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    Joins In Pandas

    Master the art of performing joins in pandas. In this blog you will learn about different types of joins and how to perform them in pandas.

    Abhishek Sharma — Published On February 27, 2020 and Last Modified On June 16th, 2022

    Beginner Data Exploration Programming Python Structured Data

    Introduction to Joins in Pandas

    “I have two different tables in Python but I’m not sure how to join them. What criteria should I consider? What are the different ways I can join these tables?”

    Sound familiar? I have come across this question plenty of times on online discussion forums. Working with one table is fairly straightforward but things become challenging when we have data spread across two or more tables.

    This is where the concept of Joins comes in. I cannot emphasize the number of times I have used these Joins in Pandas! They’ve come in especially handy during data science hackathons when I needed to quickly join multiple tables.

    We will learn about different types of Joins in Pandas here:

    Inner Join in Pandas

    Full Join in Pandas Left Join in Pandas

    Right Join in Pandas

    We will also discuss how to handle redundancy or duplicate values using joins in Pandas. Let’s begin!

    Note: If you’re new to the world of Pandas and Python, I recommend taking the below free courses:

    Pandas for Data Analysis in Python

    Python for Data Science

    Looking to learn SQL joins? We have you covered! Head over here to learn all about SQL joins.

     

    Understanding the Problem Statement

    I’m sure you’re quite familiar with e-commerce sites like Amazon and Flipkart these days. We are bombarded by their advertisements when we’re visiting non-related websites – that’s the power of targeted marketing!

    We’ll take a simple problem from a related marketing brand here. We are given two tables – one which contains data about products and the other that has customer-level information.

    We will use these tables to understand how the different types of joins work using Pandas.

     

    Inner Join in Pandas

    Inner join is the most common type of join you’ll be working with. It returns a dataframe with only those rows that have common characteristics.

    An inner join requires each row in the two joined dataframes to have matching column values. This is similar to the intersection of two sets.

    Let’s start by importing the Pandas library:

    import pandas as pd

    For this tutorial, we have two dataframes – product and customer. The product dataframe contains product details like Product_ID, Product_name, Category, Price, and Seller_City. The customer dataframe contains details like id, name, age, Product_ID, Purchased_Product, and City.

    Our task is to use our joining skills and generate meaningful information from the data. I encourage you to follow along with the code we’ll cover in this tutorial.

    Python Code:

    customer=pd.DataFrame({

       'id':[1,2,3,4,5,6,7,8,9],

       'name':['Olivia','Aditya','Cory','Isabell','Dominic','Tyler','Samuel','Daniel','Jeremy'],

       'age':[20,25,15,10,30,65,35,18,23],

       'Product_ID':[101,0,106,0,103,104,0,0,107],

       'Purchased_Product':['Watch','NA','Oil','NA','Shoes','Smartphone','NA','NA','Laptop'],

       'City':['Mumbai','Delhi','Bangalore','Chennai','Chennai','Delhi','Kolkata','Delhi','Mumbai']

    })

    Let’s say we want to know about all the products sold online and who purchased them. We can get this easily using an inner join.

    The merge() function in Pandas is our friend here. By default, the merge function performs an inner join. It takes both the dataframes as arguments and the name of the column on which the join has to be performed:

    pd.merge(product,customer,on='Product_ID')

    Here, I have performed inner join on the product and customer dataframes on the ‘Product_ID’ column.

    But, what if the column names are different in the two dataframes? Then, we have to explicitly mention both the column names.

    ‘left_on’ and ‘right_on’ are two arguments through which we can achieve this. ‘left_on’ is the name of the key in the left dataframe and ‘right_on’ in the right dataframe:

    pd.merge(product,customer,left_on='Product_name',right_on='Purchased_Product')

    Let’s try the above code in the live coding window below!!

       

    Let’s take things up a notch. The leadership team now wants more details about the products sold. They want to know about all the products sold by the seller to the same city i.e., seller and customer both belong to the same city.

    स्रोत : www.analyticsvidhya.com

    SQL

    A Computer Science portal for geeks. It contains well written, well thought and well explained computer science and programming articles, quizzes and practice/competitive programming/company interview Questions.

