CSCI 1100 - Test 3 Overview and Practice Questions

Here’s a Python function notused that takes a list of words and returns a set containing the letters of the English alphabet that are not used by any of the words in the input list:

1
2
3
4
5
6
7
8

def notused(words):
    all_letters = set("abcdefghijklmnopqrstuvwxyz")
    used_letters = set()

    for word in words:
        used_letters |= set(word.lower())

    return all_letters - used_letters

Explanation:

1. We define the function notused that takes a list of words as its parameter.

2. We create a set called all_letters that contains all the lowercase letters of the English alphabet using set("abcdefghijklmnopqrstuvwxyz").

3. We initialize an empty set called used_letters to store the letters used by the words in the input list.

4. We start a loop that iterates over each word in the words list.

5. For each word, we convert it to lowercase using word.lower() to ensure case-insensitivity. Then, we convert the lowercase word into a set of its individual letters using set(word.lower()).

6. We update the used_letters set by taking the union of itself with the set of letters from the current word using the |= operator. This adds any new letters from the current word to the used_letters set.

7. After the loop, used_letters will contain all the letters used by the words in the input list.

8. Finally, we return the set difference between all_letters and used_letters using all_letters - used_letters. This gives us the set of letters from the English alphabet that are not used by any of the words in the input list.

Here’s an example of how the function works:

1
2

print(notused(["Dog", "pony", "elephant", "Tiger", "onyx", "Zebu"]))
# Output: {'c', 'f', 'k', 'j', 'm', 'q', 's', 'w', 'v'}

In this example, the function notused is called with a list of words. It returns a set containing the letters ‘c’, ‘f’, ‘k’, ‘j’, ’m’, ‘q’, ’s’, ‘w’, and ‘v’, which are the letters of the English alphabet that are not used by any of the words in the input list.

The solution uses sets to efficiently handle the comparison and difference operations between the sets of letters.

To find the values that are in exactly one of the three sets s1, s2, and s3 without using any loops or conditionals, you can use set operations in Python. Here’s the code segment:

1
2
3

s = (s1 ^ s2 ^ s3) - (s1 & s2) - (s1 & s3) - (s2 & s3)
# Or this one
#s = (s1 - s2 - s3) | (s2 - s1 - s3) | (s3 - s1 - s2)

Explanation:

1. The ^ operator performs the symmetric difference operation between sets. It returns a new set that contains elements that are in either of the sets but not in both.

   • s1 ^ s2 ^ s3 gives us a set containing elements that are in exactly one or all three sets.

2. To remove the elements that are in more than one set, we need to subtract the intersections of each pair of sets.

   • s1 & s2 gives us the elements that are common to both s1 and s2.
   • s1 & s3 gives us the elements that are common to both s1 and s3.
   • s2 & s3 gives us the elements that are common to both s2 and s3.

3. By subtracting the intersections (s1 & s2), (s1 & s3), and (s2 & s3) from the symmetric difference (s1 ^ s2 ^ s3), we remove the elements that are in more than one set.

4. The resulting set s will contain only the elements that are in exactly one of the three sets s1, s2, and s3.

This code segment achieves the desired result without using any loops or conditionals, utilizing only set operations.

To find the number of words that appear in all three strings without using loops, you can use the set data structure and the intersection method. Here’s the solution:

1

print(len(set(w1.split()) & set(w2.split()) & set(w3.split())))

Explanation:

1. We use the split() method on each string (w1, w2, w3) to split them into lists of words. By default, split() splits the string on whitespace, which separates the words.

2. We convert each list of words into a set using set(). A set is an unordered collection of unique elements, so it eliminates any duplicate words within each string.

3. We use the & operator (set intersection) to find the common elements between the sets. The intersection of two sets returns a new set containing only the elements that are present in both sets.

4. We chain the & operator to find the intersection of all three sets, which gives us a set of words that appear in all three strings.

5. Finally, we use the len() function to count the number of elements in the resulting set, which represents the number of words that appear in all three strings.

The one-line solution combines all these steps into a single line of code, making it concise but potentially less readable. It’s important to understand the individual steps and the concept of set intersection to comprehend the one-line solution.

You can test the code with the provided example:

1
2
3
4
5

w1 = "the quick brown fox jumps over the lazy dog"
w2 = "hey diddle diddle the cat and the fiddle the cow jumps over the moon"  
w3 = "jack and jill went over the hill to fetch a pail of water"

print(len(set(w1.split()) & set(w2.split()) & set(w3.split())))  # Output: 2

The output will be 2 because the words “the” and “over” appear in all three strings.

The output of the given code will be:

1
2
3
4
5
6

{12, 7}
{'b', 7, 9, 10, 12, 'car', 'abc'}
True
False
True
{12, 7, 14, 'ab'}

Explanation:

1. s1 = set([7, 9, 12, 7, 9]): This creates a set s1 with elements 7, 9, and 12. Duplicates are automatically removed.

2. s2 = set(['abc', 12, 'b', 'car', 7, 10, 12]): This creates a set s2 with elements ‘abc’, 12, ‘b’, ‘car’, 7, and 10. Again, duplicates are removed.

3. s3 = set([12, 14, 12, 'ab']): This creates a set s3 with elements 12, 14, and ‘ab’.

4. print(s1 & s2): This performs the intersection operation between sets s1 and s2, which returns a new set containing the common elements. The output will be {12, 7}.

5. print(s1 | s2): This performs the union operation between sets s1 and s2, which returns a new set containing all the elements from both sets. The output will be {'b', 7, 9, 10, 12, 'car', 'abc'}.

6. print('b' in s2): This checks if the element ‘b’ is present in set s2. It will output True since ‘b’ is in s2.

7. print('ab' in s2): This checks if the element ‘ab’ is present in set s2. It will output False since ‘ab’ is not in s2.

8. print('ab' in s3): This checks if the element ‘ab’ is present in set s3. It will output True since ‘ab’ is in s3.

