sqlmodel/docs/tutorial/where.md

# Filter Data - WHERE

In the previous chapter we saw how to `SELECT` data from the database.

We did it using pure **SQL** and using **SQLModel**.

But we always got all the rows, the whole table:

<table>
<tr>
<th>id</th><th>name</th><th>secret_name</th><th>age</th>
</tr>
<tr>
<td>1</td><td>Deadpond</td><td>Dive Wilson</td><td>null</td>
</tr>
<tr>
<td>2</td><td>Spider-Boy</td><td>Pedro Parqueador</td><td>null</td>
</tr>
<tr>
<td>3</td><td>Rusty-Man</td><td>Tommy Sharp</td><td>48</td>
</tr>
</table>

In most of the cases we will want to get only one row, or only a group of rows.

We will see how to do that now, to filter data and get only the rows **where** a condition is true.

## Continue From Previous Code

We'll continue with the same examples we have been using in the previous chapters to create and select data.

And now we will update `select_heroes()` to filter the data.

<details>
<summary>👀 Full file preview</summary>

```Python hl_lines="36-41"
{!./docs_src/tutorial/select/tutorial001.py!}
```

</details>

If you already executed the previous examples and have a database with data, **remove the database file** before running each example, that way you won't have duplicate data and you will be able to get the same results.

## Filter Data with SQL

Let's check first how to filter data with **SQL** using the `WHERE` keyword.

```SQL hl_lines="3"
SELECT id, name, secret_name, age
FROM hero
WHERE name = "Deadpond"
```

The first part means the same as before:

> Hey SQL database 👋, please go and `SELECT` some data for me.
>
> I'll first tell you the columns I want:
>
> * `id`
> * `name`
> * `secret_name`
> * `age`
>
> And I want you to get them `FROM` the table called `"hero"`.

Then the `WHERE` keyword adds the following:

> So, SQL database, I already told you what columns to `SELECT` and where to select them `FROM`.
> But I don't want you to bring me all the rows, I only want the rows `WHERE` the `name` column has a value of `"Deadpond"`.

Then the database will bring a table like this:

<table>
<tr>
<th>id</th><th>name</th><th>secret_name</th><th>age</th>
</tr>
<tr>
<td>1</td><td>Deadpond</td><td>Dive Wilson</td><td>null</td>
</tr>
</table>

!!! tip
    Even if the result is only one row, the database always returns a **table**.

    In this case, a table with only one row.

You can try that out in **DB Browser for SQLite**:

<img class="shadow" src="/img/tutorial/where/image01.png">

### `WHERE` and `FROM` are "clauses"

These additional keywords with some sections like `WHERE` and `FROM` that go after `SELECT` (or others) have a technical name, they are called **clauses**.

There are others clauses too, with their own SQL keywords.

I won't use the term **clause** too much here, but it's good for you to know it as it will probably show up in other tutorials you could study later. 🤓

## `SELECT` and `WHERE`

Here's a quick tip that helps me think about it.

* **`SELECT`** is used to tell the SQL database what **columns** to return.
* **`WHERE`** is used to tell the SQL database what **rows** to return.

The size of the table in the two dimensions depend mostly on those two keywords.

### `SELECT` Land

If the table has too many or too few **columns**, that's changed in the **`SELECT`** part.

Starting with some table:

<table>
<tr>
<th>id</th><th>name</th><th>secret_name</th><th>age</th>
</tr>
<tr>
<td>1</td><td>Deadpond</td><td>Dive Wilson</td><td>null</td>
</tr>
<tr>
<td>2</td><td>Spider-Boy</td><td>Pedro Parqueador</td><td>null</td>
</tr>
<tr>
<td>3</td><td>Rusty-Man</td><td>Tommy Sharp</td><td>48</td>
</tr>
</table>

...and changing the number of **columns**:

<table>
<tr>
<th>name</th>
</tr>
<tr>
<td>Deadpond</td>
</tr>
<tr>
<td>Spider-Boy</td>
</tr>
<tr>
<td>Rusty-Man</td>
</tr>
</table>

...is all `SELECT` land.

