Q10Based on the plot below, which kind of data are "abc" and "efg"?sb.scatterplot (data = some_data, x = "abc", y = "efg")--INSERT--3efg21-1-2-3-4-12 -10 -8-6-4abc"abc" is nominal, "efg" is quantitativeBoth are quantitative"efg" is nominal, "abc" is quantitativeBoth are nominal-2024

The image is correctly oriented and displays a scatter plot with the x-axis labeled "abc" and the…

Answered: Based on the plot below, which kind of…

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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Create a formula that provides the start date of a project as follows: In cell H4, begin to enter a formula using the INDEX Use the Lookup table (range A2:E23) as the array. For the row_num argument, use the MATCH Use the Project ID (cell H2) as the lookup_value for the MATCH function. Use the ID column (range A2:A23) as the lookup_array for the MATCH function. Specify an exact match (0) for the MATCH function. Use the Start Date column (column 4) as the column_num for the INDEX function.
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] >>…
hello please i want SQL Statements (DDL & DML) full code based on this ERD diagram Patient patientiD PK Pharmacy BloodBank PAame Medicine pharmacylD PK bankID bloodType patientiD PK PAge treatmentiD 0.. PK treatmentID FK pHeathissue medName FK peloodType medPrice FK medPrice 0. Appointment appointmentiD Doctor DoctorClinic 1 appointmentiD FK PK doctoriD PK 0.appointmentDate 0. dName treamentiD FK medPrice doctoriD FK appointment_price FK patientiD FK FK appointmentStatus appoinment_price
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…
def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int: "''Return the id of the station from stations that is nearest to the location given by my_latidute and my_longitude. In the case of a tie, return the ID of the last station in stations with that distance. Preconditions: len(stations) > 1 » get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS) 7571 » get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS) 7486
mysql> DESC bookauthor; mysql> DESC author; mysql> DESC book; | Field | Туре | Field | Туре | Field Тур | int(11 | varchai author_order | int(11 royaltyshare | decima: author_id isbn | author_id | int(1: | ssn lastname | firstname | varch: | phone | address | city | state | zip | varch: | varch: isbn var I name | type pub_id I price | advance I ytd_sales I pubdate var var | varch: | varch: | varch: | varch: | varch: | int dec | dec int mysql> DESC bookeditor; | dat | Field | Туре mysql> DESC editor; mysql> DESC publisher; editor_id | int(11) | varchar(1 | Field isbn | Field | Туре editor_id ssn pub_id | int(11) | varchar(40) varchar(40) varchar(20) varchar(2) lastname name firstname address | phone editor_position salary | city state
Explain in brief only about the given syntax diagrams.
Pyhthon PLease.
def get_nearest_station(my_latitude: float, my_longitude: float, stations: List['Station']) -> int: """Return the id of the station from stations that is nearest to the location given by my_latidute and my_longitude. In the case of a tie, return the ID of the last station in stations with that distance. Preconditions: len(stations) > 1 >>> get_nearest_station(43.671134, -79.325164, SAMPLE_STATIONS) 7571 >>> get_nearest_station(43.674312, -79.299221, SAMPLE_STATIONS) 7486 """

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Based on the plot below, which kind of data are "abc" and "efg"? sb.scatterplot (data = some_data, x = "abc", y = "efg") --INSERT-- efg 3 2 1- -2 -3 -4 -12 -10 -4 abc "abc" is nominal, “efg" is quantitative Both are quantitative "efg" is nominal, "abc" is quantitative Both are nominal 0 2 4