var#

skrub.var(name, value=NULL, *, becomes_default=False)[source]#

Create a skrub variable.

Variables represent inputs to a DataOps plan, and the corresponding learner. They can be combined with other variables, constants, operators, function calls etc. to build up complex DataOps, which implicitly define the plan.

See the example gallery for more information about skrub DataOps.

Parameters:
namestr

The name for this input. It corresponds to a key in the dictionary that is passed to the learner’s fit() method (see Examples below). Names must be unique within a learner and must not start with "_skrub_"

valueobject, optional

Optionally, an initial value can be given to the variable. When it is available, it is used to provide a preview of the learner’s results, to detect errors in the learner early, and to provide better help and tab-completion in interactive Python shells.

becomes_defaultbool, default = False

If True, the provided value is not only used for previews but also becomes the default value for this variable when creating a learner (for example with DataOp.skb.make_learner()). Thus passing this variable in the environment is always optional.

Returns:
A skrub variable
Raises:
TypeError

If the provided value is a skrub DataOp or a skrub choose_* function.

See also

skrub.X

Create a skrub variable and mark it as being X.

skrub.y

Create a skrub variable and mark it as being y.

Examples

Variables without a value:

>>> import skrub
>>> a = skrub.var('a')
>>> a
<Var 'a'>
>>> b = skrub.var('b')
>>> c = a + b
>>> c
<BinOp: add>
>>> print(c.skb.describe_steps())
Var 'a'
Var 'b'
BinOp: add

The names of variables correspond to keys in the inputs:

>>> c.skb.eval({'a': 10, 'b': 6})
16

And also to keys to the inputs to the DataOps plan:

>>> learner = c.skb.make_learner()
>>> learner.fit_transform({'a': 5, 'b': 4})
9

When providing a value, we see what the learner produces for the values we provided:

>>> a = skrub.var('a', 2)
>>> b = skrub.var('b', 3)
>>> b
<Var 'b'>
Result:
―――――――
3
>>> c = a + b
>>> c
<BinOp: add>
Result:
―――――――
5

The values are also used for eval() when no environment is provided:

>>> c.skb.eval()
5

But we can still override them. And inputs must be provided explicitly when using the learner returned by .skb.make_learner().

>>> c.skb.eval({'a': 10, 'b': 6})
16

When passing becomes_default=True, the preview value is treated as a default value for that variable. It is kept when cloning the DataOp or creating a Learner, and is always optional (does not need to be present) in the provided environment.

>>> c = skrub.var('a', 0, becomes_default=True) + skrub.var('b', 1)
>>> c.skb.get_data()
{'a': 0, 'b': 1}
>>> c.skb.clone().skb.get_data()
{'a': 0}

For the learner ‘b’ is mandatory but ‘a’ is optional and has default value 0.

>>> c.skb.make_learner().fit_transform({'b': 10})
10
>>> c.skb.make_learner().fit_transform({'b': 10, 'a': 100})
110

Much more information about skrub variables is provided in the examples gallery.