# More Python¶

Note

These slides are also available in PDF format: 4:3 PDF, 16:9 PDF, 16:10 PDF.

## LGA & Review¶

### Learning Group Activity¶

Review the learning group activity with your group members:

1. What questions, comments, or snarky remarks did you have on the official Python style guide (PEP 8)?
2. Demonstrate what you made in Python.

### Review: Slicing¶

mylist = [1, 2, 3, 4]

# syntax is [start:stop:step], step optional
mylist[1:3]     # => [2, 3]

# unused parameters can be ommited
mylist[::-1]    # => [4, 3, 2, 1]

# without the first element
mylist[1:]      # => [2, 3, 4]

# without the last element
mylist[:-1]     # => [1, 2, 3]


### Review: Tuple Expansion & Collection¶

Multiple assignments work like so:

names = ("R. Stallman", "L. Torvalds", "B. Joy")
a, b, c = names


* can be used to collect a tuple:

# drop the lowest and highest grade
grades = (79, 81, 93, 95, 99)


The same can be done to expand a tuple in a function call:

# Each grade becomes a separate argument


## Functions¶

### Review: Functions¶

To define a function in Python, use the def syntax:

def myfun(arg1, arg2, arg3):
if arg1 == 'hello':
return arg2
return arg3


Even if your function does not take arguments, you still need the parentheses:

def noargs():
print("I'm all lonely without arguments...")


### Keyword Arguments¶

When we define a function in Python we may define keyword arguments. Keyword arguments differ from positional arguments in that keyword arguments:

• Take a default value if unspecified
• Can be placed either in order or out of order:
• In order: arguments are assigned in the order of the function definition
• Out of order: the argument name is written in the call
• Positional and keyword arguments can be mixed, so as long as the positional arguments go first.

### Keyword Arguments: Example¶

def point_twister(x, y=1, z=0):
return x + 2*z - y

# all of these are valid calls
print(point_twister(1, 2, 3))       # x=1, y=2, z=3
print(point_twister(1, 2))          # x=1, y=2, z=0
print(point_twister(1))             # x=1, y=1, z=0
print(point_twister(1, z=2, y=0))   # x=1, y=0, z=2
print(point_twister(1, z=2))        # x=1, y=1, z=2


Style Note

PEP 8 says that we should place spaces around our “=” in assignments, but these are not assignments, and should be written without spaces around the “=”.

### Passing a Dictionary as the Keyword Arguments¶

Just like a tuple or list can be expanded to the positional arguments of a function call using *some_tuple, a dictionary can be expanded to the keyword arguments of a function using **some_dict. For example:

my_point = {'x': 10, 'y': 15, 'z': 20}
print(point_twister(**my_point))


### *args and **kwargs¶

Python allows you to define functions that take a variable number of positional (*args) or keyword (**kwargs) arguments. In principle, this really just works like tuple expansion/collection.

def crazyprinter(*args, **kwargs):
for arg in args:
print(arg)
for k, v in kwargs.items():
print("{}={}".format(k, v))

crazyprinter("hello", "cheese", bar="foo")
# hello
# cheese
# bar=foo


The names args and kwargs are merely a convention. For example, you could use the names rest and kwds instead if you wanted.

### *args and **kwargs: Another Example¶

def fancy_args(a, b, *args, c=10, **kwargs):
print("a is", a)
print("b is", b)
print("c is", c)
print("args is", args)
print("kwargs is", kwargs)

fancy_args(1, 2, 3, 4, c=15, d=16, e=17)
# a is 1
# b is 2
# c is 15
# args is (3, 4)
# kwargs is {'d': 16, 'e': 17}


### Anonymous Functions¶

The Python keyword lambda creates an anonymous function. The syntax is:

lambda arg1, arg2, ...: result


For example:

double = lambda x: x * 2


But is it really cleaner?

A lot of lambda functions can make your code hard to read. But there does exist the occasion a lambda will make your life easier (defaultdict example).

## Generators¶

### Generator Functions¶

A special kind of function exists called a generator function. A generator function yields values rather than returning them: rather than exiting the function call, the function continues to run and yield more values.

def one_to(stop):
x = 1
while x <= stop:
yield x
x += 1


### Using Generator Functions¶

Calling a generator function produces a generator object:

my_gen = one_to(5)


Calling next on the generator object gets us the next thing it yields:

print(next(my_gen))     # => 1
print(next(my_gen))     # => 2
print(next(my_gen))     # => 3
print(next(my_gen))     # => 4


When the function exits, calling next raises a StopIteration exception:

print(next(my_gen))     # => 5
print(next(my_gen))     # raises StopIteration


### But we rarely use next directly…¶

for loops can use it for us:

