cpython/Doc/faq/design.rst

======================
Design and History FAQ
======================

Why does Python use indentation for grouping of statements?
-----------------------------------------------------------

Guido van Rossum believes that using indentation for grouping is extremely
elegant and contributes a lot to the clarity of the average Python program.
Most people learn to love this feature after awhile.

Since there are no begin/end brackets there cannot be a disagreement between
grouping perceived by the parser and the human reader.  Occasionally C
programmers will encounter a fragment of code like this::

   if (x <= y)
           x++;
           y--;
   z++;

Only the ``x++`` statement is executed if the condition is true, but the
indentation leads you to believe otherwise.  Even experienced C programmers will
sometimes stare at it a long time wondering why ``y`` is being decremented even
for ``x > y``.

Because there are no begin/end brackets, Python is much less prone to
coding-style conflicts.  In C there are many different ways to place the braces.
If you're used to reading and writing code that uses one style, you will feel at
least slightly uneasy when reading (or being required to write) another style.

Many coding styles place begin/end brackets on a line by themself.  This makes
programs considerably longer and wastes valuable screen space, making it harder
to get a good overview of a program.  Ideally, a function should fit on one
screen (say, 20-30 lines).  20 lines of Python can do a lot more work than 20
lines of C.  This is not solely due to the lack of begin/end brackets -- the
lack of declarations and the high-level data types are also responsible -- but
the indentation-based syntax certainly helps.


Why am I getting strange results with simple arithmetic operations?
-------------------------------------------------------------------

See the next question.


Why are floating point calculations so inaccurate?
--------------------------------------------------

People are often very surprised by results like this::

   >>> 1.2-1.0
   0.199999999999999996

and think it is a bug in Python. It's not.  This has nothing to do with Python,
but with how the underlying C platform handles floating point numbers, and
ultimately with the inaccuracies introduced when writing down numbers as a
string of a fixed number of digits.

The internal representation of floating point numbers uses a fixed number of
binary digits to represent a decimal number.  Some decimal numbers can't be
represented exactly in binary, resulting in small roundoff errors.

In decimal math, there are many numbers that can't be represented with a fixed
number of decimal digits, e.g.  1/3 = 0.3333333333.......

In base 2, 1/2 = 0.1, 1/4 = 0.01, 1/8 = 0.001, etc.  .2 equals 2/10 equals 1/5,
resulting in the binary fractional number 0.001100110011001...

Floating point numbers only have 32 or 64 bits of precision, so the digits are
cut off at some point, and the resulting number is 0.199999999999999996 in
decimal, not 0.2.

A floating point number's ``repr()`` function prints as many digits are
necessary to make ``eval(repr(f)) == f`` true for any float f.  The ``str()``
function prints fewer digits and this often results in the more sensible number
that was probably intended::

   >>> 0.2
   0.20000000000000001
   >>> print 0.2
   0.2

One of the consequences of this is that it is error-prone to compare the result
of some computation to a float with ``==``. Tiny inaccuracies may mean that
``==`` fails.  Instead, you have to check that the difference between the two
numbers is less than a certain threshold::

   epsilon = 0.0000000000001 # Tiny allowed error
   expected_result = 0.4

   if expected_result-epsilon <= computation() <= expected_result+epsilon:
       ...

Please see the chapter on :ref:`floating point arithmetic <tut-fp-issues>` in
the Python tutorial for more information.


Why are Python strings immutable?
---------------------------------

There are several advantages.

One is performance: knowing that a string is immutable means we can allocate
space for it at creation time, and the storage requirements are fixed and
unchanging.  This is also one of the reasons for the distinction between tuples
and lists.

Another advantage is that strings in Python are considered as "elemental" as
numbers.  No amount of activity will change the value 8 to anything else, and in
Python, no amount of activity will change the string "eight" to anything else.


.. _why-self:

Why must 'self' be used explicitly in method definitions and calls?
-------------------------------------------------------------------

The idea was borrowed from Modula-3.  It turns out to be very useful, for a
variety of reasons.

