1. Lists in Python

2. Lists
A list is a sequential collection of values, it is a data structure
Each value has a location (an index)
Indexes range from 0 to n-1 (where n is the length of the list) and from -1 to -n
Lists are heterogeneous = values can be of any type (strings are homogeneous because their elements are characters)

3. List syntax Values are enclosed in [], myList = [3, ‘a’, True]
One list can contain another
Empty list = []
Any type of data can be in the list
You usually refer to a list by elements, that is with the []. You can refer to a list by its name (as one whole thing) when passing it as an argument to a function.

4. List semantics Lists are mutable, that is, elements can be changed
Individual elements can be changed the same way any variable can be changed, with an assignment statement
myList = [1,9, ‘a’, 4, 7]
m = 3
myList[m] = 99
myList[m+1] = 88

5. Length function
The same as with the string type, len (lst) will return an integer, the number of elements in the list, from zero up to some max int value
If a list contains a sublist, it counts as one element L = [1, [3, 4], 7] has 3 elements
You call it as part of another statement, for example, print(len(mylist))

6. Accessing elements of a list
The index starts at 0 just like with a string and goes up to n-1 if there are n elements in the list
They also can use negative subscripts, -1 is the last element on the right, -n on the left end

7. Lists in Python
Creating lists

8. Simple ways to create a list
Hard code the values mylist = [3, 9, ‘a’, 7.2]
Using the replication operator
mylist = [0] * 100 (gives a list with 100 zeros)
Use the split method on a string
Use the readlines method on a file
Put [] around a variable
if my_num is 5, then [ my_num] is the list [5]

9. Append vs. Concatenate
Both can be used to add to an existing list but their syntax is not the same
The concatenate operator + uses two lists and creates a bigger one
You cannot say mylist = mylist + “joe”, it must be mylist = mylist + [“joe”]
Append is a method which adds an element to the right end of a list – any type of data
mylist.append(“3”) makes mylist one element longer

10. List accumulation
Either concatenation or append can be used with lists as accumulators
Initialize the list variable as an empty list []
inside your loop either concatenate or append the new item onto your list variable

11. How to output a list
Very simplest way – just print the name of the list
print(myList)
This results in output that has [] around it, quotes around strings and commas between items
More often, the output needs each item without the formatting around it
In this case, you need a loop that will process through the list, one element at a time
see “Traversing a list”
You need either a for loop that presents elements one at a time or a loop that provides the subscript for each element one at a time.

12. List methods and functions
Lists in Python
List methods and functions

13. List methods list.append(x)
Semantics
list.append(x)
Adds x to the right end of list, changes original list
list.sort()
Sorts items of list, in place. Items must be comparable
list.reverse()
Reverses order of items in list in place
list.index(x)
Returns integer index of first (leftmost) occurrence of x
list.count(x)
Returns integer count of number of occurrences of x in list

14. Notes about methods
These methods, append, sort, reverse, all change the original list.
If you need to retain the original list as well as keep the resulting list, use sorted(listname) and list(reversed(listname))
Just as with strings, index will cause an exception and program crash if its search fails

15. List functions Function Semantics min (list)
Returns the smallest element in the list, items must be comparable
max (list)
Returns the largest element in the list, items must be comparable
sum (list)
Returns the arithmetic total of all items in list, items must be numeric
sorted (list)
Returns a new copy of list in ascending order, items must be comparable
reversed (list)
Returns an iterator of the list in reverse order (use a list typecast to get the list)

16. Notes about functions and methods
When the previous two tables say “items must be comparable”, it means they must all be of numeric type, or they must all be strings. Mixing them will cause an exception and crash
When they say “in place” it means that it will change the original list.
For the sum function, items can be integer or float

17. Sequential and Random access of elements
Lists in Python
Sequential and Random access of elements

18. Random vs. Sequential Lists have two ways to be accessed
random (direct)
sequential
Both are useful in their place
If you need to access ALL elements of a list (either in order or order doesn’t matter), sequential is the way to go because it is simpler
Otherwise you need to access elements randomly

