unique plot marker for each plot in matplotlib

itertools.cycle will iterate over a list or tuple indefinitely. This is preferable to a function which randomly picks markers for you.

Python 2.x

import itertools
marker = itertools.cycle((',', '+', '.', 'o', '*')) 
for n in y:
    plt.plot(x,n, marker = marker.next(), linestyle='')

Python 3.x

import itertools
marker = itertools.cycle((',', '+', '.', 'o', '*')) 
for n in y:
    plt.plot(x,n, marker = next(marker), linestyle='')

You can use that to produce a plot like this (Python 2.x):

import numpy as np
import matplotlib.pyplot as plt
import itertools

x = np.linspace(0,2,10)
y = np.sin(x)

marker = itertools.cycle((',', '+', '.', 'o', '*')) 

fig = plt.figure()
ax = fig.add_subplot(111)

for q,p in zip(x,y):
    ax.plot(q,p, linestyle = '', marker=marker.next())
    
plt.show()

It appears that nobody has mentioned the built-in pyplot method for cycling properties yet. So here it is:

import numpy as np
import matplotlib.pyplot as plt
from cycler import cycler

x = np.linspace(0,3,20)
y = np.sin(x)

fig = plt.figure()
plt.gca().set_prop_cycle(marker=['o', '+', 'x', '*', '.', 'X']) # gca()=current axis

for q,p in zip(x,y):
    plt.plot(q,p, linestyle = '')

plt.show()

However, this way you lose the color cycle. You can add back color by multiplying or adding a color cycler and a marker cycler object, like this:

fig = plt.figure()

markercycle = cycler(marker=['o', '+', 'x', '*', '.', 'X'])
colorcycle = cycler(color=['blue', 'orange', 'green', 'magenta'])
# Or use the default color cycle:
# colorcycle = cycler(color=plt.rcParams['axes.prop_cycle'].by_key()['color'])

plt.gca().set_prop_cycle(colorcycle * markercycle) # gca()=current axis

for q,p in zip(x,y):
    plt.plot(q,p, linestyle = '')

plt.show()

When adding cycles, they need to have the same length, so we only use the first four elements of markercycle in that case:

plt.gca().set_prop_cycle(colorcycle + markercycle[:4]) # gca()=current axis