First, we need to load a few libraries:
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
In order to change the shape of the marker, you need to provide:
marker
: the shape of the markerThere 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_right1
to 4
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 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 markersalpha
: opacity of the markerss
: size of the markersfig, 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()
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