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Fix singleton hist labels #12572

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Merged
merged 2 commits into from
Oct 22, 2018
Merged

Fix singleton hist labels #12572

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5258ec0
FIX: allow singleton labels in hist
jklymak Oct 19, 2018
f38b1c9
TST: test for hist labels allowing singletons
jklymak Oct 19, 2018
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2 changes: 2 additions & 0 deletions lib/matplotlib/axes/_axes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6758,6 +6758,8 @@ def hist(self, x, bins=None, range=None, density=None, weights=None,
labels = [None]
elif isinstance(label, str):
labels = [label]
elif not np.iterable(label):
labels = [str(label)]
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I think you can combine this case with the one above it

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Unfortunately strings are iterable, so I don't think I can...

In [1]: boo = 'who'

In [2]: import numpy as np

In [3]: np.iterable(boo)
Out[3]: True

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This is exactly the code required to make int labels work.

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My mistake, I thought np.iterable ignored strings. np.ndim(boo) == 0 would work, but is perhaps slower.

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np.ndim looks useful. I quite readily get confused with the best way to test if something is a list, ndarray, or singleton, and our codebase uses a few incantations. I would be fine w/ changing this to something that is an agreed-upon standard way to check. This logic only gets called when a hist is added to the axes, so I don't think we need to worry overly much about performance.

else:
labels = [str(lab) for lab in label]

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15 changes: 15 additions & 0 deletions lib/matplotlib/tests/test_axes.py
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Expand Up @@ -3073,6 +3073,21 @@ def test_hist_emptydata():
ax.hist([[], range(10), range(10)], histtype="step")


def test_hist_labels():
# test singleton labels OK
fig, ax = plt.subplots()
l = ax.hist([0, 1], label=0)
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=[0])
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label=None)
assert l[2][0].get_label() == '_nolegend_'
l = ax.hist([0, 1], label='0')
assert l[2][0].get_label() == '0'
l = ax.hist([0, 1], label='00')
assert l[2][0].get_label() == '00'


@image_comparison(baseline_images=['transparent_markers'], remove_text=True)
def test_transparent_markers():
np.random.seed(0)
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