# Question 1
import pandas as pd
df = pd.read_csv('./data.csv')
df.head(10)

	transaction_id	transaction_date	transaction_time	sales_outlet_id	staff_id	customer_id	instore_yn	product_id	quantity	unit_price	promo_item_yn
0	7.0	01/04/2019	12:04:43	Oulet A	12.0	558.0	N	52.0	1.0	2.50	N
1	11.0	01/04/2019	15:54:39	Oulet A	17.0	781.0	N	27.0	2.0	3.50	N
2	19.0	01/04/2019	14:34:59	Oulet A	17.0	788.0	Y	46.0	2.0	2.50	N
3	32.0	01/04/2019	16:06:04	Oulet A	12.0	683.0	N	23.0	2.0	2.50	N
4	33.0	01/04/2019	19:18:37	Oulet A	17.0	99.0	Y	34.0	1.0	2.45	N
5	39.0	01/04/2019	18:54:46	Oulet A	17.0	664.0	Y	32.0	1.0	3.00	N
6	50.0	01/04/2019	13:03:49	Oulet A	12.0	316.0	N	49.0	2.0	3.00	N
7	53.0	01/04/2019	11:21:14	Oulet A	12.0	38.0	N	60.0	1.0	3.75	N
8	59.0	01/04/2019	19:30:55	Oulet A	12.0	370.0	Y	51.0	2.0	3.00	N
9	62.0	01/04/2019	12:01:00	Oulet A	12.0	180.0	Y	49.0	2.0	3.00	N

df.tail(10)

	transaction_id	transaction_date	transaction_time	sales_outlet_id	staff_id	customer_id	instore_yn	product_id	quantity	unit_price	promo_item_yn
389	1725.0	01/04/2019	13:58:36	Outlet G	12.0	246.0	N	23.0	1.0	2.50	N
390	1729.0	01/04/2019	15:50:25	Outlet G	17.0	541.0	N	35.0	1.0	3.10	N
391	1737.0	01/04/2019	19:00:45	Outlet G	12.0	60.0	N	60.0	2.0	3.75	N
392	1737.0	01/04/2019	19:00:45	Outlet G	12.0	60.0	N	69.0	1.0	3.25	N
393	1739.0	01/04/2019	18:32:25	Outlet G	17.0	697.0	N	52.0	2.0	2.50	N
394	1742.0	01/04/2019	18:39:29	Outlet G	17.0	678.0	N	22.0	1.0	2.00	N
395	1745.0	01/04/2019	16:58:42	Outlet G	17.0	753.0	Y	58.0	1.0	3.50	N
396	1745.0	01/04/2019	16:58:42	Outlet G	17.0	753.0	Y	69.0	1.0	3.25	N
397	1747.0	01/04/2019	11:41:37	Outlet G	12.0	42.0	Y	40.0	2.0	3.75	N
398	1749.0	01/04/2019	18:21:22	Outlet G	17.0	117.0	N	46.0	1.0	2.50	N

num_rows, num_cols = df.shape
print(f"The DataFrame has {num_rows} rows and {num_cols} columns") 

The DataFrame has 399 rows and 11 columns

# Question 2
outlet_counts = df['sales_outlet_id'].value_counts()
outlet_counts.plot(kind = 'bar', title = 'Sales by Outlet', xlabel = 'Sales Outlet', ylabel = 'Count')

<Axes: title={'center': 'Sales by Outlet'}, xlabel='Sales Outlet', ylabel='Count'>

# find the minimum and maximum transaction_id values
min_id = df['transaction_id'].min()
max_id = df['transaction_id'].max()

print(f"The minimum transaction_id is {min_id: .2f} and the maximum transaction_id is {max_id: .2f}")

min_customer_id = df['customer_id'].min()
max_customer_id = df['customer_id'].max()
average_customer_id = df['customer_id'].mean()

print(f"The minimum customer_id is {min_customer_id: .2f}")
print(f"The maximum customer_id is {max_customer_id: .2f}")
print(f"the average customer_id is {average_customer_id: .2f}")

# count the number of occurrences of each unique value in the column
yn_counts = df['instore_yn'].value_counts()

# plot the resulting Series as a pie chart
yn_counts.plot(kind= 'pie', title = 'In-Store Sales Proportion', legend = True)

The minimum transaction_id is  7.00 and the maximum transaction_id is  1749.00
The minimum customer_id is  2.00
The maximum customer_id is  796.00
the average customer_id is  380.92





<Axes: title={'center': 'In-Store Sales Proportion'}, ylabel='instore_yn'>

#Question 3

# Create a new column called unit_price_corrected
df['unit_price_corrected'] = df['unit_price']

# Add 1.50 to unit_price for the first 100 items
df.loc[df.index < 100, 'unit_price_corrected'] += 1.50

# Subtract 1.50 from unit_price for the remaining items
df.loc[df.index >= 100, 'unit_price_corrected'] -= 1.50

# Calculate average unit prices
average_unit_price = df['unit_price'].mean()
average_unit_price_corrected = df['unit_price_corrected'].mean()

# Print the results
print(f"Average unit price: {average_unit_price:.2f}")
print(f"Average corrected unit price: {average_unit_price_corrected:.2f}")

Average unit price: 3.16
Average corrected unit price: 2.41

# Question 4
# calculate the revenue for each transaction
df['revenue'] = df['unit_price_corrected'] * df['quantity']

# group the dataframe by sales_outlet_id and calculate the total revenue for each outlet
outlet_revenue = df.groupby('sales_outlet_id')['revenue'].sum()

