After x is defined as a NumPy array, we could use x.function() to conduct related operations such as x.sum() as shown in the following lines of code:

>>>import numpy as np
>>>x=np.array([1,2,3])
>>>x.sum()
6
>>>np.sum(x)
6

If x is a NumPy array, we could have other functions with the same dot format as well: x.mean(), x.min(), x.max(), x.var(), x.argmin(), x.clip(), x.copy(), x.diagonal(), x.reshape(), x.tolist(), x.fill(), x.transpose(), x.flatten(), and x.argmax(). Those dot functions are useful because of the convenience they offer. The following commands show two such examples:

>>>cashFlows=np.array([-100,30,50,100,30,40])
>>>np.min(cashFlows)  
-100
>>>np.argmax(cashFlows)
0

The np.min() function shows the minimum value, while the np.argmin() function gives the location (that is, index) of the minimum value.