Hi,
Is there a way to sort the columns in an array? I need to sort it so that I can easily go through and keep only the unique columns. ndarray.sort(axis=1) doesn't do what I want as it destroys the relative ordering between the various columns. For example, I would like:
[[2,1,3], [3,5,1], [0,3,1]]
to go to:
[[1,2,3], [5,3,1], [3,0,1]]
(swap the first and second columns). So I want to treat the columns as objects and sort them. I can do this if I convert to a python list, but I was hoping to avoid doing that because I ultimately need to do element-wise bitwise operations.
Thanks!
On Thu, May 6, 2010 at 10:25 AM, T J tjhnson@gmail.com wrote:
Hi,
Is there a way to sort the columns in an array? I need to sort it so that I can easily go through and keep only the unique columns. ndarray.sort(axis=1) doesn't do what I want as it destroys the relative ordering between the various columns. For example, I would like:
[[2,1,3], [3,5,1], [0,3,1]]
to go to:
[[1,2,3], [5,3,1], [3,0,1]]
(swap the first and second columns). So I want to treat the columns as objects and sort them. I can do this if I convert to a python list, but I was hoping to avoid doing that because I ultimately need to do element-wise bitwise operations.
Assuming you want to sort columns by the values in the first row:
x
array([[2, 1, 3], [3, 5, 1], [0, 3, 1]])
idx = x[0,:].argsort() x[:,idx]
array([[1, 2, 3], [5, 3, 1], [3, 0, 1]])
On Thu, May 6, 2010 at 10:34 AM, Keith Goodman kwgoodman@gmail.com wrote:
On Thu, May 6, 2010 at 10:25 AM, T J tjhnson@gmail.com wrote:
Hi,
Is there a way to sort the columns in an array? I need to sort it so that I can easily go through and keep only the unique columns. ndarray.sort(axis=1) doesn't do what I want as it destroys the relative ordering between the various columns. For example, I would like:
[[2,1,3], [3,5,1], [0,3,1]]
to go to:
[[1,2,3], [5,3,1], [3,0,1]]
(swap the first and second columns). So I want to treat the columns as objects and sort them. I can do this if I convert to a python list, but I was hoping to avoid doing that because I ultimately need to do element-wise bitwise operations.
Assuming you want to sort columns by the values in the first row:
Not quite. I want the columns treated as objects...not as the first element in the column. A better example:
x
array([[3, 2, 2, 2, 2], [2, 2, 0, 2, 2], [0, 1, 1, 0, 1], [5, 5, 3, 0, 5]])
desired
array([[2, 2, 2, 2, 3], [0, 2, 2, 2, 2], [1, 0, 1, 1, 0], [3, 0, 5, 5, 5]])
what_is_really_desired
array([0,1,2,3]) # signifying unique columns
On Thu, May 6, 2010 at 1:25 PM, T J tjhnson@gmail.com wrote:
Hi,
Is there a way to sort the columns in an array? I need to sort it so that I can easily go through and keep only the unique columns. ndarray.sort(axis=1) doesn't do what I want as it destroys the relative ordering between the various columns. For example, I would like:
[[2,1,3], [3,5,1], [0,3,1]]
to go to:
[[1,2,3], [5,3,1], [3,0,1]]
(swap the first and second columns). So I want to treat the columns as objects and sort them. I can do this if I convert to a python list, but I was hoping to avoid doing that because I ultimately need to do element-wise bitwise operations.
there is a thread last august on unique rows which might be useful, and a thread in Dec 2008 for sorting rows
something like
np.unique1d(c.view([('',c.dtype)]*c.shape[1])).view(c.dtype).reshape(-1,c.shape[1])
maybe it's np.unique with numpy 1.4.
Josef
Thanks! _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Thu, May 6, 2010 at 10:36 AM, josef.pktd@gmail.com wrote:
there is a thread last august on unique rows which might be useful, and a thread in Dec 2008 for sorting rows
something like
np.unique1d(c.view([('',c.dtype)]*c.shape[1])).view(c.dtype).reshape(-1,c.shape[1])
maybe it's np.unique with numpy 1.4.
The thread is useful:
http://www.mail-archive.com/numpy-discussion@scipy.org/msg19830.html
I'll have to see if it is quicker for me to just do:
y = x.transpose().tolist() y.sort() x = np.array(y).transpose()
On Thu, May 6, 2010 at 4:45 PM, T J tjhnson@gmail.com wrote:
On Thu, May 6, 2010 at 10:36 AM, josef.pktd@gmail.com wrote:
there is a thread last august on unique rows which might be useful, and a thread in Dec 2008 for sorting rows
something like
np.unique1d(c.view([('',c.dtype)]*c.shape[1])).view(c.dtype).reshape(-1,c.shape[1])
maybe it's np.unique with numpy 1.4.
The thread is useful:
http://www.mail-archive.com/numpy-discussion@scipy.org/msg19830.html
I'll have to see if it is quicker for me to just do:
y = x.transpose().tolist() y.sort() x = np.array(y).transpose()
for sure it's easier to read. the difference might be temporary array creation compared to using numpy.sort on a view.
Josef
NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Thu, May 6, 2010 at 11:25 AM, T J tjhnson@gmail.com wrote:
Hi,
Is there a way to sort the columns in an array? I need to sort it so that I can easily go through and keep only the unique columns. ndarray.sort(axis=1) doesn't do what I want as it destroys the relative ordering between the various columns. For example, I would like:
[[2,1,3], [3,5,1], [0,3,1]]
to go to:
[[1,2,3], [5,3,1], [3,0,1]]
(swap the first and second columns). So I want to treat the columns as objects and sort them. I can do this if I convert to a python list, but I was hoping to avoid doing that because I ultimately need to do element-wise bitwise operations.
To get the order illustrated:
In [9]: a = array([[2,1,3],[3,5,1],[0,3,1]])
In [10]: i = lexsort([a[::-1][i] for i in range(3)])
In [11]: a[:,i] Out[11]: array([[1, 2, 3], [5, 3, 1], [3, 0, 1]])
But if you just want them sorted, it is easier to do
In [12]: i = lexsort([a[i] for i in range(3)])
In [13]: a[:,i] Out[13]: array([[2, 3, 1], [3, 1, 5], [0, 1, 3]])
or just
In [18]: a[:,lexsort(a)] Out[18]: array([[2, 3, 1], [3, 1, 5], [0, 1, 3]])
For the bigger array
In [21]: a Out[21]: array([[3, 2, 2, 2, 2], [2, 2, 0, 2, 2], [0, 1, 1, 0, 1], [5, 5, 3, 0, 5]])
In [22]: a[:, lexsort(a)] Out[22]: array([[2, 2, 3, 2, 2], [2, 0, 2, 2, 2], [0, 1, 0, 1, 1], [0, 3, 5, 5, 5]])
Chuck