Only part of this can be accounted for by # of comparisons. On a given random input, msort() may do fewer or more comparisons than sort(), but doing many trials suggests that sort() has a small edge in # of compares on random data, on the order of 1 or 2% This is easy to believe, since msort does a few things it *knows* won't repay the cost if the order happens to be random. These are well worth it, since they're what allow msort to get huge wins when the data isn't random. But that's not enough to account for things like the >10% higher runtime in the NumberOfEmployees sort. I can't reproduce this magnitude of systematic slowdown when doing random sorts on floats or ints, so I conclude it has something to do with string compares. Unlike int and float compares, a string compare takes variable time, depending on how long the common prefix is. I'm not aware of specific research on this topic, but it's plausible to me that partitioning may be more effective than merging at reducing the number of comparisons specifically involving "nearly equal" elements. Indeed, the fastest string-sorting methods I know of move heaven and earth to avoid redundant prefix comparisons, and do so by partitioning. Turns out <wink> that doesn't account for it, though. Here are the total number of comparisons (first number on each line) done for each sort, and the sum across all string compares of the number of common prefix characters (second number on each line): Sorting on field Address' -- 6589 records via sort: 76188 132328 via msort: 76736 131081 Sorting on field 'Company' -- 6635 records via sort: 76288 113270 via msort: 56013 113270 Sorting on field 'Exchange' -- 6579 records via sort: 34851 207185 via msort: 37457 168402 Sorting on field 'NumberOfEmployees' -- 6531 records via sort: 76167 116322 via msort: 76554 112436 Sorting on field 'Phone' -- 6589 records via sort: 75972 278188 via msort: 76741 276012 Sorting on field 'Profile' -- 6635 records via sort: 76049 1922016 via msort: 8750 233452 Sorting on field 'Symbol' -- 6635 records via sort: 76073 73243 via msort: 8724 16424 Sorting on field 'Web' -- 6632 records via sort: 76207 863837 via msort: 58811 666852 Contrary to prediction, msort always got the smaller "# of equal prefix characters" total, even in the Exchange case, where it did nearly 10% more total comparisons. Python's string compare goes fastest if the first two characters aren't the same, so maybe sort() gets a systematic advantage there? Nope. Turns out msort() gets out early 17577 times on that basis when doing NumberOfEmployees, but sort() only gets out early 15984 times. I conclude that msort is at worst only a tiny bit slower when doing NumberOfEmployees, and possibly a little faster. The only measure that doesn't agree with that conclusion is time.clock() -- but time is such a subjective thing I won't let that bother me <wink>.

18 years, 1 month

Single- vs. Multi-pass iterability

by David Abrahams

I keep running into the problem that there is no reliable way to introspect about whether a type supports multi-pass iterability (in the sense that an input stream might support only a single pass, but a list supports multiple passes). I suppose you could check for __getitem__, but that wouldn't cover linked lists, for example. Can anyone channel Guido's intent for me? Is this an oversight or a deliberate design decision? Is there an interface for checking multi-pass-ability that I've missed? TIA, Dave

18 years, 1 month

split('') revisited

by Andrew Koenig

Back in February, there was a thread in comp.lang.python (and, I think, also on Python-Dev) that asked whether the following behavior: >>> 'abcde'.split('') Traceback (most recent call last): File "<stdin>", line 1, in ? ValueError: empty separator was a bug or a feature. The prevailing opinion at the time seemed to be that there was not a sensible, unique way of defining this operation, so rejecting it was a feature. That answer didn't bother me particularly at the time, but since then I have learned a new fact (or perhaps an old fact that I didn't notice at the time) that has changed my mind: Section 4.2.4 of the library reference says that the 'split' method of a regular expression object is defined as Identical to the split() function, using the compiled pattern. This claim does not appear to be correct: >>> import re >>> re.compile('').split('abcde') ['abcde'] This result differs from the result of using the string split method. In other words, the documentation doesn't match the actual behavior, so the status quo is broken. It seems to me that there are four reasonable courses of action: 1) Do nothing -- the problem is too trivial to worry about. 2) Change string split (and its documentation) to match regexp split. 3) Change regexp split (and its documentation) to match string split. 4) Change both string split and regexp split to do something else :-) My first impulse was to argue that (4) is right, and that the behavior should be as follows >>> 'abcde'.split('') ['a', 'b', 'c', 'd', 'e'] >>> import re >>> re.compile('').split('abcde') ['a', 'b', 'c', 'd', 'e'] When this discussion came up last time, I think there was an objection that s.split('') was ambiguous: What argument is there in favor of 'abcde'.split('') being ['a', 'b', 'c', 'd', 'e'] instead of, say, ['', 'a', 'b', 'c', 'd', 'e', ''] or, for that matter, ['', 'a', '', 'b', '', 'c', '', 'd', '', 'e', '']? I made the counterargument that one could disambiguate by adding the rule that no element of the result could be equal to the delimiter. Therefore, if s is a string, s.split('') cannot contain any empty strings. However, looking at the behavior of regular expression splitting more closely, I become more confused. Can someone explain the following behavior to me? >>> re.compile('a|(x?)').split('abracadabra') ['', None, 'br', None, 'c', None, 'd', None, 'br', None, '']

