Slide 1

Today: lambda output, lambda/def, custom sorting with lambda, wordcount sorting, introduction to modules

Slide 2

Lambda - You The Power Hacker

Lambda is powerful feature, letting you express a lot of computation in very little space. As a result, it's weird looking at first, but when it clicks, you should feel like a Power Hacker when you wield it.

These are well suited to little in-class exercises .. just one line long. Not easy, but they are short!

Slide 3

Lambda - Code as a Parameter

Countless times, you have called a function and passed in some data for it to use. The function name is the verb, and the parameters are extra nouns to guide the computation:

e.g. "draw_line" is the verb, with these int coords

canvas.draw_line(0, 0, 100, 50, color='red')

With lambda, we open up a new category, passing in code as the parameter for the function to use, e.g. with map():

map(lambda s: s.upper() + '!', ['pass', 'code'])

Result:

['PASS!', 'CODE!']

Having an easy way to pass code between functions can be very handy.

Slide 4

Recall: Lambda Steps

1. The word "lambda"

2. What type of element? - choose a good name for the parameter: n:, s:, ...

3. Write expression to produce, no "return"

Slide 5

Map With Type-Change

> lambda1 section

The output list does not need to have the same element type as the input list. The lambda can output any type it likes, and that will make the output list. See examples: super_tuple() and lens()

Slide 6

Example: lens(strs)

> lens()

lens(strs): Given a list of strings. return a list of their int lengths.

Solution

def lens(strs):
    return map(lambda s: len(s), strs)

Slide 7

Lambda vs. Def

Lambda and def are similar:

def double(n):
    return n * 2

Equivalent lambda

lambda n: n * 2

Slide 8

Def Features

• Not everything needs to be a lambda
• def introduces a name for the code
• Def has room for real code features:
• Multiple lines
• If statements
• Variables
• Loops
• Inline comments
• Lambda: best without any of that, just short, 1-line

Slide 9

Def vs. Lambda

• Shown map() with lambda many times - that's the sweet spot
• What do if computation does not fit in 1 line?
• Just write a def
• map() can use the def

Slide 10

map/def Example - map_parens()

> map_parens()

In lambda1, see the map_parens() problem.

['xx(hi)xx', 'abc(there)xyz', 'fish'] ->
  ['hi', 'there', 'fish']

Solution Code. map() works with "parens" by name

def parens(s):
    left = s.find('(')
    right = s.find(')', left)
    
    if left == -1 or right == -1:
        return s
    return s[left + 1:right]


def map_parens(strs):
    return map(parens, strs)

Slide 11

Lambda-2 Examples

> lambda-2 section

The first of these work on a list of (x, y) tuples. These are a little more complicated but packing even more power into the one line.

Slide 12

xy_sum()

> xy_sum()

xy_sum(points): Given a list of len-2 (x, y) tuples. Return a list of the sums of each tuple. Shows that the result-list does not need to hold the same type as the input list. Solve with a map/lambda.

[(4, 2), (1, 2) (2, 3)]  ->  [6, 3, 5]

The input is a list of points, that is a list of (x, y) tuples. Q: What type is the param to the lambda? A: one "point" (x, y) tuple

Solution

def xy_sum(points):
    return map(lambda point: point[0] + point[1], points)

Slide 13

xs()

> xs()

Given a points list of len-2 (x, y) tuples. Return a list of just the x value of each tuple. Solve with a map/lambda.

Solution

def xs(points):
    return map(lambda point: point[0], points)

Slide 14

min_x()

> min_x()

Given a non-empty list of len-2 (x, y) tuples. What is the leftmost x among the tuples? Return the smallest x value among all the tuples, e.g. [(4, 2), (1, 2) (2, 3)] returns the value 1. Solve with a map/lambda and the builtin min(). Recall: min([4, 1, 2]) returns 1

[(4, 2), (1, 2), (2, 3)]  -> 1

Solution

def min_x(points):
    return min(map(lambda point: point[0], points))
    # Use map/lambda to form a list of
    # just the x coords. Feed that into min()

Slide 15

Custom Sort - Power Feature

• Python sorting has a lot of power in it
• Use lambda to guide the sorting
• This code feels powerful and dense
• More examples in section!

