I have a following structure of the dataframe:

data = [
    [12, [0.1, 0.2, 0.3, 0.4, 0.5]],
    [14, [0.8, 0.7, 0.6, 0.4, 0.2]]
    # .... and so on
]
df = pd.DataFrame(data, columns=['index', 'distribution'])

How to build a boxplot chart(s) where:

1. each box-and-whisker will show the distribution (using the box/whiskers/outliers) of the distribution column above for each index
2. each box-and-whisker will aggregate the distributions with the same index (e.g. if the index value is the same, the distribution will be merged)

CodePudding user response：

You can use pandas' .explode() to convert the pesky lists into a long form dataframe. Seaborn is by far the easiest sway to create a matplotlib-style boxplot from a dataframe. Seaborn will automatically group values belonging to the same 'index'.

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

data = [
    [12, [0.1, 0.2, 0.3, 0.4, 0.5]],
    [14, [0.8, 0.7, 0.6, 0.4, 0.2]]
    # .... and so on
]
df = pd.DataFrame(data, columns=['index', 'distribution'])
sns.boxplot(data=df.explode('distribution'), x='index', y='distribution', palette='magma')