04-Comparison-Plots · Python & Machine Learning

Comparison Plots with pairplot() and jointplot()

Imports

The Data

python

df = pd.read_csv("StudentsPerformance.csv")

python

df.head()

Out · table

gender
race/ethnicity
parental level of education
lunch
test preparation course

48 more lines

math score
reading score
writing score
0
female
group B
bachelor's degree
standard
none
72
72
74
1
female
group C
some college
standard
completed
69
90
88
2
female
group B
master's degree
standard
none
90
95
93
3
male
group A
associate's degree
free/reduced
none
47
57
44
4
male
group C
some college
standard
none
76
78
75

jointplot

python

sns.jointplot(x='math score',y='reading score',data=df)

Out · result

<seaborn.axisgrid.JointGrid at 0x23f9ed8dd08>

python

sns.jointplot(x='math score',y='reading score',data=df,kind='hex')

Out · result

<seaborn.axisgrid.JointGrid at 0x23fa1027308>

python

sns.jointplot(x='math score',y='reading score',data=df,kind='kde')

Out · result

<seaborn.axisgrid.JointGrid at 0x23fa11d4108>

pairplot

python

sns.pairplot(df)

Out · result

<seaborn.axisgrid.PairGrid at 0x23fa12eb608>

python

sns.pairplot(df,hue='gender',palette='viridis')

Out · result

<seaborn.axisgrid.PairGrid at 0x23fa18c8908>

python

sns.pairplot(df,hue='gender',palette='viridis',corner=True)

Out · result

<seaborn.axisgrid.PairGrid at 0x23fa1e07948>

python

sns.pairplot(df,hue='gender',palette='viridis',diag_kind='hist')

Out · result

<seaborn.axisgrid.PairGrid at 0x23fa190ef88>