Join Operations in SQL with Python

Join Operations in SQL

Data often resides in multiple tables in databases, and combining this data effectively is crucial for meaningful insights. SQL join operations are useful in this situation. Rows from two or more tables can be combined by using a common column. In this blog, we’ll explore join operations using an example in Python with MySQL, explained in a professional yet approachable tone.

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Creating Tables

Let’s begin by creating a new table, Departments, alongside an existing Employee table.

CREATE TABLE Departments (
    Dept_id INT(20) PRIMARY KEY NOT NULL,
    Dept_Name VARCHAR(20) NOT NULL
);

Now, insert some values into the Departments table:

INSERT INTO Departments VALUES (201, 'CS');
INSERT INTO Departments VALUES (202, 'IT');

Similarly, ensure the Employee table contains relevant records with a column Dept_id to match the department details.

Join Operations in Action

Inner Join

The Inner Join retrieves records matching a condition in both tables. Here, we’ll fetch employee details along with their department names using the common column Dept_id.

Python Code for Inner Join

import mysql.connector

# Create the connection object
myconn = mysql.connector.connect(
    host="localhost", user="root", passwd="yourpassword", database="PythonDB"
)

# Create the cursor object
cur = myconn.cursor()

try:
    # Join query
    cur.execute("""
        SELECT Employee.id, Employee.name, Employee.salary, 
               Departments.Dept_id, Departments.Dept_Name 
        FROM Departments 
        JOIN Employee ON Departments.Dept_id = Employee.Dept_id
    """)
    print("ID    Name    Salary    Dept_Id    Dept_Name")
    for row in cur:
        print(f"{row[0]}    {row[1]}    {row[2]}    {row[3]}    {row[4]}")

except Exception as e:
    print("Error:", e)
    myconn.rollback()

myconn.close()

Output:

Right Join

The Right Join includes all records from the right table (Employee) and matching records from the left table (Departments). It helps identify employees without a department.

To demonstrate, let’s add a record for an employee who doesn’t belong to any department:

INSERT INTO Employee (id, name, salary, branch_name) 
VALUES (108, 'Alex', 29900, 'Mumbai');

Python Code for Right Join

try:
    cur.execute("""
        SELECT Employee.id, Employee.name, Employee.salary, 
               Departments.Dept_id, Departments.Dept_Name 
        FROM Departments 
        RIGHT JOIN Employee ON Departments.Dept_id = Employee.Dept_id
    """)
    print("ID    Name    Salary    Dept_Id    Dept_Name")
    for row in cur:
        print(f"{row[0]}    {row[1]}    {row[2]}    {row[3]}    {row[4]}")

except Exception as e:
    print("Error:", e)
    myconn.rollback()

myconn.close()

Output:

Left Join

The Left Join includes all records from the left table (Departments) and matching records from the right table (Employee). It ensures no department data is missed.

Python Code for Left Join

try:
    cur.execute("""
        SELECT Employee.id, Employee.name, Employee.salary, 
               Departments.Dept_id, Departments.Dept_Name 
        FROM Departments 
        LEFT JOIN Employee ON Departments.Dept_id = Employee.Dept_id
    """)
    print("ID    Name    Salary    Dept_Id    Dept_Name")
    for row in cur:
        print(f"{row[0]}    {row[1]}    {row[2]}    {row[3]}    {row[4]}")

except Exception as e:
    print("Error:", e)
    myconn.rollback()

myconn.close()

Output:

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