177 lines
6.5 KiB
Plaintext
177 lines
6.5 KiB
Plaintext
PEP: 290
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Title: Code Migration and Modernization
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Version: $Revision$
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Last-Modified: $Date$
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Author: python@rcn.com (Raymond D. Hettinger)
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Status: Active
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Type: Informational
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Created: 6-Jun-2002
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Post-History:
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Abstract
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This PEP is a collection of procedures and ideas for updating
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Python applications when newer versions of Python are installed.
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The migration tips highlight possible areas of incompatibility and
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make suggestions on how to find and resolve those differences. The
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modernization procedures show how older code can be updated to
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take advantage of new language features.
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Rationale
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This repository of procedures serves as a catalog or checklist of
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known migration issues and procedures for addressing those issues.
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Migration issues can arise for several reasons. Some obsolete
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features are slowly deprecated according to the guidelines in PEP 4.
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Also, some code relies on undocumented behaviors which are subject to
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change between versions. Some code may rely on behavior which was
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subsequently shown to be a bug and that behavior changes when the bug
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is fixed.
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Modernization options arise when new versions of Python add features
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that allow improved clarity or higher performance than previously
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available.
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Guidelines for New Entries
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Developers with commit access may update the PEP directly. Others
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can send their ideas to a developer for possible inclusion.
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While a consistent format makes the repository easier to use, feel
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free to add or subtract sections to improve clarity.
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Grep patterns may be supplied as tool to help maintainers locate
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code for possible updates. However, fully automated search/replace
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style regular expressions are not recommended. Instead, each code
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fragment should be evaluated individually.
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The contra-indications section is the most important part of a new
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entry. It lists known situations where the update SHOULD NOT be
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applied.
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Modernization Procedures
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Pattern: if d.has_key(k): --> if k in d:
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Idea: For testing dictionary membership, use the 'in' keyword
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instead of the 'has_key()' method.
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Version: 2.2 or greater
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Benefits: The result is shorter and more readable. The style becomes
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consistent with tests for membership in lists. The result is
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slightly faster because has_key requires an attribute search
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and uses a relatively expensive CALL FUNCTION op code.
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Locating: grep has_key
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Contra-indications:
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1. if dictlike.has_key(k) ## objects like shelve do not define
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__contains__()
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Pattern: for k in d.keys() --> for k in d
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for k in d.items() --> for k in d.iteritems()
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for k in d.values() --> for k in d.itervalues()
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Idea: Use the new iter methods for looping over dictionaries
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Version: 2.2 or greater
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Benefits: The iter methods are faster because the do not have to create
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a new list object with a complete copy of all of the keys,
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values, or items. Selecting only keys, items, or values as
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needed saves the time for creating throwaway object references
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and, in the case of items, saves a second hash look-up of
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the key.
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Contra-indications:
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1. def getids(): return d.keys() ## do not change the return type
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2. for k in dictlike.keys() ## objects like shelve do not define
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itermethods
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3. k = d.keys(); j = k[:] ## iterators do not support slicing,
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sorting or other operations
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4. for k in d.keys(): del[k] ## dict iterators prohibit modifying the
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dictionary
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Pattern: if v == None --> if v is None:
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if v != None --> if v is not None:
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Idea: Since there is only one None object, equality can be tested
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with identity.
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Version: All
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Benefits: Identity tests are slightly faster than equality tests. Also,
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some object types may overload comparison to be much slower.
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Locating: grep '== None' or grep '!= None'
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Pattern: os.stat("foo")[stat.ST_MTIME] --> os.stat("foo").st_mtime
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os.stat("foo")[stat.ST_MTIME] --> os.path.getmtime("foo")
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Idea: Replace stat contants or indices with new stat methods
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Version: 2.2 or greater
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Benefits: The methods are not order dependent and do not require an
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import of the stat module
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Locating: grep os.stat
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Pattern: import whrandom --> import random
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Idea: Replace deprecated module
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Version: 2.1 or greater
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Benefits: All random methods collected in one place
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Locating: grep whrandom
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Pattern: import types ; type(v, types.IntType) --> isinstance(v, int)
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type(s, types.StringTypes --> isinstance(s, basestring)
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Idea: The types module will likely to be deprecated in the future.
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Version: 2.3 or greater (basestring introduced in Py2.3)
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Benefits: May be slightly faster, avoid a deprecated feature.
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Locating: grep types *.py | grep import
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Pattern: import string ; string.method(s, ...) --> s.method(...)
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c in string.whitespace --> c.isspace()
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Idea: The string module will likely to be deprecated in the future.
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Version: 2.0 or greater
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Benefits: Slightly faster, avoid a deprecated feature.
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Locating: grep string *.py | grep import
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Pattern: NewError = 'NewError' --> class NewError(Exception): pass
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Idea: String exceptions are deprecated, derive from the Exception
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base class.
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Version: Any
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Benefits: Unlike the obsolete string exceptions, class exceptions all
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derive from another exception or the Exception base class.
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This allows meaningful groupings of exceptions. It also
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allows an "except Exception" clause to catch all exceptions.
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Locating: Use PyChecker
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Pattern: "foobar"[:3] == "foo" -> "foobar".startswith("foo")
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"foobar"[-3:] == "bar" -> "foobar".endswith("bar")
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Version: 2.0 or greater
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Benefits: Faster because no slice has to be created. Less risk of
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miscounting.
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References
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[1] PEP 4, Deprecation of Standard Modules
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http://www.python.org/peps/pep-0004.html
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Copyright
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This document has been placed in the public domain.
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Local Variables:
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mode: indented-text
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indent-tabs-mode: nil
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sentence-end-double-space: t
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fill-column: 70
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End:
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