IT 117: Intermediate Scripting

IT 117: Intermediate Scripting
Class 8

Microphone

Questions

Are there any questions before I begin?

Readings

If you have the textbook read Chapter 9, Dictionaries and Sets section 9.2, Sets.

Homework 4

I have posted homework 4 here.

It is due this coming Sunday at 11:59 PM.

Quiz 2

I have posted the answers to Quiz 2 here.

Let's review the answers.

Today's Class

Today I will discuss Python sets.

General Advice

Keep Each Study Period Short

Do not spend more than 1 hour on a study session
Otherwise you may find that your attention wanders ...
as your brain gets tired
If you spread out your study session over the week ...
you will learn more
The first time you read something new you might miss important details
Your brain needs time to absorb the material you have just studied
Each time you look at new material it will become more familiar
Things that you did not understand at first become clearer with time
It's also a great way to break up a big task ...
into smaller chunks
A few summers ago I needed to spread mulch over my yard
My house is on a corner so it's a big yard
It took 170 bags of mulch to cover the area
I no longer have the stamina that I had when I was your age
I could not spread 170 bags in one, two or even three sessions
But I learned that I could spread 6 bags without to much trouble
Every day when the weather permitted I spread 6 bags
By the end of the summer the job was done

Review

Values in a Boolean Context

If you use an expression whose value is a number or a string ...
in a place where Python expects a boolean expression ...
the interpreter will automatically apply the bool conversion function ...
to turn the string or number into a boolean value ...
which can be used in if statements and while loops
If the string expression is the empty string ...

Python will treat it as false

>>> value = ""
>>> if value:
...     print("True")
... else:
...     print("False")
... 
False

If the string expression is not the empty string ...

Python will treat it as true

>>> value = "x"
>>> if value:
...     print("True")
... else:
...     print("False")
... 
True

Python will so something similar with numbers

To show how this works, let's create a test function

>>> def true_or_false(value):
...     if value:
...         print("True")
...     else:
...         print("False")
...

Any expression whose value is zero will be treated as false
```
>>> true_or_false(0)
False
>>> true_or_false(0.0)
False
```
Any expression whose value is not zero will be treated as true
```
>>> true_or_false(1)
True
>>> true_or_false(4)
True
```

This even works for negative values

>>> true_or_false(-1)
True
>>> true_or_false(-100)
True

This even works with objects

Any variable that points to an object will be treated as true

>>> file = open("students.txt", "r")
>>> file
<_io.TextIOWrapper name='students.txt' mode='r' encoding='UTF-8'>
>>> true_or_false(file)
True

Can an object variable ever be false?
Python has a special value called None
An object variable whose value is None points to nothing
It's like zero for objects
So an expression whose value is None is false
```
>>> true_or_false(None)
False
```

Keys Are Immutable

There is only one constraint on dictionaries
The keys of a dictionary must be immutable
In other words they must be things that cannot be changed
The following data types can be used as keys
- Strings
- Integers
- Floats
- Tuples

But we can't create a dictionary using lists as keys

>>> lists_integers   = {[1,1]:1, [2,2]:2, [3,3]:3}
Traceback (most recent call last):
  File <stdin>, line 1, in <module>
TypeError: unhashable type: 'list'

Why this restriction?
When a entry is added to a dictionary, the hash function runs on the key
This function returns the slot number for the entry
If the key is subsequently changed ...
the function will give the wrong slot number
So you won't be able to retrieve the value

Values in a Dictionary

The keys in a dictionary must be immutable data types

But the values can be anything

>>> jumble = {1:1, 2:2.0, 3:"three", 4:(4,4), 5:[5]}
>>> for key in jumble:
...    print(jumble[key])
... 
1
2.0
three
(4, 4)
[5]

In this example all the keys are integers but all the values are of different data types
In this case all the keys were of the same data type
That is the most common situation, but it is not a requirement

Dictionary Methods

Dictionaries are objects so they have have methods

Here are some of the more useful dictionary methods

Method	Description
get(key)	Gets the value associated with a specified key. If the key is not found, the method does not raise an exception. Instead, it returns a default value. To change the default value, provide a 2nd argument.
pop(key)	Returns the value associated with a specified key and removes that key-value pair from the dictionary. If the key is not found, the method returns a default value. To change the default value, provide a 2nd argument.
popitem()	Returns the last key-value pair added to the dictionary as a tuple and removes that key-value pair from the dictionary.

