Today: tuples, dict.items() output pattern, lambda 1-2-3, map, one-liners

Each Type has a Char

Ever notice how each new type has a char that marks it literal form in the code:

• string - quote ' .. '
• list - brackets [ .. ]
• dict - curly bracket { .. }
• tuple - parenthesis ( .. ) - today!

Tuples

For more detail see guide Python Tuples

• Written with parenthesis
  e.g. ('a', 13, 42, True)
  e.g. ('Alex Smith', 12432255, '2-11-2004')
• Group a few data items together
• Access like a list:
  len(), [ ] work the same
• Fixed size, typically small
• Sometimes have different types inside
• Immutable - no change, no append

>>> t = ('Alex Smith', 12432255, '2-11-2004')
>>> t[0]
'Alex Smith'
>>> t[1]
12432255
>>> t[2]
'2-11-2004'
>>> len(t)
3
>>> t[0] = 'x'
TypeError: 'tuple' object does not support item assignment
>>>

When To Use Tuple? (vs. List)

• Use a tuple to group a few data items together
• The number of elements is small and known ahead of time
• Elements may not all be the same type
• e.g. Store sunet and id number together - tuple
  ('sally22', 123456)
• e.g. Store 3 float x,y,z coordinates all together - tuple
  (4, 5.2, 6.1)
• e.g. Store filename of an mp3 file and its length in seconds together - tuple
  ('happy.mp3', 240)

When To Use List?

• List contains many elements, possibly a large number
• Number of elements maybe not known ahead of time
• Want .append() to add more
• Typically elements are all the same type
  Same-type is not a Python requirement, but it's a good practice
• List Examples:
• Store many urls - list of strings
  ['http://foo.com', 'http://kitten.org/bleh', ...]
• Store many weights - list of floats
  [34.5, 12.0, 16, ...]
• Store many filenames - list of strings
  ['poem.txt', 'jokes.html', ... ]

Tuple Optional Parenthesis

It's possible to omit the parenthesis when writing a tuple. We will not do this in CS106A code, but you can write it if you like and it is allowed under PEP8. We will write our code more spelled-out, showing explicitly when creating a tuple.

>>> t = 1, 4     # This works
>>> t
(1, 4)
>>> t = (4, 5)   # I prefer it spelled out like this
>>> t            # PEP8 says parenthesis optional
(4, 5)

Tuple Assignment = Shortcut

Here is a neat sort of trick you can do with a tuple. This is a shortcut for the use of =, assigning multiple variables in one step. This is just a little trick, not something you need to use.

>>> (x, y) = (3, 4)
>>> x
3
>>> y
4

• It's an error if the two sides are not the same len
• Works with lists too
• A bit obscure, you do not need to use this feature
• But handy sometimes (below)

Sorting With Tuples

• Recall function: sorted(lst), return list sorted in increasing order
• Sorting of tuples
• 1. First sort each tuple by its tuple[0]
• 2. If tuple[0] is the same between two elements (a "tie")
  Fall back to sorting on tuple[1] and so on
• Lets us combine multiple data items for sorting
• Like a spreadsheet: sort by column A, then by column B, and so on
• Say we have len-2 tuples of the form: ('ca', 'palo alto')
• Try sorting these tuples

>>> cities = [('tx', 'houston'), ('ca', 'palo alto'), ('ca', 'san jose'), ('tx', 'austin'), ('ca', 'aardvark')]
>>> 
>>> sorted(cities)
[('ca', 'aardvark'), ('ca', 'palo alto'), ('ca', 'san jose'), ('tx', 'austin'), ('tx', 'houston')]
>>> 
>>> sorted(cities, reverse=True)
[('tx', 'houston'), ('tx', 'austin'), ('ca', 'san jose'), ('ca', 'palo alto'), ('ca', 'aardvark')]

Dict Output Pattern #1

Say we have a dict loaded up with data

>>> d = {'a': 'alpha', 'g': 'gamma', 'b': 'beta'}

Here is the dict-print code we used before. This is a fine, standard pattern you can continue to use. But we are going to look at another way.

