given shape with the given number of bytes per element.
-.. cfunction:: int PyBuffer_FillInfo(Py_buffer *view, void *buf, Py_ssize_t len, int readonly, int infoflags)
+.. cfunction:: int PyBuffer_FillInfo(Py_buffer *view, PyObject *obj, void *buf, Py_ssize_t len, int readonly, int infoflags)
Fill in a buffer-info structure, *view*, correctly for an exporter that can
only share a contiguous chunk of memory of "unsigned bytes" of the given
modify its value. The value is available to Python code as ``sys.version``.
-.. cfunction:: const char* Py_GetBuildNumber()
-
- Return a string representing the Subversion revision that this Python executable
- was built from. This number is a string because it may contain a trailing 'M'
- if Python was built from a mixed revision source tree.
-
- .. versionadded:: 2.5
-
-
.. cfunction:: const char* Py_GetPlatform()
.. index:: single: platform (in module sys)
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.
+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
People are often very surprised by results like this::
- >>> 1.2-1.0
+ >>> 1.2 - 1.0
0.199999999999999996
and think it is a bug in Python. It's not. This has nothing to do with Python,
``==`` 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
+ epsilon = 0.0000000000001 # Tiny allowed error
expected_result = 0.4
if expected_result-epsilon <= computation() <= expected_result+epsilon:
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.
+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
+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
+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.
+``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?
.. 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.
+.. note::
+
+ 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?
expensive. In versions of Python prior to 2.0 it was common to use this idiom::
try:
- value = dict[key]
+ value = mydict[key]
except KeyError:
- dict[key] = getvalue(key)
- value = dict[key]
+ 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 dict.has_key(key):
- value = dict[key]
+ if mydict.has_key(key):
+ value = mydict[key]
else:
- dict[key] = getvalue(key)
- value = dict[key]
+ mydict[key] = getvalue(key)
+ value = mydict[key]
+
+.. note::
-(In Python 2.0 and higher, you can code this as ``value = dict.setdefault(key,
-getvalue(key))``.)
+ In Python 2.0 and higher, you can code this as ``value =
+ mydict.setdefault(key, getvalue(key))``.
Why isn't there a switch or case statement in Python?
<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>`_.
+<http://www.scipy.org/Weave>`_.
How does Python manage memory?
difference can cause some subtle porting problems if your Python code depends on
the behavior of the reference counting implementation.
+.. XXX relevant for Python 2.6?
+
Sometimes objects get stuck in tracebacks temporarily and hence are not
deallocated when you might expect. Clear the tracebacks with::
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.
+In the absence of circularities and tracebacks, Python programs do not need to
+manage memory explicitly.
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
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>:
+ 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::
+explicitly close the file or use the :keyword:`with` statement; this will work
+regardless of GC::
- for file in <very long list of files>:
- f = open(file)
- c = f.read(1)
- f.close()
+ for file in very_long_list_of_files:
+ with open(file) as f:
+ c = f.read(1)
Why isn't all memory freed when Python exits?
- 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]]
+ mydict = {[1, 2]: '12'}
+ print mydict[[1, 2]]
- would raise a KeyError exception because the id of the ``[1,2]`` used in the
+ 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`.
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
+:meth:`__eq__` 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):
+ 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 in range(len(l)):
+ for i, el in enumerate(l):
try:
- result = result + (hash(l[i]) % 9999999) * 1001 + i
- except:
+ result = result + (hash(el) % 9999999) * 1001 + i
+ except Exception:
result = (result % 7777777) + i * 333
return result
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__()``),
+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.
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]...
+ for key in sorted(mydict):
+ ... # do whatever with mydict[key]...
How do you specify and enforce an interface spec in Python?
This type of bug commonly bites neophyte programmers. Consider this function::
- def foo(D={}): # Danger: shared reference to one dict for all calls
+ def foo(mydict={}): # Danger: shared reference to one dict for all calls
... compute something ...
- D[key] = value
- return D
+ mydict[key] = value
+ return mydict
-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.
+The first time you call this function, ``mydict`` contains a single item. The
+second time, ``mydict`` contains two items because when ``foo()`` begins
+executing, ``mydict`` 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
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={}):
+ def foo(mydict={}):
...
but::
- def foo(dict=None):
- if dict is None:
- dict = {} # create a new dict for local namespace
+ def foo(mydict=None):
+ if mydict is None:
+ mydict = {} # 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
# Callers will never provide a third parameter for this function.
def expensive (arg1, arg2, _cache={}):
- if _cache.has_key((arg1, arg2)):
+ if (arg1, arg2) in _cache:
return _cache[(arg1, arg2)]
# Calculate the value
reasonable uses of the "go" or "goto" constructs of C, Fortran, and other
languages. For example::
- class label: pass # declare a label
+ class label: pass # declare a label
try:
...
