What's new in Python 3

The Next Snake

© Eric Isselée, 123RF

© Eric Isselée, 123RF


What do Python 2.x programmers need to know about Python 3?

With the latest major Python release, creator Guido van Rossum saw the opportunity to tidy up his famous scripting language. What is different about Python 3.0? In this article, I offer some highlights for Python programmers who are thinking about making the switch to 3.x.

The first important point is that version 3.0, which is also known as Python 3000 or Py3k, broke with an old tradition in that it is not downwardly compatible. According to von Rossum, "There is no requirement that Python 2.6 code will run unmodified on Python 3.0."

Python 2.6's main purpose is to take the pain out of switching versions. Many of Python 3.0's features were backported to Python 2.6. (A similar Python 2.7 version will accompany the recent Python 3.1 release.) In addition to these transitional 2.x versions, the 2to3 command-line tool supports programmers migrating code from Python 2.x to Python 3.x.


Python has some quirks that have bugged van Rossum since the beginning of the millennium. With Python 3, he decided to sacrifice downward compatibility to remove these obstacles. The most blatant break with Python 2 is the syntactic change to print, which has mutated from a statement to a function; thus, parameters, now called keywords, are called in parentheses. This change is in line with Tim Peters' The Zen of Python: "Explicit is better than implicit." [1]. The new generic print syntax is print(*args, sep=' ', end='\n', file=sys.stdout), where args are the arguments, sep is the separator between the arguments, end is the end-of-line character, and file is the output medium.

Table 1 lists the syntax changes to the print function, including their default values. The advantage of the new version is not apparent until you take a closer look; the new print function now supports overloading. Listing 1 shows a print function that writes to standard output and to a logfile at the same time. To allow this to happen, the function instrumentalizes the built-in __builtins__.print function.

Listing 1

Overloading the Print Function


Doing no more than absolutely necessary is a virtue in many programming languages. Python 3 puts far more emphasis on lazy evaluation. Lists, dictionaries, or Python's functional building blocks no longer generate complete lists; they add just enough to support the evaluation of the output. Lazy evaluation thus saves memory and time.

The Python interpreter achieves this effect by returning only one iterable context that generates values on request. (This was what distinguished range and xrange in Python 2.) In Python 3, range acts like xrange, thus making the xrange function redundant.

The same applies to the functional building blocks map, filter, and zip. These functions have been replaced by their equivalents from the itertools library. For dictionaries, the resulting, iterable contexts are known as views. However, if the programmer needs the full expanded list, simple encapsulation of the iterable context by means of a list constructor, as shown in the following example, can help: list(range(11)) produces [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10].


That 1/2 == 0 shocks Python newcomers. This unusual feature is attributable to Python's C roots. Python 3 gets rid of this strange effect by differentiating between true division and floor division. Whereas in true division 1/2 == 0.5, floor division works like division in Python 2. The notation for floor division uses two slashes: 1//2 == 0.

In Python 2, programmers had to declare strings explicitly as Unicode strings. Python 3 strings are now implicitly Unicode. Python 3.0 only distinguishes between text and data. Text (str) relates to strings and is the same as Python 2 Unicode strings. Data (bytes) are 8-bit strings and the same as Python 2 strings. Python 3 developers need to declare data: b"8_bit_string". Table 2 gives an overview of the difference between Python 2 and 3.

The str.encode() and bytes.decode() functions are available to convert between data types. Programmers need this conversion in Python 3.0 when working with both data types; implicit type conversion no longer takes place. Programmers simply have to decide.

Syntactic Changes

Function annotations in Python 3 now allow programmers to bind metadata to a function. Decorators can then be added to the function in a second step; decorators automatically generate data from the metadata or perform type checking at run time.

The equivalent functions, sumOrig and sumMeta (Figure 1), show function declarations with and without metadata. The second function includes metadata for the signature and return value. This metadata can be referenced with the __annotations__ function attribute.

Figure 1: Function annotations bind metadata to a function.


Python 2.6's whole purpose in life is to make the move to version 3 as easy as possible, so many Python 3.0 features were backported to Python 2.6.

Resource management using with is an important new feature in Python 2.6. A resource (file, socket, mutex, …) is automatically bound when entering and released when exiting the with block. This idiom will remind C++ programmers of "Resource Acquisition Is Initialization" (RAII), wherein a resource is bound to an object. The with statement acts like try … finally from the user's point of view, in that both the try block and the finally block are always executed. All of this happens without explicit exception handling.

How does all of this work? In a with block, you can use any object provided by the context management protocol – that is, which has internal __enter()__ and __exit()__ methods. When entering the with block, the __enter()__ method is automatically called, as is __exit()__ when exiting the block.

The file object comes complete with these methods (Listing 2). Resource management is easy to code, however. A classic application would be to protect a code block against simultaneous access. Listing 3 has placeholders for the code block. The locked class objects prevent competitive access in the with block by using myLock to synchronize the block.

Listing 2

With Block with File


Listing 3

Protecting a Code Block


If this is too much work for you, you can use the contextmanager decorator from the new contextlib [2] library to benefit from its resource management functionality. Many other applications are listed in the Python Enhancement Proposal (PEP) 0343 [3].


The biggest syntactic extension to Python 2.6 is probably the introduction of abstract base classes. Whether an object could be used in a context previously depended on the object's properties and not on its formal interface specification. This idiom is known as duck typing, from a James Whitcomb Riley poem ("When I see a bird that walks like a duck and swims like a duck and quacks like a duck, I call that bird a duck.").

