# Seaborn - Visualizing Pairwise Relationship

Datasets under real-time study contain many variables. In such cases, the relation between each and every variable should be analyzed. Plotting Bivariate Distribution for (n,2) combinations will be a very complex and time taking process.

To plot multiple pairwise bivariate distributions in a dataset, you can use the pairplot() function. This shows the relationship for (n,2) combination of variable in a DataFrame as a matrix of plots and the diagonal plots are the univariate plots.

## Axes

In this section, we will learn what are Axes, their usage, parameters, and so on.

### Usage

```seaborn.pairplot(data,…)
```

### Parameters

Following table lists down the parameters for Axes −

Sr.No. Parameter & Description
1

data

Dataframe

2

hue

Variable in data to map plot aspects to different colors.

3

palette

Set of colors for mapping the hue variable

4

kind

Kind of plot for the non-identity relationships. {‘scatter’, ‘reg’}

5

diag_kind

Kind of plot for the diagonal subplots. {‘hist’, ‘kde’}

Except data, all other parameters are optional. There are few other parameters which pairplot can accept. The above mentioned are often used params.

### Example

```import pandas as pd
import seaborn as sb
from matplotlib import pyplot as plt
sb.set_style("ticks")
sb.pairplot(df,hue = 'species',diag_kind = "kde",kind = "scatter",palette = "husl")
plt.show()
```

### Output

We can observe the variations in each plot. The plots are in matrix format where the row name represents x axis and column name represents the y axis.

The diagonal plots are kernel density plots where the other plots are scatter plots as mentioned.

## Useful Video Courses

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