np sum
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so you need to sum a list what are you gonna use and P dot sum or the built in sum function ah in this video were gonna talk about the difference between NP dot some and the some built-in function so Im gonna import numpy as NP and I already have a matrix a that I created earlier so heres that matrix a and lets type in the built-in sum function that comes already in Python without importing anything so well type sum of a and we get one for negative two and four which is adding down the column so thats
the sum of every single column and if we do NP dot some of a we get one number back because its the combination or the sum of every single element now is NP dot some you can give a different parameters so lets try access equals zero now youll notice access equals zero is the same thing as our built-in sum function so its adding down the columns but what if we do NP dot some of a with access equals one now were getting the addition across the row so youre adding every single row but those arent the only parameters
heres a again lets do NP dot some of a access equals zero and Im gonna give it this parameter called keep dims equals true which is telling it to keep the dimensions so with access equals zero it was adding down the columns and with access equals one its adding across the row so youll see we have a row vector and a column vector that got spit out when we did keep dimensions heres a one more time well do NP dot some of a access equals one and Im gonna give it a different keyword that says initial equal five
now let me compare this to NP dot some without the initial equals five so you can see the difference basically with initial equals five it means you begin adding from that initial number so the initial equals 5 is 5 more than without the initial parameter because youre just adding five to every single element in the sump okay heres a again and lets do a different parameter of NP dot some will do a comma D type equals float which of course just means you can change the type you see that decimal spot there you can also use this with
axis of course to give it float types as a return type for each outlet now lets take a look at the documentation and you see weve covered all of these parameters here and that is the main difference between the built in sum and NP dot some and P dot some can do everything that some can plus it has all these other parameters that allows you to do so much more thanks for watching I hope you enjoyed this video for more videos on Python check out this playlist its full of all kinds of Python tutorials like this one 
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