I need help with three python assignment

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Lemons 5.54 511 Paprika 19.80 768 Steak 20.49 199 Mousse 4.76 918 Spinach 17.17 209 Chocolate 11.61 662 Beef pot pie 7.03 302 Salmon 17.10 637 Molasses 20.67 374 Fruit 9.88 317 Kumquats 5.92 723 Meat tenderizer 2.36 353 BBQ sauce 7.75 266 Sugar substitute 11.53 1 Cherries 3.54 194 Scrubbers 17.81 789 Doughnuts 8.54 61 Onion soup 11.37 28 Egg substitute 5.73 565 Baby shampoo 14.59 890 Apple sauce 11.35 54 Nuts 15.37 145 Perogies 15.05 961 Beans 11.52 255 Parsley 17.69 335 Sweet & sour sauce 6.34 724 Cod 20.76 588 Chicken wings 4.24 985 Hair color 20.34 558 Plastic cutlery 20.66 844 Cream of wheat 1.58 161 Garbage bags 10.39 439 Sandwich bread 12.99 979 Cottage cheese 15.23 546 Cold medicine 16.34 647 Liver 15.71 191 Corn creamed 12.17 88 Pop 7.93 290 Mashed potatoes 2.27 380 Tomatoes 5.09 152 Puffed wheat 17.81 501 Mozzarella sticks 11.41 497 Baby food 15.63 530 Facial tissue 13.81 36 Baking soda 13.79 653 Muslix 7.57 722 Ginger 3.35 240 Nuts 12.19 862 Garlic 12.09 963 Pectin 11.63 43 Non-stick spray 10.91 956 Bean salad 10.22 167 Bagels 6.99 330 Green peppers 6.70 792 Blue berries 15.45 632 Flea collar 15.39 682 Salad dressing 6.27 526 Mineral water 10.48 576 Garlic bread 6.17 454 Pumpernickel 20.50 519 Milk chocolate 1.32 554 Lunch bags 13.24 701 Roast beef 18.86 859 Soup 19.80 587 Anchovies 1.18 743 Baby wash 12.39 771 Melon 9.01 435 Turkey 12.79 621 Strawberries 12.95 714 Hand soap 17.24 3 Catnip 13.84 848 Corned beef 16.34 583 Corn chips 13.09 637 Coffee 7.54 410 Chicken nuggets 12.44 114 Pepperoni 7.82 480 Oregano 4.21 298 Flour 7.23 767 Potato soup 11.63 965 Mayonnaise 1.33 246 Mixed vegetables 14.68 761 Juice 16.71 67 Cabbage 19.04 327 Hickory sticks 7.67 894 Stew 1.46 441 Oranges 3.27 37 Cotton balls 20.47 756 Ketchup 17.91 147 Pizza 1.38 710 Wax paper 12.73 663 Burritos 9.24 358 Oatmeal 2.08 999 Potato nuggets 18.08 90 Endives 6.75 668 Rotini 4.96 343 Sorbet 2.10 146 Bathroom cleaner 7.96 930 Turkey breast 2.31 348 Hot/Cold rub 11.43 267 Hot sauce 7.90 433 Mustard 15.90 704 Vector 8.95 51 Yams 5.85 548 Chili Sauce 10.11 142 Nectarines 5.70 691 Breast pads 19.54 946 Cucumbers 17.63 359 Kitchen cleaner 10.58 678 Halibut 20.47 11 Syrup 10.23 89 Hot dogs 2.70 753 All spice 1.46 600 Ravioli 7.34 751 Sponges 14.25 211 Fabric softener 12.37 823 Sugar 11.61 934 Aluminum foil 14.21 365 Paper towel 3.60 535 Eggs 10.05 427 Plums 14.07 751 Chip dip 8.31 221 Butter 14.05 301 Bananas 6.66 675 Sourdough 5.73 274 Red snapper 19.61 97 Hash browns 16.66 263 Rutabagas 9.31 340 Body wash 6.58 932 Cauliflower 12.16 611 Parsley 7.20 647 Cheese spread 3.10 984 Macaroni 12.37 940 Vinegar 16.43 448 Shrimp 19.53 837 Salsa 17.82 301 Grapes 10.98 485 Reflective collar 2.71 152 Pineapple 11.86 786 Carpet freshener 12.42 254 Pancake mix 2.03 309 Berries 4.66 885 Veggie burgers 1.89 50 Oil 7.85 963 Radishes 6.79 489 Pears 20.64 47 Peaches 7.09 484 Tortillas 14.12 691 Oat meal 10.68 257 Pizza snacks 5.98 772 Steak sauce 9.44 638 Pizza 18.32 674 Escargot (snails) 12.62 718 Onions 13.93 661 Juice concentrate 20.60 892 Teething ointment 1.30 335 Cookies 11.51 212 Squash 20.23 413 Avocados 3.18 244 Raisin bread 15.56 286 Sprinkles 11.18 132 Chips 11.03 752 Cheezies 8.70 375 Pineapple 12.49 81 Veggie burgers 15.53 289 Bottle nipples 5.82 484 HP sauce 2.43 841 Spaghetti 6.52 287 Gum 4.75 315 French fries 9.70 73 Paper plates 18.85 264 Pot scrubbers 20.13 875 Hair gel 19.21 425 Tater tots 14.33 146 Chew toy 17.56 62 Burger