标签：情况 匹配 tar func 技术 choices obj 代码 而且
传统Python语言的主要控制结构是for循环。然而，需要注意的是for循环在Pandas中不常用，因此Python中for循环的有效执行并不适用于Pandas模式。一些常见控制结构如下。
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for循环
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while循环
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if/else语句
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try/except语句
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生成器表达式
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列表推导式
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模式匹配
所有的程序最终都需要一种控制执行流的方式。本节介绍一些控制执行流的技术。
for循环是Python的一种最基本的控制结构。使用for循环的一种常见模式是使用range函数生成数值范围，然后对其进行迭代。
res = range(3) print(list(res)) #输出：[0, 1, 2] for i in range(3): print(i) ‘‘‘输出： 0 1 2 ‘‘‘
martial_arts = ["Sambo","Muay Thai","BJJ"] for martial_art in martial_arts: print(f"{ martial_art} has influenced\ modern mixed martial arts") ‘‘‘输出： Sambo has influenced modern mixed martial arts Muay Thai has influenced modern mixed martial arts BJJ has influenced modern mixed martial arts ‘‘‘
while循环是一种条件有效就会重复执行的循环方式。while循环的常见用途是创建无限循环。在本示例中，while循环用于过滤函数，该函数返回两种攻击类型中的一种。
def attacks(): list_of_attacks = ["lower_body", "lower_body", "upper_body"] print("There are a total of {lenlist_of_attacks)}\ attacks coming!") for attack in list_of_ attacks: yield attack attack = attacks() count = 0 while next(attack) == "lower_body": count +=1 print(f"crossing legs to prevent attack #{count}") else: count += 1 print(f"This is not lower body attack, \ I will cross my arms for# count}") ‘‘‘输出： There are a total of 3 attacks coming! crossing legs to prevent attack #1 crossing legs to prevent attack #2 This is not a lower body attack, I will cross my arms for #3 ‘‘‘
if/else语句是一条在判断之间进行分支的常见语句。在本示例中，if/elif用于匹配分支。如果没有匹配项，则执行最后一条else语句。
def recommended_attack(position): """Recommends an attack based on the position""" if position == "full_guard": print(f"Try an armbar attack") elif position == "half_guard": print(f"Try a kimura attack") elif position == "fu1l_mount": print(f"Try an arm triangle") else: print(f"You‘re on your own, \ there is no suggestion for an attack") recommended_attack("full_guard")#输出：Try an armbar attack recommended_attack("z_guard") #输出：You‘re on your own, there is no suggestion for an attack
生成器表达式建立在yield语句的概念上，它允许对序列进行惰性求值。生成器表达式的益处是，在实际求值计算前不会对任何内容进行求值或将其放入内存。这就是下面的示例可以在生成的无限随机攻击序列中执行的原因。
在生成器管道中，诸如 “arm_triangle”的小写攻击被转换为“ARM_TRIANGLE”，接下来删除其中的下划线，得到“ARM TRIANGLE”。
def lazy_return_random_attacks(): """Yield attacks each time""" import random attacks = {"kimura": "upper_body", "straight_ankle_lock": "lower_body", "arm_triangle": "upper_body", "keylock": "upper_body", "knee_bar": "lower_body"} while True: random_attack random.choices(list(attacks.keys())) yield random attack #Make all attacks appear as Upper Case upper_case_attacks = \ (attack.pop().upper() for attack in \ lazy_return_random_attacks()) next(upper-case_attacks) #输出：ARM-TRIANGLE ## Generator Pipeline: One expression chains into the next #Make all attacks appear as Upper Case upper-case_attacks =\ (attack. pop().upper() for attack in\ lazy_return_random_attacks()) #remove the underscore remove underscore =\ (attack.split("_")for attack in\ upper-case_attacks) #create a new phrase new_attack_phrase =\ (" ".join(phrase) for phrase in\ remove_underscore) next(new_attack_phrase) #输出：‘STRAIGHT ANKLE LOCK‘ for number in range(10): print(next(new_attack_phrase)) ‘‘‘输出： KIMURA KEYLOCK STRAIGHT ANKLE LOCK ‘‘‘
