Porting Code to Python 3 with 2to3

Diving In

So much has changed between Python 2 and Python 3, there are vanishingly few programs that will run unmodified under both. But don’t despair! To help with this transition, Python 3 comes with a utility script called 2to3, which takes your actual Python 2 source code as input and auto-converts as much as it can to Python 3. Case study: porting chardet to Python 3 describes how to run the 2to3 script, then shows some things it can’t fix automatically. This appendix documents what it can fix automatically. 

Unicode string literals

In Python 2, print was a statement. Whatever you wanted to print simply followed the print keyword. In Python 3, print() is a function. Whatever you want to print, pass it to print() like any other function.

Notes	Python 2	Python 3
①	print	print()
②	print 1	print(1)
③	print 1, 2	print(1, 2)
④	print 1, 2,	print(1, 2, end=' ')
⑤	print >>sys.stderr, 1, 2, 3	print(1, 2, 3, file=sys.stderr)

1. To print a blank line, call print() without any arguments.
2. To print a single value, call print() with one argument.
3. To print two values separated by a space, call print() with two arguments.
4. This one is a little tricky. In Python 2, if you ended a print statement with a comma, it would print the values separated by spaces, then print a trailing space, then stop without printing a carriage return. (Technically, it’s a little more complicated than that. The print statement in Python 2 used a now-deprecated attribute called softspace. Instead of printing a space, Python 2 would set sys.stdout.softspace to 1. The space character wasn’t really printed until something else got printed on the same line. If the next print statement printed a carriage return, sys.stdout.softspace would be set to 0 and the space would never be printed. You probably never noticed the difference unless your application was sensitive to the presence or absence of trailing whitespace in print-generated output.) In Python 3, the way to do this is to pass end=' ' as a keyword argument to the print() function. The end argument defaults to '\n' (a carriage return), so overriding it will suppress the carriage return after printing the other arguments.
5. In Python 2, you could redirect the output to a pipe — like sys.stderr — by using the >>pipe_name syntax. In Python 3, the way to do this is to pass the pipe in the file keyword argument. The file argument defaults to sys.stdout (standard out), so overriding it will output to a different pipe instead.

Unicode string literals

Python 2 had two string types: Unicode strings and non-Unicode strings. Python 3 has one string type: Unicode strings.

Notes	Python 2	Python 3
①	u'PapayaWhip'	'PapayaWhip'
②	ur'PapayaWhip\foo'	r'PapayaWhip\foo'

1. Unicode string literals are simply converted into string literals, which, in Python 3, are always Unicode.
2. Unicode raw strings (in which Python does not auto-escape backslashes) are converted to raw strings. In Python 3, raw strings are always Unicode.

unicode() global function

Python 2 had two global functions to coerce objects into strings: unicode() to coerce them into Unicode strings, and str() to coerce them into non-Unicode strings. Python 3 has only one string type, Unicode strings, so the str() function is all you need. (The unicode() function no longer exists.)

Notes	Python 2	Python 3
	unicode(anything)	str(anything)

long data type

Python 2 had separate int and long types for non-floating-point numbers. An int could not be any larger than sys.maxint, which varied by platform. Longs were defined by appending an L to the end of the number, and they could be, well, longer than ints. In Python 3, there is only one integer type, called int, which mostly behaves like the long type in Python 2. Since there are no longer two types, there is no need for special syntax to distinguish them.
Further reading: PEP 237: Unifying Long Integers and Integers.

Notes	Python 2	Python 3
①	x = 1000000000000L	x = 1000000000000
②	x = 0xFFFFFFFFFFFFL	x = 0xFFFFFFFFFFFF
③	long(x)	int(x)
④	type(x) is long	type(x) is int
⑤	isinstance(x, long)	isinstance(x, int)

1. Base 10 long integer literals become base 10 integer literals.
2. Base 16 long integer literals become base 16 integer literals.
3. In Python 3, the old long() function no longer exists, since longs don’t exist. To coerce a variable to an integer, use the int() function.
4. To check whether a variable is an integer, get its type and compare it to int, not long.
5. You can also use the isinstance() function to check data types; again, use int, not long, to check for integers.

<> comparison

Python 2 supported <> as a synonym for !=, the not-equals comparison operator. Python 3 supports the != operator, but not <>.

Notes	Python 2	Python 3
①	if x <> y:	if x != y:
②	if x <> y <> z:	if x != y != z:

1. A simple comparison.
2. A more complex comparison between three values.

has_key() dictionary method

In Python 2, dictionaries had a has_key() method to test whether the dictionary had a certain key. In Python 3, this method no longer exists. Instead, you need to use the in operator.

