python.

Pilots_1 file snippet

Pilots_2 file snippet

The year is 2152. You have been hired by a major robotic pilot training and robot design contract firm to process the data from their most recent field tests in multiple training sites. To make this process easier on yourself, you have decided to write a Python script to automate the process. The company wants the following data on their robots for the training sites: 1) The information of the best pilot, as quantified by their field test average 2) The average performance of each field test 3) A histogram of robot colors from a giving training site 5) The average first and last name lengths, rounded down Many of these stats seem arbitrary, almost as if they were made for pointless reasons of testing Python skills Briefly look over the two .csv files provided on Blackboard. Both were generated using Mockaroo, a random data generation tool. For this project, perform the following steps: 1. Prompt for a file name. Read in that file name. 2. Set up a dictionary to act as a histogram for the robot colors 3. Calculate the stats specified above. Use whatever data structures you feel you may need - lists, dictionaries, etc 4. Print the final report to both the console using print and to a file called fn_report.txt. So, if the filename is Pilots 1.csv, save the data to Pilots1_report.txt Below is sample output for Pilots1.csv Enter a pilot data file name: Pilots1.csv Test Site Report: Average first name length: 6 Average last name length: 7 Average field test 1 score: 75 Average field test 2 score: 68 Average field test 3 score: 68 Best pilot data: Marietta Senn, serial RS:738_1344. Orange, with an average field test score of 99 Colors Histogram--- {'Aquamarine: 54, Blue': 66, 'Crimson': 54, 'Fuscial: 50, 'Goldenrod : 44 'Green': 56, 'Indigo: 44 Khaki': 61, "Maroon': 61, Mauv':35, Orange: 57, Pink: 47, 'Puce': 54. 'Purple': 51, 'Red': 59, 'Teal': 53, 'Turquoise: 55, "Violet': 47, 'Yellow':52} Added Requirements: Make use of functions to divide and conquer! Each chunk of code (each function) should have a comment, #using the octothorpe, on the line above it describing what the code is about to do A E F J 1 lid G H 1 field_test_1 field_test_2 field_test_3 57 39 81 58 86 56 2 3 4 57 65 78 5 78 83 92 46 6 54 85 7 66 79 97 8 96 62 62 9 60 69 95 10 74 74 73 11 98 12 B C D |first_name last_name gender 1 Joletta Dimeloe Female 2 ketti Fahy Female 3 Felizio Hobden Male 4 Ferdinande Rontree Female 5 Dorelle Kleinsmuntz Female 6 Gwenni Helstrip Female 7 Laina Huws Female 8 Whitman Harriagn Male 9 Fonz Yakubov Male 10 Edlin Pursglove Male 11 Noellyn Searjeant Female 12 Jessa McPhaden Female 13 Kermie Daid Male 14 Shelby Gueste Female 15 Onfre Astall Male 16 Moria Bowley Female 17 Julieta Coveley Female 18 Axel Togwell Male 19 Tristam Aspel Male 20 Rhys Peattie Male 21 Burl Guerri Male 22. Llicca Camalo Pilots robot_serial robot_color RS:639_7187 Indigo RS:438_5895 Mauv RS:786_0346 Fuscia RS:141_6524 Maroon RS:671_3647 Turquoise RS:016_1213 Blue RS:858_4322 Violet RS:298_6292 Turquoise RS:188_9352 Orange RS:062_0497 Violet RS:010_7415 Red RS:557_1846 Crimson RS:974_2746 Orange RS:876_7511 Green RS:768_3620 Blue RS:827_5059 Pink RS:454_7403 Violet RS:399_4443 Orange RS:502_3825 Turquoise RS:804_6072 Pink RS:257_1538 Khaki Dc2Mn M535 Dura 81 87 97 87 46 53 81 97 13 52 14 63 79 61 53 57 51 96 73 15 16 17 18 39 57 83 53 98 61 67 77 53 76 19 20 21 89 78 93 49 37 79 22 95 84 Alann 76 A 1 lid 2 3 4 5 6 7 8 9 10 11 B D first_name last_name gender 1 Richmond Coleford Male 2 Mal Niven Male 3 Mirilla Allder Female 4 Sampson Sudell Male 5 Gusella Giacomoni Female 6 Grace Hufton Female 7 Nial Mazzey Male 8 Bordie Corben Male 9 Aluin Christall Male 10 Zollie Muge Male 11 Olia Wareing Female 12 Porty Jambrozek Male 13 Gwyn Matasov Female 14 Adolphus Shird Male 15 Dareen Hyde Female 16 Ganny Salasar Male 17 Clerkclaude Merredy Male 18 Cordula Baldoni Female 19 Gareth Dewett Male 20 Neale Marham Male 21 Tabbi Pecha Female 22Crad Pilots2 E F G H robot_serial robot_color field_test_1 field_test_2 field_test_3 RS:785_6835 Violet 90 68 58 RS:613_9433 Pink 59 35 53 RS:346_6023 Yellow 94 69 78 RS:043_1361 Blue 53 87 87 RS:651_2102 Crimson 96 93 38 RS:990_0760 Turquoise 91 82 83 RS:803_4810 Blue 98 67 47 RS:857_3194 Green 76 86 91 RS:882_3008 Teal 52 54 66 RS:849_6152 Purple 55 42 87 RS:096_0171 Indigo 71 53 84 RS:498_3152 Yellow 58 51 72 RS:494_4284 Crimson 71 43 99 RS:059_9418 Pink 86 50 82 RS:051_2656 Mauv 82 94 86 RS:597_9216 Aquamarine 61 40 38 RS:276_5608 Fuscia 94 42 58 RS:158_5290 Yellow 94 86 69 RS:516_5326 Green 62 55 96 RS:799_9860 Red 94 43 99 RS:188_1377 Pink 51 74 38 12 13 14 15 16 17 18 19 20 21 22 22 Car Mala DC-157 1751 Wiolet 55 51