>>> for key in sorted(d.keys()):
...     print(key, d[key])
... 
a alpha
b beta
g gamma

Recall Dict Output: keys() values()

• How to get data out of dict?
• 1. d.keys(), or sorted(d.keys())
• 2. d.values() - collection of just values (same order as d.keys())
• These are all "list-like" collections
  The list-like collections work with for-loop and in
  But they are not lists, no [], no .append()
• If need a list, use list() to covert like this
  list(d.keys())

>>> d = {'a': 'alpha', 'g': 'gamma', 'b': 'beta'}
>>>
>>> d.keys()
dict_keys(['a', 'g', 'b'])
>>> sorted(d.keys())
['a', 'b', 'g']
>>>
>>> d.values()
dict_values(['alpha', 'gamma', 'beta'])
>>> 

dict.items()

• d.items() returns a collection of (key, value) tuples, each len-2
• d.items() returns all the (key, value) pairs, representing 100% of the dict's data
• The d.items() collection is in random order

>>> d = {'a': 'alpha', 'g': 'gamma', 'b': 'beta'}
>>> 
>>> d.items()          # (key, value) tuples - all the data
dict_items([('a', 'alpha'), ('g', 'gamma'), ('b', 'beta')])
>>> 

sorted(d.items())

>>> d.items()                      # random order
dict_items([('a', 'alpha'), ('g', 'gamma'), ('b', 'beta')])

Since sorting of tuples goes by [0] first, and [0] here is the key, the len-2 tuples are in effect sorted by key:

>>> sorted(d.items())              # sorted by key
[('a', 'alpha'), ('b', 'beta'), ('g', 'gamma')]

So it just sorts by the key. In practice, the sorting uses only key in each tuple, [0], never needing to look at [1], since the key values are all different.

Dict Output Code Almost-v2

• Say we want to output a dict
• Use sorted(dict.items() instead of dict.keys()
  Get (key, value) len-2 tuples
• Items has all the data in one step - nice
• item[0] is key, item[1] is value
• You can use this form, but there's a shortcut below

>>> for item in sorted(d.items()):
...     print(item[0], item[1])
... 
a alpha
b beta
g gamma

Dict Output Code v2

Recall the shortcut

>>> (a, b) = (6, 7)
>>> a
6
>>> b
7

Can use a similar shortcut inside a for loop. Since we are looping over tuples len-2, can specify two variables, and the loop unpacks each tuple into the variables, here key and value:

>>> for key, value in sorted(d.items()):
...     print(key, value)
... 
a alpha
b beta
g gamma

The above phrase is the v2 way to do dict output, using .items().

It's handy that the .items() gets all the data, so and we don't need to look up each value in the loop. Also it's nice that the key and value are unpacked into the variables in the loop so we have nice variable name for each.

That said, v2 is just an optional shortcut. You can always write it the old v1 way using .keys():

>>> for key in sorted(d.keys()):
...     print(key, d[key])
... 
a alpha
b beta
g gamma

Wordcount Output

Recall the wordcount.zip example. The print_counts() function prints out an alphabetical list of all the words in a text, each with its count.

$ python3 wordcount.py somefile.txt
aardvark 1
anvil 3
boat 4
...

The function can be written either with .keys() or with .items(). Both approaches are fine and are commonly used in Python code. The .items() approach is a little shorter, but using slightly more esoteric Python features.

1. sorted(dict.keys()) - v1

def print_counts(counts):
    for word in sorted(counts.keys()):
        print(word, counts[word])

2. sorted(dict.items()) - v2

Or the v2 .items() way

def print_counts(counts):
    for key, value in sorted(counts.items()):
        print(key, value)

Map/Lambda - Advanced Hacker Features

Map - a short way to transform a list - handy, but not super important

Lambda - an important way to package some code. Today we'll use map() to explore how lambda works

Lambda - Dense and Powerful

Lambda code is dense. Another way of saying that it is powerful. Sometimes you feel powerful with computer code because the code you write is long. Sometimes you feel even a little more powerful, because the code you write is short!

