By Micha Gorelick, Ian Ozsvald

Your Python code might run accurately, yet you would like it to run speedier. via exploring the elemental thought in the back of layout offerings, this useful consultant is helping you achieve a deeper knowing of Python's implementation. You'll the best way to find functionality bottlenecks and considerably accelerate your code in high-data-volume programs.

How are you able to benefit from multi-core architectures or clusters? Or construct a procedure which can scale up and down with out wasting reliability? skilled Python programmers will research concrete strategies to those and different matters, in addition to warfare tales from businesses that use excessive functionality Python for social media analytics, productionized desktop studying, and different situations.

-Get a greater grab of numpy, Cython, and profilers
-Learn how Python abstracts the underlying desktop architecture
-Use profiling to discover bottlenecks in CPU time and reminiscence usage
-Write effective courses via picking out applicable information structures
-Speed up matrix and vector computations
-Use instruments to bring together Python all the way down to desktop code
-Manage a number of I/O and computational operations concurrently
-Convert multiprocessing code to run on a neighborhood or distant cluster
-Solve huge difficulties whereas utilizing much less RAM

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Binary operations in operatorPrecedence can return multiple operation/operands if multiple operations of the same precedence level occur together. For instance, a binary operation for addition will parse "a+b+c" as [ 'a', '+', 'b', '+', 'c' ], not Getting Started with Pyparsing 54 [[ 'a', '+', 'b' ], '+', 'c' ], so BinaryOperation needs to be able to recognize and process a chain of like operations, not just a simple a op b operation. The Python [0::2] slicing notation indicates that the operands will be taken from the list of tokens beginning with the 0th element, and then stepping by 2 until the end of the list.

Following our standard practice, we start by defining the BNF for an S-expression: alphaword ::= alphas+ integer ::= nums+ sexp ::= alphaword | integer | '(' sexp* ')' Getting Started with Pyparsing 36 The first two expressions are nothing new, and you can see how we would use pyparsing's Word class to define them: alphaword = Word(alphas) integer = Word(nums) But sexp is more difficult. Our dilemma is that the definition for sexp includes sexp itself, but to define sexp we need to refer to sexp!

In this version of the BNF, AND expressions will be parsed ahead of OR expressions, and NOT expressions will be parsed ahead of AND expressions. searchExpr ::= searchAnd [ OR searchAnd ]... searchAnd ::= searchTerm [ AND searchTerm ]... setParseAction( removeQuotes ) | \ Group( LPAREN + searchExpr + RPAREN ) ) searchAnd = Group( searchTerm + ZeroOrMore( and_ + searchTerm ) ) searchExpr << Group( searchAnd + ZeroOrMore( or_ + searchAnd ) ) Using this grammar, our parsing examples evaluate as: wood and blue or red [[['wood', 'and', 'blue'], 'or', ['red']]] wood and (blue or red) [[['wood', 'and', [[['blue'], 'or', ['red']]]]]] (steel or iron) and "lime green" [[[[[['steel'], 'or', ['iron']]], 'and', 'lime green']]] not steel or iron and "lime green" [[['not', 'steel'], 'or', ['iron', 'and', 'lime green']]] not(steel or iron) and "lime green" [[['not', [[['steel'], 'or', ['iron']]], 'and', 'lime green']]] These results can now be evaluated using a depth-first recursive method, and the AND, OR, and NOT expressions will be evaluated in the proper precedence.

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