numpy: how to apply boolean index array on slice of numpy matrix cheaply

Question

I'm trying to apply a boolean mask array over a slice of my numpy matrix. I only figure out the following to implement this, by first copy this slice out to a single array, then apply boolean mask over this temporary array and copy it back to the matrix.

I'm wondering if there exists (sure it exists! but I can't find it) a cheaper way to implement it? a.k.a, without the cost of copying.

#(data is a numpy 2d matrix)

tmp = data[i,:]
tmp[tmp==0] = mean
data[i,:] = tmp

Can you show a sample input/output. What is mean?

user3483203
– user3483203

2018-07-07 03:08:51 +00:00
Commented Jul 7, 2018 at 3:08 — user3483203
– user3483203, Commented Jul 7, 2018 at 3:08
Take out the last line. tmp is a view, not a copy

Mad Physicist
– Mad Physicist

2018-07-07 05:42:05 +00:00
Commented Jul 7, 2018 at 5:42 — Mad Physicist
– Mad Physicist, Commented Jul 7, 2018 at 5:42

John Zwinck · Accepted Answer · 2018-07-07 05:34:56Z

1

What about this?

mask = data[i,:] == 0
data[i,mask] = mean

answered Jul 7, 2018 at 5:34

John Zwinck

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