NumPy ndarray — Understanding Dimensions, Shape & Attributes

In NumPy, every array you create is an ndarray — the core data structure that stores elements of the same type in a continuous block of memory. It enables fast mathematical and logical operations across dimensions. Understanding properties like ndim, shape, dtype, and strides is essential for mastering how NumPy manages multidimensional data efficiently.

Single dimensional array

import numpy as my_np 
my_array = my_np.array([0,1,2]) 
print(my_array)

[0 1 2]

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

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

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

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) 

Adding dimension while creating array

import numpy as np
npr = np.array([ 0,  1,  2],ndmin=3) # adding dimension to array
print(npr)  # [[[0 1 2]]] # print array 
print(npr.ndim) # 3 ( display dimension of the array ) 

Subhendu Mohapatra

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