Error and exception concepts
Error:
1. Syntax error: The code does not conform to Interpreter or compiler syntax
2. Logic error: Incomplete or illegal input or calculation problem
Exception: Thousands of objects occur during execution, causing the program to fail to execute
1. The program encounters logic or algorithm problems
2. Computer errors during operation (insufficient memory or IO errors)
The difference between errors and exceptions
Error:
Syntax or logic errors before the code is run,
Syntax errors must be modified before execution, logic errors cannot be modified
Exceptions are divided into two steps:
1. Exception occurs, an error is detected and the interpreter thinks it is an exception, and an exception is thrown;
2. Exception handling, intercepts the exception, ignores or terminates the program to handle the exception
Common Errors in Python
Common Errors: under ipython
1. a: NameError
Directly referencing a variable when a variable is not defined
2. if True : SyntaxError
Syntax error
3. f = open('1.txt') : IOError
When trying to open a file that does not exist
4. 10/0 : ZeroDivisionError
5. a = int('dd') : ValueError
Error encountered when performing forced type conversion
try- -except exception handling (1)
try-except: exception handling
try: try_suite except Exception [, e]: exception_block
1.try is used to capture errors in try_suite and hand them over to except for handling
2.except is used to handle exceptions. If the exception handling is consistent with setting the captured exception, use exception_block to handle the exception
Example:
try: a except Exception, e: print e
try-except captured exception analysis:
Case 1:
try: undef except: print 'catch an except'
Case 2:
try: if undef except: print 'catch an except'
case 1: The exception can be caught because it is a runtime error
case 2: The exception cannot be caught because it is a syntax error. Run Previous error
Case 3:
try: undef except NameError, e: print 'catch an except', e Case 4: try: undef except IOError, e: print 'catch an except', e
case 3: The exception can be caught because the catch NameError exception is set
case 4: The exception cannot be caught because the IOError is set and will not be handled NameError
try--except exception handling (2)
try-except: handle multiple exceptions
try: try_suite except Exception1 [e]: exception_block1 except Exception2 [e]: exception_block2 except ExceptionN [e]: exception_blockN
try-except--else use
try: try_suite except Exception1 [e]: exception_block1 else: none_exception
If there is no exception, execute the code in the else statement
try--finally statement
try-finally statement:
try: try_suite finally: do_finally
1. If try The statement does not capture the error, and the code executes the do_finally statement
2. If the try statement captures the error, the program first executes the do_finally statement, and then hands the captured error to the python interpreter for processing
try-finally Statement:
Rule: try-finally will execute the finally code regardless of whether an exception is detected
Function: Provide a cleanup mechanism for exception handling events, used to close files or release system resources
try-except-finally usage:
try: try_suite except: do_except finally: do_finally
1. If the try statement does not catch the exception, after executing the try code segment, execute finally
2. If the try catches the exception, execute it first except handles the error, and then executes finally
try-except-else-finally use:
try: try_suite except: do_except else: do_else finally: do_finally
1. If the try statement does not catch the exception, after executing the try code block, execute the else code block, Finally execute finally
2. If try catches an exception, first execute except to handle the error, and then execute finally
with statement
with statement:
with context [as var]:
with_suite
1.The with statement is used to replace the try-except-finally statement, which makes the code more concise;
2.The context expression returns An object;
3.var is used to save the context return object, a single return value or a tuple;
4.with_suite uses the var variable to operate on the context return object
……
raise and assert
raise statement
raise statement is used to actively throw exceptions
Syntax format: raise [ exception [, args]]
exception: exception class
args: tuple describing exception information
Example:
raise TypeError raise TypeError, 'Test Error' raise IOError, 'File Not Exist'
assert statement
Assertion statement: The assert statement is used to check whether the expression is true. If it is false, an AssertionError is raised;
Grammar format: assert expression [, args]
expression: expression
args: Description information of judgment conditions
Example:
assert 7==6 assert 0, 'Test Error'
Python standard and custom exceptions
Standard exceptions: python built-in exceptions, program execution
Custom exceptions have existed before:
1. python allows custom exceptions, which are used to describe abnormal situations not involved in python
2. Custom exceptions must Inherit the Exception class
3. Custom exceptions can only be actively triggered
Custom exception examples:
class FileError(IOError): pass raise FileError, 'Test FileError' class CustomError(Exception): def __init__(self, info): Exception.__init__(self) self.errorinfo = info def __str__(self): return 'CustomError: %s' % self.errorinfo try: raise CustomError('test CustomError') except CustomError, e: print 'Error Info : %s' % e
Related recommendations: "Python Tutorial"
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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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