先看map。map()函数接收两个参数,一个是函数,一个是序列,map将传入的函数依次作用到序列的每个元素,并把结果作为新的list返回。
举例说明,比如我们有一个函数a(x)=x*2,要把这个函数作用在一个list [1, 2, 3, 4, 5]上,就可以用map()实现如下:
>>> def a(x):
... return x * 2
...
>>> map(a, [1,2,3,4,5])
[2, 4, 6, 8, 10]
map传入的第一个参数a,即a函数,当然你也可以不用map函数实现这功能:
>>> list = []
>>> for i in [1, 2, 3, 4, 5]:
... list.append(a(i))
...
>>> print list
[2, 4, 6, 8, 10]
从代码量上来讲,map要精简很多,所以,map()作为高阶函数,事实上它把运算规则抽象了,因此,我们不但可以计算简单的a(x)=x*2,还可以计算任意复杂的函数,比如,把这个list所有数字转为字符串:
>>> map(str,[1,2,3,4,5])
['1', '2', '3', '4', '5']
>>>
只需要一行代码,就搞定了。让我们再看和来自顾雪峰python教程的习题:利用map()函数,把用户输入的不规范的英文名字,变为首字母大写,其他小写的规范名字。输入:[‘adam', ‘LISA', ‘barT'],输出:[‘Adam', ‘Lisa', ‘Bart']。作为我个人来说,我可能会先将不规范的英文名全转换在小写然后再通过capitalize()函数,将首字母转换在写,代码如下:
>>> def caps(name):
... return name.capitalize()
...
>>> def lowers(name):
... return name.lower()
...
>>> map(caps, map(lowers,['adam', 'LISA', 'barT']))
['Adam', 'Lisa', 'Bart']
再看reduce的用法。reduce(function, sequence, starting_value):对sequence中的item顺序迭代调用function,如果有starting_value,还可以作为初始值调用,例如可以用来对List求和:
>>> def add(x, y):
... return x + y
...
>>> reduce(add, [1, 3, 5, 7, 9])
25
>>> reduce(add, range(1, 11))
55
>>> reduce(add, range(1, 11),20)
75
当然求和运算可以直接用Python内建函数sum(),没必要动用reduce。但是如果要把序列[1,2,3,4,5,6,7]变换成整数1234567,reduce就可以派上用场:
>>> def fn(x, y):
... return x * 10 + y
...
>>> reduce(fn, [1,3,4,5,6,7])
134567

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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