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 do it in multiple ways: Here is one solution making use of tick_params: ax.tick_params(labelrotation=45) Here is another solution making use of set_xticklabels: ax.set_xticklabels(labels, rotation=45) Here is a third solution making use of set_rotation: for tick in ax.get_xticklabels(): tick.set_rotation(45)
We create the figure with the subplots: f, axes = plt.subplots(1, 2) Where axes is an array with each subplot. Then we tell each plot in which subplot we want them with the argument ax. sns.boxplot( y=”b”, x= “a”, data=df, orient=”v” , ax=axes[0]) sns.boxplot( y=”c”, x= “a”, data=df, orient=”v” , ax=axes[1]) And the result is:
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
Common grounds Both, add_axes and add_subplot add an axes to a figure. They both return a (subclass of a) matplotlib.axes.Axes object. However, the mechanism which is used to add the axes differs substantially. add_axes The calling signature of add_axes is add_axes(rect), where rect is a list [x0, y0, width, height] denoting the lower left point … Read more
Typically, you just pass the axes instance to a function. For example: import matplotlib.pyplot as plt import numpy as np def main(): x = np.linspace(0, 6 * np.pi, 100) fig1, (ax1, ax2) = plt.subplots(nrows=2) plot(x, np.sin(x), ax1) plot(x, np.random.random(100), ax2) fig2 = plt.figure() plot(x, np.cos(x)) plt.show() def plot(x, y, ax=None): if ax is None: ax … 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 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