Comparison Between SQL Joins and Python Joins

Understanding Joins in Data Science

Joins can be a challenging concept to grasp in data science, but with the right examples and explanations, they can become much clearer. Let's dive into the concept of joins by using a different example that might make it easier to understand.

What are Joins?

Imagine you are organizing a community event, and you have two separate lists: one for volunteers and one for attendees. You want to create a comprehensive list that includes all participants. This scenario can help illustrate JOINs in a real-life context.

Scenario:

1. Volunteers List: Contains columns like “Name,” “Email,” “Phone Number,” and “Role.”
2. Attendees List: Contains columns like “Name,” “Email,” “Phone Number,” and “Ticket Type.”

You need to combine these two lists into one comprehensive participant list. This is where JOINs come into play.

SQL Joins

SQL JOINS are fundamental to database management and querying, enabling the retrieval of data from multiple tables based on specified criteria. They allow us to combine related data from different tables into a single one, like in the scenario illustrated above.

Example Scenario

Suppose you have two tables in a database: one containing information about volunteers (such as volunteer ID, name, and role) and another containing details about attendees (such as attendee ID, name, and ticket type). To retrieve information about participants along with their respective roles or ticket types, you would use SQL JOINS.

Viewing the Tables

To see what both tables look like, let’s print all the columns from both tables separately and limit it to the first 5 results. You can do that using the code below:

SELECT *
FROM volunteers
LIMIT 5;

SELECT *
FROM attendees
LIMIT 5;        

Here’s what the tables look like:

A screenshot of the first 5 rows of both tables.

Components of SQL Joins

1. The tables to be joined: These tables are specified in the FROM clause of the SQL query.
2. The criteria to match rows from one table to another: This condition is specified in the ON or USING clause.
3. The type of JOIN being performed: This determines how rows are combined and what results are returned. Common types include INNER JOIN, LEFT JOIN, RIGHT JOIN, and OUTER JOIN.
4. Result Set: The output of the JOIN operation, which is a new table containing columns from both tables involved in the JOIN. The result set includes rows that meet the specified join condition.
5. Additional clauses: These can be used to further filter or manipulate the result set, such as the WHERE, ORDER BY clauses, etc.

Types of SQL Joins

There are several types of SQL JOINS, each serving a different purpose. Let’s discuss them one after the other.

INNER JOIN

This type of JOIN returns rows from both tables where there is a match based on the specified condition. It filters out rows where no match is found. Suppose we want to see participants, their roles, and their ticket types in one table, we can use an inner join to get that done.

SELECT volunteer_id, name, role, ticket_type
FROM volunteers v
INNER JOIN attendees a
ON v.email = a.email
LIMIT 10;        

LEFT JOIN

We use this when we want the query to return all rows from the left table and only rows where there is a match on the right table. Suppose you want to retrieve a list of all volunteers along with their respective ticket types, ensuring that every volunteer is included in the result set regardless of whether they have a ticket.

SELECT volunteer_id, name, role, ticket_type
FROM volunteers v
LEFT JOIN attendees a ON v.email = a.email
WHERE ticket_type IS NULL;        

RIGHT JOIN

The right join does the same thing as the left join but in reverse. We can achieve the same result as above by using the right join and switching the order of the joins.

SELECT attendee_id, name, role, ticket_type
FROM attendees a
RIGHT JOIN volunteers v ON v.email = a.email
WHERE ticket_type IS NULL;        

OUTER JOIN

Suppose you want to retrieve a comprehensive list of both volunteers and attendees, ensuring that every volunteer and every attendee are included in the result set. The columns from one table will return NULL if there is no match in the other table.

SELECT volunteer_id, name, role, ticket_type
FROM volunteers v
FULL OUTER JOIN attendees a ON v.email = a.email;        

Python Joins

Joins in Python are equally useful and effective as they are in SQL. In Python, we use joins to “merge” dataframes (Python’s version of SQL tables). This is done using the .merge() method in the pandas module.

Importing Data

First, you import your data into Python, depending on the format the data is in. For demonstration purposes, we will use the same data as above but in .csv format.

Import dependencies:

import numpy as np
import pandas as pd        

Import both tables:

# Import the volunteers table
volunteers = pd.read_csv('volunteers.csv')
# Import the attendees table
attendees = pd.read_csv('attendees.csv')        

Print the first few rows to be sure they imported correctly:

print(volunteers.head())
print(attendees.head())        

Memory updated

Understanding Joins in Data Science

Joins can be a challenging concept to grasp in data science, but with the right examples and explanations, they can become much clearer. Let's dive into the concept of joins by using a different example that might make it easier to understand.

What are Joins?

Imagine you are organizing a community event, and you have two separate lists: one for volunteers and one for attendees. You want to create a comprehensive list that includes all participants. This scenario can help illustrate JOINs in a real-life context.

Scenario:

1. Volunteers List: Contains columns like “Name,” “Email,” “Phone Number,” and “Role.”
2. Attendees List: Contains columns like “Name,” “Email,” “Phone Number,” and “Ticket Type.”

You need to combine these two lists into one comprehensive participant list. This is where JOINs come into play.

SQL Joins

SQL JOINS are fundamental to database management and querying, enabling the retrieval of data from multiple tables based on specified criteria. They allow us to combine related data from different tables into a single one, like in the scenario illustrated above.