9. s2.discard(12): This removes the element 12 from set s2 if it exists. After this operation, s2 will be {'abc', 'b', 'car', 7, 10}.

10. print((s1 & s2) ^ s3): This performs the following operations:

    • s1 & s2 calculates the intersection of sets s1 and s2, which is {7}.
    • (s1 & s2) ^ s3 performs the symmetric difference operation between the result of s1 & s2 and set s3. It returns a new set containing elements that are in either (s1 & s2) or s3, but not in both. The output will be {12, 7, 14, 'ab'}.

The order of elements in the output sets may vary since sets are unordered collections. The actual output you provided matches the expected output.

1
2
3
4
5
6
7

for restaurant, reviews in rest_reviews.items():
    count = 0
    for review in reviews:
        if any(word in review.lower() for word in ["awful", "terrible", "dump"]):
            count += 1
    if count > 0:
        print(f"{restaurant}: {count} review(s) containing the specified words")

Explanation:

• We iterate over each restaurant and its reviews in the rest_reviews dictionary.
• For each restaurant, we initialize a count variable to keep track of the number of reviews containing the specified words.
• We iterate over each review and check if any of the specified words (“awful”, “terrible”, “dump”) are present in the review (case-insensitive).
• If a review contains any of the specified words, we increment the count.
• After checking all reviews for a restaurant, if the count is greater than 0, we print the restaurant name and the count of reviews containing the specified words.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

max_reviews = 0
restaurants_with_max_reviews = []

for restaurant, reviews in rest_reviews.items():
    num_reviews = len(reviews)
    if num_reviews > max_reviews:
        max_reviews = num_reviews
        restaurants_with_max_reviews = [restaurant]
    elif num_reviews == max_reviews:
        restaurants_with_max_reviews.append(restaurant)

print("Restaurant(s) with the highest number of reviews:")
for restaurant in restaurants_with_max_reviews:
    print(restaurant)

Explanation:

• We initialize max_reviews to keep track of the highest number of reviews and restaurants_with_max_reviews to store the restaurant(s) with the highest number of reviews.
• We iterate over each restaurant and its reviews in the rest_reviews dictionary.
• For each restaurant, we calculate the number of reviews using len(reviews).
• If the number of reviews is greater than the current max_reviews, we update max_reviews and set restaurants_with_max_reviews to a list containing only the current restaurant.
• If the number of reviews is equal to max_reviews, we append the current restaurant to restaurants_with_max_reviews.
• Finally, we print the restaurant(s) with the highest number of reviews.

1
2
3
4
5

def add_review(rest_reviews, new_review, rest_name):
    if rest_name in rest_reviews:
        rest_reviews[rest_name].append(new_review)
    else:
        rest_reviews[rest_name] = [new_review]

Explanation:

• The add_review function takes the rest_reviews dictionary, a new_review, and the rest_name as arguments.
• If the rest_name is already present in the rest_reviews dictionary, we append the new_review to the existing list of reviews for that restaurant.
• If the rest_name is not in the dictionary, we create a new entry in the dictionary with the rest_name as the key and a list containing the new_review as the value.

1
2
3
4
5
6

def del_review(rest_reviews, old_review, rest_name):
    if rest_name in rest_reviews:
        if old_review in rest_reviews[rest_name]:
            rest_reviews[rest_name].remove(old_review)
            return True
    return False

Explanation:

• The del_review function takes the rest_reviews dictionary, an old_review, and the rest_name as arguments.
• If the rest_name is present in the rest_reviews dictionary, we check if the old_review is in the list of reviews for that restaurant.
• If the old_review is found, we remove it from the list using the remove method and return True to indicate successful deletion.
• If the rest_name is not in the dictionary or the old_review is not found, we return False to indicate that the deletion was not successful.

Output:

1
2

kate 3
jane 1

Explanation:

1. The code creates a dictionary x with keys 1 and 2. The value for key 1 is a list containing the string ‘joe’ and a set with elements ‘skiing’ and ‘reading’. The value for key 2 is a list containing the string ‘jane’ and a set with the element ‘hockey’.
2. The line x[1][1].add('singing') adds the element ‘singing’ to the set at index 1 of the list associated with key 1 in the dictionary x.
3. The line x[1][0] = 'kate' updates the string at index 0 of the list associated with key 1 in the dictionary x to ‘kate’.
4. The for loop iterates over the sorted keys of the dictionary x.
5. For each key item, it prints the string at index 0 of the corresponding list (x[item][0]) and the length of the set at index 1 of the list (len(x[item][1])).
6. The output shows that for key 1, the string is ‘kate’ and the set has 3 elements, and for key 2, the string is ‘jane’ and the set has 1 element.

Output:

1
2
3
4
5

** alice
** bob
** jane
!! jane
!! kristin

Explanation:

1. The code creates a dictionary y with keys ‘jane’, ‘alice’, ‘bob’, and ‘kristin’, and their corresponding values.
2. The variable m is initialized to 0.
3. The first for loop iterates over the sorted keys of the dictionary y.
4. For each person, if the value y[person] is greater than m, it prints "** " + person and updates m to the value of y[person]. This finds the maximum value in the dictionary.
5. The output of the first loop shows that ‘alice’, ‘bob’, and ‘jane’ are printed with "**" prefix because their values are greater than the initial value of m (which is 0).
6. After the first loop, m holds the maximum value found in the dictionary, which is 10.
7. The second for loop iterates over the sorted keys of the dictionary y again.
8. For each person, if the value y[person] is equal to m (the maximum value), it prints "!! " + person.
9. The output of the second loop shows that ‘jane’ and ‘kristin’ are printed with "!!" prefix because their values are equal to the maximum value m (which is 10).

Thank you for pointing out the mistake. I appreciate your attention to detail!