Notes	Python 2	Python 3
①	a_dictionary.has_key('PapayaWhip')	'PapayaWhip' in a_dictionary
②	a_dictionary.has_key(x) or a_dictionary.has_key(y)	x in a_dictionary or y in a_dictionary
③	a_dictionary.has_key(x or y)	(x or y) in a_dictionary
④	a_dictionary.has_key(x + y)	(x + y) in a_dictionary
⑤	x + a_dictionary.has_key(y)	x + (y in a_dictionary)

1. The simplest form.
2. The in operator takes precedence over the or operator, so there is no need for parentheses around x in a_dictionary or around y in a_dictionary.
3. On the other hand, you do need parentheses around x or y here, for the same reason — in takes precedence over or. (Note: this code is completely different from the previous line. Python interprets x or y first, which results in either x (if x is true in a boolean context) or y. Then it takes that singular value and checks whether it is a key in a_dictionary.)
4. The + operator takes precedence over the in operator, so this form technically doesn’t need parentheses around x + y, but 2to3 includes them anyway.
5. This form definitely needs parentheses around y in a_dictionary, since the + operator takes precedence over the in operator.

Dictionary methods that return lists

In Python 2, many dictionary methods returned lists. The most frequently used methods were keys(), items(), and values(). In Python 3, all of these methods return dynamic views. In some contexts, this is not a problem. If the method’s return value is immediately passed to another function that iterates through the entire sequence, it makes no difference whether the actual type is a list or a view. In other contexts, it matters a great deal. If you were expecting a complete list with individually addressable elements, your code will choke, because views do not support indexing.

Notes	Python 2	Python 3
①	a_dictionary.keys()	list(a_dictionary.keys())
②	a_dictionary.items()	list(a_dictionary.items())
③	a_dictionary.iterkeys()	iter(a_dictionary.keys())
④	[i for i in a_dictionary.iterkeys()]	[i for i in a_dictionary.keys()]
⑤	min(a_dictionary.keys())	no change

1. 2to3 errs on the side of safety, converting the return value from keys() to a static list with the list() function. This will always work, but it will be less efficient than using a view. You should examine the converted code to see if a list is absolutely necessary, or if a view would do.
2. Another view-to-list conversion, with the items() method. 2to3 will do the same thing with the values() method.
3. Python 3 does not support the iterkeys() method anymore. Use keys(), and if necessary, convert the view to an iterator with the iter() function.
4. 2to3 recognizes when the iterkeys() method is used inside a list comprehension, and converts it to the keys() method (without wrapping it in an extra call to iter()). This works because views are iterable.
5. 2to3 recognizes that the keys() method is immediately passed to a function which iterates through an entire sequence, so there is no need to convert the return value to a list first. The min() function will happily iterate through the view instead. This applies to min(), max(), sum(), list(), tuple(), set(), sorted(), any(), and all().

Modules that have been renamed or reorganized

Several modules in the Python Standard Library have been renamed. Several other modules which are related to each other have been combined or reorganized to make their association more logical.

http

In Python 3, several related HTTP modules have been combined into a single package, http.

Notes	Python 2	Python 3
①	import httplib	import http.client
②	import Cookie	import http.cookies
③	import cookielib	import http.cookiejar
④	import BaseHTTPServer import SimpleHTTPServer import CGIHttpServer	import http.server

1. The http.client module implements a low-level library that can request HTTP resources and interpret HTTP responses.
2. The http.cookies module provides a Pythonic interface to browser cookies that are sent in a Set-Cookie: HTTP header.
3. The http.cookiejar module manipulates the actual files on disk that popular web browsers use to store cookies.
4. The http.server module provides a basic HTTP server.

urllib

Python 2 had a rat’s nest of overlapping modules to parse, encode, and fetch URLs. In Python 3, these have all been refactored and combined in a single package, urllib.

Notes	Python 2	Python 3
①	import urllib	import urllib.request, urllib.parse, urllib.error
②	import urllib2	import urllib.request, urllib.error
③	import urlparse	import urllib.parse
④	import robotparser	import urllib.robotparser
⑤	from urllib import FancyURLopener from urllib import urlencode	from urllib.request import FancyURLopener from urllib.parse import urlencode
⑥	from urllib2 import Request from urllib2 import HTTPError	from urllib.request import Request from urllib.error import HTTPError

1. The old urllib module in Python 2 had a variety of functions, including urlopen() for fetching data and splittype(), splithost(), and splituser() for splitting a URL into its constituent parts. These functions have been reorganized more logically within the new urllib package. 2to3 will also change all calls to these functions so they use the new naming scheme.
2. The old urllib2 module in Python 2 has been folded into the urllib package in Python 3. All your urllib2 favorites — the build_opener() method, Request objects, and HTTPBasicAuthHandler and friends — are still available.
3. The urllib.parse module in Python 3 contains all the parsing functions from the old urlparse module in Python 2.
4. The urllib.robotparser module parses robots.txt files.
5. The FancyURLopener class, which handles HTTP redirects and other status codes, is still available in the new urllib.request module. The urlencode() function has moved to urllib.parse.
6. The Request object is still available in urllib.request, but constants like HTTPError have been moved to urllib.error.

Did I mention that 2to3 will rewrite your function calls too? For example, if your Python 2 code imports the urllib module and calls urllib.urlopen() to fetch data, 2to3 will fix both the import statement and the function call.

Notes	Python 2	Python 3
	import urllib print urllib.urlopen('http://diveintopython3.org/').read()	import urllib.request, urllib.parse, urllib.error print(urllib.request.urlopen('http://diveintopython3.org/').read())

dbm

All the various DBM clones are now in a single package, dbm. If you need a specific variant like GNU DBM, you can import the appropriate module within the dbm package.

