Generator Expressions in .NET framework

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Generator Expressions
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A generator expression is an object that carries out the same computation as a list comprehension, but which iteratively produces the resultThe syntax is the same as for list comprehensions except that you use parentheses instead of square brackets Here s an example:
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(expression for item1 in iterable1 if condition1 for item2 in iterable2 if condition2 for itemN in iterableN if conditionN)
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6 Functions and Functional Programming
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Unlike a list comprehension, a generator expression does not actually create a list or immediately evaluate the expression inside the parentheses Instead, it creates a generator object that produces the values on demand via iteration Here s an example:
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>>> a = [1, 2, 3, 4] >>> b = (10*i for i in a) >>> b <generator object at 0x590a8> >>> bnext() 10 >>> bnext() 20
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The difference between list and generator expressions is important, but subtleWith a list comprehension, Python actually creates a list that contains the resulting dataWith a generator expression, Python creates a generator that merely knows how to produce data on demand In certain applications, this can greatly improve performance and memory use Here s an example:
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# Read a file f = open("datatxt") lines = (tstrip() for t in f) # # # comments = (t for t in lines if t[0] == '#') # for c in comments: print(c) Open a file Read lines, strip trailing/leading whitespace All comments
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In this example, the generator expression that extracts lines and strips whitespace does not actually read the entire file into memoryThe same is true of the expression that extracts comments Instead, the lines of the file are actually read when the program starts iterating in the for loop that follows During this iteration, the lines of the file are produced upon demand and filtered accordingly In fact, at no time will the entire file be loaded into memory during this processTherefore, this would be a highly efficient way to extract comments from a gigabyte-sized Python source file Unlike a list comprehension, a generator expression does not create an object that works like a sequence It can t be indexed, and none of the usual list operations will work (for example, append()) However, a generator expression can be converted into a list using the built-in list() function:
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clist = list(comments)
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Declarative Programming
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List comprehensions and generator expressions are strongly tied to operations found in declarative languages In fact, the origin of these features is loosely derived from ideas in mathematical set theory For example, when you write a statement such as [x*x for x in a if x > 0], it s somewhat similar to specifying a set such as { x2 | x a, x > 0 } Instead of writing programs that manually iterate over data, you can use these declarative features to structure programs as a series of computations that simply operate on all of the data all at once For example, suppose you had a file portfoliotxt containing stock portfolio data like this:
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Here is a declarative-style program that calculates the total cost by summing up the second column multiplied by the third column:
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lines = open("portfoliotxt") fields = (linesplit() for line in lines) print(sum(float(f[1]) * float(f[2]) for f in fields))
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In this program, we really aren t concerned with the mechanics of looping line-by-line over the file Instead, we just declare a sequence of calculations to perform on all of the data Not only does this approach result in highly compact code, but it also tends to run faster than this more traditional version:
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total = 0 for line in open("portfoliotxt"): fields = linesplit() total += float(fields[1]) * float(fields[2]) print(total)
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The declarative programming style is somewhat tied to the kinds of operations a programmer might perform in a UNIX shell For instance, the preceding example using generator expressions is similar to the following one-line awk command:
% awk '{ total += $2 * $3} END { print total }' portfoliotxt 446712 %
The declarative style of list comprehensions and generator expressions can also be used to mimic the behavior of SQL select statements, commonly used when processing databases For example, consider these examples that work on data that has been read in a list of dictionaries:
fields = (linesplit() for line in open("portfoliotxt")) portfolio = [ {'name' : f[0], 'shares' : int(f[1]), 'price' : float(f[2]) } for f in fields] # Some queries msft = [s for s in portfolio if s['name'] == 'MSFT'] large_holdings = [s for s in portfolio if s['shares']*s['price'] >= 10000]
In fact, if you are using a module related to database access (see 17), you can often use list comprehensions and database queries together all at once For example:
sum(shares*cost for shares,cost in cursorexecute("select shares, cost from portfolio") if shares*cost >= 10000)