Combining tables is a key component of data analysis. And SQL is really good at it! Of course each case is different, but I’ve run into analytical tasks too many times in which joining two (very big) data tables took around 20-30 minutes in Python and bash – and ~10-20 seconds in SQL. I’m not saying I couldn’t have done the task in Python or bash at all… But for sure SQL JOIN was the easiest solution!
So let’s learn how to use SQL JOIN to step up your analytics projects!
Note: to get the most out of this article, you should not just read it, but actually do the coding part with me! So if you are on the phone, I suggest saving this article and continuing on your computer!
But before we start…
… I highly recommend going through these articles first – if you haven’t done so yet:
- Set up your own data server to practice: How to install Python, SQL, R and Bash (for non-devs)
- Install SQL Workbench to manage your SQL stuff better: How to install SQL Workbench for postgreSQL
- SQL for Data Analysis ep1 (SQL basics)
- SQL for Data Analysis ep2 (SQL WHERE clause)
- SQL for Data Analysis ep3 (SQL functions and GROUP BY)
- SQL for Data Analysis ep4 (SQL best practices)
What is SQL JOIN?
Okay, back to SQL JOIN!
What does it mean to join two tables? Here’s a simple example.
Let’s say we have these two datasets:
|Rubik’s Cube||Erno Rubik|
In table 1 we have inventors and inventions and in table 2 we have the same inventors and their original professions. Let’s say we want to see which inventions was invented by an inventor with what original profession. To see that, we have to merge the two tables, based on the column that shows up in both: inventor.
This is what SQL JOIN is good for. After joining the two tables, this is what we will get:
|Rubik’s Cube||Erno Rubik||architect|
Now we can finally see that the Rubik’s Cube was invented by an architect!
Extra (not SQL-related) task: find out what these 5 inventions have in common!
Perfect, but what does this look like in practice?
Get some data
To put SQL JOIN into practice, we will get some data first.
Open up SQL Workbench! We are gonna create two new temporary data tables: playlist and toplist. Run these two queries in your SQL Workbench one by one.
CREATE TABLE playlist ( artist VARCHAR, song VARCHAR);
CREATE TABLE toplist ( tophit VARCHAR, play INT);
Your tables have been created. Load some data into them! Run these two queries one by one in your SQL Workbench:
INSERT INTO playlist (artist,song) VALUES ('ABBA','Dancing Queen'), ('ABBA','Gimme!'), ('ABBA','The Winner Takes It All'), ('ABBA','Mamma Mia'), ('ABBA','Take a Chance On Me'), ('Tove Lo','Cool Girl'), ('Tove Lo','Stay High'), ('Tove Lo','Talking Body'), ('Tove Lo','Habits'), ('Tove Lo','True Disaster'), ('Avicii','Wake Me Up'), ('Avicii','Waiting For Love'), ('Avicii','The Nights'), ('Avicii','Hey Brother'), ('Avicii','Levels'), ('Zara Larsson','Lush Life');
INSERT INTO toplist (tophit,play) VALUES ('Dancing Queen',95145796), ('Gimme!',32785696), ('The Winner Takes It All',34458597), ('Mamma Mia',47901900), ('Take a Chance On Me',30654536), ('Cool Girl',227055115), ('Stay High',263901766), ('Talking Body',272334711), ('Habits',214685822), ('True Disaster',27028538), ('Wake Me Up',520259542), ('Waiting For Love',399906192), ('The Nights',278063930), ('Hey Brother',321270703), ('Levels',206004691), ('Despacito',519689490);
Note: This is actually real data that I pulled from Spotify when I wrote this article.
Let’s see if everything works! SELECT the full tables:
SELECT artist, song FROM playlist;
SELECT tophit, play FROM toplist;
Note: Why didn’t I use
SELECT *? Find out in my SQL best practices article!
Ah, cool, some nice songs from nice artists, that people have played a lot already. Can you figure out what the listed songs and artists have in common?(Actually there is one exception – for a reason…)
An important note: when you close SQL Workbench, these temporary data tables will disappear. If you do so, you have to CREATE them again and then INSERT data into them!
SQL JOIN – basics
Anyway, let’s just perform our first SQL JOIN!
SELECT * FROM toplist JOIN playlist ON tophit = song;
First of all, take a look at the results!
We have four columns, two plus two from the two tables. The values in the
tophit columns are the same. In fact, that was the column that we have joined on, so this is not a big surprise. But hey, we have just merged two tables!
Let’s see what has happened code-wise:
SELECT * –» We want to select every column…
FROM toplist –» …from the toplist data table…
JOIN playlist –» …but we also want to merge the playlist table to the the toplist table…
ON tophit = song; –» …and we want to connect those lines, where the value of the tophit column is matching with the value of the song column.
Not that complicated. Yet!
Now there is one important question here…
If you haven’t seen it yet: there is one song (“Zara Larsson – Lush Life”) that exists in the playlist table, but not in the toplist table. And there is another one (“Despacito, 519689490”) that exists in the toplist table, but not in the playlist table. I did this on purpose, because I wanted to demonstrate what happens when you have to deal with partially missing data, which actually happens quite often in real data projects.
But what happens with these songs after our SQL JOIN clause?
When you use the default SQL JOIN, your query will manage your data like this:
As you can see on the Venn diagram: this basic type of SQL JOIN keeps only the data that shows up in both tables. So Despacito and Zara Larsson were removed.
Note: this method is called sometimes “INNER JOIN”.
