Seaborn: sns.distplot calculates bandwidth as zero for arrays with many zeros
Description
Hey Everybody,
I have encountered unexpected behavior when I tried to create a seaborn distplot from an array with many zeros. I know that this is probably a weird corner case that doesn't seem very urgent but I think there is some value reporting it. I could not manage to create suitable synthetic data but I have included my original data to reproduce it, I hope that's ok.
Keep on the good work with seaborn, its a pleasure using it!
Best,
Henry
Versions:
Seaborn='0.10.0'
Matplotlib='3.1.3'
Error Message:
C:\Users\...\.conda\envs\...\python.exe C:/Users/henry/.PyCharmCE2019.3/config/scratches/scratch.py
Traceback (most recent call last):
File "C:\Users\...\.conda\envs\...\lib\site-packages\statsmodels\nonparametric\kde.py", line 451, in kdensityfft
bw = float(bw)
ValueError: could not convert string to float: 'scott'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:/Users/.../.PyCharmCE2019.3/config/scratches/scratch.py", line 555, in <module>
ax = sns.distplot(a, rug=True, hist=False, label='UW')
File "C:\Users\...\.conda\envs\...\lib\site-packages\seaborn\distributions.py", line 233, in distplot
kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws)
File "C:\Users\...\.conda\envs\...\lib\site-packages\seaborn\distributions.py", line 705, in kdeplot
cumulative=cumulative, **kwargs)
File "C:\Users\...\.conda\envs\...\lib\site-packages\seaborn\distributions.py", line 295, in _univariate_kdeplot
cumulative=cumulative)
File "C:\Users\...\.conda\envs\...\lib\site-packages\seaborn\distributions.py", line 367, in _statsmodels_univariate_kde
kde.fit(kernel, bw, fft, gridsize=gridsize, cut=cut, clip=clip)
File "C:\Users\...\.conda\envs\...\lib\site-packages\statsmodels\nonparametric\kde.py", line 140, in fit
clip=clip, cut=cut)
File "C:\Users\...\.conda\envs\....\lib\site-packages\statsmodels\nonparametric\kde.py", line 453, in kdensityfft
bw = bandwidths.select_bandwidth(X, bw, kern) # will cross-val fit this pattern?
File "C:\Users\....\.conda\envs\....\lib\site-packages\statsmodels\nonparametric\bandwidths.py", line 174, in select_bandwidth
raise RuntimeError(err)
RuntimeError: Selected KDE bandwidth is 0. Cannot estiamte density.
Process finished with exit code 1
Example code
very long example code
import seaborn as sns
a = [ 6.44158112e+00, 2.22931748e+00, 1.90461774e+00, 1.62838104e+00,
1.44450213e+00, 1.32120694e+00, -1.37449727e+00, -1.34068670e+00,
-1.22437811e+00, 1.08203506e+00, 1.00687963e+00, -1.08464463e+00,
-9.92682231e-01, 8.77397031e-01, -8.18224584e-01, -7.88574259e-01,
-7.39802046e-01, 7.26716930e-01, 7.20018861e-01, 7.05212335e-01,
-6.44688312e-01, 6.36198782e-01, -5.76895770e-01, 6.03964108e-01,
5.76204893e-01, 5.69689909e-01, 5.52118112e-01, 5.25401516e-01,
