importmatplotlib.pyplotaspltimportnumpyasnp# 创建数据x=np.linspace(0,10,100)y1=np.sin(x)y2=np.cos(x)y3=np.tan(x)y4=x**2# 创建图表fig,ax=plt.subplots(figsize=(12,8))# 绘制线条line1,=ax.plot(x,y1,label='Sine - how2matplotlib.com')line2,=ax.plot(x,y2,label...
pl.show()# show the plot on the screen 2.2.5在一个坐标系上绘制多个图 Plotting more than one plot on the same set of axes 做法是很直接的,依次作图即可: importnumpy as np importpylab as pl x1 = [1, 2, 3, 4, 5]# Make x, y arrays for each graph y1 = [1, 4, 9, 16, 25]...
ax.set_title('Multiple Lines and Scatter Plot on the Same Figure') # 显示图形 plt.show() 生成的图片如下: 当然,如果直接画也是没有问题的,只要多写一个并列的plot函数与scatter函数即可,下面举的例子是一个数值分析实例,来研究拉格朗日插值多项式以切比雪夫结点的拟合情况(恰好是这次的上机作业报告)。 impor...
pl.show()# show the plot on the screen 1. 2.2.5在一个坐标系上绘制多个图 Plotting more than one plot on the same set of axes 做法是很直接的,依次作图即可: import numpy as np 1. import pylab as pl 1. 1. x1 = [1, 2, 3, 4, 5]# Make x, y arrays for each graph 1. y1 =...
# OO stylefig,ax=plt.subplots(figsize=(13,6))# Create a figure and an axes.ax.plot(x,y1,label='Quadratic')# Plot some data on the axes.ax.plot(x,y2,label='20Sin(x)')# Plot more data on the axes...ax.plot(x,y3,label='20Cos(3x)')# ... and some more.ax.legend()plt...
然后用sca()交替让它们成为当前Axes对象,并调用plot()在其中绘图。 import matplotlib.pyplot as plt import numpy as np %matplotlib inline for idx,color in enumerate('rgbyck'): plt.subplot(321+idx,axisbg=color) plt.show() 1. 2. 3.
pl.show()# show the plot on the screen 2.2.5在一个坐标系上绘制多个图 Plotting more than one plot on the same set of axes 做法是很直接的,依次作图即可: 1 2 importnumpy as np 1 2 importpylab as pl 1 1 2 x1 = [1,2,3,4,5]# Make x, y arraysforeach graph ...
You can loosely think of it as a process where you create figures one at a time,and all commands affect the current figure and the current plot. 您可以粗略地将其视为一个一次创建一个地物的过程,所有命令都会影响当前地物和当前绘图。 We will mostly use NumPy arrays for storing the data that...
So let's 'break' or 'cut-out' the y-axis # into two portions - use the top (ax) for the outliers, and the bottom # (ax2) for the details of the majority of our data f, (ax, ax2) = plt.subplots(2, 1, sharex=True) # plot the same data on both axes ax.plot(pts) ...
ax1.plot(x, y1)ax1.set_ylabel('Y values for exp(-x)')ax1.set_title("Double Y axis")ax2 = ax1.twinx() # this is the important function ax2.plot(x, y2, 'r')ax2.set_xlim([0, np.e])ax2.set_ylabel('Y values for ln(x)')ax2.set_xlabel('Same X for both exp(-x)...