### `WHERE` Land

If the table has too many or too few **rows**, that's changed in the **`WHERE`** part.

Starting with some table:

<table>
<tr>
<th>id</th><th>name</th><th>secret_name</th><th>age</th>
</tr>
<tr>
<td>1</td><td>Deadpond</td><td>Dive Wilson</td><td>null</td>
</tr>
<tr>
<td>2</td><td>Spider-Boy</td><td>Pedro Parqueador</td><td>null</td>
</tr>
<tr>
<td>3</td><td>Rusty-Man</td><td>Tommy Sharp</td><td>48</td>
</tr>
</table>

...and changing the number of **rows**:

<table>
<tr>
<th>id</th><th>name</th><th>secret_name</th><th>age</th>
</tr>
<tr>
<td>2</td><td>Spider-Boy</td><td>Pedro Parqueador</td><td>null</td>
</tr>
</table>

...is all `WHERE` land.

## Review `SELECT` with **SQLModel**

Let's review some of the code we used to read data with **SQLModel**.

We care specially about the **select** statement:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/select/tutorial001.py[ln:36-41]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/select/tutorial001.py!}
```

</details>

## Filter Rows Using `WHERE` with **SQLModel**

Now, the same way that we add `WHERE` to a SQL statement to filter rows, we can add a `.where()` to a **SQLModel** `select()` statment to filter rows, which will filter the objects returned:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial001.py[ln:36-41]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial001.py!}
```

</details>

It's a very small change, but it's packed of details. Let's explore them.

## `select()` Objects

The object returned by `select(Hero)` is a special type of object with some methods.

One of those methods is `.where()` used to (unsurprisingly) add a `WHERE` to the SQL statement in that **select** object.

There are other methods that will will explore later. 💡

Most of these methods return the same object again after modifying it.

So we could call one after the other:

```Python
statement = select(Hero).where(Hero.name == "Deadpond").where(Hero.age == 48)
```

## Calling `.where()`

Now, this `.where()` method is special and very powerful. It is tightly integrated with **SQLModel** (actually SQLAlchemy) to let you use very familiar Python syntax and code.

Notice that we didn't call it with a single equal (`=`) sign, and with something like:

```Python
# Not supported 🚨
select(Hero).where(name="Deadpond")
```

That would have been shorter, of course, but it would have been much more error prone and limited. I'll show you why in a bit.

Instead, we used two `==`:

```Python
select(Hero).where(Hero.name == "Deadpond")
```

So, what's happening there?

## `.where()` and Expressions

In the example above we are using two equal signs (`==`). That's called the "**equality operator**".

!!! tip
    An **operator** is just a symbol that is put beside one value or in the middle of two values to do something with them.

    `==` is called the **equality** operator because it checks if two things are **equal**.

When writing Python, if you write something using this equality operator (`==`) like:

```Python
some_name == "Deadpond"
```

...that's called an equality "**comparison**", and it normally results in a value of:

```Python
True
```

...or

```Python
False
```

!!! tip
    `<`, `>`, `==`, `>=`, `<=`, and `!=` are all **operators** used for **comparisons**.

But SQLAlchemy adds some magic to the columns/fields in a **model class** to make those Python comparisons have super powers.

So, if you write something like:

```Python
Hero.name == "Deadpond"
```

...that doesn't result in a value of `True` or `False`. 🤯

Instead, it results in a special type of object. If you tried that in an interactive Python session, you'd see something like:

```Python
>>> Hero.name == "Deadpond"
<sqlalchemy.sql.elements.BinaryExpression object at 0x7f4aec0d6c90>
```

So, that result value is an **expession** object. 💡

And `.where()` takes one (or more) of these **expression** objects to update the SQL statement.

## Model Class Attributes, Expressions, and Instances

Now, let's stop for a second to make a clear distinction that is very important and easy to miss.

