Issue 1:
kdeplot() offers an argument fill. It would be nice to offer this argument to lineplot() as well.

sns.lineplot(
data=data, x="x", y="y", hue="category", fill=True, palette="crest", alpha=.5, linewidth=0
)

(image from kdeplot docu)

Keep in mind that the lineplot can be used in the context of a polar plot (axis projection: "polar").

PS: happy New Year!

Issue 2:
I'm trying to plot a seaborn histogram using the following command to get the gaussian kernel density estimation.

sns.distplot(df['charges'], kde=True)

I'm using

seaborn version 0.7.1

windows machine

jupyter notebooks

I'm getting the following error and I only see histogram but not kde.

TypeError Traceback (most recent call last)

in ()

----> 1 sns.distplot(df['charges'], bins=50, kde=True)

C:\Users\Niranjan\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)

219 if kde:

220 kde_color = kde_kws.pop("color", color)

--> 221 kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws)

222 if kde_color != color:

223 kde_kws["color"] = kde_color

C:\Users\Niranjan\Anaconda3\lib\site-packages\seaborn\distributions.py in kdeplot(data, data2, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, ax, **kwargs)

602 ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,

603 gridsize, cut, clip, legend, ax,

--> 604 cumulative=cumulative, **kwargs)

605

606 return ax

C:\Users\Niranjan\Anaconda3\lib\site-packages\seaborn\distributions.py in _univariate_kdeplot(data, shade, vertical, kernel, bw, gridsize, cut, clip, legend, ax, cumulative, **kwargs)

268 x, y = _statsmodels_univariate_kde(data, kernel, bw,

269 gridsize, cut, clip,

--> 270 cumulative=cumulative)

271 else:

272 # Fall back to scipy if missing statsmodels

C:\Users\Niranjan\Anaconda3\lib\site-packages\seaborn\distributions.py in _statsmodels_univariate_kde(data, kernel, bw, gridsize, cut, clip, cumulative)

326 fft = kernel == "gau"

327 kde = smnp.KDEUnivariate(data)

--> 328 kde.fit(kernel, bw, fft, gridsize=gridsize, cut=cut, clip=clip)

329 if cumulative:

330 grid, y = kde.support, kde.cdf

C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py in fit(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)

144 density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,

145 adjust=adjust, weights=weights, gridsize=gridsize,

--> 146 clip=clip, cut=cut)

147 else:

148 density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,

C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py in kdensityfft(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)

504 zstar = silverman_transform(bw, gridsize, RANGE)*y # 3.49 in Silverman

505 # 3.50 w Gaussian kernel

--> 506 f = revrt(zstar)

507 if retgrid:

508 return f, grid, bw

C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py in revrt(X, m)

18 if m is None:

19 m = len(X)

---> 20 y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

21 return np.fft.irfft(y)*m

22

TypeError: slice indices must be integers or None or have an index method