Notes	Python 2	Python 3
	import dbm	import dbm.ndbm
	import gdbm	import dbm.gnu
	import dbhash	import dbm.bsd
	import dumbdbm	import dbm.dumb
	import anydbm import whichdb	import dbm

xmlrpc

XML-RPC is a lightweight method of performing remote RPC calls over HTTP. The XML-RPC client library and several XML-RPC server implementations are now combined in a single package, xmlrpc.

Notes	Python 2	Python 3
	import xmlrpclib	import xmlrpc.client
	import DocXMLRPCServer import SimpleXMLRPCServer	import xmlrpc.server

Other modules

Notes	Python 2	Python 3
①	try: import cStringIO as StringIO except ImportError: import StringIO	import io
②	try: import cPickle as pickle except ImportError: import pickle	import pickle
③	import __builtin__	import builtins
④	import copy_reg	import copyreg
⑤	import Queue	import queue
⑥	import SocketServer	import socketserver
⑦	import ConfigParser	import configparser
⑧	import repr	import reprlib
⑨	import commands	import subprocess

1. A common idiom in Python 2 was to try to import cStringIO as StringIO, and if that failed, to import StringIO instead. Do not do this in Python 3; the io module does it for you. It will find the fastest implementation available and use it automatically.
2. A similar idiom was used to import the fastest pickle implementation. Do not do this in Python 3; the pickle module does it for you.
3. The builtins module contains the global functions, classes, and constants used throughout the Python language. Redefining a function in the builtins module will redefine the global function everywhere. That is exactly as powerful and scary as it sounds.
4. The copyreg module adds pickle support for custom types defined in C.
5. The queue module implements a multi-producer, multi-consumer queue.
6. The socketserver module provides generic base classes for implementing different kinds of socket servers.
7. The configparser module parses INI-style configuration files.
8. The reprlib module reimplements the built-in repr() function, with additional controls on how long the representations can be before they are truncated.
9. The subprocess module allows you to spawn processes, connect to their pipes, and obtain their return codes.

Relative imports within a package

A package is a group of related modules that function as a single entity. In Python 2, when modules within a package need to reference each other, you use import foo or from foo import Bar. The Python 2 interpreter first searches within the current package to find foo.py, and then moves on to the other directories in the Python search path (sys.path). Python 3 works a bit differently. Instead of searching the current package, it goes directly to the Python search path. If you want one module within a package to import another module in the same package, you need to explicitly provide the relative path between the two modules.
Suppose you had this package, with multiple files in the same directory:

chardet/
|
+--__init__.py
|
+--constants.py
|
+--mbcharsetprober.py
|
+--universaldetector.py

Now suppose that universaldetector.py needs to import the entire constants.py file and one class from mbcharsetprober.py. How do you do it?

Notes	Python 2	Python 3
①	import constants	from . import constants
②	from mbcharsetprober import MultiByteCharSetProber	from .mbcharsetprober import MultiByteCharsetProber

1. When you need to import an entire module from elsewhere in your package, use the new from . import syntax. The period is actually a relative path from this file (universaldetector.py) to the file you want to import (constants.py). In this case, they are in the same directory, thus the single period. You can also import from the parent directory (from .. import anothermodule) or a subdirectory.
2. To import a specific class or function from another module directly into your module’s namespace, prefix the target module with a relative path, minus the trailing slash. In this case, mbcharsetprober.py is in the same directory as universaldetector.py, so the path is a single period. You can also import form the parent directory (from ..anothermodule import AnotherClass) or a subdirectory.

next() iterator method

In Python 2, iterators had a next() method which returned the next item in the sequence. That’s still true in Python 3, but there is now also a global next() function that takes an iterator as an argument.

Notes	Python 2	Python 3
①	anIterator.next()	next(anIterator)
②	a_function_that_returns_an_iterator().next()	next(a_function_that_returns_an_iterator())
③	class A: def next(self): pass	class A: def __next__(self): pass
④	class A: def next(self, x, y): pass	no change
⑤	next = 42 for an_iterator in a_sequence_of_iterators: an_iterator.next()	next = 42 for an_iterator in a_sequence_of_iterators: an_iterator.__next__()

1. In the simplest case, instead of calling an iterator’s next() method, you now pass the iterator itself to the global next() function.
2. If you have a function that returns an iterator, call the function and pass the result to the next() function. (The 2to3 script is smart enough to convert this properly.)
3. If you define your own class and mean to use it as an iterator, define the __next__() special method.
4. If you define your own class and just happen to have a method named next() that takes one or more arguments, 2to3 will not touch it. This class can not be used as an iterator, because its next() method takes arguments.
5. This one is a bit tricky. If you have a local variable named next, then it takes precedence over the new global next() function. In this case, you need to call the iterator’s special __next__() method to get the next item in the sequence. (Alternatively, you could also refactor the code so the local variable wasn’t named next, but 2to3 will not do that for you automatically.)

filter() global function

In Python 2, the filter() function returned a list, the result of filtering a sequence through a function that returned True or False for each item in the sequence. In Python 3, the filter() function returns an iterator, not a list.

