################################## Introduction to Axes (or Subplots) ################################## Matplotlib `~.axes.Axes` are the gateway to creating your data visualizations. Once an Axes is placed on a figure there are many methods that can be used to add data to the Axes. An Axes typically has a pair of `~.axis.Axis` Artists that define the data coordinate system, and include methods to add annotations like x- and y-labels, titles, and legends. .. _anatomy_local: .. figure:: /_static/anatomy.png :width: 80% Anatomy of a Figure In the picture above, the Axes object was created with ``ax = fig.subplots()``. Everything else on the figure was created with methods on this ``ax`` object, or can be accessed from it. If we want to change the label on the x-axis, we call ``ax.set_xlabel('New Label')``, if we want to plot some data we call ``ax.plot(x, y)``. Indeed, in the figure above, the only Artist that is not part of the Axes is the Figure itself, so the `.axes.Axes` class is really the gateway to much of Matplotlib's functionality. Note that Axes are so fundamental to the operation of Matplotlib that a lot of material here is duplicate of that in :ref:`quick_start`. Creating Axes ------------- .. plot:: :include-source: import matplotlib.pyplot as plt import numpy as np fig, axs = plt.subplots(ncols=2, nrows=2, figsize=(3.5, 2.5), layout="constrained") # for each Axes, add an artist, in this case a nice label in the middle... for row in range(2): for col in range(2): axs[row, col].annotate(f'axs[{row}, {col}]', (0.5, 0.5), transform=axs[row, col].transAxes, ha='center', va='center', fontsize=18, color='darkgrey') fig.suptitle('plt.subplots()') Axes are added using methods on `~.Figure` objects, or via the `~.pyplot` interface. These methods are discussed in more detail in :ref:`creating_figures` and :doc:`arranging_axes`. However, for instance `~.Figure.add_axes` will manually position an Axes on the page. In the example above `~.pyplot.subplots` put a grid of subplots on the figure, and ``axs`` is a (2, 2) array of Axes, each of which can have data added to them. There are a number of other methods for adding Axes to a Figure: * `.Figure.add_axes`: manually position an Axes. ``fig.add_axes([0, 0, 1, 1])`` makes an Axes that fills the whole figure. * `.pyplot.subplots` and `.Figure.subplots`: add a grid of Axes as in the example above. The pyplot version returns both the Figure object and an array of Axes. Note that ``fig, ax = plt.subplots()`` adds a single Axes to a Figure. * `.pyplot.subplot_mosaic` and `.Figure.subplot_mosaic`: add a grid of named Axes and return a dictionary of axes. For ``fig, axs = plt.subplot_mosaic([['left', 'right'], ['bottom', 'bottom']])``, ``axs['left']`` is an Axes in the top row on the left, and ``axs['bottom']`` is an Axes that spans both columns on the bottom. See :doc:`arranging_axes` for more detail on how to arrange grids of Axes on a Figure. Axes plotting methods --------------------- Most of the high-level plotting methods are accessed from the `.axes.Axes` class. See the API documentation for a full curated list, and :ref:`plot_types` for examples. A basic example is `.axes.Axes.plot`: .. plot:: :include-source: fig, ax = plt.subplots(figsize=(4, 3)) np.random.seed(19680801) t = np.arange(100) x = np.cumsum(np.random.randn(100)) lines = ax.plot(t, x) Note that ``plot`` returns a list of *lines* Artists which can subsequently be manipulated, as discussed in :ref:`users_artists`. A very incomplete list of plotting methods is below. Again, see :ref:`plot_types` for more examples, and `.axes.Axes` for the full list of