# Matplotlib.axes.Axes.set_aspect() in Python

• Difficulty Level : Easy
• Last Updated : 19 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library.

The Axes Class contains most of the figure elements: Axis, Tick, Line2D, Text, Polygon, etc., and sets the coordinate system. And the instances of Axes supports callbacks through a callbacks attribute.
#Sample Code

 `# Implementation of matplotlib function``     ` `import` `matplotlib.pyplot as plt``import` `numpy as np``   ` `# make an agg figure``fig, ax ``=` `plt.subplots()``ax.plot([``1``, ``2``, ``3``])``ax.set_title(``'matplotlib.axes.Axes function'``)``fig.canvas.draw()``plt.show()`

Output:

## matplotlib.axes.Axes.set_aspect() Function

The Axes.set_aspect() function in axes module of matplotlib library is used to set the aspect of the axis scaling, i.e. the ratio of y-unit to x-unit.

Syntax:

Parameters: This method accepts the following parameters.

• aspect : This parameter accepts the following value {‘auto’, ‘equal’} or num.
• adjustable : This defines which parameter will be adjusted to meet the required aspect.
• anchor : This parameter is used to define where the Axes will be drawn if there is extra space due to aspect constraints.
• share: This parameter is used to apply the settings to all shared Axes.

Return value: This method does not return any value.

Below examples illustrate the matplotlib.axes.Axes.set_aspect() function in matplotlib.axes:

Example-1:

 `# ImpleIn Reviewtation of matplotlib function  ``import` `matplotlib.pyplot as plt`` ` `fig, (ax1, ax2) ``=` `plt.subplots(``1``, ``2``)``ax1.set_xscale(``"log"``)``ax1.set_yscale(``"log"``)``ax1.set_adjustable(``"datalim"``)``ax1.plot([``1``, ``3``, ``34``, ``4``, ``46``, ``3``, ``7``, ``45``, ``10``],``         ``[``1``, ``9``, ``27``, ``8``, ``29``, ``84``, ``78``, ``19``, ``48``],``         ``"o-"``, color ``=``"green"``)``ax1.set_xlim(``1e``-``1``, ``1e2``)``ax1.set_ylim(``1``, ``1e2``)``ax1.set_title(``"No set_aspect"``)`` ` `ax2.set_xscale(``"log"``)``ax2.set_yscale(``"log"``)``ax2.set_adjustable(``"datalim"``)``ax2.plot([``1``, ``3``, ``34``, ``4``, ``46``, ``3``, ``7``, ``45``, ``10``],``         ``[``1``, ``9``, ``27``, ``8``, ``29``, ``84``, ``78``, ``19``, ``48``],``         ``"o-"``, color ``=``"green"``)`` ` `ax2.set_xlim(``1e``-``1``, ``1e2``)``ax2.set_ylim(``1``, ``1e2``)``ax2.set_aspect(``2``)``ax2.set_title(``"set_aspect value = 2"``)`` ` `fig.suptitle('matplotlib.axes.Axes.set_aspect() \``function Example\n', fontweight ``=``"bold"``)``fig.canvas.draw()``plt.show()`

Output:

Example-2:

 `# ImpleIn Reviewtation of matplotlib function  ``import` `matplotlib.pyplot as plt``import` `matplotlib.tri as tri``import` `numpy as np`` ` `n_angles ``=` `20``n_radii ``=` `10``min_radius ``=` `2``radii ``=` `np.linspace(min_radius, ``0.95``, n_radii)`` ` `angles ``=` `np.linspace(``0``, ``4` `*` `np.pi, n_angles,``                     ``endpoint ``=` `False``)``angles ``=` `np.repeat(angles[..., np.newaxis],``                   ``n_radii, axis ``=` `1``)``angles[:, ``1``::``2``] ``+``=` `np.pi ``/` `n_angles`` ` `x ``=` `(radii ``*` `np.cos(angles)).flatten()``y ``=` `(radii ``*` `np.sin(angles)).flatten()`` ` `triang ``=` `tri.Triangulation(x, y)`` ` `triang.set_mask(np.hypot(x[triang.triangles].mean(axis ``=` `1``),``                         ``y[triang.triangles].mean(axis ``=` `1``))``                ``< min_radius)``fig, (ax, ax1) ``=` `plt.subplots(``1``, ``2``)`` ` `ax.triplot(triang, ``'bo-'``, lw ``=` `1``, color ``=` `"green"``)``ax.set_title(``"No set_aspect"``)`` ` `ax1.set_aspect(``'equal'``)``ax1.triplot(triang, ``'bo-'``, lw ``=` `1``, color ``=` `"green"``)``ax1.set_title(``"set_aspect value ='equal'"``)`` ` `fig.suptitle('matplotlib.axes.Axes.set_aspect() \``function Example\n', fontweight ``=``"bold"``)``fig.canvas.draw()``plt.show()`

Output:

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