We can use bar plots for displaying counts of our discrete data. For example, we can do the following to plot the total volume traded of Facebook stock per month from February 2018 through August 2018. Rather than passing kind to plot(), we can append a call to the specific plot type if we know the name, which is bar() here:
>>> fb['2018-02':'2018-08'].assign(... month=lambda x: x.index.month... ).groupby('month').sum().volume.plot.bar(... color='green', rot=0, title='Volume Traded'... )>>> plt.ylabel('volume') # label the y-axis (discussed in ch 6)
Note that this is also an evolution over time, but we don't use a line plot to avoid interpolating the points:
When working with categorical data, we are limited in terms ...
