Detail: Batch: Attribute

Batch.STATIC = True

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.STATIC
True

Batch.T

Transpose. Return a Frame with index as columns and vice versa.

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.T
<Batch max_workers=None>

Batch.memory

Return a MemoryDisplay, providing the size in memory of this object. For compound containers, component sizes will also be provided. Size can be interpreted through six combinations of three configurations:

L: Local: memory ignoring referenced array data provided via views. LM: Local Materialized: memory where arrays that are locally owned report their byte payload LMD: Local Materialized Data: locally owned memory of arrays byte payloads, excluding all other components

R: Referenced: memory including referenced array data provided via views RM: Referenced Materialized: memory where arrays that are locally owned or referenced report their byte payload RMD: Referenced Materialized Data: localy owned and referenced array byte payloads, excluding all other components

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.memory
      L    Lu    LM  LMu   LMD  LMDu  R     Ru    RM   RMu   RMD  RMDu
Total 4.98 KB    5.1 KB    3.85 KB    13.09 KB    5.18 KB    3.93 KB

Batch.name

A hashable label attached to this container.

Returns:: Hashable

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.name

Batch.shapes

A Series describing the shape of each iterated Frame.

Returns:: tp.Tuple[int]

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.shapes
<Series: shape>
<Index>
i               (3, 2)
j               (3, 2)
<<U1>           <object>

Batch.via_container

Return a new Batch with all values wrapped in either a Frame or Series.

>>> bt = sf.Batch((('i', sf.Frame(np.arange(6).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='x')), ('j', sf.Frame(np.arange(40, 46).reshape(3,2), index=('p', 'q', 'r'), columns=('a', 'b'), name='v'))))
>>> bt.via_container
<Batch max_workers=None>