As the title says, I was trying to verify the ordering in numpy arrays by changing the ordering in the following test script:
import numpy as np
# Standard array
arr = [[1, 2, 3], [-7, -8, -9], ['A', 'B', 'C']]
print(arr, '\n')
for row_index, row_entries in enumerate(arr):
print('Row ' str(row_index 1))
for column_index, column_entries in enumerate(row_entries):
print(' Column ' str(column_index 1) '\n', '\t [' str(column_entries) ']')
# NumPy array
arr = np.asarray([[1, 2, 3], [-7, -8, -9], ['A', 'B', 'C']], order='F') # Try 'C' vs. 'F'!!
print('\n\n', arr, '\n')
for row_index, row_entries in enumerate(arr):
print('Row ' str(row_index 1))
for column_index, column_entries in enumerate(row_entries):
print(' Column ' str(column_index 1) '\n', '\t [' str(column_entries) ']')
----------------------------------------------------------------------------------------------
Output:
[[1, 2, 3], [-7, -8, -9], ['A', 'B', 'C']]
Row 1
Column 1
[1]
Column 2
[2]
Column 3
[3]
Row 2
Column 1
[-7]
Column 2
[-8]
Column 3
[-9]
Row 3
Column 1
[A]
Column 2
[B]
Column 3
[C]
[['1' '2' '3']
['-7' '-8' '-9']
['A' 'B' 'C']]
Row 1
Column 1
[1]
Column 2
[2]
Column 3
[3]
Row 2
Column 1
[-7]
Column 2
[-8]
Column 3
[-9]
Row 3
Column 1
[A]
Column 2
[B]
Column 3
[C]
Why am I getting identical outputs?
CodePudding user response：
You start with a list (of lists):
In [29]: alist = [[1, 2, 3], [-7, -8, -9], ['A', 'B', 'C']]
In [30]: alist
Out[30]: [[1, 2, 3], [-7, -8, -9], ['A', 'B', 'C']]
Obviously we can iterate through the list, and through the sublists.
We can make an array from that list. Usually we don't specify the order, but the default is 'C':
In [31]: arr1 = np.array(alist, order='C')
In [32]: arr1
Out[32]:
array([['1', '2', '3'],
['-7', '-8', '-9'],
['A', 'B', 'C']], dtype='<U21')
Note that the dtype is strings (I suppose I could have specified object).
Same thing but with 'F':
In [34]: arr2 = np.array(alist, order='F')
In [35]: arr2
Out[35]:
array([['1', '2', '3'],
['-7', '-8', '-9'],
['A', 'B', 'C']], dtype='<U21')
Display is the same.
To see how the elements are stored we have to 'ravel' the arrays. The result is a new 1d array. See np.reshape or np.ravel docs for the use of 'K' order:
In [36]: arr1.ravel('K')
Out[36]: array(['1', '2', '3', '-7', '-8', '-9', 'A', 'B', 'C'], dtype='<U21')
In [38]: arr2.ravel('K')
Out[38]: array(['1', '-7', 'A', '2', '-8', 'B', '3', '-9', 'C'], dtype='<U21')
Here we read the values of arr2 down the columns. ravel of the first array, but with 'F' order produces the same thing:
In [39]: arr1.ravel('F')
Out[39]: array(['1', '-7', 'A', '2', '-8', 'B', '3', '-9', 'C'], dtype='<U21')
Iteration as you do, doesn't change with the order. It effectively treats the array as a list.
In [40]: [row for row in arr1]
Out[40]:
[array(['1', '2', '3'], dtype='<U21'),
array(['-7', '-8', '-9'], dtype='<U21'),
array(['A', 'B', 'C'], dtype='<U21')]
In [41]: [row for row in arr2]
Out[41]:
[array(['1', '2', '3'], dtype='<U21'),
array(['-7', '-8', '-9'], dtype='<U21'),
array(['A', 'B', 'C'], dtype='<U21')]
In [42]: arr2.tolist()
Out[42]: [['1', '2', '3'], ['-7', '-8', '-9'], ['A', 'B', 'C']]
You have to use numpy's own methods and tools to see the effect of order. order is more useful when creating an array via reshape:
In [43]: np.arange(12)
Out[43]: array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
In [44]: np.arange(12).reshape(3,4)
Out[44]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
In [45]: np.arange(12).reshape(3,4,order='F')
Out[45]:
array([[ 0, 3, 6, 9],
[ 1, 4, 7, 10],
[ 2, 5, 8, 11]])
Tweaking the shape, and then applying a transpose:
In [46]: np.arange(12).reshape(4,3,order='F')
Out[46]:
array([[ 0, 4, 8],
[ 1, 5, 9],
[ 2, 6, 10],
[ 3, 7, 11]])
In [47]: np.arange(12).reshape(4,3,order='F').T
Out[47]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
edit
It may be clearer if I make a smaller array with just 1 byte per element.
The 2 orders:
In [70]: arr1 = np.array([[1,2,3],[4,5,6]], 'uint8')
In [72]: arr2 = np.array([[1,2,3],[4,5,6]], 'uint8',order='F')
In [73]: arr1
Out[73]:
array([[1, 2, 3],
[4, 5, 6]], dtype=uint8)
In [74]: arr2
Out[74]:
array([[1, 2, 3],
[4, 5, 6]], dtype=uint8)
Instead of ravel, use tobytes with 'A' order to preserve the underlying order (see tobytes docs):
In [75]: arr1.tobytes(order='A')
Out[75]: b'\x01\x02\x03\x04\x05\x06'
In [76]: arr2.tobytes(order='A')
Out[76]: b'\x01\x04\x02\x05\x03\x06'
The difference can alse be seen in the strides:
In [77]: arr1.strides
Out[77]: (3, 1)
In [78]: arr2.strides
Out[78]: (1, 2)
strides controls how numpy iterates through the array in compiled code (but not when using python level iteration).
A comment suggested using nditer to iterate via numpy's own methods. Generally I don't recommend using nditer, but here it is is illustrative:
In [79]: [i for i in np.nditer(arr1)]
Out[79]:
[array(1, dtype=uint8),
array(2, dtype=uint8),
array(3, dtype=uint8),
array(4, dtype=uint8),
array(5, dtype=uint8),
array(6, dtype=uint8)]
In [80]: [i for i in np.nditer(arr2)]
Out[80]:
[array(1, dtype=uint8),
array(4, dtype=uint8),
array(2, dtype=uint8),
array(5, dtype=uint8),
array(3, dtype=uint8),
array(6, dtype=uint8)]
nditer takes an order, but 'K' is default (in contrast to many other cases where 'C' is the default).