seaborn 点图可视化

Question

I am plotting a point plot to show the relationship between "workclass", "sex", "occupation" and "Income exceed 50K or not". However, the result is a mess. The legends are stick together, Female and Male are both shown in blue colors in the legend etc.

#Co-relate categorical features
grid = sns.FacetGrid(train, row='occupation', size=6, aspect=1.6)
grid.map(sns.pointplot, 'workclass', 'exceeds50K', 'sex', palette='deep', markers = ["o", "x"] )
grid.add_legend()

Please advise how to fit the size of the plot. Thanks!

Answer 1

It sounds like 'exceeds50k' is a categorical variable. Your y variable needs to be continuous for a point plot. So assuming this is your dataset:

import pandas as pd
import seaborn as sns
df =pd.read_csv("https://raw.githubusercontent.com/katreparitosh/Income-Predictor-Model/master/Database/adult.csv")

We simplify some categories to plot for example sake:

df['native.country'] = [i if i == 'United-States' else 'others' for i in df['native.country']  ]
df['race'] = [i if i == 'White' else 'others' for i in df['race']  ]

df.head()

    age workclass   fnlwgt  education   education.num   marital.status  occupation  relationship    race    sex capital.gain    capital.loss    hours.per.week  native.country  income
0   90  ?   77053   HS-grad 9   Widowed ?   Not-in-family   White   Female  0   4356    40  United-States   <=50K
1   82  Private 132870  HS-grad 9   Widowed Exec-managerial Not-in-family   White   Female  0   4356    18  United

If the y variable is categorical, you might want to use a barplot:

sns.catplot(hue='income',x='sex', palette='deep',data=df,
            col='native.country',
            row='race',kind='count',height=3,aspect=1.6)

If it is continuous, for example age, you can see it works:

grid = sns.FacetGrid(df, row='race', height=3, aspect=1.6)
grid.map(sns.pointplot, 'native.country', 'age', 'sex', palette='deep', markers = ["o", "x"] )
grid.add_legend()