Notes	Python 2	Python 3
①	filter(a_function, a_sequence)	list(filter(a_function, a_sequence))
②	list(filter(a_function, a_sequence))	no change
③	filter(None, a_sequence)	[i for i in a_sequence if i]
④	for i in filter(None, a_sequence):	no change
⑤	[i for i in filter(a_function, a_sequence)]	no change

1. In the most basic case, 2to3 will wrap a call to filter() with a call to list(), which simply iterates through its argument and returns a real list.
2. However, if the call to filter() is already wrapped in list(), 2to3 will do nothing, since the fact that filter() is returning an iterator is irrelevant.
3. For the special syntax of filter(None, ...), 2to3 will transform the call into a semantically equivalent list comprehension.
4. In contexts like for loops, which iterate through the entire sequence anyway, no changes are necessary.
5. Again, no changes are necessary, because the list comprehension will iterate through the entire sequence, and it can do that just as well if filter() returns an iterator as if it returns a list.

map() global function

In much the same way as filter(), the map() function now returns an iterator. (In Python 2, it returned a list.)

Notes	Python 2	Python 3
①	map(a_function, 'PapayaWhip')	list(map(a_function, 'PapayaWhip'))
②	map(None, 'PapayaWhip')	list('PapayaWhip')
③	map(lambda x: x+1, range(42))	[x+1 for x in range(42)]
④	for i in map(a_function, a_sequence):	no change
⑤	[i for i in map(a_function, a_sequence)]	no change

1. As with filter(), in the most basic case, 2to3 will wrap a call to map() with a call to list().
2. For the special syntax of map(None, ...), the identity function, 2to3 will convert it to an equivalent call to list().
3. If the first argument to map() is a lambda function, 2to3 will convert it to an equivalent list comprehension.
4. In contexts like for loops, which iterate through the entire sequence anyway, no changes are necessary.
5. Again, no changes are necessary, because the list comprehension will iterate through the entire sequence, and it can do that just as well if map() returns an iterator as if it returns a list.

reduce() global function

In Python 3, the reduce() function has been removed from the global namespace and placed in the functools module.

Notes	Python 2	Python 3
	reduce(a, b, c)	from functools import reduce reduce(a, b, c)

apply() global function

Python 2 had a global function called apply(), which took a function f and a list [a, b, c] and returned f(a, b, c). You can accomplish the same thing by calling the function directly and passing it the list of arguments preceded by an asterisk. In Python 3, the apply() function no longer exists; you must use the asterisk notation.

Notes	Python 2	Python 3
①	apply(a_function, a_list_of_args)	a_function(*a_list_of_args)
②	apply(a_function, a_list_of_args, a_dictionary_of_named_args)	a_function(*a_list_of_args, **a_dictionary_of_named_args)
③	apply(a_function, a_list_of_args + z)	a_function(*a_list_of_args + z)
④	apply(aModule.a_function, a_list_of_args)	aModule.a_function(*a_list_of_args)

1. In the simplest form, you can call a function with a list of arguments (an actual list like [a, b, c]) by prepending the list with an asterisk (*). This is exactly equivalent to the old apply() function in Python 2.
2. In Python 2, the apply() function could actually take three parameters: a function, a list of arguments, and a dictionary of named arguments. In Python 3, you can accomplish the same thing by prepending the list of arguments with an asterisk (*) and the dictionary of named arguments with two asterisks (**).
3. The + operator, used here for list concatenation, takes precedence over the * operator, so there is no need for extra parentheses around a_list_of_args + z.
4. The 2to3 script is smart enough to convert complex apply() calls, including calling functions within imported modules.

intern() global function

In Python 2, you could call the intern() function on a string to intern it as a performance optimization. In Python 3, the intern() function has been moved to the sys module.

Notes	Python 2	Python 3
	intern(aString)	sys.intern(aString)

exec statement

Just as the print statement became a function in Python 3, so too has the exec statement. The exec() function takes a string which contains arbitrary Python code and executes it as if it were just another statement or expression. exec() is like eval(), but even more powerful and evil. The eval() function can only evaluate a single expression, but exec() can execute multiple statements, imports, function declarations — essentially an entire Python program in a string.

Notes	Python 2	Python 3
①	exec codeString	exec(codeString)
②	exec codeString in a_global_namespace	exec(codeString, a_global_namespace)
③	exec codeString in a_global_namespace, a_local_namespace	exec(codeString, a_global_namespace, a_local_namespace)

1. In the simplest form, the 2to3 script simply encloses the code-as-a-string in parentheses, since exec() is now a function instead of a statement.
2. The old exec statement could take a namespace, a private environment of globals in which the code-as-a-string would be executed. Python 3 can also do this; just pass the namespace as the second argument to the exec() function.
3. Even fancier, the old exec statement could also take a local namespace (like the variables defined within a function). In Python 3, the exec() function can do that too.

execfile statement

Like the old exec statement, the old execfile statement will execute strings as if they were Python code. Where exec took a string, execfile took a filename. In Python 3, the execfile statement has been eliminated. If you really need to take a file of Python code and execute it (but you’re not willing to simply import it), you can accomplish the same thing by opening the file, reading its contents, calling the global compile() function to force the Python interpreter to compile the code, and then call the new exec() function.

