# Matplotlib.pyplot.pcolor() function in Python

• Last Updated : 25 Nov, 2020

Matplotlib is the well-known Python package used in data visualization. Numpy is the numerical mathematics extension of Matplotlib. Matplotlib is capable of producing high-quality graphs, charts, and figures. Matplotlib produces object-oriented API for embedding plots into projects using GUI toolkits like Tkinter, wxPython, or Qt. John D. Hunter was the original developer of Matplotlib and it is distributed under a BSD-style license.

## matplotlib.pyplot.pcolor()

Matplotlib contains a wide range of functions that help in performing different tasks, one of them is matplotlib.pyplot.pcolor() function. The pcolor() function in the pyplot module of the Matplotlib library helps to create a pseudo-color plot with a non-regular rectangular grid.

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Syntax: matplotlib.pyplot.pcolor(*args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None, data=None, **kwargs)
Call Signature: pcolor([X, Y,] C, **kwargs)
Parameters:
C: Denotes a scaler 2-D array
X, Y: array_like, optional, coordinates of quadrilateral corners
cmap: str or Colormap, optional
norm: Normalize, optional
vmin, vmax: scaler, optional
edgecolors: {‘none’, None, ‘face’, color sequence}, optional
alpha: scaler, optional
snap: bool, optional
Other Parameters:
antialiaseds: bool, optional
**kwargs
Returns: The function returns a collection i.e matplotlib.collections.Collection

Note: In case of larger arrays, matplotlib.pyplot.pcolor() works very slow.

Below examples demonstrate the working of matplotlib.pyplot.pcolor() function:

Example 1: Generating images using pcolor() function

With the help of pcolor() function, we can generate 2-D image-style plots, as shown below

## Python3

 `# Demonstration of matplotlib function``import` `matplotlib.pyplot as plt``import` `numpy as np``from` `matplotlib.colors ``import` `LogNorm`` ` `Z ``=` `np.random.rand(``4``, ``12``)`` ` `fig, (ax0, ax1) ``=` `plt.subplots(``2``, ``1``)`` ` `c ``=` `ax0.pcolor(Z)``ax0.set_title(``'No edge image'``)`` ` `c ``=` `ax1.pcolor(Z, edgecolors``=``'k'``, linewidths``=``5``)``ax1.set_title(``'Thick edges image'``)`` ` `fig.tight_layout()``plt.show()`

Output: Example 2: Working of pcolor() with Log scale

## Python3

 `# Demonstration of matplotlib function``import` `matplotlib.pyplot as plt``import` `numpy as np``from` `matplotlib.colors ``import` `LogNorm`` ` `N ``=` `100``X, Y ``=` `np.mgrid[``-``4``:``4``:``complex``(``0``, N), ``-``4``:``4``:``complex``(``0``, N)]`` ` `# Image show that a low hump with a spike coming out.``# We need a z/colour axis on a log scale in order``# to watch both hump and spike.``Z1 ``=` `np.exp(``-``(X)``*``*``2` `-` `(Y)``*``*``2``)``Z2 ``=` `np.exp(``-``(X ``*` `10``)``*``*``2` `-` `(Y ``*` `10``)``*``*``2``)``Z ``=` `Z1 ``+` `50` `*` `Z2`` ` `fig, (ax0, ax1) ``=` `plt.subplots(``2``, ``1``)`` ` `c ``=` `ax0.pcolor(X, Y, Z,norm``=``LogNorm(vmin``=``Z.``min``(), vmax``=``Z.``max``()), cmap``=``plt.cm.autumn)`` ` `fig.colorbar(c, ax``=``ax0)`` ` `c ``=` `ax1.pcolor(X, Y, Z, cmap``=``plt.cm.autumn)``fig.colorbar(c, ax``=``ax1)`` ` `plt.show()`

Output: My Personal Notes arrow_drop_up