import matplotlib.pyplot as plt
import numpy as np
plt.style.use('ggplot')

#dataをplt.hist()にわたすだけでヒストグラムを作成
data = np.random.randn(10000)
plt.hist(data);
#plt.savefig("hist.png", bbox_inches = 'tight', pad_inches = 0)

#いろいろと設定を変えることで見た目を変えることができる。
plt.hist(data, bins=50, density=True, alpha=0.5,
         histtype='stepfilled', color='lightgreen',
         edgecolor='m');
#plt.savefig("hist_kai.png", bbox_inches = 'tight', pad_inches = 0)

#２つのヒストグラムを重ねて表示することもできる
x1 = np.random.normal(1,  .8, 1000)
x2 = np.random.normal(-1, .8, 1000)
kwargs = dict(histtype='stepfilled', alpha=0.5, density=True, bins=50)

plt.hist(x1, **kwargs)
plt.hist(x2, **kwargs);
#plt.savefig("hist_2ko.png", bbox_inches = 'tight', pad_inches = 0)

#,棒の数。(デフォルト:10)
n1,b1,p1 = plt.hist(data,bins=np.arange(-5,6,1),label='bins=np.arange(-5,6,1)');
n1,b2,p2 = plt.hist(data,bins=10,label='bins=10');
plt.legend()
#plt.savefig("hist_bins.png", bbox_inches = 'tight', pad_inches = 0)
b1
array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])
b2
array([-3.53911134, -2.77787418, -2.01663702, -1.25539985, -0.49416269,
        0.26707448,  1.02831164,  1.7895488 ,  2.55078597,  3.31202313,
        4.0732603 ])

#range
n3,b3,p3 = plt.hist(data, range=(-5,5),label='range=(-5,5)');
n4,b4,p4 = plt.hist(data,label='range=(np.min(data),np.max(data))');
plt.legend()
#plt.savefig("hist_range.png", bbox_inches = 'tight', pad_inches = 0)
b4
array([-3.53911134, -2.77787418, -2.01663702, -1.25539985, -0.49416269,
        0.26707448,  1.02831164,  1.7895488 ,  2.55078597,  3.31202313,
        4.0732603 ])

#正規化
plt.hist(data, density=True, label='density=True')
plt.legend()
#plt.savefig("hist_densuty.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x8342240>

#累積ヒストグラム　cumulative
plt.hist(data, density=True,cumulative=True, label='cumulative=True')
plt.legend()
#plt.savefig("hist_cumulative.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x83c5240>

#底上げ　bottom
n5,b5,p5 = plt.hist(data,bottom=3000,label='bottom=3000');
n6,b6,p6 = plt.hist(data,label='bottom=None');
plt.legend()
#plt.savefig("hist_bottom.png", bbox_inches = 'tight', pad_inches = 0)
n5==n6
array([ True,  True,  True,  True,  True,  True,  True,  True,  True,
        True])

data2 = np.random.randn(10000,2)
#histtype 'bar', 'barstacked', 'step', 'stepfilled'
plt.hist(data2,histtype='bar',label='bar')
plt.legend()
#plt.savefig("bar.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x82f0e10>

plt.hist(data2,histtype='barstacked',label='barstacked')
plt.legend()
#plt.savefig("barstacked.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x85af860>

plt.hist(data2,histtype='step',label='step')
plt.legend()
#plt.savefig("step.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x85afa20>

plt.hist(data2,histtype='stepfilled',label='stepfilled')
plt.legend()
#plt.savefig("stepfilled.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x85950b8>

#stacked
plt.hist(data2,stacked=True,label='stacked=True')
plt.legend()
#plt.savefig("stacked.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x8716358>

#align : {'left', 'mid', 'right'}, 棒が生える場所
"""
•'left': bars are centered on the left bin edges.
•'mid': bars are centered between the bin edges.
•'right': bars are centered on the right bin edges.
"""

plt.hist(data,align='left',range=(-5,5),label='align=left')
plt.legend()
plt.xlim(-5,5)
#plt.savefig("align_left.png", bbox_inches = 'tight', pad_inches = 0)
(-5, 5)

plt.hist(data,align='mid',range=(-5,5),label='align=mid')
plt.legend()
plt.xlim(-5,5)
#plt.savefig("align_mid.png", bbox_inches = 'tight', pad_inches = 0)
(-5, 5)

plt.hist(data,align='right',range=(-5,5),label='align=right')
plt.legend()
plt.xlim(-5,5)
#plt.savefig("align_right.png", bbox_inches = 'tight', pad_inches = 0)
(-5, 5)

#orientation 'horizontal', 'vertical'
plt.hist(data,orientation='horizontal',label='orientation="horizontal"')
plt.legend()
#plt.savefig("horizontal.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x97f5b38>

#rwidth
#The relative width of the bars as a fraction of the bin width
plt.hist(data,rwidth=1,label='rwidth=1')
plt.hist(data,rwidth=.5,label='rwidth=.5')
plt.hist(data,rwidth=.1,label='rwidth=.1')
plt.legend()
#plt.savefig("rwidth.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x87e75f8>

#log  logscale y軸
plt.hist(data,log=True,label='log=True')
plt.legend()
#plt.savefig("log.png", bbox_inches = 'tight', pad_inches = 0)
<matplotlib.legend.Legend at 0x98f1940>