Detail: Batch: Accessor ValuesΒΆ

Overview: Batch: Accessor Values

Batch.via_values.apply(func, *args, **kwargs)
Batch.via_values

Interface for applying a function to values in this container.

InterfaceBatchValues.apply(func, *args, **kwargs)[source]

Interface for using binary operators and methods with a pre-defined fill value.

>>> 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_values.apply(np.sin).to_frame()
<Frame>
<Index>                a                    b                   <<U1>
<IndexHierarchy>
i                p     0.0                  0.8414709848078965
i                q     0.9092974268256817   0.1411200080598672
i                r     -0.7568024953079282  -0.9589242746631385
j                p     0.7451131604793488   -0.158622668804709
j                q     -0.9165215479156338  -0.8317747426285983
j                r     0.017701925105413577 0.8509035245341184
<<U1>            <<U1> <float64>            <float64>
Batch.via_values.__array_ufunc__(ufunc, method, *args, **kwargs)
Batch.via_values

Interface for applying a function to values in this container.

InterfaceBatchValues.__array_ufunc__(ufunc, method, *args, **kwargs)[source]

Support for applying NumPy functions directly on containers, returning NumPy arrays.

>>> 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'))))
>>> np.sin(bt.via_values).to_frame()
<Frame>
<Index>                a                    b                   <<U1>
<IndexHierarchy>
i                p     0.0                  0.8414709848078965
i                q     0.9092974268256817   0.1411200080598672
i                r     -0.7568024953079282  -0.9589242746631385
j                p     0.7451131604793488   -0.158622668804709
j                q     -0.9165215479156338  -0.8317747426285983
j                r     0.017701925105413577 0.8509035245341184
<<U1>            <<U1> <float64>            <float64>
Batch.via_values.__call__(*, consolidate_blocks, unify_blocks, dtype)
Batch.via_values

Interface for applying a function to values in this container.

InterfaceBatchValues.__call__(*, consolidate_blocks=False, unify_blocks=False, dtype=None)[source]
Parameters
  • consolidate_blocks – Group adjacent same-typed arrays into 2D arrays.

  • unify_blocks – Group all arrays into single array, re-typing to an appropriate dtype.

  • dtype – specify a dtype to be used in conversion before consolidation or unification, and before function application.

>>> 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'))))
>>> np.sin(bt.via_values(unify_blocks=True)).to_frame()
<Frame>
<Index>                a                    b                   <<U1>
<IndexHierarchy>
i                p     0.0                  0.8414709848078965
i                q     0.9092974268256817   0.1411200080598672
i                r     -0.7568024953079282  -0.9589242746631385
j                p     0.7451131604793488   -0.158622668804709
j                q     -0.9165215479156338  -0.8317747426285983
j                r     0.017701925105413577 0.8509035245341184
<<U1>            <<U1> <float64>            <float64>

Batch: Constructor | Exporter | Attribute | Method | Dictionary-Like | Display | Selector | Operator Binary | Operator Unary | Accessor Values | Accessor Datetime | Accessor String | Accessor Transpose | Accessor Fill Value | Accessor Regular Expression | Accessor Hashlib