
Porting Python 2 Code to Python 3
*********************************

author:
   Brett Cannon


Abstract
^^^^^^^^

With Python 3 being the future of Python while Python 2 is still in
active use, it is good to have your project available for both major
releases of Python. This guide is meant to help you figure out how
best to support both Python 2 & 3 simultaneously.

If you are looking to port an extension module instead of pure Python
code, please see Porting Extension Modules to Python 3.

If you would like to read one core Python developer's take on why
Python 3 came into existence, you can read Nick Coghlan's Python 3 Q &
A.

For help with porting, you can email the python-porting mailing list
with questions.


The Short Explanation
=====================

To make your project be single-source Python 2/3 compatible, the basic
steps are:

1. Update your code to drop support for Python 2.5 or older
   (supporting only Python 2.7 is ideal)

2. Make sure you have good test coverage (coverage.py can help;
   "pip install coverage")

3. Learn the differences between Python 2 & 3

4. Use Modernize or Futurize to update your code ("pip install
   modernize" or "pip install future", respectively)

5. Use Pylint to help make sure you don't regress on your Python 3
   support (if only supporting Python 2.7/3.4 or newer; "pip install
   pylint")

6. Use caniusepython3 to find out which of your dependencies are
   blocking your use of Python 3 ("pip install caniusepython3")

7. Once your dependencies are no longer blocking you, use
   continuous integration to make sure you stay compatible with Python
   2 & 3 (tox can help test against multiple versions of Python; "pip
   install tox")

If you are dropping support for Python 2 entirely, then after you
learn the differences between Python 2 & 3 you can run 2to3 over your
code and skip the rest of the steps outlined above.


Details
=======

A key point about supporting Python 2 & 3 simultaneously is that you
can start **today**! Even if your dependencies are not supporting
Python 3 yet that does not mean you can't modernize your code **now**
to support Python 3. Most changes required to support Python 3 lead to
cleaner code using newer practices even in Python 2.

Another key point is that modernizing your Python 2 code to also
support Python 3 is largely automated for you. While you might have to
make some API decisions thanks to Python 3 clarifying text data versus
binary data, the lower-level work is now mostly done for you and thus
can at least benefit from the automated changes immediately.

Keep those key points in mind while you read on about the details of
porting your code to support Python 2 & 3 simultaneously.


Drop support for Python 2.5 and older (at least)
------------------------------------------------

While you can make Python 2.5 work with Python 3, it is **much**
easier if you only have to work with Python 2.6 or newer (and easier
still if you only have to work with Python 2.7). If dropping Python
2.5 is not an option then the six project can help you support Python
2.5 & 3 simultaneously ("pip install six"). Do realize, though, that
nearly all the projects listed in this HOWTO will not be available to
you.

If you are able to only support Python 2.6 or newer, then the required
changes to your code should continue to look and feel like idiomatic
Python code. At worst you will have to use a function instead of a
method in some instances or have to import a function instead of using
a built-in one, but otherwise the overall transformation should not
feel foreign to you.

But please aim for Python 2.7. Bugfixes for that version of Python
will continue until 2020 while Python 2.6 is no longer supported.
There are also some tools mentioned in this HOWTO which do not support
Python 2.6 (e.g., Pylint), and this will become more commonplace as
time goes on.


Make sure you specify the proper version support in your "setup.py" file
------------------------------------------------------------------------

In your "setup.py" file you should have the proper trove classifier
specifying what versions of Python you support. As your project does
not support Python 3 yet you should at least have "Programming
Language :: Python :: 2 :: Only" specified. Ideally you should also
specify each major/minor version of Python that you do support, e.g.
"Programming Language :: Python :: 2.7".


Have good test coverage
-----------------------

Once you have your code supporting the oldest version of Python 2 you
want it to, you will want to make sure your test suite has good
coverage. A good rule of thumb is that if you want to be confident
enough in your test suite that any failures that appear after having
tools rewrite your code are actual bugs in the tools and not in your
code. If you want a number to aim for, try to get over 80% coverage
(and don't feel bad if you can't easily get past 90%). If you don't
already have a tool to measure test coverage then coverage.py is
recommended.


Learn the differences between Python 2 & 3
------------------------------------------

Once you have your code well-tested you are ready to begin porting
your code to Python 3! But to fully understand how your code is going
to change and what you want to look out for while you code, you will
want to learn what changes Python 3 makes in terms of Python 2.
Typically the two best ways of doing that is reading the "What's New"
doc for each release of Python 3 and the Porting to Python 3 book
(which is free online). There is also a handy cheat sheet from the
Python-Future project.


Update your code
----------------

Once you feel like you know what is different in Python 3 compared to
Python 2, it's time to update your code! You have a choice between two
tools in porting your code automatically: Modernize and Futurize.
Which tool you choose will depend on how much like Python 3 you want
your code to be. Futurize does its best to make Python 3 idioms and
practices exist in Python 2, e.g. backporting the "bytes" type from
Python 3 so that you have semantic parity between the major versions
of Python. Modernize, on the other hand, is more conservative and
targets a Python 2/3 subset of Python, relying on six to help provide
compatibility.

