I have a rank-1 numpy.array of which I want to make a boxplot. However, I want to exclude all values equal to zero in the array. Currently, I solved this by looping the array and copy the value to a new array if not equal to zero. However, as the array consists of 86 000 000 values and I have to do this multiple times, this takes a lot of patience.
Is there a more intelligent way to do this?
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Answer
This is a case where you want to use masked arrays, it keeps the shape of your array and it is automatically recognized by all numpy and matplotlib functions.
X = np.random.randn(1e3, 5) X[np.abs(X)< .1]= 0 # some zeros X = np.ma.masked_equal(X,0) plt.boxplot(X) #masked values are not plotted #other functionalities of masked arrays X.compressed() # get normal array with masked values removed X.mask # get a boolean array of the mask X.mean() # it automatically discards masked values