Python / numpy problem with array / vector with empty second dimension
I have what seems like an easy question.
Observe the code:
In : x=np.array([0, 6])
Out: array([0, 6])
In : x.shape
Out: (2L,)
Which shows that the array has no second dimension, so it x
doesn't differ from x.T
.
How to make x size (2L, 1L)? The real motivation for this question is that I have an array of a y
shape [3L,4L]
and I want y.sum (1) to be a vector transposed, etc.
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As long as you can resize an array and add dimensions with [:,np.newaxis]
, you should be familiar with the most basic nested brackets, or list, notation. Note how it matches the display.
In [230]: np.array([[0],[6]])
Out[230]:
array([[0],
[6]])
In [231]: _.shape
Out[231]: (2, 1)
np.array
also takes a parameter ndmin
, although it adds extra dimensions at the beginning (default location for numpy
.)
In [232]: np.array([0,6],ndmin=2)
Out[232]: array([[0, 6]])
In [233]: _.shape
Out[233]: (1, 2)
The classic way to do something 2d is to change:
In [234]: y=np.arange(12).reshape(3,4)
In [235]: y
Out[235]:
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
sum
(and related functions) has a parameter keepdims
. Read the docs.
In [236]: y.sum(axis=1,keepdims=True)
Out[236]:
array([[ 6],
[22],
[38]])
In [237]: _.shape
Out[237]: (3, 1)
empty 2nd dimension
- not quite terminology. More like a non-existent second dimension.
The dimension can have 0 members:
In [238]: np.ones((2,0))
Out[238]: array([], shape=(2, 0), dtype=float64)
If you are more familiar with MATLAB, which has at least 2d, you might like the subclass np.matrix
. It takes steps to ensure that most operations return a different 2d matrix:
In [247]: ym=np.matrix(y)
In [248]: ym.sum(axis=1)
Out[248]:
matrix([[ 6],
[22],
[38]])
The matrix sum
does:
np.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
The bit _collapse
allows you to return a scalar for ym.sum()
.
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One more point to save size information:
In [42]: X
Out[42]:
array([[0, 0],
[0, 1],
[1, 0],
[1, 1]])
In [43]: X[1].shape
Out[43]: (2,)
In [44]: X[1:2].shape
Out[44]: (1, 2)
In [45]: X[1]
Out[45]: array([0, 1])
In [46]: X[1:2] # this way will keep dimension
Out[46]: array([[0, 1]])
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