patties 8.07 351 Peaches 7.50 440 Corn meal 1.24 378 Pabulum 15.60 97 Veggie dogs 3.30 521 Basil 8.57 623 10.74 226 Yellow peppers 15.87 136 Chicken noodle soup 6.97 611 Broccoli soup 1.72 719 Relish 8.94 677 Basil 15.55 575 Corn dogs 4.03 303 Cotton swabs 18.48 289 Bottle liners 8.70 90 Tea 3.78 680 Chick peas 12.75 865 Dental/Breath bones 17.20 963 Plastic wrap 16.50 843 Candy 1.88 891 Cat litter 14.74 807 Jell-O 9.76 523 Tartar Sauce 2.42 882 Nacho dip 2.74 589 Corn flakes 2.02 809 Ground pork 18.87 96 Shortening 9.35 513 Muffin Mix 15.59 208 Turkey 7.83 69 Bay leaf 2.76 63 Half & half 17.30 62 Montreal meat spice 6.95 170 Treats 15.78 183 Brown sugar 18.44 112 Club soda 15.93 984 Peanuts 11.55 368 Beans 13.29 160 Margarine 3.23 640 Bisquick 19.78 458 Nail clippers 3.97 958 Tofu 6.92 305 Hair spray 11.02 373 Flounder 4.53 261 Sport drink 10.36 287 Allergy medication 16.32 671 Fruit salad 18.02 312 Oregano 16.90 700 Ham 9.95 940 Hot dog buns 20.74 904 Tilapia 4.07 535 Chicken bologna 14.46 659 Buns 11.62 685 Collards 7.33 971 Napkins 18.77 347 Sprouts 6.86 745 Salami 4.07 905 Cookies 8.98 961 Cream cheese 15.11 44 Pumpkin 5.25 246 Salt 2.12 919 Worcestershire sauce 15.19 168 Grooming comb 16.33 563 Zucchini 11.57 914 Chicken loaf 18.43 996 Rye bread 20.12 734 Cake mix 12.81 467 Kohlrabi 9.77 367 Pepper 16.03 387 Asparagus 14.99 832 Baking powder 7.65 203 Toilet tank fresheners 8.80 415 Brussels sprouts 8.49 712 Limes 12.03 200 Chard 5.16 781 Cocoa powder 19.56 143 Teething ring 18.23 971 Egg plant 16.50 332 Parsnips 3.02 430 Turkey pot pie 7.65 944 Shampoo 20.38 604 Lettuce 19.10 2 Dog food 14.70 12 Kid's cereal 12.73 927 Mini wheats 10.44 463 Brownie mix 2.87 552 Toilet bowl cleaner 9.49 417 Gravy 7.82 669 Olives 1.34 728 Kiwi 10.57 551 Trail mix 3.46 82 Tomato paste 7.90 602 Crab 19.60 515 Jam 11.67 60 Cranberries 2.62 935 Chicken breast 7.70 809 Bouillon cubes 11.21 291 Ice cream 20.45 399 Couscous 4.50 261 Breath mints 14.66 205 Lobster 12.44 540 Bar soap 20.10 956 Icing 18.93 804 Crackers 11.97 655 Soy milk 7.48 547 Waffles 2.42 253 Decorations 15.88 2 TV dinners 13.35 398 French bread 16.47 685 Bologna 18.86 619 Chicken pot pie 14.23 584 Baby wipes 19.74 271 Croissants 18.32 741 Glass's cleaner 3.54 34 Broccoli 18.22 672 Paper towels 1.16 816 Beets 6.78 401 Alfredo sauce 3.51 519 Tomato sauce 2.46 988 Air freshener 9.53 292 Celery 17.61 489 Apple sauce 14.59 624 Diaper rash ointment 11.04 378 Jelly 5.94 1 Cream 5.74 144 Mangoes 18.14 531 Pasta 19.06 876 Toys 13.25 76 Sandwich bags 18.29 137 Bread crumbs 12.59 138 Cloves 17.39 600 Mixers 4.24 473 Sausage 19.91 758 Cat food 14.24 90 Diapers 4.74 209 Pasta sauce 16.64 983 Pie 7.99 82 Potatoes 1.21 130 Floss 8.38 13 Salt 7.03 135 English muffins 1.78 431 Mac&cheese 17.08 771 Mussels 19.55 294 Cilantro 20.46 615 Whipping cream 17.43 629 Dried fruit 15.17 34 Marmalade 16.70 740 Rice 5.10 539 Conditioner 18.88 626 Chicken 12.64 866 Chicken legs 6.66 303 Seafood Frozen 10.02 715 Honeydew 10.60 799 Salmon 4.80 472 Whole grain 10.45 594 Peas 7.53 778 Instant Noodles 6.25 568 Kale 6.22 952 Yogurt 3.86 584 Popsicles 6.22 452 Corn 17.35 440 Pork chops 19.95 516 Honey comb 5.77 419 Meat sauce 4.49 431 Sour cream 18.44 869 Nacho chips 4.26 255 Toilet paper 6.66 311 Pork rinds 3.85 201 Maple syrup 13.37 162 Turkey 7.08 136 Chicken flaked 15.00 564 Linguini 11.14 199 Pie 16.30 573 Laundry detergent 13.39 801 Pears 15.53 392 Fish sticks 15.57 753 Pancakes 9.67 545 Yeast 17.17 952 Cantaloupe 7.63 752 Honey 18.82 146 Tuna 8.09 833 Lemon juice 