![]() The web browser will only be able to apply a font if it is available on the system which it operates. ![]() HTML font family - the typeface that will be applied by the web browser. Note that the title's font used to be customized by the now deprecated `titlefont` attribute.Ĭode: fig.update_layout(title_font_color=)Ĭode: fig.update_layout(title_font_family=) Invalid values will be reset to the default 1.Ĭode: fig.update_layout(title_font=dict(.)) ![]() Note that when `yref='paper'`, only 1 or 0 are allowed y values. If `automargin=True` and the margins need to be expanded, then y will be set to a default 1 and yanchor will be set to an appropriate default to ensure that minimal margin space is needed. If `yref='container'` then the margins will ensure that the title doesn’t overlap with the plot area, tick labels, and axis titles. If `yref='paper'` then the margin will expand to ensure that the title doesn’t overlap with the edges of the container. If you wish to explore other plotting techniques, you can navigate backward or forward.Type: dict containing one or more of the keys listed below.Ĭode: fig.update_layout(title_automargin=)ĭetermines whether the title can automatically push the figure margins. To further enhance your matplotlib capabilities, download more examples. Plt.title( 'Legend Positioned Outside to the Right') Here’s a complete implementation: import matplotlib.pyplot as plt To locate the legend externally on the right, we need to resize the plot and set the legend’s position relative to it: chartBox = ax.get_position()Īx.set_position()Īx.legend(loc= 'upper center', bbox_to_anchor=( 1.45, 0.8), shadow= True, ncol= 1) To shift the legend to the top of your visualization, modify the bbox_to_anchor values: ax.legend(loc= 'upper center', bbox_to_anchor=( 0.5, 1.00), shadow= True, ncol= 2)Ĭomplete code for this setup: import matplotlib.pyplot as pltĪx.legend(loc= 'upper center', bbox_to_anchor=( 0.5, 1.00), shadow= True, ncol= 2) Positioning the Matplotlib Legend at the Top Additionally, a shadow effect has been added for aesthetic purposes.Ĭomplete code example: import matplotlib.pyplot as pltĪx.legend(loc= 'upper center', bbox_to_anchor=( 0.5, - 0.05), shadow= True, ncol= 2) Note the introduction of the ncol=2 parameter, which sets the number of columns in the legend. To move the legend to the bottom of your chart, you can adjust the legend() parameters as shown: ax.legend(loc= 'upper center', bbox_to_anchor=( 0.5, - 0.05), shadow= True, ncol= 2) Positioning the Matplotlib Legend at the Bottom ![]() Y2 = Īx.plot(x, y2, label= '$y2 = other numbers') Here’s a practical example: import matplotlib.pyplot as plt Utilizing matplotlib’s legend() function, one can seamlessly position the legend within the chart. Positioning the Matplotlib Legend Inside the Chart Data Visualization with Matplotlib and Python.This article offers a comprehensive guide on leveraging the legend() function in matplotlib for enhancing your data visualizations. Understanding how to position legends, whether inside or outside a chart, can enhance data interpretation. Matplotlib is a versatile Python library that provides native support for creating legends in various visualizations.
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