otp.agg.percentile#
- percentile(number_of_quantiles, input_field_names, output_field_names, running=False, bucket_interval=0, bucket_units=None, bucket_time='end', bucket_end_condition=None, boundary_tick_bucket='new', group_by=None, end_condition_per_group=False)#
Percentile running aggregation.
For each bucket, propagates its
n-1
n-quantiles
where a comparison between ticks is done using a specified set of tick fields. A new field (QUANTILE
) with the quantile number is added.- Parameters
number_of_quantiles (int) –
Specifies the number
n
of quantiles. Setting it to 2 will propagate only one tick - the median.Default: 2
input_field_names (List[Union[Union[str, onetick.py.Column], Tuple[Union[str, onetick.py.Column], str]]]) –
List of numeric field names to run aggregation on. You can use as list elements either string column name, either
Columns
.You can change default comparison order (
desc
) for the field, by passing a tuple of column name and comparison order (desc
orasc
) instead column name.output_field_names (Optional[List[str]]) –
Output columns name in the same order as columns from
input_field_names
output_field_names
andinput_field_names
must have the same number of fields.If not set,
output_field_names
will be equal toinput_field_names
.running (bool, default=False) –
Aggregation will be calculated as sliding window.
running
andbucket_interval
parameters determines when new buckets are created.running
= Trueaggregation will be calculated in a sliding window.
bucket_interval
= N (N > 0)Window size will be N. Output tick will be generated when tick “enter” window (arrival event) and when “exit” window (exit event)
bucket_interval
= 0Left boundary of window will be bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].
running
= Falsebuckets partition the [query start time, query end time) interval into non-overlapping intervals of size
bucket_interval
(with the last interval possibly of a smaller size). Ifbucket_interval
is set to 0 a single bucket for the entire interval is created.Note that in non-running mode OneTick unconditionally divides the whole time interval into specified number of buckets. It means that you will always get this specified number of ticks in the result, even if you have less ticks in the input data.
Default: False
bucket_interval (int or Operation or OnetickParameter or
symbol parameter
, default=0) –Determines the length of each bucket (units depends on
bucket_units
).If
Operation
of bool type is passed, acts asbucket_end_condition
.Bucket interval can also be set with integer
OnetickParameter
orsymbol parameter
.bucket_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –
Set bucket interval units.
By default, if
bucket_units
andbucket_end_condition
not specified, set to seconds. Ifbucket_end_condition
specified, thenbucket_units
set to flexible.If set to flexible then
bucket_end_condition
must be set.Note that seconds bucket unit doesn’t take into account daylight-saving time of the timezone, so you may not get expected results when using, for example, 24 * 60 * 60 seconds as bucket interval. In such case use days bucket unit instead. See example in
onetick.py.agg.sum()
.bucket_time (Literal['start', 'end'], default=end) –
Control output timestamp.
start
the timestamp assigned to the bucket is the start time of the bucket.
end
the timestamp assigned to the bucket is the end time of the bucket.
bucket_end_condition (condition, default=None) –
An expression that is evaluated on every tick. If it evaluates to “True”, then a new bucket is created. This parameter is only used if
bucket_units
is set to “flexible”.Also can be set via
bucket_interval
parameter by passingOperation
object.boundary_tick_bucket (Literal['new', 'previous'], default=new) –
Controls boundary tick ownership.
previous
A tick on which
bucket_end_condition
evaluates to “true” belongs to the bucket being closed.new
tick belongs to the new bucket.
This parameter is only used if
bucket_units
is set to “flexible”group_by (list, str or expression, default=None) – When specified, each bucket is broken further into additional sub-buckets based on specified field values. If
Operation
is used then GROUP_{i} column is added. Where i is index in group_by list. For example, if Operation is the only element ingroup_by
list then GROUP_0 field will be added.end_condition_per_group (bool, default=False) –
Controls application of
bucket_end_condition
in groups.end_condition_per_group
= Truebucket_end_condition
is applied only to the group defined bygroup_by
end_condition_per_group
= Falsebucket_end_condition
applied across all groups
This parameter is only used if
bucket_units
is set to “flexible”.When set to True, applies to all bucketing conditions. Useful, for example, if you need to specify
group_by
, and you want to group items first, and create buckets after that.
Examples
>>> t = otp.Ticks({'A': [1, 2, 3, 4]}) >>> t = t.percentile(['A']) >>> otp.run(t) Time A QUANTILE 0 2003-12-04 2 1
You can also pass column:
>>> t = otp.Ticks({'A': [1, 2, 3, 4]}) >>> t = t.percentile([t['A']]) >>> otp.run(t) Time A QUANTILE 0 2003-12-04 2 1
Change number of quantiles:
>>> t = otp.Ticks({'A': [1, 2, 3, 4]}) >>> t = t.percentile(['A'], number_of_quantiles=3) >>> otp.run(t) Time A QUANTILE 0 2003-12-04 3 1 1 2003-12-04 2 2
Or change default comparison order:
>>> t = otp.Ticks({'A': [1, 2, 3, 4]}) >>> t = t.percentile([('A', 'asc')], number_of_quantiles=3) >>> otp.run(t) Time A QUANTILE 0 2003-12-04 2 1 1 2003-12-04 3 2
You can also change output column name via
output_field_names
parameter:>>> t = otp.Ticks({'A': [1, 2, 3, 4]}) >>> t = t.percentile(['A'], output_field_names=['B']) >>> otp.run(t) Time B QUANTILE 0 2003-12-04 2 1
See also
PERCENTILE OneTick event processor