We have covered most of the important components in a plot figure using matplotlib. In this section, we will introduce another powerful plotting method for directly creating standard visualization from pandas data objects that are often used to manipulate data.

For Series or DataFrame objects in pandas, most plotting types are supported, such as line, bar, box, histogram, and scatter plots, and pie charts. To select a plot type, we use the kind argument of the plot function. With no kind of plot specified, the plot function will generate a line style visualization by default , as in the following example:

>>> s = pd.Series(np.random.normal(10, 8, 20))
>>> s.plot(style='ko—', alpha=0.4, label='Series plotting')
>>> plt.legend()
>>> plt.show()

The output for the preceding command is as follows:

Another example will visualize the data of a DataFrame object consisting of multiple columns:

>>> data = {'Median_Age': [24.2, 26.4, 28.5, 30.3],
         'Density': [244, 256, 268, 279]}
>>> index_label = ['2000', '2005', '2010', '2014'];
>>> df1 = pd.DataFrame(data, index=index_label)
>>> df1.plot(kind='bar', subplots=True, sharex=True)
>>> plt.tight_layout();
>>> plt.show()

The output for the preceding command is as follows:

The plot method of the DataFrame has a number of options that allow us to handle the plotting of the columns. For example, in the preceding DataFrame visualization, we chose to plot the columns in separate subplots. The following table lists more options: