11.5. Aggregation or Group By¶
One very powerful feature of SQL is that it allows us to create summary information by grouping rows together. For example, we could ask ourselves how many bike trips were taken for each subscriber type, and which subscriber type has the most bike trips.
GROUP BY member_type
takes all the rows with a given member_type and produces a
single row in the result. This means that we need to tell SQL how we want to combine the
other columns’ values into a single row. The above example uses COUNT(*)
which
reports the number of rows that were combined.
Aggregating the values for member_type
is not hard, since they are all the same, SQL
just gives us a single copy of the publisher name. Other columns, we need to either
ignore (causing them to be omitted from the output) or specify a way to aggregate them.
We must specify an aggregate function for any column that we SELECT
in our query
(except the column that we’re grouping by) in order for the command to succeed. If we
don’t specify a way to aggregate the value most database servers will complain.
However, SQLITE does not. SQLite lets you do silly things without giving you an error.
For example, the following query will work, but you have no idea what the results
actually mean.
Here you have grouped by member_type
, but without member_type
in the select
clause you have no idea which rows correspond to which member type. That is why most
databases will flag this as a error. Furthermore the duration field may be the first
duration in the group or maybe the last duration in the group or possibly in between,
But it’s not defined. The best practices for writing group by queries that work well
across database systems are as follows:
Always include the GROUP BY column(s) in your SELECT clause.
If you include a column that is not in the GROUP BY clause in your SELECT clause you must do some form of aggregation on the values in that column. For example, min, max, mean, count, etc.
Let’s go back briefly to the first query in the Aggregation section. The top result was
the count of bike trips for member_type Member
:
member_type |
COUNT(*) |
---|---|
Member |
979814 |
If you’d like to get a more granular break down of the count, you may specify multiple
columns to aggregate within the GROUP BY
clause, for example: further breakdown the
aggregate count by the start station IDs:
Great! Now that you’re familiar with how to aggregate data using SQL query by using
COUNT()
as your aggregation function, let’ take a look at other aggregation
functions.
There are many such functions. Some common ones include:
SUM
: To add the values togetherAVG
: To compute the mean of the valuesMIN
orMAX
: To compute the minimum and maximum respectively
So we could for example compute the total number of minutes of all bike trips for all subscriber types