The get Method

We usually access the value for a key using the bracket operator, like this
```
>>> quiz_scores["Mary Jones"]
[98, 95, 93]
```

But if we get the key wrong, Python will raise an exception

>>> quiz_scores["Mary Jonez"]
Traceback (most recent call last):
  File <stdin>, line 1, in <module>
KeyError: "Mary Jonez"

The get method works just like the [ ] operator when we use a valid key
```
>>> quiz_scores.get("Mary Jones")
[98, 95, 93]
```
But when we use an invalid key, get does not throw an exception

Instead it returns the special value None

>>> scores = quiz_scores.get("Mary Jonez")
>>> print(scores)
None

If you use a variable whose value is None in an if statement
Python treats this value as false
```
>>> bool(None)
False
```

The pop Method

The pop method takes a key as an argument ...
returns the value associated with that key ...

and removes the key-value pair from the dictionary

>>> quiz_scores
{"Mary Jones": [98, 95, 93], "Tim Tyler": [88, 81, 79], "John Smith": [100, 90, 85]}
>>> quiz_scores.pop("Mary Jones")
[98, 95, 93]
>>> quiz_scores
{"Tim Tyler": [88, 81, 79], "John Smith": [100, 90, 85]}

The popitem Method

popitem is like pop
But it returns returns the last key-value pair added to the dictionary

It it also removes that entry from the dictionary

>>> quiz_scores
{"Tim Tyler": [88, 81, 79], "John Smith": [100, 90, 85]}
>>> quiz_scores.popitem()
("Tim Tyler", [88, 81, 79])
>>> quiz_scores
{"John Smith": [100, 90, 85]}

Attendance

New Material

Sets in Mathematics

It's hard to take a college level Math course without encountering the concept of a set
The definition of a set is simple
It is an unordered collection of distinct objects
You can put anything into a set
But you can't have two of anything
The set with nothing in it is called the empty set

Set Membership

If the value x is contained in set A, we say that x is a member of A
In mathematics the assertion that x is an element of A is written
```
x ∈ A
```

Subsets and Supersets

If all the values in set A are also in B we say that A is subset of B
The situation is shown in the following diagram

In mathematics, the assertion that A is a subset of B is written
```
A ⊂ B
```
Another way to describe this relationship is to say that B is a superset of A
In mathematics this relationship is written
```
B ⊃ A
```

Union of Sets

There are a number of operations on sets that can create new sets
Let's say we have two sets, A and B
A set which has all the elements of the both sets ..
is called the union of A and B ...
and is written
```
A ∪ B
```
In the diagram below the union of A and B is shown in red

Intersection of Sets

A set which only has elements that are in both A and B ...
is called the intersection of A and B ...
and is written
```
A ∩ B
```
In the diagram below, the intersection of A and B is shown in red

Difference between Sets

A set of all the elements in A which are not in B ...
is called the difference between A and B ...
and is written
```
A - B
```
In the diagram below, the difference between A and B is shown in red

Symmetric Difference between Sets

The set of all elements in A not in B ...
and all the elements in B not in A ...
is called the symmetric difference between A and B ...
and is written
```
A Δ B
```
In the diagram below the symmetric difference between A and B is shown in red

Sets in Python

Sets in Python are objects
They follow the mathematical definition of a set
A Python set is an object which holds an unordered collection of unique items
The values in a Python set must be immutable
That means the elements can be
- Integers
- Floats
- Strings
- Booleans
- Tuples
But they cannot be lists or dictionaries

Set Literals

A list literal is has values, separated by commas, inside square brackets
```
>>> list_1 = [1, 2, 3, 4, 5]
>>> type(list_1)
<class "list">
```

A set literal uses curly braces

>>> nonsense = {"foo", "bar", "bletch"}
>>> type(nonsense)
<class "set">

Creating a Set in Python

You can create a set in Python using the builtin set function
set takes a single argument
That argument must be iterable
A Python object is iterable if you can use it in a for loop
This means you can use any of the following as an argument to set
- Lists
- Tuples
- Dictionaries
- Strings

Let's see this in action with a list

>>> num_list = [1,2,3]
>>> num_set  = set(num_list)
>>> num_set
{1, 2, 3}

It also works with a tuple

>>> letter_tuple = ("a", "b", "c")
>>> letter_set = set(letter_tuple)
>>> letter_set
{'a', 'c', 'b'}

