The docs indicate that numpy.correlate is not what you are looking for:
numpy.correlate(a, v, mode='valid', old_behavior=False)[source]
Cross-correlation of two 1-dimensional sequences.
This function computes the correlation as generally defined in signal processing texts:
z[k] = sum_n a[n] * conj(v[n+k])
with a and v sequences being zero-padded where necessary and conj being the conjugate.
Instead, as the other comments suggested, you are looking for a Pearson correlation coefficient. To do this with scipy try:
from scipy.stats.stats import pearsonr
a = [1,4,6]
b = [1,2,3]
print pearsonr(a,b)
This gives
(0.99339926779878274, 0.073186395040328034)
You can also use numpy.corrcoef:
import numpy
print numpy.corrcoef(a,b)
This gives:
[[ 1. 0.99339927]
[ 0.99339927 1. ]]