How to add multiple annotations to a barplot

With pandas

• Tested with pandas v1.2.4

Imports and Load Data

import pandas as pd
import matplotlib.pyplot as plt

# create the dataframe from values in the OP
counts = [29227, 102492,  53269, 504028, 802994]
df = pd.DataFrame(data=counts, columns=['counts'], index=['A','B','C','D','E'])

# add a percent column
df['%'] = df.counts.div(df.counts.sum()).mul(100).round(2)

# display(df)
   counts      %
A   29227   1.96
B  102492   6.87
C   53269   3.57
D  504028  33.78
E  802994  53.82

Plot use matplotlib from version 3.4.2

• Use matplotlib.pyplot.bar_label
• See the matplotlib: Bar Label Demo page for additional formatting options.
• Tested with pandas v1.2.4, which is using matplotlib as the plot engine.
• Some formatting can be done with the fmt parameter, but more sophisticated formatting should be done with the labels parameter.

ax = df.plot(kind='barh', y='counts', figsize=(10, 5), legend=False, width=.75,
             title='This is the plot generated by all code examples in this answer')

# customize the label to include the percent
labels = [f' {v.get_width()}\n {df.iloc[i, 1]}%' for i, v in enumerate(ax.containers[0])]

# set the bar label
ax.bar_label(ax.containers[0], labels=labels, label_type='edge', size=13)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

Annotation Resources - from matplotlib v3.4.2

• Adding value labels on a matplotlib bar chart
• How to annotate each segment of a stacked bar chart
• Stacked Bar Chart with Centered Labels
• How to plot and annotate multiple data columns in a seaborn barplot
• How to annotate a seaborn barplot with the aggregated value
• stack bar plot in matplotlib and add label to each section
• How to plot and annotate a grouped bar chart

Plot use matplotlib before version 3.4.2

# plot the dataframe
ax = df.plot(kind='barh', y='counts', figsize=(10, 5), legend=False, width=.75)
for i, y in enumerate(ax.patches):

    # get the percent label
    label_per = df.iloc[i, 1]
    
    # add the value label
    ax.text(y.get_width()+.09, y.get_y()+.3, str(round((y.get_width()), 1)), fontsize=10)
    
    # add the percent label here
    ax.text(y.get_width()+.09, y.get_y()+.1, str(f'{round((label_per), 2)}%'), fontsize=10)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

Original Answer without pandas

• Tested with matplotlib v3.3.4

import matplotlib.pyplot as plt

fig, ax = plt.subplots(figsize=(10, 5))

counts = [29227, 102492,  53269, 504028, 802994]

# calculate percents
percents = [100*x/sum(counts) for x in counts]

y_ax = ('A','B','C','D','E')
y_tick = np.arange(len(y_ax))

ax.barh(range(len(counts)), counts, align = "center", color = "tab:blue")
ax.set_yticks(y_tick)
ax.set_yticklabels(y_ax, size = 8)

#annotate bar plot with values
for i, y in enumerate(ax.patches):
    label_per = percents[i]
    ax.text(y.get_width()+.09, y.get_y()+.3, str(round((y.get_width()), 1)), fontsize=10)
    # add the percent label here
    # ax.text(y.get_width()+.09, y.get_y()+.3, str(round((label_per), 2)), ha='right', va='center', fontsize=10)
    ax.text(y.get_width()+.09, y.get_y()+.1, str(f'{round((label_per), 2)}%'), fontsize=10)

ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
plt.show()

• You can play with the positioning.
• Other formatting options mentioned by JohanC
• Print both parts of the text in one string with a \n in between to get a "natural" line spacing:
• str(f'{round((y.get_width()), 1)}\n{round((label_per), 2)}%')
• ax.text(..., va='center') to vertically center and be able to use a slightly larger font.
• ax.set_xlim(0, max(counts) * 1.18) to get a bit more space for the text.
• Start each line of text with a space to get a natural "horizontal" padding.
• str(f' {round((label_per), 2)}%'), note the space before {.
• y.get_width()+.09 is extremely close to y.get_width() when these values are in the tens of thousands.