Lets have a look at some garden-variety Python code for the Black Scholes formula.

Taken from here,

import numpy as np
import scipy.stats as ss
import time 

#Black and Scholes
def d1(S0, K, r, sigma, T):
    return (np.log(S0/K) + (r + sigma**2 / 2) * T)/(sigma * np.sqrt(T))
def d2(S0, K, r, sigma, T):
    return (np.log(S0 / K) + (r - sigma**2 / 2) * T) / (sigma * np.sqrt(T))
def BlackScholes(type, S0, K, r, sigma, T):
    if type=="C":
        return S0 * ss.norm.cdf(d1(S0, K, r, sigma, T)) - K * np.exp(-r * T) * ss.norm.cdf(d2(S0, K, r, sigma, T))
       return K * np.exp(-r * T) * ss.norm.cdf(-d2(S0, K, r, sigma, T)) - S0 * ss.norm.cdf(-d1(S0, K, r, sigma, T))

Pricing goes like this,

S0 = 100.0
K = 100.0
r = 0.1
sigma = 0.30
T = 3
Otype = 'C'

print "S0\tstock price at time 0:", S0
print "K\tstrike price:", K
print "r\tcontinuously compounded risk-free rate:", r
print "sigma\tvolatility of the stock price per year:", sigma
print "T\ttime to maturity in trading years:", T

t = time.time()
c_BS = BlackScholes(Otype,S0, K, r, sigma, T)
elapsed = time.time()-t
print "c_BS\tBlack-Scholes price:", c_BS, elapsed

The exercise is to rewrite this, but not to use any variables or functions, only lambdas!

If you do it quickly, try to do it as elegantly as possible.

Have fun.