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

a = [random.random() for i in range(100)]

val1, base1 = np.histogram(a, bins=100, range=(0.4, 0.8))

def plot_binned_data(axes, binedges, data,
*args, **kwargs):
#The dataset values are the bin centres
x = (binedges[1:] + binedges[:-1]) / 2.0
#The weights are the y-values of the input binned data
weights = data
return axes.hist(x, bins=binedges, weights=weights,
*args, **kwargs)


import numpy
import matplotlib.pyplot as plt

#Create a dataset
dataset = numpy.random.normal(size=100)
#Bin the dataset
binedges = numpy.linspace(-5.0, 5.0, num=10)
y, binedges = numpy.histogram(dataset, binedges)

#Plot the dataset
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
plot_binned_data(ax, binedges, y, histtype="step")
plt.show()

import matplotlib.pyplot as plt
import numpy as np

fig = plt.figure(figsize=[12, 12])

# もし複数枚プロットしたい時はここをいじる
# 2x2の場合は fig.add_subplot(2,2,1)として、最後の数字を1~4で変える(最初のプロットはi=0ではないことに注意)
ax = fig.add_subplot(1,1,1)

# ヒストグラムを取る時の範囲はrange指定する
# 0〜最大値*1.1とかにしておくのが吉
ax.hist(x, bins=50, range=[0, 10])
ax.set_title('hogehoge')
ax.set_xlabel('xlabel')
ax.set_ylabel('histogram')
#fig.show()
fig.savefig("hoge.png")

fig.suptitle("graphs")

fig.suptitle("graphs", y=0.90)