Assign Q9’s row and column indices to Q10 and Q11, respectively
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Assign Q9’s row and column indices to Q10 and Q11, respectively
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- Suppose you have monthly data for ten years stored in the column ‘timeframe’. They are stored as characters in R. You want to change them to R dates called ‘Period’. Assuming the data structure is easily identified as a month/date, how should you do this?Select the column that fits to the row to the followingUse the R data set “anscombe” to save a subset of all the columns of the data except x4 and y4
- In cell H15, enter a VLOOKUP function to determine the 2022 percentage of total attendance for the city listed in cell H14. Use the range A5:H11 as the table array.I need to match both columnsPlease help me with the solution:INSERT INTO LINE VALUES('1001','1','123-21UUY','1','189.99'); INSERT INTO LINE VALUES('1001','2','SRE-657UG','3','2.99'); INSERT INTO LINE VALUES('1002','1','QER-34256','2','18.63'); INSERT INTO LINE VALUES('1003','1','ZZX/3245Q','1','6.79'); INSERT INTO LINE VALUES('1003','2','SRE-657UG','1','2.99'); INSERT INTO LINE VALUES('1003','3','001278-AB','1','12.95'); INSERT INTO LINE VALUES('1004','1','001278-AB','1','12.95'); INSERT INTO LINE VALUES('1004','2','SRE-657UG','2','2.99'); /* -- */ CREATE TABLE PRODUCT ( PROD_CODE varchar(10), PROD_DESCRIPT varchar(35), PROD_PRICE numeric, PROD_ON_HAND int, VEND_CODE int ); INSERT INTO PRODUCT VALUES('001278-AB','Claw hammer','12.95','23','232'); INSERT INTO PRODUCT VALUES('123-21UUY','Houselite chain saw, 16-in. bar','189.99','4','235'); INSERT INTO PRODUCT VALUES('QER-34256','Sledge hammer, 16-lb. head','18.63','6','231'); INSERT INTO PRODUCT VALUES('SRE-657UG','Rat-tail file','2.99','15','232'); INSERT INTO…
- Q5. The following table gives the size of the floor area (ha) and the price ($A000), for 15 houses sold in a certain city. Area 694 905 802 1366 716 821 | 714 1018 | 887 790 696 771 1006 1191 Sale.price 192| 215 215 274 113 212 220 276 260 222 255 260 293 375 Type these data into a data frame with column names area and sale.price. (a) Plot sale.price versus area. (b) Use the hist() command to plot a histogram of the sale prices. (c) Repeat (a) and (b) after taking logarithms of sale prices. (d) The two histograms emphasize different parts of the range of sale prices. Describe the differences.UAA = 109 105 From left to right, use only the first 2 numbers from your UAA, and compute the multiplication of both numbers following the Russian Passant Multiplication. List all the transformations and the sums to calculate the result.Create six TIME SERIES(USING pd.series function) and store in a Pandas data frame:a)The data frame must have an index that is a range of dates from 2016-01-01 until today.b)Each column contains one set of random numbers in a range 0 to 1 (each column will haveone random number for each date). number of columns = 6 number of rows = date range between today and 2016-01-01
- Q7: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the insertion anomaly. Q8: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the deletion anomaly. Q9: Using the AIRPORT KLX Table (Textbook page 131), describe an example that illustrates the modification anomaly.Use Python Code Your task is : Visualization of confirmed cases of COVID-19 by country . (Use pandas to read the data table csv. Example df = pd.read_csv('COVID19.csv' ) then make a visualization using that csv table) Province/State Country Lat Long ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 2/1/2020 2/2/2020 2/3/2020 2/4/2020 2/5/2020 2/6/2020 2/7/2020 2/8/2020 2/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 3/1/2020 3/2/2020 3/3/2020 3/4/2020 3/5/2020 3/6/2020 3/7/2020 3/8/2020 3/9/2020 ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## ######## 4/1/2020 4/2/2020 4/3/2020 4/4/2020 4/5/2020 4/6/2020 4/7/2020 Afghanistan 33 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0…Still incorrect. Please view the errors highlighted in YELLOW and fix them please.