How to get the label values on a bar chat with seaborn on a categorical data

Question

Shown below is the syntax used to get the bar char for a categorical data on seaborn

import seaborn as sn
import matplotlib as mpl
import matplotlib.pyplot as plt


IN: data['coast'].dtypes
OUT: 
CategoricalDtype(categories=[0, 1], ordered=False)


IN: data['coast'].value_counts()  
OUT: 
0    21450
1      163
Name: coast, dtype: int64


IN: sn.factorplot('coast', data=data, kind='count')
OUT:

1. How can I get the value count on the bar chart shown below.

2. How to get the percentage value on the bar chart shown below.

Answer 1

Percentage counts

• Use sns.barplot with a custom percentage estimator
• Label the percentages using the new built-in ax.bar_label and its fmt param

ax = sns.barplot(x='coast', y='coast', estimator=lambda x: len(x) / len(data) * 100, data=data)
ax.bar_label(ax.containers[0], fmt='%.f%%')
ax.set_ylabel('%')

---

Regular counts

Save the axes handle from sns.countplot and use the new ax.bar_label:

ax = sns.countplot(x='coast', data=data)
ax.bar_label(ax.containers[0])

Or for the facet-based sns.catplot (formerly sns.factorplot), extract the axes from the facet grid before using ax.bar_label:

grid = sns.catplot(x='coast', kind='count', data=data)
ax = grid.axes[0, 0]
ax.bar_label(ax.containers[0])

Answer 2

Maybe this will work for you:

# imports
import sys # for retreiving package version matplotlib
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# package versions
print('numpy      :', np.__version__)
print('pandas     :', pd.__version__)
print('matplotlib :', sys.modules['matplotlib'].__version__)
print('seaborn    :', np.__version__)

# set seed for reproducibility
np.random.seed(100)

# generate data
n = 15
data = pd.DataFrame({'coast': np.random.randint(low=0, high=2, size=n, dtype=int)})
data['coast'] = data['coast'].astype('category')

# plot data
ax = sns.countplot(x='coast', data=data)
plt.bar_label(ax.containers[0]) # plot bar labels
plt.show()

Results:

numpy      : 1.21.0
pandas     : 1.3.0
matplotlib : 3.4.2
seaborn    : 1.21.0