API Reference#

This page gives an overview of all of Temporian's public symbols.

Check the index on the left for a more detailed description of any symbol.

Classes#

Symbol	Description
`tp.EventSetNode`	Reference to the input or output of an operator in the compute graph.
`tp.EventSet`	Container for actual temporal data.
`tp.Schema`	Description of the data inside an `EventSetNode` or `EventSet`.
`tp.FeatureSchema`	Description of a feature inside a `Schema`.
`tp.IndexSchema`	Description of an index inside a `Schema`.

Functions#

Symbol	Description
`tp.run()`	Evaluates `EventSetNodes` on `EventSets`.
`tp.plot()`	Plots `EventSets`.
`tp.event_set()`	Creates an `EventSet` from arrays (lists, NumPy arrays, Pandas Series.)
`tp.input_node()`	Creates an input `EventSetNode`, that can be used to feed data into a graph.

Input/output#

Symbol	Description
`tp.from_pandas()`	Converts a Pandas DataFrame into an `EventSet`.
`tp.to_pandas()`	Converts an `EventSet` to a pandas DataFrame.
`tp.from_csv()`	Reads an `EventSet` from a CSV file.
`tp.to_csv()`	Saves an `EventSet` to a CSV file.

Durations#

Symbols	Description
`tp.duration.milliseconds()` `tp.duration.seconds()` `tp.duration.minutes()` `tp.duration.hours()` `tp.duration.days()` `tp.duration.weeks()`	Convert input value from milliseconds / seconds / minutes / hours / days / weeks to a `Duration` in seconds.

Operators#

Symbols	Description
`tp.combine()`	Combines events from `EventSets` with different samplings.
`tp.glue()`	Concatenates features from `EventSets` with the same sampling.
`EventSet.abs()`	Computes the absolute value of the features.
`EventSet.add_index()`	Adds indexes to an `EventSet`.
`EventSet.arccos()`	Computes the inverse cosine of the features.
`EventSet.arcsin()`	Computes the inverse sine of the features.
`EventSet.arctan()`	Computes the inverse tangent of the features.
`EventSet.begin()`	Generates a single timestamp at the beginning of the input.
`EventSet.cast()`	Casts the dtype of features.
`EventSet.cos()`	Computes the cosine of the features.
`EventSet.drop_index()`	Removes indexes from an `EventSet`.
`EventSet.end()`	Generates a single timestamp at the end of the input.
`EventSet.enumerate()`	Creates an ordinal feature enumerating the events according to their timestamp.
`EventSet.equal()`	Creates boolean features with event-wise equality to another `EventSet` or to a scalar value.
`EventSet.experimental_fast_fourier_transform()`	Applies a Fast Fourier Transform.
`EventSet.filter()`	Filters out events in an `EventSet` for which a condition is false.
`EventSet.filter_empty_index()`	Filters out indexes without events.
`EventSet.filter_moving_count()`	Skips events such that no more than one event is within a time window of `window_length`.
`EventSet.isnan()`	Event-wise boolean that is `True` in the `NaN` positions of the input events. Equivalent to `~evset.notnan()`.
`EventSet.join()`	Joins `EventSets` with different samplings but the same index together.
`EventSet.lag()`	Adds a delay to an `EventSet`'s timestamps.
`EventSet.leak()`	Subtracts a duration from an `EventSet`'s timestamps.
`EventSet.log()`	Computes natural logarithm of the features
`EventSet.map()`	Applies a function on each of an `EventSet`'s values.
`EventSet.notnan()`	Event-wise boolean that is `True` if the input values are not `NaN`. Equivalent to `~evset.isnan()`.
`EventSet.prefix()`	Adds a prefix to the names of the features in an `EventSet`.
`EventSet.propagate()`	Propagates feature values over a sub index.
`EventSet.rename()`	Renames an `EventSet`'s features and index.
`EventSet.resample()`	Resamples an `EventSet` at each timestamp of another `EventSet`.
`EventSet.select()`	Selects a subset of features from an `EventSet`.
`EventSet.select_index_values()`	Selects a subset of index values from an `EventSet`.
`EventSet.set_index()`	Replaces the indexes in an `EventSet`.
`EventSet.sin()`	Computes the sine of the features.
`EventSet.since_last()`	Computes the amount of time since the last distinct timestamp.
`EventSet.tan()`	Computes the tangent of the features.
`EventSet.tick()`	Generates timestamps at regular intervals in the range of a guide.
`EventSet.tick_calendar()`	Generates timestamps at the specified calendar date-time events.
`EventSet.timestamps()`	Creates a feature from the events timestamps (`float64`).
`EventSet.unique_timestamps()`	Removes events with duplicated timestamps from an `EventSet`.
`EventSet.until_next()`	Duration until the next sampling event.
`EventSet.where()`	Choose events from two possible sources, based on boolean conditions.

Calendar operators#

Symbols	Description
`EventSet.calendar_day_of_month()` `EventSet.calendar_day_of_week()` `EventSet.calendar_day_of_year()` `EventSet.calendar_hour()` `EventSet.calendar_iso_week()` `EventSet.calendar_minute()` `EventSet.calendar_month()` `EventSet.calendar_second()` `EventSet.calendar_year()`	Obtain the day of month / day of week / day of year / hour / ISO week / minute / month / second / year the timestamps in an `EventSet` are in.

Window operators#

Symbols	Description
`EventSet.simple_moving_average()` `EventSet.moving_standard_deviation()` `EventSet.cumsum()` `EventSet.moving_sum()` `EventSet.moving_count()` `EventSet.moving_min()` `EventSet.moving_max()` `EventSet.cumprod()` `EventSet.moving_product()`	Compute an operation on the values in a sliding window over an EventSet's timestamps.

Python operators#

Symbols	Description
`+` (add), `-` (subtract), `\` (multiply), `/` (divide), `//` (floor divide), `%` (modulo), `*` (power)	Event-wise arithmetic operations between two `EventSets` or with a scalar number. See the corresponding User Guide section for more info.
`!=` (not equal), `>` (greater than), `>=` (greater or equal), `<` (less), `<=` (less or equal)	Event-wise comparison between `EventSets` or to a scalar number. Note that `==` is not supported, use `EventSet.equal()` instead. See the User Guide for more info.
`&` (and), `\\|` (or), `^` (xor)	Event-wise logic operators between boolean `EventSets`.