otp.agg.ranking#
- ranking(rank_by, show_rank_as, include_tick, 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)#
Ranking running aggregation.
Sorts a series of ticks over a bucket interval using a specified set of tick fields specified in
rank_by
and adds a new fieldRANKING
with the position of the tick in the sort order or the percentage of ticks with values less than (or equal) to the value of the tick.Does not change the order of the ticks.
- Parameters
rank_by (str or list or dict) – Set of fields to sort by. Can be one field specified by string, list of fields or dictionary with field names as keys and
asc
ordesc
string literals as values. Latter allows to specify sorting direction. Default direction isdesc
.show_rank_as (str) –
order
: calculate number that represents the position of the tick in the sort orderpercent_le_values
: calculate the percentage of ticks that have higher or equal value of the position in the sort order, relative to the tickpercent_lt_values
: calculate the percentage of ticks that have higher value of the position in the sort order, relative to the tickpercentile_standard
: calculate Percentile Rank of the tick in the sort order.
include_tick (bool, default=False) – Specifies whether the current tick should be included in calculations if
show_rank_as
ispercent_lt_values
orpercentile_standard
.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]}) >>> t = t.ranking('A') >>> otp.run(t) Time A RANKING 0 2003-12-01 00:00:00.000 1 3 1 2003-12-01 00:00:00.001 2 2 2 2003-12-01 00:00:00.002 3 1
>>> t = otp.Ticks({'A': [1, 2, 3]}) >>> t = t.ranking({'A': 'asc'}) >>> otp.run(t) Time A RANKING 0 2003-12-01 00:00:00.000 1 1 1 2003-12-01 00:00:00.001 2 2 2 2003-12-01 00:00:00.002 3 3
>>> t = otp.Ticks({'A': [1, 2, 2, 3, 2, 1]}) >>> otp.run(t.ranking({'A': 'asc'}, show_rank_as='percent_lt_values', include_tick=True)) Time A RANKING 0 2003-12-01 00:00:00.000 1 66.666667 1 2003-12-01 00:00:00.001 2 16.666667 2 2003-12-01 00:00:00.002 2 16.666667 3 2003-12-01 00:00:00.003 3 0.000000 4 2003-12-01 00:00:00.004 2 16.666667 5 2003-12-01 00:00:00.005 1 66.666667
>>> otp.run(t.ranking({'A': 'asc'}, show_rank_as='percent_lt_values', include_tick=False)) Time A RANKING 0 2003-12-01 00:00:00.000 1 80.0 1 2003-12-01 00:00:00.001 2 20.0 2 2003-12-01 00:00:00.002 2 20.0 3 2003-12-01 00:00:00.003 3 0.0 4 2003-12-01 00:00:00.004 2 20.0 5 2003-12-01 00:00:00.005 1 80.0
See also
RANKING OneTick event processor