- if (condition): raise label() # goto label
+ if (condition): raise label() # goto label
...
- except label: # where to goto
+ except label: # where to goto
pass
...
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!
+ f = open("/mydir/file.txt") # works fine!
If you're trying to build a pathname for a DOS command, try e.g. one of ::
looks like this::
with obj:
- a = 1 # equivalent to obj.a = 1
+ a = 1 # equivalent to obj.a = 1
total = total + 1 # obj.total = obj.total + 1
In Python, such a construct would be ambiguous.
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
+ 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).dict[index][index]
+ 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. If the referenced object does not have a, b and
-c attributes, of course, the end result is still a run-time exception.
+to perform the resolution once.
Why are colons required for the if/while/def/class statements?
it is much shorter and far faster to use ::
- L2 = list(L1[:3]) # "list" is redundant if L1 is a list.
+ L2 = list(L1[:3]) # "list" is redundant if L1 is a list.
Note that the functionally-oriented built-in functions such as :func:`map`,
:func:`zip`, and friends can be a convenient accelerator for loops that
perform a single task. For example to pair the elements of two lists
together::
- >>> zip([1,2,3], [4,5,6])
+ >>> zip([1, 2, 3], [4, 5, 6])
[(1, 4), (2, 5), (3, 6)]
or to compute a number of sines::
- >>> map( math.sin, (1,2,3,4))
- [0.841470984808, 0.909297426826, 0.14112000806, -0.756802495308]
+ >>> map(math.sin, (1, 2, 3, 4))
+ [0.841470984808, 0.909297426826, 0.14112000806, -0.756802495308]
The operation completes very quickly in such cases.
-Other examples include the ``join()`` and ``split()`` methods of string objects.
+Other examples include the ``join()`` and ``split()`` :ref:`methods
+of string objects <string-methods>`.
For example if s1..s7 are large (10K+) strings then
``"".join([s1,s2,s3,s4,s5,s6,s7])`` may be far faster than the more obvious
``s1+s2+s3+s4+s5+s6+s7``, since the "summation" will compute many
subexpressions, whereas ``join()`` does all the copying in one pass. For
-manipulating strings, use the ``replace()`` method on string objects. Use
-regular expressions only when you're not dealing with constant string patterns.
-Consider using the string formatting operations ``string % tuple`` and ``string
-% dictionary``.
+manipulating strings, use the ``replace()`` and the ``format()`` :ref:`methods
+on string objects <string-methods>`. Use regular expressions only when you're
+not dealing with constant string patterns. You may still use :ref:`the old %
+operations <string-formatting>` ``string % tuple`` and ``string % dictionary``.
Be sure to use the :meth:`list.sort` built-in method to do sorting, and see the
`sorting mini-HOWTO <http://wiki.python.org/moin/HowTo/Sorting>`_ for examples
Another common trick is to "push loops into functions or methods." For example
suppose you have a program that runs slowly and you use the profiler to
determine that a Python function ``ff()`` is being called lots of times. If you
-notice that ``ff ()``::
+notice that ``ff()``::
def ff(x):
... # do something with x computing result...
>>> print x
11
-In Python3, you can do a similar thing in a nested scope using the
-:keyword:`nonlocal` keyword:
-
-.. doctest::
- :options: +SKIP
-
- >>> def foo():
- ... x = 10
- ... def bar():
- ... nonlocal x
- ... print x
- ... x += 1
- ... bar()
- ... print x
- >>> foo()
- 10
- 11
-
What are the rules for local and global variables in Python?
------------------------------------------------------------
It's good practice if you import modules in the following order:
-1. standard library modules -- e.g. ``sys``, ``os``, ``getopt``, ``re``)
+1. standard library modules -- e.g. ``sys``, ``os``, ``getopt``, ``re``
2. third-party library modules (anything installed in Python's site-packages
directory) -- e.g. mx.DateTime, ZODB, PIL.Image, etc.
3. locally-developed modules
``package.sub.m1`` module and want to import ``package.sub.m2``, do not just
write ``import m2``, even though it's legal. Write ``from package.sub import
m2`` instead. Relative imports can lead to a module being initialized twice,
-leading to confusing bugs.
+leading to confusing bugs. See :pep:`328` for details.
