# A set of constants, each representing a list index for station information.ID = 0NAME1LATITUDE2%3DLONGITUDE3%3DСАРАСІTY4%3DBIKES_AVAILABLE = 5DOCKS_AVAILABLE"SMART'NO_KIOSK# For use in the get_lat_lon_distance helper functionEARTH_RADIUS = 6371# SAMPLE DATA TO USE IN DOCSTRING EXAMPLESSAMPLE_STATIONS%3D[7090, 'Danforth Ave / Lamb Ave',43.681991, -79.329455, 15, 4, 10],[7486, 'Gerrard St E / Ted Reeve Dr',43.684261, -79.299332, 24, 5, 19],[7571, 'Highfield Rd / Gerrard St E43.671685, -79.325176, 19, 14, 5]]SMART',HANDOUT_STATIONS%D[7000, 'Ft. York / Capreol Crt.',43.639832, -79.395954, 31, 20, 11],[7001, 'Lower Jarvis St SMART / The Esplanade',43.647992, -79.370907, 15, 5, 10]] def upgrade_stations(threshold: int, num_bikes: int,stations: List["Station"]) -> int:II II I|"" "Modify each station in stations that has a capacity that is less thanthreshold 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)%3D» handout_copy[0]HANDOUT_STATIONS[0]True» handout_copy[1][7001, 'Lower Jarvis St SMART / The Esplanade', \43.647992, -79.370907, 20, 10,=%3D10]TrueII II II

Note: Program plan will be the same as mentioned in the question. Program: ID = 0NAME = 1LATITUDE…