First, it's more obvious that you are using a method or instance attribute
instead of a local variable.  Reading ``self.x`` or ``self.meth()`` makes it
absolutely clear that an instance variable or method is used even if you don't
know the class definition by heart.  In C++, you can sort of tell by the lack of
a local variable declaration (assuming globals are rare or easily recognizable)
-- but in Python, there are no local variable declarations, so you'd have to
look up the class definition to be sure.  Some C++ and Java coding standards
call for instance attributes to have an ``m_`` prefix, so this explicitness is
still useful in those languages, too.

Second, it means that no special syntax is necessary if you want to explicitly
reference or call the method from a particular class.  In C++, if you want to
use a method from a base class which is overridden in a derived class, you have
to use the ``::`` operator -- in Python you can write baseclass.methodname(self,
<argument list>).  This is particularly useful for :meth:`__init__` methods, and
in general in cases where a derived class method wants to extend the base class
method of the same name and thus has to call the base class method somehow.

Finally, for instance variables it solves a syntactic problem with assignment:
since local variables in Python are (by definition!) those variables to which a
value assigned in a function body (and that aren't explicitly declared global),
there has to be some way to tell the interpreter that an assignment was meant to
assign to an instance variable instead of to a local variable, and it should
preferably be syntactic (for efficiency reasons).  C++ does this through
declarations, but Python doesn't have declarations and it would be a pity having
to introduce them just for this purpose.  Using the explicit "self.var" solves
this nicely.  Similarly, for using instance variables, having to write
"self.var" means that references to unqualified names inside a method don't have
to search the instance's directories.  To put it another way, local variables
and instance variables live in two different namespaces, and you need to tell
Python which namespace to use.


Why can't I use an assignment in an expression?
-----------------------------------------------

Many people used to C or Perl complain that they want to use this C idiom:

.. code-block:: c

   while (line = readline(f)) {
       // do something with line
   }

where in Python you're forced to write this::

   while True:
       line = f.readline()
       if not line:
           break
       ... # do something with line

The reason for not allowing assignment in Python expressions is a common,
hard-to-find bug in those other languages, caused by this construct:

.. code-block:: c

    if (x = 0) {
        // error handling
    }
    else {
        // code that only works for nonzero x
    }

The error is a simple typo: ``x = 0``, which assigns 0 to the variable ``x``,
was written while the comparison ``x == 0`` is certainly what was intended.

Many alternatives have been proposed.  Most are hacks that save some typing but
use arbitrary or cryptic syntax or keywords, and fail the simple criterion for
language change proposals: it should intuitively suggest the proper meaning to a
human reader who has not yet been introduced to the construct.

An interesting phenomenon is that most experienced Python programmers recognize
the ``while True`` idiom and don't seem to be missing the assignment in
expression construct much; it's only newcomers who express a strong desire to
add this to the language.

There's an alternative way of spelling this that seems attractive but is
generally less robust than the "while True" solution::

   line = f.readline()
   while line:
       ... # do something with line...
       line = f.readline()

The problem with this is that if you change your mind about exactly how you get
the next line (e.g. you want to change it into ``sys.stdin.readline()``) you
have to remember to change two places in your program -- the second occurrence
is hidden at the bottom of the loop.

The best approach is to use iterators, making it possible to loop through
objects using the ``for`` statement.  For example, in the current version of
Python file objects support the iterator protocol, so you can now write simply::

   for line in f:
       ... # do something with line...



Why does Python use methods for some functionality (e.g. list.index()) but functions for other (e.g. len(list))?
----------------------------------------------------------------------------------------------------------------

The major reason is history. Functions were used for those operations that were
generic for a group of types and which were intended to work even for objects
that didn't have methods at all (e.g. tuples).  It is also convenient to have a
function that can readily be applied to an amorphous collection of objects when
you use the functional features of Python (``map()``, ``apply()`` et al).

In fact, implementing ``len()``, ``max()``, ``min()`` as a built-in function is
actually less code than implementing them as methods for each type.  One can
quibble about individual cases but it's a part of Python, and it's too late to
make such fundamental changes now. The functions have to remain to avoid massive
code breakage.

.. XXX talk about protocols?

