

##############
Common Gotchas
##############

.. image:: /_static/photos/34435688380_b5a740762b_k_d.jpg

For the most part, Python aims to be a clean and consistent language that
avoids surprises. However, there are a few cases that can be confusing for
newcomers.

Some of these cases are intentional but can be potentially surprising. Some
could arguably be considered language warts. In general, what follows
is a collection of potentially tricky behavior that might seem strange at first
glance, but are generally sensible, once you're aware of the underlying cause for
the surprise.


.. _default_args:


*************************
Mutable Default Arguments
*************************

Seemingly the *most* common surprise new Python programmers encounter is
Python's treatment of mutable default arguments in function definitions.

What You Wrote
~~~~~~~~~~~~~~

.. code-block:: python

    def append_to(element, to=[]):
        to.append(element)
        return to

What You Might Have Expected to Happen
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. code-block:: python

    my_list = append_to(12)
    print(my_list)

    my_other_list = append_to(42)
    print(my_other_list)

A new list is created each time the function is called if a second argument
isn't provided, so that the output is::

    [12]
    [42]

What Actually Happens
~~~~~~~~~~~~~~~~~~~~~

.. testoutput::

    [12]
    [12, 42]

A new list is created *once* when the function is defined, and the same list is
used in each successive call.

Python's default arguments are evaluated *once* when the function is defined,
not each time the function is called (like it is in say, Ruby). This means that
if you use a mutable default argument and mutate it, you *will* and have
mutated that object for all future calls to the function as well.

What You Should Do Instead
~~~~~~~~~~~~~~~~~~~~~~~~~~

Create a new object each time the function is called, by using a default arg to
signal that no argument was provided (:py:data:`None` is often a good choice).

.. code-block:: python

    def append_to(element, to=None):
        if to is None:
            to = []
        to.append(element)
        return to

Do not forget, you are passing a *list* object as the second argument.

When the Gotcha Isn't a Gotcha
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes you can specifically "exploit" (read: use as intended) this behavior
to maintain state between calls of a function. This is often done when writing
a caching function.


****************************
Late Binding Closures
****************************

Another common source of confusion is the way Python binds its variables in
closures (or in the surrounding global scope).


What You Wrote
~~~~~~~~~~~~~~

.. testcode::

    def create_multipliers():
        return [lambda x : i * x for i in range(5)]

What You Might Have Expected to Happen
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. testcode::

    for multiplier in create_multipliers():
        print(multiplier(2))

A list containing five functions that each have their own closed-over ``i``
variable that multiplies their argument, producing::

    0
    2
    4
    6
    8

What Actually Happens
~~~~~~~~~~~~~~~~~~~~~

.. testoutput::

    8
    8
    8
    8
    8

Five functions are created; instead all of them just multiply ``x`` by 4.

Python's closures are *late binding*.
This means that the values of variables used in closures are looked
up at the time the inner function is called.

Here, whenever *any* of the returned functions are called, the value of ``i``
is looked up in the surrounding scope at call time. By then, the loop has
completed and ``i`` is left with its final value of 4.

What's particularly nasty about this gotcha is the seemingly prevalent
misinformation that this has something to do with :ref:`lambdas <python:lambda>`
in Python. Functions created with a ``lambda`` expression are in no way special,
and in fact the same exact behavior is exhibited by just using an ordinary
``def``:

.. code-block:: python

    def create_multipliers():
        multipliers = []

        for i in range(5):
            def multiplier(x):
                return i * x
            multipliers.append(multiplier)

        return multipliers

What You Should Do Instead
~~~~~~~~~~~~~~~~~~~~~~~~~~

The most general solution is arguably a bit of a hack. Due to Python's
aforementioned behavior concerning evaluating default arguments to functions
(see :ref:`default_args`), you can create a closure that binds immediately to
its arguments by using a default arg like so:

.. code-block:: python

    def create_multipliers():
        return [lambda x, i=i : i * x for i in range(5)]

Alternatively, you can use the functools.partial function:

.. code-block:: python

    from functools import partial
    from operator import mul

    def create_multipliers():
        return [partial(mul, i) for i in range(5)]

When the Gotcha Isn't a Gotcha
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Sometimes you want your closures to behave this way. Late binding is good in
lots of situations. Looping to create unique functions is unfortunately a case
where they can cause hiccups.


*********************************
Bytecode (.pyc) Files Everywhere!
*********************************

By default, when executing Python code from files, the Python interpreter
will automatically write a bytecode version of that file to disk, e.g.
``module.pyc``.

These ``.pyc`` files should not be checked into your source code repositories.

Theoretically, this behavior is on by default for performance reasons.
Without these bytecode files, Python would re-generate the bytecode
every time the file is loaded.


Disabling Bytecode (.pyc) Files
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Luckily, the process of generating the bytecode is extremely fast, and isn't
something you need to worry about while developing your code.

Those files are annoying, so let's get rid of them!

::

    $ export PYTHONDONTWRITEBYTECODE=1

With the ``$PYTHONDONTWRITEBYTECODE`` environment variable set, Python will
no longer write these files to disk, and your development environment will
remain nice and clean.

I recommend setting this environment variable in your ``~/.profile``.

Removing Bytecode (.pyc) Files
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Here's nice trick for removing all of these files, if they already exist::

    $ find . -type f -name "*.py[co]" -delete -or -type d -name "__pycache__" -delete

Run that from the root directory of your project, and all ``.pyc`` files
will suddenly vanish. Much better.

.. _version_control_ignores:

Version Control Ignores
~~~~~~~~~~~~~~~~~~~~~~~

If you still need the ``.pyc`` files for performance reasons, you can always add them
to the ignore files of your version control repositories. Popular version control
systems have the ability to use wildcards defined in a file to apply special
rules.

An ignore file will make sure the matching files don't get checked into the repository.
Git_ uses ``.gitignore`` while Mercurial_ uses ``.hgignore``.

.. _Git: https://git-scm.com/
.. _Mercurial: https://www.mercurial-scm.org/

At the minimum your ignore files should look like this.

::

    syntax:glob   # This line is not needed for .gitignore files.
    *.py[cod]     # Will match .pyc, .pyo and .pyd files.
    __pycache__/  # Exclude the whole folder

You may wish to include more files and directories depending on your needs.
The next time you commit to the repository, these files will not be included.
