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Enh mappable remapper #4490

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47b7fc8
ENH/RFC: decorators to deal with data frames
tacaswell Jun 2, 2015
270144b
ENH: add apply version of the decorators
tacaswell Jun 2, 2015
6657e65
DOC: fix up indentation in docstrings
tacaswell Jun 7, 2015
08edd22
MNT: settle on using np.asarray on input
tacaswell Jun 7, 2015
48d1cfa
DOC: updates docstrings
tacaswell Jun 7, 2015
bbd7356
MNT: use `asannayarray` instead of `asarray`
tacaswell Jun 7, 2015
1e5ae2e
DOC: make sphinx happy
tacaswell Jun 7, 2015
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DOC: updates docstrings
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@tacaswell
tacaswell committed Jun 7, 2015
commit 48d1cfa6f6c194bcc73b8b2a12e3b38a42ae97bc
102 changes: 90 additions & 12 deletions lib/matplotlib/cbook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2410,24 +2410,24 @@ def get_instancemethod(self):
def mappable_kwargs_decorator(func, map_targets):
"""Decompose labeled data for passing to base functions.

This is a decorator to easy the burden of unpacking
pandas data frames into base objects.
This is a decorator to ease the burden of unpacking
pandas data frames into numpy arrays.

In the returned function args and kwargs are passed through and
the specified columns are re-mapped to keys in the kwargs.

The args and kwargs are then unpacked into the function (which will
expect an `Axes` as the first input and a `DataFrame`-like objects
The args and kwargs are then unpacked into the function, which will
expect an `Axes` as the first input and a `DataFrame`-like object
as the second.

The function call ::

func(ax, x=data['foo'], y=data['bar'])
func(ax, x=data['foo'], y=data['bar'], *args, **kwargs)

is the same as ::

wrapped = mappable_kwargs_decorator(func, {'x': 'foo', 'y':'bar'})
wrapped(ax, data)
wrapped(ax, data, *args, **kwargs)

Parameters
----------
Expand Down Expand Up @@ -2469,25 +2469,25 @@ def inner(ax, data, *args, **kwargs):
def mappable_args_decorator(func, map_targets):
"""Decompose labeled data for passing to base functions.

This is a decorator to easy the burden of unpacking
pandas data frames into base objects.
This is a decorator to ease the burden of unpacking
pandas data frames into numpy arrays.

In the returned function args and kwargs are passed through and
the specified columns are re-mapped to precede the args.

The args and kwargs are then unpacked into the function (which will
expect an `Axes` as the first input and a `DataFrame`-like objects
The args and kwargs are then unpacked into the function, which will
expect an `Axes` as the first input and a `DataFrame`-like object
as the second.


The function call ::

func(ax, data['foo'], data['bar'])
func(ax, data['foo'], data['bar'], *args, **kwargs))

is the same as ::

wrapped = mappable_args_decorator(func, ('foo', 'bar'))
wrapped(ax, data)
wrapped(ax, data, *args, **kwargs)


Parameters
Expand Down Expand Up @@ -2519,6 +2519,48 @@ def inner(ax, data, *args, **kwargs):


def apply_kwargs_mapping(ax, func, data, map_targets, *args, **kwargs):
"""Decompose labeled data and pass to function

This is a convince to ease the burden of unpacking
pandas data frames into numpy arrays.

It calls the function with the specified columns are re-mapped
into the kwargs.


The function call ::
func(ax, x=data['foo'], y=data['bar'], *args, **kwargs)


is the same as ::

apply_kwargs_mapping(ax, func, data, {'x': 'foo', 'y':'bar'},
*args, **kwargs)


Parameters
----------
ax : Axes
The axes to plot to

function : callable
A function which expects an `Axes` objects as the first argument
and can take data as kwargs.

data : pandas.DataFrame or dict-like
Labeled data. It is assumed that data[key] will return something
that `np.asarray` can convert to a numpy array


map_targets : dict

Mapping between key expected by the function and the column name.
ex ``{'x': 'foo', 'y': 'bar'}`` ->
``kwargs['x'] == data['foo']`` and ``kwargs['y'] == data['bar']``



"""
for k, v in map_targets.items():
if k in kwargs:
raise ValueError(("Trying to map a column ({1} -> {0}) "
Expand All @@ -2529,6 +2571,42 @@ def apply_kwargs_mapping(ax, func, data, map_targets, *args, **kwargs):


def apply_args_mapping(ax, func, data, map_targets, *args, **kwargs):
"""Decompose labeled data and pass to function

This is a convince to ease the burden of unpacking
pandas data frames into numpy arrays.

It calls the function with the specified columns are re-mapped
to precede the args.


The function call ::

func(ax, data['foo'], data['bar'], *args, **kwargs))

is the same as ::

apply_args_mapping(ax, func, data, ('foo', 'bar'), *args, **kwargs)


Parameters
----------
ax : Axes
The axes to plot to

function : callable
A function which expects an `Axes` objects as the first argument
and can take data as kwargs.

data : pandas.DataFrame or dict-like
Labeled data. It is assumed that data[key] will return something
that `np.asarray` can convert to a numpy array

map_targets : tuple
Order of columns to extract to pass to the function. These column
are the 2nd -> n+1 args

"""
args = tuple(np.asarray(data[k]) for k in map_targets) + args
return func(ax, *args, **kwargs)

Expand Down