3.4 Exceptions

Programmers must be always mindful of possible errors that may arise in their programs. Examples abound: a function may not receive arguments that it is designed to accept, a necessary resource may be missing, or a connection across a network may be lost. When designing a program, one must anticipate the exceptional circumstances that may arise and take appropriate measures to handle them.

There is no single correct approach to handling errors in a program. Programs designed to provide some persistent service like a web server should be robust to errors, logging them for later consideration but continuing to service new requests as long as possible. On the other hand, the Python interpreter handles errors by terminating immediately and printing an error message, so that programmers can address issues as soon as they arise. In any case, programmers must make conscious choices about how their programs should react to exceptional conditions.

Exceptions, the topic of this section, provides a general mechanism for adding error-handling logic to programs. Raising an exception is a technique for interrupting the normal flow of execution in a program, signaling that some exceptional circumstance has arisen, and returning directly to an enclosing part of the program that was designated to react to that circumstance. The Python interpreter raises an exception each time it detects an error in an expression or statement. Users can also raise exceptions with raise and assert statements.

Raising exceptions. An exception is a object instance with a class that inherits, either directly or indirectly, from the BaseException class. The assert statement introduced in Chapter 1 raises an exception with the class AssertionError. In general, any exception instance can be raised with the raise statement. The general form of raise statements are described in the Python docs. The most common use of raise constructs an exception instance and raises it.

>>> raise Exception('An error occurred')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
Exception: an error occurred

When an exception is raised, no further statements in the current block of code are executed. Unless the exception is handled (described below), the interpreter will return directly to the interactive read-eval-print loop, or terminate entirely if Python was started with a file argument. In addition, the interpreter will print a stack backtrace, which is a structured block of text that describes the nested set of active function calls in the branch of execution in which the exception was raised. In the example above, the file name <stdin> indicates that the exception was raised by the user in an interactive session, rather than from code in a file.

Handling exceptions. An exception can be handled by an enclosing try statement. A try statement consists of multiple clauses; the first begins with try and the rest begin with except:

try:
    <try suite>
except <exception class> as <name>:
    <except suite>
...

The <try suite> is always executed immediately when the try statement is executed. Suites of the except clauses are only executed when an exception is raised during the course of executing the <try suite>. Each except clause specifies the particular class of exception to handle. For instance, if the <exception class> is AssertionError, then any instance of a class inheriting from AssertionError that is raised during the course of executing the <try suite> will be handled by the following <except suite>. Within the <except suite>, the identifier <name> is bound to the exception object that was raised, but this binding does not persist beyond the <except suite>.

For example, we can handle a ZeroDivisionError exception using a try statement that binds the name x to 0 when the exception is raised.

>>> try:
        x = 1/0
    except ZeroDivisionError as e:
        print('handling a', type(e))
        x = 0
handling a <class 'ZeroDivisionError'>
>>> x
0

A try statement will handle exceptions that occur within the body of a function that is applied (either directly or indirectly) within the <try suite>. When an exception is raised, control jumps directly to the body of the <except suite> of the most recent try statement that handles that type of exception.

>>> def invert(x):
        result = 1/x  # Raises a ZeroDivisionError if x is 0
        print('Never printed if x is 0')
        return result

>>> def invert_safe(x):
        try:
            return invert(x)
        except ZeroDivisionError as e:
            return str(e)

>>> invert_safe(2)
Never printed if x is 0
0.5
>>> invert_safe(0)
'division by zero'

This example illustrates that the print expression in invert is never evaluated, and instead control is transferred to the suite of the except clause in handler. Coercing the ZeroDivisionError e to a string gives the human-interpretable string returned by handler: 'division by zero'.

3.4.1 Exception Objects

Exception objects themselves carry attributes, such as the error message stated in an assert statement and information about where in the course of execution the exception was raised. User-defined exception classes can carry additional attributes.

In Chapter 1, we implemented Newton's method to find the zeroes of arbitrary functions. The following example defines an exception class that returns the best guess discovered in the course of iterative improvement whenever a ValueError occurs. A math domain error (a type of ValueError) is raised when sqrt is applied to a negative number. This exception is handled by raising an IterImproveError that stores the most recent guess from Newton's method as an attribute.

First, we define a new class that inherits from Exception.

>>> class IterImproveError(Exception):
        def __init__(self, last_guess):
            self.last_guess = last_guess

Next, we define a version of IterImprove, our generic iterative improvement algorithm. This version handles any ValueError by raising an IterImproveError that stores the most recent guess. As before, iter_improve takes as arguments two functions, each of which takes a single numerical argument. The update function returns new guesses, while the done function returns a boolean indicating that improvement has converged to a correct value.

>>> def iter_improve(update, done, guess=1, max_updates=1000):
        k = 0
        try:
            while not done(guess) and k < max_updates:
                guess = update(guess)
                k = k + 1
            return guess
        except ValueError:
            raise IterImproveError(guess)

Finally, we define find_root, which returns the result of iter_improve applied to a Newton update function returned by newton_update, which is defined in Chapter 1 and requires no changes for this example. This version of find_root handles an IterImproveError by returning its last guess.

>>> def find_root(f, guess=1):
        def done(x):
            return f(x) == 0
        try:
            return iter_improve(newton_update(f), done, guess)
        except IterImproveError as e:
            return e.last_guess

Consider applying find_root to find the zero of the function (2x^2 + \sqrt{x}). This function has a zero at 0, but evaluating it on any negative number will raise a ValueError. Our Chapter 1 implementation of Newton's Method would raise that error and fail to return any guess of the zero. Our revised implementation returns the last guess found before the error.

>>> from math import sqrt
>>> find_root(lambda x: 2*x*x + sqrt(x))
-0.030211203830201594

While this approximation is still far from the correct answer of 0, some applications would prefer this coarse approximation to a ValueError.

Exceptions are another technique that help us as programs to separate the concerns of our program into modular parts. In this example, Python's exception mechanism allowed us to separate the logic for iterative improvement, which appears unchanged in the suite of the try clause, from the logic for handling errors, which appears in except clauses. We will also find that exceptions are a very useful feature when implementing interpreters in Python.