matplotlib: Plot numpy arrays with None as values

Question

I have an array that looks like:

k = numpy.array([(1.,0.001), (1.1, 0.002), (None, None), 
                 (1.2, 0.003), (0.99, 0.004)])

I

Answer 1

You can use numpy.nan instead of None.

import matplotlib.pyplot as pyplot
import numpy

x = range(5)
k = numpy.array([(1.,0.001), (1.1, 0.002), (numpy.nan, numpy.nan), 
                 (1.2, 0.003), (0.99, 0.004)])

Fig, ax = pyplot.subplots()

# This plots a gap---as desired
ax.plot(x, k[:,0], 'k-')

ax.plot(range(len(y)), y[:,0]+y[:,1], 'k--')

Or you could mask the x value as well, so the indices were consistent between x and y

import matplotlib.pyplot as pyplot
import numpy

x = range(5)
y = numpy.array([(1.,0.001), (1.1, 0.002), (numpy.nan, numpy.nan), 
                 (1.2, 0.003), (0.99, 0.004)])

Fig, ax = pyplot.subplots()


ax.plot(range(len(y)), y[:,0]+y[:,1], 'k--')
import matplotlib.pyplot as pyplot
import numpy

x = range(5)
k = numpy.array([(1.,0.001), (1.1, 0.002), (None, None), 
                 (1.2, 0.003), (0.99, 0.004)])

Fig, ax = pyplot.subplots()

# This plots a gap---as desired
ax.plot(x, k[:,0], 'k-')

# I'd like to plot
#     k[:,0] + k[:,1]
# but I can't add None

arr_none = np.array([None])
mask = (k[:,0] == arr_none) | (k[:,1] == arr_none)

ax.plot(numpy.arange(len(y))[mask], k[mask,0]+k[mask,1], 'k--')

Answer 2

You can filter you array doing:

test = np.array([None])
k = k[k!=test].reshape(-1, 2).astype(float)

And then sum up the columns and make the plot. The problem of your approach is that you did not convert the None type to a numpy array, which did not allow the proper creation of the mask.