Issue 1:
Here is a screen shot that shows the dataframe in question and the resulting plot.

I am completely befuddled.

Any guidance would be very helpful!

Gus

Issue 2:
I can't get kdeplot or distplot to work with my pd.Series (or np.array)

u = np.array([ 3.41959 , 1.79315 , 1.17229 , 1.59909 , 1.27337 , 1.21917 , 2.60591 , 2.0571 , 1.83865 , 1.94869 , 1.65421 , 1.67777 , 1.23781 , 1.46352 , 1.41791 , 2.00387 , 2.51076 , 1.59734 , 1.32982 , 1.89372 , 1.40614 , 1.41747 , 1.34109 , 1.38647 , 1.49432 , 1.05335 , 1.03805 , 1.18631 , 1.06402 , 1.06929 , 1.12031 , 1.07605 , 1.25702 , 1.25158 , 1.06541 , 1.07368 , 1.00559 , 1.00555 , 1.54561 , 1.01144 , 1.79598 , 1.02094 , 1.36236 , 1.6214 , 1.09803 , 1.25866 , 0.987888, 0.993576, 1.25805 , 5.67273 , 1.20665 , 0.985643, 1.07809 , 1.22087 , 1.1201 , 1.0367 , 1.26575 , 1.0295 , 0.993422, 1.08648 , 1.32767 , 1.35376 , 0.98585 , 0.991962, 1.49533 , 1.04997 , 0.995653, 1.13044 , 1.12311 , 1.29017 , 1.6424 , 1.1599 , 1.26453 , 1.0633 , 1.00454 , 1.35529 , 0.990889, 1.19621 , 1.30717 , 1.32321 , 1.5471 , 1.13225 , 1.97847 , 1.17142 , 1.36377 , 2.14062 , 0.996708, 1.03417 , 1.03212 , 1.0082 , 1.06432 , 3.49213 , 4.28245 , 1.00274 , 1.09338 , 1.0156 , 1.13566 , 1.10697 , 1.56438 , 0.96706 ], dtype=float)
Se_u = pd.Series(u)
sns.kdeplot(Se_u)

This is the error I get:

---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-27-70aaa51b2d69> in <module>()
4 u = np.array([ 3.41959 , 1.79315 , 1.17229 , 1.59909 , 1.27337 , 1.21917 , 2.60591 , 2.0571 , 1.83865 , 1.94869 , 1.65421 , 1.67777 , 1.23781 , 1.46352 , 1.41791 , 2.00387 , 2.51076 , 1.59734 , 1.32982 , 1.89372 , 1.40614 , 1.41747 , 1.34109 , 1.38647 , 1.49432 , 1.05335 , 1.03805 , 1.18631 , 1.06402 , 1.06929 , 1.12031 , 1.07605 , 1.25702 , 1.25158 , 1.06541 , 1.07368 , 1.00559 , 1.00555 , 1.54561 , 1.01144 , 1.79598 , 1.02094 , 1.36236 , 1.6214 , 1.09803 , 1.25866 , 0.987888, 0.993576, 1.25805 , 5.67273 , 1.20665 , 0.985643, 1.07809 , 1.22087 , 1.1201 , 1.0367 , 1.26575 , 1.0295 , 0.993422, 1.08648 , 1.32767 , 1.35376 , 0.98585 , 0.991962, 1.49533 , 1.04997 , 0.995653, 1.13044 , 1.12311 , 1.29017 , 1.6424 , 1.1599 , 1.26453 , 1.0633 , 1.00454 , 1.35529 , 0.990889, 1.19621 , 1.30717 , 1.32321 , 1.5471 , 1.13225 , 1.97847 , 1.17142 , 1.36377 , 2.14062 , 0.996708, 1.03417 , 1.03212 , 1.0082 , 1.06432 , 3.49213 , 4.28245 , 1.00274 , 1.09338 , 1.0156 , 1.13566 , 1.10697 , 1.56438 , 0.96706 ], dtype=float)
5 Se_u = pd.Series(u)
----> 6 sns.kdeplot(Se_u)

/Users/jespinoz/anaconda/lib/python3.5/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

/Users/jespinoz/anaconda/lib/python3.5/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

/Users/jespinoz/anaconda/lib/python3.5/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

/Users/jespinoz/anaconda/lib/python3.5/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,

/Users/jespinoz/anaconda/lib/python3.5/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

/Users/jespinoz/anaconda/lib/python3.5/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

but this works:

Se_u.plot(kind="kde")