-4.18012528e-01, 4.59933207e-01, 4.43555421e-01, 4.41596376e-01,
4.39098156e-01, 4.28526102e-01, 4.20569936e-01, 4.15588319e-01,
3.98167467e-01, 3.79937382e-01, 3.69704675e-01, 3.48799274e-01,
3.15384843e-01, 3.15267651e-01, 3.12962586e-01, 2.89400742e-01,
2.95672458e-01, -2.66440651e-01, 2.53409343e-01, 2.45168039e-01,
2.33842507e-01, 2.18591293e-01, 2.17178734e-01, 2.14425229e-01,
2.11277792e-01, 2.10006902e-01, 1.97309425e-01, 1.82623551e-01,
1.78820952e-01, 1.75251894e-01, 1.69346510e-01, -2.23685152e-01,
-2.23374659e-01, -2.17195278e-01, -2.20288950e-01, 1.63397981e-01,
1.55450837e-01, 1.55034695e-01, 1.50501798e-01, 1.42519918e-01,
1.31807907e-01, 1.38860666e-01, 1.35390009e-01, 1.37646864e-01,
1.37242513e-01, 1.21238699e-01, 1.19726271e-01, 1.13726245e-01,
1.11266238e-01, 1.10557847e-01, 1.08976907e-01, -2.14119343e-01,
1.05026061e-01, 1.05515901e-01, 9.95445982e-02, 9.10744981e-02,
-2.09274377e-01, -2.03248740e-01, -2.01819404e-01, -2.01263355e-01,
-2.00151327e-01, -1.99609002e-01, -1.99324882e-01, -1.99020335e-01,
-1.98850700e-01, -1.97380528e-01, -1.95276893e-01, -1.92891803e-01,
-1.89409380e-01, -1.89497128e-01, -1.89755567e-01, -1.90709324e-01,
-1.91072552e-01, -1.91251750e-01, -1.91612635e-01, -1.91484739e-01,
-1.91446185e-01, -1.87141562e-01, -1.86916499e-01, -1.85361033e-01,
-1.78510889e-01, -1.79920662e-01, -1.82477178e-01, -1.83361572e-01,
-1.84418528e-01, -1.84353793e-01, -1.84203884e-01, -1.63041863e-01,
-1.79187162e-01, -1.76616619e-01, -1.76058466e-01, -1.81513868e-01,
-1.74516815e-01, -1.75429097e-01, -1.64305490e-01, -1.73209698e-01,
-1.81287911e-01, -1.72931546e-01, -1.81305962e-01, -1.67938028e-01,
-1.68547032e-01, -1.70654936e-01, -1.71224488e-01, -1.70046221e-01,
-1.69553459e-01, -1.67849033e-01, -1.68964827e-01, -1.68950514e-01,
-1.69532848e-01, 8.37116094e-02, 8.23125124e-02, 8.15529990e-02,
8.11596805e-02, 8.08502063e-02, 7.72547025e-02, 7.65640185e-02,
7.01004415e-02, 6.62348263e-02, 6.59924193e-02, 6.46424494e-02,
5.83199746e-02, -1.61669672e-01, -1.61406270e-01, -1.61529541e-01,
-1.60591827e-01, -1.60479389e-01, -1.59761896e-01, -1.59576433e-01,
-1.56387956e-01, -1.55819170e-01, -1.57978147e-01, -1.36274966e-01,
-1.43409731e-01, -1.43110906e-01, -1.58415985e-01, -1.53305601e-01,
-1.46081258e-01, -1.58444168e-01, -1.52835757e-01, -1.38571159e-01,
-1.52311674e-01, -1.38007676e-01, -1.50877098e-01, -1.50393344e-01,
-1.47580092e-01, -1.48095339e-01, -1.49929040e-01, -1.50207709e-01,
-1.47717919e-01, 5.67238171e-02, 5.56471282e-02, 5.40983928e-02,
5.38816730e-02, 5.43976521e-02, 4.58309306e-02, 5.05709835e-02,
4.96411935e-02, 4.94740307e-02, -1.29477913e-01, -1.31885780e-01,
-1.32769128e-01, -1.35228537e-01, -1.38915251e-01, -1.33084403e-01,
-1.27640698e-01, -1.35115744e-01, -1.48678080e-01, -1.49160991e-01,
