Plot density using observation weights

Question

Is there a way to plot densities using data that has observation weights?

I have a vector of observations x and a vector of integer weights y, such that y1 indicates how many observations we have of x1. That is, the density of

   x    y 
   1    2
   2    2
   2    3 

is equal to the density of 1, 1, 2, 2, 2, 2 ,2 (2x1, 5x2). As far as I understand it, matplotlib.pyplot.hist(weights=y) allow for observation weights when plotting the histogram. Is there any equivalent for computing and plotting the density?

The reason I want the package to be able to do this is that my data is very big, and I'm looking for a more efficient alternative.

Alternatively, I'm open to other packages.

Answer 1

Statsmodels' kde univariate receives weights in its fit function. See the output of the following code.

import matplotlib.pyplot as plt
import statsmodels.api as sm
import pandas as pd

df = pd.DataFrame({'x':[1.,2.],'weight':[2,4]})
weighted = sm.nonparametric.KDEUnivariate(df.x)
noweight = sm.nonparametric.KDEUnivariate(df.x)
weighted.fit(fft=False, weights=df.weight)
noweight.fit()

f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)
ax1.plot(noweight.support, noweight.density)
ax2.plot(weighted.support, weighted.density)

ax1.set_title('No Weight')
ax2.set_title('Weighted')

Output:

Note: Your time concern regarding array creation will probably not be resolved with this. Because as noted in the source code:

If FFT is False, then a ‘number_of_obs’ x ‘gridsize’ intermediate array is created