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]...
如果numRows,numCols和plotNum这三个数都小于10的话,可以把它们缩写为一个整数,例如subplot(323)和subplot(3,2,3)是相同的。subplot在plotNum指定的区域中创建一个轴对象。如果新创建的轴和之前创建的轴重叠的话,之前的轴将被删除。 subplot()返回它所创建的Axes对象,我们可以将它用变量保存起来,然后用sca()交...
# 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...
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 =...
- 'kde' : Kernel Density Estimation plot - 'density' : same as 'kde' - 'area' : area plot - 'pie' : pie plot - 'scatter' : scatter plot - 'hexbin' : hexbin plot figsize 设置图像大小 a tuple (width, height) in inches
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)...
So, if we take the same example as above, and leave out the x-points, the diagram will look like this:Example Plotting without x-points: import matplotlib.pyplot as plt import numpy as np ypoints = np.array([3, 8, 1, 10, 5, 7]) plt.plot(ypoints) plt.show() Result: Try ...