We can pass various parameters to jointplot like kind (reg, hex, kde), stat_func(spearmanr), color, size, ratio etc.
Spearmanr Parameter
- Spearmanr parameter displays the correlation between two variables.
- Value varies between -1 and +1 with 0 implying no correlation.
- Correlations of -1 or +1 imply an exact monotonic relationship.
- Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
- Spearmanr correlation does not assume that both variables are normally distributed.
Step 1: Import required libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from scipy.stats import spearmanr
Step 2: Load Tips dataset
tips=sns.load_dataset(‘tips’)
tips.head()
Step 3: Explore data using Joint Plot
sns.jointplot(x=’total_bill’, y=’tip’, data=tips)
Add regression line to scatter plot and kernel density estimate to histogram
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, kind=’reg’)
Display kernel density estimate instead of scatter plot and histogram
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, kind=’kde’)
Display hexagons instead of points in scatter plot
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, kind=’hex’)
Display correlation using spearmanr function
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, stat_func=spearmanr)
Cosmetic parameters like color, size and ratio
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, color=’green’)
sns.jointplot(x=’total_bill’, y=’tip’, data=tips, ratio=4, size=6)
You can download my Jupyter notebook from here. I recommend to also try above code with Iris dataset.
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