To achieve your goal of extracting the menu items and their quantities based on the order_type column (Breakfast, Lunch, Dinner), while cleaning up the data for easy iteration, you can proceed with the following steps:

1. Filter rows based on the order_type

You correctly filtered the rows using df.loc, but the data was returned as a Series where each entry was a list of tuples, and this is why it looked like a string.

2. Ensure proper iteration over the tuple data

To easily iterate through the menu items and quantities, you need to process the order_items column correctly. Since order_items is a list of tuples, you can extract the information by iterating over the filtered DataFrame.

Here’s how to clean up your process:

Solution

import pandas as pd

# Sample data
data = {
    'order_id': [45, 12, 44, 23],
    'order_type': ['Lunch', 'Dinner', 'Lunch', 'Breakfast'],
    'order_items': [
        [('Burger', 5), ('Fries', 6)],
        [('Shrimp', 10), ('Fish&Chips', 7)],
        [('Salad', 9), ('Steak', 9)],
        [('Coffee', 2), ('Eggs', 3)]
    ]
}

df = pd.DataFrame(data)

# Filter the rows based on order_type
breakfast_orders = df.loc[df['order_type'] == 'Breakfast', 'order_items']
lunch_orders = df.loc[df['order_type'] == 'Lunch', 'order_items']
dinner_orders = df.loc[df['order_type'] == 'Dinner', 'order_items']

# Extract the menu items and quantities as dictionaries
breakfast_menu = []
lunch_menu = []
dinner_menu = []

# Iterate through each order and extract the items and quantities
for order in breakfast_orders:
    for item, quantity in order:
        breakfast_menu.append((item, quantity))

for order in lunch_orders:
    for item, quantity in order:
        lunch_menu.append((item, quantity))

for order in dinner_orders:
    for item, quantity in order:
        dinner_menu.append((item, quantity))

# Print the results for each menu
print("Breakfast Menu:", breakfast_menu)
print("Lunch Menu:", lunch_menu)
print("Dinner Menu:", dinner_menu)

Explanation

1. Filter by order_type: You use df.loc to filter out rows based on the order_type column (Breakfast, Lunch, or Dinner).

2. Iterate through each list of tuples: The order_items column contains a list of tuples like [('Burger', 5), ('Fries', 6)]. You loop through each list (filtered by order_type) and extract the item names and quantities.

3. Store the results in lists: After extracting the items and quantities, you store them in breakfast_menu, lunch_menu, or dinner_menu as a list of tuples, making it easier to work with them.

Output

This will give you the following output:

Breakfast Menu: [('Coffee', 2), ('Eggs', 3)]
Lunch Menu: [('Burger', 5), ('Fries', 6), ('Salad', 9), ('Steak', 9)]
Dinner Menu: [('Shrimp', 10), ('Fish&Chips', 7)]

3. (Optional) If you want a dictionary by order_type

If you want to store each menu type in a dictionary for easier access, you can modify the code as follows:

# Create a dictionary to hold the menus by order_type
menu_dict = {
    'Breakfast': [],
    'Lunch': [],
    'Dinner': []
}

# Populate the dictionary
for order_type in ['Breakfast', 'Lunch', 'Dinner']:
    orders = df.loc[df['order_type'] == order_type, 'order_items']
    for order in orders:
        for item, quantity in order:
            menu_dict[order_type].append((item, quantity))

# Print the dictionary
print(menu_dict)

Output:

{
    'Breakfast': [('Coffee', 2), ('Eggs', 3)],
    'Lunch': [('Burger', 5), ('Fries', 6), ('Salad', 9), ('Steak', 9)],
    'Dinner': [('Shrimp', 10), ('Fish&Chips', 7)]
}

This approach organizes the menu items into a dictionary where each order_type is a key, and the associated value is the list of items and quantities.

Conclusion

By filtering the DataFrame and iterating over the order_items column, you can extract the menu items and their quantities into a more usable format, whether that’s a list or a dictionary. You can easily iterate over this cleaned-up data and perform further analysis or processing as needed.