Application Development - Chapter 5 - Lists - Lecture Notes 5-7

Chapter 5 Lists and Dictionaries • Introduction • A list allows the programmer to manipulate a sequence of data values of any types • A dictionary organizes data values by association with other data values rather than by sequential position • Lists and dictionaries provide powerful ways to organize data in useful and interesting applications • Lists • List: Sequence of data values (items or elements) • Some examples: • Shopping list for the grocery store • To-do list • Roster for an athletic team • Guest list for a wedding • Recipe, which is a list of instructions • Text document, which is a list of lines • Names in a phone book • Each item in a list has a unique index that specifies its position (from 0 to length – 1) • List Literals and Basic Operators (1 of 4) • Some examples: [‘apples’, ‘oranges’, ‘cherries’] [[5, 9], [541, 78]] • When an element is an expression, its value is included in the list: >>> import math >>> x = 2 >>> [x, math.sqrt(x)] [2, 1.4142135623730951] >>> [x + 1] [3] • List Literals and Basic Operators (2 of 4) • Lists of integers can be built using range: >>> first = [1, 2, 3, 4] >>> second = list(range(1, 5)) >>> first [1, 2, 3, 4] >>> second [1, 2, 3, 4] • The list function can build a list from any iterable sequence of elements: >>> third = list(“Hi there!”) >>> third [‘H’, ‘I’, ‘ ’ , ‘t’, ‘h’, ‘e’, ‘r’, ‘e’, ‘!’] • List Literals and Basic Operators (3 of 4) • len, [], +, and == work on lists as expected: >>> len(first) 4 >>> first[0] 1 >>> first[2:4] [3, 4] • Concatenation (+) and equality (==) also work as expected for lists: >>> first + [5, 6] [1, 2, 3, 4, 5, 6] >>> first == second True • List Literals and Basic Operators (4 of 4) • To print the contents of a list: >>> print(“1234”) 1234 >>> print([1, 2, 3, 4]) [1, 2, 3, 4] • in detects the presence of an element: >>> 3 in [1, 2, 3] True >>> 0 in [1, 2, 3] False • Replacing an Element in a List (1 of 2) • A list is mutable • Elements can be inserted, removed, or replaced • The list itself maintains its identity, but its state—its length and its contents—can change • Subscript operator is used to replace an element: >>> example = [1, 2, 3, 4] >>> example [1, 2, 3, 4] >>> example[3] = 0 >>> example [1, 2, 3, 0] • Subscript is used to reference the target of the assignment, which is not the list but an element’s position within it • Replacing an Element in a List (2 of 2) • The first session shows how to replace each number in a list with its square: >>> numbers = [2, 3, 4, 5] >>> numbers [2, 3, 4, 5] >>> for index in range(len(numbers)): numbers[index] = numbers[index] ** 2 >>> numbers [4, 9, 16, 25] • Next session uses the string method split to extract a list of words: >>> sentence = “This example has five words. ” >>> words = sentence.split() >>> words [‘This’, ‘example’, ‘has’, ‘five’, ‘words.’] >>> for index in range(len(words)): words[index] = words[index].upper() >>> words [‘THIS’, ‘EXAMPLE’, ‘HAS’, ‘FIVE’, ‘WORDS.’] • List Methods for Inserting and Removing Elements (1 of 4) • The list type includes several methods for inserting and removing elements • List Methods for Inserting and Removing Elements (2 of 4) • The method insert expects an integer index and the new element as arguments >>> example = [1, 2] >>> example [1, 2] >>> example.insert(1, 10) >>> example [1, 10, 2] >>> example.insert(3, 25) >>> example [1, 10, 2, 25] • List Methods for Inserting and Removing Elements (3 of 4) • The method append expects just the new element as an argument and adds the new element to the end of the list • The method extend performs a similar operation, but adds the elements of its list argument to the end of the list >>> example = [1, 2] >>> example [1, 2] >>> example.append(3) >>> example [1, 2, 3] >>> example.extend([11, 12, 13]) >>> example [1, 2, 3, 11, 12, 13] >>> example + [14, 15] [1, 2, 3, 11, 12, 13, 14, 15] >>> example [1, 2, 3, 11, 12, 13] • List Methods for Inserting and Removing Elements (4 of 4) • The method pop is used to remove an element at a given position >>> example [1, 2, 10, 11, 12, 13] # Remove the last element >>> example.pop() 13 >>> example [1, 2, 10, 11, 12] >>> example.pop(0) # Remove the first element 1 >>> example [2, 10, 11, 12] • Searching a List • in determines an element’s presence or absence, but does not return position of element (if found) • Use method index to locate an element’s position in a list • Raises an error when the target element is not found aList = [34, 45, 67] target = 45 if target in aList: print(aList.index(target)) else: print(−l) • Sorting a List • A list’s elements are always ordered by position, but you can impose a natural ordering on them • For example, in alphabetical order • When