Regardless of which tool you choose, they will update your code to run
under Python 3 while staying compatible with the version of Python 2
you started with. Depending on how conservative you want to be, you
may want to run the tool over your test suite first and visually
inspect the diff to make sure the transformation is accurate. After
you have transformed your test suite and verified that all the tests
still pass as expected, then you can transform your application code
knowing that any tests which fail is a translation failure.

Unfortunately the tools can't automate everything to make your code
work under Python 3 and so there are a handful of things you will need
to update manually to get full Python 3 support (which of these steps
are necessary vary between the tools). Read the documentation for the
tool you choose to use to see what it fixes by default and what it can
do optionally to know what will (not) be fixed for you and what you
may have to fix on your own (e.g. using "io.open()" over the built-in
"open()" function is off by default in Modernize). Luckily, though,
there are only a couple of things to watch out for which can be
considered large issues that may be hard to debug if not watched for.


Division
~~~~~~~~

In Python 3, "5 / 2 == 2.5" and not "2"; all division between "int"
values result in a "float". This change has actually been planned
since Python 2.2 which was released in 2002. Since then users have
been encouraged to add "from __future__ import division" to any and
all files which use the "/" and "//" operators or to be running the
interpreter with the "-Q" flag. If you have not been doing this then
you will need to go through your code and do two things:

1. Add "from __future__ import division" to your files

2. Update any division operator as necessary to either use "//" to
   use floor division or continue using "/" and expect a float

The reason that "/" isn't simply translated to "//" automatically is
that if an object defines its own "__div__" method but not
"__floordiv__" then your code would begin to fail.


Text versus binary data
~~~~~~~~~~~~~~~~~~~~~~~

In Python 2 you could use the "str" type for both text and binary
data. Unfortunately this confluence of two different concepts could
lead to brittle code which sometimes worked for either kind of data,
sometimes not. It also could lead to confusing APIs if people didn't
explicitly state that something that accepted "str" accepted either
text or binary data instead of one specific type. This complicated the
situation especially for anyone supporting multiple languages as APIs
wouldn't bother explicitly supporting "unicode" when they claimed text
data support.

To make the distinction between text and binary data clearer and more
pronounced, Python 3 did what most languages created in the age of the
internet have done and made text and binary data distinct types that
cannot blindly be mixed together (Python predates widespread access to
the internet). For any code that only deals with text or only binary
data, this separation doesn't pose an issue. But for code that has to
deal with both, it does mean you might have to now care about when you
are using text compared to binary data, which is why this cannot be
entirely automated.

To start, you will need to decide which APIs take text and which take
binary (it is **highly** recommended you don't design APIs that can
take both due to the difficulty of keeping the code working; as stated
earlier it is difficult to do well). In Python 2 this means making
sure the APIs that take text can work with "unicode" in Python 2 and
those that work with binary data work with the "bytes" type from
Python 3 and thus a subset of "str" in Python 2 (which the "bytes"
type in Python 2 is an alias for). Usually the biggest issue is
realizing which methods exist for which types in Python 2 & 3
simultaneously (for text that's "unicode" in Python 2 and "str" in
Python 3, for binary that's "str"/"bytes" in Python 2 and "bytes" in
Python 3). The following table lists the **unique** methods of each
data type across Python 2 & 3 (e.g., the "decode()" method is usable
on the equivalent binary data type in either Python 2 or 3, but it
can't be used by the text data type consistently between Python 2 and
3 because "str" in Python 3 doesn't have the method).

+--------------------------+-----------------------+
| **Text data**            | **Binary data**       |
+--------------------------+-----------------------+
| __mod__ ("%" operator)   |                       |
+--------------------------+-----------------------+
|                          | decode                |
+--------------------------+-----------------------+
| encode                   |                       |
+--------------------------+-----------------------+
| format                   |                       |
+--------------------------+-----------------------+
| isdecimal                |                       |
+--------------------------+-----------------------+
| isnumeric                |                       |
+--------------------------+-----------------------+

Making the distinction easier to handle can be accomplished by
encoding and decoding between binary data and text at the edge of your
code. This means that when you receive text in binary data, you should
immediately decode it. And if your code needs to send text as binary
data then encode it as late as possible. This allows your code to work
with only text internally and thus eliminates having to keep track of
what type of data you are working with.

The next issue is making sure you know whether the string literals in
your code represent text or binary data. At minimum you should add a
"b" prefix to any literal that presents binary data. For text you
should either use the "from __future__ import unicode_literals"
statement or add a "u" prefix to the text literal.

As part of this dichotomy you also need to be careful about opening
files. Unless you have been working on Windows, there is a chance you
have not always bothered to add the "b" mode when opening a binary
file (e.g., "rb" for binary reading).  Under Python 3, binary files
and text files are clearly distinct and mutually incompatible; see the
"io" module for details. Therefore, you **must** make a decision of
whether a file will be used for binary access (allowing to read and/or
write binary data) or text access (allowing to read and/or write text
data). You should also use "io.open()" for opening files instead of
the built-in "open()" function as the "io" module is consistent from
Python 2 to 3 while the built-in "open()" function is not (in Python 3
it's actually "io.open()").

