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| Introduction to Axes (or Subplots) |
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| 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. |
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| .. _anatomy_local: |
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| .. figure:: /_static/anatomy.png |
| :width: 80% |
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| Anatomy of a Figure |
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| 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. |
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| 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`. |
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| Creating Axes |
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| .. plot:: |
| :include-source: |
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| import matplotlib.pyplot as plt |
| import numpy as np |
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| fig, axs = plt.subplots(ncols=2, nrows=2, figsize=(3.5, 2.5), |
| layout="constrained") |
| |
| 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()') |
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| 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. |
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| There are a number of other methods for adding Axes to a Figure: |
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| * `.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. |
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| See :doc:`arranging_axes` for more detail on how to arrange grids of Axes on a |
| Figure. |
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| Axes plotting methods |
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| 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`: |
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| .. plot:: |
| :include-source: |
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| 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) |
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| Note that ``plot`` returns a list of *lines* Artists which can subsequently be |
| manipulated, as discussed in :ref:`users_artists`. |
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| 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. |
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| ========================= ================================================== |
| :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` |
| ========================= ================================================== |
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| Axes labelling and annotation |
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| 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. |
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| .. plot:: |
| :include-source: |
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| 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') |
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| ax.set_xlabel('Time [s]') |
| ax.set_ylabel('Distance [km]') |
| ax.set_title('Random walk example') |
| ax.legend() |
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| 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. |
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| 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. |
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| Axes limits, scales, and ticking |
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| 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`. |
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| 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: |
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| .. plot:: |
| :include-source: |
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| 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]) |
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| 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`. |
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| 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: |
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| .. plot:: |
| :include-source: |
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| 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') |
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| More fine-grained control on ticks, setting scales, and controlling the Axis can be highly customized beyond these Axes-level helpers. |
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| Axes layout |
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| Sometimes it is important to set the aspect ratio of a plot in data space, which we can do with `~.axes.Axes.set_aspect`: |
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| .. plot:: |
| :include-source: |
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| 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"') |
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| axs[1].plot(t, x) |
| axs[1].set_aspect(3) |
| axs[1].set_title('aspect=3') |
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|
