![](\"file://../../_static/anatomy.png\")
\n\n### :class:`~matplotlib.figure.Figure`\n\nThe **whole** figure. The Figure keeps\ntrack of all the child :class:`~matplotlib.axes.Axes`, a group of\n'special' Artists (titles, figure legends, colorbars, etc.), and\neven nested subfigures.\n\nTypically, you'll create a new Figure through one of the following\nfunctions::\n\n fig = plt.figure() # an empty figure with no Axes\n fig, ax = plt.subplots() # a figure with a single Axes\n fig, axs = plt.subplots(2, 2) # a figure with a 2x2 grid of Axes\n # a figure with one Axes on the left, and two on the right:\n fig, axs = plt.subplot_mosaic([['left', 'right_top'],\n ['left', 'right_bottom']])\n\n`~.pyplot.subplots()` and `~.pyplot.subplot_mosaic` are convenience functions\nthat additionally create Axes objects inside the Figure, but you can also\nmanually add Axes later on.\n\nFor more on Figures, including panning and zooming, see `figure-intro`.\n\n### :class:`~matplotlib.axes.Axes`\n\nAn Axes is an Artist attached to a Figure that contains a region for\nplotting data, and usually includes two (or three in the case of 3D)\n:class:`~matplotlib.axis.Axis` objects (be aware of the difference\nbetween **Axes** and **Axis**) that provide ticks and tick labels to\nprovide scales for the data in the Axes. Each :class:`~.axes.Axes` also\nhas a title\n(set via :meth:`~matplotlib.axes.Axes.set_title`), an x-label (set via\n:meth:`~matplotlib.axes.Axes.set_xlabel`), and a y-label set via\n:meth:`~matplotlib.axes.Axes.set_ylabel`).\n\nThe `~.axes.Axes` methods are the primary interface for configuring\nmost parts of your plot (adding data, controlling axis scales and\nlimits, adding labels etc.).\n\n### :class:`~matplotlib.axis.Axis`\n\nThese objects set the scale and limits and generate ticks (the marks\non the Axis) and ticklabels (strings labeling the ticks). The location\nof the ticks is determined by a `~matplotlib.ticker.Locator` object and the\nticklabel strings are formatted by a `~matplotlib.ticker.Formatter`. The\ncombination of the correct `.Locator` and `.Formatter` gives very fine\ncontrol over the tick locations and labels.\n\n### :class:`~matplotlib.artist.Artist`\n\nBasically, everything visible on the Figure is an Artist (even\n`.Figure`, `Axes <.axes.Axes>`, and `~.axis.Axis` objects). This includes\n`.Text` objects, `.Line2D` objects, :mod:`.collections` objects, `.Patch`\nobjects, etc. When the Figure is rendered, all of the\nArtists are drawn to the **canvas**. Most Artists are tied to an Axes; such\nan Artist cannot be shared by multiple Axes, or moved from one to another.\n\n\n## Types of inputs to plotting functions\n\nPlotting functions expect `numpy.array` or `numpy.ma.masked_array` as\ninput, or objects that can be passed to `numpy.asarray`.\nClasses that are similar to arrays ('array-like') such as `pandas`\ndata objects and `numpy.matrix` may not work as intended. Common convention\nis to convert these to `numpy.array` objects prior to plotting.\nFor example, to convert a `numpy.matrix` ::\n\n b = np.matrix([[1, 2], [3, 4]])\n b_asarray = np.asarray(b)\n\nMost methods will also parse a string-indexable object like a *dict*, a\n`structured numpy array`_, or a `pandas.DataFrame`. Matplotlib allows you\nto provide the ``data`` keyword argument and generate plots passing the\nstrings corresponding to the *x* and *y* variables.\n\n\n"
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"np.random.seed(19680801) # seed the random number generator.\ndata = {'a': np.arange(50),\n 'c': np.random.randint(0, 50, 50),\n 'd': np.random.randn(50)}\ndata['b'] = data['a'] + 10 * np.random.randn(50)\ndata['d'] = np.abs(data['d']) * 100\n\nfig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')\nax.scatter('a', 'b', c='c', s='d', data=data)\nax.set_xlabel('entry a')\nax.set_ylabel('entry b')"
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"\n## Coding styles\n\n### The explicit and the implicit interfaces\n\nAs noted above, there are essentially two ways to use Matplotlib:\n\n- Explicitly create Figures and Axes, and call methods on them (the\n \"object-oriented (OO) style\").\n- Rely on pyplot to implicitly create and manage the Figures and Axes, and\n use pyplot functions for plotting.\n\nSee `api_interfaces` for an explanation of the tradeoffs between the\nimplicit and explicit interfaces.\n\nSo one can use the OO-style\n\n"
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"x = np.linspace(0, 2, 100) # Sample data.\n\n# Note that even in the OO-style, we use `.pyplot.figure` to create the Figure.\nfig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')\nax.plot(x, x, label='linear') # Plot some data on the Axes.\nax.plot(x, x**2, label='quadratic') # Plot more data on the Axes...\nax.plot(x, x**3, label='cubic') # ... and some more.\nax.set_xlabel('x label') # Add an x-label to the Axes.\nax.set_ylabel('y label') # Add a y-label to the Axes.\nax.set_title(\"Simple Plot\") # Add a title to the Axes.\nax.legend() # Add a legend."
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"or the pyplot-style:\n\n"
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"x = np.linspace(0, 2, 100) # Sample data.\n\nplt.figure(figsize=(5, 2.7), layout='constrained')\nplt.plot(x, x, label='linear') # Plot some data on the (implicit) Axes.\nplt.plot(x, x**2, label='quadratic') # etc.\nplt.plot(x, x**3, label='cubic')\nplt.xlabel('x label')\nplt.ylabel('y label')\nplt.title(\"Simple Plot\")\nplt.legend()"
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"(In addition, there is a third approach, for the case when embedding\nMatplotlib in a GUI application, which completely drops pyplot, even for\nfigure creation. See the corresponding section in the gallery for more info:\n`user_interfaces`.)\n\nMatplotlib's documentation and examples use both the OO and the pyplot\nstyles. In general, we suggest using the OO style, particularly for\ncomplicated plots, and functions and scripts that are intended to be reused\nas part of a larger project. However, the pyplot style can be very convenient\nfor quick interactive work.\n\nYou may find older examples that use the ``pylab`` interface,\n via ``from pylab import *``. This approach is strongly deprecated.