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 * '''generator''' - A function that returns an '''iterator'''. It looks like a normal function except that the keyword `yield` is used instead of `return`. Generator functions often contain one or more `for` or `while` loops that `yield` elements. The function execution is stopped at the `yield` keyword (returning the result) and its resumed there when the next element is requested by calling the `next()` method of the returned iterator.  * '''generator function''' - A function that returns a '''generator''' '''iterator'''. Its definition looks like a normal function definition except that it uses the keyword `yield`. Generator functions often contain one or more `for` or `while` loops that `yield` elements. The function execution is stopped at the `yield` keyword (returning the result) and its resumed there when the next element is requested (e.g., by the builtin function `next()`). For details see PEP 0255 and PEP 0342.

 * '''generator''' - The common name for a generator '''iterator'''. The type of iterator returned by a generator function or a generator expression.

Python Glossary

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A symbol glossary is also available.


A-M

  • Attribute - Values associated with an individual object. Attributes are accessed using the 'dot syntax': a.x means fetch the x attribute from the 'a' object.

  • BDFL - Acronym for "Benevolent Dictator For Life" - a.k.a. Guido van Rossum, Python's primary creator, figurehead and decision-maker.

  • byte code - The internal representation of a Python program in the interpreter. The byte code is also cached in .pyc and .pyo files so that executing the same file is faster the second time (the step of 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.

  • class - A template for creating user-defined objects. Class definitions normally contain method definitions that operate on instances of the class.

  • coercion - 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 implicitly invoked with the coerce builtin function; thus, 3 + 4.5 is equivalent to operator.add(*coerce(3, 4.5)) and results in operator.add(3.0, 4.5) which is of course 7.5. Without coercion, all arguments of even compatible types would have to be normalized to the same value by the programmer, e.g., float(3) + 4.5 rather than just 3 + 4.5.

  • 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 imagary part. Imaginary numbers are real multiples of the imaginary unit, often written i in mathematics or j in engineering. Python has builtin support for complex numbers, which are written with this latter notation; the imaginary part is written with a j suffix, e.g., 3+1j. To get access to complex equivalents of the math module, use cmath. Use of complex numbers is a fairy advanced mathematical feature; if you're not aware of a need for complex numbers, it's almost certain you can safely ignore them.

  • conversion - The invocation of a well-defined mechanism from of transforming an instance of one type of object to an instance of another; for example, int('3') will convert a string ('3') to an int (3).

  • decorator - A function that modifies another function or method. Its return value is typically a callable object, possibly the original function, but most often another function that modifies the original function's behavior in some fashion.

  • descriptor - Any 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.

  • dictionary - A built-in Python data type composed of arbitrary keys and values; sometimes called a "hash" or a "hash map" in other languages, although this is technically a misnomer (hashing is one way to implement an associative array but not the only way). 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. Examples: d = {'A':65, 'B':66}, d = dict([('A', 65), ('B', 66)]), d['C'] = 67

  • docstring - A string that appears as the lexically first expression in a module, class definition or function/method definition is assigned as the __doc__ attribute of the object where it is available to documentation tools or the help() builtin function.

  • duck typing - From the "If it walks, talks, and looks like a duck, then its a duck" principle. Python uses duck typing in that if an object of some user-defined type exhibits all of the expected interfaces of some type (say the string type), then the object can be treated as if it really were of that type.

  • dynamic typing - A style of typing of variables where the type of objects to which variables are assigned can be changed merely by reassigning the variables. Python is dynamically typed. Thus, unlike as in a statically typed language such as C, a variable can first be assigned a string, then an integer, and later a list, just by making the appropriate assignment statements. This frees the programmer from managing many details, but does come at a performance cost.

  • EAFP - Acronym for the saying it's "Easier to Ask for Forgiveness than Permission". This common Python coding style assumes the existance 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 statments. The technique contrasts with the LBYL style that is common in many other languages such as C.

  • EIBTI - Acronym for "Explicit Is Better Than Implicit".

  • first-class object A first class object in a programming language is a language object that can be created dynamically, stored in a variable, passed as a parameter to a function and returned as a result by a function (from http://www.cs.unm.edu/~crowley/phdExams/1997xfall/pl.html). In Python, practically all objects are first-class, including functions, types, and classes.

  • function - A block of code that is invoked by a "calling" program, best used to provide an autonomous service or calculation.

  • generator function - A function that returns a generator iterator. Its definition looks like a normal function definition except that it uses the keyword yield. Generator functions often contain one or more for or while loops that yield elements. The function execution is stopped at the yield keyword (returning the result) and its resumed there when the next element is requested (e.g., by the builtin function next()). For details see PEP 0255 and PEP 0342.

  • generator - The common name for a generator iterator. The type of iterator returned by a generator function or a generator expression.

  • global interpreter lock or GIL - 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. See GlobalInterpreterLock.

  • greedy regular expressions - Regular expressions which match the longest string possible. The *, + and ? operators are all greedy. Their counterparts *?, +? and ?? are all non-greedy (match the shortest string possible).

  • hash - A number used to correspond to objects, usually used for 'hashing' keys for storage in a hash table. Hashing in Python is done with the builtin hash function

  • hash table - An object that maps more-or-less arbitrary keys to values. Dictionaries are the most visible and widely used objects that exhibit this behavior.

  • hashable - An object is hashable if it is immutable (ints, floats, tuples, strings, etc) or user-defined classes that define a __hash__ method.