Output:

1
2

5 0 1 6 
8 car b k 

Explanation:

1. The code creates two lists, L1 and L2, and a dictionary d with keys 5 and 8. The value for key 5 is L1, and the value for key 8 is L2.
2. The line L1[2] = 6 updates the element at index 2 of L1 to 6.
3. The line d[8].append('k') appends the element ‘k’ to the list L2, which is the value for key 8 in the dictionary d.
4. The line L2[0] = 'car' updates the element at index 0 of L2 to ‘car’.
5. The for loop iterates over the sorted keys of the dictionary d.
6. For each key k, it prints the string representation of k followed by a space.
7. The nested for loop iterates over the values v in the list d[k] and prints the string representation of each v followed by a space.
8. After each inner loop, it prints a newline character to move to the next line.
9. The output shows that for key 5, the corresponding list is [0, 1, 6], and for key 8, the corresponding list is ['car', 'b', 'k'].

Output:

1
2
3
4
5

[5, 1, 2]
0
2
4
6

Explanation:

1. The code creates a list L1 with elements [0, 1, 2, 4, 1, 0].
2. The line s1 = set(L1) creates a set s1 from the elements of L1. The set will contain only unique elements from L1, which are {0, 1, 2, 4}.
3. The lines L1.pop(), L1.pop(), and L1.pop() remove the last three elements from the list L1. After these operations, L1 becomes [0, 1, 2].
4. The line L1[0] = 5 updates the element at index 0 of L1 to 5. Now, L1 becomes [5, 1, 2].
5. The line s1.add(6) adds the element 6 to the set s1. The set s1 becomes {0, 1, 2, 4, 6}.
6. The line s1.discard(1) removes the element 1 from the set s1. The set s1 becomes {0, 2, 4, 6}.
7. The line print(L1) prints the updated list L1, which is [5, 1, 2].
8. The for loop iterates over the sorted elements v in the set s1 and prints each element on a new line.
9. The output shows the updated list L1 and the elements of the set s1 in sorted order, which are 0, 2, 4, and 6.

Thank you for bringing this to my attention. I appreciate your careful review of the answers!

To determine the relationship between two Person objects, we can compare their mother, father, and birthday attributes. Here’s the method you can add to the Person class:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

def relationship(self, other):
    if self == other:
        return -1
    elif self.mother == other.mother and self.father == other.father:
        if self.birthday == other.birthday:
            return 2
        else:
            return 1
    else:
        return 0

Explanation:

1. The method relationship takes two arguments: self (the current Person object) and other (another Person object).

2. We first check if self and other refer to the same Person object using the equality operator ==. If they are the same object, we return -1.

3. If self and other are not the same object, we check if they have the same mother and father by comparing the mother and father attributes of both objects. If they have the same mother and father, it means they are either twins or siblings.

4. If self and other have the same mother and father, we further check their birthday attribute:

   • If their birthdays are the same, we return 2 to indicate that they are twins.
   • If their birthdays are different, we return 1 to indicate that they are siblings (but not twins).

5. If self and other do not have the same mother and father, we return 0 to indicate that they are not related as siblings or twins.

With this method added to the Person class, you can create Person objects and determine their relationship using the relationship method. For example:

1
2
3
4
5
6
7
8
9

person1 = Person("John", "1990-01-01", "Mary", "David")
person2 = Person("Jane", "1990-01-01", "Mary", "David")
person3 = Person("Alice", "1992-05-10", "Mary", "David")
person4 = Person("Bob", "1995-03-15", "Lisa", "Tom")

print(person1.relationship(person2))  # Output: 2 (twins)
print(person1.relationship(person3))  # Output: 1 (siblings)
print(person1.relationship(person4))  # Output: 0 (not related)
print(person1.relationship(person1))  # Output: -1 (same person)

The relationship method correctly determines the relationship between two Person objects based on their mother, father, and birthday attributes.

Here’s a Python function that merges two dictionaries D1 and D2 into a single dictionary D based on the given requirements:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15

def merge_dict(D1, D2):
    D = {}
    
    # Merge keys from D1
    for name, numbers in D1.items():
        D[name] = numbers.copy()
    
    # Merge keys from D2
    for name, numbers in D2.items():
        if name in D:
            D[name] |= numbers
        else:
            D[name] = numbers.copy()
    
    return D

Explanation:

1. We start by creating an empty dictionary D to store the merged result.

2. We iterate over each key-value pair in D1 using the items() method:

   • For each name (key) and numbers (value) pair, we add the name as a key to D and create a copy of the numbers set using numbers.copy(). This ensures that the original sets in D1 are not modified.

3. Next, we iterate over each key-value pair in D2:

   • If the name (key) already exists in D, we merge the numbers set from D2 with the existing set in D using the |= operator (set union).
   • If the name (key) doesn’t exist in D, we add it as a new key to D and create a copy of the numbers set from D2 using numbers.copy().

4. Finally, we return the merged dictionary D.

The function correctly merges the information from both D1 and D2 into a single dictionary D. If a name exists in both dictionaries, the corresponding phone number sets are merged using set union.

You can test the function with the provided example:

1
2
3
4

D1 = {'Joe': set(['555-1111', '555-2222']), 'Jane': set(['555-3333'])}
D2 = {'Joe': set(['555-2222', '555-4444']), 'Kate': set(['555-6666'])}
merged_dict = merge_dict(D1, D2)
print(merged_dict)

Output:

1

{'Joe': {'555-1111', '555-2222', '555-4444'}, 'Jane': {'555-3333'}, 'Kate': {'555-6666'}}

The merged dictionary D contains all the information from both D1 and D2, with the phone number sets merged for common names.

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12

class Student:
    def __init__(self, name, id):
        self.name = name
        self.id = id
        self.courses = []
        self.major = "Undeclared"
    
    def add_courses(self, courses):
        self.courses.extend(courses)
    
    def common_courses(self, other_student):
        return list(set(self.courses) & set(other_student.courses))

Explanation:

(a) The __init__ method is the initializer for the Student class. It takes the name and id as parameters and initializes the name and id attributes of the Student object. It also initializes the courses attribute as an empty list and sets the major attribute to “Undeclared”.