Answered: # A set of constants, each representing…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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def return_bike(station_id: int, stations: List["Station"]) -> bool: """Update the available bike count and the docks available count for station in stations with id station_id as if a single bike was added, making an additional dock unavailable. Return True if and only if the return was successful. Precondition: station_id will appear in stations. >>> available_dock_station 43.681991, -79.329455, 15, 4, 10] >>> station_id >> original_bikes_available >> original_docks_available >> return_bike(station_id, [available_dock_station]) [7090, 'Danforth Ave / Lamb Ave', \ available_dock_station[ID] available_dock_station[BIKES_AVAILABLE] available_dock_station[DOCKS_AVAILABLE] True >> original_bikes_available + 1 available_dock_station[BIKES_AVAILABLE] == True >>> original_docks_available 1 available_dock_station[D0CKS_AVAILABLE] == True >>> no_dock_station 43.681991, -79.329455, 15, 15, 0] >>> station_id = no_dock_station[ID] >>> original_bikes_available = no_dock_station[BIKES_AVAILABLE] >>…
The Save Transaction button depicted in the screen attached is used to save relevant data to the sales table and the salesdetails tables from the depicted schema. When this button is clicked it calls the saveTransaction() function that is within the PosDAO class, it passes to this function an ArrayList of salesdetails object, this list contains the data entered into the jTable which is the products and qty being sold.Write the saveTransaction function. You are to loop through the items and get the total sales, next you are to insert the current date and the total sales into the sales table. Reminder that the sales table SalesNumber field is set to AUTO-INCREMENT, hence the reason for only entering the total sales and current date in sales table.
get_total_cases() takes the a 2D-list (similar to database) and an integer x from this set {0, 1, 2} as input parameters. Here, 0 represents Case_Reported_Date, 1 represents Age_Group and 2 represents Client_Gender (these are the fields on the header row, the integer value represents the index of each of these fields on that row). This function computes the total number of reported cases for each instance of x in the text file, and it stores this information in a dictionary in this form {an_instance_of_x : total_case}. Finally, it returns the dictionary and the total number of all reported cases saved in this dictionary.
calculate_trip_time( iata_src: str, iata_dst: str, flight_walk: List[str], flights: Flight Dir float: def ) -> """ Return a float corresponding to the amount of time required to travel from the source airport to the destination airport to the destination airport, as outlined by the flight_walk. The start time of the trip should be considered zero. In other words, assuming we start the trip at 12:00am, this function should return the time it takes for the trip to finish, including all the waiting times before, and between the flights. If there is no path available, return -1.0 >>> calculate_trip_time("AA1", "AA2", ["AA1", "AA2"], TEST_FLIGHTS_DIR_FOUR_ 2.0 "AA7", ["AA7", "AA1"], TEST_FLIGHTS_DIR_FOUR_ "AA7", ["AA1", "AA7"], TEST_FLIGHTS_DIR_FOUR_ "AA1", ["AA1"], TEST_FLIGHTS_DIR_FOUR_CITIES) "AA2", ["AA4", "AA1", "AA2"], TEST_FLIGHTS_DI "AA3", ["AA1", "AA2", "AA3"], TEST_FLIGHTS_DI >>> calculate_trip_time("AA1", "AA4", ["AA1", "AA4"], TEST_FLIGHTS_DIR_FOUR_ 2.0 || || || >>>…
Define the function number_in_trophic_class(tli3_values, classification) which takes a list of trophic level index values and a trophic classification, returning the number of lakes in that trophic class. For this version of the question you MUST use a list comprehension. You cannot use a for loop or while loop in your answer. Notes: You must include, and use/call your trophic_class function in your submission. Your tropic_class function must work in the same way it did for Question 2. You can assume that classification will always be valid, i.e. it will always be one of the states in the following list: ['Hypertrophic', 'Supertrophic', 'Eutrophic', 'Mesotrophic', 'Oligotrophic', 'Microtrophic', 'Ultra-microtrophic'] There are several ways to proceed, but you will probably find either the len function or the count method of the list class useful in conjunction with your list comprehension. For example: Test Result tli3_list_1 = [4.1061, 2.54561, 4.16276, 2.33801,…
For questions below, use the following nested data structure to answer the questions. social_network = {'bb_dawg$': ('Barry Bowden', 18, ['New York City NY', 'Langley VA'], ['Elisa Yang', 'Andrew Yamins', 'James Butler']), 'yo_jimbo2000': ('James Butler', 21, ['Boston MA'], ['Barry Bowden']), 'e_yan2002': ('Elisa Yan', 20, ['College Park MD', 'Clarksburg MD'], ['Andrew Yamins', 'Barry Bowden']), 'bmansford1990': ('Benard Mansford', 31, ['San Jose CA', 'College Park MD'], []), 'derp1234': ('who cares', 99, ['College Park MD'], [])} Q1) Notice that a removed user ('Andrew Yamins') is still contained in the friends list of a few users. Write a few lines of code to remove him from those friends lists in the social network. Q2) Write a short Python function called find_user_lived that takes a string as input along with the social network data structure; the string should be a city and state and the function should…
The following code will #define N_ROW 10 #define N_COL 10 int row; double sum=0, table[N_ROW][N_COL]; sum = 0; for (row = 0; row < N_ROW; row++) sum += table[row][1] sum the values of the column with an index of 1. sum the values of the row with an index of 1. sum the values of the row with an index of N_ROW. none of the others sum the values of the first column in the array.
• Write a simple stored function that Takes a gender as an input ● Find the staff whose gender matches the input Return the results Write a simple stored procedure that Takes a gender as an input Find the staff whose gender matches the input • Print the results
The management of your team wants to compare the team with the assigned team (the Bulls in 1996-1998). They claim that the skill level of your team in 2013-2015 is the same as the skill level of the Bulls in 1996 to 1998. In other words, the mean relative skill level of your team in 2013 to 2015 is the same as the mean relative skill level of the Bulls in 1996-1998. Test this claim using a 1% level of significance. Assume that the population standard deviation is unknown. Make the following edits to the code block below: 1. Replace ??DATAFRAME_ASSIGNED_TEAM?? with the name of assigned team's dataframe. See Step 1 for the name of assigned team's dataframe. 2. Replace ??DATAFRAME_YOUR_TEAM?? with the name of your team's dataframe. See Step 2 for the name of your team's dataframe. 3. Replace ??RELATIVE_SKILL?? with the name of the variable for relative skill. See the table included in Project Two instructions above to pick the variable name. Enclose this variable in single quotes. For…
The InstantRide Driver Relationship team wants to learn how many travels each driver has done in the month of October. You need to send them the DRIVER_ID, and two calculated columns: DAY and RIDES. The DAY column is calculated using the DAY() function with the TRAVEL_START_TIME as the argument. The RIDES column is calculated by using the COUNT() function to determine the number of rides given for each day. Filter the results with the MONTH function.
nycflights13::flights Q4<- flights %>%filter(carrier == "JFK") %>%summarise(average_dist = mean(distance)%>%summarise(max_dist = max(average_dist))%>%group_by(Q4, month, day)%>%head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) *not pictured, I did upload, tidy verse, dplyr, and the nycflights13 libraries. Error: Incomplete expression: Q4<- flights %>% filter(carrier == "JFK") %>% summarise(average_dist = mean(distance)%>% summarise(max_dist = max(average_dist))%>% group_by(Q4, month, day)%>% head(Q4[order(dat$month),(dat$day),(dat$max_dist)],n=5) RStudio Question: I can't figure out what is wrong with my code. Could someone take a look at it? See below for what I am trying to do, my code and my error output. I am trying to find what 5 days of the year had the highest mean distance from JFK airport, using the nycflights13 library. I want to format it in month, day, andvmean distance.
individual characters using their 0-based index. Hint: You will need to do this for checking all the rules. However, when you access a character .

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