Slide 16

Python Custom Sort - Food Examples

• Lamest food I could think of - Radish
• Suppose I have len-3 food tuples, each with 3 parts:
• food = (name, tasty 1-10, healthy 1-10)
• food[0] = its name
• food[1] = how tasty it is 1-10
• food[2] = how healthy it is 1-10

We'll try these food examples in the interpreter.

Slide 17

Default sorted()

By default sorted() works on list of tuples, compares [0] first, then [1], and so on

>>> foods = [('radish', 2, 8), ('donut', 10, 1), ('apple', 7, 9), ('broccoli', 6, 10)]
>>> 
>>> # By default, sorts food tuples by [0]
>>> sorted(foods)
[('apple', 7, 9), ('broccoli', 6, 10), ('donut', 10, 1), ('radish', 2, 8)]
>>> 

Slide 18

Sort By Tastiness

• Say I want to sort by tastiness
  e.g. the radish vs. donut dimension
• Control how sorted() looks a the data
• Like drawing a circle around tasty values - sort by these!
• How can we get the code to do this?

Slide 19

Project Out Sort-By Values

• How to code sort-by-tasty
• For each element in list
  "Project out" a sort-by value to be used in sorting comparisons
• Here, for each food, project out its tasty int
• aka "Proxy" strategy
  Each element, proxy value is used for sorting comparisons

Slide 20

Project Out With Lambda

• Q: how to project out these sort-by proxy values?
• A: lambda

Slide 21

Custom Sort Lambda - Plan

• 1. Call sorted() as usual
• 2. provide key=lambda to control sorting
• Lambda here takes one parameter - an elem from the list
• The lambda projects out the sort-by value to use for comparisons
• e.g. sort by tasty
  lambda food: food[1]
• e.g. sort by healthy
  lambda food: food[2]

Q: What is the parameter to the lambda?

A: One elem from the list (similar to map() function)

Slide 22

Sort By Tasty

>>> foods = [('radish', 2, 8), ('donut', 10, 1), ('apple', 7, 9), ('broccoli', 6, 10)]
>>> 
>>> sorted(foods, key=lambda food: food[1])
[('radish', 2, 8), ('broccoli', 6, 10), ('apple', 7, 9), ('donut', 10, 1)]

Slide 23

Most Tasty (reverse=True)

>>> sorted(foods, key=lambda food: food[1], reverse=True)  # most tasty
[('donut', 10, 1), ('apple', 7, 9), ('broccoli', 6, 10), ('radish', 2, 8)]

Slide 24

Can we do this for dicts?

Dicts are not inherently sortable. Let's reformat our foods example by making a dict:

foods = [{'name': 'radish', 'tasty': 2, 'healthy': 8},
          {'name': 'donut', 'tasty': 10, 'healthy': 1},
          {'name': 'apple', 'tasty': 7, 'healthy': 9},
          {'name': 'broccoli', 'tasty': 6, 'healthy': 10}]

Now, if we try sorting it, we get an error:

>>> sorted(foods)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: '<' not supported between instances of 'dict' and 'dict'
>>>

But, we can just use our key= with a lambda to tell the sorted function which element we want to sort by:

>>> sorted(foods, key=lambda x: x['name'])
>>> sorted(foods, key=lambda x: x['name'])
[{'name': 'apple', 'tasty': 7, 'healthy': 9}, {'name': 'broccoli', 'tasty': 6, 'healthy': 10}, {'name': 'donut', 'tasty': 10, 'healthy': 1}, {'name': 'radish', 'tasty': 2, 'healthy': 8}]
>>>

By the way: printing dicts in the interpreter isn't great. There is a way to "pretty print" the dict so it looks better (you could also just write a function to do this):

>>> import pprint
>>> pp = pprint.PrettyPrinter()
>>> sorted_foods = sorted(foods, key=lambda x: x['name'])
>>> pp.pprint(sorted_foods)
[{'healthy': 9, 'name': 'apple', 'tasty': 7},
 {'healthy': 10, 'name': 'broccoli', 'tasty': 6},
 {'healthy': 1, 'name': 'donut', 'tasty': 10},
 {'healthy': 8, 'name': 'radish', 'tasty': 2}]
>>>