(b) The add_courses method takes a list of courses as a parameter and adds them to the courses attribute of the Student object. It uses the extend method of the list to add multiple courses at once.

(c) The common_courses method takes another Student object (other_student) as a parameter and returns a list of courses that both students have taken in common. It uses the set intersection operation (&) to find the common courses between the current student’s courses and the other student’s courses. The resulting set is then converted back to a list using the list function.

You can use the class as follows:

1
2
3
4
5
6
7

p = Student("Chris Student", "123454321")
p.add_courses(["CSCI1100", "BASK4010", "GEOL1320"])

q = Student("Bilbo Baggins", "545454545")
q.add_courses(["MATH1240", "CSCI1100", "HIST2010", "BASK4010"])

print(q.common_courses(p))  # Output: ['CSCI1100', 'BASK4010']

This code creates two Student objects, p and q, with their respective names and IDs. It adds courses to each student using the add_courses method. Finally, it calls the common_courses method on q, passing p as the argument, to find the courses that both students have in common. The output will be ['CSCI1100', 'BASK4010'].

Assuming you have a list of Student objects called all_students, here’s a code segment that outputs the names of students who have taken at least two courses starting with “CSCI”:

1
2
3
4

for student in all_students:
    csci_courses = [course for course in student.courses if course.startswith("CSCI")]
    if len(csci_courses) >= 2:
        print(student.name)

Explanation:

1. We iterate over each Student object in the all_students list using a for loop.

2. For each student, we create a new list called csci_courses using a list comprehension. This list comprehension does the following:

   • It iterates over each course in the student’s courses list using for course in student.courses.
   • For each course, it checks if the course starts with “CSCI” using the startswith method: if course.startswith("CSCI").
   • If the course starts with “CSCI”, it is included in the csci_courses list.

3. After creating the csci_courses list, we check its length using len(csci_courses). If the length is greater than or equal to 2, it means the student has taken at least two courses starting with “CSCI”.

4. If the condition len(csci_courses) >= 2 is true, we print the student’s name using print(student.name).

This code segment will output the names of all students in the all_students list who have taken at least two courses starting with “CSCI”.

For example, if you have the following all_students list:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

p = Student("Chris Student", "123454321")
p.add_courses(["CSCI1100", "BASK4010", "GEOL1320"])

q = Student("Bilbo Baggins", "545454545")
q.add_courses(["MATH1240", "CSCI1100", "HIST2010", "BASK4010"])

r = Student("Alice Smith", "987654321")
r.add_courses(["CSCI1100", "CSCI2200", "MATH1240", "HIST2010"])

all_students = [p, q, r]

Running the code segment on this all_students list will output:

1

Alice Smith

This is because only the student “Alice Smith” has taken at least two courses starting with “CSCI” (“CSCI1100” and “CSCI2200”).

Here’s a Python function k_smallest(L, k) that returns a new list containing the k smallest values from the input list L without modifying L, using sorting and without writing any loops:

1
2
3
4

def k_smallest(L, k):
    sorted_L = sorted(L)
    Ls = sorted_L[:k]
    return Ls

Explanation:

1. We create a new list sorted_L by calling the sorted() function on the input list L. This creates a new sorted list in ascending order without modifying the original list L.

2. We create a new list Ls by slicing the sorted_L list from index 0 to index k (exclusive) using the slicing syntax sorted_L[:k]. This selects the first k elements from the sorted list, which correspond to the k smallest values.

3. Finally, we return the Ls list containing the k smallest values from the original list L.

The function achieves the desired result in just 4 lines of code without using any loops. It utilizes the built-in sorted() function to sort the list and slicing to extract the k smallest values.

You can test the function with the provided example:

1
2
3
4

L = [15, 89, 3, 56, 83, 123, 51, 14, 15, 67, 15]
k = 4
Ls = k_smallest(L, k)
print(Ls)  # Output: [3, 14, 15, 15]

The function will return the list [3, 14, 15, 15], which contains the 4 smallest values from the original list L, without modifying L.

Output:

1
2
3

2
mom
['gran', 'dad']

Explanation:

1. The code creates a dictionary dt with keys 1 and ‘hi’. The value for key 1 is a list containing the strings ‘mom’ and ‘dad’. The value for key ‘hi’ is a list containing the integers 1, 3, and 5.
2. The line print(len(dt)) prints the length of the dictionary dt, which is 2 since it has two key-value pairs.
3. The line print(dt[1][0]) prints the element at index 0 of the list associated with key 1 in the dictionary dt, which is ‘mom’.
4. The line dt['hi'].append(3) appends the integer 3 to the list associated with key ‘hi’ in the dictionary dt. After this operation, the value for key ‘hi’ becomes [1, 3, 5, 3].
5. The line dt[1][0] = 'gran' updates the element at index 0 of the list associated with key 1 in the dictionary dt to ‘gran’.
6. The line print(dt[1]) prints the list associated with key 1 in the dictionary dt, which is now [‘gran’, ‘dad’].

Output:

1
2
3
4
5

4
2
4
5
2

Explanation:

1. The code creates two lists, LP and LC, with some initial values.
2. It creates an empty dictionary called nums.
3. The line nums['pr'] = LP assigns the list LP as the value for the key ‘pr’ in the dictionary nums.
4. The line nums['co'] = LC[:] creates a shallow copy of the list LC and assigns it as the value for the key ‘co’ in the dictionary nums.
5. The line LP[1] = 5 updates the element at index 1 of the list LP to 5. This does not affect the list nums['pr'] because it is a separate copy.
6. The line print(len(nums['co'])) prints the length of the list associated with the key ‘co’ in the dictionary nums, which is 4.
7. The lines v = LC.pop(), v = LC.pop(), and v = LC.pop() remove and assign the last three elements of the list LC to the variable v. After these operations, LC becomes [4].
8. The line LC.append(12) appends the integer 12 to the list LC. Now, LC becomes [4, 12].
9. The line print(len(LC)) prints the length of the list LC, which is 2.
10. The line print(len(nums['co'])) prints the length of the list associated with the key ‘co’ in the dictionary nums, which is still 4 because it was a separate copy and not affected by the changes made to LC.
11. The lines v = nums['pr'].pop() and v = nums['pr'].pop() remove and assign the last two elements of the list associated with the key ‘pr’ in the dictionary nums to the variable v. After these operations, nums['pr'] becomes [2, 5].
12. The line print(nums['pr'][1]) prints the element at index 1 of the list associated with the key ‘pr’ in the dictionary nums, which is 5.
13. The line print(len(LP)) prints the length of the list LP, which is 2 because it was affected by the pop() operations on nums['pr'].

