Embrace Functional Programming and Iteration in Visual Studio .NET

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List comprehensions, generator expressions, generators, coroutines, and closures are much more efficient than most Python programmers realize For data processing especially, list comprehensions and generator expressions run significantly more quickly than code that manually iterates over data and carries out similar operationsThese operations also run much more quickly than legacy Python code that uses functions such as map() and filter() Generators can be used to write code that not only runs fast, but which makes efficient use of memory
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Use Decorators and Metaclasses
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Decorators and metaclasses are features that are used to modify functions and classes However, because they operate at the time of function or class definition, they can be used in ways that lead to improved performance especially if a program has many optional features that might be turned on or off 6, Functions and Functional Programming, has an example of using a decorator to enable logging of functions, but in a way that does not impact performance when logging is disabled
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The Python Library
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Built-In Functions Python Runtime Services Mathematics Data Structures, Algorithms, and Utilities String and Text Handling Python Database Access File and Directory Handling Operating System Services Threads and Concurrency Network Programming and Sockets Internet Application Programming Web Programming Internet Data Handling and Encoding Miscellaneous Library Modules
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Built-In Functions and Exceptions
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his chapter describes Python s built-in functions and exceptions Much of this material is covered less formally in earlier chapters of this bookThis chapter merely consolidates all this information into one section and expands upon some of the more subtle features of certain functions Also, Python 2 includes a number of built-in functions that are considered to be obsolete and which have been removed from Python 3Those functions are not documented here instead the focus is on modern functionality
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Built-in Functions and Types
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Certain types, functions, and variables are always available to the interpreter and can be used in any source module Although you don t need to perform any extra imports to access these functions, they are contained in a module _ _builtin_ _ in Python 2 and in a module builtins in Python 3Within other modules that you import, the variable _ _builtins_ _ is also bound to this module
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abs(x)
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Returns the absolute value of x
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all(s)
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Returns True if all of the values in the iterable s evaluate as True
any(s)
Returns True if any of the values in the iterable s evaluate as True
ascii(x)
Creates a printable representation of the object x just like the repr(), but only uses ASCII characters in the result Non-ASCII characters are turned into appropriate escape sequencesThis can be used to view Unicode strings in a terminal or shell that doesn t support Unicode Python 3 only
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12 Built-In Functions and Exceptions
basestring
This is an abstract data type that is the superclass of all strings in Python 2 (str and unicode) It is only used for type testing of strings For example, isinstance(s,basestring) returns True if s is either kind of string Python 2 only
bin(x)
Returns a string containing the binary representation of the integer x
bool([x])
Type representing Boolean values True and False If used to convert x, it returns True if x evaluates to true using the usual truth-testing semantics (that is, nonzero number, non-empty list, and so on) Otherwise, False is returned False is also the default value returned if bool() is called without any argumentsThe bool class inherits from int so the Boolean values True and False can be used as integers with values 1 and 0 in mathematical calculations
bytearray([x])
A type representing a mutable array of bytesWhen creating an instance, x may be an iterable sequence of integers in the range 0 to 255, an 8-bit string or bytes literal, or an integer that specifies the size of the byte array (in which case every entry will be initialized to 0) A bytearray object a looks like an array of integers If you perform a lookup such as a[i], you will get an integer value representing the byte value at index i Assignments such as a[i] = v also require v to be an integer byte value However, a bytearray also provides all of the operations normally associated with strings (that is, slicing, find(), split(), replace(), and so on)When using these string operations, you should be careful to preface all string literals with b in order to indicate that you re working with bytes For example, if you wanted to split a byte array a into fields using a comma character separator, you would use asplit(b',') not asplit(',')The result of these operations is always new bytearray objects, not stringsTo turn a bytearray a into a string, use the adecode(encoding) method An encoding of 'latin-1' will directly turn a bytearray of 8-bit characters into a string without any modification of the underlying character values
bytearray(s ,encoding)
An alternative calling convention for creating a bytearray instance from characters in a string s where encoding specifies the character encoding to use in the conversion
bytes([x])
A type representing an immutable array of bytes In Python 2, this is an alias for str() which creates a standard 8-bit string of characters In Python 3, bytes is a completely separate type that is an immutable version of the bytearray type described earlier In that case, the argument x has the same interpretation and can be used in the same manner One portability caution is that even though bytes is defined in Python 2, the resulting object does not behave consistently with Python 3 For example, if a is an instance created by bytes(), then a[i] returns a character string in Python 2, but returns an integer in Python 3