temporian.input_node #

input_node(
    features: Union[
        List[FeatureSchema], List[Tuple[str, DType]]
    ],
    indexes: Optional[
        Union[
            List[IndexSchema], List[Tuple[str, IndexDType]]
        ]
    ] = None,
    is_unix_timestamp: bool = False,
    same_sampling_as: Optional[EventSetNode] = None,
    name: Optional[str] = None,
) -> EventSetNode

Creates an input EventSetNode.

An input EventSetNode can be used to feed data into a graph.

Usage example:

```python
>>> # Without index
>>> a = tp.input_node(features=[("f1", tp.float64), ("f2", tp.str_)])

>>> # With an index
>>> a = tp.input_node(
...     features=[("f1", tp.float64), ("f2", tp.str_)],
...     indexes=["f2"],
... )

>>> # Two nodes with the same sampling
>>> a = tp.input_node(features=[("f1", tp.float64)])
>>> b = tp.input_node(features=[("f2", tp.float64)], same_sampling_as=a)

```

Parameters:

Name	Type	Description	Default
`features`	`Union[List[FeatureSchema], List[Tuple[str, DType]]]`	List of names and dtypes of the features.	required
`indexes`	`Optional[Union[List[IndexSchema], List[Tuple[str, IndexDType]]]]`	List of names and dtypes of the index. If empty, the data is assumed not indexed.	`None`
`is_unix_timestamp`	`bool`	If true, the timestamps are interpreted as unix timestamps in seconds.	`False`
`same_sampling_as`	`Optional[EventSetNode]`	If set, the created EventSetNode is guaranteed to have the same sampling as same_sampling_as`. In this case,`indexes`and`is_unix_timestamp` should not be provided. Some operators require for input EventSetNodes to have the same sampling.	`None`
`name`	`Optional[str]`	Name for the EventSetNode.	`None`

Returns:

Type	Description
`EventSetNode`	EventSetNode with the given specifications.