Python Optimization Generators | Lecture Note - Edubirdie

University:
Boston University
Course:
MET CS 201 | Introduction to Programming
Academic year:

2021
Views:

86

Pages:

5
Author:

kevinxcho6mju

Outline       Introduction Profiling Data Structures Syntax Generators Accelerators Generators  A Python generator is a function that behaves like an iterator.  An iterator returns every element of a collection.  Example: a for loop iterates over the elements of a Python list.  Generators can be used to create sequences of values one value at a time. range()  The range() function in Python is a generator. for x in range(1,4): print(x) # Output: # 1 # 2 # 3  Try: print(range(4))  It won’t print out any numbers!  range() returns a generator that can be iterated over to produce a sequence of integers. List comprehensions as generators  List comprehensions are handy ways to create and manipulate lists.  Intermediate lists or ones that are created and discarded still need to allocate memory.  Generator syntax: use ( ) instead of [ ]  No lists are created…little additional memory. strs = ['call','me','ishmael'] # uppercase all the strings caps = [L.upper() for L in strs] # Print them out for c in caps: print(caps) gcaps = (L.upper() for L in strs) for g in gcaps: print(g) Generator Functions  A generator function is written with the yield keyword.  It will generate values until it reaches a return statement or throws a StopIteration exception.  Every yield will return a value but the function keeps running until it returns. import random def triple_ran(N): ''' Return N tuples of 3 random numbers.''' while N > 0: vals=(random.random(), random.random(), random.random()) N -= 1 yield vals return # optional for triplet in triple_ran(4): print('%1.3f %1.3f %1.3f' % # # # # 0.070 0.668 0.384 0.633 0.363 0.705 0.817 0.303 0.821 0.235 0.071 0.591 triplet)

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