If a class contains an abstract method, it becomes an abstract base class and cannot be instantiated. Derivative classes can only be created if they implement these abstract methods. Abstract base classes in Python act in a similar way to abstract base classes in C++, in that abstract methods are specifically allowed to contain an implementation.

Besides abstract methods, Python also uses abstract properties. Python uses abstract base classes in the numbers [4] and collections [5] modules. That still leaves the question: When does a class become an abstract class? The class needs the ABCMeta metaclass. The methods can then be declared as @abstractmethod, and the properties as @abstactproperty using the corresponding decorators. In addition to this, the use of abstract base classes means the entry of static typing into a language that uses dynamic typing. In the example in Figure 2, the Cygnus class cannot be instantiated because it does not implement the abstract quack() method.

Figure 2: Failure to instantiate.

Multiple Processors

Python's response to multi-processor architectures is the new multiprocessing [6] library. This module imitates the well-known Python threading module, but instead of a thread, it creates a process, and it does this independently of the platform. The multiprocessing module became necessary because CPython, the standard Python implementation, could only run one thread in its interpreter. This behavior is dictated by the Global Interpreter Lock (GIL) [7].

Class decorators [8] round out the list in Python; from now on, you can decorate classes as well as functions.

Python 3.0 also has a new I/O library [9]. The string data type gained a new format() [10] method for improved string formatting. At the same time, the previous format operator, %, is deprecated in Python 3.1.

Clean-Up Work

Wherever changes occur, it is also necessary to ditch some ballast. This affects libraries that have been removed, that have been repackaged, or that coexist in C and Python implementations. The popular Python idiom (Listing 4) of importing the fast C implementation of a module first and then falling back on the Python implementation if this fails is no longer necessary. Python does this automatically. More details are available on changes to the standard library [11].

Listing 4

Import Idiom


All exceptions must derive from BaseException. This implies, in particular, that string exceptions are no longer supported. The exception object now has a new __traceback__ attribute, which contains the traceback of the exception. The approach to both calling and fielding exceptions with arguments has changed. Programmers can now throw exceptions with arguments using raise BaseException(args) and field them with except BaseException as variable (Figure 3).

Figure 3: The new traceback attribute for exceptions.

Python 3 also includes other changes to make life easier for programmers. For example, in cooperative super calls, it is no longer necessary to name the class instance and the class name. Old-style classes, which were deprecated at some previous time, no longer exist in Python 3.0; this removes the need to derive from object to use Python's newer features.

Direct evaluation of input via the input() command is no longer supported, as the input is available as an input string. This approach closes a critical security hole (Figure 4). It was only logical to rename the raw_input() function input() and to remove raw_input.

Figure 4: Direct evaluation of input via the input command is no longer supported in Python 3.

Of course, any description of the new features can't hope to be exclusive. If you want to know more, check out the reference document by von Rossum, "What's New In Python 3.0" [12].

Porting to Python 3.0

A clear migration path is available for porting Python 2 code to Python 3 (Figure 5), but you will need to test the code and fix any bugs at each step of the way.

Figure 5: The recommended migration path for porting Python 2.6 code to version 3.

The four lines of code in Listing 5 will serve as an example for migrating Python 2 to Python 3.0. All four lines defined functional components of Python. The first function calculates the sum of three numbers, 2, 3, and 4, by applying these arguments to the Lambda function. The reduce built-in successively reduces the list of all numbers from 1 to 10 by multiplying the results of the last multiplication with the next number in the sequence. The last two functions filter words, starting with filtering uppercase letters out of a string.

Listing 5

Code for Port


The code works on Python 2.6, and you only need to perform Steps 3 and 4 for the port. The source code for this example is stored in a file called port.py.

Calling the Python 2.6 interpreter with the -3 option (Figure 6) shows incompatibilities with version 3: Both the apply function and the reduce function are no longer built-ins in Python 3.0. The code is easily fixed (Listing 6), and the deprecation warnings then stop.

Figure 6: The Python 3 option in v.2.6 points to issues with the port.

Listing 6

Removing the Deprecation Warning


The code generator 2to3 is really useful if you need to correct your Python 2 code; the generator's final step is to automatically generate code for versions 3.0 and 3.1. The tool offers several options for this (Figure 7). The direct approach is to overwrite the original file: 2to3 port.py -w. The result is the ported source code for Python 3.0 (Listing 7).

Figure 7: The 2to3 code generator has a large number of options.

Listing 7

Code Ported to Python 3.0


When to Make the Move

Python 3.0 originally placed more emphasis on functionality, and this meant that it was about 10 percent slower than Python 2. The required optimization occurred in Python 3.1 [13]. This optimization relates to special handling of small integers. On top of this, Python 3.1's I/O library is implemented in C, which makes it between 2 and 20 times faster. Decoding of the UTF-8, UTF-16, and Latin-1 character sets is now twice to four times as fast.

If you are still waiting for third-party libraries to be ported, there is no point porting your application code to Python 3. von Rossum also recommends [14] not writing any code that will run on both Python 2.6 and Python 3 without modifications. It is preferable to maintain the source code as Python 2.6 code and then use automated tools to port to Python 3.0 or 3.1. Christopher Neugebauer has the final word in his video talk on Python 3000: "Learn 2.6, but keep 3k in mind."

The Author

Rainer Grimm has been a software developer since 1999 at Science + Computing AG in Tübingen, Germany. In particular, he holds training sessions for the in-house product SC Venus.