Customize Linear Regression Fit Line Features

This post shows the customization you can apply to a linear regression fit line such as changing the color, transparency, and line width in a scatterplot built with seaborn.

Libraries

First, we need to load a few libraries:

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

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

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)
df.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Color and width

You can custom the appearance of the regression fit in a scatterplot built with seaborn thanks to the line_kws argument.

This argument accepts a dictionary of argument names and their values. These values will be exclusively used to style the line. For example, line_kws={"color" : "red", "linewidth" : 1.5} will render the line red and with a width of 1.5.

fig, ax = plt.subplots(figsize=(8, 6))
sns.regplot(
    x=df["sepal_length"],
    y=df["sepal_width"],
    line_kws={"color": "red", "linewidth": 1.5},
    ax=ax
)
plt.show()

Opacity

You can also custom the opacity of the line with the alpha value:

fig, ax = plt.subplots(figsize=(8, 6))
sns.regplot(
    x=df["sepal_length"],
    y=df["sepal_width"],
    line_kws={
        "color": "r",
        "alpha": 0.4
    },
    ax=ax
)
plt.show()

Line width and style

You can also custom the line width and style with the linewidth and linestyle values:

fig, ax = plt.subplots(figsize=(8, 6))
sns.regplot(
    x=df["sepal_length"],
    y=df["sepal_width"],
    line_kws={
        "color": "darkred",
        "alpha": 0.4,
        "lw": 5,
        "ls": "--"
    },
    ax=ax
)
plt.show()

Going further

This post explains how to customize the appearance of a regression fit in a scatter plot with seaborn.

You might be interested in more advanced examples on:

Scatterplot

Heatmap

Correlogram

Bubble

Connected Scatter

2D Density

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