Notes	Python 2	Python 3
	execfile('a_filename')	exec(compile(open('a_filename').read(), 'a_filename', 'exec'))

repr literals (backticks)

In Python 2, there was a special syntax of wrapping any object in backticks (like `x`) to get a representation of the object. In Python 3, this capability still exists, but you can no longer use backticks to get it. Instead, use the global repr() function.

Notes	Python 2	Python 3
①	`x`	repr(x)
②	`'PapayaWhip' + `2``	repr('PapayaWhip' + repr(2))

1. Remember, x can be anything — a class, a function, a module, a primitive data type, &c. The repr() function works on everything.
2. In Python 2, backticks could be nested, leading to this sort of confusing (but valid) expression. The 2to3 tool is smart enough to convert this into nested calls to repr().

try...except statement

The syntax for catching exceptions has changed slightly between Python 2 and Python 3.

Notes	Python 2	Python 3
①	try: import mymodule except ImportError, e pass	try: import mymodule except ImportError as e: pass
②	try: import mymodule except (RuntimeError, ImportError), e pass	try: import mymodule except (RuntimeError, ImportError) as e: pass
③	try: import mymodule except ImportError: pass	no change
④	try: import mymodule except: pass	no change

1. Instead of a comma after the exception type, Python 3 uses a new keyword, as.
2. The as keyword also works for catching multiple types of exceptions at once.
3. If you catch an exception but don’t actually care about accessing the exception object itself, the syntax is identical between Python 2 and Python 3.
4. Similarly, if you use a fallback to catch all exceptions, the syntax is identical.

☞You should never use a fallback to catch all exceptions when importing modules (or most other times). Doing so will catch things like KeyboardInterrupt (if the user pressed Ctrl-C to interrupt the program) and can make it more difficult to debug errors.

raise statement

The syntax for raising your own exceptions has changed slightly between Python 2 and Python 3.

Notes	Python 2	Python 3
①	raise MyException	unchanged
②	raise MyException, 'error message'	raise MyException('error message')
③	raise MyException, 'error message', a_traceback	raise MyException('error message').with_traceback(a_traceback)
④	raise 'error message'	unsupported

1. In the simplest form, raising an exception without a custom error message, the syntax is unchanged.
2. The change becomes noticeable when you want to raise an exception with a custom error message. Python 2 separated the exception class and the message with a comma; Python 3 passes the error message as a parameter.
3. Python 2 supported a more complex syntax to raise an exception with a custom traceback (stack trace). You can do this in Python 3 as well, but the syntax is quite different.
4. In Python 2, you could raise an exception with no exception class, just an error message. In Python 3, this is no longer possible. 2to3 will warn you that it was unable to fix this automatically.

throw method on generators

In Python 2, generators have a throw() method. Calling a_generator.throw() raises an exception at the point where the generator was paused, then returns the next value yielded by the generator function. In Python 3, this functionality is still available, but the syntax is slightly different.

Notes	Python 2	Python 3
①	a_generator.throw(MyException)	no change
②	a_generator.throw(MyException, 'error message')	a_generator.throw(MyException('error message'))
③	a_generator.throw('error message')	unsupported

1. In the simplest form, a generator throws an exception without a custom error message. In this case, the syntax has not changed between Python 2 and Python 3.
2. If the generator throws an exception with a custom error message, you need to pass the error string to the exception when you create it.
3. Python 2 also supported throwing an exception with only a custom error message. Python 3 does not support this, and the 2to3 script will display a warning telling you that you will need to fix this code manually.

xrange() global function

In Python 2, there were two ways to get a range of numbers: range(), which returned a list, and xrange(), which returned an iterator. In Python 3, range() returns an iterator, and xrange() doesn’t exist.

Notes	Python 2	Python 3
①	xrange(10)	range(10)
②	a_list = range(10)	a_list = list(range(10))
③	[i for i in xrange(10)]	[i for i in range(10)]
④	for i in range(10):	no change
⑤	sum(range(10))	no change

1. In the simplest case, the 2to3 script will simply convert xrange() to range().
2. If your Python 2 code used range(), the 2to3 script does not know whether you needed a list, or whether an iterator would do. It errs on the side of caution and coerces the return value into a list by calling the list() function.
3. If the xrange() function was inside a list comprehension, the 2to3 script is clever enough not to wrap the range() function with a call to list(). The list comprehension will work just fine with the iterator that the range() function returns.
4. Similarly, a for loop will work just fine with an iterator, so there is no need to change anything here.
5. The sum() function will also work with an iterator, so 2to3 makes no changes here either. Like dictionary methods that return views instead of lists, this applies to min(), max(), sum(), list(), tuple(), set(), sorted(), any(), and all().

raw_input() and input() global functions

Python 2 had two global functions for asking the user for input on the command line. The first, called input(), expected the user to enter a Python expression (and returned the result). The second, called raw_input(), just returned whatever the user typed. This was wildly confusing for beginners and widely regarded as a “wart” in the language. Python 3 excises this wart by renaming raw_input() to input(), so it works the way everyone naively expects it to work.

