Numpy

numpy_broadcasting1.py

## Let us consider an example in which we need to calculate the % of calories from carb, protein, fat in different foods
## Each colum represent to new food like col1-> food1(apple), col2-> food2(orange) , col3-> food3(banana) =, col4->food4(mango)
## Rows represent of as-: row1->carb ,row2->protein ,row3->fat
import numpy as np

A =np.array([[56.0,0.0,4.4,68.0],
    [1.2,104.0,52.0,8.0],
    [1.8,135.0,99.0,0.9]])

print(A)

#[Output]:
#[[ 56.    0.    4.4  68. ]
# [  1.2 104.   52.    8. ]
# [  1.8 135.   99.    0.9]]

## Matrix calculation to find out the 
## axis=0  means columnwise 
calc = A.sum(axis=0)
print(calc)

#[Output]:
#[ 59.  239.  155.4  76.9]

## Calculation of amount of percentage of carb, protein, fat present in food1, food2,food3,food4
## This division is possible because of broadcasting
per = 100*A/calc
print(per)

#[Output]:
#[[94.91525424  0.          2.83140283 88.42652796]
# [ 2.03389831 43.51464435 33.46203346 10.40312094]
# [ 3.05084746 56.48535565 63.70656371  1.17035111]]

print("Food1 i.e, Apple has",per[:,0])

#[Output]:
#Food1 i.e, Apple has [94.91525424  2.03389831  3.05084746]