otp.Source.process_by_group#

Source.process_by_group(process_source_func, group_by=None, source_name=None, inplace=False)[source]#

Groups data by group_by and run process_source_func for each group and merge outputs for every group. Note process_source_func will be converted to Onetick object and passed to query, that means that python callable will be called only once.

Parameters

process_source_func (callable) – process_source_func should take Source apply necessary logic and return it or tuple of Source in this case all of them should have a common root that is the input Source.
group_by (list) –
A list of field names to group input ticks by.

If group_by is None then no group_by fields are defined and logic of process_source_func is applied to all input ticks at once
source_name (str) – A name for the source that represents all of group_by sources. Can be passed here or as a name of the inner sources; if passed by both ways, should be consistent
inplace (bool) – If True - nothing will be returned and changes will be applied to current query otherwise chanegs query will be returned

Return type

Source, Tuple[Source] or None

Examples

>>> d = otp.Ticks(X=[1, 1, 2, 2],
...               Y=[1, 2, 3, 4])
>>> def func(source):
...     return source.first()
>>> res = d.process_by_group(func, group_by=['X'])
>>> otp.run(res)[["X", "Y"]]
   X  Y
0  1  1
1  2  3

>>> d = otp.Ticks(X=[1, 1, 2, 2],
...               Y=[1, 2, 1, 3])
>>> def func(source):
...     source['Z'] = source['X']
...     source2 = source.copy()
...     source = source.first()
...     source2 = source2.last()
...     return source, source2
>>> res1, res2 = d.process_by_group(func, group_by=['Y'])
>>> df1 = otp.run(res1)
>>> df2 = otp.run(res2)
>>> df1[['X', 'Y', 'Z']]
   X  Y  Z
0  1  1  1
1  1  2  1
2  2  3  2
>>> df2[['X', 'Y', 'Z']]
   X  Y  Z
0  1  2  1
1  2  1  2
2  2  3  2