Aggregate functions in SQL are super dope. When combining these functions with clauses such as `GROUP BY` and `HAVING`, we discover ways to view our data from completely new perspectives. Instead of looking at the same old endless flat table, we can use these functions to give us entirely new insights; aggregate functions help us to understand bigger-picture things. Those things might include finding outliers in datasets, or simply figuring out which employee with a family to feed should be terminated, based on some arbitrary metric such as sales numbers.

With the basics of `JOIN`s under our belts, this is when SQL starts feel really, really powerful. Our plain two-dimensional tables suddenly gain this power to be combined, aggregated, folded on to themselves, expand infinitely outward as the universe itself, and even transcend into the fourth dimension.*

*Needs citation

## Our Base Aggregation Functions

First up, let's see what we mean by "aggregate functions" anyway. These simple functions provide us with a way to mathematically quantify what exactly is in our database. Aggregate functions are performed on table columns to give us the make-up of said column. On their own, they seem quite simple:

• `AVG`: The average of a set of values in a column.
• `COUNT`: Number of rows a column contains in a specified table or view.
• `MIN`: The minimum value in a set of values.
• `MAX`: The maximum value in a set of values.
• `SUM`: The sum of values.

### DISTINCT Aggregations

A particularly useful way of using aggregate functions on their own is when we'd like to know the number of `DISTINCT` values. While aggregate values take all records into account, using `DISTINCT` limits the data returned to specifically refer to unique values. `COUNT(column_name)` will return the number of all records in a column, where `COUNT(DISTINCT column_name)` will ignore counting records where the value in the counted column is repeated.

## Using GROUP BY

The `GROUP BY` statement is often used with aggregate functions (COUNT, MAX, MIN, SUM, AVG) to group the result-set by one or more columns.

To demonstrate how aggregate functions work moving forward, I'll be using a familiar database: the database which contains all the content for this very blog. Let's get a quick preview of what we're working with:

title slug feature_image meta_title meta_description created_at updated_at published_at custom_excerpt
Welcome to Hackers and Slackers welcome-to-hackers-and-slackers /content/images/2017/11/welcome@2x.jpg Welcome to Hackers and Slackers | Hackers and Slackers Technology for badasses 2017-11-17 20:29:13 2018-07-25 02:06:02 2017-11-13 20:37:00 Technology for badasses.
Generating Tree Hierarchies with Treelib creating-trees-in-treelib /content/images/2017/11/tree7@2x.jpg Tree Hierarchies with Treelib | Hackers and Slackers Treelib is a Python library that allows you to create a visual tree hierarchy: a simple plaintext representation of parent-child relationships. 2017-11-17 20:45:10 2019-03-28 09:02:39 2017-11-17 20:56:40 Using Python to visualize file hierarchies as trees.
About the Squad about https://hackers.nyc3.cdn.digitaloceanspaces.com/posts/2017/11/welcome@2x.jpg About | Hackers and Slackers Hackers and Slackers is a community which values technology, life, and improving the latter with the former. 2017-11-17 20:58:42 2019-04-22 08:47:02 2017-11-17 20:58:46 Hackers and Slackers is a community which values technology, life, and improving the latter with the former.
Join join https://hackers.nyc3.cdn.digitaloceanspaces.com/posts/2017/11/join@2x.jpg Join | Hackers and Slackers 2017-11-17 20:59:05 2018-07-25 02:06:02 2017-11-17 21:03:06
Merge Sets of Data in Python Using Pandas merge-dataframes-with-pandas /content/images/2017/11/pandasmerge@2x.jpg Merging Dataframes with Pandas | Hackers and Slackers Perform merges of data similar to SQL JOINs using Python's Pandas library: the essential library for data analysis in Oython. 2017-11-18 00:09:32 2018-12-26 09:29:22 2017-11-18 00:22:25 Perform SQL-like merges of data using Python's Pandas.

Item 1 on our agenda: we're going to use aggregates to find which authors have been posting most frequently:

``````SELECT
COUNT(title), author_id
FROM
posts
GROUP BY author_id;
``````

And the result:

Count author_id
102 1
280 5c12c3821345c22dced9f591
17 5c12c3821345c22dced9f592
5 5c12c3821345c22dced9f593
2 5c12c3821345c22dced9f594
2 5c12c3821345c22dced9f595

Oh look, a real-life data problem to solve! It seems like authors are represented in Ghost's posts table simply by their IDs. This isn't very useful. Luckily, we've already learned enough about JOINs to know we can fill in the missing information from the users table!

``````SELECT
COUNT(posts.title),
users.name
FROM
posts
ON
(posts.author_id = users.id)
GROUP BY users.id
ORDER BY COUNT(posts.title) DESC;
``````

Let's see the results this time around:

Count author_id
280 Matthew Alhonte
102 Todd Birchard
17 Max Mileaf
2 Graham Beckley
2 David Aquino

Now that's more like it! Matt is crushing the game with his Lynx Roundup series, with myself in second place. Max had respectable numbers for a moment but has presumably moved on to other hobbies, such as living his life.

For the remainder, well, I've got nothing to say other than we're hiring. We don't pay though. In fact, there's probably zero benefits to joining us.