And a string

>>> letter_set_2 = set("abcde")
>>> letter_set_2
{'b', 'a', 'c', 'd', 'e'}

Notice that the letters do not appear in the same order ...
as they do in the string

When we use a dictionary to create a set

>>> numb_set_2 = set({"one": 1, "two": 2, "three": 3})
>>> numb_set_2
{"one", "two", "three"}

only the keys are used

If the argument to set contains duplicate values

Only one is used

>>> letter_set_3 = set("Mississippi")
>>> letter_set_3
{"i", "M", "s", "p"}

The Empty Set

We can use empty square brackets to create an empty list
```
>>> empty = []
>>> type(empty)
<class "list">
```
But we cannot use empty curly braces to create a empty set
The empty curly braces denote an empty dictionary
```
>>> empty = {}
>>> type(empty)
<class "dict">
```
When the creators of Python got to set literals
They ran out of symbols to enclose the elements
So they had to reuse { }
So how do you create an empty set?
You run set with no arguments
```
>>> set_1 = set()
>>> set_1
set()
```
You cannot create an empty set like this
```
>>> set_2 = {}
```
This creates not a set but a dictionary
```
>>> type(set_2)
<class 'dict'>
```
That is why an empty set
```
empty_set = set()
```
Looks like this
```
>>> empty_set
set()
```

Adding Elements to a Set

Sets are mutable
They can be changed
There are two set methods that can be used to add elements to a set
- add()
- update()
add adds a single element to a set
So if we start with an empty set
```
>>> set_1 = set()
>>> set_1
set()
```

We can use add to add individual elements

>>> set_1.add(1)
>>> set_1
{1}
>>> set_1.add("two")
>>> set_1
{1, "two"}
>>> set_1.add((3,3,3))
>>> set_1
{(3, 3, 3), 1, "two"}

If you add an element to a set that is already in the set ...

nothing will change

>>> set_1.add(1)
>>> set_1
{(3, 3, 3), 1, "two"}

But this won't raise an exception
The update method adds several elements to a set

It takes one argument, which must be iterable

>>> set_2 = set()
>>> set_2
set()
>>> set_2.update([1, 2, 3])
>>> set_2
{1, 2, 3}
>>> set_2.update("foo")
>>> set_2
{1, 2, 3, "f", "o"}

Notice that only one "o" was added to the set ...
and no exception was raised

Removing Elements from a Set

To remove an element from a set, use one of two methods
- discard()
- remove()
Both methods take a single argument ...

the value that is to be removed

>>> numb_set
{1, 2, 3, 4, 5}
>>> numb_set.discard(2)
>>> numb_set
{1, 3, 4, 5}
>>> numb_set.remove(4)
>>> numb_set
{1, 3, 5}

The only difference is what happens ...
when you remove a value that is not in the set

discard will say nothing

>>> numb_set.discard(2)
>>> numb_set
{1, 3, 5}

But remove will raise an exception

>>> numb_set.remove(4)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
KeyError: 4

Why Must Set Elements Be Immutable?

The elements of a set must be immutable

That means we can add numbers, strings and tuples to a set

>>> s  = set()
>>> s
set()
>>> s.add(1)
>>> s.add(2.0)
>>> s.add("three")
>>> s.add((4,4,4,4))
>>> s
{1, 2.0, (4, 4, 4, 4), 'three'}

But not dictionaries

s.add({})
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'dict'

The reason is that Python implements sets using hash tables
It does this for efficiency
A basic question you can ask a set ...
is if it contains a specific value
If sets were implemented using lists ...
Python would have search the entire list ...
to find that some value was not in the set
Also, think what happens when a value is added to a set
Python needs to check that it is not already in the set
Because a set cannot have duplicate entries
So each time we add a value we might have to search the entire list
But with hash tables all we have to do is run the hash function
That will give us the slot number for each new element
If the slot is empty, we can add it
If not, we can't
This is why the order in which values are added to the set ...
is not the order in which the appear ...
in a for loop

If we create a set with elements in a certain order

>>> strings = set()
>>> strings.add("foo")
>>> strings.add("bar")
>>> strings.add("bletch")
>>> strings.add("ding")
>>> strings.add("dong")

We will usually not see that order when we print the set
```
>>>  strings
{'bar', 'foo', 'dong', 'ding', 'bletch'}
```
This used to be true of dictionaries too
But programmers objected
So they had to add complexity to the implementation of dictionaries ...
to order entries in the order in which they were entered