The joint plot is the concise way of understanding the relationship between two variables as well as the individual distribution of each variable.

The Joint plot is consists of 3 separate plots. In which one is the middle figure which is used to see the relationship between x and y. This area gives us information about the joint distribution while the other two areas give us marginal distribution for the x and y-axis.

## Syntax

`seaborn.jointplot(x, y, data=None, kind=â€™scatterâ€™, stat_func=None, color=None, height=6, ratio=5, space=0.2, dropna=True, xlim=None, ylim=None, joint_kws=None, marginal_kws=None, annot_kws=None, **kwargs)`

### Parameters

**x,y:**Variables that specify x-axes and y-axes.**data:**The input datasets.**kind:**The kind of plot to draw.**color:**The parameter used to take Color for the plot elements.**space:**The space between a joint distribution and marginal distribution.**xlim, ylim:**The limit of x and y-axis.

## Example

```
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
data = sns.load_dataset("geyser")
data.head(5)
```

**Output**

```
sns.jointplot(x='waiting', y='duration', data=data)
plt.show()
```

**Output:**

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