week 1: Sunday1 Monday2 week 2: Sunday1 Monday2 Tuesday3 Wednesday4 Thursday5 Friday6 Saturday7 week 3: Sunday1 Monday2 Tuesday3 Wednesday4 Thursday5 week 4: Sunday1 Monday2 Tuesday3 Wednesday4 Thursday5 Tuesday3 Wednesday4 Thursday5 Friday6 Saturday7 Friday6 Saturday7 Friday6 Saturday7
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please assist to write code in python, so the result is the same as the attached image.
please use if and elif functions.
Thanks.
Step by step
Solved in 2 steps with 1 images
- #DATE TIME-TEMPERATURE UNIT HUMIDITY%9/28/2010 15:45:14 22.248900 C 44.7219689/28/2010 16:00:07 22.738900 C 50.5399939/28/2010 16:15:07 23.388900 C 49.3393389/28/2010 16:30:07 23.918900 C 47.5392809/28/2010 16:45:08 23.668900 C 40.700378The first line is just a comment, and is there to help a human understand the data that they are seeing; it should be ignored (hint: any line beginning with '#' ought to be skipped.) Write a program to read the data from the file and produce the average temperature and average humidity for all the records in the file. This is a very large file - it is the actual temperature and humidity data from our server room. You would spend hours trying to do this manually, with a calculator, and you likely would make mistakes along the way. This is where computers are far superior to humans when dealing with boring, repetitive tasks. The output of the program will look like this: Average Temperature (C): NN.NNNNNNNNNNNNNN Average Humidity (%):…Student_Grade Student ID Course Code Semester 1-2019-2020 Mark Grade 20195605 A0313401 87 20195606 A0313402 1-2019-2020 81 20191200 A0312305 3-2018-2019 77 20205305 A0313401 1-2019-2020 91 20205306 A0313402 1-2019-2020 78 20201202 A0312305 3-2018-2019 81 1) Create the table 2) Insert the data 3) Assign a letter grade to the "Grade" according to the following rules: a. If the mark is greater or equal to 90, assign "A" b. If the mark is between 80 and 89, assign "B" c. If the mark is less than 80, assign "C"List the department IDs for those departments that have less than 5 employees: Employee Did Salarycode Ename Eid Ephone Samer 15 23 2345 70092485 Sarah 13 12 5088 71092246 Ahmad 4 26 7712 03099348 Jalal 15 22 9664 76093148 Elie 8 23 3054 70092729 Fadi 24 2976 03091945 Layal 9 21 4490 71099380 Select one: a. SELECT Did FROM Employee GROUP BY Did HAVING SUM(Eid) < 5; O b. SELECT Did, COUNT(Eid) FROM Employee GROUP BY Did; O c. None of these d. SELECT Did FROM Employee GROUP BY Did HAVING COUNT(Eid) < 5;
- Computer Science pamuk_uretimi YEAR_ birincifarki alinmis_ser var var var 1 19579 1932 2 27791 1933 8212 3 37762 1934 9971 4 52228 1935 14466 51068 1936 -1160 6 67185 1937 16117 64731 1938 -2454 8 63950 1939 -781 9 77115 1940 13165 10 64184 1941 -12931 11 74055 1942 9871 12 53392 1943 -20663 13 58916 1944 5524 14 54379 1945 -4537 15 59027 1946 4648 16 51456 1947 -7571 17 58198 1948 6742 18 104156 1949 45958 19 118377 1950 14221 20 150000 1951 31623 21 165000 1952 15000 Do it using Box and Jenkins Method and R program.Quarter Revenue 2014Q1 1050 2014Q2 1005 2014Q3 1169 2014Q4 1420 2015Q1 1566 2015Q2 1824 2015Q3 2085 2015Q4 2405 2016Q1 2566 2016Q2 2886 2016Q3 3231 2016Q4 3536 2017Q1 3661 2017Q2 4100 2017Q3 4584 2017Q4 5113 2018Q1 5442 2018Q2 6105 2018Q3 6679 2018Q4 7430 2019Q1 7696 2019Q2 8381 2019Q3 8995 2019Q4 9954 2020Q1 10219 2020Q2 10808 2020Q3 11601 2020Q4 12742 2021Q1 13503 2021Q2 14809 2021Q3 16110 2021Q4 17780 In R language, Create a time series plot of the Amazon Web Services revenue data.1) Use linear regression first to model the trend in the time series. After conducting a linear regression to model the trend in the timeseries, what is the slope coefficient for the trend variable?2) Calculate accuracy measures for predicted values based on the regression in (1).What is the MAPE based on the results of the linear regression you just conducted? (Round to two decimal places)3) Based on the pattern in the time series data,…Appendix EMPLOYEE Fnamo Minit Lname Ssn Bdato Addross Ser Salary Super ssn Dno 123456789 1965-01-00 731 Fondron, Houston, TX M 30000 333445555 333445555 1955-12-08 638 Voss, Houston, TX John B Smith Franklin Wong 40000 888665555 25000 987654321 999887777 1968-01-10 3321 Castlo, Spring, TX Wallaco 987654321 1941-06-20 201 Borry, Bollaire, TX Alicia Zolaya 4. Jonnifor 43000 888665555 4. Narayan 666884444 1962-09-15 975 Fire Oak, Humblo, TX M 38000 333445555 English 453453453 1972-07-31 5631 Rice, Houston, TX Ramosh K Joyce 25000 333445555 Ahmad V Jabbar 987987987 1969-03-20 980 Dalas, Houston, TX M 25000 087654321 4. James Borg 888665555 1937-11-10 450 Stono, Houston, TX M 55000 NULL DEPARTMENT DEPT LOCATIONS Dnamo Dnumber Mgr ssn Mgr start date Dnumbor Dlocation Rosearch 333445555 1988-05-22 Houston Administration 4. 987654321 1995-01-01 Stafford Headquarters 1. 888665555 1981-06-19 5. Bellaire 5. Sugarland Houston WORKS ON PROJECT Essn Pno Hours Pname Prumber Plocation Dnum 123456789…
