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1. Read a list of recipes from a file named "Recipes.txt" and store them in a Python list of dictionaries. Recipes.txt: Spaghetti Bolognese, Italian, Main Course, 4.5, 30 minutes Chicken Alfredo, Italian, Main Course, 4.7, 45 minutes Vegetarian Stir-Fry, Asian, Main Course, 4.2, 25 minutes Chocolate Cake, Dessert, Baking, 4.8, 60 minutes Greek Salad, Mediterranean, Salad, 4.6, 15 minutes Tacos, Mexican, Main Course, 4.4, 20 minutes 1. Output all the recipes in your list. 2. Output all recipes that are categorized as desserts. 3. Output all recipes with a rating higher than 4.5. 4. Calculate the average preparation time of all recipes in your list. 5. Create a histogram of the cuisines of the recipes. Each element in the histogram should represent the count of recipes in a specific cuisine. 6. Take all the recipes in "Recipes.txt" and store them in a Python set of tuples where each tuple represents (recipe title, cuisine). 7. Output all unique cooking times among the recipes. 8. Output the total number of entries in your set.

1. Read a list of recipes from a file named "Recipes.txt" and store them in a Python list of dictionaries. Recipes.txt: Spaghetti Bolognese, Italian, Main Course, 4.5, 30 minutes Chicken Alfredo, Italian, Main Course, 4.7, 45 minutes Vegetarian Stir-Fry, Asian, Main Course, 4.2, 25 minutes Chocolate Cake, Dessert, Baking, 4.8, 60 minutes Greek Salad, Mediterranean, Salad, 4.6, 15 minutes Tacos, Mexican, Main Course, 4.4, 20 minutes 1. Output all the recipes in your list. 2. Output all recipes that are categorized as desserts. 3. Output all recipes with a rating higher than 4.5. 4. Calculate the average preparation time of all recipes in your list. 5. Create a histogram of the cuisines of the recipes. Each element in the histogram should represent the count of recipes in a specific cuisine. 6. Take all the recipes in "Recipes.txt" and store them in a Python set of tuples where each tuple represents (recipe title, cuisine). 7. Output all unique cooking times among the recipes. 8. Output the total number of entries in your set.

Computer Networking: A Top-Down Approach (7th Edition)
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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1. Read a list of recipes from a file named "Recipes.txt" and store them in a Python list of dictionaries. Recipes.txt: Spaghetti Bolognese, Italian, Main Course, 4.5, 30 minutes Chicken Alfredo, Italian, Main Course, 4.7, 45 minutes Vegetarian Stir-Fry, Asian, Main Course, 4.2, 25 minutes Chocolate Cake, Dessert, Baking, 4.8, 60 minutes Greek Salad, Mediterranean, Salad, 4.6, 15 minutes Tacos, Mexican, Main Course, 4.4, 20 minutes 1. Output all the recipes in your list. 2. Output all recipes that are categorized as desserts. 3. Output all recipes with a rating higher than 4.5. 4. Calculate the average preparation time of all recipes in your list. 5. Create a histogram of the cuisines of the recipes. Each element in the histogram should represent the count of recipes in a specific cuisine. 6. Take all the recipes in "Recipes.txt" and store them in a Python set of tuples where each tuple represents (recipe title, cuisine). 7. Output all unique cooking times among the recipes. 8. Output the total number of entries in your set.
Transcribed Image Text:1. Read a list of recipes from a file named "Recipes.txt" and store them in a Python list of dictionaries. Recipes.txt: Spaghetti Bolognese, Italian, Main Course, 4.5, 30 minutes Chicken Alfredo, Italian, Main Course, 4.7, 45 minutes Vegetarian Stir-Fry, Asian, Main Course, 4.2, 25 minutes Chocolate Cake, Dessert, Baking, 4.8, 60 minutes Greek Salad, Mediterranean, Salad, 4.6, 15 minutes Tacos, Mexican, Main Course, 4.4, 20 minutes 1. Output all the recipes in your list. 2. Output all recipes that are categorized as desserts. 3. Output all recipes with a rating higher than 4.5. 4. Calculate the average preparation time of all recipes in your list. 5. Create a histogram of the cuisines of the recipes. Each element in the histogram should represent the count of recipes in a specific cuisine. 6. Take all the recipes in "Recipes.txt" and store them in a Python set of tuples where each tuple represents (recipe title, cuisine). 7. Output all unique cooking times among the recipes. 8. Output the total number of entries in your set.
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