6.01 921 Life 1.33 909 Lip balm 11.14 609 Chicken 14.09 220 Sword fish 6.69 552 Cake 4.01 399 Pork & beans 18.16 794 Corn whole 18.21 338 Garlic 2.95 832 Mint 15.04 192 Tub&Tile cleaner 1.34 741 Dish soap 20.25 974 Flu medicine 16.44 6 Formula 16.11 634 Peanuts 4.85 845 Bottles 19.39 889 Bleach 16.55 212 Cheese bread 5.46 930 Band-aids 6.18 869 Soy sauce 6.13 256 Granola bars 9.01 836 Sardines 16.42 301 Peas 12.79 319 Raisin bran 8.87 865 Oysters 18.63 322 Spam 7.77 338 Grapefruit 14.84 541 Protein bars 3.69 505 Apricots 1.73 338 Baguette 2.65 36 Mushrooms 15.90 269 Grape nuts 9.54 736 Pita bread 10.87 724 Medicine 9.98 477 Apples 19.73 998 Pretzels 16.77 269 Stir fry 4.06 38 Onion rings 13.38 482 Carpet cleaner 7.50 449 Peanut butter 8.21 886 Milk 1% 20.74 937 Antacids 5.69 128 Honey nut ohs 6.23 854 Vegetables Fruits 4.69 86 Hamburger buns 17.67 12 Tomatoes 7.01 589 Cinnamon buns 16.73 300 Mosquito repellant 11.69 323 Raspberries 12.88 305 Bacon 18.38 69 Carrots 16.02 80 Candles 9.00 39 Coffee whitener 13.58 339 Egg Nog 15.37 893 Cinnamon 14.65 627 Pickles 18.57 915 Baby Lotion 7.89 487 Oven cleaner 7.22 857 Carrots 7.37 589 Chives 10.07 623 Ground beef 9.88 157 Papaya 9.80 402 Plastic containers 15.65 638 Taquitos 2.59 991 Plastic cups 2.07 6 Clams 3.82 303 Oysters 10.87 605 Sealable bags 1.48 97 Rice crispies 19.48 89 Shreddies 20.90 301 Drink crystals 6.99 176 Popcorn 14.95 911 Red peppers 12.36 619 Vacuum bags 14.29 400 Milk 2% 2.11 345 Vanilla extract 10.95 882 Chocolate sauce 17.29 842 Chili 7.89 544 Dishwasher detergent 14.06 175 Cheese slices 20.42 38 Deodorant 16.08 184 Baby Items Cereal 19.86 336 Bran 11.32 324 Tuna 8.01 47 Hamburgers 4.73 295 Fruit juice 11.71 801 Pate 13.17 166 Baby cookies 20.13 675 Brownies 12.58 952 Graham crumbles 15.44 257

#1 In the movie “The Martian” 2015, the “Martian” (Matt Damon) devises a clever scheme of adapting the Ascii table to a 360° circle.  The numbers 0 thru 9 and A thru F are placed on cards spaced at 23 degrees increments in a circle around the Sojourner Martian lander: Hex Degree Range 0 = 0 - 22.5 1 = 22.5 - 45.0 2 = 45.0 - 67.5 3 = 67.5 - 90.0 4 = 90.0 - 112.5 5 = 112.5 - 135.0 6 = 135.0 - 157.5 7 = 157.5 - 180.0 8 = 180.0 - 202.5 9 = 202.5 - 225.0 A = 225.0 - 247.5 B = 247.5 - 270.0 C = 270.0 - 292.5 D = 292.5 - 315.0 E = 315.0 - 337.5 F = 337.5 - 360.0 Specifications 1. Select objects from the Sojourner image to implement an Object Oriented Design. 2. Implement a translation algorithm to convert Degrees to ASCII characters. #2 In cryptanalysis, frequency analysis is used to study the frequency of letters or groups of letters in an encrypted text. The method is used as an aid to breaking classical cyphers. Frequency analysis is based on, in any given a sample of written language, certain letters and combinations of letters occur with varying frequencies.   The encrypted file "Encrypt.txt" is a plain text file encrypted using a technique known as the "Slip" or "Caesar" cypher. The Caesar cypher is a circular permutation which, given an arbitrary shift S, replaces each letter of the plain text T by the letter S places later, T + S. ===== Original text: MEET ME AT ELEPHANT LAKE ===== Character Table Chr ASCII Frequency ' '      32        4 A     65         3 E     69         6 H    72         1 K    75         1 L     76          2 M   77          2 N    78         1 P     80         1 T     84         3 ===== Shift 3 ' ' + 3 = H A + 3 = K E + 3 = L H + 3 = M K + 3 = N L + 3 = P M + 3 = T N + 3 = ' ' P + 3 = A T + 3 = E  A Slip or Caesar encryption can be decrypted by using a linear offset (brute force) to break the cypher. The encrypted file used in this lab can also be solved using linear offset techniques.   