# print the outlet revenue
outlet_revenue.plot(xlabel = 'Sales Outlet', ylabel = 'Total Revenue', title = 'Total Revenue by Sales Outlet', kind = 'line')

<Axes: title={'center': 'Total Revenue by Sales Outlet'}, xlabel='Sales Outlet', ylabel='Total Revenue'>

# Question 5
# Calculate the revenue and product units for each transaction
df['revenue'] = df['unit_price_corrected'] * df['quantity']
df['product_units'] = df['quantity']

# Group the dataframe by staff_id and calculate the total revenue and product units sold for each staff member
staff_sales = df.groupby('staff_id')[['revenue', 'product_units']].sum()

# Plot the total product units sold by staff
staff_sales.plot(kind='bar', y='product_units', xlabel='Staff ID', ylabel='Total Product Units', title='Total Product Units Sold by Staff', legend=True)

# Plot the total revenue sold by staff
staff_sales.plot(kind='bar', y='revenue', xlabel='Staff ID', ylabel='Total Revenue', title='Total Revenue Sold by Staff', legend=True)

<Axes: title={'center': 'Total Revenue Sold by Staff'}, xlabel='Staff ID', ylabel='Total Revenue'>

# Question 6
# Create a bar chart mapping 'quantity' with 'product_id'
outlet_quantity = df.groupby('sales_outlet_id')['quantity'].sum()
outlet_quantity.plot(xlabel = 'Sales Outlet', ylabel = 'Quantity', kind = 'bar', title= "Quantity by each outlet")

<Axes: title={'center': 'Quantity by each outlet'}, xlabel='Sales Outlet', ylabel='Quantity'>

# Question 7
df['unit_price_corrected'].plot(title = 'Unit price corrected Range', kind= 'line', xlabel= 'Index', ylabel ='Unit price corrected')

<Axes: title={'center': 'Unit price corrected Range'}, xlabel='Index', ylabel='Unit price corrected'>

# Part 2
def cafe_management_system():
    menu_items = []
    items_price = {}
    items_quantity = {}
    items_rating = {}

    # Allow the café admin to enter the menu items
    while True:
        item = input("Enter a menu item (or 'quit' to stop): ")
        if item.lower() == "quit":
            break
        menu_items.append(item)

    # Create dictionary for menu item prices
    for item in menu_items:
        price = float(input(f"Enter the price for {item}: "))
        items_price[item] = price

    # Create dictionary for menu item quantities
    for item in menu_items:
        quantity = int(input(f"Enter the quantity for {item}: "))
        items_quantity[item] = quantity

    # Create dictionary for menu item ratings
    for item in menu_items:
        rating = int(input(f"Enter the rating for {item} (1-5): "))
        items_rating[item] = rating

    # Return data structures
    return items_price, items_quantity, items_rating

# Call the function and store the returned data structures
items_price, items_quantity, items_rating = cafe_management_system()

# Create a dictionary that includes all entries
all_entries = {
    "price": items_price,
    "quantity": items_quantity,
    "rating": items_rating
}

# Create a list of items with satisfaction of 3 or higher
satisfied_items = [item for item, rating in items_rating.items() if rating >= 3]

# Create a list of items with price above 10
high_price_items = [item for item, price in items_price.items() if price > 10]

# Create a list of items with quantity less than 5
few_items = [item for item, quantity in items_quantity.items() if quantity < 5]

# Print the results
print("All Entries:", all_entries)
print("Satisfied Items:", satisfied_items)
print("High Price Items:", high_price_items)
print("Few Items:", few_items)

Enter a menu item (or 'quit' to stop): capachino
Enter a menu item (or 'quit' to stop): espresso
Enter a menu item (or 'quit' to stop): three milk cake
Enter a menu item (or 'quit' to stop): latte
Enter a menu item (or 'quit' to stop): black coffee
Enter a menu item (or 'quit' to stop): cold coffee
Enter a menu item (or 'quit' to stop): hot coffee
Enter a menu item (or 'quit' to stop): quit
Enter the price for capachino: 50
Enter the price for espresso: 8
Enter the price for three milk cake: 9
Enter the price for latte: 18
Enter the price for black coffee: 5
Enter the price for cold coffee: 9
Enter the price for hot coffee: 4
Enter the quantity for capachino: 2
Enter the quantity for espresso: 40
Enter the quantity for three milk cake: 39
Enter the quantity for latte: 21
Enter the quantity for black coffee: 23
Enter the quantity for cold coffee: 43
Enter the quantity for hot coffee: 23
Enter the rating for capachino (1-5): 5
Enter the rating for espresso (1-5): 4
Enter the rating for three milk cake (1-5): 3
Enter the rating for latte (1-5): 4
Enter the rating for black coffee (1-5): 2
Enter the rating for cold coffee (1-5): 3
Enter the rating for hot coffee (1-5): 5
All Entries: {'price': {'capachino': 50.0, 'espresso': 8.0, 'three milk cake': 9.0, 'latte': 18.0, 'black coffee': 5.0, 'cold coffee': 9.0, 'hot coffee': 4.0}, 'quantity': {'capachino': 2, 'espresso': 40, 'three milk cake': 39, 'latte': 21, 'black coffee': 23, 'cold coffee': 43, 'hot coffee': 23}, 'rating': {'capachino': 5, 'espresso': 4, 'three milk cake': 3, 'latte': 4, 'black coffee': 2, 'cold coffee': 3, 'hot coffee': 5}}
Satisfied Items: ['capachino', 'espresso', 'three milk cake', 'latte', 'cold coffee', 'hot coffee']
High Price Items: ['capachino', 'latte']
Few Items: ['capachino']