Python on error resume next

on error resume next

Here are the examples of how to on error resume next in python. These are taken from open source projects. By voting up you can indicate which examples are most useful and appropriate.

4 Examples 7

 def __on_error_resume_next(self, second): """Continues an observable sequence that is terminated normally or by an exception with the next observable sequence. Keyword arguments: second -- Second observable sequence used to produce results after the first sequence terminates. Returns an observable sequence that concatenates the first and second sequence, even if the first sequence terminates exceptionally. """ if not second: raise Exception('Second observable is required') return Observable.on_error_resume_next([self, second]) 

@extensionmethod(Observable, instancemethod=True) def on_error_resume_next(self, second): """Continues an observable sequence that is terminated normally or by an exception with the next observable sequence. Keyword arguments: second -- Second observable sequence used to produce results after the first sequence terminates. Returns an observable sequence that concatenates the first and second sequence, even if the first sequence terminates exceptionally. """ if not second: raise Exception('Second observable is required') return Observable.on_error_resume_next([self, second]) 

 @classmethod def on_error_resume_next(cls, *args): """Continues an observable sequence that is terminated normally or by an exception with the next observable sequence. 1 - res = Observable.on_error_resume_next(xs, ys, zs) 2 - res = Observable.on_error_resume_next([xs, ys, zs]) Returns an observable sequence that concatenates the source sequences, even if a sequence terminates exceptionally. """ if args and isinstance(args[0], list): sources = args[0] else: sources = list(args) def subscribe(observer): subscription = SerialDisposable() pos = 0 def action(this, state=None): nonlocal pos if pos < len(sources): current = sources[pos] pos += 1 d = SingleAssignmentDisposable() subscription.disposable = d d.disposable = current.subscribe(observer.on_next, lambda ex: this(), lambda: this()) else: observer.on_completed() cancelable = immediate_scheduler.schedule_recursive(action) return CompositeDisposable(subscription, cancelable) return AnonymousObservable(subscribe) 

@extensionclassmethod(Observable) def on_error_resume_next(cls, *args): """Continues an observable sequence that is terminated normally or by an exception with the next observable sequence. 1 - res = Observable.on_error_resume_next(xs, ys, zs) 2 - res = Observable.on_error_resume_next([xs, ys, zs]) 3 - res = Observable.on_error_resume_next(xs, factory) Returns an observable sequence that concatenates the source sequences, even if a sequence terminates exceptionally. """ scheduler = current_thread_scheduler if args and isinstance(args[0], list): sources = iter(args[0]) else: sources = iter(args) def subscribe(observer): subscription = SerialDisposable() cancelable = SerialDisposable() def action(scheduler, state=None): try: source = next(sources) except StopIteration: observer.on_completed() return # Allow source to be a factory method taking an error source = source(state) if callable(source) else source current = Observable.from_future(source) d = SingleAssignmentDisposable() subscription.disposable = d def on_resume(state=None): scheduler.schedule(action, state) d.disposable = current.subscribe(observer.on_next, on_resume, on_resume) cancelable.disposable = scheduler.schedule(action) return CompositeDisposable(subscription, cancelable) return AnonymousObservable(subscribe) 

Источник

'On Error Resume Next in Python

In Basic/Visual Basic/VBS, there's a statement called On Error Resume Next which does this.

Solution 1: [1]

In Python 3.4 onwards, you can use contextlib.suppress :

from contextlib import suppress with suppress(Exception): # or, better, a more specific error (or errors) do_magic() with suppress(Exception): do_foo() with suppress(Exception): do_bar() 

Solution 2: [2]

If all three functions accept same number of parameters:

for f in (do_magic, do_foo, do_bar): try: f() except: pass 

Otherwise, wrap the function call with lambda .

for f in (do_magic, lambda: do_foo(arg1, arg2)): try: f() except: pass 

Solution 3: [3]

If there are no parameters.

funcs = do_magic, do_foo, do_bar for func in funcs: try: func() except: continue 

Solution 4: [4]

If you are the one coding the fucntions, why not program the functions to return status codes? Then they will be atomic and you wont have to capture the error in the main section. You will also be able to perform roll back or alternate coding on failure.

def do_magic(): try: #do something here return 1 except: return 0 

if do_magic() = 0: #do something useful or not. if do_foo() = 0: #do something useful or not. if do_bar() = 0: #do something useful or not. 

Solution 5: [5]

A lot of ident, but it works

try: do_magic() finally: try: do_foo() finally: try: do_bar() finally: pass 

Solution 6: [6]

you could try a nested ´try´ loop, alltho that might not be as elegantly pythonic as you might want. the ´lambda´ solution is is also a good way to go, did not mention because it was done in the previous answer

try: do_magic() finally: try: do_foo() finally: try: do_bar() except: pass 

well damnnit, this answer just got posted seconds beforehand again 😐

Solution 7: [7]

In the question, Snippet 3 does not work but will work if you don't mind splitting each line over two lines.

try: do_magic() except: pass try: do_foo() except: pass try: do_bar() except: pass 

import sys a1 = "No_Arg1" a2 = "No_Arg2" a3 = "No_Arg3" try: a1 = sys.argv[1] except: pass try: a2 = sys.argv[2] except: pass try: a3 = sys.argv[3] except: pass print a1, a2, a3 

..if you save this to test.py and then at a CMD prompt in windows simply type test.py it will return No_Arg1 No_Arg2 No_Arg3 because there were no arguments. However, if you supply some arguments, if type test.py 111 222 it will return 111 222 No_Arg3 etc. (Tested - Windows 7, python2.7).