18 years, 1 month

Sorting

by Tim Peters

An enormous amount of research has been done on sorting since the last time I wrote a sort for Python. Major developments have been in two areas: 1. Adaptive sorting. Sorting algorithms are usually tested on random data, but in real life you almost never see random data. Python's sort tries to catch some common cases of near-order via special- casing. The literature has since defined more than 15 formal measures of disorder, and developed algorithms provably optimal in the face of one or more of them. But this is O() optimality, and theoreticians aren't much concerned about how big the constant factor is. Some researchers are up front about this, and toward the end of one paper with "practical" in its title, the author was overjoyed to report that an implementation was only twice as slow as a naive quicksort <wink>. 2. Pushing the worst-case number of comparisons closer to the information-theoretic limit (ceiling(log2(N!))). I don't care much about #2 -- in experiments conducted when it was new, I measured the # of comparisons our samplesort hybrid did on random inputs, and it was never more than 2% over the theoretical lower bound, and typically closer. As N grows large, the expected case provably converges to the theoretical lower bound. There remains a vanishly small chance for a bad case, but nobody has reported one, and at the time I gave up trying to construct one. Back on Earth, among Python users the most frequent complaint I've heard is that list.sort() isn't stable. Alex is always quick to trot out the appropriate DSU (Decorate Sort Undecorate) pattern then, but the extra memory burden for that can be major (a new 2-tuple per list element costs about 32 bytes, then 4 more bytes for a pointer to it in a list, and 12 more bytes that don't go away to hold each non-small index). After reading all those papers, I couldn't resist taking a crack at a new algorithm that might be practical, and have something you might call a non-recursive adaptive stable natural mergesort / binary insertion sort hybrid. In playing with it so far, it has two bad aspects compared to our samplesort hybrid: + It may require temp memory, up to 2*N bytes worst case (one pointer each for no more than half the array elements). + It gets *some* benefit for arrays with many equal elements, but not nearly as much as I was able to hack samplesort to get. In effect, paritioning is very good at moving equal elements close to each other quickly, but merging leaves them spread across any number of runs. This is especially irksome because we're sticking to Py_LT for comparisons, so can't even detect a==b without comparing a and b twice (and then it's a deduction from that not a < b and not b < a). Given the relatively huge cost of comparisons, it's a timing disaster to do that (compare twice) unless it falls out naturally. It was fairly natural to do so in samplesort, but not at all in this sort. It also has good aspects: + It's stable (items that compare equal retain their relative order, so, e.g., if you sort first on zip code, and a second time on name, people with the same name still appear in order of increasing zip code; this is important in apps that, e.g., refine the results of queries based on user input). + The code is much simpler than samplesort's (but I think I can fix that <wink>). + It gets benefit out of more kinds of patterns, and without lumpy special-casing (a natural mergesort has to identify ascending and descending runs regardless, and then the algorithm builds on just that). + Despite that I haven't micro-optimized it, in the random case it's almost as fast as the samplesort hybrid. In fact, it might have been a bit faster had I run tests yesterday (the samplesort hybrid got sped up by 1-2% last night). This one surprised me the most, because at the time I wrote the samplesort hybrid, I tried several ways of coding mergesorts and couldn't make it as fast. + It has no bad cases (O(N log N) is worst case; N-1 compares is best). Here are some typical timings, taken from Python's sortperf.py, over identical lists of floats: Key: *sort: random data \sort: descending data /sort: ascending data 3sort: ascending data but with 3 random exchanges ~sort: many duplicates =sort: all equal !sort: worst case scenario That last one was a worst case for the last quicksort Python had before it grew the samplesort, and it was a very bad case for that. By sheer coincidence, turns out it's an exceptionally good case for the experimental sort: samplesort i 2**i *sort \sort /sort 3sort ~sort =sort !sort 15 32768 0.13 0.01 0.01 0.10 0.04 0.01 0.11 16 65536 0.24 0.02 0.02 0.23 0.08 0.02 0.24 17 131072 0.54 0.05 0.04 0.49 0.18 0.04 0.53 18 262144 1.18 0.09 0.09 1.08 0.37 0.09 1.16 19 524288 2.58 0.19 0.18 2.34 0.76 0.17 2.52 20 1048576 5.58 0.37 0.36 5.12 1.54 0.35 5.46 timsort 15 32768 0.16 0.01 0.02 0.05 0.14 0.01 0.02 16 65536 0.24 0.02 0.02 0.06 0.19 0.02 0.04 17 131072 0.55 0.04 0.04 0.13 0.42 0.04 0.09 18 262144 1.19 0.09 0.09 0.25 0.91 0.09 0.18 19 524288 2.60 0.18 0.18 0.46 1.97 0.18 0.37 20 1048576 5.61 0.37 0.35 1.00 4.26 0.35 0.74 If it weren't for the ~sort column, I'd seriously suggest replacing the samplesort with this. 2*N extra bytes isn't as bad as it might sound, given that, in the absence of massive object duplication, each list element consumes at least 12 bytes (type pointer, refcount and value) + 4 bytes for the list pointer. Add 'em all up and that's a 13% worst-case temp memory overhead.

18 years, 1 month

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Python-Dev July 2002