 # PYTHON NUMPY OPERATION

import numpy as np
a=np.array([10,20,30,40])
print(a.ndim)      # this ndim represent no. dimension
#this is one dimension so its output is 1

Output
1

import numpy as np
a=np.array([(10,20,30,40),(50,60,70,80)])
print(a.ndim)      # this ndim represent no. dimension
#this is two dimension so its output is 2

Output
2

import numpy as np
a=np.array([10,20,30,40])
print(a.itemsize)      #10 is having 4byte so it output is 4

Output
4

import numpy as np
a=np.array([10,20,30,40])
print(a.dtype)      # dtype is use for know as datatype
# so datatype is int32

Output
int32

import numpy as np
a=np.array([10,20,30,40])
print(a.size)     # size is know to the size of values in this we take four values so output is 4

Output
4

import numpy as np
a=np.array([10,20,30,40])
print(a.shape)     #shape is known to no. of columns

Output
(4,)

import numpy as np
a=np.array([(10,20,30,40),(50,60,70,80)])
print(a.shape)     #in this 2 rows and 4 columns

Output
(2,4)

import numpy as np
a=np.array([(10,20,30,40),(50,60,70,80)])     #reshape() is use in to change the rows and columns
print(a.reshape(4,2))

Output
-[[10 20]
[30 40]
[50 60]
[70 80]]

import numpy as np
a=np.array([(10,20,30,40),(50,60,70,80)])
print(a[0,2])    # 0 is row no and 2 is index no.
print(a[0:,2])    # in this we fetch both same index no.

Output
30
[30 70]

import numpy as np
a= np.linspace(1,3,10)
print(a)    # 0 is row no and 2 is index no.

Output
[1.     1.22222222 1.44444444 1.66666667 1.88888889 2.1111111
2.33333333 2.55555556 2.77777778 3.     ]

import numpy as np
a=np.array([10,20,30,40,50,100])     #reshape() is use in to change the rows and columns
print(a.min())
print(a.max())
print(a.sum())

Output
10
100
250

import numpy as np
a=np.array([(10,20,30,40),(50,60,70,80)])
print(a.sum(axis=0))     # axis 0 means (10,20,30,40)
print(a.max())      # axis 1 means (50,60,70,80)
print(a.sum())

Output
[ 60 80 100 120]
[100 260]

import numpy as np
a=np.sqrt([(10,20,30,40),(50,60,70,80)])
print(a)

Output
[[3.16227766 4.47213595 5.47722558 6.32455532]
[7.07106781 7.74596669 8.36660027 8.94427191]]

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