Notes	Python 2	Python 3
①	raw_input()	input()
②	raw_input('prompt')	input('prompt')
③	input()	eval(input())

1. In the simplest form, raw_input() becomes input().
2. In Python 2, the raw_input() function could take a prompt as a parameter. This has been retained in Python 3.
3. If you actually need to ask the user for a Python expression to evaluate, use the input() function and pass the result to eval().

func_* function attributes

In Python 2, code within functions can access special attributes about the function itself. In Python 3, these special function attributes have been renamed for consistency with other attributes.

Notes	Python 2	Python 3
①	a_function.func_name	a_function.__name__
②	a_function.func_doc	a_function.__doc__
③	a_function.func_defaults	a_function.__defaults__
④	a_function.func_dict	a_function.__dict__
⑤	a_function.func_closure	a_function.__closure__
⑥	a_function.func_globals	a_function.__globals__
⑦	a_function.func_code	a_function.__code__

1. The __name__ attribute (previously func_name) contains the function’s name.
2. The __doc__ attribute (previously func_doc) contains the docstring that you defined in the function’s source code.
3. The __defaults__ attribute (previously func_defaults) is a tuple containing default argument values for those arguments that have default values.
4. The __dict__ attribute (previously func_dict) is the namespace supporting arbitrary function attributes.
5. The __closure__ attribute (previously func_closure) is a tuple of cells that contain bindings for the function’s free variables.
6. The __globals__ attribute (previously func_globals) is a reference to the global namespace of the module in which the function was defined.
7. The __code__ attribute (previously func_code) is a code object representing the compiled function body.

xreadlines() I/O method

In Python 2, file objects had an xreadlines() method which returned an iterator that would read the file one line at a time. This was useful in for loops, among other places. In fact, it was so useful, later versions of Python 2 added the capability to file objects themselves.
In Python 3, the xreadlines() method no longer exists. 2to3 can fix the simple cases, but some edge cases will require manual intervention.

Notes	Python 2	Python 3
①	for line in a_file.xreadlines():	for line in a_file:
②	for line in a_file.xreadlines(5):	no change (broken)

1. If you used to call xreadlines() with no arguments, 2to3 will convert it to just the file object. In Python 3, this will accomplish the same thing: read the file one line at a time and execute the body of the for loop.
2. If you used to call xreadlines() with an argument (the number of lines to read at a time), 2to3 will not fix it, and your code will fail with an AttributeError: '_io.TextIOWrapper' object has no attribute 'xreadlines'. You can manually change xreadlines() to readlines() to get it to work in Python 3. (The readlines() method now returns an iterator, so it is just as efficient as xreadlines() was in Python 2.)

☃

lambda functions that take a tuple instead of multiple parameters

In Python 2, you could define anonymous lambda functions which took multiple parameters by defining the function as taking a tuple with a specific number of items. In effect, Python 2 would “unpack” the tuple into named arguments, which you could then reference (by name) within the lambda function. In Python 3, you can still pass a tuple to a lambda function, but the Python interpreter will not unpack the tuple into named arguments. Instead, you will need to reference each argument by its positional index.

Notes	Python 2	Python 3
①	lambda (x,): x + f(x)	lambda x1: x1[0] + f(x1[0])
②	lambda (x, y): x + f(y)	lambda x_y: x_y[0] + f(x_y[1])
③	lambda (x, (y, z)): x + y + z	lambda x_y_z: x_y_z[0] + x_y_z[1][0] + x_y_z[1][1]
④	lambda x, y, z: x + y + z	unchanged

1. If you had defined a lambda function that took a tuple of one item, in Python 3 that would become a lambda with references to x1[0]. The name x1 is autogenerated by the 2to3 script, based on the named arguments in the original tuple.
2. A lambda function with a two-item tuple (x, y) gets converted to x_y with positional arguments x_y[0] and x_y[1].
3. The 2to3 script can even handle lambda functions with nested tuples of named arguments. The resulting Python 3 code is a bit unreadable, but it works the same as the old code did in Python 2.
4. You can define lambda functions that take multiple arguments. Without parentheses around the arguments, Python 2 just treats it as a lambda function with multiple arguments; within the lambda function, you simply reference the arguments by name, just like any other function. This syntax still works in Python 3.

Special method attributes

In Python 2, class methods can reference the class object in which they are defined, as well as the method object itself. im_self is the class instance object; im_func is the function object; im_class is the class of im_self. In Python 3, these special method attributes have been renamed to follow the naming conventions of other attributes.

Notes	Python 2	Python 3
	aClassInstance.aClassMethod.im_func	aClassInstance.aClassMethod.__func__
	aClassInstance.aClassMethod.im_self	aClassInstance.aClassMethod.__self__
	aClassInstance.aClassMethod.im_class	aClassInstance.aClassMethod.__self__.__class__

__nonzero__ special method

In Python 2, you could build your own classes that could be used in a boolean context. For example, you could instantiate the class and then use the instance in an if statement. To do this, you defined a special __nonzero__() method which returned True or False, and it was called whenever the instance was used in a boolean context. In Python 3, you can still do this, but the name of the method has changed to __bool__().

