1. COMPSCI 107 Computer Science Fundamentals
Lecture 05 – Sequences of data

2. Recap Previously, we looked at:
how our programs can have unexpected consequences for society
the importance of writing tests before code
documenting code after it is written
using doctest to integrate testing and documentation
COMPSCI Computer Science Fundamentals

3. Learning outcomes
At the end of this lecture, students should be able to:
Use index values to refer to elements of a sequence
Use the “slice” syntax to refer to a subsequence
Use operations that apply to sequences
Use the for loop syntax to iterate over a sequence
Use list comprehensions to create a list
Visualise how lists are stored in memory
Create multi-dimensional lists
COMPSCI Computer Science Fundamentals

4. Sequences Sequences allow you to store values in an organized fashion.
Built-in types: strings, lists, tuples
COMPSCI Computer Science Fundamentals

5. Strings Strings are a sequence of characters
Strings have a number of methods that are useful
split() is especially useful
help(str)
>>> name = 'Andrew'
>>> name[0]
'A'
>>> 'd' in name
True
>>> len(name) 6
>>> name + '_' + 'Luxton-Reilly'
'Andrew_Luxton-Reilly'
COMPSCI Computer Science Fundamentals

6. Exercise
Write a function that determines whether a given string of text contains a vowel or not. The function should return True if the text contains a vowel.
COMPSCI Computer Science Fundamentals

7. Python lists my_list = [1, 2, 3] Lists are a built-in type in Python
Use square brackets to signify a list
Can contain any type of data, or any mixture of types
How do you visualise the following list?
my_list = [1, 2, 3]
Show the Python visualiser online
my_list

8. Python lists my_list = [1, 2, 3] Lists are a built-in type in Python
Use square brackets to signify a list
Can contain any type of data, or any mixture of types
How do you visualise the following list?
my_list = [1, 2, 3] 2 1 3
Show the Python visualiser online
my_list 1 2 3 4

9. Python lists 3 my_list = [1, 2, 3] len(my_list)
Numerous list functions are supported
Use help(list) to see a complete list of functions
Examples:
my_list = [1, 2, 3]
len(my_list) 3 2 1 3
Show the Python visualiser online
my_list 1 2 3 4

10. Python lists my_list = [1, 2, 3] my_list = my_list + [4]
Numerous list functions are supported
Use help(list) to see a complete list of functions
Examples:
my_list = [1, 2, 3]
my_list = my_list + [4] 4 2 1 3
Note – the + operator here creates a new list
my_list 1 2 3 4

11. Python lists my_list.append(7) Numerous list functions are supported 7
Use help(list) to see a complete list of functions
Examples:
my_list.append(7) 7 4 2 1 3
Show the Python visualiser online
my_list 1 2 3 4

12. Python lists True 3 in my_list Numerous list functions are supported 4
Use help(list) to see a complete list of functions
Examples:
3 in my_list
True 4 2 1 3
Show the Python visualiser online
my_list 1 2 3 4

13. Python lists 1 my_list[0] Numerous list functions are supported 4 2 1
Use help(list) to see a complete list of functions
Examples:
my_list[0] 1 4 2 1 3
Show the Python visualiser online
my_list 1 2 3 4

14. Python lists Numerous list functions are supported append(item)
Use help(list) to see a complete list of functions
append(item)
insert(i, item)
pop()
pop(i)
sort()
reverse()
del( my_list[i] )
index(item)
count(item)
remove(item)
Make sure you are comfortable with these functions (i.e. you can use the Python docs to call them and understand how they work)
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15. Exercises
Write a function that sums the elements of a list that contains numbers.
Write a function that accepts a list and returns a new list with the same contents, but in reverse order.
COMPSCI Computer Science Fundamentals

16. Lists of lists
Since a list element can be of any type, it can actually be a list
my_list = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
Show the Python visualiser online

17. Lists of lists
Since a list element can be of any type, it can actually be a list
my_list = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
Show the Python visualiser online

18. Lists of lists
Since a list element can be of any type, it can actually be a list
my_list = [[1, 2, 3], [4, 5, 6], [7], [8, 9]]
The online Python Tutor has a useful visualisation engine:
Show the Python visualiser online

