Customizing Matplotlib Legends (2024)

Introduction

In this lab, we will explore how to create and customize legends in Matplotlib. Legends are used to explain the meaning of the elements in a chart, including lines, bars, and markers. We will demonstrate how to create legends for specific lines, complex labels, and more complex plots. Finally, we will show how to write custom classes to stylize legends.

VM Tips

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Skills Graph

%%%%{init: {'theme':'neutral'}}%%%%flowchart RL python(("`Python`")) -.-> python/BasicConceptsGroup(["`Basic Concepts`"]) python(("`Python`")) -.-> python/FileHandlingGroup(["`File Handling`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/BasicConceptsGroup(["`Basic Concepts`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlottingDataGroup(["`Plotting Data`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/PlotCustomizationGroup(["`Plot Customization`"]) matplotlib(("`Matplotlib`")) -.-> matplotlib/AdvancedTopicsGroup(["`Advanced Topics`"]) python(("`Python`")) -.-> python/ControlFlowGroup(["`Control Flow`"]) python(("`Python`")) -.-> python/DataStructuresGroup(["`Data Structures`"]) python(("`Python`")) -.-> python/FunctionsGroup(["`Functions`"]) python(("`Python`")) -.-> python/ModulesandPackagesGroup(["`Modules and Packages`"]) python(("`Python`")) -.-> python/ObjectOrientedProgrammingGroup(["`Object-Oriented Programming`"]) python(("`Python`")) -.-> python/ErrorandExceptionHandlingGroup(["`Error and Exception Handling`"]) python(("`Python`")) -.-> python/PythonStandardLibraryGroup(["`Python Standard Library`"]) python(("`Python`")) -.-> python/DataScienceandMachineLearningGroup(["`Data Science and Machine Learning`"]) python/BasicConceptsGroup -.-> python/comments("`Comments`") python/FileHandlingGroup -.-> python/with_statement("`Using with Statement`") matplotlib/BasicConceptsGroup -.-> matplotlib/importing_matplotlib("`Importing Matplotlib`") matplotlib/BasicConceptsGroup -.-> matplotlib/figures_axes("`Understanding Figures and Axes`") matplotlib/PlottingDataGroup -.-> matplotlib/line_plots("`Line Plots`") matplotlib/PlottingDataGroup -.-> matplotlib/scatter_plots("`Scatter Plots`") matplotlib/PlottingDataGroup -.-> matplotlib/bar_charts("`Bar Charts`") matplotlib/PlottingDataGroup -.-> matplotlib/error_bars("`Error Bars`") matplotlib/PlotCustomizationGroup -.-> matplotlib/line_styles_colors("`Customizing Line Styles and Colors`") matplotlib/PlotCustomizationGroup -.-> matplotlib/legend_config("`Legend Configuration`") matplotlib/AdvancedTopicsGroup -.-> matplotlib/matplotlib_config("`Customizing Matplotlib Configurations`") python/BasicConceptsGroup -.-> python/variables_data_types("`Variables and Data Types`") python/BasicConceptsGroup -.-> python/booleans("`Booleans`") python/ControlFlowGroup -.-> python/conditional_statements("`Conditional Statements`") python/ControlFlowGroup -.-> python/for_loops("`For Loops`") python/DataStructuresGroup -.-> python/lists("`Lists`") python/DataStructuresGroup -.-> python/tuples("`Tuples`") python/DataStructuresGroup -.-> python/dictionaries("`Dictionaries`") python/DataStructuresGroup -.-> python/sets("`Sets`") python/FunctionsGroup -.-> python/function_definition("`Function Definition`") python/ModulesandPackagesGroup -.-> python/importing_modules("`Importing Modules`") python/ModulesandPackagesGroup -.-> python/standard_libraries("`Common Standard Libraries`") python/ObjectOrientedProgrammingGroup -.-> python/classes_objects("`Classes and Objects`") python/ObjectOrientedProgrammingGroup -.-> python/encapsulation("`Encapsulation`") python/ErrorandExceptionHandlingGroup -.-> python/catching_exceptions("`Catching Exceptions`") python/PythonStandardLibraryGroup -.-> python/data_collections("`Data Collections`") python/DataScienceandMachineLearningGroup -.-> python/numerical_computing("`Numerical Computing`") python/DataScienceandMachineLearningGroup -.-> python/data_visualization("`Data Visualization`") python/FunctionsGroup -.-> python/build_in_functions("`Build-in Functions`") subgraph Lab Skills python/comments -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/with_statement -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/importing_matplotlib -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/figures_axes -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/line_plots -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/scatter_plots -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/bar_charts -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/error_bars -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/line_styles_colors -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/legend_config -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} matplotlib/matplotlib_config -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/variables_data_types -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/booleans -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/conditional_statements -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/for_loops -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/lists -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/tuples -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/dictionaries -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/sets -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/function_definition -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/importing_modules -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/standard_libraries -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/classes_objects -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/encapsulation -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/catching_exceptions -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/data_collections -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/numerical_computing -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/data_visualization -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} python/build_in_functions -.-> lab-48801{{"`Customizing Matplotlib Legends`"}} end

