可视化——matploblib常见api（二）

本文介绍了使用Matplotlib进行数据可视化的多种技巧，包括如何利用fill_between制作不同效果的填充图，以及step和bar函数的区别应用。同时展示了如何通过tight_layout调整布局，确保图表元素不被截断。

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基本命令	说明
plt.tight_layout()	针对`fig, ax = plt.subplots(1, 3)`
plt.axis(‘equal’)	坐标轴刻度一致
plt.legend(loc=”)	center left/center/center right upper left/upper center/upper right lower left/lower center/lower right right/best
cmap	colormap: plt.cm.jet

ListedColormap

class ListedColormap(Colormap):
    """Colormap object generated from a list of colors.
    ...
    """

from matplotlib.colors import ListedColormap
colors = ('lightgreen', 'cyan', 'gray', 'r', 'b')
cmp = ListedColormap(colors[:np.unique(y_train)])

plt.fill_between()

我们要做出如下的效果图：


import numpy as np
import matplotlib.pyplot as plt


def main():
    x = np.arange(0, 2, .01)
    y1 = np.sin(2*np.pi*x)
    y2 = 1.2*np.sin(4*np.pi*x)

    fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1, sharex=True)
    ax1.fill_between(x, 0, y1)
    ax1.set_title('between 0 and '+r'$y_1=\sin(2\pi x)$')
    ax2.fill_between(x, y1, 1)
    ax2.set_title('between'+ r'$y_1=\sin(2\pi x)$ and 1')
    ax3.fill_between(x, y1, y2)
    ax3.set_title('between ' +r'$y_1=\sin(2\pi x)$'+ ' and '+r'$ y_2=1.2 \sin(4\pi x)$')
    ax3.set_xlabel('x')
    plt.savefig('./1.png')
    plt.show()

if __name__ == '__main__':
    main()

fill between where


import numpy as np
import matplotlib.pyplot as plt


def main():
    x = np.arange(0, 2, .01)
    y1 = np.sin(2*np.pi*x)
    y2 = 1.2*np.sin(4*np.pi*x)
    plt.figure(figsize=(6, 4.5))
    plt.plot(x, y1, x, y2, 'k')
    plt.fill_between(x, y1, y2, y1>=y2, facecolor='g')
                            # where=y1>=y2
    plt.fill_between(x, y1, y2, y1<y2, facecolor='r')
    plt.savefig('./2.png')
    plt.show()

if __name__ == '__main__':
    main()

plt.step() 与 plt.bar()

plt.bar(range(1, len(x)), x, alpha=.4, align=’center’, label=’individual’)
plt.step(range(1, 14), np.cumsum(x), where=’mid’, label=’cumsum’)

x = [ 4.8923083   2.46635032  1.42809973  1.01233462  0.84906459  0.60181514  0.52251546  0.08414846  0.33051429  0.29595018  0.16831254  0.21432212  0.2399553 ]
tot = sum(x)
x = [i/tol for i in x]
plt.bar(range(1, len(x)), x, alpha=.4, align='center', label='individual')
plt.step(range(1, len(x)), np.cumsum(x), where='mid', label='cumsum')
plt.legend(loc='best', frameon=False)
plt.show()