Note that for string operations Python has moved from external functions (the
``string`` module) to methods.  However, ``len()`` is still a function.


Why is join() a string method instead of a list or tuple method?
----------------------------------------------------------------

Strings became much more like other standard types starting in Python 1.6, when
methods were added which give the same functionality that has always been
available using the functions of the string module.  Most of these new methods
have been widely accepted, but the one which appears to make some programmers
feel uncomfortable is::

   ", ".join(['1', '2', '4', '8', '16'])

which gives the result::

   "1, 2, 4, 8, 16"

There are two common arguments against this usage.

The first runs along the lines of: "It looks really ugly using a method of a
string literal (string constant)", to which the answer is that it might, but a
string literal is just a fixed value. If the methods are to be allowed on names
bound to strings there is no logical reason to make them unavailable on
literals.

The second objection is typically cast as: "I am really telling a sequence to
join its members together with a string constant".  Sadly, you aren't.  For some
reason there seems to be much less difficulty with having :meth:`~str.split` as
a string method, since in that case it is easy to see that ::

   "1, 2, 4, 8, 16".split(", ")

is an instruction to a string literal to return the substrings delimited by the
given separator (or, by default, arbitrary runs of white space).  In this case a
Unicode string returns a list of Unicode strings, an ASCII string returns a list
of ASCII strings, and everyone is happy.

:meth:`~str.join` is a string method because in using it you are telling the
separator string to iterate over a sequence of strings and insert itself between
adjacent elements.  This method can be used with any argument which obeys the
rules for sequence objects, including any new classes you might define yourself.

Because this is a string method it can work for Unicode strings as well as plain
ASCII strings.  If ``join()`` were a method of the sequence types then the
sequence types would have to decide which type of string to return depending on
the type of the separator.

.. XXX remove next paragraph eventually

If none of these arguments persuade you, then for the moment you can continue to
use the ``join()`` function from the string module, which allows you to write ::

   string.join(['1', '2', '4', '8', '16'], ", ")


How fast are exceptions?
------------------------

A try/except block is extremely efficient.  Actually catching an exception is
expensive.  In versions of Python prior to 2.0 it was common to use this idiom::

   try:
       value = dict[key]
   except KeyError:
       dict[key] = getvalue(key)
       value = dict[key]

This only made sense when you expected the dict to have the key almost all the
time.  If that wasn't the case, you coded it like this::

   if dict.has_key(key):
       value = dict[key]
   else:
       dict[key] = getvalue(key)
       value = dict[key]

(In Python 2.0 and higher, you can code this as ``value = dict.setdefault(key,
getvalue(key))``.)


Why isn't there a switch or case statement in Python?
-----------------------------------------------------

You can do this easily enough with a sequence of ``if... elif... elif... else``.
There have been some proposals for switch statement syntax, but there is no
consensus (yet) on whether and how to do range tests.  See :pep:`275` for
complete details and the current status.

For cases where you need to choose from a very large number of possibilities,
you can create a dictionary mapping case values to functions to call.  For
example::

   def function_1(...):
       ...

   functions = {'a': function_1,
                'b': function_2,
                'c': self.method_1, ...}

   func = functions[value]
   func()

For calling methods on objects, you can simplify yet further by using the
:func:`getattr` built-in to retrieve methods with a particular name::

   def visit_a(self, ...):
       ...
   ...

   def dispatch(self, value):
       method_name = 'visit_' + str(value)
       method = getattr(self, method_name)
       method()

It's suggested that you use a prefix for the method names, such as ``visit_`` in
this example.  Without such a prefix, if values are coming from an untrusted
source, an attacker would be able to call any method on your object.


Can't you emulate threads in the interpreter instead of relying on an OS-specific thread implementation?
--------------------------------------------------------------------------------------------------------

Answer 1: Unfortunately, the interpreter pushes at least one C stack frame for
each Python stack frame.  Also, extensions can call back into Python at almost
random moments.  Therefore, a complete threads implementation requires thread
support for C.

