import numpy as np
import scipy as sp
import scipy.stats

def mean_confidence_interval(data, confidence=0.95):
    a = 1.0*np.array(data)
    n = len(a)
    m, se = np.mean(a), scipy.stats.sem(a)
    h = se * sp.stats.t._ppf((1+confidence)/2., n-1)
    # return m, m-h, m+h, h
    return m, h

iostimeline, h = np.apply_along_axis(mean_confidence_interval, 0, ioshourmatrix)
androidtimeline, h = np.apply_along_axis(mean_confidence_interval, 0, androidhourmatrix)

# plt.plot(iostimeline, 'b-', label='iOS')
# plt.plot(androidtimeline, 'g-', label='Android')

line, caps, bars = plt.errorbar(list(range(24)), iostimeline, yerr=h)
plt.setp(line, label="iOS") # give label to returned line
line, caps, bars = plt.errorbar(list(range(24)), androidtimeline, yerr=h)
plt.setp(line, label="Android") # give label to returned line

plt.legend(loc=0)