Python: How to replace negative values with "0" in a matrix?

Question

Let's say I have a matrix

x = [[0.708, 0.000, -0.070],
[-0.004, 0.561, -0.088 ],
[ 0.001, -0.001 -0.023]]

I want to replace all the negative values if it begins -0.0 with 0.

Is there any way to do it python. Since my matrix is really big, it also need to be computationally efficient ?

if the value is -1.23 you don't want to replace it?

Maximilian Peters
– Maximilian Peters

2016-06-16 15:21:21 +00:00
Commented Jun 16, 2016 at 15:21 — Maximilian Peters
– Maximilian Peters, Commented Jun 16, 2016 at 15:21

J91321 · Accepted Answer · 2016-06-16 16:12:53Z

4

Easiest solution I would suggest is using numpy for handling any matrix operations so you won't have to reimplement the wheel. Maybe it would be worth looking into numpy masking operations, like masked_where

I'm not saying this is the most efficient solution, really don't know what you consider big matrix and what sort of efficiency you need, I would probably try several approaches, and compare them using profiler and decide which is best for you.

>>> import numpy
>>> x = numpy.array([1, 2, 3, -99, 5])
>>> c = numpy.ma.masked_where(x < 0, x, copy=False)
>>> c
masked_array(data = [1 2 3 -- 5],
             mask = [False False False  True False],
       fill_value = 999999)

>>> x2 = c.filled(0)
>>> x2
array([1, 2, 3, 0, 5])

answered Jun 16, 2016 at 16:12

J91321

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2 Comments

Rangooski Over a year ago

My data is numpy array. I want to replace all the elements starts with "- 0.0" not " -0.5". But your answer helps to replace only "-99" , which is negative integer.

J91321 Over a year ago

What I posted is just an example, you can have the array filled with floats and it'll still work. And you can specify any interval in masked_where or use masked_inside which is a shortcut like this c = numpy.ma.masked_inside(x, 0, -0.1, copy=False)

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