Python Crib Sheet #3

Modulo

Modulo in Python and Scala differ for negative numbers. In Python, you see something like:

>>> print(-1 % 10)
9
>>> print(-11 % 10)
9

In Scala:

scala> println(-1 % 10)
-1

scala> println(-11 % 10)
-1

Arrays

Negative indices can be used to count from the end, for example:

>>> xs = range(0, 10)
>>> xs
range(0, 10)
>>> list(xs)

[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

>>> xs[-1]
9
>>> xs[-2]
8

etc

Numpy

Similarly, you can reshape Numpy arrays:

>>> xs = [0, 1, 2]
>>> np.array(xs).shape
(3,)
>>> np.array(xs).reshape(-1, 1).shape
(3, 1)

If you don't reshape the Numpy array then operators can change the shape of those that have been reshaped.

>>> xs = [0, 1, 2]
>>>  np.array(xs).reshape(-1, 1)
array([[0],
       [1],
       [2]])
>>> np.array(xs).reshape(-1, 1) * np.array(xs)
array([[0, 0, 0],
       [0, 1, 2],
       [0, 2, 4]])

That is, a Cartesian product operations.

But note that an array that is not reshaped implicitly acts like a row vector:

>>> np.array(xs).reshape(1, -1) * np.array(xs)
array([[0, 1, 4]])

That is, an element-wise product.

Also note that some actions on the reshaped array can reverse the reshaping. For instance, summing over rows:

>>> a = np.array(xs).reshape(-1, 1)

>>> a.shape

(3, 1)

>>> np.sum(a, 1).shape  # 0 would sum columns

(3,)

Generators

Generators are (to my understanding at least) like a Java/Scala stream.

Iterables, generators and yield are related and this excellent SO answer weaves them together.

“When you use a list comprehension, you create a list, and so an iterable:
…
Generators are iterators, a kind of iterable you can only iterate over once. Generators do not store all the values in memory, they generate the values on the fly. [The syntax for creating generators is] just the same except you used () instead of []
…
yield is a keyword that is used like return, except the function will return a generator… To master yield, you must understand that when you call the function, the code you have written in the function body does not run. The first time the for calls the generator object created from your function, it will run the code in your function from the beginning until it hits yield, then it'll return the first value of the loop. Then, each other call will run the loop you have written in the function one more time, and return the next value until there is no value to return. The generator is considered empty once the function runs, but does not hit yield anymore.

>>> def createGenerator():
...    mylist = range(3)
...    for i in mylist:
...        yield i*i
...
>>> mygenerator = createGenerator() # create a generator
>>> print(mygenerator) # mygenerator is an object!

>>> for i in mygenerator:
...     print(i)
0
1
4

Here it's a useless example, but it's handy when you know your function will return a huge set of values that you will only need to read once."

Note that "Local variables in a generator function are saved from one call to the next, unlike in normal functions." [Python Wiki]