Water_Quality_Monitoring

month1_student_management/student_data.py

@@ -5,16 +5,64 @@ def add_student():
     TODO: Prompt the user to enter student name, age, and grade.
     Append the student as a dictionary to the students list.
     """
-    pass
+    name = input("Enter student name: ")
+    age_input = input("Enter student age: ")
+    grade_input = input("Enter student grade: ")
+
+    try:
+        age = int(age_input)
+        grade = float(grade_input)
+    except ValueError:
+        print("Invalid input. Age must be an integer and grade must be a number.")
+        return
+
+    student = {"name": name, "age": age, "grade": grade}
+    students.append(student)
+    print(f"{name} added successfully!\n")
+
 
 def view_students():
     """
     TODO: Loop through the students list and print each student's info.
     """
-    pass
+    if not students:
+        print("No students in the list.\n")
+        return
+
+    for i, student in enumerate(students, start=1):
+        print(f"{i}. Name: {student['name']}, Age: {student['age']}, Grade: {student['grade']}")
+    print()  # blank line
+
 
 def get_average_grade():
     """
     TODO: Return the average grade of all students.
     """
-    pass
+    if not students:
+        print("No students to calculate average.\n")
+        return 0
+
+    total = sum(student["grade"] for student in students)
+    average = total / len(students)
+    return average
+
+# Menu loop
+if __name__ == "__main__":
+    while True:
+        print("1. Add student")
+        print("2. View students")
+        print("3. Get average grade")
+        print("4. Exit")
+        choice = input("Choose an option: ")
+
+        if choice == "1":
+            add_student()
+        elif choice == "2":
+            view_students()
+        elif choice == "3":
+            avg = get_average_grade()
+            print(f"Average grade: {avg}\n")
+        elif choice == "4":
+            break
+        else:
+            print("Invalid option. Try again.\n")

month2_minimart_analysis/python/main.py

@@ -1 +1,46 @@
 # Entry point for the MiniMart data reporting system
+from utils import load_data, apply_discount, convert_currency, flag_large_order
+from report_generator import generate_report
+import pandas as pd
+import json
+
+def main():
+    # Load base data
+    customers, products, orders = load_data()
+
+    # ✅ Simulate new orders using a list of dicts
+    new_orders = [
+        {"order_id": 6, "customer_id": 2, "product_id": 1, "quantity": 3},
+        {"order_id": 7, "customer_id": 3, "product_id": 2, "quantity": 5},
+        {"order_id": 8, "customer_id": 1, "product_id": 3, "quantity": 10},
+    ]
+    new_orders_df = pd.DataFrame(new_orders)
+
+    # Merge new orders into existing ones
+    orders = pd.concat([orders, new_orders_df], ignore_index=True)
+
+    # ✅ Apply discounts and currency conversion on products
+    products["discounted_price"] = products["price"].apply(apply_discount)
+    products["price_in_kes"] = products["discounted_price"].apply(convert_currency)
+
+    # ✅ Flag large orders (> $100 before conversion)
+    orders_with_prices = orders.merge(products, on="product_id")
+    orders_with_prices["order_total"] = orders_with_prices["discounted_price"] * orders_with_prices["quantity"]
+    orders_with_prices["large_order"] = orders_with_prices["order_total"].apply(flag_large_order)
+
+    # Generate report
+    report = generate_report(customers, products, orders)
+
+    # Print results
+    print("==== MiniMart Report ====")
+    for key, value in report.items():
+        print(f"{key}: {value}")
+
+    # Save report to JSON
+    with open("report.json", "w") as f:
+        json.dump(report, f, indent=4)
+
+    print("\n✅ Report saved to report.json")
+
+if __name__ == "__main__":
+    main()

month2_minimart_analysis/python/report.json

@@ -0,0 +1,11 @@
+{
+    "total_products_sold": 30,
+    "most_popular_product": "Bread Loaf",
+    "revenue_per_customer": {
+        "1": 23.0,
+        "2": 6.5,
+        "3": 17.5,
+        "4": 5.6,
+        "5": 3.6
+    }
+}

month2_minimart_analysis/python/report_generator.py

@@ -1 +1,26 @@
 # Code to generate dictionary summary reports
+def generate_report(customers, products, orders):
+    """Generate a dictionary report with key metrics."""
+
+    # Merge orders with products
+    orders_with_prices = orders.merge(products, on="product_id")
+    orders_with_prices["revenue"] = orders_with_prices["price"] * orders_with_prices["quantity"]
+
+    # ✅ Total products sold
+    total_products_sold = int(orders["quantity"].sum())
+
+    # ✅ Most popular product
+    popular_product_id = orders.groupby("product_id")["quantity"].sum().idxmax()
+    popular_product = products.loc[products["product_id"] == popular_product_id, "product_name"].values[0]
+
+    # ✅ Revenue per customer
+    revenue_per_customer = (
+        orders_with_prices.groupby("customer_id")["revenue"].sum().to_dict()
+    )
+
+    report = {
+        "total_products_sold": total_products_sold,
+        "most_popular_product": popular_product,
+        "revenue_per_customer": revenue_per_customer
+    }
+    return report

month2_minimart_analysis/python/utils.py

@@ -1 +1,21 @@
 # Utility functions for data conversion and filtering
+import pandas as pd
+
+def load_data():
+    """Load customers, products, and orders CSVs from sql folder."""
+    customers = pd.read_csv("../sql/customers_data.csv")
+    products = pd.read_csv("../sql/products_data.csv")
+    orders = pd.read_csv("../sql/orders_data.csv")
+    return customers, products, orders
+
+def apply_discount(price, threshold=50, discount=0.1):
+    """Apply discount if price exceeds threshold."""
+    return price * (1 - discount) if price > threshold else price
+
+def convert_currency(price, rate=150):
+    """Convert product price to another currency (e.g., USD→KES)."""
+    return price * rate
+
+def flag_large_order(total, threshold=100):
+    """Flag orders exceeding a given total threshold."""
+    return total > threshold

