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(expressions)=

# Expressions

In DataFusion an expression is an abstraction that represents a computation.
Expressions are used as the primary inputs and outputs for most functions within
DataFusion. As such, expressions can be combined to create expression trees, a
concept shared across most compilers and databases.

## Column

The first expression most new users will interact with is the Column, which is created by calling {py:func}`~datafusion.col`.
This expression represents a column within a DataFrame. The function {py:func}`~datafusion.col` takes as in input a string
and returns an expression as it's output.

## Literal

Literal expressions represent a single value. These are helpful in a wide range of operations where
a specific, known value is of interest. You can create a literal expression using the function {py:func}`~datafusion.lit`.
The type of the object passed to the {py:func}`~datafusion.lit` function will be used to convert it to a known data type.

In the following example we create expressions for the column named `color` and the literal scalar string `red`.
The resultant variable `red_units` is itself also an expression.

```{code-cell} ipython3
from datafusion import col, lit

red_units = col("color") == lit("red")
```

## Boolean

When combining expressions that evaluate to a boolean value, you can combine these expressions using boolean operators.
It is important to note that in order to combine these expressions, you *must* use bitwise operators. See the following
examples for the and, or, and not operations.

```{code-cell} ipython3
red_or_green_units = (col("color") == lit("red")) | (col("color") == lit("green"))
heavy_red_units = (col("color") == lit("red")) & (col("weight") > lit(42))
not_red_units = ~(col("color") == lit("red"))
```

## Arrays

For columns that contain arrays of values, you can access individual elements of the array by index
using bracket indexing. This is similar to calling the function
{py:func}`datafusion.functions.array_element`, except that array indexing using brackets is 0 based,
similar to Python arrays and `array_element` is 1 based indexing to be compatible with other SQL
approaches.

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

ctx = SessionContext()
df = ctx.from_pydict({"a": [[1, 2, 3], [4, 5, 6]]})
df.select(col("a")[0].alias("a0"))
```

:::{warning}
Indexing an element of an array via `[]` starts at index 0 whereas
{py:func}`~datafusion.functions.array_element` starts at index 1.
:::

Starting in DataFusion 49.0.0 you can also create slices of array elements using
slice syntax from Python.

```{code-cell} ipython3
df.select(col("a")[1:3].alias("second_two_elements"))
```

To check if an array is empty, you can use the function {py:func}`datafusion.functions.array_empty` or `datafusion.functions.empty`.
This function returns a boolean indicating whether the array is empty.

```{code-cell} ipython3
from datafusion import SessionContext, col
from datafusion.functions import array_empty

ctx = SessionContext()
df = ctx.from_pydict({"a": [[], [1, 2, 3]]})
df.select(array_empty(col("a")).alias("is_empty"))
```

In this example, the `is_empty` column will contain `True` for the first row and `False` for the second row.

To get the total number of elements in an array, you can use the function {py:func}`datafusion.functions.cardinality`.
This function returns an integer indicating the total number of elements in the array.

```{code-cell} ipython3
from datafusion import SessionContext, col
from datafusion.functions import cardinality

ctx = SessionContext()
df = ctx.from_pydict({"a": [[1, 2, 3], [4, 5, 6]]})
df.select(cardinality(col("a")).alias("num_elements"))
```

In this example, the `num_elements` column will contain `3` for both rows.

To concatenate two arrays, you can use the function {py:func}`datafusion.functions.array_cat` or {py:func}`datafusion.functions.array_concat`.
These functions return a new array that is the concatenation of the input arrays.

```{code-cell} ipython3
from datafusion import SessionContext, col
from datafusion.functions import array_cat, array_concat

ctx = SessionContext()
df = ctx.from_pydict({"a": [[1, 2, 3]], "b": [[4, 5, 6]]})
df.select(array_cat(col("a"), col("b")).alias("concatenated_array"))
```

In this example, the `concatenated_array` column will contain `[1, 2, 3, 4, 5, 6]`.

To repeat the elements of an array a specified number of times, you can use the function {py:func}`datafusion.functions.array_repeat`.
This function returns a new array with the elements repeated.

```{code-cell} ipython3
from datafusion import SessionContext, col, literal
from datafusion.functions import array_repeat

ctx = SessionContext()
df = ctx.from_pydict({"a": [[1, 2, 3]]})
df.select(array_repeat(col("a"), literal(2)).alias("repeated_array"))
```

In this example, the `repeated_array` column will contain `[[1, 2, 3], [1, 2, 3]]`.

## Lambda functions

Some array functions take a *lambda function*: a small function that runs once
per element. {py:func}`~datafusion.functions.array_transform` maps a lambda over
every element, {py:func}`~datafusion.functions.array_filter` keeps the elements
for which a predicate lambda is true, and
{py:func}`~datafusion.functions.array_any_match` returns whether any element
satisfies a predicate lambda. (Functions that take another function as an
argument are sometimes called *higher-order* functions.)

The simplest way to supply a lambda is a Python `lambda`. Its parameter names
become the lambda parameters, and its return value becomes the body.

```{code-cell} ipython3
from datafusion import SessionContext, col
from datafusion import functions as f

ctx = SessionContext()
df = ctx.from_pydict({"a": [[1, 2, 3], [4, 5]]})
df.select(f.array_transform(col("a"), lambda v: v * 2).alias("doubled"))
df.select(f.array_filter(col("a"), lambda v: v > 2).alias("big_only"))
df.select(f.array_any_match(col("a"), lambda v: v > 3).alias("has_big"))
```

If you need explicit control over parameter names, build the lambda with
{py:func}`~datafusion.functions.lambda_` and reference its parameters with
{py:func}`~datafusion.functions.lambda_var`. The following is equivalent to the
`array_transform` call above.

