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sns.distplot calculates bandwidth as zero for arrays with many zeros

See original GitHub issue

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,
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        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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        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
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        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
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        0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,
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        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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        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

grafik

Issue Analytics

  • State:closed
  • Created 4 years ago
  • Reactions:2
  • Comments:11 (6 by maintainers)

github_iconTop GitHub Comments

5reactions
henrymartin1commented, Mar 23, 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?

1reaction
mwaskomcommented, Aug 5, 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.

Read more comments on GitHub >

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