To find and output the names of people whose age is known to be at least 30, you can use a list comprehension to filter the dictionaries based on the ‘age’ key and then sort the resulting names alphabetically. Here’s the code to achieve this:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12

people = [
    {'name': 'Paul', 'age': 25, 'weight': 165},
    {'height': 155, 'name': 'Sue', 'age': 30, 'weight': 123},
    {'weight': 205, 'name': 'Sam'},
    {'height': 156, 'name': 'Andre', 'age': 39, 'weight': 123}
]

names = [person['name'] for person in people if 'age' in person and person['age'] >= 30]
names.sort()

for name in names:
    print(name)

Explanation:

1. We start with the given list of dictionaries called people.

2. We use a list comprehension to create a new list called names. The list comprehension iterates over each dictionary person in the people list.

3. For each person dictionary, we check two conditions:

   • First, we check if the ‘age’ key exists in the dictionary using the 'age' in person condition. This ensures that we only consider dictionaries that have an ‘age’ key.
   • Second, we check if the value of the ‘age’ key is greater than or equal to 30 using the person['age'] >= 30 condition.

4. If both conditions are satisfied, we include the value of the ’name’ key from the person dictionary in the names list.

5. After the list comprehension, we have a list names that contains the names of people whose age is known to be at least 30.

6. We use the sort() method to sort the names list alphabetically.

7. Finally, we iterate over each name in the sorted names list and print them one by one.

The output of the code will be:

1
2

Andre
Sue

This code effectively filters the list of dictionaries based on the age condition, extracts the names, sorts them alphabetically, and prints them as required.

Here’s a Python function that creates a new dictionary associating cities with the states they appear in and outputs the unique cities in alphabetical order:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21

def create_cities(states):
    cities = {}
    unique_cities = []

    for state, city_list in states.items():
        for city in city_list:
            if city not in cities:
                cities[city] = [state]
            else:
                cities[city].append(state)

    for city, state_list in cities.items():
        if len(state_list) == 1:
            unique_cities.append(city)

    unique_cities.sort()

    for city in unique_cities:
        print(city)

    return cities

Explanation:

1. We define the function create_cities that takes the states dictionary as input.

2. We initialize an empty dictionary called cities to store the city-to-state mappings.

3. We also initialize an empty list called unique_cities to store the names of the unique cities.

4. We iterate over each state and its corresponding list of cities in the states dictionary using the items() method.

5. For each city in the city list, we check if it already exists as a key in the cities dictionary:

   • If the city is not in the cities dictionary, we add it as a key with a list containing the current state as the value.
   • If the city is already in the cities dictionary, we append the current state to the existing list of states for that city.

6. After creating the cities dictionary, we iterate over each city and its corresponding list of states in the cities dictionary using the items() method.

7. For each city, we check if the length of its state list is equal to 1. If it is, it means the city appears in only one state, so we append the city name to the unique_cities list.

8. We sort the unique_cities list in alphabetical order using the sort() method.

9. We iterate over each city in the sorted unique_cities list and print them one by one.

10. Finally, we return the cities dictionary.

Using the provided example states dictionary, the output of the function will be:

1
2
3
4

Boston
Chicago
Hanover
Peoria

And the returned cities dictionary will be:

1
2
3
4
5
6
7
8

{
    'Concord': ['New Hampshire', 'Massachusetts'],
    'Hanover': ['New Hampshire'],
    'Boston': ['Massachusetts'],
    'Springfield': ['Massachusetts', 'Illinois'],
    'Chicago': ['Illinois'],
    'Peoria': ['Illinois']
}

This function creates the desired city-to-state dictionary, outputs the unique cities in alphabetical order, and returns the cities dictionary.

Here’s the implementation of the contains method for the Rectangle class:

1
2

def contains(self, x, y):
    return self.x0 <= x <= self.x1 and self.y0 <= y <= self.y1

Explanation:

• The contains method takes two parameters: x and y, representing the coordinates of the point to check.
• It checks if the given x and y values fall within the boundaries of the rectangle.
• The condition self.x0 <= x <= self.x1 checks if x is between the left and right boundaries of the rectangle (inclusive).
• The condition self.y0 <= y <= self.y1 checks if y is between the bottom and top boundaries of the rectangle (inclusive).
• If both conditions are true, it means the point is inside the rectangle or on its boundary, so the method returns True.
• Otherwise, if either condition is false, the point is outside the rectangle, and the method returns False.

Example usage:

1
2
3

r = Rectangle(1, 3, 7, 10)
print(r.contains(1, 4))  # Output: True
print(r.contains(2, 11))  # Output: False

In the first example, the point (1, 4) is inside the rectangle, so contains returns True. In the second example, the point (2, 11) is outside the rectangle, so contains returns False.

Here’s the implementation of the add_points method for the Rectangle class:

1
2
3
4

def add_points(self, points):
    for point in points:
        if self.contains(point.x, point.y) and not any(p.id == point.id for p in self.points):
            self.points.append(point)

Explanation:

• The add_points method takes a list of Point objects as a parameter.
• It iterates over each point in the points list.
• For each point, it checks two conditions:
  1. It calls the contains method (from part a) to check if the point’s coordinates (point.x, point.y) are inside the rectangle’s boundaries.
  2. It checks if a point with the same id as the current point already exists in the rectangle’s points list using the any function and a generator expression.
• If both conditions are satisfied (i.e., the point is inside the rectangle and no point with the same id exists in the points list), the point is appended to the rectangle’s points list using self.points.append(point).
• If either condition is not satisfied (i.e., the point is outside the rectangle or a point with the same id already exists), the point is ignored and not added to the points list.

