Adeyeye ayomide

README.md

@@ -130,6 +130,6 @@ python main.py
 
 - Python 3.x
 - PostgreSQL (recommended for Month 2)
-- Any text editor or IDE (VSCode, PyCharm, etc.)
+- VSCode
 
 Happy coding! 🎉

month1_student_management/student_data.py

@@ -1,20 +1,35 @@
 students = []
 
 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 = int(input('Enter student age:  '))
+    grade = float(input('Enter student grade: '))
+
+    student = {
+        'student_name' : name,
+        'age' : age,
+        'grade' : grade
+    }
 
+    students.append(student)
+
+
+    # TODO: Prompt the user to enter student name, age, and grade.
+    # Append the student as a dictionary to the students list.
+
 def view_students():
-    """
-    TODO: Loop through the students list and print each student's info.
-    """
-    pass
+    print('======Student Profiles=====')
+    for student in students:
+        print(f"Student name: {student['name']} \n Age: {student['age']} \n grade: {student['grade']}")
+
+
+    # TODO: Loop through the students list and print each student's info.
+
+
 
 def get_average_grade():
-    """
-    TODO: Return the average grade of all students.
-    """
-    pass
+   total_grade = sum(student['grade'] for student in students)
+   avg_grade = total_grade/len(students)
+
+
+    # TODO: Return the average grade of all students.

month2_minimart_analysis/python/customers.csv

@@ -0,0 +1,11 @@
+"customer_id","name","email","join_date"
+1,Michael Salazar,woneal@example.net,2023-12-28
+2,Rodney Mckinney,lukehernandez@example.org,2024-11-15
+3,Stephanie Edwards,tiffanydunn@example.com,2024-03-06
+4,David Dunn,lindasmith@example.com,2024-09-20
+5,Betty Cook,savageandrea@example.org,2023-12-08
+6,Crystal Mccormick,melendeztara@example.org,2023-07-01
+7,Joy Garrett,bryan71@example.net,2025-01-18
+8,Katie Peters,epeterson@example.com,2025-03-10
+9,Michael Freeman,ctyler@example.net,2024-03-22
+10,Grace Mendoza,carl61@example.org,2023-12-06

month2_minimart_analysis/python/main.py

@@ -1 +1,25 @@
 # Entry point for the MiniMart data reporting system
+# Entry point for the student management system
+from utils import  new_order, merge_product, convert_currency
+import report_generator as rgt
+
+
+print(" MiniMart Sales System")
+
+while True:
+    print("\n1. New_orders \n2. Merge_products \n3. Convert_currency \n4. Report Generator \n5. Exit")
+    choice = input("Choose an option: ")
+
+    if choice == "1":
+        new_order()
+    elif choice == "2":
+        merge_product()
+    elif choice == "3":
+        convert_currency()
+    elif choice == "4":
+        rgt() 
+    elif choice == "5":
+        print("Goodbye!")
+        break
+    else:
+        print("Invalid option.")

month2_minimart_analysis/python/orders.csv

@@ -0,0 +1,21 @@
+"order_id","customer_id","product_id","quantity","order_date"
+1,9,1,6,2025-01-07
+2,5,5,4,2025-02-01
+3,1,2,1,2024-09-06
+4,5,2,3,2025-03-07
+5,9,3,1,2024-09-08
+6,7,5,4,2025-04-16
+7,8,4,1,2024-12-11
+8,3,5,1,2025-02-22
+9,8,4,6,2025-05-26
+10,5,3,4,2025-02-28
+11,8,4,8,2024-08-16
+12,5,5,6,2024-10-13
+13,6,3,8,2024-12-18
+14,4,5,3,2025-03-12
+15,4,1,8,2024-12-11
+16,9,5,8,2024-09-12
+17,4,2,2,2024-08-01
+18,9,4,8,2025-04-24
+19,7,5,6,2024-09-10
+20,7,4,7,2025-04-10

month2_minimart_analysis/python/products.csv

@@ -0,0 +1,6 @@
+"product_id","product_name","category","price"
+5,Military,Books,197.08
+1,TV,Electronics,228.78
+2,Lego,Toys,171.9
+3,Alchemy ,Books,482.86
+4,Business World,Books,336.73

