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# SQL

DataFusion also offers a SQL API, read the full reference [here](https://arrow.apache.org/datafusion/user-guide/sql/index.html)

```{code-cell} ipython3
import datafusion
from datafusion import DataFrame, SessionContext

# create a context
ctx = datafusion.SessionContext()

# register a CSV
ctx.register_csv("pokemon", "pokemon.csv")

# create a new statement via SQL
df = ctx.sql('SELECT "Attack"+"Defense", "Attack"-"Defense" FROM pokemon')

# collect and convert to pandas DataFrame
df.to_pandas()
```

## Parameterized queries

In DataFusion-Python 51.0.0 we introduced the ability to pass parameters
in a SQL query. These are similar in concept to
[prepared statements](https://datafusion.apache.org/user-guide/sql/prepared_statements.html),
but allow passing named parameters into a SQL query. Consider this simple
example.

```{code-cell} ipython3
def show_attacks(ctx: SessionContext, threshold: int) -> None:
    ctx.sql(
        'SELECT "Name", "Attack" FROM pokemon WHERE "Attack" > $val', val=threshold
    ).show(num=5)
show_attacks(ctx, 75)
```

When passing parameters like the example above we convert the Python objects
into their string representation. We also have special case handling
for {py:class}`~datafusion.dataframe.DataFrame` objects, since they cannot simply
be turned into string representations for an SQL query. In these cases we
will register a temporary view in the {py:class}`~datafusion.context.SessionContext`
using a generated table name.

The formatting for passing string replacement objects is to precede the
variable name with a single `$`. This works for all dialects in
the SQL parser except `hive` and `mysql`. Since these dialects do not
support named placeholders, we are unable to do this type of replacement.
We recommend either switching to another dialect or using Python
f-string style replacement.

:::{warning}
To support DataFrame parameterized queries, your session must support
registration of temporary views. The default
{py:class}`~datafusion.catalog.CatalogProvider` and
{py:class}`~datafusion.catalog.SchemaProvider` do have this capability.
If you have implemented custom providers, it is important that temporary
views do not persist across {py:class}`~datafusion.context.SessionContext`
or you may get unintended consequences.
:::

The following example shows passing in both a {py:class}`~datafusion.dataframe.DataFrame`
object as well as a Python object to be used in parameterized replacement.

```{code-cell} ipython3
def show_column(
    ctx: SessionContext, column: str, df: DataFrame, threshold: int
) -> None:
    ctx.sql(
        'SELECT "Name", $col FROM $df WHERE $col > $val',
        col=column,
        df=df,
        val=threshold,
    ).show(num=5)
df = ctx.table("pokemon")
show_column(ctx, '"Defense"', df, 75)
```

The approach implemented for conversion of variables into a SQL query
relies on string conversion. This has the potential for data loss,
specifically for cases like floating point numbers. If you need to pass
variables into a parameterized query and it is important to maintain the
original value without conversion to a string, then you can use the
optional parameter `param_values` to specify these. This parameter
expects a dictionary mapping from the parameter name to a Python
object. Those objects will be cast into a
[PyArrow Scalar Value](https://arrow.apache.org/docs/python/generated/pyarrow.Scalar.html).

Using `param_values` will rely on the SQL dialect you have configured
for your session. This can be set using the {ref}`configuration options <configuration>`
of your {py:class}`~datafusion.context.SessionContext`. Similar to how
[prepared statements](https://datafusion.apache.org/user-guide/sql/prepared_statements.html)
work, these parameters are limited to places where you would pass in a
scalar value, such as a comparison.

```{code-cell} ipython3
def param_attacks(ctx: SessionContext, threshold: int) -> None:
    ctx.sql(
        'SELECT "Name", "Attack" FROM pokemon WHERE "Attack" > $val',
        param_values={"val": threshold},
    ).show(num=5)
param_attacks(ctx, 75)
```