Python 3 upgrade

About ten years ago, Guido Van Rossum, the Python author and Benevolent Dictator for Life (BDFL), along with the Python community, decided to make several concurrent backward incompatible changes to Python 2.5 and release a new version, Python 3.0. The main changes were:

  • Using unicode strings as default, with the old string type becoming a full featured binary type
  • Changing several builtins, for example
    • The print statement became a function, allowing more consistent syntax and the use of the word print as a name
    • The confusing input removed, and raw_input now renamed to input
    • Simpler exec
    • Division is now split between / float division and // truncating division
  • Improved exception tracing, with chaining
  • Improved function call syntax with annotations and keyword only arguments, replacing little used tuple parameter unpacking
  • More class constructor features, such as nicer metaclass syntax, keyword arguments, __prepare__
  • Renamed standard libraries, to be more consistent
  • Removal of a lot of depreciated features, including old-style classes
  • Removal of a lot of depreciated syntax that had become learner stumbling blocks
  • Adding nonlocal variables
  • Extended tuple unpacking, like first, *rest = makes_a_tuple()
  • Removing the proliferation of .pyc files, instead using __pycache__ directories
  • Automatic selection of C-based standard library modules over pure Python ones if available
  • Unified the int and long types into one unlimited length integer type

Unfortunately, this list was comprehensive enough to break virtually every python script ever written. So, to ease the transition, 3.0 and 2.6 were released simultaneously, with the other, backward compatible new features of 3.0 being also included in 2.6. This happened again with the releases of 3.1 and 2.7. Not wanting to maintain two Pythons, the BDFL declared that 2.7 was the last Python 2 series release.

These changes (mostly the unicode one) also made Python much slower in version 3.0. Since then, however, there have been many speed and memory improvements. Combined with new C extensions for some modules, Python 3 is now usually as fast or faster than Python 2.

The original, officially sanctioned upgrade path was one of the biggest issues with moving to Python 3. A script, 2to3, was supposed to convert code to Python 3, and then the old version could be eventually dropped. This script required a lot of manual intervention (things like the unicode strings require knowledge of the programmer’s intent), and required library authors to maintain two separate versions of the code. This hindered initial adoption with many major libraries unwilling to support two versions for Python 3 support.

Unofficial authors tried making a new script, 3to2, which worked significantly better, but still was hindered by the dual copies of code issue.

Another decision also may have slowed adoption. Part way through the development of Python 3.2 up to 3.4, the decision was made to avoid adding any new features, to give authors time to adopt code to a stable Python 3. This statement could be taken in reverse; why update to Python 3 when it does not have any new features to improve your program? The original changes (as listed above) were not enough to cause mass adoption.

This dreary time in Python development is now drawing to a close, thanks to a change in the way authors started approaching Python compatibility. There is such a good overlap between Python 2.6 or Python 2.7 and Python 3.3+ that a single code base can support them both. The reason for this is the following three things:

  • Good Python 2 is almost the same as Python 3. The things that were dropped were mostly things you shouldn’t do in Python 2 anyway.
  • Several changes in syntax are available in Python 2 using __future__
  • The remaining changes can mostly be wrapped in libraries

These were capitalized by the unofficial library authors, and now almost every library is available as a single code base for Python 2 and 3. Most of the new standard libraries, and even a few language features, are regularly backported to Python 2, as well.

Libraries to ease in the transition


The original compatibility library, six (so named because 2 times 3 is 6), provides tools to make writing 2 and 3 compatible code easy. You just import six, and then access the renamed standard libraries from six.moves. There are wrappers for the changed features, such as six.with_metaclass.

These features are not hard to wrap yourself, so many libraries implement their own six wrapper to reduce dependencies and overhead.

See also: Future library (click to expand)


This is a newer library with a unique approach. Instead of forcing a usage of a special wrapper, the idea of future is to simply allow code to be written in Python 3, but work in Python 3. For example, from builtins import input will do nothing on Python 3 (builtins is where input lives), but on Python 2 with future installed, builtins is part of future and will import the future version. You can even patch in the Python 3 standard library names with a standard_library.install_aliases() function.

Future also comes with it’s own version of the conversion scripts, called futurize and pasteurize, which use the future library to make code that runs on one version run on both versions. An alpha feature, the autotranslate function, can turn a library that supports only Python 2 into a Python 3 version on import.


Several of the new libraries and features have been backported to Python 2. I’m not including ones that were backported in an official Python release, like argparse.

  • pathlib2: A simple, object oriented path library from Python 3.4
  • enum34: A python package for enumerations from Python 3.4
  • mock: A version of unittest.mock from Python 3.3
  • futures: This is the concurrent.futures package in Python 3.2
  • statistics: From Python 3.4
  • selectors34: The selectors package from Python 3.4
  • typing: Type hints from Python 3.5
  • trollius: The asyncio package, with a new syntax for yield from, from Python 3.4
  • Smaller changes: configparser, subprocess32, functools32, and the various backports-dot-something packages.