语法上列表推导式与生成器表达式类似，然而直接对比它们，会发现列表推导式是在内存中求值。此外，列表推导式是优化的C代码，可以认为这是对传统for循环的重大改进。
martial_arts = ["Sambo", "Muay Thai", "BJJ"] new_phrases [f"mixed Martial Arts is influenced by \ (martial_art)" for martial_art in martial_arts] print(new_phrases) [‘Mixed Martial Arts is influenced by Sambo‘, \ ‘Mixed Martial Arts is influenced by Muay Thai‘, \ ‘Mixed Martial Arts is influenced by BJJ‘]
有了这些基础知识后，重要的是不仅要了解如何创建代码，还要了解如何创建可维护的代码。创建可维护代码的一种方法是创建一个库，另一种方法是使用已经安装的第三方库编写的代码。其总体思想是最小化和分解复杂性。
使用Python编写库非常重要，之后将该库导入项目无须很长时间。下面这些示例是编写库的基础知识：在存储库中有一个名为funclib的文件夹，其中有一个_init_ .py文件。要创建库，在该目录中需要有一个包含函数的模块。
touch funclib/funcmod.py
"""This is a simple module""" def list_of_belts_in_bjj(): """Returns a list of the belts in Brazilian jiu-jitsu""" belts= ["white", "blue", "purple", "brown", "black"] return belts import sys;sys.path.append("..") from funclib import funcmod funcmod.list_of_belts_in-bjj() #输出：[‘white‘, ‘blue‘, ‘purple‘, ‘brown‘, ‘black‘]
如果库是上面的目录，则可以用Jupyter添加sys.path.append方法来将库导入。接下来，使用前面创建的文件夹/文件名/函数名的命名空间导入模块。
可使用pip install命令安装第三方库。请注意，conda命令(
https://conda.io/docs/user-guide/tasks/manage-pkgs.html)是pip命令的可选替代命令。如果使用conda命令，那么pip命令也会工作得很好，因为pip是virtualenv虚拟环境的替代品，但它也能直接安装软件包。
pip install pandas
另外，还可使用requirements.txt文件安装包。
> ca requirements.txt pylint pytest pytest-cov click jupyter nbval > pip install -r requirements.txt
下面是在Jupyter Notebook中使用小型库的示例。值得指出的是，在Jupyter Notebook中创建程序代码组成的巨型蜘蛛网很容易，而且非常简单的解决方法就是创建一些库，然后测试并导入这些库。
"""This is a simple module""" import pandas as pd def list_of_belts_in_bjj(): """Returns a list of the belts in Brazilian jiu-jitsu""" belts = ["white", "blue", "purple", "brown", "black"] return belts def count_belts(): """Uses Pandas to count number of belts""" belts = list_of_belts_in_bjj() df = pd.Dataframe(belts) res = df.count() count = res.values.tolist()[0] return count from funclib.funcmod import count_belts print(count_belts()) #输出：5
可在Jupyter Notebook中重复使用类并与类进行交互。最简单的类类型就是一个名称，类的定义形式如下。
class Competitor: pass
class Competitor: pass conor = Competitor() conor.name = "Conor McGregor" conor.age = 29 conor.weight = 155 nate = Competitor() nate.name = "Nate Diaz" nate.age = 30 nate.weight = 170 def print_competitor _age(object): """Print out age statistics about a competitor""" print(f"{object.name} is {object.age} years old") print_competitor_age(nate) #输出：Nate Diaz is 30 years old print_competitor_age(conor) #输出：Conor McGregor is 29 years old
到此这篇关于Python的控制结构：For、While、If…的文章就介绍到这了
python的控制结构之For、While、If循环问题
标签：情况 匹配 tar func 技术 choices obj 代码 而且
原文地址：https://www.cnblogs.com/nanhe/p/13214569.html
