1 lambda函数
函数格式是lambda keys:express 匿名函数lambda是一个表达式函数,接受keys参数,返回表达式的值。所以不用return,也没有函数名,经常用在需要key参数的函数中,比如sorted。
2 元组(),它是以逗号辨别的,而不是小括号。比如一个元素的元组新手经常写成(12),其实他会被解释成单个元素12.正确的写法应该是(12,),在元素后面加上逗号。
3 模块导入。比如
import random
print random.choice(range(10))
和
from random import choice
print choice(range(10))
新手会有一种误解,第二种方法只导入了一个函数,而没有把整个模块导入,这是错误的。整个模块其实已经被导入,只是那个函数的引用被保存了起来。所以from-import这种语法不会带来性能上的差异,也没有节省内存。
4 当你有许多module,比如几百个,想要使用时可能会想一个一个导入太麻烦,有没有简便的方法?答案是有的,就是将这些模块组织成一个package。其实就是将模块都放在一个目录里,然后再加一个__init__.py文件,python会将其看作为package,使用里面的函数就可以以dotted-attribute方式来访问。
5 参数传递可变对象是传引用的,不可变对象是传值的。那么什么对象是可变的,什么是不可变的。所有python对象都有三个属性:类型、标识符和值,如果值是可变的就是可变对象,如果值不可变就是不可变对象。像数字、字符串、元组都是不可变对象,剩下的列表、字典、类、类实例等都是可变对象。
6 迭代器的理解,是实现了迭代器协议的容器对象。自己实现一个迭代器,类中要有__iter__()方法,该方法返回一个对象。这个对象要有__next__()方法,在next方法中的适当位置返回StopIteration异常。迭代器不经常使用,所以不用太担心。有替代方法就是生成器。
class MyIterator(object): """docstring for MyIterator""" def __init__(self, num): self.num = num def __iter__(self): return self; def __next__(self): if self.num <= 0: raise StopIteration; self.num -= 1; return self.num; for each in MyIterator(5): print(each); -> 结果
7 生成器。函数中只要出现了yield语句就会将其转变成一个生成器。在遇见yield语句后会保存上下文环境,并退出函数。
注意:生成器中没有return语句。
def fun2(num): print("start generator"); while(num>0): yield num; num -=1; a=[each for each in fun2(5)] print(a);->结果 start generator [5, 4, 3, 2, 1]
学习过程中,难免出错。如果您在阅读过程中遇到不太明白,或者有疑问。
以上这篇浅谈python新手中常见的疑惑及解答就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持脚本之家。

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.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


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