19. Examples sequential for i in range(5): print(myList[i]) random
Note that it is the order of elements accessed that makes it sequential or random, both will use the []

20. Lists as Arguments/Parameters
Lists in Python
Lists as Arguments/Parameters

21. Lists as arguments to functions
Just like other data types, lists can be sent to functions as arguments
The function must be aware of the fact that a parameter is a list, so it can treat it as a list (use subscripts)
Lists are passed to functions in a different way than other arguments you have seen so far

22. Passed by value
The process y ou have seen so far is that the values of arguments are copied into the spaces reserved for parameters
The function can do anything you want with the copies
The copies are deleted when the function finishes and returns
This is called ‘passing arguments by value’

23. Passing by reference Lists are handled differently
Their address is sent to the function in the place of a parameter
Using the address (also called the reference) the function can change the elements of the list – in other words, the function is changing the original contents of the original list
In other words, the list contents can be changed permanently

24. Python versus other languages
In languages like C, C++ and Java, passing by reference is fairly common
A main reason for this is that those languages can return no more than one value via a return statement
If a function needs to return two things, it must use pass by reference to get one of the things back to the calling code

25. Python vs. other languages
Python on the other hand can return many different values through the return statement
Typically functions do not need to pass things by reference because of this fact
The main thing to be aware of is that a function can change a list, which will affect the calling function’s variable. Most Python programmers would consider doing this a mistake by the programmer.
If you do use passing by reference, document it WELL in the header, so that anyone using the function knows that a list argument can be changed!

26. Selection Sort Algorithm
Lists in Python
Selection Sort Algorithm

27. Sorting A very common activity for computers
Not just in business, sorting is used in databases, graphics, simulations, games, etc.
Many different algorithms for sorting
different ones are better for sorting data in different formats, environments, conditions
Python has a sort algorithm builtin, for lists

28. So why learn a sort algorithm?
Good exercise for manipulating lists
Not all languages have one builtin
Why selection sort?
one of the simpler algorithms
easy to debug if necessary

29. Selection sort for n elements (ascending)
for n-1 passes
find the largest value in unsorted part of list
swap it with the value at the end of the unsorted part
There are variations based on using smallest instead of largest, on descending instead of ascending

30. Lists in Python
Parallel lists

31. Parallel lists
This is a technique for using lists which goes back to the early days of programming
It’s not new syntax, just a slightly different way to look at arrays
Many languages have a restriction that an array can be only one type, an integer array, a string array, etc.

32. Sometimes the situation required storing both floats and strings, for example (GPAs and names).
One piece of data, a GPA, belongs with another piece of data, a name.
Since they could not be put in the same array, they were put in separate arrays, but in the same position.

33. Parallel arrays
By using the same subscript for both arrays, you are referring to related data.
The main thing to remember is if you move data around in one of the arrays, you must make the same moves in the other array
Nothing in the language enforces this relationship, it’s up to your programming

34. Two-dimensional arrays
Lists in Python
Two-dimensional arrays

35. Two dimensions
You will sometimes have data which has a two-dimensional structure (days and hours for example, or rows and columns)
You could store the data in one big list and keep track of the structure some other way but most modern languages provide a way to express the two dimensions directly

36. List of lists
In Python you can think of this structure as a list of lists
mat = [[1,2,3], [5, 9, 2], [7, 8, 3], [6, 7, 0]]
Of course in Python any of these lists can be any size (they don’t all have to be 3 elements long!)
Typically a 2-d array is thought of as having so many rows and so many columns (a grid like Excel)

37. Syntax of 2-d arrays
One way to create a 2-d array is as given on the last slide, just write it out
Another that is quicker if you want to initialize all elements to one value:
mat2 = [[0] * 3 for i in range(4)]
creates a 2-d array with 4 rows, 3 columns full of zeros O

38. Accessing elements of a 2-d array
You use two subscripts, each in a pair of []
mat [2][1] is the element in the third row and second column – just like 1-d arrays, they start numbering at [0][0]