Answer 2: Fortunately, there is `Stackless Python <http://www.stackless.com>`_,
which has a completely redesigned interpreter loop that avoids the C stack.
It's still experimental but looks very promising.  Although it is binary
compatible with standard Python, it's still unclear whether Stackless will make
it into the core -- maybe it's just too revolutionary.


Why can't lambda forms contain statements?
------------------------------------------

Python lambda forms cannot contain statements because Python's syntactic
framework can't handle statements nested inside expressions.  However, in
Python, this is not a serious problem.  Unlike lambda forms in other languages,
where they add functionality, Python lambdas are only a shorthand notation if
you're too lazy to define a function.

Functions are already first class objects in Python, and can be declared in a
local scope.  Therefore the only advantage of using a lambda form instead of a
locally-defined function is that you don't need to invent a name for the
function -- but that's just a local variable to which the function object (which
is exactly the same type of object that a lambda form yields) is assigned!


Can Python be compiled to machine code, C or some other language?
-----------------------------------------------------------------

Not easily.  Python's high level data types, dynamic typing of objects and
run-time invocation of the interpreter (using :func:`eval` or :keyword:`exec`)
together mean that a "compiled" Python program would probably consist mostly of
calls into the Python run-time system, even for seemingly simple operations like
``x+1``.

Several projects described in the Python newsgroup or at past `Python
conferences <http://python.org/community/workshops/>`_ have shown that this approach is feasible,
although the speedups reached so far are only modest (e.g. 2x).  Jython uses the
same strategy for compiling to Java bytecode.  (Jim Hugunin has demonstrated
that in combination with whole-program analysis, speedups of 1000x are feasible
for small demo programs.  See the proceedings from the `1997 Python conference
<http://python.org/community/workshops/1997-10/proceedings/>`_ for more information.)

Internally, Python source code is always translated into a bytecode
representation, and this bytecode is then executed by the Python virtual
machine.  In order to avoid the overhead of repeatedly parsing and translating
modules that rarely change, this byte code is written into a file whose name
ends in ".pyc" whenever a module is parsed.  When the corresponding .py file is
changed, it is parsed and translated again and the .pyc file is rewritten.

There is no performance difference once the .pyc file has been loaded, as the
bytecode read from the .pyc file is exactly the same as the bytecode created by
direct translation.  The only difference is that loading code from a .pyc file
is faster than parsing and translating a .py file, so the presence of
precompiled .pyc files improves the start-up time of Python scripts.  If
desired, the Lib/compileall.py module can be used to create valid .pyc files for
a given set of modules.

Note that the main script executed by Python, even if its filename ends in .py,
is not compiled to a .pyc file.  It is compiled to bytecode, but the bytecode is
not saved to a file.  Usually main scripts are quite short, so this doesn't cost
much speed.

.. XXX check which of these projects are still alive

There are also several programs which make it easier to intermingle Python and C
code in various ways to increase performance.  See, for example, `Psyco
<http://psyco.sourceforge.net/>`_, `Pyrex
<http://www.cosc.canterbury.ac.nz/~greg/python/Pyrex/>`_, `PyInline
<http://pyinline.sourceforge.net/>`_, `Py2Cmod
<http://sourceforge.net/projects/py2cmod/>`_, and `Weave
<http://www.scipy.org/site_content/weave>`_.


How does Python manage memory?
------------------------------

The details of Python memory management depend on the implementation.  The
standard C implementation of Python uses reference counting to detect
inaccessible objects, and another mechanism to collect reference cycles,
periodically executing a cycle detection algorithm which looks for inaccessible
cycles and deletes the objects involved. The :mod:`gc` module provides functions
to perform a garbage collection, obtain debugging statistics, and tune the
collector's parameters.

Jython relies on the Java runtime so the JVM's garbage collector is used.  This
difference can cause some subtle porting problems if your Python code depends on
the behavior of the reference counting implementation.

Sometimes objects get stuck in tracebacks temporarily and hence are not
deallocated when you might expect.  Clear the tracebacks with::

   import sys
   sys.exc_clear()
   sys.exc_traceback = sys.last_traceback = None

Tracebacks are used for reporting errors, implementing debuggers and related
things.  They contain a portion of the program state extracted during the
handling of an exception (usually the most recent exception).