**Model class attributes** for each of the columns/fields are special and can be used for expressions.

But that's only for the **model class attributes**. 🚨

**Instance** attributes behave like normal Python values. ✅

So, using the class (`Hero`, with capital `H`) in a Python comparison:

```Python
Hero.name == "Deadpond"
```

...results in one of those **expression** objects to be used with `.where()`:

```Python
<sqlalchemy.sql.elements.BinaryExpression object at 0x7f4aec0d6c90>
```

But if you take an instance:

```Python
some_hero = Hero(name="Deadpond", secret_name="Dive Wilson")
```

...and use it in a comparison:

```Python
some_hero.name == "Deadpond"
```

...that results in a Python value of:

```Python
True
```

...or if it was a different object with a different name, it could have been:

```Python
False
```

The difference is that one is using the **model class**, the other is using an **instance**.

## Class or Instance

It's quite probable that you will end up having some variable `hero` (with lowercase `h`) like:

```Python
hero = Hero(name="Deadpond", secret_name="Dive Wilson")
```

And now the class is `Hero` (with capital `H`) and the instance is `hero` (with a lowercase `h`).

So now you have `Hero.name` and `hero.name` that look very similar, but are two different things:

```Python
>>> Hero.name == "Deadpond"
<sqlalchemy.sql.elements.BinaryExpression object at 0x7f4aec0d6c90>

>>> hero.name == "Deadpond"
True
```

It's just something to pay attention to. 🤓

But after understanding that difference between classes and instances it can feel natural, and you can do very powerful things. 🚀

For example, as `hero.name` works like a `str` and `Hero.name` works like a special object for comparisons, you could write some code like:

```Python
select(Hero).where(Hero.name == hero.name)
```

That would mean:

> Hey SQL Database 👋, please `SELECT` all the columns
>
> `FROM` the table for the model class `Hero` (the table `"hero"`)
>
> `WHERE` the column `"name"` is equal to the name of this hero instance I have here: `hero.name` (in the example above, the value `"Deadpond"`).

## `.where()` and Expressions Instead of Keyword Arguments

Now, let me tell you why I think that for this use case of interacting with SQL databases it's better to have these expressions:

```Python
# Expression ✨
select(Hero).where(Hero.name == "Deadpond")
```

...instead of keyword arguments like this:

```Python
# Not supported, keyword argument 🚨
select(Hero).where(name="Deadpond")
```

Of course, the keyword arguments would have been a bit shorter.

But with the **expressions** your editor can help you a lot with autocompletion and inline error checks. ✨

Let me give you an example. Let's imagine that keword arguments were supported in SQLModel and you wanted to filter using the secret identity of Spider-Boy.

You could write:

```Python
# Don't copy this 🚨
select(Hero).where(secret_identity="Pedro Parqueador")
```

The editor would see the code, and because it doesn't have any information of which keyword arguments are allowed and which not, it would have no way to help you **detect the error**.

Maybe your code could even run and seem like it's all fine, and then some months later you would be wondering why your app *never finds rows* although you were sure that there was one `"Pedro Parqueador"`. 😱

And maybe finally you would realize that we wrote the code using `secret_identity` which is not a column in the table. We should have written `secret_name` instead.

Now, with the the expressions, your editor would show you an error right away if you tried this:

```Python
# Expression ✨
select(Hero).where(Hero.secret_identity == "Pedro Parqueador")
```

Even better, it would autocomplete the correct one for you, to get:

```Python
select(Hero).where(Hero.secret_name == "Pedro Parqueador")
```

I think that alone, having better editor support, autocompletion, and inline errors, is enough to make it worth having expressions instead of keyword arguments. ✨

!!! tip
    **Expressions** also provide more features for other types of comparisons, shown down below. 👇

## Exec the Statement

Now that we know how `.where()` works, let's finish the code.

It's actually the same as in previous chapters for selecting data:

```Python hl_lines="6-8"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial001.py[ln:36-41]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial001.py!}
```

</details>

We take that statement, that now includes a `WHERE`, and we `exec()` it to get the results.

And in this case the results will be just one:

<div class="termy">

```console
$ python app.py

// Some boilerplate output omitted 😉

// Now the important part, the SELECT with WHERE 💡

INFO Engine SELECT hero.id, hero.name, hero.secret_name, hero.age 
FROM hero 
WHERE hero.name = ?
INFO Engine [no key 0.00014s] ('Deadpond',)

// Here's the only printed hero
secret_name='Dive Wilson' age=None id=1 name='Deadpond'
```

</div>


!!! tip
    The `results` object is an iterable to be used in a `for` loop.

