This is the second episode of my SQL for Data Analysis (for beginners) series, and today I’ll show you every tiny little detail of the SQL WHERE clause. It’s not by accident that I’ve dedicated a whole article to this topic; the
WHERE clause is essential if you want to select the right bit of your data from your data table!
In the first half of this article I’ll show you the different operators. In the second half, we will finally import our favorite 7.000.000+ rows dataset (the one with the airplane delays). And at the end of the tutorial, I’ll give you a few assignments to test your SQL knowledge and practice a bit!
If you are new here, let’s start with these articles first:
- How to install Python, SQL, R and Bash (for non-devs)
- SQL for Data Analysis – Tutorial for Beginners – ep1
- How to install SQL Workbench for postgreSQL
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Make sure your SQL data environment works!
If you have already done the above mentioned articles, you most probably have these in place… but just in case, please double-check if you have these three things:
- Your fully-functioning data server (from this article)
- SQL Workbench with an established connection to your data server (from this article)
zoodata set (from this article)
Check-check-check? Okay, you are ready to learn new things!
SQL WHERE Clause
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 you save this article and continue on a computer!
In SQL for Data Analysis episode 1 we did this from the Terminal, but this time please re-run this query from SQL Workbench:
SELECT * FROM zoo WHERE animal = 'elephant';
This is the most basic example of how to use the SQL WHERE clause. But you can (and have to) be more sophisticated.
SQL WHERE with comparison operators
Well first of all – you can do more than “something equals something.” It can be “greater than,” “less than”,” “not equals to,” etc…
Here’s a short list of the comparison operators that work with the SQL
|comparison operator||What does it mean?|
|not equals to|
|not equals to|
Let’s see some concrete examples:
1. How would you select all the animals, that are not zebras?
SELECT * FROM zoo WHERE animal <> 'zebra';
2. How would you select all the animals for whom the
water_need is less than
SELECT * FROM zoo WHERE water_need < 300;
Okay, I think you got it!
So let’s take this to the next level!
SQL WHERE clause with LIKE operator
Sometimes you want to do your selection based on a pattern. Take a look at this fictional data set:
Let’s say, you want to select all the rows where the
user_name starts with ‘
Now it’s time to boost your
WHERE clause with a
SELECT * FROM franks WHERE user_name LIKE 'frank%';
How does it work?
LIKE logical operator is just like the
!= — so it’s a comparsion operator. But it tells SQL that you want to filter results based on a pattern — and not on an exact value. Once you typed
LIKE you specify what the pattern is exactly. Let’s say it’s
'frank%'! Do you see anything tricky here? Yepp, it’s the
% character – which in this case means that any string of characters (of any length) can follow
Hence your result will be:
To hammer this home, let’s go back to our zoo dataset and try to answer these questions:
1. How would you select all animals whose name contains at least one
SELECT * FROM zoo WHERE animal LIKE '%e%';
It needed a
% at the beginning and also at the end of the pattern, because the ‘
e‘ character could be anywhere in the word, not just at the end or the beginning. (If you don’t get it, try removing the first
% and you will understand it immediately!)
2. How would you select all animals whose name ends with ‘
SELECT * FROM zoo WHERE animal LIKE '%roo';
There is another type of tricky character:
_. (Oh, by the way, from now on I’ll just use its official name: wildcard.)
%means any string of characters of any length.
_means any character you want, but only one.
E.g. the word
tiger can be found using wildcards like
't%'. But you can use
Knowing this trick, here’s another short question:
3. How would you select all animals whose species name is exactly five characters long?
SELECT * FROM zoo WHERE animal LIKE '_____';
This is how the SQL
WHERE clause works with the
How to combine different conditions in an SQL WHERE clause
You can combine multiple conditions — using logical operators! There are more, but here I’ll highlight the two most important:
Let’s say we want to select all the animals whose name is exactly five characters long, except for
tigers. We have two conditions and we can do that with SQL
WHERE boosted with the
AND logical operator.
SELECT * FROM zoo WHERE animal LIKE '_____' AND animal <> 'tiger';
Nice, zebras only!
Now twist it a bit and select only those animals, that:
- have a name exactly five characters long
- are not
- have a
SELECT * FROM zoo WHERE animal LIKE '_____' AND animal <> 'tiger' AND water_need > 200;
Yes: you can use multiple
Finally, let’s see an example of the
SELECT * FROM zoo WHERE water_need < 300 OR animal = 'lion';
It returns all the animals that are lions, plus all the animals that have less than
ANDreturns every row where all the conditions are true.
ORreturns every row where at least one of the conditions is true.
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The SQL IN operator
Imagine a situation where you want to select all the animals whose unique id is any of these:
You have already learned a way to do that! Can you find it out?
It’s the SQL WHERE clause with multiple
OR operators. Something like this:
SELECT * FROM zoo WHERE uniq_id = 1001 OR uniq_id = 1008 OR uniq_id = 1012 OR uniq_id = 1015 OR uniq_id = 1018;
While it’s a valid solution, it’s certainly not the most elegant way to get the job done! If you want to reduce the number of
ORs in your SQL WHERE clause, you should use the
IN operator instead.
This SQL query returns the exact same result as the previous one:
SELECT * FROM zoo WHERE uniq_id IN (1001,1008,1012,1015,1018);
It’s a little bit shorter — so it’s much nicer. (You know, when it gets to coding, we don’t like to repeat things.)
The SQL NOT operator
There is an extra logical operator, which is by the way extremely simple.
