CS 111: Program Design I Lecture 24: List comprehension, social networks

Transcription

1 CS 111: Program Desig I Lecture 24: List comprehesio, social etworks Robert H. Sloa & Richard Warer Uiversity of Illiois at Chicago April 19, 2018

2 SPECIAL EMERGENCY TOPIC 1:.DEGREE(), ITERABLES

3 etworkx chaged from versio 1.x to 2.0 Type of thig retured by.degree() is ow somethig of type DegreeView that is kid of sort of like a dictioary, but it does ot covert to a padas Series i a ice way for sortig o the secod (degree, wats to be it) compoet Iteds for us to walk over it with for loop of form: for (, d) i G.degree(): Use the ame of the ode (here strigs) ad d the degree

4 Thigs we ca walk over with for loop Sequece types: strig, list, dictioary rage().degree() Lots of others Sometimes called "iterables" because we ca iterate (repeat) over them

5 What's our problem? The ew DegreeView data structure whe coverted directly to a padas Series does't give us a series of all itegers that we ca sort Simplest solutio, foud by a studet q q Covert DegreeView to dictioary, which will have ode ames as keys ad degrees of it type as values Covert that Dictioary to a padas Series, which will have the keys as the labels (colum headers) ad the keys as the value

6 Solutio from the etworkx maual Use a list comprehesio to get a list of the degrees

7 List Comprehesios 1: A geeral patter ew_list = [] for item i a_list: ew_list.apped(f(item)) Pythoistas write: ew_list = [f(item) for item i a_list]

8 SPECIAL EMERGENCY TOPIC 2: LIST COMPREHENSIONS

9 Example: Shoutig fruit fruit_list = ["apple", "orage", "blueberry"] loud_fruit = [item.capitalize() for item i fruit_list] loud_fruit ["Apple", "Orage", "Blueberry"]

10 Why do Pythoistas love list comprehesios? Mai reaso: Pytho sometimes rus them much faster tha for loops Mior reasos: q q q Shorter to write Perhaps easier to uderstad after you've see tos of them? Perhaps less likely to make errors after you've see tos of them?

11 Geeralizatio 1: a_list à iterable ew_list = [] for stuff i iterable: ew_list.apped(f(item)) Pythoistas write: ew_list = [f(stuff) for stuff i iterable]

12 What is ls? ls = [x ** 2 for x i rage(-2, 2)] A. [4, 1, 0, 1, 4] B. [4, 1, 0, 1] C. Noe D. Error E. [-2, -1, 0, 1]

13 Importat Notes List comprehesio writte i [ ]'s q Remider that we always get a list out It's always a ew list q To modify origial list my_list must do: my_list = [f(item) for item i my_list]

14 Our problem We have a iterable G.degree() that gives two thigs, ad we wat list of secod thig [degree for ame, degree i G.degree()] Ad we would like that sorted with largest first

15 Our problem cotiued We have a iterable G.degree() that gives two thigs, ad we wat list of secod thig [degree for ame, degree i G.degree()] Ca apply either.sort() method or sorted fuctio, both with optioal argumet reverse=true

16 Sorted list of degrees sorted_degrees = sorted([degree for ame, degree i G.degree()], reverse=true) Must use sorted fuctio which takes list as iput ad returs ew list with origial list elemets sorted q Caot use.sort() method for subtle reaso:.sort() chages a existig list ad returs Noe. With ew list comprehesio do't have ame/hadle for ew list at all. Could do: degs = [degree for ame, degree i G.degree()] degs.sort(reverse=true)

17 List Comprehesios 3: Coditios ew_list = [] for item i a_list: if coditio(item): ew_list.apped(f(item)) Pythoistas write: ew_list = [f(item) for item i a_list if coditio(item)]

18 List of strigs i list ls loger tha [s for s i ls if le(s) > ]

19 List of odd umbers < squared [i ** 2 for i i rage() if i % 2 == 1] OR [i ** 2 for i i rage() if i % 2] # Bous poits the Pythoista use of 0 false, 1 true?

20 SOCIAL MEDIA AND PRIVACY

21 Data Breaches From Jauary 1, 2018 to April 1, 2018: q q 273 data breaches 5,498,547 records compromised The iformatio eters the dark web for sale to crimials. mmary2018.pdf

22 Cambridge Aalytical Data Leak Data from 87 millio users For purposes of predictive aalytical aalysis of political attitudes ad votig tedecies. Massive amouts of data is legitimately collected for that purpose. So why are people so upset?