Create a legend for specific lines

In this step, we will create a legend for specific lines.

## Import necessary librariesimport matplotlib.pyplot as pltimport numpy as np## Define data for the chartt1 = np.arange(0.0, 2.0, 0.1)t2 = np.arange(0.0, 2.0, 0.01)## Create a plot with multiple linesfig, ax = plt.subplots()l1, = ax.plot(t2, np.exp(-t2))l2, l3 = ax.plot(t2, np.sin(2 * np.pi * t2), '--o', t1, np.log(1 + t1), '.')l4, = ax.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 's-.')## Create a legend for two of the linesax.legend((l2, l4), ('oscillatory', 'damped'), loc='upper right', shadow=True)## Add labels and title to the chartax.set_xlabel('time')ax.set_ylabel('volts')ax.set_title('Damped oscillation')## Display the chartplt.show()

Plot more complex labels

In this step, we will plot more complex labels.

## Define data for the chartx = np.linspace(0, 1)## Create a chart with multiple linesfig, (ax0, ax1) = plt.subplots(2, 1)for n in range(1, 5): ax0.plot(x, x**n, label=f"{n=}")## Create a legend with multiple columns and a titleleg = ax0.legend(loc="upper left", bbox_to_anchor=[0, 1], ncols=2, shadow=True, title="Legend", fancybox=True)leg.get_title().set_color("red")## Create a chart with multiple lines and markersax1.plot(x, x**2, label="multi\nline")half_pi = np.linspace(0, np.pi / 2)ax1.plot(np.sin(half_pi), np.cos(half_pi), label=r"$\frac{1}{2}\pi$")ax1.plot(x, 2**(x**2), label="$2^{x^2}$")## Create a legend with a shadowax1.legend(shadow=True, fancybox=True)## Display the chartplt.show()

Attach legends to more complex plots

In this step, we will attach legends to more complex plots.

## Define data for the chartfig, axs = plt.subplots(3, 1, layout="constrained")top_ax, middle_ax, bottom_ax = axs## Create a bar chart with multiple barstop_ax.bar([0, 1, 2], [0.2, 0.3, 0.1], width=0.4, label="Bar 1", align="center")top_ax.bar([0.5, 1.5, 2.5], [0.3, 0.2, 0.2], color="red", width=0.4, label="Bar 2", align="center")top_ax.legend()## Create an error bar chart with multiple errorsmiddle_ax.errorbar([0, 1, 2], [2, 3, 1], xerr=0.4, fmt="s", label="test 1")middle_ax.errorbar([0, 1, 2], [3, 2, 4], yerr=0.3, fmt="o", label="test 2")middle_ax.errorbar([0, 1, 2], [1, 1, 3], xerr=0.4, yerr=0.3, fmt="^", label="test 3")middle_ax.legend()## Create a stem chart with a legendbottom_ax.stem([0.3, 1.5, 2.7], [1, 3.6, 2.7], label="stem test")bottom_ax.legend()## Display the chartplt.show()

Create legend entries with more than one legend key

In this step, we will create legend entries with more than one legend key.

