Lets visualize our data with Joint Plot which is present in Seaborn library. By default, Joint Plot uses Scatter Plot and Histogram. Joint Plot can also display data using Kernel Density Estimate (KDE) and Hexagons. We can also draw a Regression Line in Scatter Plot. By using

We can pass various parameters to

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

%matplotlib inline

from scipy.stats import spearmanr

tips=sns.load_dataset('tips')

tips.head()

sns.jointplot(x='total_bill', y='tip', data=tips)

sns.jointplot(x='total_bill', y='tip', data=tips,

sns.jointplot(x='total_bill', y='tip', data=tips,

sns.jointplot(x='total_bill', y='tip', data=tips,

sns.jointplot(x='total_bill', y='tip', data=tips,

sns.jointplot(x='total_bill', y='tip', data=tips,

You can download my Jupyter notebook from here. I recommend to also try above code with Iris dataset.

**spearmanr**function, we can print the correlation between two variables.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.

For more details on spearmanr parameter, please visit documentation.

Lets explore Joint Plot using Tips dataset.

**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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