在numpy中用零填充数组

Question

h = numpy.zeros((2,2,2)) 

What is the last 2 for? Is it creating a multidimensional array or something?

Output:

array([[[ 0.,  0.],
    [ 0.,  0.]],
   [[ 0.,  0.],
    [ 0.,  0.]]])

If it is creating number of copies, then what is happening when i do the following?

h = numpy.zeros((2,2,1))

Output:

array([[[ 0.],
    [ 0.]],
   [[ 0.],
    [ 0.]]])

I understand that it is getting filled by zeros, and the first two values are specifying the row and column, what about the third? Thank you in advance. And I tried Google, but I could not word my questions.

Answer 1

by giving three arguments you're creating a three-dimensional array:

numpy.array((2,2,2)) results in an array of size 2x2x2:

  0---0
 /   /|
0---0 0
|   |/
0---0

numpy.array((2,2,1)) results in an array of size 2x2x1:

0---0
|   |
0---0

numpy.array((2,1,2)) results in an array of size 2x2x1:

  0---0
 /   /
0---0

numpy.array((1,2,2)) results in an array of size 2x2x1:

  0
 /|
0 0
|/
0

in these representations the matrix "might look like numpy.array((2,2)) " (a 2x2 array) however the underlying structure is still three dimensional.

Answer 2

Read (4,3,2) as: There's a building with 4 floors, each floor has 3 rows and 2 columns of rooms. Hence it is a 3-D array.

In [4]: np.zeros((4, 3, 2))                                                                      
Out[4]: 
array([[[ 0.,  0.],                                                                              
        [ 0.,  0.],                                                                              
        [ 0.,  0.]],                                                                             

       [[ 0.,  0.],                                                                              
        [ 0.,  0.],                                                                              
        [ 0.,  0.]],                                                                             

       [[ 0.,  0.],                                                                              
        [ 0.,  0.],                                                                              
        [ 0.,  0.]],                                                                             

       [[ 0.,  0.],                                                                              
        [ 0.,  0.],                                                                              
        [ 0.,  0.]]])      

Answer 3

The argument is specifying the shape of the array :

In [72]: import numpy as np

In [73]: h = np.zeros((2,2,2))

In [74]: h.shape
Out[74]: (2, 2, 2)

In [75]: h = np.zeros((2,2,1))

In [76]: h.shape
Out[76]: (2, 2, 1)

If the shape of an array is (a,b,c) , then it has in NumPy parlance 3 "axes" (or in common English, 3 "dimensions"). Axis 0 has length a , axis 1 has length b , and axis 2 has length c .

---

When you define h = np.zeros((2,2,1)) notice that the result has 3 levels of brackets:

In [77]: h
Out[77]: 
array([[[ 0.],
        [ 0.]],

       [[ 0.],
        [ 0.]]])

The outermost bracket contains 2 items, the middle brackets also contain 2 items each. The innermost bracket contains just a single item. Thus, the shape is (2, 2, 1).

Answer 4

The last digit always means number of elements. All the others are arrays or lists So, (3, 4, 3) means I need a list with 3 arrays each of the 3 containing 4 arrays, each of those 4 contains 3 elements .

{ [[0,0,0,], [0,0,0], [0,0,0], [0,0,0]],
[[0,0,0], [0,0,0], [0,0,0], [0,0,0]],
[[0,0,0], [0,0,0], [0,0,0], [0,0,0]] }

2, 2, 1 means , list with 2 arrays each containing 2 arrays each with 1 element

{ [[0], [0}],
[[0], [0]] }