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import numpy as np

a = np.arange(6).reshape((3,2))
print(a)

#[Output]:
#array([[0, 1],
#       [2, 3],
#       [4, 5]])

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print(np.cos(np.deg2rad(np.array((0.,30.,45.)))))

#[Output]:
#array([1.        , 0.8660254 , 0.70710678])

Robofied

import numpy as np

## 1. np.sin()
print(np.sin(np.pi/2.))

#[Output]:
#1.0

"""In this we are taking an array of angles in degree and calculating the sine of that, so we are converting 
them to radians first"""

Robofied

## 1. moveaxis routine
import numpy as np

x = np.zeros((3, 4, 5))

## In this all the elements shape gets shifted in the direction of source to destonation, so for this example 
## it is just like cyclic rotation in clockwise direction.
print(np.moveaxis(x, 0, -1).shape)

#[Output]:

Robofied

import numpy as np
a = np.arange(6)

print(a)

#[Output]:
#[0 1 2 3 4 5]

## 1. Reshaping
a.reshape(2,3)

Robofied

## Here order doesn't matter
a = np.arange(6).reshape(2,3)

for x in np.nditer(a, order='F'):
     print (x,end=",")
    
#[Output]:
#0,3,1,4,2,5,

Robofied

import numpy as np

a = np.arange(6).reshape(2,3)

for x in np.nditer(a):
     print (x,end=",")
    
#[Output]:
#0,1,2,3,4,5,

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## 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)

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import numpy as np

x = np.arange(35)
print(x)

#[Output]:
#[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
# 24 25 26 27 28 29 30 31 32 33 34]

Robofied

import numpy as np

x = np.arange(10,1,-1)
print(x)

#[Output]:
#[10  9  8  7  6  5  4  3  2]


print(x[np.array([3,3,4,7])])