python numpy pandas histogram kernel-density

Python/Scipy kde fit, scaling

I have a Series in Python and I'd like to fit a density to its histogram. Question: is there a slick way to use the values from np.histogram() to achieve this result? (see Update below)

My current problem is that the kde fit I perform has (seemingly) unwanted kinks, as depicted in the second plot below. I was hoping for a kde fit that is monotone decreasing based on a histogram, which is the first figure depicted. Below I've included my current code. Thanks in advance

import numpy as np
from matplotlib import pyplot as plt
from scipy.stats import gaussian_kde as kde

df[var].hist()
plt.show()  # shows the original histogram
density = kde(df[var])
xs = np.arange(0, df[var].max(), 0.1)
ys = density(xs)
plt.plot(xs, ys)  # a pdf with kinks

Alternatively, is there a slick way to use

count, div = np.histogram(df[var])

and then scale the count array to apply kde() to it?

original historgram

kde_fit

Update

Based on cel's comment below (should've been obvious, but I missed it!), I was implicitly under-binning in this case using the default params in pandas.DataFrame.hist(). In the updated plot I used

df[var].hist(bins=100)

I'll leave this post up in case others find it useful but won't mind if it gets taken down as 'too localized' etc.

enter image description here

Solution

The problem was under-binning as mentioned by cel, see comments above. It was clarifying to set bins=100 in pd.DataFrame.histo() which defaults to bins=10.