# matplotlib.pyplot.imshow() in Python

• Last Updated : 22 Apr, 2020

Matplotlib is a library in Python and it is numerical – mathematical extension for NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface.

## matplotlib.pyplot.imshow() Function:

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The imshow() function in pyplot module of matplotlib library is used to display data as an image; i.e. on a 2D regular raster.

Syntax: matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None, vmax=None, origin=None, extent=None, shape=, filternorm=1, filterrad=4.0, imlim=, resample=None, url=None, \*, data=None, \*\*kwargs)

Parameters: This method accept the following parameters that are described below:

• X: This parameter is the data of the image.
• cmap : This parameter is a colormap instance or registered colormap name.
• norm : This parameter is the Normalize instance scales the data values to the canonical colormap range [0, 1] for mapping to colors
• vmin, vmax : These parameter are optional in nature and they are colorbar range.
• alpha : This parameter is a intensity of the color.
• aspect : This parameter is used to controls the aspect ratio of the axes.
• interpolation : This parameter is the interpolation method which used to display an image.
• origin : This parameter is used to place the [0, 0] index of the array in the upper left or lower left corner of the axes.
• resample : This parameter is the method which is used for resembling.
• extent : This parameter is the bounding box in data coordinates.
• filternorm : This parameter is used for the antigrain image resize filter.
• url : This parameter sets the url of the created AxesImage.

Returns: This returns the following:

• image : This returns the AxesImage

Below examples illustrate the matplotlib.pyplot.imshow() function in matplotlib.pyplot:

Example #1:

 `# Implementation of matplotlib function``import` `matplotlib.pyplot as plt``import` `numpy as np``from` `matplotlib.colors ``import` `LogNorm``     ` `dx, dy ``=` `0.015``, ``0.05``y, x ``=` `np.mgrid[``slice``(``-``4``, ``4` `+` `dy, dy),``                ``slice``(``-``4``, ``4` `+` `dx, dx)]``z ``=` `(``1` `-` `x ``/` `3.` `+` `x ``*``*` `5` `+` `y ``*``*` `5``) ``*` `np.exp(``-``x ``*``*` `2` `-` `y ``*``*` `2``)``z ``=` `z[:``-``1``, :``-``1``]``z_min, z_max ``=` `-``np.``abs``(z).``max``(), np.``abs``(z).``max``()`` ` `c ``=` `plt.imshow(z, cmap ``=``'Greens'``, vmin ``=` `z_min, vmax ``=` `z_max,``                 ``extent ``=``[x.``min``(), x.``max``(), y.``min``(), y.``max``()],``                    ``interpolation ``=``'nearest'``, origin ``=``'lower'``)``plt.colorbar(c)`` ` `plt.title(``'matplotlib.pyplot.imshow() function Example'``, ``                                     ``fontweight ``=``"bold"``)``plt.show()`

Output:

Example #2:

 `# Implementation of matplotlib function``import` `matplotlib.pyplot as plt``import` `numpy as np``from` `matplotlib.colors ``import` `LogNorm``     ` `dx, dy ``=` `0.015``, ``0.05``x ``=` `np.arange(``-``4.0``, ``4.0``, dx)``y ``=` `np.arange(``-``4.0``, ``4.0``, dy)``X, Y ``=` `np.meshgrid(x, y)``  ` `extent ``=` `np.``min``(x), np.``max``(x), np.``min``(y), np.``max``(y)``  ` `Z1 ``=` `np.add.outer(``range``(``8``), ``range``(``8``)) ``%` `2``plt.imshow(Z1, cmap ``=``"binary_r"``, interpolation ``=``'nearest'``,``                               ``extent ``=` `extent, alpha ``=` `1``)``  ` `def` `geeks(x, y):``    ``return` `(``1` `-` `x ``/` `2` `+` `x``*``*``5` `+` `y``*``*``6``) ``*` `np.exp(``-``(x``*``*``2` `+` `y``*``*``2``))``  ` `Z2 ``=` `geeks(X, Y)``  ` `plt.imshow(Z2, cmap ``=``"Greens"``, alpha ``=` `0.7``, ``           ``interpolation ``=``'bilinear'``, extent ``=` `extent)`` ` `plt.title(``'matplotlib.pyplot.imshow() function Example'``, ``                                     ``fontweight ``=``"bold"``)``plt.show()`

Output:

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