How to make two markers on one label in a legend using matplotlib?

Question

I want it to be like this:

What I get is:

So how to combine markers into one label? Of course, for lines, for lines, you can implement this without assigning a label to the second line when using the same type of line, but for markers you cannot, because they have different shapes.

Answer 1

I think it’s best to use the full legend - otherwise, how will your readers know the difference between two models or two data sets? I would do it like this:

But if you really want to do it your own way, you can use a custom legend, as shown in this guide . You need to create your own class, like it, which defines the legend_artist method, which then adds squares and circles as needed. Here is the generated plot and the code used to create it:

 #!/usr/bin/env python import matplotlib.pyplot as plt import matplotlib.patches as mpatches import numpy as np # ================================== # Define the form of the function # ================================== def model(x, A=190, k=1): return A * np.exp(-k*x/50) # ================================== # How many data points are generated # ================================== num_samples = 15 # ================================== # Create data for plots # ================================== x_model = np.linspace(0, 130, 200) x_data1 = np.random.rand(num_samples) * 130 x_data1.sort() x_data2 = np.random.rand(num_samples) * 130 x_data2.sort() data1 = model(x_data1, k=1) * (1 + np.random.randn(num_samples) * 0.2) data2 = model(x_data2, k=2) * (1 + np.random.randn(num_samples) * 0.15) model1 = model(x_model, k=1) model2 = model(x_model, k=2) # ================================== # Plot everything normally # ================================== fig = plt.figure() ax = fig.add_subplot('111') ax.plot(x_data1, data1, 'ok', markerfacecolor='none', label='Data (k=1)') ax.plot(x_data2, data2, 'sk', markeredgecolor='0.5', markerfacecolor='0.5', label='Data (k=2)') ax.plot(x_model, model1, '-k', label='Model (k=1)') ax.plot(x_model, model2, '--k', label='Model (k=2)') # ================================== # Format plot # ================================== ax.set_xlabel('Distance from heated face($10^{-2}$ m)') ax.set_ylabel('Temperature ($^\circ$C)') ax.set_xlim((0, 130)) ax.set_title('Normal way to plot') ax.legend() fig.tight_layout() plt.show() # ================================== # ================================== # Do it again, but with custom # legend # ================================== # ================================== class AnyObject(object): pass class data_handler(object): def legend_artist(self, legend, orig_handle, fontsize, handlebox): scale = fontsize / 22 x0, y0 = handlebox.xdescent, handlebox.ydescent width, height = handlebox.width, handlebox.height patch_sq = mpatches.Rectangle([x0, y0 + height/2 * (1 - scale) ], height * scale, height * scale, facecolor='0.5', edgecolor='0.5', transform=handlebox.get_transform()) patch_circ = mpatches.Circle([x0 + width - height/2, y0 + height/2], height/2 * scale, facecolor='none', edgecolor='black', transform=handlebox.get_transform()) handlebox.add_artist(patch_sq) handlebox.add_artist(patch_circ) return patch_sq # ================================== # Plot everything # ================================== fig = plt.figure() ax = fig.add_subplot('111') d1 = ax.plot(x_data1, data1, 'ok', markerfacecolor='none', label='Data (k=2)') d2 = ax.plot(x_data2, data2, 'sk', markeredgecolor='0.5', markerfacecolor='0.5', label='Data (k=1)') m1 = ax.plot(x_model, model1, '-k', label='Model (k=1)') m2 = ax.plot(x_model, model2, '-k', label='Model (k=2)') # ax.legend([d1], handler_map={ax.plot: data_handler()}) ax.legend([AnyObject(), m1[0]], ['Data', 'Model'], handler_map={AnyObject: data_handler()}) # ================================== # Format plot # ================================== ax.set_xlabel('Distance from heated face($10^{-2}$ m)') ax.set_ylabel('Temperature ($^\circ$C)') ax.set_xlim((0, 130)) ax.set_title('Custom legend') fig.tight_layout() plt.show() 

Answer 2

Here is a new solution that will build any collection of markers with the same label. I did not understand how to make it work with markers from a linear plot, but you can probably make a spread chart over a line if you need to.

 from matplotlib import pyplot as plt import matplotlib.collections as mcol import matplotlib.transforms as mtransforms import numpy as np from matplotlib.legend_handler import HandlerPathCollection from matplotlib import cm class HandlerMultiPathCollection(HandlerPathCollection): """ Handler for PathCollections, which are used by scatter """ def create_collection(self, orig_handle, sizes, offsets, transOffset): p = type(orig_handle)(orig_handle.get_paths(), sizes=sizes, offsets=offsets, transOffset=transOffset, ) return p fig, ax = plt.subplots() #make some data to plot x = np.arange(0, 100, 10) models = [.05 * x, 8 * np.exp(- .1 * x), np.log(x + 1), .01 * x] tests = [model + np.random.rand(len(model)) - .5 for model in models] #make colors and markers colors = cm.brg(np.linspace(0, 1, len(models))) markers = ['o', 'D', '*', 's'] markersize = 50 plots = [] #plot points and lines for i in xrange(len(models)): line, = plt.plot(x, models[i], linestyle = 'dashed', color = 'black', label = 'Model') plot = plt.scatter(x, tests[i], c = colors[i], s = markersize, marker = markers[i]) plots.append(plot) #get attributes paths = [] sizes = [] facecolors = [] edgecolors = [] for plot in plots: paths.append(plot.get_paths()[0]) sizes.append(plot.get_sizes()[0]) edgecolors.append(plot.get_edgecolors()[0]) facecolors.append(plot.get_facecolors()[0]) #make proxy artist out of a collection of markers PC = mcol.PathCollection(paths, sizes, transOffset = ax.transData, facecolors = colors, edgecolors = edgecolors) PC.set_transform(mtransforms.IdentityTransform()) plt.legend([PC, line], ['Test', 'Model'], handler_map = {type(PC) : HandlerMultiPathCollection()}, scatterpoints = len(paths), scatteryoffsets = [.5], handlelength = len(paths)) plt.show() 

