How to add percentages on top of bars in seaborn

Given the following count plot how do I place percentages on top of the bars?

import seaborn as sns
sns.set(style="darkgrid")
titanic = sns.load_dataset("titanic")
ax = sns.countplot(x="class", hue="who", data=titanic)

For example for “First” I want total First men/total First, total First women/total First, and total First children/total First on top of their respective bars.

Answers:

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Method 1

The seaborn.catplot organizing function returns a FacetGrid, which gives you access to the fig, the ax, and its patches. If you add the labels when nothing else has been plotted you know which bar-patches came from which variables. From @LordZsolt’s answer I picked up the order argument to catplot: I like making that explicit because now we aren’t relying on the barplot function using the order we think of as default.

import seaborn as sns
from itertools import product

titanic = sns.load_dataset("titanic")

class_order = ['First','Second','Third'] 
hue_order = ['child', 'man', 'woman']
bar_order = product(class_order, hue_order)

catp = sns.catplot(data=titanic, kind='count', 
                   x='class', hue='who',
                   order = class_order, 
                   hue_order = hue_order )

# As long as we haven't plotted anything else into this axis,
# we know the rectangles in it are our barplot bars
# and we know the order, so we can match up graphic and calculations:

spots = zip(catp.ax.patches, bar_order)
for spot in spots:
    class_total = len(titanic[titanic['class']==spot[1][0]])
    class_who_total = len(titanic[(titanic['class']==spot[1][0]) & 
        (titanic['who']==spot[1][1])])
    height = spot[0].get_height() 
    catp.ax.text(spot[0].get_x(), height+3, '{:1.2f}'.format(class_who_total/class_total))

    #checking the patch order, not for final:
    #catp.ax.text(spot[0].get_x(), -3, spot[1][0][0]+spot[1][1][0])

produces

An alternate approach is to do the sub-summing explicitly, e.g. with the excellent pandas, and plot with matplotlib, and also do the styling yourself. (Though you can get quite a lot of styling from sns context even when using matplotlib plotting functions. Try it out — )

Method 2

with_hue function will plot percentages on the bar graphs if you have the ‘hue’ parameter in your plots. It takes the actual graph, feature, Number_of_categories in feature, and hue_categories(number of categories in hue feature) as a parameter.

without_hue function will plot percentages on the bar graphs if you have a normal plot. It takes the actual graph and feature as a parameter.

def with_hue(ax, feature, Number_of_categories, hue_categories):
    a = [p.get_height() for p in ax.patches]
    patch = [p for p in ax.patches]
    for i in range(Number_of_categories):
        total = feature.value_counts().values[i]
        for j in range(hue_categories):
            percentage = '{:.1f}%'.format(100 * a[(j*Number_of_categories + i)]/total)
            x = patch[(j*Number_of_categories + i)].get_x() + patch[(j*Number_of_categories + i)].get_width() / 2 - 0.15
            y = patch[(j*Number_of_categories + i)].get_y() + patch[(j*Number_of_categories + i)].get_height() 
            ax.annotate(percentage, (x, y), size = 12)

def without_hue(ax, feature):
    total = len(feature)
    for p in ax.patches:
        percentage = '{:.1f}%'.format(100 * p.get_height()/total)
        x = p.get_x() + p.get_width() / 2 - 0.05
        y = p.get_y() + p.get_height()
        ax.annotate(percentage, (x, y), size = 12)

Method 3

With the help of cphlewis’s solution, I managed to put the correct percentages on top of the chart, so the classes sum up to one.

for index, category in enumerate(categorical):
    plt.subplot(plot_count, 1, index + 1)

    order = sorted(data[category].unique())
    ax = sns.countplot(category, data=data, hue="churn", order=order)
    ax.set_ylabel('')

    bars = ax.patches
    half = int(len(bars)/2)
    left_bars = bars[:half]
    right_bars = bars[half:]

    for left, right in zip(left_bars, right_bars):
        height_l = left.get_height()
        height_r = right.get_height()
        total = height_l + height_r

        ax.text(left.get_x() + left.get_width()/2., height_l + 40, '{0:.0%}'.format(height_l/total), ha="center")
        ax.text(right.get_x() + right.get_width()/2., height_r + 40, '{0:.0%}'.format(height_r/total), ha="center")

However, the solution assumes there are 2 options (man, woman) as opposed to 3 (man, woman, child).

Since Axes.patches are ordered in a weird way (first all the blue bars, then all the green bars, then all red bars), you would have to split them and zip them back together accordingly.

Method 4

Answer is inspire from jrjc and cphlewis answer as above but more simple and understandable

sns.set(style="whitegrid")
plt.figure(figsize=(8,5))
total = float(len(train_df))
ax = sns.countplot(x="event", hue="event", data=train_df)
plt.title('Data provided for each event', fontsize=20)
for p in ax.patches:
    percentage = '{:.1f}%'.format(100 * p.get_height()/total)
    x = p.get_x() + p.get_width()
    y = p.get_height()
    ax.annotate(percentage, (x, y),ha='center')
plt.show()

Method 5

• The easiest option beginning with matplotlib 3.4.2 is to use matplotlib.pyplot.bar_label.
• See this answer for more options and information about using .bar_label.
• The list comprehension for labels uses an assignment expression (:=), which requires python >= 3.8. This can be rewritten as a standard for loop.
  • labels = [f'{v.get_height()/data.who.count()*100:0.1f}' for v in c] works without an assignment expression.
  • Annotations for horizontal bars should use v.get_width().
• The annotations in the example are percent of the total. For adding annotations based upon the total of a group, see this answer.
• Also see How to plot percentage with seaborn distplot / histplot / displot

Imports and Sample DataFrame

import matplotlib.pyplot as plt
import seaborn as sns

# load the data
data = sns.load_dataset('titanic')[['survived', 'class', 'who']]

   survived  class    who
0         0  Third    man
1         1  First  woman
2         1  Third  woman

Axes Level Plot

• Works with seaborn.countplot or seaborn.barplot

# plot
ax = sns.countplot(x="class", hue="who", data=data)
ax.set(ylabel='Bar Count', title='Bar Count and Percent of Total')

# add annotations
for c in ax.containers:
    
    # custom label calculates percent and add an empty string so 0 value bars don't have a number
    labels = [f'{h/data.who.count()*100:0.1f}%' if (h := v.get_height()) > 0 else '' for v in c]
    
    ax.bar_label(c, labels=labels, label_type='edge')

plt.show()

Figure Level Plot

fg = sns.catplot(data=data, kind='count', x='class', hue='who', col='survived')
fg.fig.subplots_adjust(top=0.9)
fg.fig.suptitle('Bar Count and Percent of Total')

for ax in fg.axes.ravel():
    
    # add annotations
    for c in ax.containers:

        # custom label calculates percent and add an empty string so 0 value bars don't have a number
        labels = [f'{h/data.who.count()*100:0.1f}%' if (h := v.get_height()) > 0 else '' for v in c]

        ax.bar_label(c, labels=labels, label_type='edge')

plt.show()

Method 6

If there are more than 2 hue categories, I couldn’t get these approaches to work.

I used the approach of @Lord Zsolt , augmented for any number of hue categories.

def barPerc(df,xVar,ax):
    '''
    barPerc(): Add percentage for hues to bar plots
    args:
        df: pandas dataframe
        xVar: (string) X variable 
        ax: Axes object (for Seaborn Countplot/Bar plot or
                         pandas bar plot)
    '''
    # 1. how many X categories
    ##   check for NaN and remove
    numX=len([x for x in df[xVar].unique() if x==x])

    # 2. The bars are created in hue order, organize them
    bars = ax.patches
    ## 2a. For each X variable
    for ind in range(numX):
        ## 2b. Get every hue bar
        ##     ex. 8 X categories, 4 hues =>
        ##    [0, 8, 16, 24] are hue bars for 1st X category
        hueBars=bars[ind:][::numX]
        ## 2c. Get the total height (for percentages)
        total = sum([x.get_height() for x in hueBars])

        # 3. Print the percentage on the bars
        for bar in hueBars:
            ax.text(bar.get_x() + bar.get_width()/2.,
                    bar.get_height(),
                    f'{bar.get_height()/total:.0%}',
                    ha="center",va="bottom")

As you can see, this approach does what the original poster requested:

I want total First men/total First, total First women/total First, and total First children/total First on top of their respective bars.

That is, the values added are the Percentage of each Hue (for each X category) – so that for each X category the percentages add to 100%

(This also works with Seaborn’s .barplot())

All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0