Python matplotlib.pyplot.hist()绘制直方图的方法实例

hist(x, bins=None, range=None, density=False,weights=None, cumulative=False, 
bottom=None, histtype='bar', align='mid', orientation='vertical', rwidth=None,
 log=False, color=None, label=None, stacked=False, *, data=None, **kwargs)

plt.hist(
    x,# 指定要绘制直方图的数据
    bins,# 设置长条形的数目
    range,# 指定直方图数据的上下界，默认包含绘图数据的最大值和最小值（范围）
    density=True or False, # 如果"True"，将y轴转化为密度刻度 默认为None
    weights,# 该参数可为每一个数据点设置权重
    cumulative=True or False,# 是否需要计算累计频数或频率 默认值False
    bottom=0, # 可以为直方图的每个条形添加基准线，默认为0
    histtype={'bar', 'barstacked', 'step', 'stepfilled'} # 设置样式
               # bar柱状形数据并排，默认值。
               # barstacked在柱状形数据重叠并排（相同的在一起）
               # step柱状形颜色不填充 
               # stepfilled填充的线性
    align='mid' or 'left' or 'right', # 设置条形边界值的对其方式，默认为mid，除此还有'left'和'right'
    orientation={'vertical', 'horizontal'},# 设置直方图的摆放方向，默认为垂直方向vertical
    rwidth,# 设置直方图条形宽度的百分比
    log=True or False,# 是否需要对绘图数据进行log变换 默认值False
    color='r',# 设置直方图的填充色
    label, # 设置直方图的标签
    stacked=True or False, # 当有多个数据时，是否需要将直方图呈堆叠摆放，默认False水平摆放；
    facecolor,# 设置长条形颜色(和color效果一致，设置color就不用再设置facecolor)
    edgecolor,# 设置边框的颜色
    alpha # 设置透明度  
)
# 注意组距，得到满意的展示效果
# 注意y轴所代表的变量是频数还是频率

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
# bins设置长条形的数目
plt.hist(data,bins=10)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,histtype='barstacked')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,histtype='step')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,histtype='stepfilled')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,histtype='bar',range=(150,170))
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,density=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal',density=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,weights=data)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal',weights=data)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,cumulative=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal',cumulative=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,bottom=170)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal',bottom=170)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,align='left')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,align='right')
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,rwidth=0.5)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,log=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
data=np.random.randint(140,180,200)
 
plt.hist(data,bins=10,orientation='horizontal',log=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
x=np.random.randint(140,180,200)
y=np.random.randint(140,180,200)
 
plt.hist([x,y], bins=10)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
x=np.random.randint(140,180,200)
y=np.random.randint(140,180,200)
 
plt.hist([x,y], bins=10,stacked=True)
 
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
plt.rcParams['font.sans-serif']=['FangSong']
 
fig=plt.figure(figsize=(8,8))
data=np.random.randint(140,180,200)
 
# data数据
# bins设置长条形的个数
# histtype设置样式 barstacked：在柱状形数据重叠并排（相同的在一起）
# range显示范围
# cumulative累计频数
# align设置边界对齐值为中心对齐
# orientation设置摆放方向为horizontal水平方向
# rwidth设置长条形宽度的百分比为20
# color设置长条形的填充颜色为#FFB6C1
# label设置直方图的标签
# edgecolor设置长条形边框线为#FFD700
# alpha设置长条形的透明度为0.5
# density=True 长条形呈水平方向：density将x轴转换为密度刻度  长条形呈垂直方向：density将y轴转换为密度刻度
# weights=data为每个数据点设置权重
# bottom设置基准线为15000
# log=True是否对数据进行log转换
plt.hist(data,bins=10,histtype='barstacked',range=(140,170),cumulative=True,align='mid',orientation='horizontal',rwidth=20,color='#FFB6C1',
        label='数量',edgecolor='#FFD700',alpha=0.5,weights=data,bottom=10000,log=False)
 
plt.xticks(size=20) # x轴刻度值大小
plt.yticks(size=20) # y轴刻度值大小
 
plt.title('hist',size=30) # 设置直方图标签
plt.xlabel('x轴',size=15) # 设置x轴标签
plt.ylabel('y轴',size=20) # 设置y轴标签
 
plt.rcParams.update({'font.size':20})  # 修改图例字体大小
 
plt.legend()
plt.show()

import matplotlib.pyplot as plt
import numpy as np
 
x=np.random.normal(100,15,10000)
y=np.random.normal(80,15,10000)
 
# density=True设置为密度刻度
n1, bins1, patches1 = plt.hist(x, bins=50,  density=True, color='#00B8B8', alpha=1)
n2, bins2, patches2 = plt.hist(y, bins=50,  density=True, color='r', alpha=0.2)
 
plt.plot(bins1[:-1],n1,':',lw=3)
plt.plot(bins2[:-1],n2,'--',lw=3)
 
plt.show()