```{code-cell} ipython3
from datafusion import lit

double_fn = f.lambda_(["v"], f.lambda_var("v") * lit(2))
df.select(f.array_transform(col("a"), double_fn).alias("doubled"))
```

:::{note}
Lambda expressions cannot yet be serialized: calling
{py:meth}`~datafusion.expr.Expr.to_bytes` or pickling an expression that
contains a lambda raises `Lambda not implemented`. SQL lambda syntax is
only parsed by dialects that support lambdas; set
`datafusion.sql_parser.dialect` to one of `DuckDB`, `ClickHouse`,
`Snowflake`, or `Databricks`. Both arrow syntax (`x -> x * 2`) and
keyword syntax (`lambda x: x * 2`) parse. DuckDB will drop the arrow
form in v2.1, so prefer `lambda x: x * 2` for forward compatibility.
The Python expression builder shown above works regardless of dialect.
:::

## Testing membership in a list

A common need is filtering rows where a column equals *any* of a small set of
values. DataFusion offers three forms; they differ in readability and in how
they scale:

1. A compound boolean using `|` across explicit equalities.
2. {py:func}`~datafusion.functions.in_list`, which accepts a list of
   expressions and tests equality against all of them in one call.
3. A trick with {py:func}`~datafusion.functions.array_position` and
   {py:func}`~datafusion.functions.make_array`, which returns the 1-based
   index of the value in a constructed array, or null if it is not present.

```{code-cell} ipython3
from datafusion import SessionContext, col, lit
from datafusion import functions as f

ctx = SessionContext()
df = ctx.from_pydict({"shipmode": ["MAIL", "SHIP", "AIR", "TRUCK", "RAIL"]})

# Option 1: compound boolean. Fine for two values; awkward past three.
df.filter((col("shipmode") == lit("MAIL")) | (col("shipmode") == lit("SHIP")))

# Option 2: in_list. Preferred for readability as the set grows.
df.filter(f.in_list(col("shipmode"), [lit("MAIL"), lit("SHIP")]))

# Option 3: array_position / make_array. Useful when you already have the
# set as an array column and want "is in that array" semantics.
df.filter(
    ~f.array_position(
        f.make_array(lit("MAIL"), lit("SHIP")), col("shipmode")
    ).is_null()
)
```

Use `in_list` as the default. It is explicit, readable, and matches the
semantics users expect from SQL's `IN (...)`. Reach for the
`array_position` form only when the membership set is itself an array
column rather than a literal list.

## Conditional expressions

DataFusion provides {py:func}`~datafusion.functions.case` for the SQL
`CASE` expression in both its switched and searched forms, along with
{py:func}`~datafusion.functions.when` as a standalone builder for the
searched form.

**Switched CASE** (one expression compared against several literal values):

```{code-cell} ipython3
df = ctx.from_pydict(
    {"priority": ["1-URGENT", "2-HIGH", "3-MEDIUM", "5-LOW"]},
)

df.select(
    col("priority"),
    f.case(col("priority"))
     .when(lit("1-URGENT"), lit(1))
     .when(lit("2-HIGH"), lit(1))
     .otherwise(lit(0))
     .alias("is_high_priority"),
)
```

**Searched CASE** (an independent boolean predicate per branch). Use this
form whenever a branch tests more than simple equality — for example,
checking whether a joined column is `NULL` to gate a computed value:

```{code-cell} ipython3
df = ctx.from_pydict(
    {"volume": [10.0, 20.0, 30.0], "supplier_id": [1, None, 2]},
)

df.select(
    col("volume"),
    col("supplier_id"),
    f.when(col("supplier_id").is_not_null(), col("volume"))
     .otherwise(lit(0.0))
     .alias("attributed_volume"),
)
```

This searched-CASE pattern is idiomatic for "attribute the measure to the
matching side of a left join, otherwise contribute zero" — a shape that
appears in TPC-H Q08 and similar market-share calculations.

If a switched CASE only groups several equality matches into one bucket,
`f.when(f.in_list(col(...), [...]), value).otherwise(default)` is often
simpler than the full `case` builder.

## Structs

Columns that contain struct elements can be accessed using the bracket notation as if they were
Python dictionary style objects. This expects a string key as the parameter passed.

```{code-cell} ipython3
ctx = SessionContext()
data = {"a": [{"size": 15, "color": "green"}, {"size": 10, "color": "blue"}]}
df = ctx.from_pydict(data)
df.select(col("a")["size"].alias("a_size"))

```

## Functions

As mentioned before, most functions in DataFusion return an expression at their output. This allows us to create
a wide variety of expressions built up from other expressions. For example, {py:func}`~datafusion.expr.Expr.alias` is a function that takes
as it input a single expression and returns an expression in which the name of the expression has changed.

The following example shows a series of expressions that are built up from functions operating on expressions.

```{code-cell} ipython3
from datafusion import SessionContext
from datafusion import column, lit
from datafusion import functions as f
import random

ctx = SessionContext()
df = ctx.from_pydict(
    {
        "name": ["Albert", "Becca", "Carlos", "Dante"],
        "age": [42, 67, 27, 71],
        "years_in_position": [13, 21, 10, 54],
    },
    name="employees"
)

age_col = col("age")
renamed_age = age_col.alias("age_in_years")
start_age = age_col - col("years_in_position")
started_young = start_age < lit(18)
can_retire = age_col > lit(65)
long_timer = started_young & can_retire

df.filter(long_timer).select(col("name"), renamed_age, col("years_in_position"))
```
