To generate a statistical summary for each of our numerical attributes, use the Pandas DataFrame describe() function on our forestfire_df.   # TODO 1 ff_describe = display(ff_describe) todo_check([ (ff_describe.shape == (8,11),'ff_describe shape did not match (8, 11)'), (np.all(np.isclose(ff_describe.values[:4,2:4].flatten(), np.array([517. ,517. ,90.64468085,110.87234043,5.52011085,64.04648225,18.7,1.1]),rtol=.01)),'The values of ff_describe were wrong!'), ])

7. group_friends_by_food For this problem, you are given a mostly-working version of the Friend and FriendsDB classes from hw6 and hw7, and we will add new method to FriendsDB -- group_friends_by_food. group_friends_by_food returns a dictionary mapping from each of the favorite foods enjoyed by any friend to a list of the friends who enjoy that food, sorted in alphabetical order. (You can use the friends_who_love method to generate these lists.) A sample run should look like this: |>>> friend1 = Friend ("sarah", 165) >>> friend1.add_favorite_food ("strawberries") >>> friend2 - Friend ("dweezil", 175) >>> friend2.add_favorite_food("pizza") >>> friend3 = Friend("bimmy", 60) >>> friend3.add_favorite_food("pizza") >>> friend3.add_favorite_food("strawberries") >>> db = FriendsDB() >>> db.add friend (friend1) >>> db.add_friend(friend2) >>> db.add friend(friend3) >>> db.group_friends_by_food() {'strawberries': ['bimmy', 'sarah'], 'pizza': [' bimmy', 'dweezil']}

Heres what I have roster = {}for i in range(5):jersey_number = int(input(f"Enter player{i+1}'s jersey number:\n"))rating = int(input(f"Enter player{i+1}'s rating:\n"))roster[jersey_number] = ratingsorted_keys = sorted(roster.keys())print("ROSTER")for key in sorted_keys:print(f"Jersey number:{key}, Rating:{roster[key]}")menu = "MENU\na - Add player\nd - Remove player\nu - Update player rating\nr - Output players above a rating\no - Output roster\nq - Quit\n"while True:print(menu)option = input("Choose an option:\n")if option == "a":jersey_number = int(input("Enter a new player's jersey number:"))rating = int(input("Enter the player's rating:\n"))roster[jersey_number] = ratingelif option == "d":jersey_number = int(input("Enter a jersey number: "))if jersey_number in roster:del roster[jersey_number]else:print("Jersey number not found.")elif option == "u":jersey_number = int(input("Enter a jersey number:\n"))if jersey_number in roster:rating = int(input("Enter a new rating for…

Seq is a sequence object that can be imported from Biopython using the following statement: from Bio.Seq import Seq If my_seq is a Seq object, what is the correct Biopython code to print the reverse complement of my_seq? Hint. Use the built-in function help you find out the methods of the Seq object. print('reverse complement is %s' % complement(my_seq.reverse())) print('reverse complement is %s' % my_seq.reverse()) print('reverse complement is %s' % my_seq.reverse_complement()) print('reverse complement is %s' % reverse(my_seq.complement()))

Write a struct ‘Student’ that has member variables: (string) first name, (int) age and (double) fee. Create a sorted (in ascending order according to the age) linked list of three instances of the struct Student. The age of the students must be 21, 24 and 27. Write a function to insert the new instances (elements of the linked list), and insert three instance whose member variable have age (i) 20, (ii) 23 and (iii) 29. Write a function to remove the instances from the linked list, and remove the instances whose member variables have age (i) 21 and (ii) 29. Write a function to count the elements of the linked list. Write a function to search the elements from the linked list, and implement the function to search an element whose age is 23. Write a function to print the linked list on the console. Consider the Student struct as defined above. Create a stack of 5objects of the class. Implement the following (i) push an element to the stack (ii) pop an element from the stack (iii) get the…

The shape class variable is used to save the shape of the forestfire_df dataframe. Save the result to ff_shape. # TODO 1.1 ff_shape = print(f'The forest fire dataset shape is: {ff_shape}') todo_check([ (ff_shape == (517,13),'The shape recieved for ff_shape did not match the shape (517, 13)') ])

Complete the docstring using the information attached (images):def upgrade_stations(threshold: int, num_bikes: int,                     stations: List["Station"]) -> int:    """Modify each station in stations that has a capacity that is less than    threshold by adding num_bikes to the capacity and bikes available counts.    Modify each station at most once.     Return the total number of bikes that were added to the bike share network.     Precondition: num_bikes >= 0     >>> handout_copy = [HANDOUT_STATIONS[0][:], HANDOUT_STATIONS[1][:]]    >>> upgrade_stations(25, 5, handout_copy)    5    >>> handout_copy[0] == HANDOUT_STATIONS[0]    True    >>> handout_copy[1] == [7001, 'Lower Jarvis St SMART / The Esplanade', \                            43.647992, -79.370907, 20, 10, 10]    True    """

TODO 12 Let's now split our input data X and labels y into a train and test set using the train_test_split() function (docs). Here we'll use the 80-20 split rule where we use 80% of the data for training and 20% for testing. Lastly, we'll seed our split using the random_state keyword argument which will make sure we create the same split every time we run the function. Use the train_test_split() function to get a train and test split. Store the output into X_train, X_test, y_train, and y_test. Pass the required arguments X and y. Further specify we want to use 20% of the training data by setting the test_size keyword argument. Lastly, pass the keyword argument random_state=42 to set the random seed so we get the same split every time we run this code. Print the shape for X_train. Print the shape for y_train. Print the shape for X_test. Print the shape for y_test. # TODO 12.1X_train, X_test, y_train, y_test =  todo_check([    (X_train.shape == (413, 29), 'X_train does not have the…

Use an appropriate scikit-learn library we used in class to create y_train, y_test, X_train and X_test by splitting the data into 70% train and 30% test datasets. Set random_state to 4 and stratify the subsamples so that train and test datasets have roughly equal proportions of the target's class labels Standardise the data using StandardScaler library

In Checkpoint B, the game will be playable as a two-player game, albeit the competition will be a computer player who makes moves randomly. Implement these functions from the template following the description (specification) in their docstring: shuffle() replace_deck() deal_initial_hands() play_hand() should be extended to support 'random' mode The main function to drive the game has been provided. In 'interactive' mode, play_hand() should behave the same way as in Part A In 'random' mode, the computer selects actions randomly. Compared to 'interactive' mode: Every call to input() for the player should be replaced with a random choice Extra printed feedback for the player should be omitted For testing, we have hard-coded the seed 26 for the random source Hint: you can use random.randint(x, y) to select a number between x and y, inclusive. In particular, "flipping a coin" would be accomplished via random.randint(0, 1) Sample input/output When the inputs are: yes 1 no yes 9 The…