Reduce a sparse matrix in numpy -


It seems that I'm missing something very basic. I have a large square matrix, which is mostly zero. What I want to reduce is the matrix in which all rows and columns can be with zero-zero entries. For example: 

  1 1 0 1 1 1 0 1 0 0 0 1 1 0 1   
 The following should be done in the following:  <<>  1 1 1 1 1 1 1 1 1   
 Is there a quick way to do this?   
 
 
 How about something like this:  
  & gt; & Gt; & Gt; arr Array ([[1., 1., 0., 1.], [1., 1., 0., 1], [0., 0., 0., 0.], [1., 1 ., 0., 1.]])> Gt; & Gt; & Gt; Mask = (arr == 0) arr = arr [~ np.all (mask, axis = 0)] arr = arr [:, ~ np.all (mask, axis = 1)]> gt; & Gt; & Gt; arr Array ([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]])

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