methods. ========================= ================================================== :ref:`basic_plots` `~.axes.Axes.plot`, `~.axes.Axes.scatter`, `~.axes.Axes.bar`, `~.axes.Axes.step`, :ref:`arrays` `~.axes.Axes.pcolormesh`, `~.axes.Axes.contour`, `~.axes.Axes.quiver`, `~.axes.Axes.streamplot`, `~.axes.Axes.imshow` :ref:`stats_plots` `~.axes.Axes.hist`, `~.axes.Axes.errorbar`, `~.axes.Axes.hist2d`, `~.axes.Axes.pie`, `~.axes.Axes.boxplot`, `~.axes.Axes.violinplot` :ref:`unstructured_plots` `~.axes.Axes.tricontour`, `~.axes.Axes.tripcolor` ========================= ================================================== Axes labelling and annotation ----------------------------- Usually we want to label the Axes with an xlabel, ylabel, and title, and often we want to have a legend to differentiate plot elements. The `~.axes.Axes` class has a number of methods to create these annotations. .. plot:: :include-source: fig, ax = plt.subplots(figsize=(5, 3), layout='constrained') np.random.seed(19680801) t = np.arange(200) x = np.cumsum(np.random.randn(200)) y = np.cumsum(np.random.randn(200)) linesx = ax.plot(t, x, label='Random walk x') linesy = ax.plot(t, y, label='Random walk y') ax.set_xlabel('Time [s]') ax.set_ylabel('Distance [km]') ax.set_title('Random walk example') ax.legend() These methods are relatively straight-forward, though there are a number of :ref:`text_props` that can be set on the text objects, like *fontsize*, *fontname*, *horizontalalignment*. Legends can be much more complicated; see :ref:`legend_guide` for more details. Note that text can also be added to axes using `~.axes.Axes.text`, and `~.axes.Axes.annotate`. This can be quite sophisticated: see :ref:`text_props` and :ref:`annotations` for more information. Axes limits, scales, and ticking -------------------------------- Each Axes has two (or more) `~.axis.Axis` objects, that can be accessed via :attr:`~matplotlib.axes.Axes.xaxis` and :attr:`~matplotlib.axes.Axes.yaxis` properties. These have substantial number of methods on them, and for highly customizable Axis-es it is useful to read the API at `~.axis.Axis`. However, the Axes class offers a number of helpers for the most common of these methods. Indeed, the `~.axes.Axes.set_xlabel`, discussed above, is a helper for the `~.Axis.set_label_text`. Other important methods set the extent on the axes (`~.axes.Axes.set_xlim`, `~.axes.Axes.set_ylim`), or more fundamentally the scale of the axes. So for instance, we can make an Axis have a logarithmic scale, and zoom in on a sub-portion of the data: .. plot:: :include-source: fig, ax = plt.subplots(figsize=(4, 2.5), layout='constrained') np.random.seed(19680801) t = np.arange(200) x = 2**np.cumsum(np.random.randn(200)) linesx = ax.plot(t, x) ax.set_yscale('log') ax.set_xlim([20, 180]) The Axes class also has helpers to deal with Axis ticks and their labels. Most straight-forward is `~.axes.Axes.set_xticks` and `~.axes.Axes.set_yticks` which manually set the tick locations and optionally their labels. Minor ticks can be toggled with `~.axes.Axes.minorticks_on` or `~.axes.Axes.minorticks_off`. Many aspects of Axes ticks and tick labeling can be adjusted using `~.axes.Axes.tick_params`. For instance, to label the top of the axes instead of the bottom,color the ticks red, and color the ticklabels green: .. plot:: :include-source: fig, ax = plt.subplots(figsize=(4, 2.5)) ax.plot(np.arange(10)) ax.tick_params(top=True, labeltop=True, color='red', axis='x', labelcolor='green') More fine-grained control on ticks, setting scales, and controlling the Axis can be highly customized beyond these Axes-level helpers. Axes layout ----------- Sometimes it is important to set the aspect ratio of a plot in data space, which we can do with `~.axes.Axes.set_aspect`: .. plot:: :include-source: fig, axs = plt.subplots(ncols=2, figsize=(7, 2.5), layout='constrained') np.random.seed(19680801) t = np.arange(200) x = np.cumsum(np.random.randn(200)) axs[0].plot(t, x) axs[0].set_title('aspect="auto"') axs[1].plot(t, x) axs[1].set_aspect(3) axs[1].set_title('aspect=3')