-1.49138897e-01, -1.24029421e-01, -1.49157217e-01, -1.22302996e-01,
-1.21170579e-01, -9.81788195e-02, -1.18786846e-01, -1.20252946e-01,
-1.20895307e-01, -1.19927552e-01, -1.15299137e-01, -1.16483578e-01,
-1.00593212e-01, -9.98744104e-02, -1.00191733e-01, -1.15729095e-01,
-1.13453486e-01, -1.11293466e-01, -1.09606570e-01, -1.11860069e-01,
-1.04643493e-01, -1.13444140e-01, -1.05704060e-01, -1.07932356e-01,
-1.07308795e-01, -1.07061131e-01, -1.06332775e-01, -1.05680215e-01,
-1.05556510e-01, -1.06531647e-01, -1.06546074e-01, 4.46522709e-02,
4.26326376e-02, 4.18579030e-02, 3.95789224e-02, 3.67029722e-02,
-9.07252905e-02, -9.31991170e-02, -9.48009629e-02, -9.37511452e-02,
3.77194782e-02, 3.63218933e-02, 3.64345478e-02, 3.50365062e-02,
3.47210397e-02, 3.22271144e-02, 3.27813039e-02, 3.01730225e-02,
2.75490276e-02, -8.58690329e-02, -8.38566794e-02, 2.55098638e-02,
2.63265642e-02, 2.69203628e-02, -8.25406187e-02, -7.89080276e-02,
-8.18018371e-02, -8.08433316e-02, -8.27116088e-02, 2.80862357e-02,
2.26070834e-02, 2.11098929e-02, 1.94462773e-02, 1.87655915e-02,
1.79644481e-02, 1.61079425e-02, 1.67037111e-02, 1.74944810e-02,
-7.88106740e-02, -8.07984234e-02, -8.07135524e-02, 1.07818841e-02,
1.20588749e-02, 1.37124959e-02, -7.70532180e-02, -7.62938575e-02,
-7.78147109e-02, -7.59550947e-02, -7.57975654e-02, -7.35855022e-02,
1.40408613e-02, -6.76752283e-02, -6.87429319e-02, -6.94180556e-02,
-7.14036197e-02, -7.11021845e-02, -6.99441693e-02, 1.18585177e-02,
-6.16563322e-02, -7.46773776e-02, -6.82789447e-02, -6.11811485e-02,
-5.89809954e-02, -5.66514754e-02, -5.58221355e-02, -5.51303038e-02,
-5.39643569e-02, -5.33207840e-02, -5.27445706e-02, -5.21709381e-02,
-4.61021790e-02, -4.42446168e-02, -4.41405884e-02, -4.45180547e-02,
-5.04652409e-02, -4.80008972e-02, -4.40915878e-02, -4.74504462e-02,
-4.73400376e-02, -4.78504585e-02, -4.85186333e-02, -5.04339005e-02,
-5.02457771e-02, -5.03974339e-02, 1.33749380e-02, 1.10408305e-02,
9.01743685e-03, 8.43389771e-03, -3.87831428e-02, -4.28769616e-02,
-4.14306446e-02, -4.10137655e-02, -4.39109163e-02, -4.38856318e-02,
6.11191333e-03, 5.97722565e-03, 5.49410749e-03, 4.09716218e-03,
2.72398131e-03, 2.27430876e-03, 2.01532595e-03, 1.52393434e-03,
6.78264082e-04, 2.86254191e-04, 1.72368287e-04, -3.90050641e-02,
-3.60705360e-04, -3.84656223e-02, -3.85343367e-02, -3.81052277e-02,
-3.80782952e-02, -3.71953838e-02, -3.69504859e-02, -3.67808849e-02,
-3.66022877e-02, -3.59646413e-02, -3.52132050e-02, -3.36462876e-02,
-3.40490035e-02, -3.18415071e-02, -3.27403419e-02, -3.43256771e-02,
-3.43204148e-02, -3.24156593e-02, -3.05766265e-02, -3.30033619e-02,
-2.98220002e-02, -3.01131585e-02, -3.03290421e-02, -2.99423896e-02,
-2.88595658e-02, -2.87151243e-02, -2.80879787e-02, -2.74451726e-02,
-2.71362640e-02, -2.69734837e-02, -2.66375744e-02, -7.54322044e-04,
