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- The typical Python prompt of the interactive shell. Often seen for code examples
that can be tried right away in the interpreter.
- The typical Python prompt of the interactive shell when entering code for an
indented code block.
- Benevolent Dictator For Life, a.k.a. Guido van Rossum, Python's creator.
- byte code
- The internal representation of a Python program in the interpreter. The byte code is
also cached in the
.pyo files so that executing the
same file is faster the second time (compilation from source to byte code can be
saved). This ``intermediate language'' is said to run on a ``virtual machine'' that
calls the subroutines corresponding to each bytecode.
- classic class
- Any class which does not inherit from object. See new-style
- Converting data from one type to another. For example,
coerces the floating point number to the integer,
3. Most mathematical
operations have rules for coercing their arguments to a common type. For instance,
3+4.5, causes the integer
3 to be coerced to be a
3.0 before adding to
4.5 resulting in the float
- Any new-style object that defines the methods __get__(),
__set__(), or __delete__(). When a
class attribute is a descriptor, its special binding behavior is triggered upon
attribute lookup. Normally, writing a.b looks up the object b in
the class dictionary for a, but if b is a descriptor, the
defined method gets called. Understanding descriptors is a key to a deep understanding
of Python because they are the basis for many features including functions, methods,
properties, class methods, static methods, and reference to super classes.
- An associative array, where arbitrary keys are mapped to values. The use of dict much resembles that for list, but the
keys can be any object with a __hash__() function, not just
integers starting from zero. Called a hash in Perl.
- Easier to ask for forgiveness than permission. This common Python coding style
assumes the existence of valid keys or attributes and catches exceptions if the
assumption proves false. This clean and fast style is characterized by the presence of
many try and except statements. The
technique contrasts with the LBYL style that is common in many other languages
such as C.
- A pseudo module which programmers can use to enable new language features which are
not compatible with the current interpreter. For example, the expression
currently evaluates to
2. If the module in which it is executed had
enabled true division by executing:
from __future__ import division
11/4 would evaluate to
2.75. By actually
importing the __future__
module and evaluating its variables, you can see when a new feature was first added to
the language and when it will become the default:
>>> import __future__
_Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 8192)
- A function that returns an iterator. It looks like a normal function except that the
yield keyword is used instead of return.
Generator functions often contain one or more for or while loops that yield elements back to
the caller. The function execution is stopped at the yield
keyword (returning the result) and is resumed there when the next element is requested
by calling the next() method of the returned iterator.
- See global interpreter lock.
- global interpreter lock
- The lock used by Python threads to assure that only one thread can be run at a time.
This simplifies Python by assuring that no two processes can access the same memory at
the same time. Locking the entire interpreter makes it easier for the interpreter to
be multi-threaded, at the expense of some parallelism on multi-processor machines.
Efforts have been made in the past to create a ``free-threaded'' interpreter (one
which locks shared data at a much finer granularity), but performance suffered in the
common single-processor case.
- An Integrated Development Environment for Python. IDLE is a basic editor and
interpreter environment that ships with the standard distribution of Python. Good for
beginners, it also serves as clear example code for those wanting to implement a
moderately sophisticated, multi-platform GUI application.
- A object with fixed value. Immutable objects are numbers, strings or tuples (and
more). Such an object cannot be altered. A new object has to be created if a different
value has to be stored. They play an important role in places where a constant hash
value is needed. For example as a key in a dictionary.
- integer division
- Mathematical division discarding any remainder. For example, the expression
currently evaluates to
2 in contrast to the
2.75 returned by
float division. Also called floor division. When dividing two integers the
outcome will always be another integer (having the floor function applied to it).
However, if one of the operands is another numeric type (such as a float),
the result will be coerced (see coercion) to a common type. For example, a
integer divided by a float will result in a float value, possibly with a decimal
fraction. Integer division can be forced by using the
// operator instead
/ operator. See also __future__.
- Python has an interactive interpreter which means that you can try out things and
directly see its result. Just launch
python with no arguments (possibly
by selecting it from your computer's main menu). It is a very powerful way to test out
new ideas or inspect modules and packages (remember
- Python is an interpreted language, opposed to a compiled one. This means that the
source files can be run right away without first making an executable which is then
run. Interpreted languages typically have a shorter development/debug cycle than
compiled ones. See also interactive.
