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
sns.set_style("darkgrid")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 |
Simple splitting with a categorical variable
The lmplot() function requires the following 4 arguments to create a faceted scatterplot:
x: column of the variable for the x axisy: column of the variable for the y axishue: column of the variable for the facet.data: data frame with all columns
sns.lmplot(
x="sepal_length",
y="sepal_width",
data=df,
hue="species",
)
plt.show()
Multiple subplots
You can also use the col argument and assign it the same value as the hue to create subplots instead of a single one:
sns.lmplot(
x="sepal_length",
y="sepal_width",
data=df,
hue="species",
col="species"
)
plt.show()
Split with a 4th column
You can also custom the line width and style with the linewidth and linestyle values:
import numpy as np
df["other_column"] = np.random.choice(['A', 'B', 'C', 'D'], size=len(df))
sns.lmplot(
x="sepal_length",
y="sepal_width",
data=df,
hue="species",
col="other_column",
col_wrap=2 # maximum of 2 columns
)
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:
