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 or asc) 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 and input_field_names must have the same number of fields.

If not set, output_field_names will be equal to input_field_names.
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, 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 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

>>> 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