query

class timeflux.nodes.query.SelectRange(ranges, axis=0, inclusive=False)[source]

Bases: timeflux.core.node.Node

Select a subset of the given data along vertical (index) or horizontal (columns) axis.

Variables:

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

Parameters:

ranges (dict) – Dict with keys are level names and values are selection ranges.
axis (int) – If 0, the level concerns row index, if 1, columns index (0 or 1). Default: 0.
inclusive (bool) – Whether the boundaries are strict or included. Default: False.

Example

In this example, we have an input DataFrame with multi level columns and we want to select data with index from level of name second in range [1,1.5]. We set:

ranges = {“second”: [1, 1.5]}
axis = 1
inclusive = True

If the data received on port i is:

first                                 A                        ...            B
second                              1.3       1.6       1.9                 1.3       1.6       1.9
2017-12-31 23:59:59.998745401  0.185133  0.541901  0.806561    ...     0.732225  0.806561  0.658783
2018-01-01 00:00:00.104507143  0.692277  0.849196  0.987668    ...     0.489425  0.221209  0.987668
2018-01-01 00:00:00.202319939  0.944059  0.039427  0.567945    ...     0.925248  0.180575  0.567945

The data provided on port o will be:

first                                 A         B
second                              1.3       1.3
2017-12-31 23:59:59.998745401  0.185133  0.732225
2018-01-01 00:00:00.104507143  0.692277  0.489425
2018-01-01 00:00:00.202319939  0.944059  0.925248

Instantiate the node.

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

class timeflux.nodes.query.XsQuery(key, **kwargs)[source]

Bases: timeflux.core.node.Node

Returns a cross-section (row(s) or column(s)) from the data.

Variables:

i (Port) – default input, expects DataFrame with eventually MultiIndex.
o (Port) – default output, provides DataFrame with eventually MultiIndex.

Parameters:

key (str|tuple) – Some label contained in the index, or partially in a MultiIndex index.
axis (int) – Axis to retrieve cross-section on (0 or 1). Default: 0.
level (str|int|tuple) – In case of a key partially contained in a MultiIndex, indicates which levels are used. Levels can be referred by label or position.
drop_level (bool) – If False, returns DataFrame with same level. Default: False.

Example

In this example, we have an input DataFrame with multi level columns and we want to select cross section between B from level of name first and 1 from level of name second. We set:

key = (“B”, 1)

axis = 1

level = [“first”, “second”]

drop_level = False

If the data received on port i is:

first                                 A              ...            B
second                                1         2    ...            1         2
2017-12-31 23:59:59.998745401  0.185133  0.541901    ...     0.297349  0.806561
2018-01-01 00:00:00.104507143  0.692277  0.849196    ...     0.844549  0.221209
2018-01-01 00:00:00.202319939  0.944059  0.039427    ...     0.120567  0.180575

The data provided on port o will be:

first                                 B
second                                1
2018-01-01 00:00:00.300986584  0.297349
2018-01-01 00:00:00.396560186  0.844549
2018-01-01 00:00:00.496559945  0.120567

References

See the documentation of pandas.DataFrame.xs .

Parameters:

key (str|tuple) – Some label contained in the index, or partially in a MultiIndex index.
kwargs – Keyword arguments to call pandas xs method: axis, level, drop_level

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

class timeflux.nodes.query.LocQuery(key, axis=1)[source]

Bases: timeflux.core.node.Node

Slices DataFrame on group of rows and columns by label(s)

Variables:

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

Parameters:

key (str|list|tuple) – Label selection specification.
axis (int) – Axis to query the label from (0 or 1). Default: 1.

Example

In this example, we have an input DataFrame with 5 columns [A, B, C, D, E] and we want to select columns A and E. We set:

key = [“A”, “E”]
axis = 1

If the data received on port i is:

                                  A         B        ...         E         F
2017-12-31 23:59:59.998745401  0.185133  0.541901    ...     0.806561  0.658783
2018-01-01 00:00:00.104507143  0.692277  0.849196    ...     0.221209  0.987668
2018-01-01 00:00:00.202319939  0.944059  0.039427    ...     0.180575  0.567945

The data provided on port o will be:

                                   A         E
2017-12-31 23:59:59.998745401  0.185133  0.806561
2018-01-01 00:00:00.104507143  0.692277  0.221209
2018-01-01 00:00:00.202319939  0.944059  0.180575

References

See the documentation of pandas.DataFrame.loc .

Instantiate the node.

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

class timeflux.nodes.query.Match(expression)[source]

Bases: timeflux.core.node.Node

Extract columns matching a regular expression

Variables:

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

Parameters:

expression (str) – Regular expression to match against.

Instantiate the node.

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