Vectorized operations are quicker than loops in Python. That’s because loops are executed in Python, while vectorized operations done by NumPy call C routines under the hood, and C is overwhelmingly faster than Python
Python’s way of indexing vectors is weird coming from Julia, but look at this: to split a list l before and after index i, you just do before=l[:i]; after=l[i:].
Turns out in Python you can add attributes to an object at runtime, attributes that were not in the class definition! Things like obj.new_attribute = 3. It sounds pretty dangerous to me, but ok.
Adding a __call__ method to a class will enable instances to behave as functions. This is what is called “functor” behavior in Julia.
A context manager is a Python construct that calls __enter__ when the object is created in a with clause, and __exit__ at the end of the with clause. For instance, this is how Python handles the with open(...) as f: construct that you’ll often see for opening files without requiring an explicit close(f) at the end.
Generators in Python are iterators, just like those in Julia, i.e. lazy iterators over iterables, except in Python once you can’t iterate more than once. Iterating a second time returns nothing
>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator:
... print(i)
0
1
4
>>> for i in mygenerator:
... print(i)
Python iterators are stateful, meaning that an iterator is an object containing a state which is modified every time an iteration is performed. Instead, Julia’s iterate accepts an iterator and a state, and returns a (item , newstate) tuple.
In Python, iterators are managed using the yield keyword. Placed instead of a return statement, it transforms the return of the parent function into an iterator which, once iterated over, executes the code until the next yield statement. Automatically, enough information is stored in the iterator object such that it’s possible to resume execution at each iteration.
os.walk: generates the file names in a directory tree by walking the tree either top-down or bottom-up. For each directory in the tree rooted at directory top (including top itself), it yields a 3-tuple (dirpath, dirnames, filenames).
partial is Python’s equivalent of a closure in Julia.