Slide 1: “Practical Python Applications”

Slide 2: The Tale of a Traveling Salesman The salesman needs to visit multiple cities and find the most efficient route to minimize travel time and costs. This problem can be solved using a greedy algorithm.

Slide 3: Code Snippet 1 (Traveling Salesman)

Ready for some cool stuff? Here’s how we can tackle this:

# Define city names and distances
cities = ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix']
distances = [
    [0, 2789, 713, 1592, 2145],
    [2789, 0, 1744, 1373, 370],
    [713, 1744, 0, 923, 1472],
    [1592, 1373, 923, 0, 879],
    [2145, 370, 1472, 879, 0]
]

This code defines the city names as a list and the distances between each pair of cities as a 2D list. We’ll use these data structures in our algorithm.

Slide 4: Code Snippet 2 (Traveling Salesman)

Don’t worry, this is easier than it looks! Here’s how we can tackle this:

def traveling_salesman(start):
    unvisited = cities[:]
    route = [start]
    unvisited.remove(start)

    while unvisited:
        nearest_city = min([(distances[cities.index(start)][cities.index(city)], city) for city in unvisited], key=lambda x: x[0])[1]
        route.append(nearest_city)
        unvisited.remove(nearest_city)
        start = nearest_city

    return route

This function builds a greedy algorithm to find the shortest path. It starts from the given city, iteratively finds the nearest unvisited city, and adds it to the route. The algorithm returns the completed route.

Slide 5: Real-life Use Case (Traveling Salesman) Logistics and transportation companies can use this algorithm to optimize delivery routes, saving time and fuel costs. For example, a courier service can use this algorithm to plan the most efficient route for delivering packages across multiple locations.

Slide 6: The Chef’s Special Algorithm The chef needs to manage ingredients and create new recipes by combining them in the right proportions. We’ll use lists to store ingredients, dictionaries to map ingredients to quantities, and tuples to represent recipes.

Slide 7: Code Snippet 1 (Chef’s Algorithm)

Let’s make this super clear! Here’s how we can tackle this:

# Define ingredients and quantities
ingredients = ['flour', 'sugar', 'butter', 'eggs', 'milk']
quantities = {'flour': 500, 'sugar': 200, 'butter': 150, 'eggs': 12, 'milk': 1000}

This code defines a list of ingredients and a dictionary that maps each ingredient to its available quantity.

Slide 8: Code Snippet 2 (Chef’s Algorithm)

Here’s where it gets exciting! Here’s how we can tackle this:

def create_recipe(name, recipe_ingredients):
    recipe = []
    for ingredient, quantity in recipe_ingredients:
        if ingredient in quantities and quantities[ingredient] >= quantity:
            recipe.append((ingredient, quantity))
            quantities[ingredient] -= quantity
        else:
            print(f"Not enough {ingredient} to create {name}")
            return None

    print(f"Created recipe: {name} with ingredients: {recipe}")
    return recipe

This function takes a recipe name and a list of tuples representing the required ingredients and quantities. It checks if there are enough ingredients available, updates the quantities dictionary, and returns the recipe if successful.

Slide 9: Real-life Use Case (Chef’s Algorithm) Recipe management software for professional kitchens or food manufacturing companies can use this algorithm to calculate ingredient requirements and track inventory. For example, a bakery can use this program to manage their ingredients and create new product recipes based on available stock.

Slide 10: The Artist’s Palette The artist wants to mix primary colors to achieve a desired shade for painting. We’ll use functions to encapsulate color mixing logic, modules to import external libraries, and external libraries like Pillow or OpenCV for color operations.

Slide 11: Code Snippet 1 (Artist’s Palette)

Ready for some cool stuff? Here’s how we can tackle this:

from PIL import Image, ImageDraw

def mix_colors(color1, color2, ratio):
    r1, g1, b1 = color1
    r2, g2, b2 = color2
    ratio1, ratio2 = ratio

    r = (r1 * ratio1 + r2 * ratio2) // (ratio1 + ratio2)
    g = (g1 * ratio1 + g2 * ratio2) // (ratio1 + ratio2)
    b = (b1 * ratio1 + b2 * ratio2) // (ratio1 + ratio2)

    return (r, g, b)

This function takes two RGB color tuples and a ratio tuple. It calculates the weighted average of the color components based on the given ratio and returns the mixed color as an RGB tuple.

Slide 12: Real-life Use Case (Artist’s Palette) Digital art and graphic design software can incorporate this algorithm to provide cool color mixing and blending tools for artists and designers. For example, a photo editing application can use this code to allow users to mix and create custom colors for image editing tasks.

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🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