一：什么是异常？
异常即是一个事件，该事件会在程序执行过程中发生，影响了程序的正常执行。
一般情况下，在python无法正常处理程序时就会发生一个异常（异常是python对象，表示一个错误）
异常就是程序运行时候发生错误的信号（在程序出现错误的时候，则会产生一个异常，若程序没有处理他，则会抛出该异常，程序的运行也随之终止），在python中，错误触发的异常如下：
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alt="" width="699" height="385" />
而错误分为两种（语法错误和逻辑错误）：
1，语法错误（这种错误，根本过不了python解释器的语法检测，必须在程序执行前就改正）
#语法错误示范一
if
#语法错误示范二
def test:
pass
#语法错误示范三
class Foo
pass
#语法错误示范四
print(haha
2，逻辑错误
#TypeError:int类型不可迭代
for i in 3:
pass
#ValueError
num=input(">>: ") #输入hello
int(num) #NameError
aaa #IndexError
l=['egon','aa']
l[3] #KeyError
dic={'name':'egon'}
dic['age'] #AttributeError
class Foo:pass
Foo.x #ZeroDivisionError:无法完成计算
res1=1/0
res2=1+'str'
二：异常的种类有哪些？
在python中不同的异常可以用不同的类型（python中统一了类与类别，类型即类）取标识，一个异常标识一种错误。
1，常见语法错误
AttributeError 试图访问一个对象没有的属性，比如foo.x，但是foo没有属性x IOError 输入/输出异常；基本上是无法打开文件 ImportError 无法引入模块或包；基本上是路径问题或名称错误 IndentationError 语法错误（的子类） ；代码没有正确对齐 IndexError 下标索引超出序列边界，比如当x只有三个元素，却试图访问x[5] KeyError 试图访问字典里不存在的键 KeyboardInterrupt Ctrl+C被按下 NameError 使用一个还未被赋予对象的变量 SyntaxError Python代码非法，代码不能编译(个人认为这是语法错误，写错了） TypeError 传入对象类型与要求的不符合 UnboundLocalError 试图访问一个还未被设置的局部变量，基本上是由于另有一
个同名的全局变量，导致你以为正在访问它 ValueError 传入一个调用者不期望的值，即使值的类型是正确的
2，更多错误
ArithmeticError
AssertionError
AttributeError
BaseException
BufferError
BytesWarning
DeprecationWarning
EnvironmentError
EOFError
Exception
FloatingPointError
FutureWarning
GeneratorExit
ImportError
ImportWarning
IndentationError
IndexError
IOError
KeyboardInterrupt
KeyError
LookupError
MemoryError
NameError
NotImplementedError
OSError
OverflowError
PendingDeprecationWarning
ReferenceError
RuntimeError
RuntimeWarning
StandardError
StopIteration
SyntaxError
SyntaxWarning
SystemError
SystemExit
TabError
TypeError
UnboundLocalError
UnicodeDecodeError
UnicodeEncodeError
UnicodeError
UnicodeTranslateError
UnicodeWarning
UserWarning
ValueError
Warning
ZeroDivisionError
3，python所有标准异常类
| 异常名称 | 描述 |
|---|---|
| BaseException | 所有异常的基类 |
| SystemExit | 解释器请求退出 |
| KeyboardInterrupt | 用户中断执行(通常是输入^C) |
| Exception | 常规错误的基类 |
| StopIteration | 迭代器没有更多的值 |
| GeneratorExit | 生成器(generator)发生异常来通知退出 |
| SystemExit | Python 解释器请求退出 |
| StandardError | 所有的内建标准异常的基类 |
| ArithmeticError | 所有数值计算错误的基类 |
| FloatingPointError | 浮点计算错误 |
| OverflowError | 数值运算超出最大限制 |
