Beeswarm Chart

Source: Better Data Visualization Using Beeswarm Chart

• One-dimensional chart (see also Box plot and a Violin plot)
• Up to 2 variables:
  • X axis (quantitative variable)
  • Dot color (qualitative variable)
• It emphasizes individual points in a distribution instead of binning them like a histogram.
• Limitations:
  • data size capacity: too many same values
    • solution: change scale (linear and logarithmic)

import pandas as pd
import seaborn as sns

import kaggle

Dataset

Dataset comes from Kaggle: Titanic - Suited for binary logistic regression

kaggle.api.authenticate()
kaggle.api.dataset_download_files('heptapod/titanic', path='.', unzip=True)

df = pd.read_csv('train_and_test2.csv', sep=',')

df.rename(columns={'2urvived': 'survived'}, inplace=True)

df['Sex'] = df['Sex'].replace({0: 'Male', 1: 'Female'}).astype('category')
df['Pclass'] = df['Pclass'].replace({1: '1st', 2: '2nd', 3: '3th'}).astype('category')
# df['Age'] = pd.cut(df['Age'].round(), 5).astype(str)

ax = sns.catplot(x="Sex", y="Age", hue='survived', col='Pclass', kind="swarm", data=df)

/home/pablo/miniconda3/envs/datamining/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 6.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/home/pablo/miniconda3/envs/datamining/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 20.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)
/home/pablo/miniconda3/envs/datamining/lib/python3.8/site-packages/seaborn/categorical.py:1296: UserWarning: 33.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.
  warnings.warn(msg, UserWarning)