It is sometimes necessary to move imports to a function or class to avoid
problems with circular imports. Gordon McMillan says:
a = B()
b = a
print b
- <__main__.A instance at 016D07CC>
+ <__main__.A instance at 0x16D07CC>
print a
- <__main__.A instance at 016D07CC>
+ <__main__.A instance at 0x16D07CC>
Arguably the class has a name: even though it is bound to two names and invoked
through the name B the created instance is still reported as an instance of
Comma is not an operator in Python. Consider this session::
>>> "a" in "b", "a"
- (False, '1')
+ (False, 'a')
Since the comma is not an operator, but a separator between expressions the
above is evaluated as if you had entered::
not::
- >>> "a" in ("5", "a")
+ >>> "a" in ("b", "a")
The same is true of the various assignment operators (``=``, ``+=`` etc). They
are not truly operators but syntactic delimiters in assignment statements.
if not isfunction(on_true):
return on_true
else:
- return apply(on_true)
+ return on_true()
else:
if not isfunction(on_false):
return on_false
else:
- return apply(on_false)
+ return on_false()
In most cases you'll pass b and c directly: ``q(a, b, c)``. To avoid evaluating
b or c when they shouldn't be, encapsulate them within a lambda function, e.g.:
map(lambda x,y=y:y%x,range(2,int(pow(y,0.5)+1))),1),range(2,1000)))
# First 10 Fibonacci numbers
- print map(lambda x,f=lambda x,f:(x<=1) or (f(x-1,f)+f(x-2,f)): f(x,f),
+ print map(lambda x,f=lambda x,f:(f(x-1,f)+f(x-2,f)) if x>1 else 1: f(x,f),
range(10))
# Mandelbrot set
How do I specify hexadecimal and octal integers?
------------------------------------------------
-To specify an octal digit, precede the octal value with a zero. For example, to
-set the variable "a" to the octal value "10" (8 in decimal), type::
+To specify an octal digit, precede the octal value with a zero, and then a lower
+or uppercase "o". For example, to set the variable "a" to the octal value "10"
+(8 in decimal), type::
- >>> a = 010
+ >>> a = 0o10
>>> a
8
178
-Why does -22 / 10 return -3?
-----------------------------
+Why does -22 // 10 return -3?
+-----------------------------
It's primarily driven by the desire that ``i % j`` have the same sign as ``j``.
If you want that, and also want::
- i == (i / j) * j + (i % j)
+ i == (i // j) * j + (i % j)
then integer division has to return the floor. C also requires that identity to
-hold, and then compilers that truncate ``i / j`` need to make ``i % j`` have the
-same sign as ``i``.
+hold, and then compilers that truncate ``i // j`` need to make ``i % j`` have
+the same sign as ``i``.
There are few real use cases for ``i % j`` when ``j`` is negative. When ``j``
is positive, there are many, and in virtually all of them it's more useful for
ago? ``-190 % 12 == 2`` is useful; ``-190 % 12 == -10`` is a bug waiting to
bite.
+.. note::
+
+ On Python 2, ``a / b`` returns the same as ``a // b`` if
+ ``__future__.division`` is not in effect. This is also known as "classic"
+ division.
+
How do I convert a string to a number?
--------------------------------------
To convert, e.g., the number 144 to the string '144', use the built-in type
constructor :func:`str`. If you want a hexadecimal or octal representation, use
-the built-in functions ``hex()`` or ``oct()``. For fancy formatting, use
-:ref:`the % operator <string-formatting>` on strings, e.g. ``"%04d" % 144``
-yields ``'0144'`` and ``"%.3f" % (1/3.0)`` yields ``'0.333'``. See the library
-reference manual for details.
+the built-in functions :func:`hex` or :func:`oct`. For fancy formatting, see
+the :ref:`formatstrings` section, e.g. ``"{:04d}".format(144)`` yields
+``'0144'`` and ``"{:.3f}".format(1/3)`` yields ``'0.333'``. You may also use
+:ref:`the % operator <string-formatting>` on strings. See the library reference
+manual for details.
How do I modify a string in place?
... "\r\n"
... "\r\n")
>>> lines.rstrip("\n\r")
- "line 1 "
+ 'line 1 '
Since this is typically only desired when reading text one line at a time, using
``S.rstrip()`` this way works well.