SQL JOIN – same query, better syntax
Remember that in the SQL best practices article I suggested not to use * in a SELECT statement, but the columns’ name instead. And it’s even more important when you use SQL JOIN! So instead of this query…
SELECT * FROM toplist JOIN playlist ON tophit = song;
…I recommend using this one:
SELECT tophit, play, artist, song FROM toplist JOIN playlist ON tophit = song;
And this is still not the best solution. The problem is that when you look at this code, you can’t instantly see what columns belong to which table. Thus I like to add the table names also to the column names. It’s really simple. E.g. if the
tophit column is in the
toplist table, then instead of
tophit, I’ll write:
If you apply this to the above query:
SELECT toplist.tophit, toplist.play, playlist.artist, playlist.song FROM toplist JOIN playlist ON toplist.tophit = playlist.song;
Much better. But one last small tweak! The
playlist.song and the
toplist.tophit columns are actually the same. We don’t need both of them… so remove one:
SELECT toplist.tophit, toplist.play, playlist.artist FROM toplist JOIN playlist ON toplist.tophit = playlist.song;
Now this is finally a pretty decent SQL JOIN.
But you might ask: ”What should we do to keep Despacito and Zara Larsson in the data set, even though they don’t show up in both data tables?” And I’d answer: great question! To continue with the visualization, this is what we want to achieve:
This SQL JOIN is called FULL JOIN, and to execute it, you should change only one tiny thing in our previous query: add
FULL before the JOIN clause.
SELECT toplist.tophit, toplist.play, playlist.artist, playlist.song FROM toplist FULL JOIN playlist ON toplist.tophit = playlist.song;
Boom! The magic has happened!
As you can see, Zara Larsson and Despacito are there, but on the joint fields they don’t have data, hence those fields stay empty. These empty fields are called NULLs in SQL. I have already mentioned “NULL” and its importance – but I’ll get back to that in more detail later!
However, now you know: just the fact that a given value doesn’t exist in both data tables you want to join doesn’t mean that you can’t join every line. You can, but if you do so, the missing values will stay empty.
LEFT JOIN AND RIGHT JOIN
And this brings us right to the next question. What if we want to apply the FULL JOIN thinking only on one of the tables?
Back to the visualization! Either this…
… or this:
Before I reveal the (by the way, very simple) solution, I’d like to emphasize that this problem occurs quite often in real data projects too.
E.g. let’s say,you are running an A/B test in which you have 2 data tables.
|user||feature usage timestamp|
You want to include only 80% of your audience (user1, user2, user3, user4) in your A/B test, and you put these users into a table with the info about which bucket they belong to.
And you have another table that works as a feature log (see more here: data collection) and collects the feature usage for all users (user1, user2, user3, user4 and even user5).
To evaluate your A/B test, you have to combine the two tables. And you want to keep all users from the first table (even if they didn’t use the feature, ergo didn’t show up in the other table at all), but you don’t want to keep the users who showed up only in the second table (used the feature, but not part of the A/B test).
What do you do?
This is called LEFT JOIN. If you want to perform a LEFT JOIN, you simply have to add a
LEFT to your JOIN clause.
Getting back to our playlist+toplist data sets, try something like this:
SELECT toplist.tophit, toplist.play, playlist.artist FROM toplist LEFT JOIN playlist ON toplist.tophit = playlist.song;
It keeps every line from the
toplist table even if it doesn’t exist in the
playlist table. But it keeps only those lines from the
playlist table that exist in the
toplist table too. Good!
If you want to execute the opposite, you should do a RIGHT JOIN instead:
SELECT toplist.tophit, toplist.play, playlist.artist FROM toplist RIGHT JOIN playlist ON toplist.tophit = playlist.song;
Test yourself #1
You now know everything you have to know at this point about SQL JOIN! But the best way of learning is practicing! Here’s an analysis, that you should perform on our playlist and toplist tables:
Given the information in the
toplist data tables:
How many plays does each artist have in total?
Here’s my solution:
SELECT playlist.artist, SUM(toplist.play) FROM playlist FULL JOIN toplist ON playlist.song = toplist.tophit GROUP BY artist;
And a little explanation:
SELECT –» We select…
playlist.artist, –» the artists from the playlist table…
SUM(toplist.play) –» and we summarize the number of plays from the toplist table…
FROM playlist –» we actually specify that we will use the playlist table…
FULL JOIN toplist –» and we also want to merge the toplist table (using the empty fields too)…
ON playlist.song = toplist.tophit –» the join matches those lines where the song and tophit columns have the same data…
GROUP BY artist; –» and GROUP BY refers to the SUM function, above – we want to see the sums by artist.
Test yourself #2
One more test, before I let you go!
Print the top 5 ABBA songs ordered by number of plays!
And the solution is:
SELECT playlist.artist, playlist.song, toplist.play FROM playlist FULL JOIN toplist ON playlist.song = toplist.tophit WHERE playlist.artist = 'ABBA' ORDER BY toplist.play DESC;
Well, nothing new here. 😉 But if you need an explanation, just let me know in the comment section and I’ll give you one!
JOIN is really important and you will use it quite often as a data analyst or scientist. This article has given you a solid base of knowledge. Further down the road you will meet even more advanced applications, but using what you have learned from this article – combined with what you have learned from the previous ones – will cover most cases for now.
And there is only one more article left from my SQL for Data Analysis – Tutorial for Beginners series. In that I’ll introduce some advanced methods: the most interesting one will be the query-in-a-query, but I’ll also show you two more exciting clauses:
CASE! Here it is: Advanced SQL.
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