otp.Source.transpose#

Source.transpose(inplace=False, direction='rows', n=None)#

Data transposing. The main idea is joining many ticks into one or splitting one tick to many.

Parameters

inplace (bool, default=False) – if True method will modify current object, otherwise it will return modified copy of the object.
direction ('rows', 'columns', default='rows') –
- rows: join certain input ticks (depending on other parameters) with preceding ones. Fields of each tick will be added to the output tick and their names will be suffixed with _K where K is the positional number of tick (starting from 1) in reverse order. So fields of current tick will be suffixed with _1, fields of previous tick will be suffixed with _2 and so on.
- columns: the operation is opposite to rows. It splits each input tick to several output ticks. Each input tick must have fields with names suffixed with _K where K is the positional number of tick (starting from 1) in reverse order.
n (Optional[int], default=None) – must be specified only if direction is ‘rows’. Joins every n number of ticks with n-1 preceding ticks.

Returns

If inplace parameter is True method will return None,
otherwise it will return modified copy of the object.

Return type

Optional[onetick.py.core.source.Source]

Examples

Merging two ticks into one.

>>> data = otp.Ticks(dict(A=[1, 2],
...                       B=[3, 4]))
>>> data = data.transpose(direction='rows', n=2)
>>> otp.run(data)
                     Time             TIMESTAMP_1  A_1  B_1 TIMESTAMP_2  A_2  B_2
0 2003-12-01 00:00:00.001 2003-12-01 00:00:00.001    2    4  2003-12-01    1    3

And splitting them back into two.

>>> data = data.transpose(direction='columns')
>>> otp.run(data)
                     Time  A  B
0 2003-12-01 00:00:00.000  1  3
1 2003-12-01 00:00:00.001  2  4