[numpy] array properties

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Import numpy Data packets

#  introduce Numpy library 
import numpy as np

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1. Array dimension number query

ndarray.ndmin Query the dimensions of the array  

import numpy as np
#  Array dimensions 
##  Dimension for 1
arr1 = np.array([1,2,3])
arr1.ndim # 1
##  Dimension for 2
arr2 = np.array([[1,2,3],[4,5,6]])
arr2.ndim # 2
##  Dimension for 3
arr3 = np.array([
        [[1,2,3],[4,5,6]],
        [[7,8,9],[10,11,12]]
       ])
arr3.ndim # 3

2. Array shape query   

ndarray.shape Query the shape of the array ( A few lines and columns ), The return value is a tuple , There are several elements in it, which represent a multi-dimensional array

import numpy as np
arr1 = np.array([1,2,3])
arr1.shape # (3,)

arr2 = np.array([[1,2,3],[4,5,6]])
arr2.shape # (2,3)

arr3 = np.array([
        [[1,2,3],[4,5,6]],
        [[7,8,9],[10,11,12]]
       ])
arr3.shape # (2,2,3)

ndarray.shape You can also change the shape of the array

import numpy as np
arr4 = np.array([[1,2,3],[4,5,6]]) 
arr4.shape =  (3,2) 

arr4

arr4( After processing ) 

3. Modify array shape  

.reshape function You can change the shape of the original array , Create a new array , Change the elements of the new array , The value of the corresponding element of the original array will also change  

# NumPy Provides .reshape Function to adjust the size and shape of the array 
import numpy as np

data = np.array([[1,2,3],[4,5,6]]) # array([[1, 2, 3],
                                   #        [4, 5, 6]])
data.shape # (2,3)

arr = data.reshape(6,) # array([1, 2, 3, 4, 5, 6])
arr.shape # (6,)

arr[0] = 437
arr # array([437, 2, 3, 4, 5, 6])

data # array([[437,   2,   3],
     #        [  4,   5,   6]])

.flatten function Flat can be achieved ( Convert multidimensional array into one-dimensional array )  

import numpy as np

arr = np.array([
        [[1,2,3],[4,5,6]],
        [[7,8,9],[10,11,12]]
      ])
arr.ndim # 3

deal_arr = arr.flatten() # array([1,2,3,4,5,6,7,8,9,10,11,12])
deal_arr.ndim # 1

4. Query the number of array elements and the memory occupied  

ndarray.size Query the number of array elements

ndarray.itemsize Query the memory size of each element in the array ( In bytes )

import numpy as np

arr = np.array([
        [[1,2,3],[4,5,6]],
        [[7,8,9],[10,11,12]]
      ])
#  The number of elements in the array 
arr.size # 12
#  Memory occupied by each element 
arr.itemsize # 4
#  The data type of each element 
arr.dtype # dtype('int32')
#  Memory occupied by array 
arr.itemsize * arr.size # 48

#  Array of dtype by int8( A byte )
data1 = np.array([1,2,3,4,5], dtype = np.int8)
data1.itemsize # 1

#  Array of dtype For now float64( Eight bytes )
data2 = np.array([1,2,3,4,5], dtype = np.float64)
data2.itemsize # 8

5. Element data type query

ndarray.dtype For return ndarray The element type of the object  

import numpy as np
arr1 = np.array([[1,2,3],[4,5,6]])  
arr1.dtype # dtype('int32')
arr2 = np.array([1.2, 2.3, 3.4])
arr2.dtype # dtype('float64')