I think this would be best explained by the following picture: To initialize the above, one would type: import matplotlib.pyplot as plt fig = plt.figure() fig.add_subplot(221) #top left fig.add_subplot(222) #top right fig.add_subplot(223) #bottom left fig.add_subplot(224) #bottom right plt.show()
Change your subplot settings to: plt.subplot(1, 2, 1) … plt.subplot(1, 2, 2) The parameters for subplot are: number of rows, number of columns, and which subplot you’re currently on. So 1, 2, 1 means “a 1-row, 2-column figure: go to the first subplot.” Then 1, 2, 2 means “a 1-row, 2-column figure: go to the … Read more
Global title: In newer releases of matplotlib one can use Figure.suptitle() method of Figure: import matplotlib.pyplot as plt fig = plt.gcf() fig.suptitle(“Title centered above all subplots”, fontsize=14) Alternatively (based on @Steven C. Howell’s comment below (thank you!)), use the matplotlib.pyplot.suptitle() function: import matplotlib.pyplot as plt # plot stuff # … plt.suptitle(“Title centered above all subplots”, … Read more
You can manually create the subplots with matplotlib, and then plot the dataframes on a specific subplot using the ax keyword. For example for 4 subplots (2×2): import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) df1.plot(ax=axes[0,0]) df2.plot(ax=axes[0,1]) … Here axes is an array which holds the different subplot axes, and you can access one … Read more
There are several ways to do it. The subplots method creates the figure along with the subplots that are then stored in the ax array. For example: import matplotlib.pyplot as plt x = range(10) y = range(10) fig, ax = plt.subplots(nrows=2, ncols=2) for row in ax: for col in row: col.plot(x, y) plt.show() However, something … Read more
You should use the OO interface to matplotlib, rather than the state machine interface. Almost all of the plt.* function are thin wrappers that basically do gca().*. plt.subplot returns an axes object. Once you have a reference to the axes object you can plot directly to it, change its limits, etc. import matplotlib.pyplot as plt … Read more
You can create a big subplot that covers the two subplots and then set the common labels. import random import matplotlib.pyplot as plt x = range(1, 101) y1 = [random.randint(1, 100) for _ in range(len(x))] y2 = [random.randint(1, 100) for _ in range(len(x))] fig = plt.figure() ax = fig.add_subplot(111) # The big subplot ax1 = … Read more
You have pylab.ylim: pylab.ylim([0,1000]) Note: The command has to be executed after the plot! Update 2021 Since the use of pylab is now strongly discouraged by matplotlib, you should instead use pyplot: from matplotlib import pyplot as plt plt.ylim(0, 100) #corresponding function for the x-axis plt.xlim(1, 1000)
Just place the colorbar in its own axis and use subplots_adjust to make room for it. As a quick example: import numpy as np import matplotlib.pyplot as plt fig, axes = plt.subplots(nrows=2, ncols=2) for ax in axes.flat: im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1) fig.subplots_adjust(right=0.8) cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7]) fig.colorbar(im, cax=cbar_ax) plt.show() Note that the … Read more
Another way is to use the subplots function and pass the width ratio with gridspec_kw matplotlib Tutorial: Customizing Figure Layouts Using GridSpec and Other Functions matplotlib.gridspec.GridSpec has available gridspect_kw options import numpy as np import matplotlib.pyplot as plt # generate some data x = np.arange(0, 10, 0.2) y = np.sin(x) # plot it f, (a0, … Read more