In the absence of circularities and tracebacks, Python programs need not
explicitly manage memory.

Why doesn't Python use a more traditional garbage collection scheme?  For one
thing, this is not a C standard feature and hence it's not portable.  (Yes, we
know about the Boehm GC library.  It has bits of assembler code for *most*
common platforms, not for all of them, and although it is mostly transparent, it
isn't completely transparent; patches are required to get Python to work with
it.)

Traditional GC also becomes a problem when Python is embedded into other
applications.  While in a standalone Python it's fine to replace the standard
malloc() and free() with versions provided by the GC library, an application
embedding Python may want to have its *own* substitute for malloc() and free(),
and may not want Python's.  Right now, Python works with anything that
implements malloc() and free() properly.

In Jython, the following code (which is fine in CPython) will probably run out
of file descriptors long before it runs out of memory::

   for file in <very long list of files>:
       f = open(file)
       c = f.read(1)

Using the current reference counting and destructor scheme, each new assignment
to f closes the previous file.  Using GC, this is not guaranteed.  If you want
to write code that will work with any Python implementation, you should
explicitly close the file; this will work regardless of GC::

   for file in <very long list of files>:
       f = open(file)
       c = f.read(1)
       f.close()


Why isn't all memory freed when Python exits?
---------------------------------------------

Objects referenced from the global namespaces of Python modules are not always
deallocated when Python exits.  This may happen if there are circular
references.  There are also certain bits of memory that are allocated by the C
library that are impossible to free (e.g. a tool like Purify will complain about
these).  Python is, however, aggressive about cleaning up memory on exit and
does try to destroy every single object.

If you want to force Python to delete certain things on deallocation use the
:mod:`atexit` module to run a function that will force those deletions.


Why are there separate tuple and list data types?
-------------------------------------------------

Lists and tuples, while similar in many respects, are generally used in
fundamentally different ways.  Tuples can be thought of as being similar to
Pascal records or C structs; they're small collections of related data which may
be of different types which are operated on as a group.  For example, a
Cartesian coordinate is appropriately represented as a tuple of two or three
numbers.

Lists, on the other hand, are more like arrays in other languages.  They tend to
hold a varying number of objects all of which have the same type and which are
operated on one-by-one.  For example, ``os.listdir('.')`` returns a list of
strings representing the files in the current directory.  Functions which
operate on this output would generally not break if you added another file or
two to the directory.

Tuples are immutable, meaning that once a tuple has been created, you can't
replace any of its elements with a new value.  Lists are mutable, meaning that
you can always change a list's elements.  Only immutable elements can be used as
dictionary keys, and hence only tuples and not lists can be used as keys.


How are lists implemented?
--------------------------

Python's lists are really variable-length arrays, not Lisp-style linked lists.
The implementation uses a contiguous array of references to other objects, and
keeps a pointer to this array and the array's length in a list head structure.

This makes indexing a list ``a[i]`` an operation whose cost is independent of
the size of the list or the value of the index.

When items are appended or inserted, the array of references is resized.  Some
cleverness is applied to improve the performance of appending items repeatedly;
when the array must be grown, some extra space is allocated so the next few
times don't require an actual resize.


How are dictionaries implemented?
---------------------------------

Python's dictionaries are implemented as resizable hash tables.  Compared to
B-trees, this gives better performance for lookup (the most common operation by
far) under most circumstances, and the implementation is simpler.

Dictionaries work by computing a hash code for each key stored in the dictionary
using the :func:`hash` built-in function.  The hash code varies widely depending
on the key; for example, "Python" hashes to -539294296 while "python", a string
that differs by a single bit, hashes to 1142331976.  The hash code is then used
to calculate a location in an internal array where the value will be stored.
Assuming that you're storing keys that all have different hash values, this
means that dictionaries take constant time -- O(1), in computer science notation
-- to retrieve a key.  It also means that no sorted order of the keys is
maintained, and traversing the array as the ``.keys()`` and ``.items()`` do will
output the dictionary's content in some arbitrary jumbled order.