Slide 25

Most Healthy

>>> sorted(foods, key=lambda food: food[2], reverse=True)  # most healthy
[('broccoli', 6, 10), ('apple', 7, 9), ('radish', 2, 8), ('donut', 10, 1)]

Slide 26

Most tasty * healthy

Not limited to just projecting out existing values. We can project out a computed value. Here we compute tasty * healthy and sort on that. So apple is first, 7 * 9 = 63, broccoli is second with 6 * 10 = 60. Donut is last :(

>>> sorted(foods, key=lambda food: food[1] * food[2], reverse=True)
[('apple', 7, 9), ('broccoli', 6, 10), ('radish', 2, 8), ('donut', 10, 1)]
>>>

Slide 27

Sorted vs. Min Max

• What code give us the most tasty food?
  Or the least tasty?
• Sorting n things is kind of expensive
• Could sort, take the last item - overly expensive approach
• Use max(), max takes a key=lambda just like sorted()
• e.g. pull out most or least tasty food - change "sorted" to "max" or "min"

>>> foods = [('radish', 2, 8), ('donut', 10, 1), ('apple', 7, 9), ('broccoli', 6, 10)]
>>> max(foods)     # uses [0] by default - tragic!
('radish', 2, 8)
>>> 
>>> sorted(foods, key=lambda food: food[1])
[('radish', 2, 8), ('broccoli', 6, 10), ('apple', 7, 9), ('donut', 10, 1)]
>>> 
>>> max(foods, key=lambda food: food[1])  # most tasty
('donut', 10, 1)
>>> min(foods, key=lambda food: food[1])  # least tasty
('radish', 2, 8)

Key performance point: computing one max/min element is much faster than sorting all n elements.

Slide 28

Python Custom Sort String Examples

• Default sorted() uses "<"
• With strings, < places uppercase before lowercase, rarely what we want

>>> # The default sorting is not good with upper/lower case
>>> strs = ['coffee', 'Donut', 'Zebra', 'apple', 'Banana']
>>> sorted(strs)
['Banana', 'Donut', 'Zebra', 'apple', 'coffee']

Slide 29

String Sort Lambda

• Fix: project out lowercase version of string as sort-by
• The lambda takes in one elem from list - in this case 1 string
• e.g. lambda s: s.lower()
• Examples: sort not case-sensitive, sort by last char

>>> strs = ['coffee', 'Donut', 'Zebra', 'apple', 'Banana']
>>> 
>>> sorted(strs, key=lambda s: s.lower())    # not case sensitive
['apple', 'Banana', 'coffee', 'Donut', 'Zebra']
>>> 
>>> sorted(strs, key=lambda s: s[len(s)-1])  # by last char
['Zebra', 'Banana', 'coffee', 'apple', 'Donut']
>>> 

Slide 30

Movie Examples

Given a list of movie tuples, (name, score, date-score), e.g.

[('alien', 8, 1), ('titanic', 6, 9), ('parasite', 10, 6), ('caddyshack', 4, 5)]

Slide 31

sort_score(movies)

> sort_score()

Given a list of movie tuples, (name, score, date-score), where score is a rating 1-10, and date 1-10 is a rating as a "date" movie. Return a list sorted in increasing order by score.

Slide 32

sort_date(movies)

> sort_date()

Given a list of movie tuples, (name, score, date-score), where score is a rating 1-10, and date-score 1-10 is a rating as a "date" movie. Return the list sorted in decreasing by date score.

Slide 33

Put It All Together - WordCount + Sorted

Look at wordcount project, apply custom sorting to the output stage.

Slide 34

Sorted vs. Dict Count Items

• Wordcount has a "counts" dict, key is a word, value is its count
• Use counts.items()
• Gives us a "items" list of pairs: (char, count)
• I'll use "items" as the var name here, echoing the "d.items()" function name
• Use "pair" as lambda parameter, (char, count)

>>> items = [('z', 1), ('a', 3), ('e', 11), ('b', 3), ('c', 2)]

• Copy that items list into interpreter, try these code challenges
• Questions we could ask of the items - demo or you-try-it
• 1. How to sort items in increasing order by char (easy!)
• 2: How to sort items in increasing order by count?
• 3. How to sort items in decreasing order by count?
• 4: How to access the pair with the largest count?

