preprocessing-data-for-machine-learning-and-deep-learning_code5.py

def plotDataAndCov(data):
    ACov = np.cov(data, rowvar=False, bias=True)
    print 'Covariance matrix:\n', ACov

    fig, ax = plt.subplots(nrows=1, ncols=2)
    fig.set_size_inches(10, 10)

    ax0 = plt.subplot(2, 2, 1)
    
    # Choosing the colors
    cmap = sns.color_palette("GnBu", 10)
    sns.heatmap(ACov, cmap=cmap, vmin=0)

    ax1 = plt.subplot(2, 2, 2)
    
    # data can include the colors
    if data.shape[1]==3:
        c=data[:,2]
    else:
        c="#0A98BE"
    ax1.scatter(data[:,0], data[:,1], c=c, s=40)
    
    # Remove the top and right axes from the data plot
    ax1.spines['right'].set_visible(False)
    ax1.spines['top'].set_visible(False)