Dense One-Liner Code Solutions

There is something satisfying about solving a real problem with 1 line of code. The 1-liner code is so dense, we'll will write it a little more deliberately. See how this works below!

1. What does def do?

Consider the following "double" def. What does this provide to the rest of the program? Does it run the code? No.

def double(n):
    return 2 * n

The def defines that name within the program, and setting it to point to that body of code. Later line can refer to the code by that name. The drawing below shows a form of this - the name "double" now points to this black-box of code that anybody can call.

def: name + code

2. What is the double() code?

The code in this story is essentially a black box. Code that takes in one input, does its computation, and returns one output.

This does not depend on the name. The computation is the nature ofthe black box itself.

3. double() In The Interpreter

Normally we don't define a function in the interpreter, but here we'll do it to see the parts at work.

1. Define function

2. Call the function using its name

3. Ask interpreter what the value of "double" is

>>> def double(n):
...   return 2 * n
... 
>>>
>>> double(10)
20
>>> double(144)
288
>>>
>>> double
<function double at 0x7fe2caad0430>
>>>

The function name "double" points to the double code, printed as "0x7fe2caad0430" which is a bit obscure. It's the location in memory of the bytes of code that implement the function. When the function is called, the code at that location is run.

Aside: Memory locations are usually written in hexadecimal base-16, using the digits 0-9 and the letters a-f. The prefix "0x" at the start marks it as a hexadecimal number. This behind-the-scenes material comes up more in CS106B and CS107.

4. What map() Does: map(fn, list)

The map function takes in a function and a list of elements. For each element in the original list, map() calls the function, passing in one element from the original list.

Each function call to the function returns one result. Map() gathers all the results together into a new list. So if the original list is length 5, the function will be called 5 times, each call getting as input one element from the original list.

A visual of what map() does

map(double, [1, 2, 3, 4, 5]) -> [2, 4, 6, 8, 10]

We might say this example: "maps the double function over the list"

map() Example - double()

>>> # We have a "double" def
>>> def double(n):
...   return 2 * n
... 
>>>
>>> map(double, [1, 2, 3, 4, 5])
<map object at 0x7f9a25969910>     # why we need list()
>>> 
>>> list(map(double, [1, 2, 3, 4, 5]))
[2, 4, 6, 8, 10]
>>> 
>>> list(map(double, [3, -1, 10]))
[6, -2, 20]
>>> 
>>> list(map(double, range(20)))
[0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38]
>>> 

Note: map() Result + list()

• The result that map() returns is list-like, but is not exactly a list and does not display clearly in the interpreter
• Therefore we call it like this in the interpreter
• list(map(...))
• Thus making a list of the map() result so it prints out correctly
• This use of list() is not needed generally in production code, we're using it here just to get printing in the interpreter

map() Example - minus()

Create a minus() function that takes in n, returns -n.

>>> def minus(n):
...   return -1 * n
... 
>>> list(map(minus, [1, 2, 3, 4]))
[-1, -2, -3, -4]
>>> 
>>> list(map(minus, range(20)))
[0, -1, -2, -3, -4, -5, -6, -7, -8, -9, -10, -11, -12, -13, -14, -15, -16, -17, -18, -19]
>>>

map() Example - exclaim()

Say we have an exclaim(s) function that takes in a string and returns it uppercase with an exclamation mark at the end. Use map to run exclaim() over a list of strings.

>>> def exclaim(s):
...   return s.upper() + '!'
... 
>>>
>>> list(map(exclaim, ['hi', 'woot', 'donut']))
['HI!', 'WOOT!', 'DONUT!']
>>> 
>>> list(map(exclaim, ['meh']))
['MEH!']

Thus Far - Function into Function

The map() function saves us some bookkeeping - it takes care of calling a function a bunch of times, once for each element in the input list, and giving us a list of the results. This is a first example of passing a function in as a parameter, which is an important advanced technique.