  • id - id is a built-in function which returns a number identifying the object, referred to as the object's id. It will be unique during the lifetime of the object, but is very often reused after the object is deleted.

  • IDLE - an Integrated Development Environment for Python. IDLE is a basic editor and intepreter environment that ships with the standard distribution of Python. Good for beginners and those on a budget, it also serves as clear example code for those wanting to implement a moderately sophisticated, multi-platform GUI application.

  • immutable - An object with fixed value. Immutable objects include numbers, strings and tuples. 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 such as the keys of a dictionary.

  • integer division - Mathematical division discarding any remainder, for example 3 / 2 returns 1, in contrast to the 1.5 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, 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.

  • interactive - Python has an interactive interpreter which means that you can try out things and directly see its result, just launch python with no arguments. A very powerful way to test out new ideas, inspect libraries (remember x.__doc__ and help(x)) and improve programming skills.

  • interpreted - Python is an interpreted language (like Perl), as opposed to a compiled one (like C). This means that the source files can be run directly without first creating an executable which is then run. Interpreted languages typicaly have a shorter development/debug cycle than compiled ones, though their programs generally also run more slowly. See also interactive.

  • iterable - A container object capable of returning its members one at a time. Examples of iterables include all sequence types (list, str, tuple, etc.) 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, generator and reiterable.

  • iterator - 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 (e.g. 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 and on, making it appear like an empty container.

  • LBYL - 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.

  • list - A built-in Python datatype, which is a mutable sorted sequence of values. Note that only sequence itself is mutable; it can contain immutable values like strings and numbers. Any Python first-class object can be placed in a tuple as a value.

  • list comprehension - A neat syntactical way to process 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 part is optional' all elements are processed when it is omitted.

  • mapping - A container object (such as dict) that supports arbitrary key lookups using __getitem__.

  • metaclass - 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 - Mutable objects can change their value but keep their id(). See also immutable.

N-Z

  • namespace - The place where a variable is stored in a Python program's memory. Namespaces are implemented as a dictionary. There are the local, global and builtins namespaces and the nested namespaces in objects (in methods). Namespaces support modularity by preventing naming conflicts. For instance, __builtins__.open() and os.open() are distinguished by their namespaces. Namespaces also aid readability and maintainabilty by making it clear which modules implement a function. For instance, writing random.seed() and itertools.izip() will make 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 list and dict. Only new style classes can use Python's newer, versatile features like __slots__, descriptors, properties, __getattribute__, class methods, and static methods.

  • object - Any data with state (attributes or value) and defined behavior (methods).

  • object oriented - Programming typified by a data-centered (as opposed to a function-centered) approach to program design.

  • old-style class - Any class that does not inherit (directly or indirectly) from object.

  • pie syntax - A syntax using '@' for decorators that was committed to an alpha version of Python 2.4. So called because the '@' vaguely resembles a pie and the commital came on the heels of the Pie-thon at an open source conference in 2004.

  • Python 3000 - A mythical Python release, allowed to be backward incompatible, with telepathic interface.

  • regular expression - A formula for matching strings that follow some pattern. Regular expressions are made up of normal characters and metacharacters. In the simplest case, a regular expression looks like a standard search string. For example, the regular expression "testing" contains no metacharacters. It will match "testing" and "123testing" but it will not match "Testing". Metacharacters match some expressions like '.' metacharacter match any single character in a search string.

  • reiterable - An iterable object which can be iterated over multiple times. Reiterables must not return themselves when used as an argument to iter().

  • sequence - An iterable that also supports random access using __getitem__ and len. Some builtin sequence types are list, str, tuple, and unicode. Note that dict also supports these two operations but is considered a mapping rather than a sequence because the lookups use arbitrary keys rather than consecutive numbers and it should be considered unsorted.

  • __slots__ - 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.

  • static typing - A style of typing of variables common to many programming languages (such as C) where a variable, having been assigned an object of a given type, cannot be assigned objects of different types subsequently.

  • string - One of the basic types in Python that store text. In Python 2.X strings store text as a 'string of bytes', and so the string type can also be used to store binary data. Also see Unicode.

  • triple-quoted string - A string which is bound by three instances of either the double quote mark (") or the single quote mark ('). For instance This's such a string. They are useful for a number of reasons: they allow you to include both single and double quotes within a string quite easily, they can span multiple lines without the use of the continuation character, and they are used to create docstrings.

  • tuple - (pronounced TUH-pul or TOO-pul) A built-in Python datatype, which is an immutable ordered sequence of values. Note that only the sequence itself is immutable. If it contains a mutable value such as a dictionary, that value's content may be changed (e.g. adding new key/value pair). Any Python first-class object can be placed in a tuple as a value.

  • type - A "sort" or "category" of data that can be represented by a programming language. Types differ in their properties (such as mutability and immutability), the methods and functions applicable to them, and in their representations. Python includes, among others, the string, integer, long, floating point, list, tuple, and dictionary types.

  • unicode - The unicode type is the companion to the string type. They are used to store text with characters represented as Unicode code points.

  • whitespace - The unconventional use of space characters (' ') to control the flow of a program. Instead of a loosely-enforced ideal, this is an integral part of Python syntax. It's a tradeoff between readability and flexibility in favor of the former.

  • Zen of Python - listing of Python design principles and philosophies that are helpful in understanding and using the language effectively. The listing can be found by typing "import this" at the interactive prompt.

misc

  • >>> - The typical Python prompt of the interactive shell. Often seen for code examples that can be tried right away in the interpreter. Not to be confused with ">" symbols used for the indentation of cited material on email lists.


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