1. Introduction to Computational Thinking
Abstraction & Data
Structures
…more complex data structures
(C) Dennis Kafura 2016

2. Combining lists and dictionaries
Lists and dictionaries are two forms of data structures – different schemes to organize and access data
Representing the abstractions of complex real-world artifacts often requires some combination of these data structures
Combining simple structures to form more complex ones
(C) Dennis Kafura 2016

3. List of dictionaries A weather report abstraction has three properties
temperature
humidity
wind
A weather forecast is a collection of instance of weather reports
(C) Dennis Kafura 2016

4. A collection of dictionaries
Each instance of a weather report in the weather forecast can be represented separately
Each instance of a weather report can be represented as a dictionary
{ “temperature”: 30, “humidity”: 20, “wind”: 3 }
(C) Dennis Kafura 2016

5. A list of dictionaries
The individual weather report instances (dictionaries) can be collectively represented in a list
{ ‘temperature’: 18, ‘humidity’: 90, ‘wind’: 15 }
{ ‘temperature’: 29, ‘humidity’: 100, ‘wind’: 5 }
{ ‘temperature’: 24, ‘humidity’: 30, ‘wind’: 19 }
{ ‘temperature’: 25, ‘humidity’: 50, ‘wind’: 10 }
{ ‘temperature’: 30, ‘humidity’: 20, ‘wind’: 3 }
(C) Dennis Kafura 2016

6. List/dictionary in Python
{ ‘temperature’: 18, ‘humidity’: 90, ‘wind’: 15 }
{ ‘temperature’: 29, ‘humidity’: 100, ‘wind’: 5 }
{ ‘temperature’: 24, ‘humidity’: 30, ‘wind’: 19 }
{ ‘temperature’: 25, ‘humidity’: 50, ‘wind’: 10 }
{ ‘temperature’: 30, ‘humidity’: 20, ‘wind’: 3 }
[ {‘temperature’ : 30, ‘humidity’ : 20, ‘wind’ : 3} ,
{‘temperature’ : 25, ‘humidity’ : 50, ‘wind’ : 10} ,
{‘temperature’ : 29, ‘humidity’ :100, ‘wind’ : 5} ,
{‘temperature’ : 18, ‘humidity’ : 90, ‘wind’ : 15} ,
{‘temperature’ : 24, ‘humidity’ : 30, ‘wind’ : 19} ],
(C) Dennis Kafura 2016

7. Next steps Remember: Now With a list  iterate through each element
for <item> in <list>:
With a dictionary  use a key to access a value
value = dictionary[“key”]
Now
Do the first two problems
Report/discuss
(C) Dennis Kafura 2016

8. A dictionary with list values
An abstraction of a temperature forecast for multiple cities
City
Forecast
“Blacksburg, VA”
[76, 54, 72, 52, 64, 43, 66, 48, 71, 54]
“Seattle, WA”
[63, 56, 69, 55, 69, 54, 67, 52, 63, 51]
“Miami, FL”
[86, 69, 87, 71, 88, 64, 88, 72, 87, 73]
“San Jose, CA”
[79, 57, 82, 57, 83, 58, 84, 60, 80, 84]
“New York, NY”
[5, 8, 8, 3, 2, 1, 9, 5, -4, -10]
(C) Dennis Kafura 2016

9. “Nested” iteration
Nested: one (inner) iteration in the body of another (outer) iteration
City
Forecast
“Blacksburg, VA”
[ ]
“Seattle, WA”
“Miami, FL”
“San Jose, CA”
“New York, NY”
for ….. :
# each city
# each temperature
(C) Dennis Kafura 2016

10. Another graphic form [ list ] { City , Forecast } [ list ] City
“Blacksburg, VA”
[76, 54, 72, 52, 64, 43, 66, 48, 71, 54]
“Seattle, WA”
[63, 56, 69, 55, 69, 54, 67, 52, 63, 51]
“Miami, FL”
[86, 69, 87, 71, 88, 64, 88, 72, 87, 73]
“San Jose, CA”
[79, 57, 82, 57, 83, 58, 84, 60, 80, 84]
“New York, NY”
[5, 8, 8, 3, 2, 1, 9, 5, -4, -10]
{ City , Forecast }
string
[ list ]
int(eger)
(C) Dennis Kafura 2016

11. Classwork
Do problem 3
Report/discussion
(C) Dennis Kafura 2016

12. Layers of Abstraction
Many properties are often needed make our abstractions of the real world accurate
Humans are not “wired” to handle many properties at one time (our short term memory allows about 7 +/- 1 things)
We have devised ways of coping with this complexity through layers or hierarchy
An entity is represented by few parts
Each part is represented by a few sub-parts
Each sub-part is ….
(C) Dennis Kafura 2016

13. Layers of Abstraction Place Report Date . . . Temperature Humidity
Wind 76 55 18
City
State
Zip
Blacksburg VA
24061
Month
Day
Year
Time
May 20
2015
Hour
Min
Sec 11 32 52
(C) Dennis Kafura 2016

14. Accessing a value forecast = To find the Zip code:
where = forecast[“Place’]
code = where[“Zip”]
or just
code = forecast[“Place”][“Zip”]
To find the Hour:
when = forecast[“Date”][“Time”][“Hour”]
(C) Dennis Kafura 2016

15. Demonstration Mapping the earthquakes data stream
Using Spyder’s variable explorer
(C) Dennis Kafura 2016

16. Mapping diagram
(C) Dennis Kafura 2016

17. To do Work on class assignments Self-assess
Are you solid on the concepts?
If not, see me
Are you up to date on homework?
If not, catch up
Have you identified your project data stream and questions?
If not, see me and work on this
(C) Dennis Kafura 2016