YouTip

We can use the **subplot()** and **subplots()** methods in pyplot to draw multiple subplots. The **subplot()** method requires specifying the position when plotting, while the **subplots()** method can generate multiple subplots at once. When calling it, you only need to use the `ax` attribute of the generated object. ### subplot subplot(nrows, ncols, index, **kwargs) subplot(pos, **kwargs) subplot(**kwargs) subplot(ax) The above functions divide the entire plotting area into `nrows` rows and `ncols` columns, then number each sub-area from 1 to N in order from left to right, top to bottom. The top-left sub-area is numbered 1, and the bottom-right is numbered N. The numbering can be set via the **index** parameter. Setting `numRows = 1` and `numCols = 2` means plotting in a 1x2 image area. The corresponding coordinates are: (1, 1), (1, 2) **plotNum = 1** corresponds to the coordinate (1, 1), which is the subplot in the first row and first column. **plotNum = 2** corresponds to the coordinate (1, 2), which is the subplot in the first row and second column. ## Example import matplotlib.pyplot as plt import numpy as np #plot 1: xpoints = np.array([0,6]) ypoints = np.array([0,100]) plt.subplot(1,2,1) plt.plot(xpoints,ypoints) plt.title("plot 1") #plot 2: x = np.array([1,2,3,4]) y = np.array([1,4,9,16]) plt.subplot(1,2,2) plt.plot(x,y) plt.title("plot 2") plt.suptitle(" subplot Test") plt.show() The result is shown below: !(#) Setting `numRows = 2` and `numCols = 2` means plotting in a 2x2 image area. The corresponding coordinates are: (1, 1), (1, 2)(2, 1), (2, 2) **plotNum = 1** corresponds to the coordinate (1, 1), which is the subplot in the first row and first column. **plotNum = 2** corresponds to the coordinate (1, 2), which is the subplot in the first row and second column. **plotNum = 3** corresponds to the coordinate (2, 1), which is the subplot in the second row and first column. **plotNum = 4** corresponds to the coordinate (2, 2), which is the subplot in the second row and second column. ## Example import matplotlib.pyplot as plt import numpy as np #plot 1: x = np.array([0,6]) y = np.array([0,100]) plt.subplot(2,2,1) plt.plot(x,y) plt.title("plot 1") #plot 2: x = np.array([1,2,3,4]) y = np.array([1,4,9,16]) plt.subplot(2,2,2) plt.plot(x,y) plt.title("plot 2") #plot 3: x = np.array([1,2,3,4]) y = np.array([3,5,7,9]) plt.subplot(2,2,3) plt.plot(x,y) plt.title("plot 3") #plot 4: x = np.array([1,2,3,4]) y = np.array([4,5,6,7]) plt.subplot(2,2,4) plt.plot(x,y) plt.title("plot 4") plt.suptitle(" subplot Test") plt.show() The result is shown below: !(#) ### subplots() The syntax for the subplots() method is as follows: matplotlib.pyplot.subplots(nrows=1, ncols=1, *, sharex=False, sharey=False, squeeze=True, subplot_kw=None, gridspec_kw=None, **fig_kw) **Parameter Description:** * **nrows**: Default is 1, sets the number of rows in the chart. * **ncols**: Default is 1, sets the number of columns in the chart. * **sharex, sharey**: Sets whether the x and y axes share attributes. Default is false, can be set to 'none', 'all', 'row', or 'col'. False or 'none' means each subplot's x or y axis is independent. True or 'all' means all subplots share the x or y axis. 'row' sets each subplot row to share one x or y axis. 'col' sets each subplot column to share one x or y axis. * **squeeze**: Boolean, default is True. Indicates that extra dimensions are squeezed out from the returned Axes object. For N*1 or 1*N subplots, returns a 1D array. For N*M, where N>1 and M>1, returns a 2D array. If set to False, no squeezing is performed, and a 2D array of Axes instances is returned, even if it is ultimately 1x1. * **subplot_kw**: Optional, dictionary type. Passes the dictionary keywords to add_subplot() to create each subplot. * **gridspec_kw**: Optional, dictionary type. Passes the dictionary keywords to the GridSpec constructor to place subplots in a grid. * ****fig_kw**: Passes detailed keyword arguments to the figure() function. ## Example import matplotlib.pyplot as plt import numpy as np # Create some test data -- Figure 1 x = np.linspace(0,2*np.pi,400) y = np.sin(x**2) # Create a figure and subplot -- Figure 2 fig, ax = plt.subplots() ax.plot(x, y) ax.set_title('Simple plot') # Create two subplots -- Figure 3 f,(ax1, ax2)= plt.subplots(1,2, sharey=True) ax1.plot(x, y) ax1.set_title('Sharing Y axis') ax2.scatter(x, y) # Create four subplots -- Figure 4 fig, axs = plt.subplots(2,2, subplot_kw=dict(projection="polar")) axs[0,0].plot(x, y) axs[1,1].scatter(x, y) # Share x axis plt.subplots(2,2, sharex='col') # Share y axis plt.subplots(2,2, sharey='row') # Share x and y axes plt.subplots(2,2, sharex='all', sharey='all') # This also shares x and y axes plt.subplots(2,2, sharex=True, sharey=True) # Create a figure with label 10, delete if it already exists fig, ax = plt.subplots(num=10, clear=True) plt.show() Some of the chart results are shown below: **Figure 1** !(#) **Figure 2** !(#) **Figure 3** !(#) **Figure 4** !(#)