I have a solution for you if you want to use all circles for markers and distinguish only by color. You can use the collection of circles to represent markers, and then have a legend label for the collection as a whole.

Code example:

 import matplotlib.pyplot as plt import matplotlib.collections as collections from matplotlib import cm import numpy as np #make some data to plot x = np.arange(0, 100, 10) models = [.05 * x, 8 * np.exp(- .1 * x), np.log(x + 1), .01 * x] tests = [model + np.random.rand(len(model)) - .5 for model in models] #make colors colors = cm.brg(np.linspace(0, 1, len(models))) markersize = 50 #plot points and lines for i in xrange(len(models)): line, = plt.plot(x, models[i], linestyle = 'dashed', color = 'black', label = 'Model') plt.scatter(x, tests[i], c = colors[i], s = markersize) #create collection of circles corresponding to markers circles = collections.CircleCollection([markersize] * len(models), facecolor = colors) #make the legend -- scatterpoints needs to be the same as the number #of markers so that all the markers show up in the legend plt.legend([circles, line], ['Test', 'Model'], scatterpoints = len(models), scatteryoffsets = [.5], handlelength = len(models)) plt.show() 

Answer 3

I also found this link very useful (code below), this is an easier way to deal with this problem. It mainly uses the descriptor descriptor list to make one of the markers on the first handle invisible and overlap it with the marker on the second handle. This way, you have both markers next to each other with the same label.

 fig, ax = plt.subplots() p1 = ax.scatter([0.1],[0.5],c='r',marker='s') p2 = ax.scatter([0.3],[0.2],c='b',marker='o') l = ax.legend([(p1,p2)],['points'],scatterpoints=2) 

With the above code, a TupleHandler used to create description descriptors that simply drag two handles (there are red squares behind the blue circles if you look carefully. What you want to do is make the second marker of the first pen and the first marker of the second pen invisible. Unfortunately, TupleHandler is a fairly recent addition and you need a special function to get all the pens, otherwise you can use the Legend.legendHandles attribute (it only shows the first handle for TupleHandler ).

 def get_handle_lists(l): """returns a list of lists of handles. """ tree = l._legend_box.get_children()[1] for column in tree.get_children(): for row in column.get_children(): yield row.get_children()[0].get_children() 

 handles_list = list(get_handle_lists(l)) handles = handles_list[0] # handles is a list of two PathCollection. # The first one is for red squares, and the second # is for blue circles. handles[0].set_facecolors(["r", "none"]) # for the fist # PathCollection, make the # second marker invisible by # setting their facecolor and # edgecolor to "none." handles[0].set_edgecolors(["k", "none"]) handles[1].set_facecolors(["none", "b"]) handles[1].set_edgecolors(["none", "k"]) fig 

Answer 4

You can do this by building the data without any label, and then adding the label separately:

 from matplotlib import pyplot as plt from numpy import random xs = range(10) data = random.rand(10, 2) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) kwargs = {'color': 'r', 'linewidth': 2, 'linestyle': '--'} ax.plot(xs, data, **kwargs) ax.plot([], [], label='Model', **kwargs) ax.legend() plt.show() 

Answer 5

Note that in recent versions of matplotlib you can achieve this using class matplotlib.legend_handler.HandlerTuple , as shown in this answer , as well as in this guide :

 import matplotlib.pyplot as plt from matplotlib.legend_handler import HandlerTuple fig, ax1 = plt.subplots(1, 1) # First plot: two legend keys for a single entry p2, = ax1.plot([3, 4], [2, 3], 'o', mfc="white", mec="k") p1, = ax1.plot([1, 2], [5, 6], 's', mfc="gray", mec="gray") # 'plot' returns a list, but we want the handle - thus the comma on the left p3, = ax1.plot([1, 5], [4, 4], "-k") p4, = ax1.plot([2, 6], [3, 2], "-k") # Assign two of the handles to the same legend entry by putting them in a tuple # and using a generic handler map (which would be used for any additional # tuples of handles like (p1, p3)). l = ax1.legend([(p1, p2), p3], ['data', 'models'], handler_map={tuple: HandlerTuple(ndivide=None)}) plt.savefig("demo.png")