python generator comprehension

One: What is an iterator protocol1. The iterator protocol means that the object must provide a next method that either returns the next item in the iterator, or causes a Stoplteration exception to terminate the protocol (only backward cannot go forward)2. An iterative object: An object that implements an iterator protocol (how to: Define an _iter_ () method within an object)3. The Protocol is a convention that iterates over an object, implements an iterator protocol, and Python's internal tools

back, and cannot be accessed again4, the elements that have been accessed, and those that are accessed using subscripts are not supported. When all of the elements have been accessed, if you need to revisit the elements, you must recreate the generator object, and the enumerate, filter, map, zip, and other iterator objects have the same characteristics.　　　　#1. Creating Builder Objectsg = ((i+2) **2 forIinchRange (10))Printg##2. Converting a

loop catches the stopiteration exception to terminate the iteration6. Generator: Can be understood as a data type, this data type automatically implements the iterator protocol (other data types need to call their own built-in __iter__ method), so the generator is an iterative object7. Can be understood as a data type, this data type automatically implements the iterator protocol (other data types need to

, 8, 12]]This in turn writes the same as follows:transposed = []for i in Range (4): transposed_row = [] for row in matrix: transposed_row.append (Row[i]) Transposed.append (transposed_row) Print transposed# [[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]]There is also a python built-in function zip (), also can be implemented as above functionZip (*matrix) # [(1, 5, 9), (2, 6, 10), (3, 7, 11), (4, 8, 12)]2.del statement (The DEL stateme

Tornado 4.0 has been released for a long time, and the new version has widely applied the Future feature. We have now upgraded the Tornado to the latest version, and have also used a lot of the coprocessor features. A long time no update blog, today is a simple introduction to the implementation of the Tornado, the Tornado is based on the Python generator implementation, so first of all to review the

this example:import disdef foo(): bar()def bar(): pass print(dis.dis(foo))We can use the DIS module to view the Python program's bytecode, the following is the byte code of Function foo ():0 LOAD_GLOBAL 0 (bar)2 CALL_FUNCTION 04 POP_TOP6 LOAD_CONST 0 (None)8 RETURN_VALUEThe Foo function loads the bar into the stack and invokes it, pops the return value from the stack, loads and returns none, and when Pye

mistake.3. Using the generator to write Fibonacci sequencesWe already know how to create a generator, and then we'll create a Fibonacci sequence by creating a second method of the generator.First we first understand the Fibonacci sequence, the Fibonacci sequence defines the first number as 0, the second is 1, and then each number is the sum of the first two numbers, which is simply similar to the 0,1,1,2,3

"", Line 1,inchMyg.next () stopiteration because the For Loop has next () calls and handles to the stopiteration, you can iterate through a generator with a for loop iteration. >>> forEachiteminchSimplegen ():PrintEachitem---punch!Enhanced generator FeaturesInstead of using next () to get the next generated value, the user can send the value back to the generator

generator. __next__ () - Print(Next (ML2), end=" ,") - Print(Next (ML2), end=" ,") the Print(Next (ML2))The result of the above code execution is as follows:　　 3.2 for in mode traversal, code as follows, 1 for inch if i% 2 = = 0)2print("For in"iterates over theresult; ", end="")3 for in ml:4 Print(i, end="")The result of the above code execution is:　　　　4 Closing the generator　　command :

The simple generator introduced in Python 2.2 can be used to simplify the state machine and emulate the collaboration program. David describes an abstract pattern of state machine processing in an earlier section of the "Lovely Python" column. Since then, the introduction of simple generators has provided some more natural examples of describing machines. A colla

. These simple examples should give you a little idea of how the generator works. In addition to next () to get the next generated value, the user can return the value to the generator [Send ()], throw an exception in the generator, and require the generator to exit [Close ()] Here is a simple example that shows these

Range (1,5): print (g.__next__ ()) 3.def func (argv): For I in range (1,argv ): yield ' Knight class%s '%ifor j in Range (1,5): Print (func (5). __next__ ())Attention:def func (argv): For I in range (1,ARGV): yield ' Knight class%s period '%ig = func (Ten) for J in Range (1,5): print (g.__next__ ()) for J in Range (1,6): print (g.__next__ ())B. Generator expressiong = [I for I in range (100)] is a list-derivedg = (I-I

# !/usr/bin/python3 def mygenerator (): value=yield 1 yield value return Donegen=mygenerator ()print(next (gen))print(Gen.send ( " I am Value "))There is a method send inside the generator that can pass in a value again.The above sentence may not understand, but it does not matter, we first look at the code,#!/usr/bin/python3def MyGenerator(): value=yield 1 yield value return donegen=MyGenerator()print(next(gen))print(gen.send("I

List-generated--A list can be generated quickly , and another listcan be deduced from a list , and the code is simple:>>> [x * x for x in range (1, 11)][1, 4, 9, 16, 25, 36, 49, 64, 81, 100]>>> [x * x for x in range (1, one) if x% 2 = = 0][4, 16, 36, 64, 100]>>> [m + N for M in ' ABC ' for n in ' XYZ '][' AX ', ' AY ', ' AZ ', ' BX ', ' by ', ' BZ ', ' CX ', ' CY ', ' CZ ']Generator (Generator)--wit

Python learning diary: day13 ------ iterator and generator, pythonday13 ------I. Import 1. the dir function print (dir ([]) tells me that all the methods in this list contain double underscores. 2. The returned value after a list executes _ iter _ () is an iterator 3, the number of _ length_hint _ elements 4, and the value of _ setstate _ from the specified position is 5, []. _ iter _ () iterator ----> _ ne

exactly the same:>>> for N in Fab (5): ... Print N ... 1 1 2) 3 5　　Simply put, the function of yield is to turn a function into a generator, the function with yield is no longer a normal function, the Python interpreter treats it as a generator, and the Call to FAB (5) does not execute the FAB function, but instead returns a Iterab Le Object! When the F

Programming Python, 3rd Edition translation See the latest version: Http://wiki.woodpecker.org.cn/moin/PP3eD 22.6. The SWIG Integration Code generator 22.6. Integrated code generator Swig But don ' t does that. As can probably tell, manual coding of the C extensions can become fairly involved (this are almost inevitable in C LANGUAG e work). I ' ve introduc

= (yield)6 ifPatterninchLine :7 #if the received data meets the requirements,8 #is sent to the next coroutine for processing9Target.send (line)As you can see from the code, GREP_FILTER_CO () has a dead loop, hangs in the loop waiting to receive data, once the data is received, if there is a pattern in the data, the received data is sent to target, so that target will process the data next, Then wait for the data to be received and hang again.Similarly, now combin

Welcome to personal website: www.comingnext.cn1. About Python built-in sequence typesA. Differentiate the container sequence by the ability to store different types of data:list, tuple, and collections.deque these sequences can hold different types of dataFlat sequence:STR, Bytes, ByteArray, Memoryview, and array.array, such sequences can only hold one type.B. Classify variable sequences according to whether they can be modified:List, ByteArray, Array

Understand:We first use a popular analogy to understand the next generator, to the restaurant to eat, we ordered the menu, the chef is not a one-time dish are all up, but a dish. The chef here is a generator object. The builder is a Python feature, and Python uses the generator