### Conditional Grouping With "HAVING"

`HAVING` is like the `WHERE` of aggregations. We can't use `WHERE` on aggregate values, so that's why `HAVING` exists. `HAVING` can't accept any conditional value, but instead it must accept a numerical conditional derived from a `GROUP BY`. Perhaps this would be easier to visualize in a query:

``````SELECT
tags.name,
COUNT(DISTINCT posts_tags.post_id)
FROM posts_tags
LEFT JOIN tags ON tags.id = posts_tags.tag_id
LEFT JOIN posts ON posts.id = posts_tags.post_id
GROUP BY
tags.id
HAVING
COUNT(DISTINCT posts_tags.post_id) > 10
ORDER BY
COUNT(DISTINCT posts_tags.post_id)
DESC;
``````

In this scenario, we want to see which tags on our blog have the highest number of associated posts. The query is very similar to the one we made previously, only this time we have a special guest:

``````HAVING
COUNT(DISTINCT posts_tags.post_id) > 10
``````

This usage of `HAVING` only gives us tags which have ten posts or more. This should clean up our report by letting Darwinism takes its course. Here's how it worked out:

tag Count
Roundup 263
Python 80
Machine Learning 29
DevOps 28
Data Science 28
Software Development 27
Data Engineering 23
Excel 19
SQL 18
Architecture 18
REST APIs 16
Pandas 15
Data Analysis 12
JavaScript 12
AWS 11
MySQL 11

As expected, Matt's roundup posts take the lead (and if we compare this to previous data, we can see Matt has made a total of 17 non-Lynx posts: meaning Max and Matt are officially TIED).

If we hadn't included our `HAVING` statement, this list would be much longer, filled with tags nobody cares about. Thanks to explicit omission, now we don't need to experience the dark depression that comes when confronting those sad pathetic tags. Out of sight, out of mind.

## More Aggregates

To explore some of the other aggregates, we're going to switch datasets. This time, we're going to look at wind speeds across US cities:

datetime Vancouver Portland San Francisco Seattle Los Angeles San Diego Las Vegas Phoenix Albuquerque Denver San Antonio Dallas Houston Kansas City Minneapolis Saint Louis Chicago Nashville Indianapolis Atlanta Detroit Jacksonville Charlotte Miami Pittsburgh Toronto Philadelphia New York Montreal Boston Beersheba Tel Aviv District Eilat Haifa Nahariyya Jerusalem
2012-10-01 12:00:00 8
2012-10-01 13:00:00 0 0 2 0 0 0 0 2 4 4 0 3 1 0 3 4 0 4 4 3 0 3 4 3 0 3 4 7 4 3 1 0 8 2 2 2
2012-10-01 14:00:00 0 0 2 0 0 0 0 2 4 4 0 3 1 0 3 4 0 4 4 3 0 3 4 3 0 3 4 7 4 3 3 0 8 2 2 2
2012-10-01 15:00:00 0 0 2 0 0 0 0 2 4 3 0 3 1 0 3 4 0 4 4 3 0 3 4 3 0 3 3 7 4 3 3 0 8 2 2 2
2012-10-01 16:00:00 0 0 2 0 0 0 0 2 4 3 0 3 1 0 3 3 0 4 4 3 0 3 4 3 0 3 3 7 4 3 3 0 8 2 2 2
2012-10-01 17:00:00 0 0 2 0 0 0 0 2 4 3 0 3 1 0 3 3 0 4 4 3 0 3 4 3 0 3 3 6 3 3 3 0 8 2 2 2
2012-10-01 18:00:00 0 0 2 0 0 0 0 2 4 3 0 3 2 0 3 3 0 4 4 3 0 3 4 3 0 3 3 6 3 3 3 0 8 2 2 2
2012-10-01 19:00:00 0 0 2 0 0 0 0 2 4 3 0 3 2 0 3 3 0 4 4 3 0 3 4 4 0 3 3 6 3 3 2 1 8 2 2 2
2012-10-01 20:00:00 0 0 1 0 0 0 0 1 4 3 0 3 2 0 3 3 0 4 4 3 0 3 4 4 0 3 3 6 3 3 2 1 8 2 2 2
2012-10-01 21:00:00 0 0 1 0 0 0 0 1 4 3 0 3 2 0 3 3 0 4 4 3 0 3 4 4 0 3 3 6 3 3 2 1 8 2 2 2

Let's figure our if Chicago really is the windy city, shall we?

``````SELECT
AVG(Chicago),
AVG(`San Francisco`),
AVG(`Los Angeles`),
AVG (Seattle),
AVG(`New York`),
AVG(`Boston`),
AVG(Vancouver),
AVG(Miami)
FROM
wind_speed;``````

...Aaand the results!:

AVG(Chicago) AVG(`San Francisco`) AVG(`Los Angeles`) AVG (Seattle) AVG(`New York`) AVG(`Boston`) AVG(Vancouver) AVG(Miami)
3.7593 2.7867 1.2195 2.1181 3.2110 3.3809 2.4327 3.2365

Wow, so it looks like (at first glance), Chicago really is the windiest city! I'm... not sure if I was expecting that, for some reason. Let's see the range of wind speeds in Chicago as well:

``````SELECT
AVG(Chicago),
MIN(Chicago),
MAX(Chicago)
FROM
wind_speed;``````
AVG(Chicago) MIN(Chicago) MAX(Chicago)
3.7593 0 25

Then lowest wind speed in our dataset for Chicago appears to be 0 (not shocking). On the flip side, the highest wind speed we have recorded for Chicago was 25mph! Wow! Isn't that... dangerous?

### Get Creative

Aggregate functions aren't just about counting values or finding averages. Especially in Data Science, these functions are critical to drawing any statistical conclusions from data. That said, attention spans only last so long, and I'm not a scientist. Perhaps that can be your job.