    SQL | Join (Inner, Left, Right and Full Joins)

    Difficulty Level : Easy

    Last Updated : 15 Jul, 2022

    SQL Join statement is used to combine data or rows from two or more tables based on a common field between them. Different types of Joins are as follows:

    INNER JOIN LEFT JOIN RIGHT JOIN FULL JOIN

    Consider the two tables below:

    Student

    StudentCourse

    The simplest Join is INNER JOIN.

    A. INNER JOIN

    The INNER JOIN keyword selects all rows from both the tables as long as the condition is satisfied. This keyword will create the result-set by combining all rows from both the tables where the condition satisfies i.e value of the common field will be the same.

    Syntax:

    SELECT table1.column1,table1.column2,table2.column1,....

    FROM table1 INNER JOIN table2

    ON table1.matching_column = table2.matching_column;

    table1: First table.table2: Second tablematching_column: Column common to both the tables.Note: We can also write JOIN instead of INNER JOIN. JOIN is same as INNER JOIN.

    Example Queries(INNER JOIN)

    This query will show the names and age of students enrolled in different courses.

    SELECT StudentCourse.COURSE_ID, Student.NAME, Student.AGE FROM Student

    INNER JOIN StudentCourse

    ON Student.ROLL_NO = StudentCourse.ROLL_NO;

    Output:

    B. LEFT JOIN

    This join returns all the rows of the table on the left side of the join and matches rows for the table on the right side of the join. For the rows for which there is no matching row on the right side, the result-set will contain . LEFT JOIN is also known as LEFT OUTER JOIN.

    Syntax:

    SELECT table1.column1,table1.column2,table2.column1,....

    FROM table1 LEFT JOIN table2

    ON table1.matching_column = table2.matching_column;

    table1: First table.

    table2: Second table

    matching_column: Column common to both the tables.

    Note: We can also use LEFT OUTER JOIN instead of LEFT JOIN, both are the same.

    Example Queries(LEFT JOIN):

    SELECT Student.NAME,StudentCourse.COURSE_ID

    FROM Student

    LEFT JOIN StudentCourse

    ON StudentCourse.ROLL_NO = Student.ROLL_NO;

    Output:

    C. RIGHT JOIN

    RIGHT JOIN is similar to LEFT JOIN. This join returns all the rows of the table on the right side of the join and matching rows for the table on the left side of the join. For the rows for which there is no matching row on the left side, the result-set will contain . RIGHT JOIN is also known as RIGHT OUTER JOIN.

    Syntax:

    SELECT table1.column1,table1.column2,table2.column1,....

    FROM table1 RIGHT JOIN table2

    ON table1.matching_column = table2.matching_column;

    table1: First table.

    table2: Second table

    matching_column: Column common to both the tables.

    Note: We can also use RIGHT OUTER JOIN instead of RIGHT JOIN, both are the same.

    Example Queries(RIGHT JOIN):

    SELECT Student.NAME,StudentCourse.COURSE_ID

    FROM Student

    RIGHT JOIN StudentCourse

    ON StudentCourse.ROLL_NO = Student.ROLL_NO;

    Output:

    D. FULL JOIN

    FULL JOIN creates the result-set by combining results of both LEFT JOIN and RIGHT JOIN. The result-set will contain all the rows from both tables. For the rows for which there is no matching, the result-set will contain values.

    Syntax:

    SELECT table1.column1,table1.column2,table2.column1,....

    FROM table1 FULL JOIN table2

    ON table1.matching_column = table2.matching_column;

    table1: First table.

    table2: Second table

    matching_column: Column common to both the tables.

    Example Queries(FULL JOIN):

    SELECT Student.NAME,StudentCourse.COURSE_ID

    FROM Student

    FULL JOIN StudentCourse

    ON StudentCourse.ROLL_NO = Student.ROLL_NO;

    Output:

    NAME COURSE_ID HARSH 1 PRATIK 2 RIYANKA 2 DEEP 3 SAPTARHI 1 DHANRAJ NULL ROHIT

    स्रोत : www.geeksforgeeks.org

    SQL Joins Tutorial: Cross Join, Full Outer Join, Inner Join, Left Join, and Right Join.

    SQL joins allow our relational database management systems to be, well, relational. Joins allow us to re-construct our separated database tables back into the relationships that power our applications. In this article, we'll look at each of the different join types in SQL and how to use them. Here's

    AUGUST 28, 2020 / #SQL

    SQL Joins Tutorial: Cross Join, Full Outer Join, Inner Join, Left Join, and Right Join.

