Having seen how to create, lay out, and annotate time-series charts, we will now look at creating a number of charts, other than time series that are commonplace in presenting statistical information.
Bar plots are useful in order to visualize the relative differences in values of non time-series data. Bar plots can be created using the kind='bar'
parameter of the .plot()
method:
In [24]: # make a bar plot # create a small series of 10 random values centered at 0.0 np.random.seed(seedval) s = pd.Series(np.random.rand(10) - 0.5) # plot the bar chart s.plot(kind='bar');
If the data being plotted consists of multiple columns, a multiple series bar plot will be created:
In [25]: # draw a multiple series bar chart # generate 4 columns of 10 random values np.random.seed(seedval) df2 = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd']) # draw the multi-series bar chart df2...