Example usage:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11

r = Rectangle(1, 3, 7, 10)

p1 = Point(2, 5, 1)
p2 = Point(4, 8, 2)
p3 = Point(6, 12, 3)  # Outside the rectangle
p4 = Point(3, 6, 1)  # Same id as p1

r.add_points([p1, p2, p3, p4])

for point in r.points:
    print(point.id)

Output:

1
2

1
2

In this example:

• p1 and p2 are inside the rectangle and have unique ids, so they are added to the points list.
• p3 is outside the rectangle, so it is ignored.
• p4 has the same id as p1, so it is also ignored.

After adding the points, the rectangle’s points list contains only p1 and p2, as shown in the output.

Apologies for the mistake in my previous answer. Here’s the corrected output of the given code:

1
2
3
4
5
6

set()
['Portland', 'Eugene', 'Albany', 'NY']
Eugene
Albany
NY
{'Harlem', 'Chelsea'}

Explanation:

1. print(places['OR']['Eugene']):

   • This line accesses the value associated with the key ‘Eugene’ in the dictionary places['OR'].
   • The value associated with ‘Eugene’ is an empty set set(), so it prints set().

2. a = [] and for place in places: a += places[place].keys():

   • These lines initialize an empty list a and then iterate over the keys of the places dictionary.
   • For each key place, it retrieves the keys of the nested dictionary places[place] using places[place].keys() and appends them to the list a.
   • After the loop, a contains the list ['Portland', 'Eugene', 'Albany', 'NY'].

3. print(a):

   • This line prints the list a, which is ['Portland', 'Eugene', 'Albany', 'NY'].

4. for x in a: if len(x) < 7: print(x):

   • This loop iterates over each element x in the list a.
   • If the length of x is less than 7, it prints x.
   • The output of this loop is:

     1 2 3

     Eugene Albany NY

5. for place in places: if x in places[place]: print(places[place][x]):

   • This loop iterates over each key place in the places dictionary.
   • It checks if the last value of x from the previous loop (‘NY’) is a key in the nested dictionary places[place].
   • If ‘NY’ is a key in places[place], it prints the value associated with that key.
   • The output of this loop is:

     1	{'Harlem', 'Chelsea'}

   • The line {'Harlem', 'Chelsea'} is printed because ‘NY’ is a key in places['NY'], and it prints the set associated with ‘NY’.

So, the complete output of the code is:

1
2
3
4
5
6

set()
['Portland', 'Eugene', 'Albany', 'NY']
Eugene
Albany
NY
{'Harlem', 'Chelsea'}

I apologize for the confusion in my previous answer. Thank you for pointing out the correction.

Here’s a Python script that reads the businesses.txt file, calculates the average review score for each business, and prints the required information:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31

# Read the businesses.txt file
with open('businesses.txt', 'r') as file:
    lines = file.readlines()

# Create a dictionary to store the category and its corresponding businesses
categories = {}

# Process each line in the file
for line in lines:
    # Split the line into business name, category, and review scores
    data = line.strip().split('|')
    business_name = data[0]
    category = data[1]
    scores = [int(score) for score in data[2:]]

    # Calculate the average review score
    average_score = sum(scores) / len(scores)

    # Add the business to the corresponding category in the dictionary
    if category in categories:
        categories[category].append((business_name, average_score))
    else:
        categories[category] = [(business_name, average_score)]

# Print the business information
for category, businesses in categories.items():
    for business_name, average_score in businesses:
        print(f"{business_name} ({category}): Score {average_score:.3f}")

# Print the total number of unique categories
print(f"{len(categories)} categories found.")

Explanation:

1. We start by opening the businesses.txt file in read mode using a with statement. This ensures that the file is properly closed after we’re done reading from it.

2. We read all the lines from the file using file.readlines() and store them in the lines list.

3. We create an empty dictionary called categories to store the category and its corresponding businesses.

4. We iterate over each line in the lines list.

5. For each line, we split it into the business name, category, and review scores using line.strip().split('|'). The strip() method is used to remove any leading or trailing whitespace from the line.

6. We convert the review scores from strings to integers using a list comprehension: [int(score) for score in data[2:]].

7. We calculate the average review score by summing up all the scores and dividing by the number of scores.

8. We check if the category already exists in the categories dictionary. If it does, we append a tuple of the business name and average score to the corresponding list. If it doesn’t exist, we create a new list with the business name and average score and add it to the dictionary.

9. After processing all the lines, we iterate over each category and its corresponding businesses in the categories dictionary.

10. For each business, we print its name, category, and average review score using an f-string with the desired formatting.

11. Finally, we print the total number of unique categories by getting the length of the categories dictionary.

When you run this script with the provided businesses.txt file, it will produce the desired output:

1
2
3
4

Dinosaur Bar-B-Que (BBQ): Score 4.125
DeFazio's Pizzeria (Pizza): Score 4.857
I Love NY Pizza (Pizza): Score 4.250
2 categories found.

This script reads the file, calculates the average review score for each business, and prints the business information along with the total number of unique categories found in the file.

Here’s a Python function that generates a histogram using a dictionary:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

def histogram_dict(numbers):
    # Create a dictionary to store the count of each number
    count_dict = {}

    # Count the occurrences of each number in the list
    for num in numbers:
        if num in count_dict:
            count_dict[num] += 1
        else:
            count_dict[num] = 1

    # Print the histogram
    for num in sorted(count_dict.keys()):
        print(f"{num}: {'*' * count_dict[num]}")

Explanation:

1. We define a function called histogram_dict that takes a list of numbers as input.

2. We create an empty dictionary called count_dict to store the count of each number in the list.

3. We iterate over each number num in the numbers list.

4. For each number, we check if it already exists as a key in the count_dict:

   • If it exists, we increment its count by 1.
   • If it doesn’t exist, we add it to the dictionary with an initial count of 1.

