|Title:||Code Migration and Modernization|
|Author:||Raymond Hettinger <python at rcn.com>|
- Guidelines for New Entries
- Migration Issues
- Modernization Procedures
- Python 2.4 or Later
- Python 2.3 or Later
- Python 2.2 or Later
- Python 2.1 or Later
- Python 2.0 or Later
- Python 1.5 or Later
- All Python Versions
This PEP is a collection of procedures and ideas for updating Python applications when newer versions of Python are installed.
The migration tips highlight possible areas of incompatibility and make suggestions on how to find and resolve those differences. The modernization procedures show how older code can be updated to take advantage of new language features.
This repository of procedures serves as a catalog or checklist of known migration issues and procedures for addressing those issues.
Migration issues can arise for several reasons. Some obsolete features are slowly deprecated according to the guidelines in PEP 4 . Also, some code relies on undocumented behaviors which are subject to change between versions. Some code may rely on behavior which was subsequently shown to be a bug and that behavior changes when the bug is fixed.
Modernization options arise when new versions of Python add features that allow improved clarity or higher performance than previously available.
Developers with commit access may update this PEP directly. Others can send their ideas to a developer for possible inclusion.
While a consistent format makes the repository easier to use, feel free to add or subtract sections to improve clarity.
Grep patterns may be supplied as tool to help maintainers locate code for possible updates. However, fully automated search/replace style regular expressions are not recommended. Instead, each code fragment should be evaluated individually.
The contra-indications section is the most important part of a new entry. It lists known situations where the update SHOULD NOT be applied.
Prior to Python 2.3, comparison operations returned 0 or 1 rather than True or False. Some code may have used this as a shortcut for producing zero or one in places where their boolean counterparts are not appropriate. For example:
def identity(m=1): """Create and m-by-m identity matrix""" return [[i==j for i in range(m)] for j in range(m)]
In Python 2.2, a call to identity(2) would produce:
[[1, 0], [0, 1]]
In Python 2.3, the same call would produce:
[[True, False], [False, True]]
Since booleans are a subclass of integers, the matrix would continue to calculate normally, but it will not print as expected. The list comprehension should be changed to read:
return [[int(i==j) for i in range(m)] for j in range(m)]
There are similar concerns when storing data to be used by other applications which may expect a number instead of True or False.
Procedures are grouped by the Python version required to be able to take advantage of the modernization.
Python's lists are implemented to perform best with appends and pops on the right. Use of pop(0) or insert(0, x) triggers O(n) data movement for the entire list. To help address this need, Python 2.4 introduces a new container, collections.deque() which has efficient append and pop operations on the both the left and right (the trade-off is much slower getitem/setitem access). The new container is especially helpful for implementing data queues:
c = list(data) --> c = collections.deque(data) c.pop(0) --> c.popleft() c.insert(0, x) --> c.appendleft()
grep pop(0 or grep insert(0
In Python 2.4, the sort method for lists and the new sorted built-in function both accept a key function for computing sort keys. Unlike the cmp function which gets applied to every comparison, the key function gets applied only once to each record. It is much faster than cmp and typically more readable while using less code. The key function also maintains the stability of the sort (records with the same key are left in their original order.
Original code using a comparison function:
names.sort(lambda x,y: cmp(x.lower(), y.lower()))
Alternative original code with explicit decoration:
tempnames = [(n.lower(), n) for n in names] tempnames.sort() names = [original for decorated, original in tempnames]
Revised code using a key function:
names.sort(key=str.lower) # case-insensitive sort
Locating: grep sort *.py
In Python 2.4, the operator module gained two new functions, itemgetter() and attrgetter() that can replace common uses of the lambda keyword. The new functions run faster and are considered by some to improve readability.
lambda r: r --> itemgetter(2) lambda r: r.myattr --> attrgetter('myattr')
sort(studentrecords, key=attrgetter('gpa')) # set a sort field map(attrgetter('lastname'), studentrecords) # extract a field
Locating: grep lambda *.py
Python 2.4 introduced the reversed builtin function for reverse iteration. The existing approaches to reverse iteration suffered from wordiness, performance issues (speed and memory consumption), and/or lack of clarity. A preferred style is to express the sequence in a forwards direction, apply reversed to the result, and then loop over the resulting fast, memory friendly iterator.
