Composite Types in Python (PDF)

 Composite Types in Python Introduction to Computational Thinking 2 Lesson Objectives At the end of this lesson, you should be able to: Discuss the concept of composite types...

 Composite Types in Python Introduction to Computational Thinking 2 Lesson Objectives At the end of this lesson, you should be able to: Discuss the concept of composite types Explain the importance of composite types Use composite types in Python to solve problems Introduction to Computational Thinking 3 Topic Outline What are Composite Types/ Data Structure? Why are Composite Types/ Data Structure Important? Three Common Data Structures in Python (and Their Operations): List Tuples Dictionaries Introduction to Computational Thinking 4 What is a Composite Type? A data type, which is constructed (composed) using primitive and other composite types. A new data type made from existing ones. Introduction to Computational Thinking 5 Data Structures Particular ways of storing data to make some operations easier or more efficient - They are tuned for certain tasks, and they are often associated with algorithms Different data structures have different characteristics - One suited to solving a certain problem may not be suited for another problem Dictionaries DATA STRUCTRES lists[…] Introduction to Computational Thinking 6 Data Structures (Cont’d) Kinds of Data Structures Built-in User-defined Data structures Data structures that (classes in are so common as to object-oriented be provided by default programming) that are designed for a particular task Introduction to Computational Thinking 7 Composite Types in Python lists[…] Composite Types Convenient and efficient ways to store data How to use them? Dictionaries Focus of this lesson Introduction to Computational Thinking 8  Composite lists[…] Types Convenient and efficient ways to store data. Dictionaries Introduction to Computational Thinking 9 Python Lists Python List is an ordered sequence of items. We have already covered a type of sequence: Strings Recall A string is a sequence of characters. Introduction to Computational Thinking 10 Creating a List As with all data structures, lists have a constructor. Constructors have the same name as the data structures. l = list() Creates an empty list l = list(arg) Takes an iterable data structure as an argument and add each item of arg to the constructed list 1 Shortcut: use of square brackets [] to indicate explicit items. l = […] Introduction to Computational Thinking 11 Creating a List: Example aList = list('abc') aList  ['a', 'b', 'c'] newList = [1, 3.14159, 'a', True] Introduction to Computational Thinking 12 Lists: Similarities with Strings concatenate: + (only for lists – not string + list) repeat: * indexing: the [ ] operator), e.g., lst 4th item in the list slicing: [:] membership: the in operator length: the len() function Introduction to Computational Thinking 13 Lists: Differences with Strings Lists can contain a mixture of python objects (types); strings can only hold characters. E.g. l = [1, 'bill', 1.2345, True] Lists are mutable; their values can be changed, while strings are immutable. Lists are designated with [ ], with elements separated by commas; strings use "". Introduction to Computational Thinking 14 List Structure myList = [1, 'a', 3.14159, True] myList 1 'a' 3.14159 True Index Forward 0 1 2 3 Index Backward –4 –3 –2 –1 myList 'a' myList[:3] [1, 'a', 3.14159] Introduction to Computational Thinking 15 []? Indexing on Lists [] means a list and it is also used to retrieve index. ['a', 'b', 'c'] ‘b’ [0, 1, 2] 0 0 Content is important! Index is always at the end of the expression and is preceded by something (variable, sequence). Introduction to Computational Thinking 16 Lists of Lists myLst = ['a', [1, 2, 3], 'a'] What is the second element of the list? myLst #apply from left to right myLst [1, 2, 3] [1, 2, 3] 1 [1, [2, [3, 4]], 5] ? Introduction to Computational Thinking 17 Operators + e.g. [1, 2, 3] + [1, 2, 3, 4] * e.g. [1, 2, 3] * 2 [1, 2, 3, 1, 2, 3] in e.g. 1 in [1, 2, 3] True Introduction to Computational Thinking 18 List Functions len(lst) Number of elements in list (top level) e.g. len([1, [1, 2], 3]) 3 min(lst) Minimum element in the list max(lst) Maximum element in the list sum(lst) Sum of the elements, numeric only Introduction to Computational Thinking 19 Iterate on the List for element in [1, [1, 2], 'a', True]: print(element) What do you think is the print output? Answer: 1 [1, 2] ‘a’ True Introduction to Computational Thinking 20 Mutable vs. Immutable Mutability The quality of Mutable Immutable being capable of After creation, it can mutation After creation, it be changed. cannot