-For older versions of Python, There are two partial substitutes:
+For older versions of Python, there are two partial substitutes:
- If you want to remove all trailing whitespace, use the ``rstrip()`` method of
string objects. This removes all trailing whitespace, not just a single
If you don't mind reordering the list, sort it and then scan from the end of the
list, deleting duplicates as you go::
- if List:
- List.sort()
- last = List[-1]
- for i in range(len(List)-2, -1, -1):
- if last == List[i]:
- del List[i]
+ if mylist:
+ mylist.sort()
+ last = mylist[-1]
+ for i in range(len(mylist)-2, -1, -1):
+ if last == mylist[i]:
+ del mylist[i]
else:
- last = List[i]
+ last = mylist[i]
If all elements of the list may be used as dictionary keys (i.e. they are all
hashable) this is often faster ::
d = {}
- for x in List:
- d[x] = x
- List = d.values()
+ for x in mylist:
+ d[x] = 1
+ mylist = list(d.keys())
In Python 2.5 and later, the following is possible instead::
- List = list(set(List))
+ mylist = list(set(mylist))
This converts the list into a set, thereby removing duplicates, and then back
into a list.
Use a list comprehension::
- result = [obj.method() for obj in List]
+ result = [obj.method() for obj in mylist]
More generically, you can try the following function::
case, use the ``pprint`` module to pretty-print the dictionary; the items will
be presented in order sorted by the key.
-A more complicated solution is to subclass ``UserDict.UserDict`` to create a
+A more complicated solution is to subclass ``dict`` to create a
``SortedDict`` class that prints itself in a predictable order. Here's one
simpleminded implementation of such a class::
- import UserDict, string
-
- class SortedDict(UserDict.UserDict):
+ class SortedDict(dict):
def __repr__(self):
- result = []
- append = result.append
- keys = self.data.keys()
- keys.sort()
- for k in keys:
- append("%s: %s" % (`k`, `self.data[k]`))
- return "{%s}" % string.join(result, ", ")
+ keys = sorted(self.keys())
+ result = ("{!r}: {!r}".format(k, self[k]) for k in keys)
+ return "{{{}}}".format(", ".join(result))
- __str__ = __repr__
+ __str__ = __repr__
This will work for many common situations you might encounter, though it's far
from a perfect solution. The largest flaw is that if some values in the
sorting is quite simple to do with list comprehensions. To sort a list of
strings by their uppercase values::
- tmp1 = [(x.upper(), x) for x in L] # Schwartzian transform
+ tmp1 = [(x.upper(), x) for x in L] # Schwartzian transform
tmp1.sort()
Usorted = [x[1] for x in tmp1]
To sort by the integer value of a subfield extending from positions 10-15 in
each string::
- tmp2 = [(int(s[10:15]), s) for s in L] # Schwartzian transform
+ tmp2 = [(int(s[10:15]), s) for s in L] # Schwartzian transform
tmp2.sort()
Isorted = [x[1] for x in tmp2]
An alternative for the last step is::
- result = []
- for p in pairs: result.append(p[1])
+ >>> result = []
+ >>> for p in pairs: result.append(p[1])
If you find this more legible, you might prefer to use this instead of the final
list comprehension. However, it is almost twice as slow for long lists. Why?
different thing based on what class it is. For example, if you have a function
that does something::
- def search (obj):
+ def search(obj):
if isinstance(obj, Mailbox):
# ... code to search a mailbox
elif isinstance(obj, Document):
How do I create static class data and static class methods?
-----------------------------------------------------------
-Static data (in the sense of C++ or Java) is easy; static methods (again in the
-sense of C++ or Java) are not supported directly.
+Both static data and static methods (in the sense of C++ or Java) are supported
+in Python.
For static data, simply define a class attribute. To assign a new value to the
attribute, you have to explicitly use the class name in the assignment::
search path from ``c.__class__`` back to ``C``.
Caution: within a method of C, an assignment like ``self.count = 42`` creates a
-new and unrelated instance vrbl named "count" in ``self``'s own dict. Rebinding
-of a class-static data name must always specify the class whether inside a
-method or not::
+new and unrelated instance named "count" in ``self``'s own dict. Rebinding of a
+class-static data name must always specify the class whether inside a method or
+not::
C.count = 314
Point: x= 3.000 y= 4.000 hypot= 5.000
Point: x=14.000 y= 0.714 hypot=14.018
-The subclass shown above sets ``__slots__`` to an empty tuple. This keeps
+The subclass shown above sets ``__slots__`` to an empty tuple. This helps
keep memory requirements low by preventing the creation of instance dictionaries.
Subclassing is not useful for adding new, stored fields. Instead, simply
.. function:: set([iterable])
:noindex:
- Return a new set, optionally with elements are taken from *iterable*.
+ Return a new set, optionally with elements taken from *iterable*.