month2_minimart_analysis/python/report_generator.py

@@ -1 +1,28 @@
 # Code to generate dictionary summary reports
+from utils import customers_df, products_df, orders_df
+import json
+
+# Total products sold
+total_sold = orders_df['quantity'].sum()
+
+# Most popular product
+merged = orders_df.merge(products_df, on='product_id')
+popular = merged.groupby('product_name')['quantity'].sum().idxmax()
+
+# Revenue per customer
+revenue_df = merged.copy()
+revenue_df['revenue'] = revenue_df['price'] * revenue_df['quantity']
+revenue_per_customer = revenue_df.groupby('customer_id')['revenue'].sum().to_dict()
+
+# Report dictionary
+report = {
+    "total_products_sold": int(total_sold),
+    "most_popular_product": popular,
+    "revenue_per_customer": revenue_per_customer
+}
+
+
+# Save to JSON file
+with open("sales_report.json", "w") as f:
+    json.dump(report, f, indent=4)
+

month2_minimart_analysis/python/sales_report.json

@@ -0,0 +1,14 @@
+{
+    "total_products_sold": 95,
+    "most_popular_product": "Military",
+    "revenue_per_customer": {
+        "1": 171.9,
+        "3": 197.08,
+        "4": 2765.28,
+        "5": 4417.9400000000005,
+        "6": 3862.88,
+        "7": 4327.91,
+        "8": 5050.950000000001,
+        "9": 6126.02
+    }
+}

month2_minimart_analysis/python/utils.py

@@ -1 +1,64 @@
 # Utility functions for data conversion and filtering
+import pandas as pd
+import random
+from datetime import datetime
+
+
+# Simulate new orders using lists or dictionaries
+# Use conditionals to flag large orders (e.g., total > $100)
+# Convert product prices to another currency and apply conditional discounts
+# Generate a dictionary report including:
+# ✅ Total products sold
+# ✅ Most popular product
+# ✅ Revenue per customer
+
+
+customers_df = pd.read_csv("customers.csv")
+products_df = pd.read_csv("products.csv")
+orders_df = pd.read_csv("orders.csv")
+
+new_orders = [ ]
+
+
+## Simulate new orders using lists or dictionaries
+def new_order():
+    for _ in range(5):
+        orders = {
+            "customer_id": random.choice(customers_df['customer_id']),
+            "product_id": random.choice(products_df['product_id']),
+            "quantity": random.randint(1, 5),
+            "order_date": datetime.now().date()
+        }
+        new_orders.append(orders)
+
+    #  Save to CSV
+    new_orders_df = pd.DataFrame(new_orders)
+    new_orders_df[['customer_id', 'product_id', 'quantity', 'order_date']].to_csv(
+    "orders.csv", mode='a', index=False)
+
+
+#Convert product prices to another currency 
+EXCHANGE_RATE = 1500  # e.g., USD to NGN
+def convert_currency():
+    for order in new_orders:
+        order['total_ngn'] = order['total'] * EXCHANGE_RATE
+    # Apply 10% discount if order > 200 in USD
+    for order in new_orders:
+        if order['total'] > 200:
+                order['discount_applied'] = True
+                order['discounted_total'] = order['total'] * 0.9
+        else:
+            order['discount_applied'] = False
+            order['discounted_total'] = order['total']
+
+
+#Use conditionals to flag large orders (e.g., total > $100)
+def merge_product():    
+    for order in new_orders:
+        product_price = products_df.loc[products_df['product_id'] == order['product_id'], 'price']
+        order['total'] = order['quantity'] * product_price
+        order['is_large_order'] = order['total'] > 100
+
+
+
+

month2_minimart_analysis/sql/create_tables.sql

@@ -1 +1,24 @@
 -- SQL script to create necessary tables
+CREATE TABLE customers
+(customer_id SERIAL PRIMARY KEY,
+  name VARCHAR(125),
+  email VARCHAR(125),
+  join_date DATE)
+
+CREATE TABLE products(
+  product_id SERIAL PRIMARY KEY,
+  product_name VARCHAR(125),
+  category VARCHAR(125),
+  price INT
+  )
+
+  ALTER TABLE products 
+  ALTER COLUMN price FLOAT
+
+CREATE TABLE orders (
+  order_id SERIAL PRIMARY KEY,
+  quantity INT,
+  order_date DATE,
+  Foreign Key customer_id REFERENCES customers(customer_id), 
+  Foreign Key product_id REFERENCES products(product_id)
+)

