Generator yield in Python

Generator yield: Using yield statements
You can let a function generate a result sequence, not just a value

def  Countdow (n):       print ("start!");       While n>0:            yield n;            n-= 1;  c = Countdow (5);  Print (c.__next__ ())   print (c.__next__ ())

Output Result:

start!54

The __next__ () method causes the generator function to run until the next yield statement, at which point the __next__ () method passes the return value to yield and the function temporarily aborts execution
Call __next__ () again when the function continues to perform this procedure until the generator function returns to the end
The __next__ () method is not usually called manually
Instead, use loops

For I in  Countdow (5):   print (i);

Output Result:

54321

Generators are a powerful way to write programs based on processing pipelines, streams, or data streams;
Such as:

def tail (f): For line in     F:      If is not line: #如果 non-true temporarily hibernate and try     Time.sleep again (0.1);     Continue;    Yield line; # generates a sequence of values from the obtained file values  filecount = tail (open (' e:/work.txt '));    #grep方法 used to look up a specific substring in the method builder above    def grep (lines,searchtext): For line in   lines:    if searchtext on line:     Yield line  ;  Lines  = grep (FileCount, ' Tom ');  #查找带有tom substring for line in  lines:    print (line);

Output Result:
' Tom ', 120,132
Summarize:
The role of the generator: The value that will run when the program runs to yield
Passed to the yield program does not output can be considered at this time the program is in a paused state when using the __next__ () method The function continues execution
Until the yield is met again

Advantage: Yield stores not a single value, but saves the current execution state of the program without having to compute all the elements at once, but using the same time to save memory space

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