The set type is described in :ref:`types-set`.
For other containers see the built in :class:`dict`, :class:`list`, and
an option that must be supplied on the command-line; note that the phrase
"required option" is self-contradictory in English. :mod:`optparse` doesn't
prevent you from implementing required options, but doesn't give you much
- help at it either. See ``examples/required_1.py`` and
- ``examples/required_2.py`` in the :mod:`optparse` source distribution for two
- ways to implement required options with :mod:`optparse`.
+ help at it either.
For example, consider this hypothetical command-line::
module, and for high-level file and directory handling see the :mod:`shutil`
module.
-The design of all built-in operating system dependent modules of Python is such
-that as long as the same functionality is available, it uses the same interface;
-for example, the function ``os.stat(path)`` returns stat information about
-*path* in the same format (which happens to have originated with the POSIX
-interface).
+Notes on the availability of these functions:
-Extensions peculiar to a particular operating system are also available through
-the :mod:`os` module, but using them is of course a threat to portability!
+* The design of all built-in operating system dependent modules of Python is
+ such that as long as the same functionality is available, it uses the same
+ interface; for example, the function ``os.stat(path)`` returns stat
+ information about *path* in the same format (which happens to have originated
+ with the POSIX interface).
-.. note::
+* Extensions peculiar to a particular operating system are also available
+ through the :mod:`os` module, but using them is of course a threat to
+ portability.
+
+* An "Availability: Unix" note means that this function is commonly found on
+ Unix systems. It does not make any claims about its existence on a specific
+ operating system.
- If not separately noted, all functions that claim "Availability: Unix" are
- supported on Mac OS X, which builds on a Unix core.
+* If not separately noted, all functions that claim "Availability: Unix" are
+ supported on Mac OS X, which builds on a Unix core.
.. note::
.. data:: name
- The name of the operating system dependent module imported. The following names
- have currently been registered: ``'posix'``, ``'nt'``, ``'mac'``, ``'os2'``,
- ``'ce'``, ``'java'``, ``'riscos'``.
+ The name of the operating system dependent module imported. The following
+ names have currently been registered: ``'posix'``, ``'nt'``, ``'mac'``,
+ ``'os2'``, ``'ce'``, ``'java'``, ``'riscos'``.
.. _os-procinfo:
.. attribute:: class.__bases__
- The tuple of base classes of a class object. If there are no base classes, this
- will be an empty tuple.
+ The tuple of base classes of a class object.
.. attribute:: class.__name__
String Formatting
-----------------
-Starting in Python 2.6, the built-in str and unicode classes provide the ability
+.. versionadded:: 2.6
+
+The built-in str and unicode classes provide the ability
to do complex variable substitutions and value formatting via the
:meth:`str.format` method described in :pep:`3101`. The :class:`Formatter`
class in the :mod:`string` module allows you to create and customize your own
Template strings
----------------
+.. versionadded:: 2.4
+
Templates provide simpler string substitutions as described in :pep:`292`.
Instead of the normal ``%``\ -based substitutions, Templates support ``$``\
-based substitutions, using the following rules:
Any other appearance of ``$`` in the string will result in a :exc:`ValueError`
being raised.
-.. versionadded:: 2.4
-
The :mod:`string` module provides a :class:`Template` class that implements
these rules. The methods of :class:`Template` are:
.. note::
- This feature is only available if Python is built with universal newline support
- (the default). Also, the newlines attribute of the file objects :attr:`stdout`,
- :attr:`stdin` and :attr:`stderr` are not updated by the communicate() method.
+ This feature is only available if Python is built with universal newline
+ support (the default). Also, the newlines attribute of the file objects
+ :attr:`stdout`, :attr:`stdin` and :attr:`stderr` are not updated by the
+ communicate() method.
The *startupinfo* and *creationflags*, if given, will be passed to the
underlying CreateProcess() function. They can specify things such as appearance
The arguments are the same as for the Popen constructor. Example::
- retcode = call(["ls", "-l"])
+ >>> retcode = subprocess.call(["ls", "-l"])
.. function:: check_call(*popenargs, **kwargs)
The arguments are the same as for the Popen constructor. Example::
- check_call(["ls", "-l"])
+ >>> subprocess.check_call(["ls", "-l"])
+ 0
.. versionadded:: 2.5
def _cleanup():
for inst in _active[:]:
- if inst._internal_poll(_deadstate=sys.maxint) >= 0:
+ res = inst._internal_poll(_deadstate=sys.maxint)
+ if res is not None and res >= 0:
try:
_active.remove(inst)
except ValueError:
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