## Define data for the chartfig, (ax1, ax2) = plt.subplots(2, 1, layout='constrained')p1 = ax1.scatter([1], [5], c='r', marker='s', s=100)p2 = ax1.scatter([3], [2], c='b', marker='o', s=100)p3, = ax1.plot([1, 5], [4, 4], 'm-d')## Create a legend with two keys for one entryl = ax1.legend([(p1, p3), p2], ['two keys', 'one key'], scatterpoints=1, numpoints=1, handler_map={tuple: HandlerTuple(ndivide=None)})## Create two bar charts on top of each other and change the padding between the legend keysx_left = [1, 2, 3]y_pos = [1, 3, 2]y_neg = [2, 1, 4]rneg = ax2.bar(x_left, y_neg, width=0.5, color='w', hatch='///', label='-1')rpos = ax2.bar(x_left, y_pos, width=0.5, color='k', label='+1')## Treat each legend entry differently by using specific `HandlerTuple`sl = ax2.legend([(rpos, rneg), (rneg, rpos)], ['pad!=0', 'pad=0'], handler_map={(rpos, rneg): HandlerTuple(ndivide=None), (rneg, rpos): HandlerTuple(ndivide=None, pad=0.)})## Display the chartplt.show()

Write custom classes to stylize legends

In this step, we will write custom classes to stylize legends.

## Define data for the chartclass HandlerDashedLines(HandlerLineCollection): """ Custom Handler for LineCollection instances. """ def create_artists(self, legend, orig_handle, xdescent, ydescent, width, height, fontsize, trans): ## figure out how many lines there are numlines = len(orig_handle.get_segments()) xdata, xdata_marker = self.get_xdata(legend, xdescent, ydescent, width, height, fontsize) leglines = [] ## divide the vertical space where the lines will go ## into equal parts based on the number of lines ydata = np.full_like(xdata, height / (numlines + 1)) ## for each line, create the line at the proper location ## and set the dash pattern for i in range(numlines): legline = Line2D(xdata, ydata * (numlines - i) - ydescent) self.update_prop(legline, orig_handle, legend) ## set color, dash pattern, and linewidth to that ## of the lines in linecollection try: color = orig_handle.get_colors()[i] except IndexError: color = orig_handle.get_colors()[0] try: dashes = orig_handle.get_dashes()[i] except IndexError: dashes = orig_handle.get_dashes()[0] try: lw = orig_handle.get_linewidths()[i] except IndexError: lw = orig_handle.get_linewidths()[0] if dashes[1] is not None: legline.set_dashes(dashes[1]) legline.set_color(color) legline.set_transform(trans) legline.set_linewidth(lw) leglines.append(legline) return leglines## Create a chart with multiple linesx = np.linspace(0, 5, 100)fig, ax = plt.subplots()colors = plt.rcParams['axes.prop_cycle'].by_key()['color'][:5]styles = ['solid', 'dashed', 'dashed', 'dashed', 'solid']for i, color, style in zip(range(5), colors, styles): ax.plot(x, np.sin(x) - .1 * i, c=color, ls=style)## Create proxy artists and a legendline = [[(0, 0)]]lc = mcol.LineCollection(5 * line, linestyles=styles, colors=colors)ax.legend([lc], ['multi-line'], handler_map={type(lc): HandlerDashedLines()}, handlelength=2.5, handleheight=3)## Display the chartplt.show()

Summary

In this lab, we learned how to create and customize legends in Matplotlib. We demonstrated how to create legends for specific lines, complex labels, and more complex plots. We also showed how to write custom classes to stylize legends. Legends are an important part of any chart, and understanding how to create and customize them is essential for creating effective visualizations.