


This article mainly introduces the relevant information about the detailed explanation of iterator and generator instances in Python. Friends in need can refer to
Detailed explanation of iterator and generator instances in Python
This article summarizes some related knowledge of iterators and generators in Python by focusing on different application scenarios and their solutions, as follows:
1. Manually traverse iterators
Application scenario: Want to traverse all elements in an iterable object, but do not want to use a for loop
Solution: Use the next() function, and Capturing StopIteration exception
def manual_iter(): with open('/etc/passwd') as f: try: while True: line=next(f) if line is None: break print(line,end='') except StopIteration: pass
#test case items=[1,2,3] it=iter(items) next(it) next(it) next(it)
2. Agent iteration
Application scenario : Want to perform iteration operations directly on a container object containing a list, tuple or other iterable object
Solution: Define an iter() method to proxy the iteration operation to the container inside the container
on the object Example:
##
class Node: def init(self,value): self._value=value self._children=[] def repr(self): return 'Node({!r})'.fromat(self._value) def add_child(self,node): self._children.append(node) def iter(self): #将迭代请求传递给内部的_children属性 return iter(self._children)
#test case if name='main': root=Node(0) child1=Node(1) child2=Nide(2) root.add_child(child1) root.add_child(child2) for ch in root: print(ch)
3. Reverse iteration
a=[1,2,3,4] for x in reversed(a): print(x) #4 3 2 1 f=open('somefile') for line in reversed(list(f)): print(line,end='') #test case for rr in reversed(Countdown(30)): print(rr) for rr in Countdown(30): print(rr)Example 2
class Countdown: def init(self,start): self.start=start #常规迭代 def iter(self): n=self.start while n > 0: yield n n -= 1 #反向迭代 def reversed(self): n=1 while n <= self.start: yield n n +=1
4 .Selective iteration
##
with open('/etc/passwd') as f: for line in f: print(line,end='')
Example 2
from itertools import dropwhile with open('/etc/passwd') as f: for line in dropwhile(lambda line:line.startwith('#'),f): print(line,end='')
5. Iterate multiple sequences at the same time
Application scenario: Want to iterate multiple sequences at the same time and take an element from one sequence each time
Solution: Use zip() function
##6. Iteration of elements on different collections
Application scenario: Want to perform the same operation on multiple objects, but these objects are in different In the container
Solution: Use itertool.chain() function
7. Expand the nested sequence
Application scenario: Want to expand a multi-level nested sequence into a single-level list
Solution: Use a recursive generator containing a yield from statement
from collections import Iterable def flatten(items,ignore_types=(str,bytes)): for x in items: if isinstance(x,Iterable) and not isinstance(x,ignore_types): yield from flatten(x) else: yield x
#test case items=[1,2,[3,4,[5,6],7],8] for x in flatten(items): print(x)
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Python and C each have their own advantages, and the choice should be based on project requirements. 1) Python is suitable for rapid development and data processing due to its concise syntax and dynamic typing. 2)C is suitable for high performance and system programming due to its static typing and manual memory management.

Choosing Python or C depends on project requirements: 1) If you need rapid development, data processing and prototype design, choose Python; 2) If you need high performance, low latency and close hardware control, choose C.

By investing 2 hours of Python learning every day, you can effectively improve your programming skills. 1. Learn new knowledge: read documents or watch tutorials. 2. Practice: Write code and complete exercises. 3. Review: Consolidate the content you have learned. 4. Project practice: Apply what you have learned in actual projects. Such a structured learning plan can help you systematically master Python and achieve career goals.

Methods to learn Python efficiently within two hours include: 1. Review the basic knowledge and ensure that you are familiar with Python installation and basic syntax; 2. Understand the core concepts of Python, such as variables, lists, functions, etc.; 3. Master basic and advanced usage by using examples; 4. Learn common errors and debugging techniques; 5. Apply performance optimization and best practices, such as using list comprehensions and following the PEP8 style guide.

Python is suitable for beginners and data science, and C is suitable for system programming and game development. 1. Python is simple and easy to use, suitable for data science and web development. 2.C provides high performance and control, suitable for game development and system programming. The choice should be based on project needs and personal interests.

Python is more suitable for data science and rapid development, while C is more suitable for high performance and system programming. 1. Python syntax is concise and easy to learn, suitable for data processing and scientific computing. 2.C has complex syntax but excellent performance and is often used in game development and system programming.

It is feasible to invest two hours a day to learn Python. 1. Learn new knowledge: Learn new concepts in one hour, such as lists and dictionaries. 2. Practice and exercises: Use one hour to perform programming exercises, such as writing small programs. Through reasonable planning and perseverance, you can master the core concepts of Python in a short time.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.


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