How to Increase subplot text size and add custom bar plot annotations

Question

I have the following dataframe that represents the total of employees per department/area in each region.

               Finance   HR   IT  Marketing  Medical  Operations  Sales
Business Unit                                                          
Cardiology           0    2    1          0        3           0      0
Genetics             1    4    3          1        3           1      1
Imaging             34   74   70         38       68          18     33
Labs                63  130  131         66      130          32     68
Pathology            2    5   10          4        8           3      6

Using this dataframe, I generated bellow graph using this code:

#Plot the graph
fig, ax = plt.subplots(1, 5, figsize=(30, 15), sharey = True)
iax = iter(ax.flatten())
for n, g in df.groupby('Business Unit'):
    g.loc[n, :].plot.bar(ax=next(iax),  title=f'{n}', stacked=True, legend = True, fontsize = 30)

As you can see, the text size of the subplot where the Business Unit is informed and also the legend size (in the top corner) is way too small. How could I increase the text of both of them?

Also, is there a way I could add in this code a way to display the % from the total in each of those columns?

Thank you so much for your help!

Answer 1

• I would use pandas.DataFrame.plot, which plots the index as the xaxis. As such, the dataframe needs to be transposed with .T
  • This uses matplotlib and the default plotting backend
• Using subplots=True, df.plot is assigned to axes, which is an array of arrays of AxesSubplots
• Annotations can be added with .bar_label, which can have customized labels using the labels= parameter. The following links have examples of customizing the labels.
  • Bar Label Demo
  • stack bar plot in matplotlib and add label to each section
  • How to add multiple annotations to a barplot
  • How to plot a horizontal stacked bar with annotations
• Tested in pandas 1.3.0 and matplotlib 3.4.2

import pandas as pd

# load dataframe
data = {'Cardiology': {'Finance': 0, 'HR': 2, 'IT': 1, 'Marketing': 0, 'Medical': 3, 'Operations': 0, 'Sales': 0}, 'Genetics': {'Finance': 1, 'HR': 4, 'IT': 3, 'Marketing': 1, 'Medical': 3, 'Operations': 1, 'Sales': 1}, 'Imaging': {'Finance': 34, 'HR': 74, 'IT': 70, 'Marketing': 38, 'Medical': 68, 'Operations': 18, 'Sales': 33}, 'Labs': {'Finance': 63, 'HR': 130, 'IT': 131, 'Marketing': 66, 'Medical': 130, 'Operations': 32, 'Sales': 68}, 'Pathology': {'Finance': 2, 'HR': 5, 'IT': 10, 'Marketing': 4, 'Medical': 8, 'Operations': 3, 'Sales': 6}}
df = pd.DataFrame.from_dict(data, orient='index')

# get the total for each business unit; used to calculate percent
bu_total = df.sum(axis=1)

# get the total for each division; used to calculate percent
div_total = df.sum(axis=0)

# plot
axes = df.T.plot(kind='bar', subplots=True, layout=(1, 5), figsize=(22, 6), sharey=True, ylabel='Expenditures ($)')

# iterate through axes subplots
for ax in axes[0]:
    # title is used to get the total from bu_total
    title = ax.get_title()
    
    ax.legend(fontsize=15)
    ax.set_xticklabels(ax.get_xticklabels(), fontdict={'fontsize':24})
    ax.set_title(title, fontdict={'fontsize':24})
    ax.set_ylabel(ax.get_ylabel(), fontdict={'fontsize':24})
    
    # customized labels for % business unit total
    bu_labels = [f'{(v.get_height() / bu_total[title])*100 :.0f}%' for v in ax.containers[0]]
    
    # customized labels for % division total
    div_labels = [f'{(v.get_height() / div_total[i])*100 :.0f}%' for i, v in enumerate(ax.containers[0])]
    
    # annotate as desired: use bu_lables or div_labels
    ax.bar_label(ax.containers[0], labels=bu_labels, label_type='edge', fontsize=10)

    # pad the spacing between the number and the edge of the figure
    ax.margins(y=0.1)