# # import matplotlib.pyplot as plt

# # plt.figure(1) # the first figure

# # plt.subplot(211) # the first subplot in the first figure

# # plt.plot([1, 2, 3])

# # plt.subplot(212) # the second subplot in the first figure

# # plt.plot([4, 5, 6])

# #

# #

# # plt.figure(2) # a second figure

# # plt.plot([4, 5, 6]) # creates a subplot(111) by default

# #

# # plt.figure(1) # figure 1 current; subplot(212) still current

# # plt.subplot(211) # make subplot(211) in figure1 current

# # plt.title('Easy as 1, 2, 3') # subplot 211 title

# # plt.show()

#

# import numpy as np

# import matplotlib.pyplot as plt

# from matplotlib import style

#

# style.use('ggplot')

# # Fixing random state for reproducibility

# np.random.seed(19680801)

#

# mu, sigma = 100, 15

# x = mu + sigma * np.random.randn(10000)

#

# # the histogram of the data

# n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)

#

#

# plt.xlabel('$\sigma_i=15$')

# plt.ylabel('Probability')

# plt.title('Histogram of IQ')

# plt.text(60, .025, r'$\mu=100,\ \sigma=15$')

# plt.axis([40, 160, 0, 0.03])

# # plt.grid(True)

# plt.show()

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.ticker import MaxNLocator

from collections import namedtuple

n_groups = 5

means_men = (20, 35, 30, 35, 27)

std_men = (2, 3, 4, 1, 2)

means_women = (25, 32, 34, 20, 25)

std_women = (3, 5, 2, 3, 3)

fig, ax = plt.subplots()

index = np.arange(n_groups)

bar_width = 0.35

opacity = 0.4

error_config = {'ecolor': '0.3'}

rects1 = ax.bar(index, means_men, bar_width,

alpha=opacity, color='b',

yerr=std_men, error_kw=error_config,

label='Men')

rects2 = ax.bar(index + bar_width, means_women, bar_width,

alpha=opacity, color='r',

yerr=std_women, error_kw=error_config,

label='Women')

ax.set_xlabel('Group')

ax.set_ylabel('Scores')

ax.set_title('Scores by group and gender')

ax.set_xticks(index + bar_width / 2)

ax.set_xticklabels(('A', 'B', 'C', 'D', 'E'))

ax.legend()

fig.tight_layout()

plt.show()