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Fix ValueError being raised when plotting hist and hexbin on empty dataset (Fix #3886) #4119

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Merged
merged 7 commits into from
Mar 30, 2015
Merged

Fix ValueError being raised when plotting hist and hexbin on empty dataset (Fix #3886) #4119

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e864d94
Fix hexbin raising ValueError on empty dataset
Feb 17, 2015
287a815
Fix hist raising ValueError on empty dataset
Feb 17, 2015
6deba68
Add explanation for test_hist_empty
Feb 17, 2015
74a4266
Fix test_hist_empty and test_hexbin_empty test cases. Improve docstri…
Feb 18, 2015
a567658
Fix hist to return correct patches on empty input. Add test for hist …
Feb 18, 2015
2bf9777
Fix width and weights for hist on empty input. Update tests for empty…
Mar 14, 2015
22c6c7f
Fix hexbin to use len instead of any. Fix hist to pass in bin_range a…
Mar 19, 2015
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Fix hist raising ValueError on empty dataset
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Umair Idris committed Feb 17, 2015
commit 287a815f4ac96c93f2bf82b46257b1d4aaa5e21a
2 changes: 2 additions & 0 deletions 2 lib/matplotlib/axes/_axes.py
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Expand Up @@ -5605,6 +5605,8 @@ def hist(self, x, bins=10, range=None, normed=False, weights=None,

# basic input validation
flat = np.ravel(x)
if len(flat) == 0:
return [], [], cbook.silent_list('No Patches')
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Do we want do draw no patches or patches of zero size? On consideration I think patches of zero size might be a better option here so client code which expects a non-zero length list does not have to check that it is true. For example if the user passes in bin edges, they should get back a list of zeros telling them that there are no counts in any of the bins.

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@efiring @mdboom @WeatherGod @pelson Thoughts, I think that this is an important API choice that needs to be made.


# Massage 'x' for processing.
# NOTE: Be sure any changes here is also done below to 'weights'
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3 changes: 3 additions & 0 deletions 3 lib/matplotlib/tests/test_axes.py
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Expand Up @@ -1005,6 +1005,9 @@ def test_hist_log():
ax.hist(data, fill=False, log=True)


def test_hist_empty():
ax.hist([])
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Same comment as for the other test.

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Done.


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