# Queries with multiple tables¶

Now that we have more than one table in our data pipeline, you can perform more exciting queries! In this section, we’ll take a look at how we can form queries using multiple tables, and we’ll look at a few new exciting query operators as well!

## Restriction by other table¶

In Querying and fetching data, we have seen how you can restrict a table by strings and/or dictionaries to narrow down the query results, returning only the entries that you are interested in. For example, you can find all male mice with:

>> tutorial.Mouse & 'sex="M"'

ans =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID        dob         sex
________    ____________    ___

0           '2017-03-01'    'M'
1           '2016-11-19'    'M'

2 tuples (0.0254 s)


It turns out that we can also use another table to restrict a table in a query! Consider the Session table that we created in Defining dependent tables.

 >> tutorial.Session

ans =

Object tutorial.Session

:: experiment session ::

MOUSE_ID    SESSION_DATE    experiment_setup     experimenter
________    ____________    ________________    ______________

0         '2017-05-15'    0                   'Edgar Walker'
0         '2017-05-19'    0                   'Edgar Walker'
5         '2017-01-05'    1                   'Fabian Sinz'
100         '2017-05-25'    1                   'Jake Reimer'

4 tuples (0.0172 s)


Note

Notice that we have added a few more entries into the Session table using insert methods covered in Populating the table. Go ahead and add more entries into the table before proceeding.

We can ask questions like “What are all the mice that I have at least done at least one experiment on?” by restricting a table with another table:

 >> tutorial.Mouse & tutorial.Session

ans =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID        dob         sex
________    ____________    ___

0         '2017-03-01'    'M'
5         '2016-12-25'    'F'
100         '2017-05-12'    'F'

3 tuples (0.0379 s)


and there it goes! Restricting a table A with another table B asin A & B returns all entries in A with corresponding entries in B!

### Combining restrictions¶

We can now combine multiple restrictions to get more complex queries intuitively! Let’s take a look at a few examples:

Q. Give me all sessions recorded for male mice

A. We can first get all male mice, and then get sessions corresponding to them.

>> male_mice = tutorial.Mouse & 'sex = "M"'  % get all male mice

male_mice =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID        dob         sex
________    ____________    ___

0           '2017-03-01'    'M'
1           '2016-11-19'    'M'

2 tuples (0.0159 s)

>> tutorial.Session & male_mice

ans =

Object tutorial.Session

:: experiment session ::

MOUSE_ID    SESSION_DATE    experiment_setup     experimenter
________    ____________    ________________    ______________

0           '2017-05-15'    0                   'Edgar Walker'
0           '2017-05-19'    0                   'Edgar Walker'

2 tuples (0.0336 s)


or you could have combine this into one statement as in:

>> tutorial.Session & (tutorial.Mouse & 'sex = "M"')

ans =

Object tutorial.Session

:: experiment session ::

MOUSE_ID    SESSION_DATE    experiment_setup     experimenter
________    ____________    ________________    ______________

0           '2017-05-15'    0                   'Edgar Walker'
0           '2017-05-19'    0                   'Edgar Walker'

2 tuples (0.0286 s)


As you get used to the DataJoint queries, you will quickly learn to read above queries as “all sessions for male mice”!

Q. Give me all mice that have had an experimental session done on or after 2017-05-19

A. Again we can break this into parts first - get all sessions done on or after 2017-05-19 and then find all mice corresponding to those sessions. Or simply do it in one statement:

>> tutorial.Mouse & (tutorial.Session & 'session_date >= "2017-05-19"')

ans =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID        dob         sex
________    ____________    ___

0         '2017-03-01'    'M'
100         '2017-05-12'    'F'

2 tuples (0.0264 s)


Q. I want to know all female mice that have an experiment performed before 2017-05-20

A. This hs more parts but you can again break things up in a couple of way. For example, you can first look for all female mice and then restrict by all sessions performed before 2017-05-20. Or, once again you could express the query in a single line without losing much readability.

>> tutorial.Mouse & 'sex="F"' & (tutorial.Session & 'session_date<"2017-05-20"')

ans =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID       dob        sex
________    __________    ___

5           2016-12-25    F

1 tuples (0.0345 s)


Let’s now take a look at one more example.

Q. I want all mouse that has no experiment sessions performed on it yet.

A. Aha! This is one example that would be rather difficult to obtain with what we know already, but there is an operator just for this - the difference operator -

## Difference Operator -¶

Sometimes you want to find entries that do not have any matching entries in another table. Well, DataJoint has an operator just for that! You can use the difference - operator to find entries that does not have a corresponding entry in another table! Let’s see how we can use this to find “all mice without experiement session”

>> tutorial.Mouse - tutorial.Session

ans =

Object tutorial.Mouse

:: mouse class ::

MOUSE_ID        dob         sex
________    ____________    ___

1          '2016-11-19'    'M'
2          '2016-11-20'    'U'
10          '2017-01-01'    'F'
11          '2017-01-03'    'F'

4 tuples (0.0396 s)


and that’s it! When you say A - B, you find all entries in A that does not have a corresponding entries in B. The difference operator comes in really handy when you want to look for things like missing entries just like we did.

Note

Unlike restriction &, the difference operator - only works on tables. If you want to negate the condition when restricting by a string (e.g. sex = "M"), simply the negate the statement itself (e.g. sex != "M").

We will wrap up this section by covering one more of the basic but very powerful query operator - join *.

## Join operator *¶

When working with multiple tables (as in the case of Mouse and Session), you would sometimes wish to combine these tables into one so that you have all information together. Well, there is an operator for that! The join operator * allows you to combine two tables by matching corresponding entries, returning you one table that has all columns from both tables. Let’s take a look at an example:

>> tutorial.Mouse * tutorial.Session

ans =

Object dj.GeneralRelvar

MOUSE_ID    SESSION_DATE        dob         sex    experiment_setup     experimenter
________    ____________    ____________    ___    ________________    ______________

0         '2017-05-15'    '2017-03-01'    'M'    0                   'Edgar Walker'
0         '2017-05-19'    '2017-03-01'    'M'    0                   'Edgar Walker'
5         '2017-01-05'    '2016-12-25'    'F'    1                   'Fabian Sinz'
100         '2017-05-25'    '2017-05-12'    'F'    1                   'Jake Reimer'

4 tuples (0.0653 s)


Notice that you are returne a single table with columns from both the Mouse and the Session table, giving you all the information you want! You might have noticed that this does not list all mice, however.

Why is that? This is because join * only combines the matching entries from the two tables. Because some mice did not have matching entries in the Session table (e.g. mouse_id = 2), it was left out from the join results. This policy ensures that the returned joined table will not have any missing entries.

You can easily use the result of the join in further queries. For example, we can find all mouse-session combination for male mice with experiment session performed on or after 2017-05-19:

>> tutorial.Mouse * tutorial.Session & 'sex="M"' & 'session_date >="2017-05-19"'

ans =

Object dj.GeneralRelvar

MOUSE_ID    SESSION_DATE       dob        sex    experiment_setup    experimenter
________    ____________    __________    ___    ________________    ____________

0           2017-05-19      2017-03-01    M      0                     [1x12 char]

1 tuples (0.0439 s)


Notice how we were able to use attributes from both Mouse (sex) and Session (session_date) together.

## What’s next?¶

Phew! That was a lot of material but hopefully you saw how you can form powerful queries using DataJoint’s intuitive query language! Go ahead and spend some more time playing with the queries and see if you can come up with queries to answer any question you can ask about your data! In the next section we will look into building a table that can load external data automatically!