NOT will modify your logical operator to do its opposite instead. Let’s get back to a previous example with the 5 characters long animals:
SELECT * FROM zoo WHERE animal LIKE '_____';
How to select all the animals that are not 5 characters long?
SELECT * FROM zoo WHERE animal NOT LIKE '_____';
Simple as that.
Import a bigger dataset into postgreSQL
Are you bored with our 22-row
zoo dataset? Me too! It’s time to go a bit bigger! Let’s import the 7.000.000+ row air-delays dataset we have used before in the bash tutorials! Follow these steps to have it imported into your SQL database!
UPDATE: I’ve realized that this might be a bit complex – not difficult, just complex – so I put all the instructions into a short video too! Click here or read below:
Note: we will work in bash. If you haven’t done my bash tutorial series yet, I highly recommend doing at least the first episode, but if you don’t want to, it’s also okay to simply follow my lead step by step below.
- Open Terminal and login (
ssh) to your data server!
- Download the
Note: If you have this already, skip forward to 5.As of 29 December 2019, I realized that the dataset has been removed from its original place. Now, you can download the dataset using this code instead:
- Set up
dtrx! That’s a command line tool for unzipping stuff!
(Note: this might have been already set up; if so, skip this step!)
sudo apt-get install dtrx
- Unzip the .csv file!
Note: It will take around ~60 seconds to process the whole file, so don’t worry, your Terminal is not freezing, it just needs some time.
- Format your data!
cat 2007.csv |cut -d',' -f1,2,3,4,5,7,10,11,14,15,16,17,18,19 | grep -v ',NA' > sql_ready.csv
- Now we have to give permission to our postgreSQL user to create tables and load data into them. This will need multiple steps. Here’s a gif first (note: my username is
dataguy– yours might be something else).
sudoto the user called
sudo -u postgres -i
Then start postgreSQL:
The prompt will change to this:
ALTER USER [your_user_name] WITH SUPERUSER;
This turns your original user into a super user.
Exiting from postgreSQL. Type:
Then exit from the user called
Finally access your original user’s postgreSQL database from the command line:
psql -d postgres
Okay, this was the hard part…
- Now all you need to do is create the table by simply copy-pasting these lines into your terminal:
CREATE TABLE flight_delays ( year INTEGER, month INTEGER, dayofmonth INTEGER, dayofweek INTEGER, deptime INTEGER, arrtime INTEGER, flightnum INTEGER, tailnum VARCHAR, airtime INTEGER, arrdelay INTEGER, depdelay INTEGER, origin VARCHAR, dest VARCHAR, distance INTEGER);
- And finally, copy the data from the .csv file you have just downloaded!
COPY flight_delays FROM '/home/tomi/sql_ready.csv' DELIMITER ',' CSV HEADER;
Note: make sure that you type your user name where I’ve typed
dataguy(which are my user names…)
- Go back to SQL Workbench and make a simple
SELECTstatement… but make sure that you use the
LIMITclause, too. Why?
Because now you have over 7.000.000 rows of data. PostgreSQL can handle it easily, sure, but your computer might be frozen if you try print all that data on your screen.
So try something like this first:
SELECT * FROM flight_delays LIMIT 10;
Test yourself #1
Here’s assignment number one:
How many flights did the plane with the tail-number
N253WN take on 23 April, 2007?
(Hint: as we haven’t learned the
COUNT function yet, it’s enough if you print all the flights with the given conditions and count the lines for yourself.)
Done? Here’s my solution:
SELECT * FROM flight_delays WHERE month = 4 AND year = 2007 AND dayofmonth = 23 AND tailnum = 'N253WN';
The only trick is that the day, the month and the year are in different columns, so you have to use multiple
AND operators to filter simultaneously for all the conditions.
(Note: since the
dayofmonth fields are integers, you don’t need apostrophes. But you need it for the
tailnum field, because it contains letters too. I’ll get back to this topic later.)
Test yourself #2
Select all the rows with these conditions:
- the flight was in April 2007
- it was an even weekday (2nd, 4th or 6th day of the week)
- the flight distance was less than 50 miles
- either the departure or the arrival delay was less than 0 (means the plane took off or landed earlier than planned)
(Hint: you will have to use
AND operators together!)
And my solution is:
SELECT * FROM flight_delays WHERE month = 4 AND dayofweek IN (2,4,6) AND distance < 50 AND (arrdelay < 0 OR depdelay < 0);
Can you see the trick here? In SQL WHERE clauses
AND is stronger by default than
OR – thus if you don’t use the parentheses, the execution order of the different filters would be like this:
WHERE (month = 4 AND dayofweek IN (2,4,6) AND distance < 50 AND arrdelay < 0) OR (depdelay < 0);
And that would have returned wrong results! Take-away is: always use parentheses when adding multiple conditions in your
WHERE clause — so SQL can priortize the right filters.
Knowing, understanding and using SQL WHERE clauses with the right operators is crucial. In this article you have learned the most important parts of it.
Now – having a good base knowledge – it’s time to continue with the next important SQL topic! In my next article, I’ll introduce the basic SQL functions (
COUNT) and I’ll show you some more important clauses (
You can continue by clicking here.
- If you want to learn more about how to become a data scientist, take my 50-minute video course: How to Become a Data Scientist. (It’s free!)
- Also check out my 6-week online course: The Junior Data Scientist’s First Month video course.
- http://stat-computing.org/dataexpo/ — shout out for the awesome dataset!