As promised, let's learn an easier way to generate heatmaps compared to what we had to do in Chapter 5, Visualizing Data with Pandas and Matplotlib. Once again, we will make a heatmap of the correlations between the OHLC stock prices, the log of volume traded, and the daily difference between the highest and lowest prices (max_abs_change); however, this time, we will use seaborn, which gives us the heatmap() function for an easier way to produce this visualization:
>>> sns.heatmap(... fb.sort_index().assign(... volume=np.log(fb.volume), ... max_abs_change=fb.high - fb.low... ).corr(), ... annot=True, center=0... )
We also pass center=0 so that seaborn puts values of 0 (no correlation) at the center of the colormap ...
