How to do it...

The following code block draws a table chart for the types of batteries sold in each year:

1. Prepare the data:

rows = ['2011', '2012', '2013', '2014', '2015']
columns = ('7Ah', '35Ah', '40Ah', '135Ah', '150Ah')
data = [[75, 144, 114, 102, 108], [90, 126, 102, 84, 126], 
        [96, 114, 75, 105, 135], [105, 90, 150, 90, 75], 
        [90, 75, 135, 75, 90]]

2. Define the range and scale for the y axis:

values = np.arange(0, 600, 100)

3. Specify the color spectrum to be used. Each year will be represented in a different color:

colors = plt.cm.OrRd(np.linspace(0, 0.5, len(rows)))

4. Define x axis ticks where the bars are to be plotted:

index = np.arange(len(columns)) + 0.3
bar_width = 0.5

5. Initialize the vertical offset for the stacked bar chart:

y_offset = np.zeros(len(columns))

6. Specify the area for the plot in terms of figure and axes:

fig, ax = plt.subplots()

7. Plot bars and create text labels for the table. Initialize the list in which data for the table is saved:

cell_text = []

8. Each iteration of the for loop plots one year of data for all battery ratings in one color:

n_rows = len(data)
for row in range(n_rows):
    plot = plt.bar(index, data[row], bar_width, bottom=y_offset, 
                   color=colors[row])
    y_offset = y_offset + data[row]
    cell_text.append(['%1.1f' % (x) for x in y_offset])
    i=0
# Each iteration of this for loop, labels each bar with   
  corresponding value for the given year
    for rect in plot:
        height = rect.get_height()
        ax.text(rect.get_x() + rect.get_width()/2, y_offset[i],'%d' 
                % int(y_offset[i]), 
                ha='center', va='bottom')
        i = i+1 

9. Add a table to the bottom of the axes:

the_table = plt.table(cellText=cell_text, rowLabels=rows, 
                rowColours=colors, colLabels=columns, loc='bottom')
plt.ylabel("Units Sold")

10. There are n ticks on the x axis, since this table covers the labels:

plt.xticks([])
plt.title('Number of Batteries Sold/Year')

11. Display the plot on the screen:

plt.show()