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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?
839
Using the NumPy function np. delete() , you can delete any row and column from the NumPy array ndarray . Specify the axis (dimension) and position (row number, column number, etc.). It is also possible to select multiple rows and columns using a slice or a list.
If you want to replace NaN with 0 : a[np. isnan(a)] = 0 print(a) # [[11. 12.
For a NumPy array a, you can use
a[a != 0] to extract the values not equal to zero.
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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 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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