Question

I have 1000 large numbers, randomly distributed in range 37231 to 56661.

I am trying to use the stats.gaussian_kde but something does not work.
(maybe because of my poor knowledge of statistics?).

Here is the code:

from scipy import stats.gaussian_kde
import matplotlib.pyplot as plt

# 'data' is a 1D array that contains the initial numbers 37231 to 56661
xmin = min(data)
xmax = max(data)   

# get evenly distributed numbers for X axis.
x = linspace(xmin, xmax, 1000)   # get 1000 points on x axis
nPoints = len(x)

# get actual kernel density.
density = gaussian_kde(data)
y = density(x)

# print the output data
for i in range(nPoints):
    print "%s   %s" % (x[i], y[i])

plt.plot(x, density(x))
plt.show()

In the printout, I get x values in the column 1, and zeros in the column 2.
The plot shows a flat line.

I simply can not find the solution.
I tried a very wide range of X-es, the same result.

What is the problem? What am I doing wrong?
Could the large numbers be the cause?

Answer 1

I think what’s happening is that your data array is made up of integers, which leads to problems:

>>> import numpy, scipy.stats
>>> 
>>> data = numpy.random.randint(37231, 56661,size=10)
>>> xmin, xmax = min(data), max(data)
>>> x = numpy.linspace(xmin, xmax, 10)
>>> 
>>> density = scipy.stats.gaussian_kde(data)
>>> density.dataset
array([[52605, 45451, 46029, 40379, 48885, 41262, 39248, 38247, 55987,
        44019]])
>>> density(x)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

but if we use floats:

>>> density = scipy.stats.gaussian_kde(data*1.0)
>>> density.dataset
array([[ 52605.,  45451.,  46029.,  40379.,  48885.,  41262.,  39248.,
         38247.,  55987.,  44019.]])
>>> density(x)
array([  4.42201513e-05,   5.51130237e-05,   5.94470211e-05,
         5.78485526e-05,   5.21379448e-05,   4.43176188e-05,
         3.66725694e-05,   3.06297511e-05,   2.56191024e-05,
         2.01305127e-05])