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 a while.

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 many to believe otherwise.  Even experienced C
programmers will sometimes stare at it a long time wondering as to 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. After becoming used to reading and writing code using a
particular style, it is normal to feel somewhat uneasy when reading
(or being required to write) in a different one.

Many coding styles place begin/end brackets on a line by themselves.
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?
==================================================

Users are often surprised by results like this:

   >>> 1.2 - 1.0
   0.19999999999999996

and think it is a bug in Python.  It’s not.  This has little to do
with Python, and much more to do with how the underlying platform
handles floating-point numbers.

The "float" type in CPython uses a C "double" for storage.  A "float"
object’s value is stored in binary floating-point with a fixed
precision (typically 53 bits) and Python uses C operations, which in
turn rely on the hardware implementation in the processor, to perform
floating-point operations. This means that as far as floating-point
operations are concerned, Python behaves like many popular languages
including C and Java.

Many numbers that can be written easily in decimal notation cannot be
expressed exactly in binary floating-point.  For example, after:

   >>> x = 1.2

the value stored for "x" is a (very good) approximation to the decimal
value "1.2", but is not exactly equal to it.  On a typical machine,
the actual stored value is:

   1.0011001100110011001100110011001100110011001100110011 (binary)

which is exactly:

   1.1999999999999999555910790149937383830547332763671875 (decimal)

The typical precision of 53 bits provides Python floats with 15–16
decimal digits of accuracy.

For a fuller explanation, please see the floating point arithmetic
chapter in the Python tutorial.


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 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 "__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 is 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?
===============================================

Starting in Python 3.8, you can!

Assignment expressions using the walrus operator *:=* assign a
variable in an expression:

   while chunk := fp.read(200):
      print(chunk)

See **PEP 572** for more information.


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

As Guido said:

   (a) For some operations, prefix notation just reads better than
   postfix – prefix (and infix!) operations have a long tradition in
   mathematics which likes notations where the visuals help the
   mathematician thinking about a problem. Compare the easy with which
   we rewrite a formula like x*(a+b) into x*a + x*b to the clumsiness
   of doing the same thing using a raw OO notation.

   (b) When I read code that says len(x) I *know* that it is asking
   for the length of something. This tells me two things: the result
   is an integer, and the argument is some kind of container. To the
   contrary, when I read x.len(), I have to already know that x is
   some kind of container implementing an interface or inheriting from
   a class that has a standard len(). Witness the confusion we
   occasionally have when a class that is not implementing a mapping
   has a get() or keys() method, or something that isn’t a file has a
   write() method.

   -- https://mail.python.org/pipermail/python-3000/2006-November/004
   643.html


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 "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).

"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. Similar methods exist for bytes and
bytearray objects.


How fast are exceptions?
========================

A try/except block is extremely efficient if no exceptions are raised.
Actually catching an exception is expensive.  In versions of Python
prior to 2.0 it was common to use this idiom:

   try:
       value = mydict[key]
   except KeyError:
       mydict[key] = getvalue(key)
       value = mydict[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 key in mydict:
       value = mydict[key]
   else:
       value = mydict[key] = getvalue(key)

For this specific case, you could also use "value =
dict.setdefault(key, getvalue(key))", but only if the "getvalue()"
call is cheap enough because it is evaluated in all cases.


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 "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, which has a
completely redesigned interpreter loop that avoids the C stack.


Why can’t lambda expressions contain statements?
================================================

Python lambda expressions 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 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 expression yields) is assigned!


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

Cython compiles a modified version of Python with optional annotations
into C extensions.  Nuitka is an up-and-coming compiler of Python into
C++ code, aiming to support the full Python language. For compiling to
Java you can consider VOC.


How does Python manage memory?
==============================

The details of Python memory management depend on the implementation.
The standard implementation of Python, *CPython*, 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 "gc" module provides functions to perform a garbage
collection, obtain debugging statistics, and tune the collector’s
parameters.

Other implementations (such as Jython or PyPy), however, can rely on a
different mechanism such as a full-blown garbage collector.  This
difference can cause some subtle porting problems if your Python code
depends on the behavior of the reference counting implementation.

In some Python implementations, the following code (which is fine in
CPython) will probably run out of file descriptors:

   for file in very_long_list_of_files:
       f = open(file)
       c = f.read(1)

Indeed, using CPython’s reference counting and destructor scheme, each
new assignment to *f* closes the previous file.  With a traditional
GC, however, those file objects will only get collected (and closed)
at varying and possibly long intervals.

If you want to write code that will work with any Python
implementation, you should explicitly close the file or use the "with"
statement; this will work regardless of memory management scheme:

   for file in very_long_list_of_files:
       with open(file) as f:
           c = f.read(1)


Why doesn’t CPython 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, CPython works with anything that implements malloc() and free()
properly.


Why isn’t all memory freed when CPython 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 "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 in CPython?
=====================================

CPython’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 in CPython?
============================================

CPython’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 "hash()" built-in function.  The hash code varies
widely depending on the key and a per-process seed; for example,
“Python” could hash to -539294296 while “python”, a string that
differs by a single bit, could hash 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 Big-O notation – to retrieve a key.


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.:

     mydict = {[1, 2]: '12'}
     print(mydict[[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 "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 "__eq__()" and a "__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 __eq__(self, other):
           return self.the_list == other.the_list

       def __hash__(self):
           l = self.the_list
           result = 98767 - len(l)*555
           for i, el in enumerate(l):
               try:
                   result = result + (hash(el) % 9999999) * 1001 + i
               except Exception:
                   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.__eq__(o2) is True") 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, "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.

If you want to return a new list, use the built-in "sorted()" function
instead.  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(mydict):
       ...  # do whatever with mydict[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 "abc" module that lets you define Abstract Base
Classes (ABCs).  You can then use "isinstance()" and "issubclass()" to
check whether an instance or a class implements a particular ABC.  The
"collections.abc" module defines a set of useful ABCs such as
"Iterable", "Container", and "MutableMapping".

For Python, many of the advantages of interface specifications can be
obtained by an appropriate test discipline for components.

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 "doctest" and "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
"append()" method is expected to add new elements to the end of some
internal list; an interface specification cannot test that your
"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 to make it easily tested. One increasingly popular technique,
test-driven 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 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(Exception): 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 languages
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).mydict[index][index].a = 21
   function(args).mydict[index][index].b = 42
   function(args).mydict[index][index].c = 63

write this:

   ref = function(args).mydict[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.


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 reordered 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.