Notes	Python 2	Python 3
①	class A: def __nonzero__(self): pass	class A: def __bool__(self): pass
②	class A: def __nonzero__(self, x, y): pass	no change

1. Instead of __nonzero__(), Python 3 calls the __bool__() method when evaluating an instance in a boolean context.
2. However, if you have a __nonzero__() method that takes arguments, the 2to3 tool will assume that you were using it for some other purpose, and it will not make any changes.

Octal literals

The syntax for defining base 8 (octal) numbers has changed slightly between Python 2 and Python 3.

Notes	Python 2	Python 3
	x = 0755	x = 0o755

sys.maxint

Due to the integration of the long and int types, the sys.maxint constant is no longer accurate. Because the value may still be useful in determining platform-specific capabilities, it has been retained but renamed as sys.maxsize.

Notes	Python 2	Python 3
①	from sys import maxint	from sys import maxsize
②	a_function(sys.maxint)	a_function(sys.maxsize)

1. maxint becomes maxsize.
2. Any usage of sys.maxint becomes sys.maxsize.

callable() global function

In Python 2, you could check whether an object was callable (like a function) with the global callable() function. In Python 3, this global function has been eliminated. To check whether an object is callable, check for the existence of the __call__() special method.

Notes	Python 2	Python 3
	callable(anything)	hasattr(anything, '__call__')

zip() global function

In Python 2, the global zip() function took any number of sequences and returned a list of tuples. The first tuple contained the first item from each sequence; the second tuple contained the second item from each sequence; and so on. In Python 3, zip() returns an iterator instead of a list.

Notes	Python 2	Python 3
①	zip(a, b, c)	list(zip(a, b, c))
②	d.join(zip(a, b, c))	no change

1. In the simplest form, you can get the old behavior of the zip() function by wrapping the return value in a call to list(), which will run through the iterator that zip() returns and return a real list of the results.
2. In contexts that already iterate through all the items of a sequence (such as this call to the join() method), the iterator that zip() returns will work just fine. The 2to3 script is smart enough to detect these cases and make no change to your code.

StandardError exception

In Python 2, StandardError was the base class for all built-in exceptions other than StopIteration, GeneratorExit, KeyboardInterrupt, and SystemExit. In Python 3, StandardError has been eliminated; use Exception instead.

Notes	Python 2	Python 3
	x = StandardError()	x = Exception()
	x = StandardError(a, b, c)	x = Exception(a, b, c)

types module constants

The types module contains a variety of constants to help you determine the type of an object. In Python 2, it contained constants for all primitive types like dict and int. In Python 3, these constants have been eliminated; just use the primitive type name instead.

Notes	Python 2	Python 3
	types.UnicodeType	str
	types.StringType	bytes
	types.DictType	dict
	types.IntType	int
	types.LongType	int
	types.ListType	list
	types.NoneType	type(None)
	types.BooleanType	bool
	types.BufferType	memoryview
	types.ClassType	type
	types.ComplexType	complex
	types.EllipsisType	type(Ellipsis)
	types.FloatType	float
	types.ObjectType	object
	types.NotImplementedType	type(NotImplemented)
	types.SliceType	slice
	types.TupleType	tuple
	types.TypeType	type
	types.XRangeType	range

☞types.StringType gets mapped to bytes instead of str because a Python 2 “string” (not a Unicode string, just a regular string) is really just a sequence of bytes in a particular character encoding.

isinstance() global function

The isinstance() function checks whether an object is an instance of a particular class or type. In Python 2, you could pass a tuple of types, and isinstance() would return True if the object was any of those types. In Python 3, you can still do this, but passing the same type twice is deprecated.

Notes	Python 2	Python 3
	isinstance(x, (int, float, int))	isinstance(x, (int, float))

basestring datatype

Python 2 had two string types: Unicode and non-Unicode. But there was also another type, basestring. It was an abstract type, a superclass for both the str and unicode types. It couldn’t be called or instantiated directly, but you could pass it to the global isinstance() function to check whether an object was either a Unicode or non-Unicode string. In Python 3, there is only one string type, so basestring has no reason to exist.

Notes	Python 2	Python 3
	isinstance(x, basestring)	isinstance(x, str)

itertools module

Python 2.3 introduced the itertools module, which defined variants of the global zip(), map(), and filter() functions that returned iterators instead of lists. In Python 3, those global functions return iterators, so those functions in the itertools module have been eliminated. (There are still lots of useful functions in the itertools module, just not these.)

Notes	Python 2	Python 3
①	itertools.izip(a, b)	zip(a, b)
②	itertools.imap(a, b)	map(a, b)
③	itertools.ifilter(a, b)	filter(a, b)
④	from itertools import imap, izip, foo	from itertools import foo

1. Instead of itertools.izip(), just use the global zip() function.
2. Instead of itertools.imap(), just use map().
3. itertools.ifilter() becomes filter().
4. The itertools module still exists in Python 3, it just doesn’t have the functions that have migrated to the global namespace. The 2to3 script is smart enough to remove the specific imports that no longer exist, while leaving other imports intact.

sys.exc_type, sys.exc_value, sys.exc_traceback

Python 2 had three variables in the sys module that you could access while an exception was being handled: sys.exc_type, sys.exc_value, sys.exc_traceback. (Actually, these date all the way back to Python 1.) Ever since Python 1.5, these variables have been deprecated in favor of sys.exc_info(), which is a function that returns a tuple containing those three values. In Python 3, these individual variables have finally gone away; you must use the sys.exc_info() function.