Example Scenario

Suppose you have two tables in a database: one containing information about volunteers (such as volunteer ID, name, and role) and another containing details about attendees (such as attendee ID, name, and ticket type). To retrieve information about participants along with their respective roles or ticket types, you would use SQL JOINS.

Viewing the Tables

To see what both tables look like, let’s print all the columns from both tables separately and limit it to the first 5 results. You can do that using the code below:

sql        

Copy code

SELECT * FROM volunteers LIMIT 5; SELECT * FROM attendees LIMIT 5;

Here’s what the tables look like:

A screenshot of the first 5 rows of both tables.

Components of SQL Joins

1. The tables to be joined: These tables are specified in the FROM clause of the SQL query.
2. The criteria to match rows from one table to another: This condition is specified in the ON or USING clause.
3. The type of JOIN being performed: This determines how rows are combined and what results are returned. Common types include INNER JOIN, LEFT JOIN, RIGHT JOIN, and OUTER JOIN.
4. Result Set: The output of the JOIN operation, which is a new table containing columns from both tables involved in the JOIN. The result set includes rows that meet the specified join condition.
5. Additional clauses: These can be used to further filter or manipulate the result set, such as the WHERE, ORDER BY clauses, etc.

Types of SQL Joins

There are several types of SQL JOINS, each serving a different purpose. Let’s discuss them one after the other.

INNER JOIN

This type of JOIN returns rows from both tables where there is a match based on the specified condition. It filters out rows where no match is found. Suppose we want to see participants, their roles, and their ticket types in one table, we can use an inner join to get that done.

sql        

Copy code

SELECT volunteer_id, name, role, ticket_type FROM volunteers v INNER JOIN attendees a ON v.email = a.email LIMIT 10;

LEFT JOIN

We use this when we want the query to return all rows from the left table and only rows where there is a match on the right table. Suppose you want to retrieve a list of all volunteers along with their respective ticket types, ensuring that every volunteer is included in the result set regardless of whether they have a ticket.

sql        

Copy code

SELECT volunteer_id, name, role, ticket_type FROM volunteers v LEFT JOIN attendees a ON v.email = a.email WHERE ticket_type IS NULL;

RIGHT JOIN

The right join does the same thing as the left join but in reverse. We can achieve the same result as above by using the right join and switching the order of the joins.

sql        

Copy code

SELECT attendee_id, name, role, ticket_type FROM attendees a RIGHT JOIN volunteers v ON v.email = a.email WHERE ticket_type IS NULL;

OUTER JOIN

Suppose you want to retrieve a comprehensive list of both volunteers and attendees, ensuring that every volunteer and every attendee are included in the result set. The columns from one table will return NULL if there is no match in the other table.

sql        

Copy code

SELECT volunteer_id, name, role, ticket_type FROM volunteers v FULL OUTER JOIN attendees a ON v.email = a.email;

Python Joins

Joins in Python are equally useful and effective as they are in SQL. In Python, we use joins to “merge” dataframes (Python’s version of SQL tables). This is done using the .merge() method in the pandas module.

Importing Data

First, you import your data into Python, depending on the format the data is in. For demonstration purposes, we will use the same data as above but in .csv format.

1. Import dependencies:

python        

Copy code

import numpy as np import pandas as pd

1. Import both tables:

python        

Copy code

# Import the volunteers table volunteers = pd.read_csv('volunteers.csv') # Import the attendees table attendees = pd.read_csv('attendees.csv')

1. Print the first few rows to be sure they imported correctly:

python        

Copy code

print(volunteers.head()) print(attendees.head())

Components of Python Joins

1. The dataframes to be joined: The tables are read using the pd.read... method in the pandas module as seen above.
2. The .merge() method.
3. The criteria to match the tables on: This is specified using the on, left_on, or right_on parameters. If no column is specified, results are joined on a column common to both tables.
4. The type of JOIN to be performed: This is specified using the how parameter. The accepted arguments are inner, outer, left, and right. This is set to inner by default.
5. Additional parameters: These can be specified like left_index, right_index, suffixes, sort, validate.
6. The result: This result can be assigned a name and saved to memory for further analysis.

Types of Python Joins

Similar to SQL, we have the INNER, OUTER, LEFT, and RIGHT JOINS. They mean the same in both languages. Here is how to get these done:

INNER JOIN

# Inner Join of the volunteers and attendees tables on the email column
participants_inner = volunteers.merge(attendees, on="email", sort=True)
print(participants_inner)        

LEFT JOIN

participants_left = volunteers.merge(attendees, how='left', on="email", sort=True)
print(participants_left)        

This will return rows of null values for the columns from the right table if there are volunteers without matching attendees.

RIGHT JOIN

participants_right = volunteers.merge(attendees, how='right', on="email", sort=True)
print(participants_right)        

OUTER JOIN

participants_outer = volunteers.merge(attendees, how='outer', on="email", sort=True)
print(participants_outer)        

An outer join returns the matched rows as well as the unmatched rows of both tables.

Conclusion

Proficiency in joining tables is a foundational skill in data science and data manipulation, particularly when handling structured data. This article demonstrates that regardless of the tool, you can achieve the desired results.

We have discussed the various types of joins: inner join, outer join, left join, and right join. We illustrated the execution of these operations using the JOIN keyword in SQL and the .merge() function in Python. We also examined the differences between the JOIN operations of both languages.

Click here to get access to the files used on my GitHub. I hope you find some value in this piece of writing. If you did, feel free to send feedback; it’d be really appreciated.