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Fixed KDE Plot to drop the missing values #14820

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Merged
merged 10 commits into from
Dec 15, 2016
Merged

Fixed KDE Plot to drop the missing values #14820

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45a99ec
fixed kde plot to ignore the missing values
Dec 8, 2016
101b0d4
added comment to elaborate the changes made
keshavramaswamy Dec 8, 2016
d2657e3
added a release note in whatsnew/0.19.2
Dec 8, 2016
24a6286
Merge branch 'kde-fix' of https://github.com/keshavramaswamy/pandas i…
Dec 8, 2016
bba56a0
merged with master
Dec 10, 2016
59db2c6
added test to check for missing values and cleaned up whatsnew doc
Dec 11, 2016
e9f67e3
added comment to refer the issue
Dec 11, 2016
613cd72
modified to fit lint checks
Dec 11, 2016
c3237f0
Merge remote-tracking branch 'upstream/master' into kde-fix
Dec 14, 2016
4273f8e
replaced ._xorig with .get_xdata()
keshavramaswamy Dec 14, 2016
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2 changes: 2 additions & 0 deletions doc/source/whatsnew/v0.19.2.txt
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Expand Up @@ -79,4 +79,6 @@ Bug Fixes

- Explicit check in ``to_stata`` and ``StataWriter`` for out-of-range values when writing doubles (:issue:`14618`)

- Bug in ``.plot(kind='kde')`` which did not drop missing values to generate the KDE Plot, instead generating an empty plot. (:issue:`14821`)

- Bug in ``unstack()`` if called with a list of column(s) as an argument, regardless of the dtypes of all columns, they get coerced to ``object`` (:issue:`11847`)
6 changes: 5 additions & 1 deletion pandas/tests/plotting/test_series.py
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Expand Up @@ -569,7 +569,11 @@ def test_kde_missing_vals(self):
_skip_if_no_scipy_gaussian_kde()
s = Series(np.random.uniform(size=50))
s[0] = np.nan
_check_plot_works(s.plot.kde)
axes = _check_plot_works(s.plot.kde)
# check if the values have any missing values
# GH14821
self.assertTrue(any(~np.isnan(axes.lines[0].get_xdata())),
msg='Missing Values not dropped')

@slow
def test_hist_kwargs(self):
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7 changes: 4 additions & 3 deletions pandas/tools/plotting.py
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Expand Up @@ -2153,9 +2153,10 @@ def _args_adjust(self):

def _get_ind(self, y):
if self.ind is None:
sample_range = max(y) - min(y)
ind = np.linspace(min(y) - 0.5 * sample_range,
max(y) + 0.5 * sample_range, 1000)
# np.nanmax() and np.nanmin() ignores the missing values
sample_range = np.nanmax(y) - np.nanmin(y)
ind = np.linspace(np.nanmin(y) - 0.5 * sample_range,
np.nanmax(y) + 0.5 * sample_range, 1000)
else:
ind = self.ind
return ind
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