![]() xlabel() adds an x-axis label to your plot We can add axis titles using the following methods: This is part of the incredible flexibility that Matplotlib offers. Matplotlib handles the styling of axis labels in the same way that you learned above. Axis labels provide descriptive titles to your data to help your readers understand what your dad is communicating. In this section, you’ll learn how to add axis labels to your Matplotlib plot. ![]() In the next section, you’ll learn how to add and style axis labels in a Matplotlib plot. While this is an official way to add a subtitle to a Matplotlib plot, it does provide the option to visually represent a subtitle. Y = Īdding a subtitle to your Matplotlib plot Let’s see how we can use these parameters to style our plot: # Adding style to our plot's title The ones above represent the key parameters that we can use to control the styling. There are many, many more attributes that you can learn about in the official documentation. family= controls the font family of the font. ![]() ![]() fontweight= controls the the weight of the font.loc= controls the positioning of the text.fontsize= controls the size of the font and accepts an integer or a string.title() method in order to style our text: Let’s take a look at the parameters we can pass into the. Matplotlib provides you with incredible flexibility to style your plot’s title in terms of size, style, and positioning (and many more). Changing Font Sizes and Positioning in Matplotlib Titles This is what you’ll learn in the next section. We can easily control the font styling, sizing, and positioning using Matplotlib. There is probably a general solution that takes padding between figures into account.We can see that the title is applied with Matplotlib’s default values. Throws an exception when the height of the top part is 0. The formula does not take space between the parts into account. The horizontal distance to the plot is based on the top part, the bottom ticks might extend into the label. Mid = 0.5-somePlot.get_height_ratios()/(2.*somePlot.get_height_ratios()) # Simplified to 0.5 - height(bottom)/(2*height(top)) # The center is (height(top)-height(bottom))/(2*height(top)) Plt.setp(partA.get_xticklabels(), visible=False) Subplot_spec=outerGrid, height_ratios=, hspace = 0) SomePlot = gridspec.GridSpecFromSubplotSpec(2, 1, OuterGrid = gridspec.GridSpec(2, 3, width_ratios=, height_ratios=) As the padding between the parts (hspace) in my code was zero, I could calculate the middle of the two parts relative to the upper part. It is usually 0.5, the middle of the plot it is added to. I did not want to use a solution that depends on knowing the position in the outer figure (like fig.text()), so I manipulated the y-position of the set_ylabel() function. The y-label was supposed to be centered over both parts. The graphs consisted of two parts (top and bottom). I ran into a similar problem while plotting a grid of graphs. I'm guessing this is because when the label is finally drawn, matplotlib uses 0.5 for the y-coordinate without checking whether the underlying coordinate transform has changed. Notably, if you omit the set_position call, the ylabel will show up exactly halfway up the figure. and you should see that the label still appropriately adjusts left-right to keep from overlapping with labels, just like normal, but will also position itself exactly between the desired subplots. Transform = mtransforms.blended_transform_factory(mtransforms.IdentityTransform(), fig.transFigure) # specify x, y transformĪxs._transform(transform) # changed from default blend (IdentityTransform(), axs.transAxes)Īxs._position((0, avepos)) Import ansforms as mtransformsįig, axs = plt.subplots(nrows=2, ncols=1, bottom=bottom, top=top) If you know the bottom and top kwargs that went into a GridSpec initialization, or you otherwise know the edges positions of your axes in Figure coordinates, you can also specify the ylabel position in Figure coordinates with some fancy "transform" magic.įor example: import matplotlib.pyplot as plt By default, when you make figures, the labels are "shared" between subplots. This feature is now part of the proplot matplotlib package that I recently released on pypi.
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