5. After counting the occurrences of each number, we iterate over the sorted keys of the count_dict using sorted(count_dict.keys()). This ensures that the histogram is printed in ascending order of the numbers.

6. For each number num, we print the number followed by a colon and the corresponding number of stars (*) based on its count in the count_dict. We use the '*' * count_dict[num] expression to repeat the star character count_dict[num] times.

Example usage:

1
2

numbers = [5, 4, 1, 1, 3, 1, 2, 2, 4, 1]
histogram_dict(numbers)

Output:

1
2
3
4
5

1: ****
2: **
3: *
4: **
5: *

This function uses a dictionary to count the occurrences of each number and then prints the histogram based on the counts.

Here’s a Python function that generates a histogram using a set:

1
2
3
4
5
6
7
8

def histogram_set(numbers):
    # Create a set to store the unique numbers
    unique_nums = set(numbers)

    # Print the histogram
    for num in sorted(unique_nums):
        count = numbers.count(num)
        print(f"{num}: {'*' * count}")

Explanation:

1. We define a function called histogram_set that takes a list of numbers as input.

2. We create a set called unique_nums by passing the numbers list to the set() constructor. This automatically removes any duplicate numbers and gives us a set of unique numbers.

3. We iterate over each number num in the sorted unique_nums set using sorted(unique_nums). This ensures that the histogram is printed in ascending order of the numbers.

4. For each unique number num, we use the count() method on the numbers list to count the occurrences of that number in the original list. We store the count in the count variable.

5. We print the number followed by a colon and the corresponding number of stars (*) based on its count. We use the '*' * count expression to repeat the star character count times.

Example usage:

1
2

numbers = [5, 4, 1, 1, 3, 1, 2, 2, 4, 1]
histogram_set(numbers)

Output:

1
2
3
4
5

1: ****
2: **
3: *
4: **
5: *

This function uses a set to store the unique numbers and then uses the count() method to count the occurrences of each unique number in the original list. It then prints the histogram based on the counts.

Note: Using a set eliminates the need for a separate counting step, as we can directly use the count() method on the original list for each unique number.

Here’s the Python code that prints the name and addresses of each person who graduated before 2013:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11

alums = [
    {'fname': 'Abed', 'lname': 'Nadir', 'graduated': 2012, 'addresses': ['Troy&Abed apt.', 'Abed&Annie apt.']},
    {'fname': 'Troy', 'lname': 'Barnes', 'graduated': 2013, 'addresses': ['Troy&Abed apt.']},
    {'fname': 'Britta', 'lname': 'Perry', 'graduated': 2012, 'addresses': ['1 Revolution lane']}
]

for alum in alums:
    if alum['graduated'] < 2013:
        print(alum['fname'], alum['lname'])
        for address in alum['addresses']:
            print(address)

Explanation:

1. We start with the given list of alumni, where each alumnus is represented by a dictionary containing their information.

2. We iterate over each alum dictionary in the alums list.

3. For each alum, we check if their graduation year (alum['graduated']) is less than 2013. This condition filters out the alumni who graduated before 2013.

4. If the condition is true, we proceed to print the name and addresses of the alumnus.

5. We print the first name (alum['fname']) and last name (alum['lname']) of the alumnus using the print() function.

6. We then iterate over each address in the alum['addresses'] list.

7. For each address, we print it on a separate line using the print() function.

8. After processing all the alumni, the code will have printed the name and addresses of each person who graduated before 2013.

Output:

1
2
3
4
5

Abed Nadir
Troy&Abed apt.
Abed&Annie apt.
Britta Perry
1 Revolution lane

This code iterates over the list of alumni, checks their graduation year, and prints the name and addresses of those who graduated before 2013. The information is printed in the order it appears in the list.

Here’s the implementation of the get_line function in Python:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14

def get_line(fname, lno, start, end):
    try:
        with open(fname, 'r') as file:
            lines = file.readlines()
            if lno <= len(lines):
                line = lines[lno - 1]
                if end <= len(line):
                    return line[start:end]
                else:
                    return ''
            else:
                return None
    except FileNotFoundError:
        return None

Explanation:

1. We define the get_line function that takes four parameters: fname (file name), lno (line number), start (starting point), and end (end point).

2. We use a try block to handle the case where the file might not exist.

3. Inside the try block, we open the file specified by fname in read mode using the with statement. This ensures that the file is properly closed after we’re done reading from it.

4. We read all the lines from the file using file.readlines() and store them in the lines list.

5. We check if the requested line number lno is less than or equal to the total number of lines in the file (len(lines)).

6. If the condition is true, we retrieve the line at index lno - 1 from the lines list (since line numbers start at 1, but list indices start at 0).

7. We then check if the requested end point end is less than or equal to the length of the retrieved line (len(line)).

8. If the condition is true, we return the substring of the line starting from index start up to (but not including) index end using string slicing (line[start:end]).

9. If the end point end is greater than the length of the line, we return an empty string ('').

10. If the requested line number lno is greater than the total number of lines in the file, we return None.

11. If a FileNotFoundError occurs (i.e., the file doesn’t exist), we catch the exception and return None.

Now, let’s test the get_line function with the given program:

1
2
3
4

print('1:', get_line('hpss.txt', 2, 9, 15))
print('2:', get_line('hpss.txt', 5, 5, 9))
print('3:', get_line('hpss.txt', 5, 0, 4))
print('4:', get_line('hpss.txt', 8, 0, 10))

Output:

1
2
3
4

1: to fin
2: 
3: all.
4: None

Explanation of the output:

1. For the first case, the function returns the substring from index 9 to 14 (inclusive) of the second line, which is "to fin".

2. For the second case, the function returns an empty string because the fifth line doesn’t have enough characters to extract from index 5 to 8.

