Numpy n dimensional Arrays ( ndarray )

Axis of Two dimensional array We can create powerful multidimensional arrays in numpy. We can arrage the elements in number of rows and columns.

Let us try some attributes to know details of the array. These attributes we will apply to different types of arrays ( dimension ).
baseBase object, if memory is from other object
dtypetype of data. Integer, float, Python object, etc
databuffer object showing start of the data
flagsDetails about Memory Layout
itemsizeSize in bytes
ndimNumber of dimensions or axis
nbytes Total bytes used by all the elements
shaperows and columns ,tuple , length indicates numbrer of axes,
product of elements is same as size
sizeTotal number of elements
stridesReturns a tuple with bytes consumed by each dimension

Single dimensional array

Single dimensional array
import numpy as my_np 
my_array = my_np.array([0,1,2]) 
print(my_array)
Output
[0 1 2]
Let us try above attributes to know details of the array.
import numpy as my_np 

my_array = my_np.array([ 0,  1,  2])

print(my_array.base)        # None  
print(my_array.dtype)       # int32
print(my_array.data)        # <memory at 0x0000021772F6F108>
print(my_array.flags)       #  	  C_CONTIGUOUS : True
                            #     F_CONTIGUOUS : False
                            #     OWNDATA : True
                            #     WRITEABLE : True
                            #     ALIGNED : True
                            #     WRITEBACKIFCOPY : False
                            #     UPDATEIFCOPY : False 
print(my_array.itemsize)    # 4
print(my_array.ndim)        # 1 
print(my_array.nbytes)      # 12 
print(my_array.shape)       # (3,) 
print(my_array.size)        # 3 
print(my_array.strides)     # (4,) 

Two dimensional array

Two dimensional array
import numpy as my_np 
my_array = my_np.array([[0,  1,  2],[3,  4,  5],[6,  7,  8]])
print(my_array[1][2]) # output is 5
import numpy as my_np 

my_array = my_np.array([[0,  1,  2],[3,  4,  5],[6,  7,  8]])

print(my_array.base)        # None  
print(my_array.dtype)       # int32
print(my_array.data)        # <memory at 0x0000021772EC28B8>
print(my_array.flags)       #  C_CONTIGUOUS : True
                            #     F_CONTIGUOUS : True
                            #     OWNDATA : True
                            #     WRITEABLE : True
                            #     ALIGNED : True
                            #     WRITEBACKIFCOPY : False
                            #     UPDATEIFCOPY : False 
print(my_array.itemsize)    # 4
print(my_array.ndim)        # 2 
print(my_array.nbytes)      # 36 
print(my_array.shape)       # (3,3) 
print(my_array.size)        # 9 
print(my_array.strides)     # (12,4) 

Three Dimensional array

Three dimensional array
import numpy as my_np 
#x=complex(98,97)
my_array = my_np.array([[[ 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]]])
print(my_array[2][2][1]) # 25
Let us try above attributes to know details of the array.
import numpy as my_np 
my_array = my_np.array([[[ 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]]])
print(my_array.base)        # None  
print(my_array.dtype)       # int32
print(my_array.data)        # <memory at 0x00000217728F4E58>
print(my_array.flags)       #  C_CONTIGUOUS : True
                            #     F_CONTIGUOUS : False
                            #     OWNDATA : True
                            #     WRITEABLE : True
                            #     ALIGNED : True
                            #     WRITEBACKIFCOPY : False
                            #     UPDATEIFCOPY : False 
print(my_array.itemsize)    # 4
print(my_array.ndim)        # 3 
print(my_array.nbytes)      # 108 
print(my_array.shape)       # (3,3,3) 
print(my_array.size)        # 27 
print(my_array.strides)     # (36,12,4) 
Numpy array methods to get output
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