| ZeroDivisionError | 除(或取模)零 (所有数据类型) |
| AssertionError | 断言语句失败 |
| AttributeError | 对象没有这个属性 |
| EOFError | 没有内建输入,到达EOF 标记 |
| EnvironmentError | 操作系统错误的基类 |
| IOError | 输入/输出操作失败 |
| OSError | 操作系统错误 |
| WindowsError | 系统调用失败 |
| ImportError | 导入模块/对象失败 |
| KeyboardInterrupt | 用户中断执行(通常是输入^C) |
| LookupError | 无效数据查询的基类 |
| IndexError | 序列中没有没有此索引(index) |
| KeyError | 映射中没有这个键 |
| MemoryError | 内存溢出错误(对于Python 解释器不是致命的) |
| NameError | 未声明/初始化对象 (没有属性) |
| UnboundLocalError | 访问未初始化的本地变量 |
| ReferenceError | 弱引用(Weak reference)试图访问已经垃圾回收了的对象 |
| RuntimeError | 一般的运行时错误 |
| NotImplementedError | 尚未实现的方法 |
| SyntaxError | Python 语法错误 |
| IndentationError | 缩进错误 |
| TabError | Tab 和空格混用 |
| SystemError | 一般的解释器系统错误 |
| TypeError | 对类型无效的操作 |
| ValueError | 传入无效的参数 |
| UnicodeError | Unicode 相关的错误 |
| UnicodeDecodeError | Unicode 解码时的错误 |
| UnicodeEncodeError | Unicode 编码时错误 |
| UnicodeTranslateError | Unicode 转换时错误 |
| Warning | 警告的基类 |
| DeprecationWarning | 关于被弃用的特征的警告 |
| FutureWarning | 关于构造将来语义会有改变的警告 |
| OverflowWarning | 旧的关于自动提升为长整型(long)的警告 |
| PendingDeprecationWarning | 关于特性将会被废弃的警告 |
| RuntimeWarning | 可疑的运行时行为(runtime behavior)的警告 |
| SyntaxWarning | 可疑的语法的警告 |
| UserWarning | 用户代码生成的警告 |
三：异常处理的定义
python解释器检测到错误，触发异常（也允许程序员自己触发异常）
程序员编写特定的代码，专门用来捕捉这个异常（这段代码与程序逻辑无关，与异常处理有关）
如果捕捉成功则进入另外一个处理分支，执行你为其定制的逻辑，使程序不会崩溃，这就是异常处理
四：异常处理的用法
为了保证程序的健壮性与容错性，即在遇到错误时候程序不会崩溃，我们需要对异常进行处理，
1，如果错误发生的条件是可预知的，我们需要用if进行处理，在错误发生之前进行预防
AGE=10
while True:
age=input('>>: ').strip()
if age.isdigit(): #只有在age为字符串形式的整数时,下列代码才不会出错,该条件是可预知的
age=int(age)
if age == AGE:
print('you got it')
break
2，如果错误发生的条件是不可预知的，则需要用到try..except：在错误发生之后进行处理
#基本语法为
try:
被检测的代码块
except 异常类型：
try中一旦检测到异常，就执行这个位置的逻辑
#举例
try:
f=open('a.txt')
g=(line.strip() for line in f)
print(next(g))
print(next(g))
print(next(g))
print(next(g))
print(next(g))
except StopIteration:
f.close()
五，try...except...的详细用法
我们把可能发生错误的语句放在try模块里，用except来处理异常。except可以处理一个专门的异常，也可以处理一组圆括号中的异常，如果except后没有指定异常，则默认处理所有的异常。每一个try，都必须至少有一个except
1，异常类只能来处理指定的异常情况，如果非指定异常则无法处理
s1 = 'hello'
try:
int(s1)
except IndexError as e: # 未捕获到异常，程序直接报错
print e
2，多分支
s1 = 'hello'
try:
int(s1)
except IndexError as e:
print(e)
except KeyError as e:
print(e)
except ValueError as e:
