temporian.EventSet.cast #

cast(
    target: TargetDtypes, check_overflow: bool = True
) -> EventSetOrNode

Casts the data types of an EventSet's features.

Features not impacted by cast are kept.

Usage example

>>> a = tp.event_set(
...     timestamps=[1, 2],
...     features={"A": [0, 2], "B": ['a', 'b'], "C": [5.0, 5.5]},
... )

>>> # Cast all input features to the same dtype
>>> b = a[["A", "C"]].cast(tp.float32)
>>> b
indexes: []
features: [('A', float32), ('C', float32)]
events:
    (2 events):
        timestamps: [1. 2.]
        'A': [0. 2.]
        'C': [5.  5.5]
...


>>> # Cast by feature name
>>> b = a.cast({'A': bool, 'C': int})
>>> b
indexes: []
features: [('A', bool_), ('B', str_), ('C', int64)]
events:
    (2 events):
        timestamps: [1. 2.]
        'A': [False  True]
        'B': [b'a' b'b']
        'C': [5  5]
...

>>> # Map original_dtype -> target_dtype
>>> b = a.cast({float: int, int: float})
>>> b
indexes: []
features: [('A', float64), ('B', str_), ('C', int64)]
events:
    (2 events):
        timestamps: [1. 2.]
        'A': [0. 2.]
        'B': [b'a' b'b']
        'C': [5  5]
...

Parameters:

Name	Type	Description	Default
`target`	`TargetDtypes`	Single dtype or a map. Providing a single dtype will cast all columns to it. The mapping keys can be either feature names or the original dtypes (and not both types mixed), and the values are the target dtypes for them. All dtypes must be Temporian types (see `dtype.py`).	required
`check_overflow`	`bool`	Flag to check overflow when casting to a dtype with a shorter range (e.g: `INT64`->`INT32`). Note that this check adds some computation overhead. Defaults to `True`.	`True`

Returns:

Type	Description
`EventSetOrNode`	New EventSet (or the same if no features actually changed dtype), with the same feature names as the input one, but with the new dtypes as specified in `target`.

Raises:

Type	Description
`ValueError`	If `check_overflow=True` and some value is out of the range of the `target` dtype.
`ValueError`	If trying to cast a non-numeric string to numeric dtype.
`ValueError`	If `target` is neither a dtype nor a mapping.
`ValueError`	If `target` is a mapping, but some of the keys are not a dtype nor a feature in `input.feature_names`, or if those types are mixed.