Why are the limits of the histogram data autodetected as [nan, nan] instead of discarding NaNs? - pandas

Question

The following code generates an error
print(g['resp'])
par = {'hist': True, 'kde': False, 'fit': scipy.stats.norm, 'bins': 'auto'}
sns.distplot(g['resp'], color='blue', **par)
31 23.0
32 28.0
33 29.0
34 31.0
35 32.0
36 35.0
37 35.0
38 36.0
39 37.0
40 38.0
41 38.0
42 38.0
43 41.0
44 42.0
45 42.0
46 42.0
47 42.0
48 46.0
49 48.0
50 49.0
51 50.0
52 52.0
53 55.0
54 56.0
55 60.0
56 60.0
57 100.0
58 NaN
59 NaN
60 NaN
61 NaN
Name: resp, dtype: float64
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-23-42944bf1e405> in <module>
1 print(g['resp'])
2 par = {'hist': True, 'kde': False, 'fit': scipy.stats.norm, 'bins': 'auto'}
----> 3 sns.distplot(g['resp'], color='blue', **par)
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\distributions.py in distplot(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)
223 hist_color = hist_kws.pop("color", color)
224 ax.hist(a, bins, orientation=orientation,
--> 225 color=hist_color, **hist_kws)
226 if hist_color != color:
227 hist_kws["color"] = hist_color
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\__init__.py in inner(ax, data, *args, **kwargs)
1808 "the Matplotlib list!)" % (label_namer, func.__name__),
1809 RuntimeWarning, stacklevel=2)
-> 1810 return func(ax, *args, **kwargs)
1811
1812 inner.__doc__ = _add_data_doc(inner.__doc__,
C:\ProgramData\Anaconda3\lib\site-packages\matplotlib\axes\_axes.py in hist(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, **kwargs)
6589 # this will automatically overwrite bins,
6590 # so that each histogram uses the same bins
-> 6591 m, bins = np.histogram(x[i], bins, weights=w[i], **hist_kwargs)
6592 m = m.astype(float) # causes problems later if it's an int
6593 if mlast is None:
C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\histograms.py in histogram(a, bins, range, normed, weights, density)
708 a, weights = _ravel_and_check_weights(a, weights)
709
--> 710 bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
711
712 # Histogram is an integer or a float array depending on the weights.
C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\histograms.py in _get_bin_edges(a, bins, range, weights)
331 "bins is not supported for weighted data")
332
--> 333 first_edge, last_edge = _get_outer_edges(a, range)
334
335 # truncate the range if needed
C:\ProgramData\Anaconda3\lib\site-packages\numpy\lib\histograms.py in _get_outer_edges(a, range)
259 if not (np.isfinite(first_edge) and np.isfinite(last_edge)):
260 raise ValueError(
--> 261 "autodetected range of [{}, {}] is not finite".format(first_edge, last_edge))
262
263 # expand empty range to avoid divide by zero
ValueError: autodetected range of [nan, nan] is not finite
It looks like the NaN values are causing trouble - how to discard them?

Answer 1

I think not, so possible solution is Series.dropna for remove missing values:
sns.distplot(g['resp'].dropna(), color='blue', **par)