<p dir="auto"><a href="https://seaborn.pydata.org/generated/seaborn.kdeplot.html" rel="nofollow">kdeplot()</a> offers an argument fill. It would be nice to offer this argument to lineplot() as well.</p>
<div class="highlight highlight-source-python notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="sns.lineplot(
   data=data, x=&quot;x&quot;, y=&quot;y&quot;, hue=&quot;category&quot;, fill=True, palette=&quot;crest&quot;, alpha=.5, linewidth=0
)"><pre class="notranslate"><span class="pl-s1">sns</span>.<span class="pl-en">lineplot</span>(
   <span class="pl-s1">data</span><span class="pl-c1">=</span><span class="pl-s1">data</span>, <span class="pl-s1">x</span><span class="pl-c1">=</span><span class="pl-s">"x"</span>, <span class="pl-s1">y</span><span class="pl-c1">=</span><span class="pl-s">"y"</span>, <span class="pl-s1">hue</span><span class="pl-c1">=</span><span class="pl-s">"category"</span>, <span class="pl-s1">fill</span><span class="pl-c1">=</span><span class="pl-c1">True</span>, <span class="pl-s1">palette</span><span class="pl-c1">=</span><span class="pl-s">"crest"</span>, <span class="pl-s1">alpha</span><span class="pl-c1">=</span><span class="pl-c1">.5</span>, <span class="pl-s1">linewidth</span><span class="pl-c1">=</span><span class="pl-c1">0</span>
)</pre></div>
<p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/11406002/103433277-e7e86980-4bee-11eb-8bca-961b6e6a81ff.png"><img src="https://user-images.githubusercontent.com/11406002/103433277-e7e86980-4bee-11eb-8bca-961b6e6a81ff.png" alt="image" style="max-width: 100%;"></a><br>
(image from kdeplot docu)</p>
<p dir="auto">Keep in mind that the lineplot can be used in the context of a polar plot (axis projection: "polar").</p>
<p dir="auto">PS: happy New Year!</p>
<p dir="auto">I'm trying to plot a seaborn histogram using the following command to get the gaussian kernel density estimation.</p>
<p dir="auto"><code class="notranslate">sns.distplot(df['charges'], kde=True)</code></p>
<p dir="auto">I'm using</p>
<ul dir="auto">
<li>seaborn version 0.7.1</li>
<li>windows machine</li>
<li>jupyter notebooks</li>
</ul>
<p dir="auto">I'm getting the following error and I only see histogram but not kde.</p>
<hr>
<p dir="auto">TypeError                                 Traceback (most recent call last)<br>
 in ()<br>
----&gt; 1 sns.distplot(df['charges'], bins=50, kde=True)</p>
<p dir="auto">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)<br>
219     if kde:<br>
220         kde_color = kde_kws.pop("color", color)<br>
--&gt; 221         kdeplot(a, vertical=vertical, ax=ax, color=kde_color, **kde_kws)<br>
222         if kde_color != color:<br>
223             kde_kws["color"] = kde_color</p>
<p dir="auto">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)<br>
602         ax = _univariate_kdeplot(data, shade, vertical, kernel, bw,<br>
603                                  gridsize, cut, clip, legend, ax,<br>
--&gt; 604                                  cumulative=cumulative, **kwargs)<br>
605<br>
606     return ax</p>
<p dir="auto">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)<br>
268         x, y = _statsmodels_univariate_kde(data, kernel, bw,<br>
269                                            gridsize, cut, clip,<br>
--&gt; 270                                            cumulative=cumulative)<br>
271     else:<br>
272         # Fall back to scipy if missing statsmodels</p>
<p dir="auto">C:\Users\Niranjan\Anaconda3\lib\site-packages\seaborn\distributions.py in _statsmodels_univariate_kde(data, kernel, bw, gridsize, cut, clip, cumulative)<br>
326     fft = kernel == "gau"<br>
327     kde = smnp.KDEUnivariate(data)<br>
--&gt; 328     kde.fit(kernel, bw, fft, gridsize=gridsize, cut=cut, clip=clip)<br>
329     if cumulative:<br>
330         grid, y = kde.support, kde.cdf</p>
<p dir="auto">C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py in fit(self, kernel, bw, fft, weights, gridsize, adjust, cut, clip)<br>
144             density, grid, bw = kdensityfft(endog, kernel=kernel, bw=bw,<br>
145                     adjust=adjust, weights=weights, gridsize=gridsize,<br>
--&gt; 146                     clip=clip, cut=cut)<br>
147         else:<br>
148             density, grid, bw = kdensity(endog, kernel=kernel, bw=bw,</p>
<p dir="auto">C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py in kdensityfft(X, kernel, bw, weights, gridsize, adjust, clip, cut, retgrid)<br>
504     zstar = silverman_transform(bw, gridsize, RANGE)*y # 3.49 in Silverman<br>
505                                                    # 3.50 w Gaussian kernel<br>
--&gt; 506     f = revrt(zstar)<br>
507     if retgrid:<br>
508         return f, grid, bw</p>
<p dir="auto">C:\Users\Niranjan\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py in revrt(X, m)<br>
18     if m is None:<br>
19         m = len(X)<br>
---&gt; 20     y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j<br>
21     return np.fft.irfft(y)*m<br>
22</p>
<p dir="auto">TypeError: slice indices must be integers or None or have an <strong>index</strong> method</p>