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()