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 snsDataset
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)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
1to4 - and many more...
You can find them by running the following code:
from matplotlib import markers
print(markers.MarkerStyle.markers.keys())
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. The following arguments must be provided:
color: color of the markersalpha: opacity of the markerss: size of the markers
fig, ax = plt.subplots(figsize=(8, 6))
sns.regplot(
x=df["sepal_length"],
y=df["sepal_width"],
fit_reg=False,
scatter_kws={
"color":"darkred",
"alpha":0.3,
"s":200
},
ax=ax
)
plt.show()
Going further
This post explains how to customize the appearance of the markers in a scatter plot with seaborn.
You might be interested in how to create a bubble plot, a kind of scatter plot where the size of the marker is proportional to a third variable or how to colors dots according to a variable.