print(e)
3，万能异常Exception
s1 = 'hello'
try:
int(s1)
except Exception as e:
print(e)
4，多分支＋Exception
s1 = 'hello'
try:
int(s1)
except IndexError as e:
print(e)
except KeyError as e:
print(e)
except ValueError as e:
print(e)
except Exception as e:
print(e)
5，异常的其他机构（try...finally语法）
try...finally语句无论是否发生异常都将会执行最后的代码。语法如下：
try:
<语句>
finally:
<语句> #退出try时总会执行
raise
示例：
s1 = 'hello'
try:
int(s1)
except IndexError as e:
print(e)
except KeyError as e:
print(e)
except ValueError as e:
print(e)
#except Exception as e:
# print(e)
else:
print('try内代码块没有异常则执行我')
finally:
print('无论异常与否,都会执行该模块,通常是进行清理工作')
6，主动触发异常（raise语句）
我们可以使用raise语句自己触发异常，raise语法格式如下：
raise [Exception [, args [, traceback]]]
语句中Exception是异常的类型（例如，NameError）参数是一个异常参数值。该参数是可选的，如果不提供，异常的参数是"None"。
最后一个参数是可选的（在实践中很少使用），如果存在，是跟踪异常对象。
示例：
一个异常可以是一个字符串，类或对象。 Python的内核提供的异常，大多数都是实例化的类，这是一个类的实例的参数。
定义一个异常非常简单，如下所示：
def functionName( level ):
if level < 1:
raise Exception("Invalid level!", level)
# 触发异常后，后面的代码就不会再执行
try:
raise TypeError('类型错误')
except Exception as e:
print(e)
7，自定义异常
通过创建一个新的异常类，程序可以命名它们自己的异常。异常应该是典型的继承自Exception类，通过直接或间接的方式。
以下为与BaseException相关的实例,实例中创建了一个类，基类为BaseException，用于在异常触发时输出更多的信息。
在try语句块中，用户自定义的异常后执行except块语句，变量 e 是用于创建Networkerror类的实例。
class Networkerror(BaseException):
def __init__(self,msg):
self.msg=msg
def __str__(self):
return self.msg try:
raise Networkerror('类型错误')
except Networkerror as e:
print(e)
8，断言：assert条件
assert 1 == 1
assert 1 == 2
9，总结try...except
1，把错误处理和真正的工作分开来 2，代码更易组织，更清晰，复杂的工作任务更容易实现 3，毫无疑问，更安全了，不至于由于一些小的疏忽而使程序意外崩溃了
六：什么时候用异常处理？
有的同学会这么想，学完了异常处理后，好强大，我要为我的每一段程序都加上try...except，干毛线去思考它会不会有逻辑错误啊，这样就很好啊，多省脑细胞，这样其实并不好，为什么呢？
首先try...except是你附加给你的程序的一种异常处理的逻辑，与你的主要的工作是没有关系的，这种东西加的多了，会导致你的代码可读性变差
然后异常处理本就不是你的擦屁股纸，只有在错误发生的条件无法预知的情况下，才应该加上try...except
七，异常问题的解决方法
7.1 TabError的解决方法
问题：Python文件运行时报错如下：
TabError: inconsistent use of tabs and spaces in indentation
原因：说明Python文件中混有Tab和Space用作格式缩进。这通常是使用外部编辑器编辑Python文件时，自动采用Tab进行格式缩进。
解决：将Tab转换成4个Space（通常）或者用Python编辑器（如pyDev）格式化。
7.2 EOFError的解决方法
使用pickle.load(f) 加载 pickle 文件时，报错：
EOFError: Ran out of input
可能原因：文件为空
解决方法：加载非空文件，或者加载前判断文件是否为空。
此文参考：https://www.luffycity.com/python-book/di-5-zhang-mian-xiang-dui-xiang-bian-cheng-she-ji-yu-kai-fa/514-yi-chang-chu-li.html
主要是自己复习和巩固知识点。