The last possible visualization that we explore using matplotlib has to do with images. Resorting to plt.imgshow is useful when you are working with image data. Let's take as an example the Olivetti dataset, an open source set of images of 40 people who provided 10 images of themselves at different times (and with different expressions, a fact that makes it more challenging for testing face recognition algorithms). The images from this dataset are provided as feature vectors of pixel intensities. Therefore, it is important to reshape the vectors in order to make them resemble a matrix of pixels. Setting the interpolation to 'nearest' helps to smooth the picture:
In: from sklearn.datasets import fetch_olivetti_faces import ...
