## Libraries

First, we need to load a few libraries:

- seaborn: for creating the scatterplot
- matplotlib: for displaying the plot
- pandas: for data manipulation

```
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
```

## Dataset

Since scatter plot are made for visualizing **relationships between two numerical variables**, we need a dataset that contains at least two numerical columns.

Here, we will use the `iris`

dataset that we **load** directly from the gallery:

```
path = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/iris.csv'
df = pd.read_csv(path)
```

## Scatter plot

A scatterplot can be made using `regplot()`

function of seaborn library. An example dataset from seaborn repository, iris dataset, is used in the example. The plot shows the relationship between sepal lenght and width of plants. In order to show the most basic utilization of this function, the following parameters should be provided:

`x`

: positions of points on the X axis`y`

: positions of points on the Y axis

```
sns.regplot(
x=df["sepal_length"],
y=df["sepal_width"],
fit_reg=False
)
plt.show()
```

## Fit a linear regression model

By just removing the `fit_reg=False`

parameter, the `regplot()`

function will **fit and plot** a linear regression model to the scatterplot.

This is a good way to **visualize the relationship** between two variables.

```
sns.regplot(
x=df["sepal_length"],
y=df["sepal_width"]
)
plt.show()
```

## Going further

This post explains how to create a scatterplot with seaborn.

You might be interested in more advanced examples on how to visualize linear regression or how to color points according to a third variable.