Pre Lambda

Structure of above examples without lambda:

1. def - Do the def first, define the name + code we want to use - e.g. double

2. map - Later, map() refers to function by name - double to get the code

The two steps here seem a little needless? Like could we just do this in one step?

Lambda - Function In One Step

Write an expression that represents the code of a function in one step - no def needed.

Lambda History

• History: Alonzo Church 1930
  Lambda Calculus
• Church studied computation before computers were invented, used lambda as a notation

Python Lambda

• Python lambda is a way to make 1-line function
• Without using def - a sort of shortcut
• Appropriate when we need a short function
• Continue to use def for regular multi-line functions

Lambda: (1) lambda, (2) param, (3) exprression

• "lambda" is a stripped-down syntax to define computation in 1 line
• lambda has the absolute minimum needed
• Below is a lambda which defines code to double a number
• Syntax:
• 1. The word "lambda"
• 2. A single parameter and colon
• 3. An expression defining the output of the lambda, no "return"
• All of today's lambdas will have 1 parameter
  Possible to have different numbers, but 1 is very common
• Lambda is great when you need a short function expressed easily
• But it's dense code!

Here is a lambda that takes in a number, returns double that number

lambda n: 2 * n

Lambda Black Box

It's like the lambda just defines the black box code, not bothering with giving it a name.

Lambda works with map()

Want to double a bunch of numbers? Instead of a separate def, write the lambda inside the map() like this:

>>> list(map(lambda n: 2 * n, [1, 2, 3, 4, 5]))
[2, 4, 6, 8, 10]

How To Write Lambda - 1, 2, 3

1. "lambda"

Write the word "lambda"

2. Param:

• What is the lambda input?
• Give the lambda param a good name, followed by colon
• A an int - "n:"
• A string - "s:"
• A url = "url:"
• A mass in kilograms - "mass:" or "kg:"
• Having a good name helps with the next step

3. Expression

• Write the expression for the output
• Fit on one line, use the param
• No "return"
• If the code is long .. consider using a "def" see below

Lambda Examples in Interpreter

Do these in interpreter >>>. Just hit the up-arrow to change the body of the lambda.

>>> nums = [1, 2, 3, 4, 5]
>>> 
>>> # n * 10
>>> list(map(lambda n: n * 10, nums))
[10, 20, 30, 40, 50]
>>>
>>> # n * -1
>>> list(map(lambda n: n * -1, nums))
[-1, -2, -3, -4, -5]
>>> 
>>> # 100 - n
>>> list(map(lambda n: 100 - n, nums))
[99, 98, 97, 96, 95]
>>>
>>>

Lambda String Examples

Have a list of strings. Map a lambda over this list. What is the parameter to the lambda? One string. Whatever the lambda returns, that's what makes up the list of results.

1. Compute list of lowercase string forms

2. Compute list of first char of each string

>>> strs = ['Banana', 'apple', 'Zebra', 'coffee', 'Donut']
>>> 
>>> # 1. Lowercase form
>>> list(map(lambda s: s.lower(), strs))
['banana', 'apple', 'zebra', 'coffee', 'donut']
>>> 
>>> # 2. First char
>>> list(map(lambda s: s[0], strs))
['B', 'a', 'Z', 'c', 'D']
>>> 

Remember: Density

Map/lambda is powerful, but the line is quite dense too. It's fine to slow down a little, write each bit of the line carefuly.

Map Lambda - Examples

One liners!

> squared()

> shout()

Map Lambda - Exercises

These are true one-liner exercises. We'll do a few of them in class, and you can look at the others in the lambda1 section on the server.

Solve each of these with a 1 line map/lambda .. though it's a dense line!

You do not call list() for these. That was needed in the interpreter, but here just plain map() works.

For reference, here is the syntax for our "double" example:

map(lambda n: 2 * n, [1, 2, 3, 4, 5])

> negate

> power10

> first2x

> first_up