The following code first appeared as A literate program to compute the inverse of the normal CDF. See that page for a detailed explanation of the algorithm.

import math def rational_approximation(t): # Abramowitz and Stegun formula 26.2.23. # The absolute value of the error should be less than 4.5 e-4. c = [2.515517, 0.802853, 0.010328] d = [1.432788, 0.189269, 0.001308] numerator = (c[2]*t + c[1])*t + c[0] denominator = ((d[2]*t + d[1])*t + d[0])*t + 1.0 return t - numerator / denominator def normal_CDF_inverse(p): assert p > 0.0 and p < 1 # See article above for explanation of this section. if p < 0.5: # F^-1(p) = - G^-1(p) return -rational_approximation( math.sqrt(-2.0*math.log(p)) ) else: # F^-1(p) = G^-1(1-p) return rational_approximation( math.sqrt(-2.0*math.log(1.0-p)) ) def demo(): print "\nShow that the NormalCDFInverse function is accurate at" print "0.05, 0.15, 0.25, ..., 0.95 and at a few extreme values.\n\n" p = [ 0.0000001, 0.00001, 0.001, 0.05, 0.15, 0.25, 0.35, 0.45, 0.55, 0.65, 0.75, 0.85, 0.95, 0.999, 0.99999, 0.9999999 ] # Exact values computed by Mathematica. exact = [ -5.199337582187471, -4.264890793922602, -3.090232306167813, -1.6448536269514729, -1.0364333894937896, -0.6744897501960817, -0.38532046640756773, -0.12566134685507402, 0.12566134685507402, 0.38532046640756773, 0.6744897501960817, 1.0364333894937896, 1.6448536269514729, 3.090232306167813, 4.264890793922602, 5.199337582187471 ] maxerror = 0.0 num_values = len(p) print "p, exact CDF inverse, computed CDF inverse, diff\n\n"; for i in range(num_values): computed = normal_CDF_inverse(p[i]) error = exact[i] - computed print p[i], ",", exact[i], ",", computed, ",", error if (abs(error) > maxerror): maxerror = abs(error) print "\nMaximum error:" , maxerror , "\n" if __name__ == "__main__": demo()

The code is based on an algorithm given in Handbook of Mathematical Functions by Abramowitz and Stegun.

This code is in the public domain. Do whatever you want to with it, no strings attached.

More stand-alone numerical code