# Why does np.array**3 lead to a different solution than (np.array/1)**3 in my code?

I wrote this code to calculate the integral in time of:
Q = (Q_max*(1 – (time/t_0 – 1)**2))
which I derived analytically.

``````import numpy as np
Q_max       = 400                   # W m-2
t_0         = 6*3600                # seconds
dt          = 60                    # seconds
time        = np.arange(0,2*t_0,dt)
Q_integral_A = Q_max*((time)**2/(t_0) - (time)**3/(3*(t_0)**2))
``````

However, I found that Q_integral_A gives the wrong solution. After trying a lot of stuff, I found out that doing the following leads to the right solution (dividing the second "time" by 1):

``````Q_integral_B = Q_max*((time)**2/(t_0) - (time/1)**3/(3*(t_0)**2))
``````

What is happening here? Why is there a difference between Q_integral_A and Q_integral_B?

screenshot of output

Versions used:
Python 3.8.5
Numpy 1.20.3
Spyder 4.2.5

### >Solution :

I looked into problem myself, and I get the same result. So at first `time` is a int32, but when you do `time / 1` it becomes float64. It shouldn’t bring problems by itself, but `time` contains some big numbers, and raising them to 3rd power results in overflowing (here’s what i get), but it doesn’t effect float, because it works in different way.

To solve it just pass `dtype="int64"` `time = np.arange(0, 2*t_0, dt, dtype = "int64")`, but it won’t solve the problem for even larger numbers.