-1.23093022e-03, -1.38938584e-03, -1.40447606e-03, -2.55350500e-02,
-2.53044848e-02, -2.52834725e-02, -2.50011349e-02, -2.41489003e-02,
-2.45273573e-02, -1.70504288e-03, -1.78253366e-03, -2.03871152e-03,
-2.49232162e-03, -2.10988084e-03, -2.26496525e-03, -2.69011455e-03,
-2.43301779e-02, -2.43109116e-02, -5.61679141e-03, -4.75819585e-03,
-4.57157121e-03, -2.30341475e-03, -7.26984792e-03, -3.52972031e-03,
-6.32520347e-03, -7.49145245e-03, -3.94233363e-03, -3.96198695e-03,
-8.13305576e-03, -6.71888881e-03, -7.55099622e-03, -6.52408512e-03,
-3.74535407e-03, -3.95916921e-03, -3.63936469e-03, -3.82913567e-03,
-6.54316719e-03, -2.39414903e-02, -2.39133181e-02, -2.37050536e-02,
-2.36659159e-02, -2.32695605e-02, -2.31764590e-02, -8.54702157e-03,
-8.99214600e-03, -8.86464018e-03, -2.30837945e-02, -2.30769429e-02,
-1.60867691e-02, -9.28590513e-03, -9.87791006e-03, -9.83266794e-03,
-2.27264161e-02, -2.28972009e-02, -2.28894535e-02, -2.28506962e-02,
-2.28652122e-02, -1.14488089e-02, -1.26773164e-02, -1.40918099e-02,
-1.34593134e-02, -1.33454488e-02, -1.01530025e-02, -1.34095187e-02,
-1.46555593e-02, -1.12614537e-02, -1.29164302e-02, -1.49605177e-02,
-1.08296044e-02, -1.07256005e-02, -2.15098885e-02, -1.22558325e-02,
-1.48544055e-02, -1.05348111e-02, -1.05276558e-02, -1.48605454e-02,
-2.17955599e-02, -2.21009741e-02, -2.20810729e-02, -2.20625219e-02,
-2.20169857e-02, -2.16666111e-02, -2.16598543e-02, -2.16819682e-02,
-1.22342029e-02, -1.10130980e-02, -1.02922707e-02, -1.65756767e-02,
-2.07313924e-02, -1.44611227e-02, -1.54267371e-02, -1.41747379e-02,
-2.11662878e-02, -1.52330202e-02, -2.11738629e-02, -2.10542458e-02,
-1.56997291e-02, -1.50555111e-02, -1.64983511e-02, -1.70710367e-02,
-1.77221893e-02, -1.78515526e-02, -2.08755995e-02, -1.87110205e-02,
-1.89590547e-02, -1.69000058e-02, -1.73204121e-02, -1.93520404e-02,
-2.03202500e-02, -2.06333452e-02, -1.92398459e-02, -2.01127992e-02,
-2.07540415e-02, -1.94179367e-02, -2.01220855e-02, -2.07082950e-02,
-2.05509375e-02, -2.04702399e-02, -1.70837884e-02, -1.72166447e-02,
-1.75795969e-02, -1.79566412e-02, -1.98717610e-02, -1.77591278e-02,
-1.97493766e-02, -1.80378840e-02, -1.94786238e-02, -1.99131232e-02,
-1.98986727e-02, -1.82544655e-02, -1.83911627e-02, -1.84204920e-02,
-1.84059917e-02, -1.86930325e-02, -1.95818499e-02, -1.95779855e-02,
-1.96101750e-02, -2.02479476e-02, -1.78562265e-02, -1.81803334e-02,
-1.81768789e-02, -1.76010627e-02, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
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ax = sns.distplot(a, rug=True, hist=False, label='UW')
Plot of example data
henrymartin1
👍2
All 11 comments
Thanks for reporting. This is indeed a corner case 鈥斅爄f you encounter this error, you very likely have a data set that will not be represented well by a Gaussian distribution estimate.
