otp.agg.find_value_for_percentile#

find_value_for_percentile(percentile, show_percentile_as, 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)#

For each bucket, finds the value which has percentile rank closest to percentile parameter.

For more details, see Percentile rank.

Parameters
  • percentile (int) – Specifies the percentile number in 0-100% range for which find the value of input field that has percentile rank close to it.

  • show_percentile_as (str) –

    • By default (when parameter is not set), the output field contains the value which has percentile rank closest to percentile parameter.

    • When set to interpolated_value, the output field contains the interpolated value of: The value above and below specified percentile, or one of them if it’s biggest or smallest value.

    • When set to first_value_with_ge_percentile, the output field contains the value which has greater or equal than specified percentile.

  • running (bool, default=False) –

    Aggregation will be calculated as sliding window. running and bucket_interval parameters determines when new buckets are created.

    • running = True

      aggregation 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 = 0

        Left boundary of window will be bound to start time. For each tick aggregation will be calculated in [start_time; tick_t].

    • running = False

      buckets 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). If bucket_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 or datetime offset object, default=0) –

    Determines the length of each bucket (units depends on bucket_units).

    If Operation of bool type is passed, acts as bucket_end_condition.

    Bucket interval can be set via datetime offset objects like otp.Second, otp.Minute, otp.Hour, otp.Day, otp.Month. In this case you could omit setting bucket_units parameter.

    Bucket interval can also be set with integer OnetickParameter or symbol parameter.

  • bucket_units (Optional[Literal['seconds', 'ticks', 'days', 'months', 'flexible']], default=None) –

    Set bucket interval units.

    By default, if bucket_units and bucket_end_condition not specified, set to seconds. If bucket_end_condition specified, then bucket_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 passing Operation 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 in group_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 = True

      bucket_end_condition is applied only to the group defined by group_by

    • end_condition_per_group = False

      bucket_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

Find interpolated value of 50% percentile rank:

t = otp.Ticks({'A': [1, 2, 3, 4]})
data = t.find_value_for_percentile('A', 50, 'interpolated_value')
df = otp.run(data)
print(df)
        Time    A
0 2003-12-04  2.5

Find first value greater or equal to 50% percentile rank:

data = t.find_value_for_percentile('A', 50, 'first_value_with_ge_percentile')
df = otp.run(data)
print(df)
        Time    A
0 2003-12-04  3.0

Use agg() method to apply several aggregations at the same time:

data = t.agg({
    'X': otp.agg.find_value_for_percentile('A', 50, 'interpolated_value'),
    'COUNT': otp.agg.count(),
})
df = otp.run(data)
print(df)
        Time    X  COUNT
0 2003-12-04  2.5      4

See also

FIND_VALUE_FOR_PERCENTILE OneTick event processor