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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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ENH: add apply version of the decorators
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@tacaswell
tacaswell committed Jun 2, 2015
commit 270144b01a9da4e691390ef2009df84a6e9ee23d
15 changes: 15 additions & 0 deletions lib/matplotlib/cbook.py
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Expand Up @@ -2515,6 +2515,21 @@ def inner(ax, data, *args, **kwargs):
return inner


def apply_kwargs_mapping(ax, func, data, map_targets, *args, **kwargs):
for k, v in map_targets.items():
if k in kwargs:
raise ValueError(("Trying to map a column ({1} -> {0}) "
"on to a passed in "
"keyword arg ({0})").format(k, v))
kwargs[k] = data[v].values
return func(ax, *args, **kwargs)


def apply_args_mapping(ax, func, data, map_targets, *args, **kwargs):
args = tuple(data[k] for k in map_targets) + args
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Why are you using the values attribute for the kwargs cases but not for the args cases?

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Oversight, but I am not fully sure which way is better. .values gets us a numpy array full-stop, but just [] gets use a Series which more-or-less behave like a numpy array.

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Not using .values also means that these decorators will work with dicts of numpy arrays. It might be better to use np.asarray.

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Omitting .values also means it will work with a numpy structured array; but I'm not sure if there would be any point in this.
Maybe use np.asanyarray in case something might yield a masked array?
I presume with a DataFrame, missing data will end up as NaN after conversion, correct?

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I think it is always NaN internally in pandas and they don't do masking/sparse at all

return func(ax, *args, **kwargs)


# Numpy > 1.6.x deprecates putmask in favor of the new copyto.
# So long as we support versions 1.6.x and less, we need the
# following local version of putmask. We choose to make a
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