How to visualize categorical frequency difference-CodePudding

Data: Diabetes dataset found here:

See figure above. This is where I need help. You cant really make heads or tails of this. There is a class imbalance in this age group - infact 312 samples are not-diabetic while only 84 are. How can I adjust the plot to better depict this class imbalance?

CodePudding user response：

The difference in 'Outcome' for each 'Age' can most easily be seen with a bar plot showing the count, which can be done directly with a seaborn.countplot, or calculating the counts in pandas, and plotting with pandas.DataFrmame.plot.
Tested in python 3.8.12, pandas 1.3.3, matplotlib 3.4.3, seaborn 0.11.2

Data and Imports

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

# data
df = pd.read_csv('https://raw.githubusercontent.com/LahiruTjay/Machine-Learning-With-Python/master/datasets/diabetes.csv')

# filter for less than 30
u30 = df[df.Age.lt(30)]

Use

Data

Incase the data at the GitHub link is no longer available

Age,Outcome
21,0
26,1
29,0
27,0
29,1
22,0
28,1
22,0
28,0
27,1
26,0
25,1
29,0
22,0
24,0
22,0
26,0
21,0
22,0
21,0
24,0
25,0
27,0
28,1
26,0
23,0
22,0
22,0
27,0
26,1
24,0
22,0
22,0
22,0
27,0
26,0
24,0
21,0
21,0
24,0
22,0
23,0
22,0
21,0
24,0
27,0
21,0
27,0
25,0
24,1
24,1
23,0
25,0
25,0
22,0
21,0
25,1
24,0
23,0
23,1
26,1
23,0
26,0
21,0
22,0
29,0
28,0
22,0
23,0
21,0
22,0
24,0
23,0
21,0
23,0
22,0
27,0
21,0
22,0
29,0
29,0
29,1
25,0
23,0
26,1
23,0
21,0
27,0
25,1
21,0
29,1
21,0
23,1
26,1
29,1
21,0
28,0
27,0
27,0
21,0
25,0
24,0
24,1
25,1
21,1
26,0
22,0
26,0
24,1
24,0
22,1
22,0
29,0
23,0
26,1
23,1
27,0
21,0
22,0
22,1
29,0
23,0
23,0
27,0
24,0
25,0
21,1
25,0
24,0
27,1
24,0
25,1
24,0
21,0
28,1
21,0
21,0
25,0
29,1
23,0
22,0
28,1
29,1
26,0
21,0
25,1
24,1
28,0
29,1
24,0
25,1
28,1
29,0
21,0
25,1
22,0
27,1
25,0
26,0
29,1
28,0
25,1
21,0
24,0
23,1
25,0
22,0
26,0
22,0
22,0
22,0
23,0
26,0
29,0
24,0
21,0
28,1
29,1
29,1
29,1
21,0
22,0
25,1
21,0
21,0
25,0
28,0
22,0
22,0
24,0
22,0
21,0
25,0
25,0
24,0
28,0
27,1
21,0
25,0
22,1
25,0
25,1
26,0
25,0
28,1
28,0
25,0
22,0
21,0
21,1
22,1
22,0
27,0
28,1
26,0
21,0
21,0
21,0
25,0
26,0
23,0
22,0
29,0
29,1
28,0
21,0
22,0
24,0
25,1
28,0
26,0
22,1
26,0
23,0
23,1
25,0
24,0
24,0
26,0
21,0
22,0
25,0
27,0
28,0
22,0
22,0
24,0
29,1
29,0
28,0
23,0
24,1
21,0
28,0
24,0
22,0
25,0
21,0
28,0
21,0
21,0
21,0
22,0
24,0
28,1
25,0
26,0
26,0
24,0
21,0
21,0
24,0
22,0
22,0
24,0
29,0
24,0
23,1
23,0
27,1
25,0
29,0
28,0
21,0
25,0
23,0
28,0
28,1
24,0
27,0
22,0
21,0
21,0
22,0
22,0
23,0
25,0
21,1
21,1
27,0
22,0
29,0
25,0
24,0
25,0
22,1
21,0
26,0
24,0
28,0
21,0
22,1
25,0
27,0
23,0
24,0
26,0
27,0
23,0
24,1
28,0
28,0
21,0
21,0
29,0
21,0
21,0
21,0
24,0
23,0
22,0
23,0
28,0
27,0
24,0
27,0
22,1
23,0
23,0
27,0
28,0
27,0
22,0
25,1
22,0
27,1
22,1
24,0
21,0
22,0
25,0
25,1
23,0
22,0
26,1
22,0
27,1
25,0
22,0
29,0
23,0
23,0
25,0
22,0
28,0
26,0
26,0
27,0
28,0
22,0
23,1
24,0
21,0
24,0
21,0
25,0
22,0
22,0
22,0
22,1
24,1
22,0
28,0
21,0
21,0
26,0
22,0
27,1
22,1
28,0
25,0
26,1
26,0
22,0
27,0
23,0