New features in modern Python

These are features that have been released in a version of Python after 3.0 that are not in the older Python 2 series:

  • Matrix multiplication operator, @ (3.5)
  • Special async and await syntax for asynchronous operations (3.5, 3.7)
  • Unpacking improvements, so that the * and ** operators work in more places like you’d expect (3.5)
  • Function signatures now in easy to use object (3.3)
  • Improvements to Windows support (Windows launcher, recent versions of Visual C++) (3.2, 3.4, 3.5, 3.6, 3.7)
  • Delegation to a subgenerator, yield from, finally allows safe factorisation of generators (3.3)
  • Context variables and AsyncIO improvments, include a simple run function (3.7)
  • importlib.resources, which allows files that don’t end in .py to be accessed (FINALLY!) (3.7, backports available)
  • breakpoint() built-in function for debugging (3.7)
  • Modules can have custom __dir__ and __getattr__ (3.7)
  • Lots of new debugging options in CPython for developers, like timing module import and better stacktraces (3.7)
  • Positional only arguments, to avoid name clashes and for eventual speed optimizations (3.8)

Formatted string literals (3.6)

Finally! You can write code such as the following now:

x = 2
print(f"The value of x is {x}")

This is indicated by the f prefix, and can take almost any valid python expression. It does not have the scope issues that the old workaround, .format(**locals()) encounters.

In Python 3.8, you can use an equals:

x = 2
print(f"{x = }")

to print a variable or expression and its name:

x = 2

Syntax for variable annotations (3.6)

This will be great for type hints, IDE’s, and Cython, but the syntax is a little odd for Python. It’s based on function annotations. A quick example:

an_empty_list_of_ints: List[int] = []
will_be_a_str_later: str 

This stores the variable name and the annotation in an __annotations__ dictionary for the module or the class that they are in.

Simi-ordered dictionaries (3.6 and 3.7)

Python dictionaries are now partially ordered; due to huge speedups in the C definition of ordered dicts, the dict class is now guarantied to iterate in order as long as nothing has been changed since the dict creation. This may sound restrictive, but it enables many features; you can now discover the order keyword arguments were passed, the order class members were added, and the order of {} dicts. If you want to continue to keep or control the order, you should move the dict to an OrderedDict, as before. This makes ordered dictionaries much easier to create, too.

Only class member order and keyword argument order are ensured by the language; the ordering of {} is an implementation detail. This detail works in both CPython 3.6 and all versions PyPy, however. This became language mandated in Python 3.7.

DataClasses (3.7)

Most programmers coming from other languages want some form of class designed to store data. Creation of these data-centric classes is verbose and ugly in python, since you hace to put all the setup in the __init__ method rather than directly in the class like other languages, and you have to manage initilization, print, comparison, etc. yourself. Now, with DataClasses, you can do it with a nice syntax:

from dataclasses import dataclass

class Vector:
    x: float
    y: float
    z: float

This will create (by default) __init__, __repr__, and __eq__. You can also ask for order, unsafe_hash, and frozen.

This is similar to, and less powerful than, the popular attrs library (available for all versions of Python). This library module, like many others, was also backported to older versions of Python. However, the variable type annotations are not available in older versions.

Walrus operator (3.8)

You can now use a special assignment operator, := (called the walrus operator due to the eyes + tusks appearance) almost anywhere that a normal = was not allowed. So, for example, you can now do this:

if (x := long_check()):
# x is no longer in scope!

This might be very handy for setting up machine learning tools, where you set a number of layers then refer to it further down in the same dict or function call.

Other smaller features:

  • Underscores in numeric literals. You can add arbitrary spacers to numbers now, such as 1_000_000. (3.6)
  • Windows encoding improvements. (3.6)
  • Simpler customization of class creation, using __init_subclass__ class method. (3.6)
  • Descriptor access to the name of the class and the descriptor, using __set_name__. (3.6)
  • A file system path protocol, __fspath__(), allows any object to indicate that it represents a path. Finally pathlib works without wrapping it in a str()! (3.6)
  • Better support for async list comprehensions, and async generators. (3.6)
  • A secrets module for password related randomization functions. (3.6)
  • __slots__ can contain docstrings as a dict (3.8)

Status of Python

The current status of the python releases is as follows:

  • Python 2.5: Dead.
  • Python 2.6: Most libraries are dropping support, officially discontinued, but still on some legacy systems, like the default environment in SL6.
  • Python 2.7: The officially supported Python 2 release, critical security flaws fixed till January 1, 2020. PyPy supports 2.7.13. Windows version is stuck requiring Visual Studio 2008 for builds (Careful memory design can allow use of new VS). Numpy, Pandas, IPython, and more have dropped support.
  • Python 3.0-3.2: Never used significantly, no library support.
  • Python 3.3: Better backwards compatibility makes this the first generally used Python 3, with Windows downloads outpacing Python 2.7 for the first time. u"" was added back in as a no-op.
  • Python 3.4: Addition of asyncio features and pathlib provided even more interest. No longer in use.
  • Python 3.5: New features, such as matrix multiplication, are accelerating the transition from Python 2. Note that PyPy3 is currently based on Python 3.5.3.
  • Python 3.6: The addition of format strings make simple scripts much easier and cleaner.
  • Python 3.7: Big performance improvements make this the fastest CPython ever; dataclasses, typing, and threading improvements.
  • Python 3.8 beta: New walrus operator, positional only arguments, and fast call.

Further reading

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