Python 2 的程式碼移植到Python 3
*******************************

作者:
   Brett Cannon


摘要
^^^^

Python 3 是 Python 的未来，但 Python 2 仍处于活跃使用阶段，最好让您的
项目在两个主要版本的Python 上都可用。本指南旨在帮助您了解如何最好地同
时支持 Python 2 和 3。

如果您希望迁移扩展模块而不是纯 Python 代码，请参阅 遷移延伸模組到
Python 3。

如果你想了解核心 Python 开发者对于 Python 3 的出现有何看法，你可以阅读
Nick Coghlan 的 Python 3 Q & A 或 Brett Cannon 的 为什么要有 Python 3.

For help with porting, you can view the archived python-porting
mailing list.


簡短的說明
==========

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

1. Only worry about supporting Python 2.7

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

3. 瞭解Python 2 & 3的差異

4. Use Futurize (or Modernize) to update your code (e.g. "python -m
   pip install future")

5. Use Pylint to help make sure you don't regress on your Python 3
   support ("python -m pip install pylint")

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

8. Consider using optional static type checking to make sure your type
   usage works in both Python 2 & 3 (e.g. use mypy to check your
   typing under both Python 2 & Python 3; "python -m pip install
   mypy").

備註:

  Note: Using "python -m pip install" guarantees that the "pip" you
  invoke is the one installed for the Python currently in use, whether
  it be a system-wide "pip" or one installed within a virtual
  environment.


詳細的
======

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 code.

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.


向下支援Python 2.6和更舊的版本
------------------------------

While you can make Python 2.5 work with Python 3, it is **much**
easier 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 ("python -m 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 skip Python 2.5 and older, 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 you should aim for only supporting Python 2.7. Python 2.6 is no
longer freely supported and thus is not receiving bugfixes. This means
**you** will have to work around any issues you come across with
Python 2.6. 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. It will simply be easier for you if you
only support the versions of Python that you have to support.


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".


有好的測試覆蓋率
----------------

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 find it hard to get better than 90%
coverage). If you don't already have a tool to measure test coverage
then coverage.py is recommended.


瞭解Python 2 & 3的差異
----------------------

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.


更新你的程式碼
--------------

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: Futurize and Modernize.
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, directly relying on six to help
provide compatibility. As Python 3 is the future, it might be best to
consider Futurize to begin adjusting to any new practices that Python
3 introduces which you are not accustomed to yet.

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.


除法
~~~~

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 a "__truediv__" method but not
"__floordiv__" then your code would begin to fail (e.g. a user-defined
class that uses "/" to signify some operation but not "//" for the
same thing or at all).


文本与二进制数据
~~~~~~~~~~~~~~~~

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 deals only 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" and those that
work with binary data work with the "bytes" type from Python 3 (which
is a subset of "str" in Python 2 and acts as an alias for "bytes" type
in Python 2). Usually the biggest issue is realizing which methods
exist on 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 textual data
type consistently between Python 2 and 3 because "str" in Python 3
doesn't have the method). Do note that as of Python 3.5 the "__mod__"
method was added to the bytes type.

+--------------------------+-----------------------+
| **文本数据**             | **二进制数据**        |
+--------------------------+-----------------------+
|                          | 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. You should add a "b" prefix
to any literal that presents binary data. For text you should add a
"u" prefix to the text literal. (there is a "__future__" import to
force all unspecified literals to be Unicode, but usage has shown it
isn't as effective as adding a "b" or "u" prefix to all literals
explicitly)

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 binary data to
be read and/or written) or textual access (allowing text data to be
read and/or written). 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()"). Do not bother with the
outdated practice of using "codecs.open()" as that's only necessary
for keeping compatibility with Python 2.5.

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, textual literals with a
   "u" prefix

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 into binary data


Use feature detection instead of version detection
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Inevitably you will have code that has to choose what to do based on
what version of Python is running. The best way to do this is with
feature detection of whether the version of Python you're running
under supports what you need. If for some reason that doesn't work
then you should make the version check be against Python 2 and not
Python 3. To help explain this, let's look at an example.

Let's pretend that you need access to a feature of "importlib" that is
available in Python's standard library since Python 3.3 and available
for Python 2 through importlib2 on PyPI. You might be tempted to write
code to access e.g. the "importlib.abc" module by doing the following:

   import sys

   if sys.version_info[0] == 3:
       from importlib import abc
   else:
       from importlib2 import abc

The problem with this code is what happens when Python 4 comes out? It
would be better to treat Python 2 as the exceptional case instead of
Python 3 and assume that future Python versions will be more
compatible with Python 3 than Python 2:

   import sys

   if sys.version_info[0] > 2:
       from importlib import abc
   else:
       from importlib2 import abc

The best solution, though, is to do no version detection at all and
instead rely on feature detection. That avoids any potential issues of
getting the version detection wrong and helps keep you future-
compatible:

   try:
       from importlib import abc
   except ImportError:
       from importlib2 import abc


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

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 the "-bb" flag with the Python 3 interpreter
to trigger an exception when you are comparing bytes to strings or
bytes to an int (the latter is available starting in Python 3.5). By
default type-differing comparisons simply return "False", but if you
made a mistake in your separation of text/binary data handling or
indexing on bytes you wouldn't easily find the mistake. This flag will
raise an exception when these kinds of comparisons occur, making the
mistake much easier to track down.

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.


考虑使用可选的静态类型检查
--------------------------

Another way to help port your code is to use a static type checker
like mypy or pytype on your code. These tools can be used to analyze
your code as if it's being run under Python 2, then you can run the
tool a second time as if your code is running under Python 3. By
running a static type checker twice like this you can discover if
you're e.g. misusing binary data type in one version of Python
compared to another. If you add optional type hints to your code you
can also explicitly state whether your APIs use textual or binary
data, helping to make sure everything functions as expected in both
versions of Python.