Linear Offset Example using uppercase A-Z    Word = "Program"    Subset = Upper Case Alphabet    Shift 00: P R O G R A M    Shift 01: Q S P H S B N    Shift 02: R T Q I T C O    Shift 03: S U R J U D P    Shift 04: T V S K V E Q    Shift 05: U W T L W F R    Shift 06: V X U M X G S    Shift 07: W Y V N Y H T    Shift 08: X Z W O Z I U    Shift 09: Y A X P A J V    Shift 10: Z B Y Q B K W    Shift 11: A C Z R C L X    Shift 12: B D A S D M Y    Shift 13: C E B T E N Z    Shift 14: D F C U F O A    Shift 15: E G D V G P B    Shift 16: F H E W H Q C    Shift 17: G I F X I R D    Shift 18: H J G Y J S E    Shift 19: I K H Z K T F    Shift 20: J L I A L U G    Shift 21: K M J B M V H    Shift 22: L N K C N W I    Shift 23: M O L D O X J    Shift 24: N P M E P Y K    Shift 25: O Q N F Q Z L    Shift 26: P R O G R A M    The encrypted file is based on 'Unicode' characters, and are inherited from the ASCII character set.  This cypher contains Upper case, Lower case, Numbers and common Punctuation.  Also, the subsets of Upper case, Lower case, Numbers and Punctuation do not use the complete ranges for Upper, Lower, Numbers or Punctuation.  For example, the Upper case subset does NOT contain ALL the upper case letters.    Adjustments will have to be made to programmatically determine which of the Upper case letters are in the subset.  The first step is to determine the frequencies of each character in the encrypted file. Lets say the most frequent character in the encrypted file is the letter 'H'.  By mapping the letter 'H' to the most frequent character in the file (it is likely to be the SPACE ' ' character), the letter 'H' corresponds to a word separator.   After finding the most frequent character, try to find a space character followed by a space character three characters away.  When you find the 3 space seperation, the most likely letter combination is either an 'and' or 'the'.  Assuming it is an 'and', you have found the letters for 'a', 'n', and 'd'.  The offset into the character frequency table can then be calculated.    This lab assignment is based on the idea of building "Artificial Intelligence" into your program.  The Brute Force method, shown above, will not work based on the assumption that all the lower ascii characters are present in the file.  The characters used in the file determine the table of characters used.    Suppose the Encrypted file only contains this text:  "THE QUICK BROWN FOX JUMPED OVER THE LAZY POODLE".  The frequency table would contain ALL the UPPER case letters, no LOWER case, no Numbers and no Punctuation.    The Brute force method will work if you use the character frequency table.  However, since the purpose of this assignment is to build Artifical Intelligence (described above) into your program, only a score of 70% will be awarded to programs that only use the Brute Force method. #3 Data File: Products.csv Read the Products.csv file and parse the product data using Regular Expressions. Store each Product information into a Dictionary. Each line in the data file corresponds to a Product. For example: Paper towels,1.16,816 Product name: Paper Towels Product price: 1.16 Product quantity: 816 Write a function to lookup a Product, by name, in the Dictionary.  The function takes a Product name and returns the Product price.

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