117 lines
3.6 KiB
Python
117 lines
3.6 KiB
Python
#===============================================================
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# 画图：柱状图，散点图，拆线图，3D图，概率分布图，累计概率分布图
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#===============================================================
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import numpy as np
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import matplotlib.pyplot as plt
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#===============================================================
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# 柱状图
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plt.subplot(221)
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size = 5
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x = np.arange(size)
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a = np.random.random(size)
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b = np.random.random(size)
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width=0.3
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plt.bar(x+width/2, a, label='a',width=width)
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plt.bar(x+width*3/2, b, label='b',width=width)
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plt.legend()
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plt.xlabel('x-axis')
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plt.ylabel('y-axis')
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plt.title('plot')
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#plt.show()
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#===============================================================
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# 散点图
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import matplotlib.pyplot as plt
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plt.subplot(222)
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#plt.figure(figsize=(6,5))
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x=np.random.random(10)
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y=np.random.random(10)
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plt.scatter(x,y,s=15,c='b',marker=(6,1),alpha=0.7,lw=2)
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plt.xlim(0,1)
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plt.ylim(0,1)
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#plt.show()
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#===============================================================
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# 拆线图
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import matplotlib.pyplot as plt
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plt.subplot(223)
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size = 5
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x = np.arange(size)
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a = np.random.random(size)
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b = np.random.random(size)
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plt.plot(x,a, marker='o', mec='b', mfc='b',label=u'y=a')
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plt.plot(x,b, marker='*', ms=10,label=u'y=b')
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plt.legend()
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plt.xlabel('x-axis')
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plt.ylabel('y-axis')
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plt.title('plot')
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plt.show()
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#===============================================================
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# 3D图
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#===============================================================
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# plt.subplot(224)
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from mpl_toolkits.mplot3d import Axes3D
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import matplotlib.pyplot as plt
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# 定义函数
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def rand_range(n, vmin, vmax):
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'''
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make an array of random numbers having shape (n, )
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with each number distributed Uniform(vmin, vmax).
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'''
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return (vmax - vmin) * np.random.rand(n) + vmin
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fig = plt.figure()
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ax = fig.add_subplot(111, projection='3d')
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# plot n random points in the box
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# defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].
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n = 5
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for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:
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xs = rand_range(n, 20, 30)
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ys = rand_range(n, 30, 100)
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zs = rand_range(n, zlow, zhigh)
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ax.scatter(xs, ys, zs, c=c, marker=m)
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# 单独一个散点
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ax.scatter(25, 50, -25, c='g', marker="*")
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_zlabel('z')
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plt.show()
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#===============================================================
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# 概率分布图,累计概率分布图
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#===============================================================
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import matplotlib.pyplot as plt
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import numpy as np
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#概率分布直方图
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#高斯分布
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mean = 0 #均值为0
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sigma = 1 #标准差为1，反应数据集中还是分散的值
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x=mean+sigma*np.random.randn(10000)
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#第二个参数是柱子宽一些还是窄一些，越大越窄越密
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fig,(ax0,ax1) = plt.subplots(nrows=2,figsize=(6,6))
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##pdf概率分布图，一万个数落在某个区间内的数有多少个
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ax0.hist(x,bins=40,density=True,histtype='bar',facecolor='green',alpha=0.8,rwidth=0.8) # bins参数表示将数据分成几组
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ax0.set_title('pdf')
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#cdf累计概率函数，cumulative累计。比如需要统计小于5的数的概率
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# bins参数表示将数据分成几组
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# normed 是否对y轴数据进行标准化:True表是在本区间的点在所有的点中所占的概率，如果 normed 为False， 则是显示点的数量
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ax1.hist(x,bins=20,density=False,histtype='step',facecolor='blue',alpha=0.8,cumulative=True,rwidth=0.8)
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ax1.set_title("cdf")
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fig.subplots_adjust(hspace=0.4)
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plt.show()
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#plt.draw()
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# 可用 help(function) 查看函数帮助，如：help(ax.plot_surface)
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#=============================================================== |