How do I downsample a 1d numpy array?

Question

This seems like a very straight forward problem but I can't figure out a solution. Suppose I have a sine function y with 8000 samples:

import numpy as np

Fs = 8000
f = 1
npts = 8000
x = np.arange(npts)
y = np.sin(2 * np.pi * f * x / Fs)

I want to downsample this function to 6000 samples, so I tried the method of this answer to a similar question...

import math
from scipy import nanmean

#number of samples I want to downsample to
npts2 = 6000

#calculating the number of NaN values to pad to the array
n = math.ceil(float(y.size)/npts2)
pad_size = n*npts2 - len(y)
padded = np.append(y, np.zeros(int(pad_size))*np.NaN)

#downsampling the reshaped padded array with nanmean
downsampled = nanmean(padded.reshape((npts2, int(n))), axis = 1)

This gives me an array of the correct length (6000) but the last 2000 samples (i.e. the difference between the original npts and npts2) are NaN, and the function itself only occupies the first 4000 samples.

Is there a better way I can make this sine function 6000 samples in length? Thanks!

Edit

Thanks for the replies - I realize now I was attacking this the wrong way. I decided to use the scipy.interpolate.interp1d function on the y function, and then pass it an np.linspace array generated with the desired number of points to interpolate to. This gives me the correctly scaled output.

from scipy.interpolate import interp1d

def downsample(array, npts):
    interpolated = interp1d(np.arange(len(array)), array, axis = 0, fill_value = 'extrapolate')
    downsampled = interpolated(np.linspace(0, len(array), npts))
    return downsampled

downsampled_y = downsample(y, 6000)

Answer 1

Your initial sample rate of 8000 is not divisible by 6000, so this cannot be simply downsampled like the referenced post. In your scenario, scipy's resample should work.

from scipy import signal
downsampled =  signal.resample(y, 6000)