month2_minimart_analysis/sql/customers.csv

@@ -0,0 +1,11 @@
+"customer_id","name","email","join_date"
+1,Michael Salazar,woneal@example.net,2023-12-28
+2,Rodney Mckinney,lukehernandez@example.org,2024-11-15
+3,Stephanie Edwards,tiffanydunn@example.com,2024-03-06
+4,David Dunn,lindasmith@example.com,2024-09-20
+5,Betty Cook,savageandrea@example.org,2023-12-08
+6,Crystal Mccormick,melendeztara@example.org,2023-07-01
+7,Joy Garrett,bryan71@example.net,2025-01-18
+8,Katie Peters,epeterson@example.com,2025-03-10
+9,Michael Freeman,ctyler@example.net,2024-03-22
+10,Grace Mendoza,carl61@example.org,2023-12-06

month2_minimart_analysis/sql/insert.py

@@ -0,0 +1,70 @@
+from faker import Faker
+import pandas as pd
+import random
+
+fake = Faker()
+
+
+def generate_customers(n=10):
+    data = []
+    for i in range(1, n + 1):
+        name = fake.name()
+        email = fake.email()
+        join_date = fake.date_between(start_date='-2y', end_date='today')
+        data.append({
+            "customer_id": i,
+            "name": name,
+            "email": email,
+            "join_date": join_date
+        })
+    return pd.DataFrame(data)
+
+
+def generate_products(n=5):
+    categories = ["Books", "Electronics", "Toys", "Books", "Books"]
+    data = []
+    for i in range(1, n + 1):
+        product_name = fake.word().capitalize()
+        category = random.choice(categories)
+        price = round(random.uniform(10, 500), 2)
+        data.append({
+            "product_id": i,
+            "product_name": product_name,
+            "category": category,
+            "price": price
+        })
+    return pd.DataFrame(data)
+
+
+def generate_orders(customers_df, products_df, n=20):
+    data = []
+    for i in range(1, n + 1):
+        customer_id = random.choice(customers_df["customer_id"].tolist())
+        product_id = random.choice(products_df["product_id"].tolist())
+        quantity = random.randint(1, 10)
+        order_date = fake.date_between(start_date='-1y', end_date='today')
+        data.append({
+            "order_id": i,
+            "customer_id": customer_id,
+            "product_id": product_id,
+            "quantity": quantity,
+            "order_date": order_date
+        })
+    return pd.DataFrame(data)
+
+
+customers_df = generate_customers(10)
+products_df = generate_products(5)
+orders_df = generate_orders(customers_df, products_df, 20)
+
+
+customers_df.dropna(inplace=True)
+products_df.dropna(inplace=True)
+orders_df.dropna(inplace=True)
+
+
+customers_df.to_csv("customers.csv", index=False)
+products_df.to_csv("products.csv", index=False)
+orders_df.to_csv("orders.csv", index=False)
+
+print("✅ All data generated with no NULLs and saved as CSVs.")

month2_minimart_analysis/sql/insert_data.sql

@@ -1 +1,3 @@
 -- SQL script to insert sample data
+---using the faker library, I populated the data table 
+--kindly find the attached insert.py file

month2_minimart_analysis/sql/orders.csv

@@ -0,0 +1,21 @@
+"order_id","customer_id","product_id","quantity","order_date"
+1,9,1,6,2025-01-07
+2,5,5,4,2025-02-01
+3,1,2,1,2024-09-06
+4,5,2,3,2025-03-07
+5,9,3,1,2024-09-08
+6,7,5,4,2025-04-16
+7,8,4,1,2024-12-11
+8,3,5,1,2025-02-22
+9,8,4,6,2025-05-26
+10,5,3,4,2025-02-28
+11,8,4,8,2024-08-16
+12,5,5,6,2024-10-13
+13,6,3,8,2024-12-18
+14,4,5,3,2025-03-12
+15,4,1,8,2024-12-11
+16,9,5,8,2024-09-12
+17,4,2,2,2024-08-01
+18,9,4,8,2025-04-24
+19,7,5,6,2024-09-10
+20,7,4,7,2025-04-10

month2_minimart_analysis/sql/products.csv

@@ -0,0 +1,6 @@
+"product_id","product_name","category","price"
+5,Military,Books,197.08
+1,TV,Electronics,228.78
+2,Lego,Toys,171.9
+3,Alchemy ,Books,482.86
+4,Business World,Books,336.73