the elements can be related by comparing them <, >, and ==, they can be sorted • The method sort mutates a list by arranging its elements in ascending order >>> example = [4, 2, 10, 8] >>> example [4, 2, 10, 8] >>> example.sort() >>> example [2, 4, 8, 10] • Mutator Methods and the Value None • All of the functions and methods examined in previous chapters return a value that the caller can then use to complete its work • Mutator methods (e.g., insert, append,extend,pop,and sort) usually return no value of interest to caller • Python automatically returns the special value None >>> aList = aList.sort() >>> print(aList) None • Aliasing and Side Effects (1 of 2) • Mutable property of lists leads to interesting phenomena: >>> first = [10, 20, 30] >>> second = first >>> first [10, 20, 30] >>> second [10, 20, 30] >>> first[1] = 99 >>> first [10, 99, 30] >>> second [10, 99, 30] • First and second are aliases • They refer to the exact same list object • Aliasing and Side Effects (2 of 2) • To prevent aliasing, create a new object and copy contents of original: >>> third = [] >>> for element in first: third.append(element) >>> first [10, 99, 30] >>> third [10, 99, 30] >>> first[l] = 100 >>> first [10, 100, 30] >>> third [10, 99, 30] • Equality: Object Identity and Structural Equivalence (1 of 2) • Programmers might need to see whether two variables refer to the exact same object or to different objects • Example, you might want to determine whether one variable is an alias for another • The == operator returns True if the variables are aliases for the same object. • Unfortunately, == also returns True if the contents of two different objects are the same • The first relation is called object identity • The second relation is called structural equivalence. • The == operator has no way of distinguishing between these two types of relations. • Equality: Object Identity and Structural Equivalence (2 of 2) • Python’s is operator can be used to test for object identity >>> first = [20, 30, 40] >>> second = first >>> third = list(first) # Or first[:] >>> first == second True >>> first == third True >>> first is second True >>> first is third False • Example: Using a List to Find the Median of a Set of Numbers • To find the median of a set of numbers: “““ File: median.py Demon: median.py Prints the median of a set of numbers in a file. ””” fileName = input(“Enter the filename: ”) f = open(fileName, ‘r’) # Input the text, convert it to numbers, and # add the numbers to a list numbers = [] for line in f: words = line.split() for word in words: numbers.append(float(word)) # Sort the list and print the number at its midpoint numbers.sort() midpoint = len(numbers) // 2 print(“The median is’’, end = “ ”) if len(numbers) % 2 == 1: print(numbers[midpoint]) else: print((numbers[midpoint] + numbers[midpoint − 1]) / 2) • Tuples • A tuple resembles a list, but is immutable • Indicate by enclosing its elements in () >>> fruits = (“apple”, “banana”) >>> fruits (‘apple’, ‘banana’) >>> meats = (“fish”, “poultry”) >>> meats (‘fish’, ‘poultry’) >>> food = meats + fruits >>> food (‘fish’, ‘poultry’, ‘apple’, ‘banana’) >>> veggies = [“celery”, “beans”] >>> tuple(veggies) (‘celery’, ‘beans’) Defining Simple Functions Defining our own functions allows us to organize our code in existing scripts more effectively • This section provides a brief overview of how to do this • The Syntax of Simple Function Definitions • Definition of a function consists of header and body • Docstring contains information about what the function does; to display, enter help(square) • A function can be defined in a Python shell, but it is more convenient to define it in an I D L E window • Syntax of a function definition: def (, ..., ): def square(x): “““Returns the square of x.””” return x * x • Parameters and Arguments • A parameter is the name used in the function definition for an argument that is passed to the function when it is called • For now, the number and positions of arguments of a function call should match the number and positions of the parameters in the definition • Some functions expect no arguments • They are defined with no parameters • The Return Statement • Place a return statement at each exit point of a function when function should explicitly return a value • Syntax: return • If a function contains no return statement, Python transfers control to the caller after the last statement in the function’s body is executed • The special value None is automatically returned • Boolean Functions • A Boolean function usually tests its argument for the presence or absence of some property • Returns True if property is present; False otherwise • Example: >>> odd(5) True >>> odd(6) False def odd(x): “““ Returns True if x is odd or