Ignoring negative and extreme values

Masked arrays are useful when we want to ignore negative values, for instance, when taking the logarithm of array values. Another use case for masked arrays is excluding extreme values. This works based on an upper and lower bound for extreme values.

In this tutorial, we will apply these techniques to stock price data. We will skip the steps for downloading data, as they are repeated in previous chapters.

How to do it...

We will take the logarithm of an array that contains negative numbers.

1. Take the logarithm of negative numbers.

   First, let's create an array containing numbers divisible by three:

   triples = numpy.arange(0, len(close), 3)
   print "Triples", triples[:10], "..."

   Next, we will create an array with the ones that have the same size as the price data array:

   signs = numpy.ones(len(close))
   print "Signs", signs[:10], "..."

   We will set each third number to be negative, with the help of indexing tricks we learned about in Chapter 2, Advanced Indexing and Array...