# Prints 1, 2, then 3
# The loop exits on StopIteration
for x in one_to(3):
print(x)


We can create lists, sets, and many other things from generator objects:

list(one_to(8))   # => [1, 2, 3, 4, 5, 6, 7, 8]
set(one_to(8))    # => {1, 2, 3, 4, 5, 6, 7, 8}
tuple(one_to(8))  # => (1, 2, 3, 4, 5, 6, 7, 8)


### Generator Functions: Another Example¶

We could define a function (similar to) range that we talked about last time:

def range(start, stop, step=1):
i = start
while i < stop:
yield i
i += step


### Generator Expressions (Anonymous Generator Functions)¶

A generator function can be created anonymously:

(x * 2 for x in nums if x % 2 == 0)


Consider this similar to the following Haskell list comprehension:

[x * 2 | x <- nums, x mod 2 == 0]


There’s three parts to a generator expression:

1. The output expression which computes each value, this is x * 2 above
2. Preforming something for every element in a sequence, this is for x in nums above
3. Selecting a subset of elements to operate on, this is if x % 2 == 0 above

### GEs: Multiple Loops¶

Multiple loops can be written inside of a GE, and the loops will be evaluated outside-in:

>>> gen = ((x, y) for x in range(15)
if happy(x)
for y in range(2))
>>> list(gen)
[(1, 0), (1, 1),
(7, 0), (7, 1),
(10, 0), (10, 1),
(13, 0), (13, 1)]


Note

The function happy is not included in Python, but can be found on the course website.

Looks like you are viewing the HTML version of these slides, so here is a link for you!

### GEs: Syntax Details¶

If a GE is the only argument to a function call, the second set of parentheses can be omitted:

print("The smallest was:",
min(input("Give me a number: ") for _ in range(5)))


You could use this to build lists or sets, for example:

list(x + 1 for x in range(3))   # => [1, 2, 3]
set(x + 1 for x in range(3))    # => {1, 2, 3}


But Python provides a more convenient syntax for that…

### Comprehensions¶

A list comprehension is written as a GE with brackets. Think of it as a eager generator expression:

[x * 2 for x in nums if x % 2 == 0]


Similarly, a set comprehension is written as a GE with braces:

{x % 7 for x in range(0, 20, 5)}


And we can even write dictionary comprehensions:

{x: f(x) for x in range(10)}


### Applications of GEs¶

reader = (float(line) for line in f)
while event_queue:

• Hash function pRNGs

rng = (h(x)/MAX_HASH for x in count())

• The possibilities are endless! I use GEs and comprehensions all the time since they are highly expressive.

## Modules¶

### Modules¶

Often times, we wish to break our software into several files and namespaces. Python provides a very simple way to do this:

1. Write your functions in a file called somemodule.py
2. Type import somemodule at the top of your program.
3. You’ll now have access to an object named somemodule whose members are the objects from somemodule.py

See happy.py on the course website for a simple example.

### Bringing Things Into our Namespace¶

Typing import somemodule will provide you with a module object which you can access members, but does not declare any new variables in your namespace except for the somemodule object.

To bring in certain members, you can use a from statement:

from somemodule import f1, f2


### Aliasing¶

Often times we don’t want to call the module in our namespace what the filename is, so we can use as to rename:

import somemodule as mod

mod.f1(...)


Or, using a from:

from somemodule import f1 as somefunc

somefunc(...)


### More Complex Modules¶

We may wish to make very complex modules, which are composed of multiple files. To do so:

1. Create a directory with the desired module name (e.g., somemodule)
2. Put a file in that directory named __init__.py. When import somemodule is typed, this is the file that will be imported.
3. Create other parts of the module under other file names, these can be imported by typing import somemodule.somefile. From within our module, we can type from .somefile import x.

## Functional Programming¶

### Partial Application¶

The partial function from the functools library provides us with a partial applicator:

from functools import partial

value_print = partial(print, "The value is: ")

value_print(10)
# The value is: 10


### min/max¶

The min and max functions select the minimum/maximum element from an sequence or generator, optionally based on a key function:

closest_point = min(points, key=partial(dist, ref))


Compare to the equivalent procedural code:

closest_dist = float('inf')
closest_point = None
for p in points:
d = dist(ref, p)
if d < closest_dist:
closest_dist = d
closest_point = p


You tell me which code snippet is more expressive. ;)

### zip¶

Python takes a little inspiration from Haskell and provides a zip generator function which yields pairwise tuples from each of its arguments.

small = [1, 2, 3]
med = [10, 20, 30]
large = [100, 200, 300]
for a, b, c in zip(small, med, large):
print(a, b, c)
# 1 10 100
# 2 20 200
# 3 30 300


Pro Tip: Iterating over the columns of a row-major 2D list

for col in zip(*arr)

• map(f, *sequences) is a generator function that applies a function to a sequence of elements. Anything that can be done with map could also be done using a GE (and potentially zip), so your choice on whether to use this.
• reduce(f, seq) is the general-case reduction function: it takes a function and folds it across the sequence. (from functools)