False otherwise.””” if x % 2 == 1: return True • • else: return False • Defining a Main Function (1 of 2) • main serves as the entry point for a script • Usually expects no arguments and returns no value • Definition of main and other functions can appear in no particular order in the script • As long as main is called at the end of the script • Script can be run from I D L E, imported into the shell, or run from a terminal command prompt • Defining a Main Function (2 of 2) • Example: “““ File: computesquare.py (Demon: computesquare.py) Illustrates the definition of a main function. ””” def main(): “““ The main function for this script.””” number = float(input(“Enter a number: ”)) result = square(number) print(“The square of”, number, “is”, result) def square(x): “““Returns the square of x.””” return x * x # The entry point for program execution if __name__ == “__main:”__ main() • Dictionaries • A dictionary organizes information by association, not position • Example: When you use a dictionary to look up the definition of “mammal,” you don’t start at page 1; instead, you turn to the words beginning with “M” • Data structures organized by association are also called tables or association lists • In Python, a dictionary associates a set of keys with data values • Dictionary Literals • A Python dictionary is written as a sequence of key/value pairs separated by commas • Pairs are sometimes called entries • Enclosed in curly braces ({ and }) • A colon (:) separates a key and its value • Examples: A phone book: {‘Savannah’:‘476-3321’, ‘Nathaniel’:‘351-7743’} Personal information: {‘Name’:‘Molly’, ‘Age’:18} An empty dictionary: {} • Keys in a dictionary can be data of any immutable types, including other data structures • They are normally strings or integers • Adding Keys and Replacing Values • Add a new key/value pair to a dictionary using []: [] = • Example: >>> info = {} >>> info[“name”] = “Sandy” >>> info[“occupation”] = “hacker” >>> info {‘name’:‘Sandy’, ‘occupation’:‘hacker’} • Use [] also to replace a value at an existing key: >>> info[“occupation”] = “manager” >>> info {‘name’:‘Sandy’, ‘occupation’: ‘manager’} • Accessing Values (1 of 2) • Use [] to obtain the value associated with a key • If key is not present in dictionary, an error is raised >>> info[“name”] ‘Sandy’ >>> info[“job”] Trace back (most recent call last): File “”, line 1, in info[“job”] Key Error: ‘job’ • Accessing Values (2 of 2) • If the existence of a key is uncertain, test for it using the dictionary method has_key • Easier strategy is to use the method get >>> if “job” in info: print(info.[“job”]) • Removing Keys • To delete an entry from a dictionary, remove its key using the method pop • pop expects a key and an optional default value as arguments >>> print(info.pop(“job”, None)) None >>> print(info.pop(“occupation”)) manager >>> info {‘name’:‘Sandy’} • Traversing a Dictionary (1 of 3) • To print all of the keys and their values: for key in info: print(key, info[key]) • Alternative: Use the dictionary method items() >>> grades = {90:‘A’, 80:‘B’, 70:‘C’} >>> list(grades.items()) [(80, ‘B’), (90,‘A’), (70,‘C’)] • Entries are represented as tuples within the list for (key, value) in grades.items(): print(key, value) • Traversing a Dictionary (2 of 3) • You can sort the list first then traverse it to print the entries of the dictionary in alphabetical order: the Keys = list(info.keys()) the Keys.sort() for key in the Keys: print(key, info[key]) • Traversing a Dictionary (3 of 3) • Example: The Hexadecimal System Revisited (1 of 2) • You can keep a hex-to-binary lookup table to aid in the conversion process hexToBinaryTable = {‘0’:‘0000’, ‘1’:‘0001’, ‘2’:‘0010’, ‘3’:‘0011’, ‘4’:‘0100’, ‘5’:‘0101’, ‘6’:‘0110’, ‘7’:‘0111’, ‘8’:‘1000’, ‘9’:‘1001’, ‘A’:‘1010’, ‘B’:‘1011’, ‘C’:‘1100’, ‘D’:‘1101’, ‘E’:‘1110’, ‘F’:‘1111’} • Example: The Hexadecimal System Revisited (2 of 2) • The function convert expects two parameters: a string representing the number to be converted and a table of associations of digits • Example: def convert(number, table): “““Builds and returns the base two representation of number.””” binary = “ ” for digit in number: binary = table[digit] + binary return binary >>> convert(“35A”, hexToBinaryTable) ‘001101011010’ • Example: Finding the Mode of a List of Values (1 of 2) • The mode of a list of values is the value that occurs most frequently • The following script inputs a list of words from a text file and prints their mode fileName = input(“Enter the filename:”) f = open(fileName, ‘r’) # Input the text, convert its words to uppercase, and # add the words to a list words = [] for line in f: for word in line.split(): words.append(word.upper()) • Example: Finding the Mode of a List of Values (2 of 2)