《Python数据可视化之matplotlib实践》 源码 第二篇 精进 第七章

《Python数据可视化之matplotlib实践》 源码 第二篇 精进 第七章

图   7.1

import matplotlib as mplimport matplotlib.pyplot as pltimport numpy as npmpl.rcParams["font.sans-serif"]=["SimHei"]mpl.rcParams["axes.unicode_minus"]=Falsefig, ax1 = plt.subplots()t=np.arange(0.05, 10.0, 0.01)s1=np.exp(t)ax1.plot(t, s1, c="b", ls="-")ax1.set_xlabel("x坐标轴")ax1.set_ylabel("以e为底指数函数", color="b")ax1.tick_params("y", colors="b")ax2=ax1.twinx()s2=np.cos(t**2)ax2.plot(t, s2, c="r", ls=":")ax2.set_ylabel("余弦函数", color="r")ax2.tick_params("y", colors="r")plt.show()

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图   7.2

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2)ax1=ax[0, 0]ax1.plot(x1, y1)ax2=ax[0, 1]ax2.plot(x2, y2)ax3=ax[1, 0]ax3.scatter(x3, y3)ax4=ax[1, 1]ax4.scatter(x4, y4)plt.show()

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图   7.3

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2, sharex="all")ax1=ax[0, 0]ax1.plot(x1, y1)ax2=ax[0, 1]ax2.plot(x2, y2)ax3=ax[1, 0]ax3.scatter(x3, y3)ax4=ax[1, 1]ax4.scatter(x4, y4)plt.show()

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图   7.4

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2, sharex="none")ax1=ax[0, 0]ax1.plot(x1, y1)ax2=ax[0, 1]ax2.plot(x2, y2)ax3=ax[1, 0]ax3.scatter(x3, y3)ax4=ax[1, 1]ax4.scatter(x4, y4)plt.show()

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图   7.5

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2, sharex="row")ax1=ax[0, 0]ax1.plot(x1, y1)ax2=ax[0, 1]ax2.plot(x2, y2)ax3=ax[1, 0]ax3.scatter(x3, y3)ax4=ax[1, 1]ax4.scatter(x4, y4)plt.show()

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图   7.6

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2, sharex="col")ax1=ax[0, 0]ax1.plot(x1, y1)ax2=ax[0, 1]ax2.plot(x2, y2)ax3=ax[1, 0]ax3.scatter(x3, y3)ax4=ax[1, 1]ax4.scatter(x4, y4)plt.show()

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图   7.7

import matplotlib.pyplot as pltimport numpy as npx=np.linspace(0.0, 10.0, 200)y=np.cos(x)*np.sin(x)y2=np.exp(-x)*np.sin(x)y3=3*np.sin(x)y4=np.power(x, 0.5)fig, (ax1, ax2, ax3, ax4)=plt.subplots(4, 1, sharex="all")fig.subplots_adjust(hspace=0)ax1.plot(x, y, ls="-", lw=2)ax1.set_yticks(np.arange(-0.6, 0.7, 0.2))ax1.set_ylim(-0.7, 0.7)ax2.plot(x, y2, ls="-", lw=2)ax2.set_yticks(np.arange(-0.05, 0.36, 0.1))ax2.set_ylim(-0.1, 0.4)ax3.plot(x, y3, ls="-", lw=2)ax3.set_yticks(np.arange(-3, 4, 1))ax3.set_ylim(-3.5, 3.5)ax4.plot(x, y4, ls="-", lw=2)ax4.set_yticks(np.arange(0.0, 3.6, 0.5))ax4.set_ylim(0.0, 4.0)plt.show()

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图   7.8

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2)ax1=plt.subplot(221)ax1.plot(x1, y1)ax2=plt.subplot(222)ax2.plot(x2, y2)ax3=plt.subplot(223)ax3.scatter(x3, y3)ax4=plt.subplot(224, sharex=ax1)ax4.scatter(x4, y4)plt.show()

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图   7.9

import matplotlib.pyplot as pltimport numpy as npx1=np.linspace(0, 2*np.pi, 400)y1=np.cos(x1**2)x2=np.linspace(0.01, 10, 100)y2=np.sin(x2)x3=np.random.rand(100)y3=np.linspace(0, 3, 100)x4=np.arange(0, 6, 0.5)y4=np.power(x4, 3)fig, ax=plt.subplots(2, 2)ax1=plt.subplot(221)ax1.plot(x1, y1)ax2=plt.subplot(222)ax2.plot(x2, y2)ax3=plt.subplot(223)plt.autoscale(enable=True, axis="both", tight=True)ax3.scatter(x3, y3)ax4=plt.subplot(224, sharex=ax1)ax4.scatter(x4, y4)plt.show()

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