    Even if we got only one row, we iterate over that `results` object. Just as if it was a list of one element.

    We'll see other ways to get the data later.

## Other Comparisons

Here's another great advantage of these special **expressions**  passed to `.where()`.

Above, we have been using an "equality" comparison (using `==`), only checking if two things are the same value.

But we can use other standard Python comparisons. ✨

### Not Equal

We could get the rows where a column is **not** equal to a value using `!=`:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial002.py[ln:36-41]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial002.py!}
```

</details>

That would output:

```
secret_name='Pedro Parqueador' age=None id=2 name='Spider-Boy'
secret_name='Tommy Sharp' age=48 id=3 name='Rusty-Man'
```

### Pause to Add Data

Let's update the function `create_heroes()` and add some more rows to make the next comparison examples clearer:

```Python hl_lines="4-10  13-19"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial003.py[ln:23-41]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial003.py!}
```

</details>

Now that we have several heroes with different ages, it's gonna be more obvious what the next comparisons do.

### More Than

Now let's use `>` to get the rows where a column is **more than** a value:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial003.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial003.py!}
```

</details>

That would output:

```
age=48 id=3 name='Rusty-Man' secret_name='Tommy Sharp'
age=36 id=6 name='Dr. Weird' secret_name='Steve Weird'
age=93 id=7 name='Captain North America' secret_name='Esteban Rogelios'
```

!!! tip
    Notice that it didn't select `Black Lion`, because the age is not *strictly* greater than `35`.

### More Than or Equal

Let's do that again, but with `>=` to get the rows where a column is **more than or equal** to a value:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial004.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial004.py!}
```

</details>

Because we are using `>=`, the age `35` will be included in the output:

``` hl_lines="2"
age=48 id=3 name='Rusty-Man' secret_name='Tommy Sharp'
age=35 id=5 name='Black Lion' secret_name='Trevor Challa'
age=36 id=6 name='Dr. Weird' secret_name='Steve Weird'
age=93 id=7 name='Captain North America' secret_name='Esteban Rogelios'
```

!!! tip
    This time we got `Black Lion` too because although the age is not *strictly* greater than `35`it is *equal* to `35`.

### Less Than

Similarly, we can use `<` to get the rows where a column is **less than** a value:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial005.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial005.py!}
```

</details>

And we get the younger one with an age in the database:

```
age=32 id=4 name='Tarantula' secret_name='Natalia Roman-on'
```

!!! tip
    We could imagine that **Spider-Boy** is even **younger**. But because we don't know the age, it is `NULL` in the database (`None` in Python), it doesn't match any of these age comparisons with numbers.

### Less Than or Equal

Finally, we can use `<=` to get the rows where a column is **less than or equal** to a value:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial006.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial006.py!}
```

</details>

And we get the younger ones, `35` and below:

``` hl_lines="2"
age=32 id=4 name='Tarantula' secret_name='Natalia Roman-on'
age=35 id=5 name='Black Lion' secret_name='Trevor Challa'
```

!!! tip
    We get `Black Lion` here too because although the age is not *strictly* less than `35` it is *equal* to `35`.

### Benefits of Expresions

Here's a good moment to see that being able to use these pure Python expressions instead of keyword arguments can help a lot. ✨

We can use the same standard Python comparison operators like `.

## Multiple `.where()`

Because `.where()` returns the same special select object back, we can add more `.where()` calls to it:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial007.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial007.py!}
```

</details>

This will select the rows `WHERE` the `age` is **greater than or equal** to `35`, `AND` also the `age` is **less than** `40`.

The equivalent SQL would be:

```SQL hl_lines="3"
SELECT id, name, secret_name, age 
FROM hero 
WHERE age >= 35 AND age < 40
```

This uses `AND` to put both comparisons together.