be changed. Lists are mutable. Strings are immutable. Immutable (Strings): Examples myStr = 'abc' myStr = 'z' #not possible newStr= myStr.replace('a', 'z') #make a new string Introduction to Computational Thinking 21 Lists as Mutable The object’s contents can be changed. myLst = [1, 2, 3] myLst = 127 print(myLst) What do you think is the output? Answer: [127, 2, 3] Introduction to Computational Thinking 22 List Methods A list is mutable and can be changed: myList = 'a' #index assignment myList.append(e) // e: element to append myList.extend(L) // L: a list myList.pop(i) // i: index (default: -1) myList.insert(i,e) myList.remove(e) myList.sort() myList.reverse() Introduction to Computational Thinking 23 List Methods: Example myList = [1,3] [1, 3] myList = 'a' ['a', 3] myList.append(2) ['a', 3, 2] lst = [6,5] myList.extend(lst) ['a', 3, 2, 6, 5] myList.extend(5) ERROR! element = myList.pop() ['a', 3, 2, 6] print(element) 5 myList.append([8,9]) ['a', 3, 2, 6, [8,9]] Introduction to Computational Thinking 24 List Methods: Example (Cont’d) ['a', 3, 2, 6] myList.insert(0, 'b') ['b', 'a', 3, 2, 6] myList.insert(-1, 'b') ['b', 'a', 3, 2, 'b', 6] myList.insert(10, 'c') ['b', 'a', 3, 2, 'b', 6, 'c'] myList.remove('b') ['a', 3, 2, 'b', 6, 'c'] myList.sort() TypeError!! myList.remove('b') ['a', 3, 2, 6, 'c'] myList.remove('a') [3, 2, 6, 'c'] myList.remove('c') [3, 2, 6] myList.remove('d') ValueError!! myList.sort() [2, 3, 6] myList.reverse() [6, 3, 2] Introduction to Computational Thinking 25 Return Values When compared to string methods, most of these list methods do not return a value. NO Return Value This is because lists are mutable so the methods modify the list directly; there is no need to return a new list. Remember the python standard is your friend! Introduction to Computational Thinking 26 Warning about Results myLst = [4, 7, 1, 2] Be careful of what you are myLst = myLst.sort() assigning! myLst  None #what happened? What is the return value of myLst.sort()? WARNING Introduction to Computational Thinking 27 String Method: split() The string method split() generates a sequence of characters by splitting the string at certain split-characters. - Default split-character: white space. The string method, split(), returns a list. splitLst = 'this is a test'.split() print(splitLst) ['this', 'is', 'a', 'test'] Introduction to Computational Thinking 28 Sorting Only lists have a built-in sorting method. Thus, data could be converted to a list if it needs sorting. myLst = list('xyzabc') #iterable to constructor myLst ['x', 'y', 'z', 'a', 'b', 'c'] myLst.sort() ['a', 'b', 'c', 'x', 'y', 'z'] # convert back to a string sortStr = ''.join(myLst) 'abcxyz' Introduction to Computational Thinking 29  lists[…] Composite Types Convenient and efficient ways to store data. Dictionaries Introduction to Computational Thinking 30 Tuples Tuples are immutable lists. They are designated with (,). Example: Why Immutable Lists? myTuple = (1, 'a', 3.14, True) Provides a data structure with some integrity and some permanency To avoid accidentally changing one Introduction to Computational Thinking 31 Lists vs. Tuples Everything that works for a list works for a tuple except methods that modify the tuple. What works? What doesn’t work? indexing Mutable methods slicing append() len() extend() print() remove(), etc. Introduction to Computational Thinking 32 Commas Create Tuples For tuples: comma can be thought of as the operator that makes a tuple while the round bracket ( ) simply acts as a grouping myTuple = 1,2 # creates (1,2) myTuple = (1,) # creates (1) myTuple = (1) # creates 1 not (1) myTuple = 1, # creates (1) Introduction to Computational Thinking 33  List Comprehension Introduction to Computational Thinking 34 Constructing List List comprehension: syntactic structure for concise construction of lists mark the comprehension with [ ] [ n for n in range(1,5) ] what we Iteration condition collect Note that we iterate over a set of values and collect some (in this [1, 2, 3, 4] case all) of them. Introduction to Computational Thinking 35 Other Examples [n**2 for n in range(1,6)] [1, 4, 9, 16, 25] [x + y for x in range(1,5) for y in range (1,4)] ? It is as if we had done the following: myList = [ ] for x in range (1,5): for y in range (1,4): myList.append(x+y) [c for c in "Hi There Mom" if c.isupper()] [‘H’, ‘T’, ‘M’] Introduction to Computational Thinking 36  lists[…] Composite Dictionaries Types Convenient and efficient ways to store data. Introduction to Computational Thinking 37 What is Dictionary? In data structure terms, a dictionary is better termed as an associative array, or associative list, or a map. Dictionaries You can