The NumPy array has some useful properties that can help you to use it easily. This article will show you some examples of how to use them correctly.

### 1. Common NumPy Array Properties.

**ndarray.flags**: Returns the memory information of the ndarray array, such as the storage method of the array and whether it is a copy of other arrays.>>> import numpy as np >>> >>> arr = np.array(['python', 'java', 'javascript']) >>> >>> print(arr.flags) C_CONTIGUOUS : True F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False UPDATEIFCOPY : False

**ndarray.itemsize**: Returns the size of each element in the array in bytes.>>> import numpy as np >>> >>> arr = np.array(['python', 'java', 'javascript']) >>> >>> print(arr.itemsize) 40 >>> >>> arr1 = np.array([6, 7, 8, 9, 10], dtype = np.int8) >>> >>> print(arr1.itemsize) 1

**ndarray.ndim**: Returns the dimension of the array.>>> import numpy as np >>> >>> arr1 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]]) >>> >>> print(arr1.ndim) 2

**ndarray.shape**: The return value of the shape property is a tuple composed of array dimensions. For example, a two-dimensional array with 2 rows and 3 columns can be expressed as (2,3). This property can be used to adjust the size of array dimensions.>>> import numpy as np >>> # create the Numpy array. >>> arr1 = np.array([[1, 2, 3],[4, 5, 6],[7, 8, 9]]) >>> # print it's dimension. >>> print(arr1.ndim) 2 # print the NumPy array shape. >>> print(arr1.shape) (3, 3) >>> # change the NumPy array shape. >>> arr1.shape = (1, 9) >>> # print the reshaped array. >>> print(arr1) [[1 2 3 4 5 6 7 8 9]]

**ndarray.reshape()**: This method is used to adjust the array shape.>>> import numpy as np >>> # create the original 2 dimension array. >>> arr1 = np.array([[1, 2, 3],[4, 5, 6]]) >>> # print out the array shape. >>> print(arr1.shape) (2, 3) >>> # reshape the above array to (3,2) >>> arr1.reshape(3, 2) array([[1, 2], [3, 4], [5, 6]]) >>> # you can find that the original array's shape is not changed. >>> print(arr1) [[1 2 3] [4 5 6]]