23 A Repeat of the 1960 s? Credit professioals had log marveled at the igorace of the America public whe it came to cosumer credit surveillace. I am costatly amazed at the average perso s complete lack of uderstadig of the fuctios of a credit bureau, the atioal associatio s educatio director cofessed i This abruptly chaged i the mid-1960s, but ot i the idustry s favor. The public was awakeed, ad Americas were outraged. q Josh Lauer, Creditworthy: A History of Cosumer Surveillace ad Fiacial Idetity i America

24 A Iitial Example I 2015, 21 year old UIC udergraduate Jabari Dea posted: q This is my oly warig. At 10 a.m. o Moday mourig, I am goig to the campus quad of the Uiversity of Chicago. I will be armed with a M-4 Carbie ad 2 Desert Eagles all fully loaded. I will execute approximately 16 white males studets ad or staff, which is the same umber of times [Laquad] McDoald was killed, the will die killig ay umber of white policeme i the process. This is ot a joke. I am to do my part to rid the world of white devils. I expect you to do the same. He was agry, quickly realized he had made a mistake, ad deleted the post.

25 FBI Traced the Post to Dea A teeager took a screeshot of the post ad otified police. The FBI arrested Dea q For trasmittig a threat i iterstate commerce. If Dea had just told his frieds, he would mostly likely ot have committed a crime. Moral: Our actios ca have a differet sigificace i coected world.

26 What Facebook Kows Facebook activity q Likes, clicks, type of access device, where, ad whe, time spet o various types of cotet. Activity o other sites q Collects iformatio whether or ot you remaied logged ito Facebook. It kows whe you load a webpage cotaiig a Facebook like or share buttos. Iformatio it buys q It buys a vast amout of iformatio from third parties. About 30,000 differet categorizatios of users ito groups of iterest to advertisers.

27 Facebook s 98 Data Poits (2016) Locatio Household compositio Age Geeratio Geder Laguage Educatio level Field of study School Ethic affiity Users who have a aiversary withi 30 days Users who are away from family or hometow Users who are frieds with someoe who has a aiversary, is ewly married or egaged, recetly moved, or has a upcomig birthday Icome ad et worth Home owership ad type Home value Property size Square footage of home Users i log-distace relatioships Users i ew relatioships Users who have ew jobs Users who are ewly egaged Year home was built Users who are ewly married

28 Facebook s 98 Data Poits (2016) Users who have recetly moved Users who have birthdays soo Parets Users who pla to buy a car (ad what kid/brad of car, ad how soo) Expectat parets Mothers, divided by type (soccer, tredy, etc.) Users who are likely to egage i politics Users who bought auto parts or accessories recetly Users who are likely to eed auto parts or services Style ad brad of car you drive Coservatives ad liberals Year car was bought Relatioship status Age of car Employer Idustry How much moey user is likely to sped o ext car Job title Office type Iterests Users who ow motorcycles Where user is likely to buy ext car How may employees your compay has Users who ow small busiesses

29 Facebook s 98 Data Poits (2016) Users who work i maagemet or are executives Users who have recetly uploaded photos to Facebook Users who have doated to charity (divided by type) Operatig system Users who play cavas games Users who ow a gamig cosole Iteret browser service Early/late adopters of techology Expats (divided by what coutry they are from origially) Users who have created a Facebook evet Users who have used Facebook Paymets Users who have spet more tha average o Facebook Paymets Users who belog to a credit uio, atioal bak or regioal bak Users who ivestor (divided by ivestmet type) Number of credit lies Users who admiister a Facebook page Users who are active credit card users Credit card type

30 Facebook s 98 Data Poits (2016) Users who have a debit card Time of year user s household shops most Users who carry a balace o their credit card Users who liste to the radio Preferece i TV shows Users who use a mobile device (divided by what brad they use) Iteret coectio type Users who recetly acquired a smartphoe or tablet Users who access the Iteret through a smartphoe or tablet Users who use coupos Users who are heavy buyers of beer, wie or spirits Users who buy groceries (ad what kids) Users who buy beauty products Users who buy allergy medicatios, cough/cold medicatios, pai relief products, ad over-thecouter meds Users who sped moey o household products Users who sped moey o products for kids or pets, ad what kids of pets Types of clothig user s household buys

31 Facebook s 98 Data Poits (2016) Users whose household makes more purchases tha is average Users who ted to shop olie (or off) Types of restaurats user eats at Kids of stores user shops at Users who are receptive to offers from compaies offerig olie auto isurace, higher educatio or mortgages, ad prepaid debit cards/satellite TV Users who travel frequetly, for work or pleasure Users who commute to work Types of vacatios user teds to go o Users who recetly retured from a trip Users who recetly used a travel app Users who participate i a timeshare Legth of time user has lived i house Users who are likely to move soo Users who are iterested i the Olympics, fall football, cricket or Ramada

32 The DHS Example DHS collects social media hadles, aliases, associated idetifiable iformatio, ad search results o immigrats, icludig aturalized citizes ad permaet residets, ad it stores public-source data (icludig iformatio from social media) ad gathers iformatio from commercial data providers ad public sources such as social media, ews media outlets, ad the Iteret.