1+ Below is a complete Python solution using a class with methods to solve the problem with Pandas . This code creates the data (mimicking the SQL tables ), then provides methods to run each query .
2+ 3+ `` `python
4+ import pandas as pd
5+ 6+ class ProsnalDatabase :
7+ def __init__ (self ):
8+ # Create tables (as Pandas DataFrames)
9+ self .create_tables ()
10+ 11+ def create_tables (self ):
12+ # Employees Table
13+ self .employees = pd .DataFrame ({
14+ 'Id' : [1 , 2 , 3 , 4 , 5 ],
15+ 'Name' : ['John Doe' , 'Jane Smith' , 'Mike Johnson' , 'Sarah Black' , 'David White' ],
16+ 'Salary' : [50000 , 60000 , 70000 , 60000 , 70000 ],
17+ 'Department' : ['HR' , 'Finance' , 'IT' , 'Finance' , 'IT' ]
18+ })
19+ 20+ # Projects Table
21+ self .projects = pd .DataFrame ({
22+ 'Id' : [1 , 2 , 3 ],
23+ 'Name' : ['Project A' , 'Project B' , 'Project C' ],
24+ 'Department' : ['IT' , 'Finance' , 'IT' ]
25+ })
26+ 27+ # EmployeeProjects Table
28+ self .employee_projects = pd .DataFrame ({
29+ 'EmployeeId' : [1 , 1 , 2 , 3 , 3 , 4 , 5 ],
30+ 'ProjectId' : [1 , 2 , 1 , 1 , 3 , 2 , 1 ]
31+ })
32+ 33+ # Users Table
34+ self .users = pd .DataFrame ({
35+ 'id' : [1 , 2 ],
36+ 'name' : ['John Doe' , 'Jane Smith' ],
37+ 'email' : ['john.doe@example.com' , 'antima@example.com' ]
38+ })
39+ 40+ # Sessions Table
41+ self .sessions = pd .DataFrame ({
42+ 'id' : [1 , 2 , 3 ],
43+ 'user_id' : [1 , 1 , 2 ],
44+ 'session_date' : pd .to_datetime (['2022年01月01日' , '2022年01月02日' , '2022年01月03日' ])
45+ })
46+ 47+ # Products Table
48+ self .products = pd .DataFrame ({
49+ 'id' : [1 , 2 , 3 ],
50+ 'name' : ['Product A' , 'Product B' , 'Product C' ],
51+ 'price' : [10.99 , 20.99 , 30.99 ]
52+ })
53+ 54+ # Orders Table
55+ self .orders = pd .DataFrame ({
56+ 'id' : [1 , 2 , 3 , 4 , 5 ],
57+ 'product_id' : [1 , 2 , 3 , 1 , 2 ],
58+ 'quantity' : [5 , 3 , 1 , 2 , 4 ]
59+ })
60+ 61+ def query_employee_projects (self ):
62+ """
63+ Returns a DataFrame that lists each employee's project details.
64+ This joins the Employees, EmployeeProjects, and Projects DataFrames.
65+ """
66+ # Merge Employees with EmployeeProjects
67+ emp_proj = self .employees .merge (
68+ self .employee_projects , left_on = 'Id' , right_on = 'EmployeeId'
69+ )
70+ # Merge with Projects to get project details
71+ emp_proj = emp_proj .merge (
72+ self .projects , left_on = 'ProjectId' , right_on = 'Id' , suffixes = ('_Employee' , '_Project' )
73+ )
74+ # Select and rename desired columns
75+ result = emp_proj [['Name_Employee' , 'Department_Employee' , 'Name_Project' , 'Department_Project' ]]
76+ result = result .rename (columns = {
77+ 'Name_Employee' : 'EmployeeName' ,
78+ 'Department_Employee' : 'EmployeeDepartment' ,
79+ 'Name_Project' : 'ProjectName' ,
80+ 'Department_Project' : 'ProjectDepartment'
81+ })
82+ return result
83+ 84+ def query_user_sessions (self ):
85+ """
86+ Returns a DataFrame that lists the session dates for each user.
87+ This joins the Users and Sessions DataFrames.
88+ """
89+ user_sessions = self .users .merge (
90+ self .sessions , left_on = 'id' , right_on = 'user_id'
91+ )
92+ result = user_sessions [['name' , 'session_date' ]].rename (columns = {'name' : 'user_name' })
93+ return result
94+ 95+ def query_order_totals (self ):
96+ """
97+ Returns a DataFrame that calculates the total price for each order.
98+ This joins the Products and Orders DataFrames and computes total price = price * quantity.
99+ """
100+ orders_merged = self .orders .merge (
101+ self .products , left_on = 'product_id' , right_on = 'id'
102+ )
103+ orders_merged ['total_price' ] = orders_merged ['price' ] * orders_merged ['quantity' ]
104+ result = orders_merged [['name' , 'quantity' , 'total_price' ]].rename (columns = {'name' : 'product_name' })
105+ return result
106+ 107+ # Example usage:
108+ if __name__ == '__main__' :
109+ db = ProsnalDatabase ()
110+ 111+ print ("Employee Projects:" )
112+ print (db .query_employee_projects (), "\n " )
113+ 114+ print ("User Sessions:" )
115+ print (db .query_user_sessions (), "\n " )
116+ 117+ print ("Order Totals:" )
118+ print (db .query_order_totals ())
119+ `` `
120+
121+ ### Explanation:
122+ - **Class Initialization (` __init__ `)** :
123+ The `ProsnalDatabase` class initializes by calling `create_tables()` , which creates sample DataFrames for all tables .
124+ 125+ - * * `create_tables()` ** :
126+ This method creates DataFrames for `Employees` , `Projects` , `EmployeeProjects` , `Users` , `Sessions` , `Products` , and `Orders` with sample data .
127+ 128+ - * * Query Methods ** :
129+ - * * `query_employee_projects()` ** :
130+ Merges the `employees` , `employee_projects` , and `projects` DataFrames to show which employee works on which project . Columns are renamed for clarity .
131+ 132+ - * * `query_user_sessions()` ** :
133+ Merges the `users` and `sessions` DataFrames to list session dates for each user .
134+ 135+ - * * `query_order_totals()` ** :
136+ Merges the `orders` DataFrame with the `products` DataFrame and calculates the total price for each order .
137+ 138+ - * * Main Block ** :
139+ An instance of `ProsnalDatabase` is created , and the query methods are executed to display results .
140+ 141+ This modular , class - based approach using Pandas helps encapsulate data and query logic in a single , easy - to - manage unit . Let me know if you need any further modifications !
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