Why must dictionary keys be immutable?
--------------------------------------

The hash table implementation of dictionaries uses a hash value calculated from
the key value to find the key.  If the key were a mutable object, its value
could change, and thus its hash could also change.  But since whoever changes
the key object can't tell that it was being used as a dictionary key, it can't
move the entry around in the dictionary.  Then, when you try to look up the same
object in the dictionary it won't be found because its hash value is different.
If you tried to look up the old value it wouldn't be found either, because the
value of the object found in that hash bin would be different.

If you want a dictionary indexed with a list, simply convert the list to a tuple
first; the function ``tuple(L)`` creates a tuple with the same entries as the
list ``L``.  Tuples are immutable and can therefore be used as dictionary keys.

Some unacceptable solutions that have been proposed:

- Hash lists by their address (object ID).  This doesn't work because if you
  construct a new list with the same value it won't be found; e.g.::

     d = {[1,2]: '12'}
     print d[[1,2]]

  would raise a KeyError exception because the id of the ``[1,2]`` used in the
  second line differs from that in the first line.  In other words, dictionary
  keys should be compared using ``==``, not using :keyword:`is`.

- Make a copy when using a list as a key.  This doesn't work because the list,
  being a mutable object, could contain a reference to itself, and then the
  copying code would run into an infinite loop.

- Allow lists as keys but tell the user not to modify them.  This would allow a
  class of hard-to-track bugs in programs when you forgot or modified a list by
  accident. It also invalidates an important invariant of dictionaries: every
  value in ``d.keys()`` is usable as a key of the dictionary.

- Mark lists as read-only once they are used as a dictionary key.  The problem
  is that it's not just the top-level object that could change its value; you
  could use a tuple containing a list as a key.  Entering anything as a key into
  a dictionary would require marking all objects reachable from there as
  read-only -- and again, self-referential objects could cause an infinite loop.

There is a trick to get around this if you need to, but use it at your own risk:
You can wrap a mutable structure inside a class instance which has both a
:meth:`__cmp_` and a :meth:`__hash__` method.  You must then make sure that the
hash value for all such wrapper objects that reside in a dictionary (or other
hash based structure), remain fixed while the object is in the dictionary (or
other structure). ::

   class ListWrapper:
       def __init__(self, the_list):
           self.the_list = the_list
       def __cmp__(self, other):
           return self.the_list == other.the_list
       def __hash__(self):
           l = self.the_list
           result = 98767 - len(l)*555
           for i in range(len(l)):
               try:
                   result = result + (hash(l[i]) % 9999999) * 1001 + i
               except:
                   result = (result % 7777777) + i * 333
           return result

Note that the hash computation is complicated by the possibility that some
members of the list may be unhashable and also by the possibility of arithmetic
overflow.

Furthermore it must always be the case that if ``o1 == o2`` (ie ``o1.__cmp__(o2)
== 0``) then ``hash(o1) == hash(o2)`` (ie, ``o1.__hash__() == o2.__hash__()``),
regardless of whether the object is in a dictionary or not.  If you fail to meet
these restrictions dictionaries and other hash based structures will misbehave.

In the case of ListWrapper, whenever the wrapper object is in a dictionary the
wrapped list must not change to avoid anomalies.  Don't do this unless you are
prepared to think hard about the requirements and the consequences of not
meeting them correctly.  Consider yourself warned.


Why doesn't list.sort() return the sorted list?
-----------------------------------------------

In situations where performance matters, making a copy of the list just to sort
it would be wasteful. Therefore, :meth:`list.sort` sorts the list in place. In
order to remind you of that fact, it does not return the sorted list.  This way,
you won't be fooled into accidentally overwriting a list when you need a sorted
copy but also need to keep the unsorted version around.

In Python 2.4 a new builtin -- :func:`sorted` -- has been added.  This function
creates a new list from a provided iterable, sorts it and returns it.  For
example, here's how to iterate over the keys of a dictionary in sorted order::

   for key in sorted(dict.iterkeys()):
       ... # do whatever with dict[key]...


How do you specify and enforce an interface spec in Python?
-----------------------------------------------------------

An interface specification for a module as provided by languages such as C++ and
Java describes the prototypes for the methods and functions of the module.  Many
feel that compile-time enforcement of interface specifications helps in the
construction of large programs.

