otp.Source.schema

property Source.schema: onetick.py.core._source.schema.Schema

Represents actual python data schema in the column-name -> type format. For example, could be used after the Source.sink() to adjust the schema.

Return type

Schema

Examples

>>> data = otp.Ticks([['X', 'Y',   'Z'],
...                   [  1, 0.5, 'abc']])
>>> data['T'] = data['Time']
>>> data.schema
{'X': <class 'int'>, 'Y': <class 'float'>, 'Z': <class 'str'>, 'T': <class 'onetick.py.types.nsectime'>}

>>> data.schema['X']
<class 'int'>

>>> data.schema['X'] = float
>>> data.schema['X']
<class 'float'>

>>> 'W' in data.schema
False
>>> data.schema['W'] = otp.nsectime
>>> 'W' in data.schema
True
>>> data.schema['W']
<class 'onetick.py.types.nsectime'>

See also

Source.sink

class Schema

Bases: collections.abc.Mapping

A source data schema proxy. It allows to work with source schema in a frozen-dict manner. It also allows to set using the set and update a source schema using the update methods.

items() → a set-like object providing a view on D's items

keys() → a set-like object providing a view on D's keys

copy()

set(**new_schema)

Drops the python schema representation of a source and sets the new one from the new_schema

Parameters

new_schema (Dict[str, type]) – schema in the column-name -> type format

Return type

None

update(**other_schema)

Updates the python schema representation of a source: values from matching keys will be overridden from the other_schema, values from new keys will be added.

Parameters

other_schema (type) – schema in the column-name -> type format

Return type

None