Notes	Python 2	Python 3
	sys.exc_type	sys.exc_info()[0]
	sys.exc_value	sys.exc_info()[1]
	sys.exc_traceback	sys.exc_info()[2]

List comprehensions over tuples

In Python 2, if you wanted to code a list comprehension that iterated over a tuple, you did not need to put parentheses around the tuple values. In Python 3, explicit parentheses are required.

Notes	Python 2	Python 3
	[i for i in 1, 2]	[i for i in (1, 2)]

os.getcwdu() function

Python 2 had a function named os.getcwd(), which returned the current working directory as a (non-Unicode) string. Because modern file systems can handle directory names in any character encoding, Python 2.3 introduced os.getcwdu(). The os.getcwdu() function returned the current working directory as a Unicode string. In Python 3, there is only one string type (Unicode), so os.getcwd() is all you need.

Notes	Python 2	Python 3
	os.getcwdu()	os.getcwd()

Metaclasses

In Python 2, you could create metaclasses either by defining the metaclass argument in the class declaration, or by defining a special class-level __metaclass__ attribute. In Python 3, the class-level attribute has been eliminated.

Notes	Python 2	Python 3
①	class C(metaclass=PapayaMeta): pass	unchanged
②	class Whip: __metaclass__ = PapayaMeta	class Whip(metaclass=PapayaMeta): pass
③	class C(Whipper, Beater): __metaclass__ = PapayaMeta	class C(Whipper, Beater, metaclass=PapayaMeta): pass

1. Declaring the metaclass in the class declaration worked in Python 2, and it still works the same in Python 3.
2. Declaring the metaclass in a class attribute worked in Python 2, but doesn’t work in Python 3.
3. The 2to3 script is smart enough to construct a valid class declaration, even if the class is inherited from one or more base classes.

Matters of style

The rest of the “fixes” listed here aren’t really fixes per se. That is, the things they change are matters of style, not substance. They work just as well in Python 3 as they do in Python 2, but the developers of Python have a vested interest in making Python code as uniform as possible. To that end, there is an official Python style guide which outlines — in excruciating detail — all sorts of nitpicky details that you almost certainly don’t care about. And given that 2to3 provides such a great infrastructure for converting Python code from one thing to another, the authors took it upon themselves to add a few optional features to improve the readability of your Python programs.

set() literals (explicit)

In Python 2, the only way to define a literal set in your code was to call set(a_sequence). This still works in Python 3, but a clearer way of doing it is to use the new set literal notation: curly braces. This works for everything except empty sets, because dictionaries also use curly braces, so {} is an empty dictionary, not an empty set.

☞The 2to3 script will not fix set() literals by default. To enable this fix, specify -f set_literal on the command line when you call 2to3.

Notes	Before	After
	set([1, 2, 3])	{1, 2, 3}
	set((1, 2, 3))	{1, 2, 3}
	set([i for i in a_sequence])	{i for i in a_sequence}

buffer() global function (explicit)

Python objects implemented in C can export a “buffer interface,” which allows other Python code to directly read and write a block of memory. (That is exactly as powerful and scary as it sounds.) In Python 3, buffer() has been renamed to memoryview(). (It’s a little more complicated than that, but you can almost certainly ignore the differences.)

☞The 2to3 script will not fix the buffer() function by default. To enable this fix, specify -f buffer on the command line when you call 2to3.

Notes	Before	After
	x = buffer(y)	x = memoryview(y)

Whitespace around commas (explicit)

Despite being draconian about whitespace for indenting and outdenting, Python is actually quite liberal about whitespace in other areas. Within lists, tuples, sets, and dictionaries, whitespace can appear before and after commas with no ill effects. However, the Python style guide states that commas should be preceded by zero spaces and followed by one. Although this is purely an aesthetic issue (the code works either way, in both Python 2 and Python 3), the 2to3 script can optionally fix this for you.

☞The 2to3 script will not fix whitespace around commas by default. To enable this fix, specify -f wscomma on the command line when you call 2to3.

Notes	Before	After
	a ,b	a, b
	{a :b}	{a: b}

Common idioms (explicit)

There were a number of common idioms built up in the Python community. Some, like the while 1: loop, date back to Python 1. (Python didn’t have a true boolean type until version 2.3, so developers used 1 and 0 instead.) Modern Python programmers should train their brains to use modern versions of these idioms instead.

☞The 2to3 script will not fix common idioms by default. To enable this fix, specify -f idioms on the command line when you call 2to3.

Notes	Before	After
	while 1: do_stuff()	while True: do_stuff()
	type(x) == T	isinstance(x, T)
	type(x) is T	isinstance(x, T)
	a_list = list(a_sequence) a_list.sort() do_stuff(a_list)	a_list = sorted(a_sequence) do_stuff(a_list)

© 2001–11 Mark Pilgrim