The constructors of both "str" and "bytes" have different semantics
for the same arguments between Python 2 & 3. Passing an integer to
"bytes" in Python 2 will give you the string representation of the
integer: "bytes(3) == '3'". But in Python 3, an integer argument to
"bytes" will give you a bytes object as long as the integer specified,
filled with null bytes: "bytes(3) == b'\x00\x00\x00'". A similar worry
is necessary when passing a bytes object to "str". In Python 2 you
just get the bytes object back: "str(b'3') == b'3'". But in Python 3
you get the string representation of the bytes object: "str(b'3') ==
"b'3'"".

Finally, the indexing of binary data requires careful handling
(slicing does **not** require any special handling). In Python 2,
"b'123'[1] == b'2'" while in Python 3 "b'123'[1] == 50". Because
binary data is simply a collection of binary numbers, Python 3 returns
the integer value for the byte you index on. But in Python 2 because
"bytes == str", indexing returns a one-item slice of bytes. The six
project has a function named "six.indexbytes()" which will return an
integer like in Python 3: "six.indexbytes(b'123', 1)".

To summarize:

1. Decide which of your APIs take text and which take binary data

2. Make sure that your code that works with text also works with
   "unicode" and code for binary data works with "bytes" in Python 2
   (see the table above for what methods you cannot use for each type)

3. Mark all binary literals with a "b" prefix, use a "u" prefix or
   "__future__" import statement for text literals

4. Decode binary data to text as soon as possible, encode text as
   binary data as late as possible

5. Open files using "io.open()" and make sure to specify the "b"
   mode when appropriate

6. Be careful when indexing binary data


Prevent compatibility regressions
---------------------------------

Once you have fully translated your code to be compatible with Python
3, you will want to make sure your code doesn't regress and stop
working under Python 3. This is especially true if you have a
dependency which is blocking you from actually running under Python 3
at the moment.

To help with staying compatible, any new modules you create should
have at least the following block of code at the top of it:

   from __future__ import absolute_import
   from __future__ import division
   from __future__ import print_function
   from __future__ import unicode_literals

You can also run Python 2 with the "-3" flag to be warned about
various compatibility issues your code triggers during execution. If
you turn warnings into errors with "-Werror" then you can make sure
that you don't accidentally miss a warning.

You can also use the Pylint project and its "--py3k" flag to lint your
code to receive warnings when your code begins to deviate from Python
3 compatibility. This also prevents you from having to run Modernize
or Futurize over your code regularly to catch compatibility
regressions. This does require you only support Python 2.7 and Python
3.4 or newer as that is Pylint's minimum Python version support.


Check which dependencies block your transition
----------------------------------------------

**After** you have made your code compatible with Python 3 you should
begin to care about whether your dependencies have also been ported.
The caniusepython3 project was created to help you determine which
projects -- directly or indirectly -- are blocking you from supporting
Python 3. There is both a command-line tool as well as a web interface
at https://caniusepython3.com .

The project also provides code which you can integrate into your test
suite so that you will have a failing test when you no longer have
dependencies blocking you from using Python 3. This allows you to
avoid having to manually check your dependencies and to be notified
quickly when you can start running on Python 3.


Update your "setup.py" file to denote Python 3 compatibility
------------------------------------------------------------

Once your code works under Python 3, you should update the classifiers
in your "setup.py" to contain "Programming Language :: Python :: 3"
and to not specify sole Python 2 support. This will tell anyone using
your code that you support Python 2 **and** 3. Ideally you will also
want to add classifiers for each major/minor version of Python you now
support.


Use continuous integration to stay compatible
---------------------------------------------

Once you are able to fully run under Python 3 you will want to make
sure your code always works under both Python 2 & 3. Probably the best
tool for running your tests under multiple Python interpreters is tox.
You can then integrate tox with your continuous integration system so
that you never accidentally break Python 2 or 3 support.

You may also want to use use the "-bb" flag with the Python 3
interpreter to trigger an exception when you are comparing bytes to
strings. Usually it's simply "False", but if you made a mistake in
your separation of text/binary data handling you may be accidentally
comparing text and binary data. This flag will raise an exception when
that occurs to help track down such cases.

And that's mostly it! At this point your code base is compatible with
both Python 2 and 3 simultaneously. Your testing will also be set up
so that you don't accidentally break Python 2 or 3 compatibility
regardless of which version you typically run your tests under while
developing.


Dropping Python 2 support completely
====================================

If you are able to fully drop support for Python 2, then the steps
required to transition to Python 3 simplify greatly.

1. Update your code to only support Python 2.7

2. Make sure you have good test coverage (coverage.py can help)

3. Learn the differences between Python 2 & 3

4. Use 2to3 to rewrite your code to run only under Python 3

After this your code will be fully Python 3 compliant but in a way
that is not supported by Python 2. You should also update the
classifiers in your "setup.py" to contain "Programming Language ::
Python :: 3 :: Only".
