matplotlib-次坐标轴
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代码123456789101112131415161718import matplotlib.pyplot as pltimport numpy as npx = np.arange(0, 10, 0.1)y1 = 0.05 * x**2y2 = -2 *y1f
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matplotlib-图中图
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代码123456789101112131415161718192021222324252627282930import matplotlib.pyplot as pltfig = plt.figure()x = [1, 2, 3, 4, 5, 6, 7]y =
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matplotlib-subplot分格显示
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代码1234567891011121314151617181920212223242526272829303132333435363738import matplotlib.pyplot as pltimport matplotlib.gridspec as g
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matplotlib-subplot多合一显示
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代码12345678910111213141516171819202122232425262728293031323334353637383940414243import matplotlib.pyplot as plt# example 1:#########
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matplotlib-3D显示
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代码12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849import numpy as npimport matplotlib.pypl
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matplotlib-图像
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代码1234567891011121314151617181920import matplotlib.pyplot as pltimport numpy as np# image dataa = np.array([0.313660827978, 0.36534
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matplotlib-柱状图
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代码123456789101112131415161718192021222324252627import matplotlib.pyplot as pltimport numpy as npn = 12X = np.arange(n)Y1 = (1 - X /
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matplotlib-散点图
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代码12345678910111213141516import matplotlib.pyplot as pltimport numpy as npn = 1024 # data sizeX = np.random.normal(0, 1, n)Y = n
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matplotlib-tick能见度
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代码123456789101112131415161718192021222324import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3, 3, 50)y = 0.1*xplt.fi
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matplotlib-对图像进行标注
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代码1234567891011121314151617181920212223242526272829303132333435import matplotlib.pyplot as pltimport numpy as npx = np.linspace(-3,
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