在“循环”一节,我们已经讨论了Python基本的循环语法。这一节,我们将接触更加灵活的循环方式。
range()
在Python中,for循环后的in跟随一个序列的话,循环每次使用的序列元素,而不是序列的下标。
之前我们已经使用过range()来控制for循环。现在,我们继续开发range的功能,以实现下标对循环的控制:
代码如下:
S = 'abcdefghijk'
for i in range(0,len(S),2):
print S[i]
在该例子中,我们利用len()函数和range()函数,用i作为S序列的下标来控制循环。在range函数中,分别定义上限,下限和每次循环的步长。这就和C语言中的for循环相类似了。
enumerate()
利用enumerate()函数,可以在每次循环中同时得到下标和元素:
代码如下:
S = 'abcdefghijk'
for (index,char) in enumerate(S):
print index
print char
实际上,enumerate()在每次循环中,返回的是一个包含两个元素的定值表(tuple),两个元素分别赋予index和char
zip()
如果你多个等长的序列,然后想要每次循环时从各个序列分别取出一个元素,可以利用zip()方便地实现:
代码如下:
ta = [1,2,3]
tb = [9,8,7]
tc = ['a','b','c']
for (a,b,c) in zip(ta,tb,tc):
print(a,b,c)
每次循环时,从各个序列分别从左到右取出一个元素,合并成一个tuple,然后tuple的元素赋予给a,b,c
zip()函数的功能,就是从多个列表中,依次各取出一个元素。每次取出的(来自不同列表的)元素合成一个元组,合并成的元组放入zip()返回的列表中。zip()函数起到了聚合列表的功能。
我们可以分解聚合后的列表,如下:
代码如下:
ta = [1,2,3]
tb = [9,8,7]
# cluster
zipped = zip(ta,tb)
print(zipped)
# decompose
na, nb = zip(*zipped)
print(na, nb)
总结
range()
enumerate()
zip()

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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