- A container object capable of returning its members one at a time. Examples of
iterables include all sequence types (such as list, str, and tuple) and some non-sequence types
like dict and file and objects of any
classes you define with an __iter__() or __getitem__()
method. Iterables can be used in a for loop and in many other
places where a sequence is needed (zip(), map(), ...). When an iterable object is passed as an argument to
the builtin function iter(), it returns an iterator for the
object. This iterator is good for one pass over the set of values. When using
iterables, it is usually not necessary to call iter() or
deal with iterator objects yourself. The
for statement does that
automatically for you, creating a temporary unnamed variable to hold the iterator for
the duration of the loop. See also iterator, sequence, and generator.
- An object representing a stream of data. Repeated calls to the iterator's next() method return successive items in the stream. When no more
data is available a StopIteration exception is raised
instead. At this point, the iterator object is exhausted and any further calls to its next() method just raise StopIteration
again. Iterators are required to have an __iter__() method
that returns the iterator object itself so every iterator is also iterable and may be
used in most places where other iterables are accepted. One notable exception is code
that attempts multiple iteration passes. A container object (such as a list) produces a fresh new iterator each time you pass it to the iter() function or use it in a for
loop. Attempting this with an iterator will just return the same exhausted iterator
object from the second iteration pass, making it appear like an empty container.
- list comprehension
- A compact way to process all or a subset of elements in a sequence and return a list
with the results.
result = ["0x%02x" % x for x in range(256) if x % 2
== 0] generates a list of strings containing hex numbers (0x..) that are even
and in the range from 0 to 255. The if clause is optional. If
omitted, all elements in
range(256) are processed in that case.
- A container object (such as dict) that supports arbitrary key
lookups using the special method __getitem__().
- The class of a class. Class definitions create a class name, a class dictionary, and
a list of base classes. The metaclass is responsible for taking those three arguments
and creating the class. Most object oriented programming languages provide a default
implementation. What makes Python special is that it is possible to create custom
metaclasses. Most users never need this tool, but when the need arises, metaclasses
can provide powerful, elegant solutions. They have been used for logging attribute
access, adding thread-safety, tracking object creation, implementing singletons, and
many other tasks.
- Look before you leap. This coding style explicitly tests for pre-conditions before
making calls or lookups. This style contrasts with the EAFP approach and is
characterized the presence of many if statements.
- Mutable objects can change their value but keep their id().
See also immutable.
- The place where a variable is stored. Namespaces are implemented as dictionary.
There is the local, global and builtins namespace and the nested namespaces in objects
(in methods). Namespaces support modularity by preventing naming conflicts. For
instance, the functions __builtin__.open() and os.open() are distinguished by their namespaces. Namespaces also
aid readability and maintainability by making it clear which modules implement a
function. For instance, writing random.seed() or itertools.izip() makes it clear that those functions are
implemented by the random and itertools modules
- nested scope
- The ability to refer to a variable in an enclosing definition. For instance, a
function defined inside another function can refer to variables in the outer function.
Note that nested scopes work only for reference and not for assignment which will
always write to the innermost scope. In contrast, local variables both read and write
in the innermost scope. Likewise, global variables read and write to the global
- new-style class
- Any class that inherits from object. This includes all
built-in types like list and dict. Only
new-style classes can use Python's newer, versatile features like __slots__,
descriptors, properties, __getattribute__(), class methods,
and static methods.
- A mythical python release, allowed not to be backward compatible, with telepathic
- A declaration inside a new-style class that saves memory by pre-declaring
space for instance attributes and eliminating instance dictionaries. Though popular,
the technique is somewhat tricky to get right and is best reserved for rare cases
where there are large numbers of instances in a memory critical application.
- An iterable which supports efficient element access using integer indices via
the __getitem__() and __len__()
special methods. Some built-in sequence types are list, str, tuple, and unicode.
Note that dict also supports __getitem__()
and __len__(), but is considered a mapping rather than a
sequence because the lookups use arbitrary immutable keys rather than integers.
- Zen of Python
- Listing of Python design principles and philosophies that are helpful in
understanding and using the language. The listing can be found by typing ``
this'' at the interactive prompt.
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