Original code expressed with half-open intervals:
for i in range(n-1, -1, -1): print seqn[i]
Alternative original code reversed in multiple steps:
rseqn = list(seqn) rseqn.reverse() for value in rseqn: print value
Alternative original code expressed with extending slicing:
for value in seqn[::-1]: print value
Revised code using the reversed function:
for value in reversed(seqn): print value
In Python 2.3, for string2 in string1, the length restriction on string2 is lifted; it can now be a string of any length. When searching for a substring, where you don't care about the position of the substring in the original string, using the in operator makes the meaning clear.
string1.find(string2) >= 0 --> string2 in string1 string1.find(string2) != -1 --> string2 in string1
In Python 2.3, apply() was marked for Pending Deprecation because it was made obsolete by Python 1.6's introduction of * and ** in function calls. Using a direct function call was always a little faster than apply() because it saved the lookup for the builtin. Now, apply() is even slower due to its use of the warnings module.
apply(f, args, kwds) --> f(*args, **kwds)
Note: The Pending Deprecation was removed from apply() in Python 2.3.3 since it creates pain for people who need to maintain code that works with Python versions as far back as 1.5.2, where there was no alternative to apply(). The function remains deprecated, however.
For testing dictionary membership, use the 'in' keyword instead of the 'has_key()' method. The result is shorter and more readable. The style becomes consistent with tests for membership in lists. The result is slightly faster because has_key requires an attribute search and uses a relatively expensive function call.
if d.has_key(k): --> if k in d:
Some dictionary-like objects may not define a __contains__() method:
Locating: grep has_key
Use the new iter methods for looping over dictionaries. The iter methods are faster because they do not have to create a new list object with a complete copy of all of the keys, values, or items. Selecting only keys, values, or items (key/value pairs) as needed saves the time for creating throwaway object references and, in the case of items, saves a second hash look-up of the key.
for key in d.keys(): --> for key in d: for value in d.values(): --> for value in d.itervalues(): for key, value in d.items(): --> for key, value in d.iteritems():
If you need a list, do not change the return type:
def getids(): return d.keys()
Some dictionary-like objects may not define iter methods:
for k in dictlike.keys():
Iterators do not support slicing, sorting or other operations:
k = d.keys(); j = k[:]
Dictionary iterators prohibit modifying the dictionary:
for k in d.keys(): del[k]
Replace stat constants or indices with new os.stat attributes and methods. The os.stat attributes and methods are not order-dependent and do not require an import of the stat module.
os.stat("foo")[stat.ST_MTIME] --> os.stat("foo").st_mtime os.stat("foo")[stat.ST_MTIME] --> os.path.getmtime("foo")
Locating: grep os.stat or grep stat.S
The types module is likely to be deprecated in the future. Use built-in constructor functions instead. They may be slightly faster.
isinstance(v, types.IntType) --> isinstance(v, int) isinstance(s, types.StringTypes) --> isinstance(s, basestring)
Full use of this technique requires Python 2.3 or later (basestring was introduced in Python 2.3), but Python 2.2 is sufficient for most uses.
Locating: grep types *.py | grep import
In Python 2.2, new built-in types were added for dict and file. Scripts should avoid assigning variable names that mask those types. The same advice also applies to existing builtins like list.
file = open('myfile.txt') --> f = open('myfile.txt') dict = obj.__dict__ --> d = obj.__dict__
Locating: grep 'file ' *.py
All random-related methods have been collected in one place, the random module.
import whrandom --> import random
Locating: grep whrandom
The string module is likely to be deprecated in the future. Use string methods instead. They're faster too.
import string ; string.method(s, ...) --> s.method(...) c in string.whitespace --> c.isspace()
Locating: grep string *.py | grep import
Use these string methods instead of slicing. No slice has to be created and there's no risk of miscounting.
"foobar"[:3] == "foo" --> "foobar".startswith("foo") "foobar"[-3:] == "bar" --> "foobar".endswith("bar")
The atexit module supports multiple functions to be executed upon program termination. Also, it supports parameterized functions. Unfortunately, its implementation conflicts with the sys.exitfunc attribute which only supports a single exit function. Code relying on sys.exitfunc may interfere with other modules (including library modules) that elect to use the newer and more versatile atexit module.
sys.exitfunc = myfunc --> atexit.register(myfunc)
String exceptions are deprecated, so derive from the Exception base class. Unlike the obsolete string exceptions, class exceptions all derive from another exception or the Exception base class. This allows meaningful groupings of exceptions. It also allows an "except Exception" clause to catch all exceptions.
NewError = 'NewError' --> class NewError(Exception): pass
Since there is only one None object, equality can be tested with identity. Identity tests are slightly faster than equality tests. Also, some object types may overload comparison, so equality testing may be much slower.
if v == None --> if v is None: if v != None --> if v is not None:
Locating: grep '== None' or grep '!= None'
|||PEP 4, Deprecation of Standard Modules, von Loewis (http://www.python.org/dev/peps/pep-0004/)|
This document has been placed in the public domain.