seaborn visualize point count in point plot

Question

How can I add a point count in the point plot in seaborn?

df = pd.DataFrame({'group':[0,1,1],'percentage':[0.5, .3,.7],'value_group':[1,2,3], 'count':[1, 10, 20]}) df['value_group'] = pd.qcut(df.value_group, 2) group percentage value_group count 0 0 0.5 1 1 1 1 0.3 2 10 2 1 0.7 3 20

With

sns.pointplot(x="value_group", y="percentage", hue="group", data=df)

I get: But instead I would want:

How can this be achieved in seaborn?

edit

They are similar, but not the same. My value_group is obtained using pd.qcut and the code referenced i.e. cannot handle these.

python Seaborn - annotations with pointplot

import matplotlib.pyplot as plt import seaborn as sns tips = sns.load_dataset("tips") ax = sns.pointplot(x="time", y="total_bill", hue="smoker", data=tips) for c in ax.collections: for of in c.get_offsets(): ax.annotate("Label", of) plt.show()

looks interesting, but so far I do not yet know how to match the right labels/indices with my counts.

Possible duplicate of How to add counts of points as a label in a sparse scatter plot — Vishnu Dasu
– Vishnu Dasu, Commented Jun 10, 2019 at 6:58

Sheldore · Accepted Answer · 2019-06-10 15:11:45Z

The problem was bit nontrivial that I was thinking. Since you have used hue="group", there are two groups in the plot and therefore, ax.collections has a length of 2. So to have the annotations in the correct order, I used a tacking index j.

You can zip the offsets and the DataFrame values you want to show, and annotate them using a for loop as

import matplotlib.pyplot as plt import seaborn as sns sns.set() df = pd.DataFrame({'group':[0,1,1],'percentage':[0.5, .3,.7],'value_group':[1,2,3], 'count':[1, 10, 20]}) ax = sns.pointplot(x="value_group", y="percentage", hue="group", data=df) j = 0 # <--- Index to keep rack of values values = df['count'].values # <--- store the values in a variable for easy access for c in ax.collections: for i, of in zip(range(len(c.get_offsets())), c.get_offsets()): ax.annotate(values[j], of, color='red', fontsize=24) j += 1 ax.legend(loc=(0.8, 0.1)) plt.show()

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