>>> items = [('z', 1), ('a', 3), ('e', 11), ('b', 3), ('c', 2)]
>>> 
>>> # sort by [0]=word is the default
>>> sorted(items)
[('a', 3), ('b', 3), ('c', 2), ('e', 11), ('z', 1)]
>>> 
>>> sorted(items, key=lambda pair: pair[1])   # sort by count
[('z', 1), ('c', 2), ('a', 3), ('b', 3), ('e', 11)]
>>> 
>>> sorted(items, key=lambda pair: pair[1], reverse=True)
[('e', 11), ('a', 3), ('b', 3), ('c', 2), ('z', 1)]
>>> 
>>> max(pairs, key=lambda pair: pair[1])      # largest count
('e', 11)

Slide 35

Wordcount - Top-Count - Lambda

Here is the WordCount project we had before. This time look at the print_counts() and print_top() functions.

> wordcount.zip

Slide 36

print_counts() - Alphabetic Output

Here is the output of the regular print_counts() function, which prints out in alphabetic order. Output looks like:

$ python3 wordcount.py poem.txt 
are 2
blue 2
red 2
roses 1
violets 1
$

Slide 37

print_counts() Solution

This is the standard dict-output sorted loop.

def print_counts(counts):
    """
    Given counts dict, print out each word and count
    one per line in alphabetical order, like this
    aardvark 1
    apple 13
    ...
    """
    for word in sorted(counts.keys()):
        print(word, counts[word])
    # Alternately use .items() to access all the key/value data
    # for key, value in sorted(counts.items()):
    #    print(key, value)

Slide 38

print_top()

The print_top(counts, n) function - print the n most common words in decreasing order by count.

$ python3 wordcount-solution.py -top 10 alice-book.txt 
the 1639
and 866
to 725
a 631
she 541
it 530
of 511
said 462
i 410
alice 386

• Look at print_top() function
• Print the items list to see what we have (Python debug technique)
• Recall: dict.items() - random order of word/count pairs
  [('sister', 12), ('rabbit', 5), ...]
• Need to sort the pairs: decreasing order by count
• Use sorted/lambda
• This code is incredibly short and powerful

Slide 39

print_top() Solution

def print_top(counts, n):
    """
    Given counts dict and int N, print the N most common words
    in decreasing order of count
    the 1045
    a 672
    ...
    """
    items = counts.items()
    # Could print the items in raw form, just to see what we have
    # print(items)
    pass
    # Your code - my solution is 3 lines long, but it's dense!
    # Sort the items with a lambda so the most common words are first.
    # Then print just the first N word,count pairs with a slice
    # 1. Sort largest count first
    items = sorted(items, key=lambda pair: pair[1], reverse=True)
    # 2. Slice to grab first N
    for word, count in items[:n]:
        print(word, count)

Slide 40

Modules and Modern Coding

May or may not get to this

• "module" - unit of code to use, aka "library"
• Every module has a name, e.g. "math"
• Module contains lots of functions, solving common problems
• Modern coding:
• 1. Writing custom code
• 2. Calling built-in functions e.g. sorted(), str.lower()
• 3. Calling module code, e.g. math - today

Slide 41

Standard Modules - import math

• Python comes with "standard" modules
• Standard modules are installed when Python is installed, so no extra step required
• These are preferred
• e.g. the "math" module - mathematics
• e.g. the "sys" module - interface with operating system
• import module by name
• Use dot to refer to functions etc. in the module
  math.sqrt(2) - function call
  math.pi - pi constant within math
• Shown here in interpreter, but works in .py too
• Common error: forgetting to do the import
• Aside: there are other import forms, but this name/dot form is the most important

>>> import math
>>> math.sqrt(2)  # call sqrt() fn
1.4142135623730951
>>> math.sqrt

>>> 
>>> math.log(10)
2.302585092994046
>>> math.pi       # constants in module too
3.141592653589793

Quit and restart the interpreter without the import, see common error:

>>> # quit and restart interpreter
>>> math.sqrt(2)  # OOPS forgot the import
Traceback (most recent call last):
NameError: name 'math' is not defined
>>>
>>> import math
>>> math.sqrt(2)  # now it works
1.4142135623730951

Slide 42

Random Module Exercise

Try "random" module. Import it, call its "randrange(20)" function.

>>> import random
>>> 
>>> random.randrange(4)
3
>>>