3. For the third case, the function returns the substring from index 0 to 3 (inclusive) of the fifth line, which is "all.". The newline character is also included in the returned string.

4. For the fourth case, the function returns None because there is no eighth line in the file.

The get_line function correctly handles the cases where the requested line number or end point exceeds the available lines or characters in the file.

To find the top three coldest years based on the mean temperature data provided in the dictionary, you can sort the dictionary items based on the temperature values in ascending order and print the first three items. Here’s the code to achieve that:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10

temp = {2001: 36.4, 2002: 27.4, 2003: 29.3, 2004: 28.6, 2005: 27.8,
        2006: 37.3, 2007: 28.1, 2008: 30.2, 2010: 26.0, 2011: 35.4,
        2012: 33.8, 2013: 27.9, 2014: 32.8}

# Sort the dictionary items based on temperature values in ascending order
sorted_temp = sorted(temp.items(), key=lambda x: x[1])

# Print the top three coldest years
for year, temperature in sorted_temp[:3]:
    print(f"{year}: {temperature}")

Explanation:

1. The temp dictionary is defined with the given mean temperature data for each year.

2. We use the sorted() function to sort the dictionary items based on the temperature values in ascending order. The items() method is used to get a list of key-value pairs from the dictionary. The key parameter is set to lambda x: x[1], which means the sorting will be based on the second element of each item tuple (i.e., the temperature value).

3. The sorted items are stored in the sorted_temp list, where each item is a tuple containing the year and its corresponding temperature.

4. We iterate over the first three items of sorted_temp using a for loop and unpack each item into the variables year and temperature.

5. Finally, we print each year and its corresponding temperature using an f-string, which allows us to embed variables directly in the string.

The output will be:

1
2
3

2010: 26.0
2002: 27.4
2005: 27.8

This code will find and print the top three coldest years based on the mean temperature data provided in the dictionary. If there are ties in temperature values, any ordering of the tied years is acceptable.

To find the items that are in the menu for all three dinners and the items that are in the menu for exactly one dinner, you can use set operations. Here’s the code to achieve that:

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11

t1 = set(['Turkey', 'Potatoes', 'Green Beans', 'Cranberry', 'Gravy'])
t2 = set(['Turkey', 'Yams', 'Stuffing', 'Cranberry', 'Marshmallows'])
t3 = set(['Turkey', 'Gravy', 'Yams', 'Green Beans', 'Cranberry', 'Turducken'])

# Find items in all three dinners
common_items = t1.intersection(t2, t3)
print("Items in all three dinners:", ', '.join(sorted(common_items)))

# Find items in exactly one dinner
unique_items = (t1 ^ t2 ^ t3) - (t1 & t2) - (t1 & t3) - (t2 & t3)
print("Items in exactly one dinner:", ', '.join(sorted(unique_items)))

Explanation:

1. The sets t1, t2, and t3 are defined with the given menu items for each Thanksgiving dinner.

2. To find the items that are in the menu for all three dinners, we use the intersection() method. It returns a new set containing the common elements from all the sets. We pass t1, t2, and t3 as arguments to intersection(), which gives us the items that are present in all three sets.

3. We print the common items using print(). The sorted() function is used to sort the items in alphabetical order, and ', '.join() is used to join the items into a comma-separated string.

4. To find the items that are in the menu for exactly one dinner, we use the following set operations:

   • t1 ^ t2 ^ t3 performs the symmetric difference (XOR) operation on all three sets. It gives us the items that are in exactly one of the sets.
   • (t1 & t2), (t1 & t3), and (t2 & t3) perform the intersection operation on pairs of sets. These give us the items that are common between each pair of sets.
   • We subtract the common items between pairs of sets from the result of the symmetric difference operation. This gives us the items that are in exactly one dinner.

5. Finally, we print the unique items using print(), again sorting them in alphabetical order and joining them into a comma-separated string.

The output will be:

1
2

Items in all three dinners: Cranberry, Turkey
Items in exactly one dinner: Marshmallows, Potatoes, Stuffing, Turducken

This code finds and prints the items that are in the menu for all three dinners and the items that are in the menu for exactly one dinner, in alphabetical order.

The running time of the membership test in a list using list.index(value) is O(n), where n is the number of elements in the list.

Explanation:

• The list.index(value) method searches for the first occurrence of the specified value in the list.
• In the worst case, the value may be located at the end of the list or not present in the list at all.
• The method needs to traverse the list from the beginning until it finds the value or reaches the end of the list.
• As the size of the list grows, the number of elements to be searched increases linearly.
• Therefore, the running time of list.index(value) is proportional to the size of the list, resulting in a time complexity of O(n).

The running time of the membership test in a set using value in set is O(1) on average.

Explanation:

• Sets in Python are implemented using hash tables.
• The value in set operation checks if the specified value exists in the set.
• In a hash table, elements are stored based on their hash values, which allows for efficient retrieval.
• On average, the time complexity of searching for an element in a hash table is O(1), constant time.
• This is because the hash function used by the set distributes the elements uniformly across the hash table, minimizing collisions.
• Even if there are collisions, the number of elements in each “bucket” of the hash table is expected to be small, resulting in a constant-time lookup on average.
• Therefore, the running time of value in set is O(1) on average, regardless of the size of the set.

The running time of nested for loops over an entire list is O(n^2), where n is the number of elements in the list.

Explanation:

• When you have nested for loops iterating over the same list, the total number of iterations is the product of the number of iterations in each loop.
• If the list has n elements, the outer loop will iterate n times.
• For each iteration of the outer loop, the inner loop will also iterate n times.
• Therefore, the total number of iterations is n * n = n^2.
• As the size of the list grows, the number of iterations increases quadratically.
• The running time of nested for loops over an entire list is proportional to the square of the size of the list, resulting in a time complexity of O(n^2).
• This indicates that the running time grows rapidly as the size of the list increases, making it less efficient for large lists compared to algorithms with lower time complexities.