Python 2.6 adds an :mod:`abc` module that lets you define Abstract Base Classes
(ABCs).  You can then use :func:`isinstance` and :func:`issubclass` to check
whether an instance or a class implements a particular ABC.  The
:mod:`collections` modules defines a set of useful ABCs such as
:class:`Iterable`, :class:`Container`, and :class:`MutableMapping`.

For Python, many of the advantages of interface specifications can be obtained
by an appropriate test discipline for components.  There is also a tool,
PyChecker, which can be used to find problems due to subclassing.

A good test suite for a module can both provide a regression test and serve as a
module interface specification and a set of examples.  Many Python modules can
be run as a script to provide a simple "self test."  Even modules which use
complex external interfaces can often be tested in isolation using trivial
"stub" emulations of the external interface.  The :mod:`doctest` and
:mod:`unittest` modules or third-party test frameworks can be used to construct
exhaustive test suites that exercise every line of code in a module.

An appropriate testing discipline can help build large complex applications in
Python as well as having interface specifications would.  In fact, it can be
better because an interface specification cannot test certain properties of a
program.  For example, the :meth:`append` method is expected to add new elements
to the end of some internal list; an interface specification cannot test that
your :meth:`append` implementation will actually do this correctly, but it's
trivial to check this property in a test suite.

Writing test suites is very helpful, and you might want to design your code with
an eye to making it easily tested.  One increasingly popular technique,
test-directed development, calls for writing parts of the test suite first,
before you write any of the actual code.  Of course Python allows you to be
sloppy and not write test cases at all.


Why are default values shared between objects?
----------------------------------------------

This type of bug commonly bites neophyte programmers.  Consider this function::

   def foo(D={}):  # Danger: shared reference to one dict for all calls
       ... compute something ...
       D[key] = value
       return D

The first time you call this function, ``D`` contains a single item.  The second
time, ``D`` contains two items because when ``foo()`` begins executing, ``D``
starts out with an item already in it.

It is often expected that a function call creates new objects for default
values. This is not what happens. Default values are created exactly once, when
the function is defined.  If that object is changed, like the dictionary in this
example, subsequent calls to the function will refer to this changed object.

By definition, immutable objects such as numbers, strings, tuples, and ``None``,
are safe from change. Changes to mutable objects such as dictionaries, lists,
and class instances can lead to confusion.

Because of this feature, it is good programming practice to not use mutable
objects as default values.  Instead, use ``None`` as the default value and
inside the function, check if the parameter is ``None`` and create a new
list/dictionary/whatever if it is.  For example, don't write::

   def foo(dict={}):
       ...

but::

   def foo(dict=None):
       if dict is None:
           dict = {} # create a new dict for local namespace

This feature can be useful.  When you have a function that's time-consuming to
compute, a common technique is to cache the parameters and the resulting value
of each call to the function, and return the cached value if the same value is
requested again.  This is called "memoizing", and can be implemented like this::

   # Callers will never provide a third parameter for this function.
   def expensive (arg1, arg2, _cache={}):
       if _cache.has_key((arg1, arg2)):
           return _cache[(arg1, arg2)]

       # Calculate the value
       result = ... expensive computation ...
       _cache[(arg1, arg2)] = result           # Store result in the cache
       return result

You could use a global variable containing a dictionary instead of the default
value; it's a matter of taste.


Why is there no goto?
---------------------

You can use exceptions to provide a "structured goto" that even works across
function calls.  Many feel that exceptions can conveniently emulate all
reasonable uses of the "go" or "goto" constructs of C, Fortran, and other
languages.  For example::

   class label: pass # declare a label

   try:
        ...
        if (condition): raise label() # goto label
        ...
   except label: # where to goto
        pass
   ...

This doesn't allow you to jump into the middle of a loop, but that's usually
considered an abuse of goto anyway.  Use sparingly.


Why can't raw strings (r-strings) end with a backslash?
-------------------------------------------------------

More precisely, they can't end with an odd number of backslashes: the unpaired
backslash at the end escapes the closing quote character, leaving an
unterminated string.

