The x.sum() dot function
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.