Control Marker Features

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

plt.rcParams["figure.dpi"] = 300

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

Control Marker Shape

In order to change the shape of the marker, you need to provide:

marker: the shape of the marker

There are actually lots of different shapes available. Here are a few examples:

'o': circle
's': square
'+': plus
'x': cross
'D': diamond
'h': hexagon
'p': pentagon
'v': triangle_down
'^': triangle_up
'<': triangle_left
'>': triangle_right
all numbers from 1 to 4
and many more...

You can find them by running the following code:

from matplotlib import markers
print(markers.MarkerStyle.markers.keys())

Control Marker Features: Control Marker Shape

fig, ax = plt.subplots(figsize=(8, 6))
sns.regplot(
    x=df["sepal_length"],
    y=df["sepal_width"],
    marker="+",
    fit_reg=False,
    ax=ax
)

plt.show()

Changing Color, Transparency and Size of Markers

You can also change the other features of markers in a plot thanks to scatter_kws.

This argument accepts a dictionary of argument names and their values. These values will be exclusively used to style the dots. For example, scatter_kws={"color" : "red", "alpha" : 1.5} will render the line red and with an opacity of 0.3.

color : color of the markers
alpha : opacity of the markers
s : size of the markers

Control Marker Features: Changing Color, Transparency and Size of Markers

fig, ax = plt.subplots(figsize=(8, 6))
sns.scatterplot(
    x=df["sepal_length"],
    y=df["sepal_width"],
    color="skyblue",
    alpha=1,
    s=100,
    edgecolor="black",
    lw=3,
    ax=ax
)
plt.show()

Going further

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