In DataFusion an expression is an abstraction that represents a computation. Expressions are used as the primary inputs and ouputs for most functions within DataFusion. As such, expressions can be combined to create expression trees, a concept shared across most compilers and databases.
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 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.
.. ipython:: python
red_units = col("color") == lit("red")
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.
.. ipython:: python
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"))
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.
.. ipython:: python
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"))