think of it as a list of pairs. - The key, which is the first element of the pair, is used to retrieve the second element, which is the value. Thus, we map a key to a value. Introduction to Computational Thinking 38 Key:Value The key acts as a “lookup” to find the associated value. Just like a dictionary, you look up a word by its spelling to find the associated definition. A dictionary can be searched to locate the value associated with a key. Introduction to Computational Thinking 39 Python Dictionary { } marker: used to create a dictionary : marker: used to create key:value pairs Name PhoneNumbers contacts = {'bill': '353-1234', ‘bill’ ‘353-1234’ 'rich': '269-1234', 'jane': '352-1234’} ‘rich’ ‘269-1234’ print(contacts) {'jane': '352-1234', 'bill': '353-1234', 'rich': '269-1234'} ‘jane’ ‘352-1234’ Introduction to Computational Thinking 40 What are Keys and Values? Key:value Must be immutable Can be anything Strings, Integers, Tuples NOT Allowed Allowed Lists Introduction to Computational Thinking 41 Collection vs. Sequence Dictionaries are collections but they are not sequences like lists, strings, or tuples. There is no order to the elements of a dictionary. In fact, the order (for example, when printed) might change as elements are added or deleted. So, how do you access dictionary elements? Introduction to Computational Thinking 42 Access to Dictionary Access requires [ ] and the key is the index. myDict = {} an empty dictionary myDict['bill'] = 25 add the pair 'bill':25 print(myDict['bill']) print 25 del myDic['bill'] remove the pair 'bill':25 Introduction to Computational Thinking 43 Dictionaries are Mutable Like lists, dictionaries are mutable. You can change the object via various operations, such as index assignment. myDict = {'bill':3, 'rich':10} print(myDict['bill']) # prints 3 myDict['bill'] = 100 # change value print(myDict['bill']) # prints 100 del myDict['rich'] # remove 'rich':10 del myDict['rich'] # KeyError Introduction to Computational Thinking 44 Dictionary Operations Like others, dictionaries respond to these: len(myDict) → number of key:value pairs in the dictionary element in myDict → boolean; is element a key in the dictionary? for key in myDict → iterate through the keys of a dictionary Introduction to Computational Thinking 45 Other Methods and Operations myDict.items() → return all the key:value pairs myDict.keys() → return all the keys myDict.values() → return all the values myDict.clear() → empty the dictionary myDict.update(yourDict) → for each key in yourDict, update myDict with that key:value pair Introduction to Computational Thinking 46 Iterating on a Dictionary for key in myDict: print(key) prints all the keys for key,value in myDict.items(): print(key, value) prints all the key:value pairs for value in myDict.values(): print(value) prints all the values Introduction to Computational Thinking 47 Summary In this lesson, we have learnt: The concept of composite types Built-in composite types in the Python programming language: ‒ List ‒ Tuple ‒ Dictionary Introduction to Computational Thinking 48 References for Images No. Slide No. Image Reference Gabovitch, I. (2014). AV Out In HDMI In Jack Plug Red White Yellow Audio and 1 5 Video Mixer Backside [Online Image]. Retrieved May 17, 2018 from https://www.flickr.com/photos/qubodup/12248078123. Python Logo [Online Image]. Retrieved April 24, 2018 from 2 6 https://pixabay.com/en/language-logo-python-2024210/. By Ephemeron - Own work, based on File:Dynamic Dictionary Logo.png, CC BY-SA 3 6, 8, 37, 38 3.0, retrieved May 18, 2018 from https://commons.wikimedia.org/w/index.php?curid=7361291. String [Online Image]. Retrieved April 24, 2018 https://pixabay.com/en/string-twine- 4 6, 8 ball-twined-isolated-314346/. Search [Online Image]. Retrieved April 18, 2018 from 5 10 https://pixabay.com/en/database-search-database-search-icon-2797375/. Introduction to Computational Thinking 49 References for Images No. Slide No. Image Reference Question problem [Online Image]. Retrieved April 18, 2018 from 6 17, 20, 22, 42 https://pixabay.com/en/question-problem-think-thinking-622164/. Survey icon [Online Image]. Retrieved April 18, 2018 from 7 21 https://pixabay.com/en/survey-icon-survey-icon-2316468/. Smiley 11 [Online Image]. Retrieved April 18, 2018 from 8 26 http://www.publicdomainfiles.com/show_file.php?id=13545100814144. By Unknown - From the Open Clip Art Gallery - http://openclipart.org/, CC0, retrieved 9 27 May 16, 2018 from https://commons.wikimedia.org/w/index.php?curid=1849852. Alphabet [Online Image]. Retrieved May 17, 2018 from 10 29 https://pixabay.com/en/alphabet-letters-numbers-digits-40515/. Introduction to Computational Thinking 50