- TABLE 4-6 Parking Tickets at Millennium College Parking Ticket Table St ID L Name F Name Phone No St Lic Lic No Ticket # Date Code Fine 38249 Brown Thomas 111-7804 FL BRY 123 15634 10/17/12 2 $25 16017 11/13/12 1 $15 82453 Green Sally 391-1689 AL TRE 141 14987 10/05/12 $100 16293 11/18/12 1 $15 17892 12/13/12 2 $25 1. Convert to INF Relation 2. Draw dependency diagram showing all functional dependencies 3. Identify anomalies 4. Convert to 3NF Relations 5. Develop EER Diagram with appropriate cardinalities46 Refer to the given table. Salesperson Number 137 137 137 106 106 106 106 204 204 361 361 361 Product Salesperson Number Name 19440 Baker 24013 Baker 26722 Baker 16386 Adams 19440 Adams 21765 Adams 24013 Adams 21765 Dickens 26722 Dickens 16386 Caryle 21765 Caryle 26722 Caryle SALESPERSON/PRODUCT table Commission Year of Department Manager Product Unit Percentage Hire Number Name Name Price 10 1995 73 Scott Hammer 17.50 10 1995 73 Scott Saw 26.25 10 1995 73 Scott Pliers 11.50 15 2001 59 Lopez Wrench 12.95 15 2001 59 Lopez Hammer 17.50 15 2001 59 Lopez 32.99 15 2001 59 Lopez 26.25 10 1998 73 Scott 32.99 10 1998 73 Scott 11.50 20 2001 73 Scott 12.95 20 2001 73 Scott 32.99 20 2001 73 Scott 11.50 4 Which quality of the table demonstrates why it is not in first normal form? O There are only non-key attributes visible. O There is data redundancy. There is no primary key. Each attribute has multiple values. Drill Saw Drill Pliers Wrench Drill Pliers Quantity 473 170 688 1475 2529 1962 3031…A semiconductor manufacturer produces devices used as central processing units in personal computers. The speed of the devices (in megahertz) is important because it determines the price that the manufacturer can charge for the devices. The following table contains measurements on 120 devices. 680 669 719 699 670 710 677 669 700 718 690 681 702 696 692 649 675 701 721 683 735 688 763 672 681 722 663 658 634 720 690 679 691 683 705 746 706 649 668 717 727 683 652 720 660 695 701 724 668 698 668 660 653 637 660 693 679 682 704 652 664 702 661 720 695 670 656 723 710 680 684 705 681 662 644 683 695 678 674 724 748 697 690 656 667 683 698 672 642 694 660 659 704 690 724 680 739 704 695 718 660 648 703 660 722 691 680 685 681 715 665 676 665 675 655 659 720 675 697 663 This data set: Homework4, has been uploaded with our Homework#4 folder, download and read this data to R studio and use it to answer the following questions. a) Calculate the following using R, mean, median, standard…
- Using KNN algorithm to find the 05 similar movies from the movies below. Movie ID Movie Name IMDB Rating Biography Drama Thriller Comedy Crime Mystery History Label 58 The Imitation Game 8 1 1 1 0 0 0 0 0 8 Ex Machina 7.7 0 1 0 0 0 1 0 0 46 A Beautiful Mind 8.2 1 1 0 0 0 0 0 0 62 Good Will Hunting 8.3 0 1 0 0 0 0 0 0 97 Forrest Gump 8.8 0 1 0 0 0 0 0 0 98 21 6.8 0 1 0 0 1 0 1 0 31 Gifted 7.6 0 1 0 0 0 0 0 0 3 Travelling Salesman 5.9 0 1 0 0 0 1 0 0 51 Avatar 7.9 0 0 0 0 0 0 0 0 47 The Karate Kid 7.2 0 1 0 0 0 0 0 0 50 A Brilliant Young Mind 7.2 0 1 0 0 0 0 0 0 49 A Time To Kill 7.4 0 1 1 0 1 0 0 0 30 Interstellar 8.6 0 1 0 0 0 0 0 0 94 The Wolf of…LUNCH SALES Days of Week Lunch Sold Qty Unit Price Total Sold Monday Chicken 25 15.00 $ 375.00 Monday Pork 15 20.00 $ 300.00 Monday Fish 20 25.00 $ 500.00 Tuesday Mutton 15 23.00 $ 345.00 Tuesday Fish 30 25.00 $ 750.00 Wednesday Chicken 55 15.00 $ 825.00 Thursday Turkey 45 18.00 $ 810.00 Friday Turkey 60 18.00 $ 1,080.00 Saturday Chicken 15 15.00 $ 225.00 Saturday Mutton 45 23.00 $ 1,035.00 Saturday Fish 55 25.00 $ 1,375.00 Saturday Turkey 50 18.00 $ 900.00 Saturday Pork 20 20.00 $ 400.00 Sunday Fish 55 25.00 $ 1,375.00 Sunday Mutton 35 23.00 $ 805.00 Sunday Chicken 30 15.00 $ 450.00 #NAME? Fruit # of Days Sold Total Sold Sum of Total Sold Chicken Mutton Fish Turkey Pork Instructions: (Fill in the columns of the "Calculations Table") 1.…Set up Order [ single order ) Assign seats [ subscription ) Assign chairs Award bonus Debit Account charge credit card Mail packet