How to use np.prod to rewrite python for loop function-CodePudding

I am using pandas and numpy to deal with data, and I have a python function:

mt= np.array([1,2,3,4,5,6,7])

def getnpx(mt, age, interest):
    val = 1
    initval = 1
    for i in range(age, 6):
        val = val * mt[i]
        intval = val / (1   interest) ** (i   1 - age)
        initval = initval   intval
    return initval

If directly use pd.apply(getnpx),it will be very slow since the data size is very large ,so I want to convert this function into numpy,I think I maybe can use np.prod to rewrite it,but have no idea how to continue,since this function is very complicated!

Any friend can help?

CodePudding user response：

You don't want np.prod, you want np.cumprod:

def getnpx_(mt, age, interest):
    return 1   (np.cumprod(mt[age:6]) / (1 interest)**np.arange(1,(7-age))).sum()

age, interest = 3, 0.5
print(np.isclose(getnpx(mt, age, interest), getnpx_(mt, age, interest) ))