We can then run it to see the output from the program:

<div class="termy">

```console
$ python app.py

// Some boilerplate output omitted 😉

// The SELECT statement with WHERE, also using AND
INFO Engine SELECT hero.id, hero.name, hero.secret_name, hero.age 
FROM hero 
WHERE hero.age >= ? AND hero.age < ?
INFO Engine [no key 0.00014s] (35, 40)

// The two heros printed
age=35 id=5 name='Black Lion' secret_name='Trevor Challa'
age=36 id=6 name='Dr. Weird' secret_name='Steve Weird'

```

</div>

## `.where()` With Multiple Expressions

As an alternative to using multiple `.where()` we can also pass several expressions to a single `.where()`:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial008.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial008.py!}
```

</details>

This is the same as the above, and will result in the same output with the two heroes:

```
age=35 id=5 name='Black Lion' secret_name='Trevor Challa'
age=36 id=6 name='Dr. Weird' secret_name='Steve Weird'
```

## `.where()` With Multiple Expressions Using `OR`

These last examples use `where()` with multiple expressions. And then those are combined in the final SQL using `AND`, which means that *all* of the expressions must be true in a row for it to be included in the results.

But we can also combine expressions using `OR`. Which means that **any** (but not necessarily all) of the expressions should be true in a row for it to be included.

To do it, you can import `or_`:

```Python hl_lines="3"
{!./docs_src/tutorial/where/tutorial009.py[ln:1-3]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial009.py!}
```

</details>

And then pass both expressions to `or_()` and put it inside `.where()`.

For example, here we select the heroes that are the youngest OR the oldest:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial009.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial009.py!}
```

</details>

When we run it, this generates the output:

<div class="termy">

```console
$ python app.py

// Some boilerplate output omitted 😉

// The SELECT statement with WHERE, also using OR 🔍
INFO Engine SELECT hero.id, hero.name, hero.secret_name, hero.age 
FROM hero 
WHERE hero.age <= ? OR hero.age > ?
INFO Engine [no key 0.00021s] (35, 90)

// The results include the youngest and oldest ✨
secret_name='Natalia Roman-on' age=32 id=4 name='Tarantula'
secret_name='Trevor Challa' age=35 id=5 name='Black Lion'
secret_name='Esteban Rogelios' age=93 id=7 name='Captain North America'
```

</div>

## Type Annotations and Errors

There's a chance that your editor gives you an error when using these comparisons, like:

```Python
Hero.age > 35
```

It would be an error telling you that

> `Hero.age` is potentially `None`, and you cannot compare `None` with `>`

This is because as we are using pure and plain Python annotations for the fields, `age` is indeed annotated as `Optional[int]`, which means `int` or `None`.

By using this simple and standard Python type annotations We get the benefit of the extra simplicity and the inline error checks when creating or using instances. ✨

And when we use these special **class attributes** in a `.where()`, during execution of the program, the special class attribute will know that the comparison only applies for the values that are not `NULL` in the database, and it will work correctly.

But the editor doesn't know that it's a special **class attribute**, so it tries to help us preventing an error (that in this case is a false alarm).

Nevertheless, we can easily fix. 🎉

We can tell the editor that this class attribute is actually a special **SQLModel** column (instead of an instance attribute with a normal value).

To do that, we can import `col()` (as short for "column"):

```Python hl_lines="3"
{!./docs_src/tutorial/where/tutorial011.py[ln:1-3]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial011.py!}
```

</details>

And then put the **class attribute** inside `col()` when using it in a `.where()`:

```Python hl_lines="5"
# Code above omitted 👆

{!./docs_src/tutorial/where/tutorial011.py[ln:44-49]!}

# Code below omitted 👇
```

<details>
<summary>👀 Full file preview</summary>

```Python
{!./docs_src/tutorial/where/tutorial011.py!}
```

</details>

So, now the comparison is not:

```Python
Hero.age > 35
```

...but:

```Python
col(Hero.age) > 35
```

And with that the editor knows this code is actually fine, because this is a special **SQLModel** column.

!!! tip
    That `col()` will come handy later, giving autocompletion to several other things we can do with these special **class attributes** for columns.

    But we'll get there later.

## Recap

You can use `.where()` with powerful expressions using **SQLModel** columns (the special class attributes) to filter the rows that you want. 🚀