|An Introduction to Python|
by Guido van Rossum and Fred L. Drake, Jr.
Paperback (6"x9"), 124 pages
RRP £12.95 ($19.95)
Sales of this book support the Python Software Foundation! Get a printed copy>>>
- 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
.pyofiles so that executing the same file is faster the second time (recompilation from source to byte code can be avoided). 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 class.
The implicit conversion of an instance of one type to another during an
operation which involves two arguments of the same type. For example,
int(3.15)converts the floating point number to the integer
3, but in
3+4.5, each argument is of a different type (one int, one float), and both must be converted to the same type before they can be added or it will raise a
TypeError. Coercion between two operands can be performed with the
coercebuiltin function; thus,
3+4.5is equivalent to calling
operator.add(*coerce(3, 4.5))and results in
operator.add(3.0, 4.5).(7) Without coercion, all arguments of even compatible types would have to be normalized to the same value by the programmer, e.g.,
float(3)+4.5rather than just
- ‘complex number’
An extension of the familiar real number system in which all numbers are
expressed as a sum of a real part and an imaginary part. Imaginary numbers
are real multiples of the imaginary unit (the square root of
-1), often written
iin mathematics or
jin engineering. Python has builtin support for complex numbers, which are written with this latter notation; the imaginary part is written with a
3+1j. To get access to complex equivalents of the ‘math’ module, use ‘cmath’. Use of complex numbers is a fairly advanced mathematical feature. If you're not aware of a need for them, it's almost certain you can safely ignore them.
Any new-style object that defines the methods
__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
dictmuch 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.
Pythonic programming style that determines an object's type by inspection
of its method or attribute signature rather than by explicit relationship
to some type object ("If it looks like a duck and quacks like a duck, it
must be a duck.") By emphasizing interfaces rather than specific types,
well-designed code improves its flexibility by allowing polymorphic
substitution. Duck-typing avoids tests using
isinstance(). Instead, it typically employs
hasattr()tests or EAFP programming.
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
exceptstatements. 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
11/4currently evaluates to
2. If the module in which it is executed had enabled true division by executing:
from __future__ import divisionthe expression
11/4would evaluate to
2.75. By 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__ >>> __future__.division _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 values are returned to the caller using a
yieldstatement instead of a
returnstatement. Generator functions often contain one or more
yieldelements back to the caller. The function execution is stopped at the
yieldkeyword (returning the result) and is resumed there when the next element is requested by calling the
next()method of the returned iterator.
- ‘generator expression’
An expression that returns a generator. It looks like a normal expression
followed by a
forexpression defining a loop variable, range, and an optional
ifexpression. The combined expression generates values for an enclosing function:
>>> sum(i*i for i in range(10)) # sum of squares # 0, 1, 4, ... 81 285
- 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.
- An 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
11/4currently evaluates to
2in contrast to the
2.75returned 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, an 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 of the
/operator. See also __future__.
Python has an interactive interpreter which means that you can try out
things and immediately see their results. Just launch
pythonwith 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, as opposed to a compiled one. This means that the source files can be run directly without first creating an executable which is then run. Interpreted languages typically have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive.
A container object capable of returning its members one at a time.
Examples of iterables include all sequence types (such as
tuple) and some non-sequence types like
fileand objects of any classes you define with an
__getitem__()method. Iterables can be used in a
forloop and in many other places where a sequence is needed (
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
forstatement 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
next()method return successive items in the stream. When no more data is available a
StopIterationexception is raised instead. At this point, the iterator object is exhausted and any further calls to its
next()method just raise
StopIterationagain. 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
forloop. Attempting this with an iterator will just return the same exhausted iterator object used in the previous iteration pass, making it appear like an empty container.
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 by the presence of
- ‘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
ifclause is optional. If omitted, all elements in
A container object (such as
dict) that supports arbitrary key lookups using the special method
- 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.
Mutable objects can change their value but keep their
id(). See also immutable.
The place where a variable is stored. Namespaces are implemented as
dictionaries. There are the local, global and builtin namespaces
as well as nested namespaces in objects (in methods). Namespaces support
modularity by preventing naming conflicts. For instance, the
os.open()are distinguished by their namespaces. Namespaces also aid readability and maintainability by making it clear which module implements a function. For instance, writing
itertools.izip()makes it clear that those functions are implemented by the ‘random’ and ‘itertools’ modules respectively.
- ‘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 namespace.
- ‘new-style class’
Any class that inherits from
object. This includes all built-in types like
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, not required to be backward compatible, with telepathic interface.
- 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
__len__()special methods. Some built-in sequence types are
unicode. Note that
__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
import this" at the interactive prompt.
|ISBN 0954161769||An Introduction to Python||See the print edition|