UPDATE 2018 There is a github project https://github.com/bendichter/brokenaxes which probably is more convenient to use. My own solution looks like (using gridspec, assuming that the units of the two y-axis should be equal): “”” Broken axis example, where the y-axis will have a portion cut out. “”” import matplotlib.pylab as plt import matplotlib.gridspec as gridspec … Read more
Here’s an automated layout with lots of groups (of random fake data) and playing around with grouped.get_group(key) will show you how to do more elegant plots. import pandas as pd from numpy.random import randint import matplotlib.pyplot as plt df = pd.DataFrame(randint(0,10,(200,6)),columns=list(‘abcdef’)) grouped = df.groupby(‘a’) rowlength = grouped.ngroups/2 # fix up if odd number of groups … Read more
If you debug your program by simply printing ax, you’ll quickly find out that ax is a two-dimensional array: one dimension for the rows, one for the columns. Thus, you need two indices to index ax to retrieve the actual AxesSubplot instance, like: ax[1,1].plot(…) If you want to iterate through the subplots in the way … Read more
There are two ways: Use the axes methods of the subplot object (e.g. ax.set_xticks and ax.set_xticklabels) or Use plt.sca to set the current axes for the pyplot state machine (i.e. the plt interface). As an example (this also illustrates using setp to change the properties of all of the subplots): import matplotlib.pyplot as plt fig, … Read more
You can set the height_ratios of the subplots using the gridspec_kw argument in the call to plt.subplots, and use the heights of the different images to set that ratio. From the docs to plt.subplots(): gridspec_kw : dict, optional Dict with keywords passed to the GridSpec constructor used to create the grid the subplots are placed … Read more
You can nest your GridSpec using SubplotSpec. The outer grid will be a 2 x 2 and the inner grids will be 2 x 1. The following code should give you the basic idea. import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec fig = plt.figure(figsize=(10, 8)) outer = gridspec.GridSpec(2, 2, wspace=0.2, hspace=0.2) for i in … Read more
You can simply do: import numpy as np import matplotlib.pyplot as plt x = np.arange(0, 7, 0.01) plt.subplot(2, 1, 1) plt.plot(x, np.sin(x)) plt.subplot(2, 2, 3) plt.plot(x, np.cos(x)) plt.subplot(2, 2, 4) plt.plot(x, np.sin(x)*np.cos(x)) i.e., the first plot is really a plot in the upper half (the figure is only divided into 2×1 = 2 cells), and … Read more
ax.title.set_text(‘My Plot Title’) seems to work too. fig = plt.figure() ax1 = fig.add_subplot(221) ax2 = fig.add_subplot(222) ax3 = fig.add_subplot(223) ax4 = fig.add_subplot(224) ax1.title.set_text(‘First Plot’) ax2.title.set_text(‘Second Plot’) ax3.title.set_text(‘Third Plot’) ax4.title.set_text(‘Fourth Plot’) plt.show()
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