temporian.from_pandas #

from_pandas(
    df: pandas.DataFrame,
    indexes: Optional[List[str]] = None,
    timestamps: str = "timestamp",
    name: Optional[str] = None,
    same_sampling_as: Optional[EventSet] = None,
) -> EventSet

Converts a Pandas DataFrame into an EventSet.

The column timestamps (defaults to "timestamp") contains the timestamps. Columns indexes (default to None, equivalent to []), contains the indexes. The remaining columns are converted into features.

See tp.event_set() for the list of supported timestamp and feature types.

Usage example

>>> df = pd.DataFrame(
...     data=[
...         [1.0, 5, "A"],
...         [2.0, 6, "A"],
...         [3.0, 7, "B"],
...     ],
...     columns=["timestamp", "feature_1", "feature_2"],
... )
>>> evset = tp.from_pandas(df, indexes=["feature_2"])

Parameters:

Name	Type	Description	Default
`df`	`DataFrame`	A non indexed Pandas dataframe.	required
`indexes`	`Optional[List[str]]`	Names of the columns to use as indexes. If empty (default), the data is not indexed. Only integer and string columns can be used as indexes.	`None`
`timestamps`	`str`	Name of the column containing the timestamps. See `tp.event_set()` for the list of supported timestamp types.	`'timestamp'`
`name`	`Optional[str]`	Optional name of the EventSet. Used for debugging, and graph serialization.	`None`
`same_sampling_as`	`Optional[EventSet]`	If set, the new EventSet is checked and tagged as having the same sampling as `same_sampling_as`. Some operators, such as `EventSet.filter()`, require their inputs to have the same sampling.	`None`

Returns:

Type	Description
`EventSet`	An EventSet.

Raises:

Type	Description
`ValueError`	If `indexes` or `timestamps` are not in `df`'s columns.
`ValueError`	If a column has an unsupported dtype.