Legends are especially useful when multiple lines, markers, or colors are used in a plot, as they provide a visual guide to understand the meaning behind these graphical representations. The simplest way to create a legend is by using the plt.legend() command, which automatically generates a legend for labeled plot elements. Let’s delve into the details of how you can create, display, and perform various actions with legends. To further expand your understanding of Python programming, don’t hesitate to explore additional tutorials available at Python Programming Tutorials. In this tutorial, we will learn how to add a legend to a Matplotlib plot in Python and customization of legends, thereby enhancing the overall clarity and interpretability of your graphs. Particularly, when dealing with graphs that contain multiple lines of varying colors, legends play a crucial role in clarifying the meaning behind these color-coded lines. As a powerful 2D plotting library, it allows you to input arrays of numbers and plot the data in various graphical representations, including bar graphs, histograms, scatter plots, and line plots. Of instance to Handler as a keyword to legend.Matplotlib is a widely used Python package for data visualization. On the legend() function for convenience). Which accepts a numpoints argument (numpoints is also a keyword Sake of simplicity, let's choose legend_handler.HandlerLine2D The simplest example of using custom handlers is to instantiate one of theĮxisting legend_handler.HandlerBase subclasses. With the value in the handler_map keyword.Ĭheck if the handle is in the newly created handler_map.Ĭheck if the type of handle is in the newly created handler_map.Ĭheck if any of the types in the handle's mro is in the newlyįor completeness, this logic is mostly implemented inĪll of this flexibility means that we have the necessary hooks to implementĬustom handlers for our own type of legend key. The choice of handler subclass is determined by the following rules: In order to create legend entries, handles are given as an argument to an legend ( handles =, loc = 'lower right' ) plt. add_artist ( first_legend ) # Create another legend for the second line. legend ( handles =, loc = 'upper right' ) # Add the legend manually to the Axes. plot (, label = "Line 2", linewidth = 4 ) # Create a legend for the first line. plot (, label = "Line 1", linestyle = '-' ) line2, = ax. To keep old legend instances, we must add themįig, ax = plt. To call legend() repeatedly to update the legend to the latest This has been done so that it is possible The legend() function multiple times, you will find that only one Whilst the instinctive approach to doing this might be to call Sometimes it is more clear to split legend entries across multiple plot (,, label = 'test' ) for loc in : fig. subplots ( figsize = ( 6, 4 ), layout = 'constrained', facecolor = '0.7' ) ax. legend ( loc = loc, title = loc ) fig, ax = plt. plot (,, label = 'TEST' ) # Place a legend to the right of this smaller subplot. The legend is drawn outside the Axes on the (sub)figure. Specifying "outside" at the beginning of the loc keyword argument, Sometimes it makes more sense to place a legend relative to the (sub)figure legend ( bbox_to_anchor = ( 1.05, 1 ), loc = 'upper left', borderaxespad = 0. plot (, label = "test2" ) # Place a legend to the right of this smaller subplot. 102 ), loc = 'lower left', ncols = 2, mode = "expand", borderaxespad = 0. plot (, label = "test2" ) # Place a legend above this subplot, expanding itself to # fully use the given bounding box. subplot_mosaic (, ], empty_sentinel = "BLANK" ) ax_dict. Text rendering with XeLaTeX/LuaLaTeX via the pgf backendįig, ax_dict = plt.Customizing Matplotlib with style sheets and rcParams.Understanding the extent keyword argument of imshow.Tight layout guide (mildly discouraged).Writing a backend - the pyplot interface.Interactive figures and asynchronous programming.Matplotlib Application Interfaces (APIs).
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