Arbitrary operations on DataFrames

apply

class timeflux.nodes.apply.ApplyMethod(method, apply_mode='universal', axis=0, closed='right', func=None, **kwargs)[source]

Bases: timeflux.core.node.Node

Apply a function along an axis of the DataFrame.

This node applies a function along an axis of the DataFrame. Objects passed to the function are Series objects whose index is either the DataFrame’s index (axis = 0) or the DataFrame’s columns (axis = 1).

Variables

i (Port) – default data input, expects DataFrame.
o (Port) – default output, provides DataFrame.

Parameters

func (func) – custom function specified directely that takes as input a n_array (eg. lambda x: x+1). Default: None.
method (str) – name of the module to import, in which the method is defined. eg. numpy.mean.
apply_mode (str`) – {universal, reduce, expand }. Default: universal. - universal if function is a transformation from n_array to n_array - reduce if function is a transformation from n_array to scalar - expand if function is a transformation from n_array to nk_array [not yet implemented]
axis (int) – if 0, the transformation is applied to columns, if 1 to rows. Default: 0.
closed (str) – {left, right, center}: timestamp to transfer in the output, only when method_type is “reduce” and axis = 0, in which case, the output port’s lenght is 1. Default: right.
kwargs – additional keyword arguments to pass as keywords arguments to func.

Notes

Note that the passed function will receive ndarray objects for performance purposes. For universal functions, ie. transformation from n_array to n_array, input and output ports have the same size. For reducing function, ie. from n_array to scalar, output ports’s index is set to first (if closed = left), last (if closed = right), or middle (if closed = center)

Todo

Allow expanding functions such as n_array to nk_array (with XArray usage)

Example

Universal function: in this example, we apply numpy.sqrt to each value of the data. Shapes of input and output data are the same.

method = numpy.sqrt
method_type = universal

If data in input port is i is:

                                   0
2018-10-25 07:33:41.871131         9.0
2018-10-25 07:33:41.873084         16.0
2018-10-25 07:33:41.875037         1.0
2018-10-25 07:33:41.876990         4.0

It returns the squared root of the data on port o:

                                   0
2018-10-25 07:33:41.871131         3.0
2018-10-25 07:33:41.873084         4.0
2018-10-25 07:33:41.875037         1.0
2018-10-25 07:33:41.876990         2.0

Example

Reducing function: in this example, we apply numpy.sum to each value of the data. Shapes of input and output data are not the same. We set:

method = numpy.sum
method_type = reduce
axis = 0
closed = right

If data in input port is i is:

                                   0        1
2018-10-25 07:33:41.871131         9.0      10.0
2018-10-25 07:33:41.873084         16.0     2.0
2018-10-25 07:33:41.875037         1.0      5.0
2018-10-25 07:33:41.876990         4.0      2.0

It returns the sum amongst row axis on port o:

                                   0        1
2018-10-25 07:33:41.876990         30.0     19.0

References

See the documentation of pandas.apply .

Instantiate the node.

update()[source]: Update the input and output ports.