    John Mosesman

    SQL joins allow our relational database management systems to be, well, relational.

    Joins allow us to re-construct our separated database tables back into the relationships that power our applications.

    In this article, we'll look at each of the different join types in SQL and how to use them.

    Here's what we'll cover:

    What is a join?

    Setting up your database

    CROSS JOIN

    Setting up our example data (directors and movies)

    FULL OUTER JOIN INNER JOIN

    LEFT JOIN / RIGHT JOIN

    Filtering using LEFT JOIN

    Multiple joins

    Joins with extra conditions

    The reality about writing queries with joins

    (Spoiler alert: we'll cover five different types—but you really only need to know two of them!)

    What is a join?

    A join is an operation that combines two rows together into one row.

    These rows are usually from two different tables—but they don't have to be.

    Before we look at how to write the join itself, let's look at what the result of a join would look like.

    Let's take for example a system that stores information about users and their addresses.

    The rows from the table that stores user information might look like this:

    id | name | email | age

    ----+--------------+---------------------+-----

    1 | John Smith | [email protected] | 25

    2 | Jane Doe | [email protected] | 28

    3 | Xavier Wills | [email protected] | 3

    ... (7 rows)

    And the rows from the table that stores address information might look like this:

    id | street | city | state | user_id

    ----+-------------------+---------------+-------+---------

    1 | 1234 Main Street | Oklahoma City | OK | 1

    2 | 4444 Broadway Ave | Oklahoma City | OK | 2

    3 | 5678 Party Ln | Tulsa | OK | 3

    (3 rows)

    We could write separate queries to retrieve both the user information and the address information—but ideally we could write one query and receive all of the users and their addresses in the same result set.

    This is exactly what a join lets us do!

    We'll look at how to write these joins soon, but if we joined our user information to our address information we could get a result like this:

    id | name | email | age | id | street | city | state | user_id

    ----+--------------+---------------------+-----+----+-------------------+---------------+-------+---------

    1 | John Smith | [email protected] | 25 | 1 | 1234 Main Street | Oklahoma City | OK | 1

    2 | Jane Doe | [email protected] | 28 | 2 | 4444 Broadway Ave | Oklahoma City | OK | 2

    3 | Xavier Wills | [email protected] | 35 | 3 | 5678 Party Ln | Tulsa | OK | 3

    (3 rows)

    Here we see all of our users and their addresses in one nice result set.

    Besides producing a combined result set, another important use of joins is to pull extra information into our query that we can filter against.

    For example, if we wanted to send some physical mail to all users who live in Oklahoma City, we could use this joined-together result set and filter based on the city column.

    Now that we know the purpose of a joins—let's start writing some!

    Setting up your database

    Before we can write our queries we need to setup our database.

    For these examples we'll be using PostgreSQL, but the queries and concepts shown here will easily translate to any other modern database system (like MySQL, SQL Server, etc.).

    To work with our PostgreSQL database, we can use psql—the interactive PostgreSQL command line program. If you have another database client that you enjoy working with that's fine too.

    To begin, let's create our database. With PostgreSQL already installed, we can run the command createdb at our terminal to create a new database. I called mine fcc:Next let's start the interactive console by using the command psql and connect to the database we just made using \c

    $ createdb fcc

    :

    $ psql psql (11.5)

    Type "help" for help.

    john=# \c fcc

    You are now connected to database "fcc" as user "john".

    fcc=#

    Note: I've cleaned up the psql output in these examples to make it easier to read, so don't worry if the output shown here isn't exactly what you've seen in your terminal.

    I encourage you to follow along with these examples and run these queries for yourself. You will learn and remember far more by working through these examples rather than just reading them.

    Now onto the joins!

    CROSS JOIN

    The simplest kind of join we can do is a CROSS JOIN or "Cartesian product."

    This join takes each row from one table and joins it with each row of the other table.

    If we had two lists—one containing 1, 2, 3 and the other containing A, B, C—the Cartesian product of those two lists would be this:

    1A, 1B, 1C 2A, 2B, 2C 3A, 3B, 3C

    Each value from the first list is paired with each value of the second list.

    Let's write this same example as a SQL query.

    First let's create two very simple tables and insert some data into them:

    स्रोत : www.freecodecamp.org

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