A 2-D Heatmap is an information perception instrument that assists with addressing the size of the peculiarity in type of shadings. In python, we can plot 2-D Heatmaps utilizing Matplotlib bundle. There are various strategies to plot 2-D Heatmaps, some of them are examined underneath.

Utilizing Matplotlib, I need to plot a 2D hotness map. My information is a n-by-n Numpy exhibit, each with a worth somewhere in the range of 0 and 1. So for the (I, j) component of this exhibit, I need to plot a square at the (I, j) coordinate in my hotness map, whose tone is relative to the component’s worth in the cluster.

**Method 1: Using matplotlib.pyplot.imshow() Function**

Syntaxmatplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, alpha=None, vmin=None,:

vmax=None, origin=None, extent=None, shape=<deprecated parameter>, filternorm=1, filterrad=4.0,

imlim=<deprecated parameter>, resample=None, url=None, \*, data=None, \*\*kwargs)

- Python3

`# Program to plot 2-D Heat map` `# using matplotlib.pyplot.imshow() method` `import` `numpy as np` `import` `matplotlib.pyplot as plt` ` ` `data ` `=` `np.random.random(( ` `12` `, ` `12` `))` `plt.imshow( data , cmap ` `=` `'autumn'` `, interpolation ` `=` `'nearest'` `)` ` ` `plt.title( ` `"2-D Heat Map"` `)` `plt.show()` |

**Output:**

eevibes

**Method 2: Using Seaborn Library**

For this we use** seaborn.heatmap() function**

Syntaxseaborn.heatmap(data, *, vmin=None, vmax=None, cmap=None, center=None, robust=False,annot=None,:

fmt=’.2g’, annot_kws=None, linewidths=0, linecolor=’white’, cbar=True, cbar_kws=None, cbar_ax=None,

square=False, xticklabels=’auto’, yticklabels=’auto’, mask=None, ax=None, **kwargs)

- Python3

`# Program to plot 2-D Heat map` `# using seaborn.heatmap() method` `import` `numpy as np` `import` `seaborn as sns` `import` `matplotlib.pylab as plt` ` ` `data_set ` `=` `np.random.rand( ` `10` `, ` `10` `)` `ax ` `=` `sns.heatmap( data_set , linewidth ` `=` `0.5` `, cmap ` `=` `'coolwarm'` `)` ` ` `plt.title( ` `"2-D Heat Map"` `)` `plt.show()` |

**Output:**

eevibes

**Method 3: Using matplotlib.pyplot.pcolormesh() Function**

Syntaxmatplotlib.pyplot.pcolormesh(*args, alpha=None, norm=None, cmap=None, vmin=None, vmax=None,:

shading=’flat’, antialiased=False, data=None, **kwargs)

- Python3

`# Program to plot 2-D Heat map` `# using matplotlib.pyplot.pcolormesh() method` `import` `matplotlib.pyplot as plt` `import` `numpy as np` ` ` `Z ` `=` `np.random.rand( ` `15` `, ` `15` `)` ` ` `plt.pcolormesh( Z , cmap ` `=` `'summer'` `)` ` ` `plt.title( ` `'2-D Heat Map'` `)` `plt.show()` |

**Output:**

eevibes

**Also Read**: **Learn Arrays in C and C++**