19. Slices of sequences
A piece of a sequence can be obtained using the following syntax:
sequence_name[start : stop]
Where start is the index of the first element, and stop is the index after the last element
>>> name = "Andrew"
>>> values = [5,8,2,3,7,1]
>>> print(name[1:2]) n
>>> print(values[2:5])
[2, 3, 7]
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20. Slices step value
Actually, the syntax allows for a third value, used to define the step size between elements included in the slice. If a value is omitted, it defaults to :
[ start : stop : step]
length 1
Using a step size of -1, and not providing start and stop means that Python will step through the word right to left, from end to beginning
>>> word = 'Computer'
>>> print(word[:4:2]) Cm
>>> print(word[::-1])
retupmoC
Sure, the [::] is the extended slice operator. It allows you to take substrings. Basically, it works by specifying which elements you want as [begin:end:step], and it works for all sequences. Two neat things about it:
You can omit one or more of the elements and it does "the right thing"
Negative numbers for begin, end, and step have meaning
For begin and end, if you give a negative number, it means to count from the end of the sequence. For instance, if I have a list:
l = [1,2,3] Then l[-1] is 3, l[-2] is 2, and l[-3] is 1.
For the step argument, a negative number means to work backwards through the sequence. So for a list::
l = [1,2,3,4,5,6,7,8,9,10] You could write l[::-1] which basically means to use a step size of -1 while reading through the list. Python will "do the right thing" when filling in the start and stop so it iterates through the list backwards and gives you [10,9,8,7,6,5,4,3,2,1].
I've given the examples with lists, but strings are just another sequence and work the same way. So a[::-1] means to build a string by joining the characters you get by walking backwards through the string.
word C o m p u t e r
Positive index 1 2 3 4 5 6 7
Negative index -8 -7 -6 -5 -4 -3 -2 -1

21. Exercise

22. For loops Used to iterate through a sequence 2 3 57 11
numbers = [2, 3, 5, 7, 11]
for i in numbers:
print(i) A n d r e w
name = 'Andrew'
for c in name:
print(c)
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23. List comprehensions
A list can be created using instructions that appear within the square brackets
The general format of a list comprehension is:
my_list = [x for x in range(90, 100)]
print(my_list)
[90, 91, 92, 93, 94, 95, 96, 97, 98, 99]
Show the Python visualiser online
[expression for variable in sequence]

24. List comprehensions Examples
Converting a string into a list of characters:
my_list = [c for c in 'COMPSCI107']
print(my_list)
['C', 'O', 'M', 'P', 'S', 'C', 'I', '1', '0', ‘7']
Generating all even numbers between 1 and 20
my_list = [n * 2 for n in range(1, 10)]
print(my_list)
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[2, 4, 6, 8, 10, 12, 14, 16, 18]

25. List comprehensions using conditions
We can extend the syntax for a list comprehension to include a condition:
The general format of a list comprehension using a condition is:
my_list = [x for x in range(0, 10) if x % 2 == 0]
print(my_list)
[0, 2, 4, 6, 8]
Show the Python visualiser online
[expression for variable in sequence if condition]

26. Exercises What is the output? (a) Result = [2, 4] (b) Result = [3, 4]
values = [x + x for x in range(5) if x + x < x * x]
print('Result =', values)
(a) Result = [2, 4]
(b) Result = [3, 4]
(c) Result = [0, 2, 4, 6, 8]
(d) Result = [6, 8]
(e) Result = [0, 1, 2, 3, 4]
Answer = D

27. Exercise What is the output? numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
primes = [2, 3, 5, 7]
my_list = [x+x for x in numbers if x not in primes]
print(my_list)
Numbers are immutable, lists are mutable – more tomorrow!

28. List comprehensions using conditions
Example
Generate a list of the non-vowel letters that appear in a string:
phrase = 'The quick brown fox jumps over the lazy dog'
vowels = 'aeiou'
my_list = [c for c in phrase if c not in vowels]
for c in my_list:
print(c, end = '')
Show the Python visualiser online
Th qck brwn fx jmps vr th lzy dg

29. Summary
Information in a list is stored contiguously in memory (i.e. in consecutive memory locations)
Location (memory address) of the information can be calculated as:
location = address of start of list +
index * size of each element
Efficiency issues
It takes the same amount of time to access any of the elements
It is slow to move elements around (i.e. add and delete elements from within the list)
COMPSCI Computer Science Fundamentals