IMHO this is far more elegant than the nesting example replies. It also works exactly like On Error Resume Next and I use it when translating from VB6. One issue is that the try lines cannot contain a conditional. I have found that as a rule, python cannot contain more than one : in a line. That said, it simply means splitting the statement over 3 lines etc.

Sources

This article follows the attribution requirements of Stack Overflow and is licensed under CC BY-SA 3.0.

Источник

How to fix on error resume next in python?

In VBA (Visual Basic for Applications), the "On Error Resume Next" statement is used to ignore errors and continue executing the code. However, in Python, there is no equivalent to this statement as it encourages the idea of handling exceptions rather than ignoring them. Ignoring errors can lead to unexpected behavior and make it difficult to debug issues in your code. Therefore, it is recommended to handle exceptions in a proper manner in Python.

Method 1: Try-Except Block

To handle errors in Python, you can use the try-except block. This block allows you to catch and handle exceptions that may occur during the execution of your code. Here's an example of how to use the try-except block to handle errors in Python:

try: # code that may raise an exception except Exception as e: # code to handle the exception

In this example, the try block contains the code that may raise an exception. If an exception is raised, the code in the except block will be executed. The as keyword is used to assign the exception to a variable ( e in this case) so that you can access information about the exception.

Here's an example of how to use the try-except block to handle the On Error Resume Next functionality in Python:

try: # code that may raise an exception except: pass

In this example, the except block contains the pass keyword, which tells Python to do nothing if an exception is raised. This is similar to the On Error Resume Next functionality in other languages.

However, it's generally not recommended to use this approach as it can hide errors and make debugging more difficult. It's better to handle exceptions explicitly and gracefully. Here's an example of how to handle a specific exception:

try: # code that may raise a specific exception except ValueError as e: # code to handle the ValueError exception

In this example, the except block will only be executed if a ValueError exception is raised. You can replace ValueError with any other exception type to handle different types of exceptions.

Overall, it's important to handle exceptions in your code to ensure that it runs smoothly and to make debugging easier. The try-except block is a powerful tool for handling exceptions in Python.

Method 2: Raising Exceptions

To fix On Error Resume Next in Python, you can use the "Raising Exceptions" method. This method allows you to handle errors in a more controlled manner by explicitly raising an exception when an error occurs. Here's how you can use this method:

Step 1: Identify the code block where you want to handle the error.

Step 2: Use a try-except block to catch the error. Within the try block, include the code that you want to execute.

try: # code to execute except: # code to handle the error

Step 3: Within the except block, raise an exception using the "raise" keyword. You can specify the type of exception to raise, along with an error message.

try: # code to execute except: raise Exception("An error occurred")

Step 4: Add additional except blocks to handle specific types of exceptions. This allows you to handle different types of errors in different ways.

try: # code to execute except ValueError: # code to handle a ValueError except TypeError: # code to handle a TypeError except: # code to handle any other type of error

Here's an example of how you can use this method to handle a division by zero error:

try: result = 10 / 0 except ZeroDivisionError: raise Exception("Cannot divide by zero")

In this example, the code attempts to divide 10 by 0, which raises a ZeroDivisionError. The except block catches this error and raises a new exception with a custom error message.

Overall, using the "Raising Exceptions" method allows you to handle errors in a more controlled and explicit way, making your code more robust and reliable.

Method 3: Assertions

When it comes to error handling in Python, the try-except block is the most commonly used method. However, there is another way to handle errors that is often overlooked - assertions. In this tutorial, we will discuss how to use assertions to fix the problem of On Error Resume Next in Python.

Step 1: Understanding Assertions

Assertions are statements that check whether a given condition is true or false. If the condition is false, an error is raised, and the program stops executing. Assertions are used to check for programming errors and ensure that the code is working as expected.

Step 2: Implementing Assertions in Your Code

To use assertions, you need to include a statement that checks for a condition. Here's an example:

x = 10 assert x == 10, "x should be 10"

In this example, the assert statement checks whether x is equal to 10. If the condition is true, the program continues executing. If the condition is false, an error is raised, and the program stops executing.

Step 3: Handling Errors with Assertions

Now that we know how to use assertions, let's see how we can use them to handle errors in our code. Here's an example:

def divide(x, y): assert y != 0, "Cannot divide by zero" return x / y print(divide(10, 0))

In this example, we have defined a function that performs division. The assert statement checks whether the denominator is not equal to zero. If the denominator is zero, an error is raised, and the program stops executing.

Step 4: Fixing On Error Resume Next with Assertions

To fix the problem of On Error Resume Next in Python, we need to use assertions to check for errors in our code. Here's an example:

def get_value(data, index): assert index  len(data), "Index out of range" return data[index] data = [1, 2, 3] print(get_value(data, 5))

In this example, we have defined a function that gets a value from a list. The assert statement checks whether the index is less than the length of the list. If the index is out of range, an error is raised, and the program stops executing.

Источник