Python runner technique code#
It would be easier to spot errors if we could make our code clearer. An algorithm with a lot of loops and branches would be hard to follow and may even confuse ourselves. If we need some numerical algorithms, check if you can get one from NumPy.Īnother way to avoid bugs is to use better logic. We have a JSON library, and we shouldn’t write our own. The same attitude applies to functions too. But the dictionary is highly optimized, and we should consider using the dictionary whenever possible. Python doesn’t come with a balanced search tree or linked list. Similarly, if you need a queue, we have deque in the collections module from the standard library. Your implementation would not be any faster. You should never try to create a stack data structure yourself since a list supports append() and pop(). In Python, we have a lot of nice containers and optimized operations. The other technique to avoid bugs is not to reinvent the wheel. In summary, we should be careful if the argument to our function is a mutable object. Then we will see the three distinct names in the log. The principle here is that you should never let the anomaly proceed silently as your algorithm will not behave correctly and sometimes have dangerous effects (e.g., deleting wrong files or creating cybersecurity issues). That’s why in Python, we prefer “it’s easier to ask for forgiveness than permission” (EAFP) over “look before you leap” (LBYL). From the performance perspective, you will also find that raising exceptions is faster than using if-statements to check. Your code won’t work in these cases anyway, and raising an appropriate exception can help future reuse. Similarly, when something unexpected happens, e.g., a temporary file you created disappeared at the midway point, raise a RuntimeError. When you never expect the input to be negative, raise a ValueError with an appropriate message. In fact, the exception handling system in Python is mature, and we should use it. It is also a good practice to make our code harder to be misused, i.e., not to let variables go out of our intended range without notice. Having these guard rails in place will guarantee our half-baked code is never used in the way it is not supposed to. This is useful when we gradually develop our program, which we implement one case at a time and address some corner cases later. NotImplementedError: Function tanh is not implementedĪs you can imagine, we can raise NotImplementedError in places where the condition is not entirely invalid, but it’s just that we are not ready to handle those cases yet. However, we want to point out that the following is a wrong but common way to do sanitation:
We do this only where it can go wrong, namely, on the interface functions that we expose as API for other users or on the main function where we take the input from a user’s command line. Usually, we do not do this on every function to save our effort as well as not to compromise the computation efficiency. Certainly, that is a balance you need to decide on. You may wonder if it is necessary to make our code lengthier by adding these sanitations. Canonicalized input is easier to check for conformation (e.g., we know /etc/passwd contains sensitive system data, but we’re not so sure about /tmp/./etc/././passwd).
Python runner technique full#
For example, a URL should start with “ and a file path should always be a full absolute path like /etc/passwd instead of something like /tmp/./etc/././passwd. This means we should make the input in a standardized format. Otherwise, we have to consider three different cases that we call range(), namely, range(10), range(2,10), and range(2,10,3), which will make our while loop more complicated and error-prone.Īnother reason to sanitize the input is for canonicalization. Then, the while loop can be written as such. But with the two if statements at the beginning of the function, we know there are always values for variables a, b, and c. This is a simplified version of range() that we can get from Python’s built-in library.
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