But efforts have been made elsewhere to make kdeplot robust to corner cases as it is used in exploratory plots (pairplot) which would be good practice here too.
The bandwidth calculation and the error itself are coming out of statsmodels. This presents two challenges. First, seaborn probably does not want to catch and ignore all RuntimeError instances raised by statsmodels here. Second, if statsmodels is not installed, scipy will be used to compute the KDE, and scipy actually doesn't raise here, and generates a reasonable density estimate (check yourself by setting seaborn.distributions._has_statsmodels to False. It would be good to understand why that is.
mwaskom
on 14 Mar 2020
👍1
If IQR = 0, statsmodels selects that for bw and it fails due to that. (they do min(iqr, sd))
So any array with many repeating numbers are affected by this bug.
Statsmodels raise an error for this, but it is eaten by some try except.
One way to fix this is to add small amount of noise, or just use sd for bandwidth. Not sure, but you could calculate bw yourself with sd and use that if iqr == 0.
ahartikainen
on 22 Mar 2020
Thanks for your answers and your interest for this corner case. I have worked around this by catching the error and setting a small bandwidth manually:
ax = sns.distplot(a, rug=True, hist=False, label='UW', kde_kws={'bw':0.1})
So I am not really in need of a solution anymore. I posted this more in case that it might reveal a hidden problem anywhere.
@mwaskom Do you think I should post it to statsmodels then?
henrymartin1
on 23 Mar 2020
👍5
An example of this issue affecting kdeplot:
Referencing a statsmodels issue (#5419, for which I just added a comment) because it affects the kde plots in seaborn when IQR = 0 leads to bw = 0
I wonder if there is a possibility to handle it with try except.
As suggested by @ahartikainen a small amount of noise is a workaround.
mycarta
on 23 Mar 2020
I wonder if there is a possibility to handle it with try except.
As I said up-thread, I don't think catching and ignoring all instances of RuntimeError from statsmodels is a good idea. If they raised a more specific error class, then it might be possible.
mwaskom
on 23 Mar 2020
Hello, I encountered the same problem in Kaggle kernel. However, the issue didn't seem to occur in my system. My data has a huge number for zeros for the column which seems to cause the problem.
System version for Seaborn - '0.9.0'
Kaggle's version - '0.10.0'
swaroop-nath
on 12 Apr 2020
Right, if you read the thread you鈥檒l see that statsmodels and scipy (which seaborn uses to compute the kde if statsmodels is not available) handle this case a bit differently.
mwaskom
on 12 Apr 2020
As a note, it looks like this behavior was introduced in statsmodels 0.11.
mwaskom
on 26 Apr 2020
Hi, I have encountered the exact same problem just with sns.jointplot and playing with bw does not help. It helps if I disable statsmodels as @mwaskom says (ie. sns.distributions._has_statsmodels = False) but then all the rest looks quite bad. Any comments on this?
jankaWIS
on 5 Aug 2020
jointplot is just using distplot behind the scenes, so I would have no comments on this beyond what you can see in this thread, because it is not a different problem.
mwaskom
on 5 Aug 2020
But for people who end up here, there are a number of suggested hacks (adding noise, setting a small-but-nonzero bandwidth) in the thread. While these successfully avoid an exception, I believe they produce a plot that misrepresents the data. If the IQR of the dataset is 0, showing a smooth curve will be misleading. It is important to think clearly about the visualization method that you are using and whether it is well-suited to the nature of the data that you want to visualize.
mwaskom
on 5 Aug 2020
👍1
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Most helpful comment
Thanks for your answers and your interest for this corner case. I have worked around this by catching the error and setting a small bandwidth manually:
So I am not really in need of a solution anymore. I posted this more in case that it might reveal a hidden problem anywhere.
@mwaskom Do you think I should post it to statsmodels then?