Raw strings were designed to ease creating input for processors (chiefly regular
expression engines) that want to do their own backslash escape processing. Such
processors consider an unmatched trailing backslash to be an error anyway, so
raw strings disallow that.  In return, they allow you to pass on the string
quote character by escaping it with a backslash.  These rules work well when
r-strings are used for their intended purpose.

If you're trying to build Windows pathnames, note that all Windows system calls
accept forward slashes too::

   f = open("/mydir/file.txt") # works fine!

If you're trying to build a pathname for a DOS command, try e.g. one of ::

   dir = r"\this\is\my\dos\dir" "\\"
   dir = r"\this\is\my\dos\dir\ "[:-1]
   dir = "\\this\\is\\my\\dos\\dir\\"


Why doesn't Python have a "with" statement for attribute assignments?
---------------------------------------------------------------------

Python has a 'with' statement that wraps the execution of a block, calling code
on the entrance and exit from the block.  Some language have a construct that
looks like this::

   with obj:
       a = 1    # equivalent to obj.a = 1
       total = total + 1   # obj.total = obj.total + 1

In Python, such a construct would be ambiguous.

Other languages, such as Object Pascal, Delphi, and C++, use static types, so
it's possible to know, in an unambiguous way, what member is being assigned
to. This is the main point of static typing -- the compiler *always* knows the
scope of every variable at compile time.

Python uses dynamic types. It is impossible to know in advance which attribute
will be referenced at runtime. Member attributes may be added or removed from
objects on the fly. This makes it impossible to know, from a simple reading,
what attribute is being referenced: a local one, a global one, or a member
attribute?

For instance, take the following incomplete snippet::

   def foo(a):
       with a:
           print x

The snippet assumes that "a" must have a member attribute called "x".  However,
there is nothing in Python that tells the interpreter this. What should happen
if "a" is, let us say, an integer?  If there is a global variable named "x",
will it be used inside the with block?  As you see, the dynamic nature of Python
makes such choices much harder.

The primary benefit of "with" and similar language features (reduction of code
volume) can, however, easily be achieved in Python by assignment.  Instead of::

   function(args).dict[index][index].a = 21
   function(args).dict[index][index].b = 42
   function(args).dict[index][index].c = 63

write this::

   ref = function(args).dict[index][index]
   ref.a = 21
   ref.b = 42
   ref.c = 63

This also has the side-effect of increasing execution speed because name
bindings are resolved at run-time in Python, and the second version only needs
to perform the resolution once.  If the referenced object does not have a, b and
c attributes, of course, the end result is still a run-time exception.


Why are colons required for the if/while/def/class statements?
--------------------------------------------------------------

The colon is required primarily to enhance readability (one of the results of
the experimental ABC language).  Consider this::

   if a == b
       print a

versus ::

   if a == b:
       print a

Notice how the second one is slightly easier to read.  Notice further how a
colon sets off the example in this FAQ answer; it's a standard usage in English.

Another minor reason is that the colon makes it easier for editors with syntax
highlighting; they can look for colons to decide when indentation needs to be
increased instead of having to do a more elaborate parsing of the program text.


Why does Python allow commas at the end of lists and tuples?
------------------------------------------------------------

Python lets you add a trailing comma at the end of lists, tuples, and
dictionaries::

   [1, 2, 3,]
   ('a', 'b', 'c',)
   d = {
       "A": [1, 5],
       "B": [6, 7],  # last trailing comma is optional but good style
   }


There are several reasons to allow this.

When you have a literal value for a list, tuple, or dictionary spread across
multiple lines, it's easier to add more elements because you don't have to
remember to add a comma to the previous line.  The lines can also be sorted in
your editor without creating a syntax error.

Accidentally omitting the comma can lead to errors that are hard to diagnose.
For example::

       x = [
         "fee",
         "fie"
         "foo",
         "fum"
       ]

This list looks like it has four elements, but it actually contains three:
"fee", "fiefoo" and "fum".  Always adding the comma avoids this source of error.

Allowing the trailing comma may also make programmatic code generation easier.