month2_minimart_analysis/sql/create_tables.sql

@@ -1 +1,38 @@
+-- create databse first
+CREATE DATABASE minimart;
+USE minimart;
+
 -- SQL script to create necessary tables
+-- Customers table
+CREATE TABLE customers (
+    customer_id SERIAL PRIMARY KEY,
+    name VARCHAR(100),
+    email VARCHAR(100)
+);
+
+-- Products table
+CREATE TABLE products (
+    product_id SERIAL PRIMARY KEY,
+    product_name VARCHAR(100),
+    price DECIMAL(10,2)
+);
+
+-- Orders table
+CREATE TABLE orders (
+    order_id SERIAL PRIMARY KEY,
+    customer_id INT,
+    order_date DATE,
+    FOREIGN KEY (customer_id) REFERENCES customers(customer_id)
+);
+
+-- Order details table (to link orders and products)
+CREATE TABLE order_details (
+    detail_id SERIAL PRIMARY KEY,
+    order_id INT,
+    product_id INT,
+    quantity INT,
+    FOREIGN KEY (order_id) REFERENCES orders(order_id),
+    FOREIGN KEY (product_id) REFERENCES products(product_id)
+);
+
+

month2_minimart_analysis/sql/customers_data.csv

@@ -0,0 +1,6 @@
+customer_id,name,email,join_date
+1,"Alice Johnson",alice.johnson@example.com,2024-01-15
+2,"Brian Smith",brian.smith@example.com,2024-02-10
+3,"Catherine Lee",catherine.lee@example.com,2024-03-05
+4,"David Kim",david.kim@example.com,2024-03-20
+5,"Emily Davis",emily.davis@example.com,2024-04-01

month2_minimart_analysis/sql/insert_data.sql

@@ -1 +1,23 @@
 -- SQL script to insert sample data
+INSERT INTO customers (name, email, join_date) VALUES
+('Alice Johnson', 'alice.johnson@example.com', '2024-01-15'),
+('Brian Smith', 'brian.smith@example.com', '2024-02-10'),
+('Catherine Lee', 'catherine.lee@example.com', '2024-03-05'),
+('David Kim', 'david.kim@example.com', '2024-03-20'),
+('Emily Davis', 'emily.davis@example.com', '2024-04-01');
+
+INSERT INTO products (product_name, category, price) VALUES
+('Coca Cola 500ml', 'Drinks', 1.50),
+('Pepsi 500ml', 'Drinks', 1.40),
+('Bread Loaf', 'Bakery', 2.00),
+('Apples 1kg', 'Fruits', 3.50),
+('Milk 1L', 'Dairy', 1.80);
+
+
+INSERT INTO orders (customer_id, product_id, quantity, order_date) VALUES
+(1, 1, 2, '2024-04-05'),  -- Alice buys 2 Coca Cola
+(2, 3, 1, '2024-04-06'),  -- Brian buys 1 Bread
+(3, 4, 3, '2024-04-06'),  -- Catherine buys 3 kg Apples
+(4, 2, 4, '2024-04-07'),  -- David buys 4 Pepsi
+(5, 5, 2, '2024-04-07');  -- Emily buys 2 Milk
+

month2_minimart_analysis/sql/orders_data.csv

@@ -0,0 +1,6 @@
+order_id,customer_id,product_id,quantity,order_date
+4,4,2,4,2024-04-07
+5,5,5,2,2024-04-07
+2,2,3,1,2024-04-06
+3,3,4,3,2024-04-06
+1,1,1,2,2024-04-05

month2_minimart_analysis/sql/products_data.csv

@@ -0,0 +1,6 @@
+product_id,product_name,category,price
+1,"Coca Cola 500ml",Drinks,1.50
+2,"Pepsi 500ml",Drinks,1.40
+3,"Bread Loaf",Bakery,2.00
+4,"Apples 1kg",Fruits,3.50
+5,"Milk 1L",Dairy,1.80

month2_minimart_analysis/sql/queries.sql

@@ -1 +1,58 @@
 -- SQL queries for retrieving insights
+SELECT * 
+FROM customers;
+
+SELECT * 
+FROM products;
+
+SELECT product_id, product_name, price
+FROM products
+WHERE category = 'Drinks';
+
+SELECT p.product_name,
+       SUM(p.price * o.quantity) AS total_revenue
+FROM orders o
+JOIN products p ON o.product_id = p.product_id
+GROUP BY p.product_name;
+
+SELECT AVG(price) AS average_price
+FROM products;
+
+
+SELECT * 
+FROM orders
+ORDER BY order_date DESC;
+
+SELECT COUNT(*) AS total_orders
+FROM orders;
+
+SELECT o.order_id,
+       c.name AS customer_name,
+       c.email,
+       p.product_name,
+       p.category,
+       o.quantity,
+       o.order_date,
+       (p.price * o.quantity) AS total_price
+FROM orders o
+INNER JOIN customers c ON o.customer_id = c.customer_id
+INNER JOIN products p ON o.product_id = p.product_id;
+
+SELECT c.customer_id,
+       c.name,
+       c.email,
+       o.order_id,
+       o.order_date,
+       o.quantity
+FROM customers c
+LEFT JOIN orders o ON c.customer_id = o.customer_id;
+
+-- left join for all data 
+SELECT p.product_id,
+       p.product_name,
+       p.category,
+       p.price,
+       o.order_id,
+       o.quantity
+FROM products p
+LEFT JOIN orders o ON p.product_id = o.product_id;