add_axes([0.1, 0.1, 0.8, 0.8]) line, = ax.plot([0,1], [0,1]) ax.set_title("a straight line (OO)") ax.set_xlabel("x value") ax.set_ylabel("y value") canvas.print_figure('demo.jpg') 新的demo.jpg如下: 10、理解对象 上面的例子中
line, = ax1.plot([0,1], [0,1]) ax1.set_title("ax1") # second axes ax2 = fig.add_axes([0.4, 0.3, 0.4, 0.5]) sca = ax2.scatter([1,3,5],[2,1,2]) ax2.set_title("ax2") canvas.print_figure('demo.jpg') 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. ...
这个函数会同时返回我们需要的figure对象和axes对象（也可以使用plt.figure()和plt.add_subplot()，但在...
canvas=FigureCanvas(fig)#first axesax1 = fig.add_axes([0.1, 0.1, 0.2, 0.2]) line,= ax1.plot([0,1], [0,1]) ax1.set_title("ax1")#second axesax2 = fig.add_axes([0.4, 0.3, 0.4, 0.5]) sca= ax2.scatter([1,3,5],[2,1,2]) ax2.set_title("ax2") canvas.print_figure(...
ax= fig.add_subplot(111, autoscale_on=False, xlim=(-1, 5), ylim=(-4, 3)) t= np.arange(0.0, 5.0, 0.01) s= np.cos(2*np.pi*t) line,= ax.plot(t, s, lw=3) ax.annotate('straight', xy=(0, 1), xycoords='data', ...
4from matplotlib.backends.backend_aggimportFigureCanvasAggasFigureCanvas56fig=Figure()7canvas=FigureCanvas(fig)8ax=fig.add_axes([0.1,0.1,0.8,0.8])910line,=ax.plot([0,1],[0,1])11ax.set_title("a straight line (OO)")12ax.set_xlabel("x value")13ax.set_ylabel("y value")1415canvas....
add_axes方法的四个参数分别表示画布区域的左下角坐标(left,bottom)和整块儿画布的宽高，需要注意的是...
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8]) line, = ax.plot([0,1], [0,1]) ax.set_title("a straight line (OO)") ax.set_xlabel("x value") ax.set_ylabel("y value") canvas.print_figure('demo.jpg') 1. 2. 3. 4.
Aline chartis a type of chart which displays information as a series of data points calledmarkersconnected by straight line segments. linechart.py #!/usr/bin/python import numpy as np import matplotlib.pyplot as plt t = np.arange(0.0, 3.0, 0.01) ...
This tutorial should be enough to get you started making line charts with matplotlib. But, this is really only the beginning. If you’re serious about data visualization and data science with Python, you’ll need to learn more. You’ll need to learn how to add titles to your plots, form...