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Showing content from https://python-graph-gallery.com/41-control-marker-features/ below:

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Control Marker Features

Libraries

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:

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

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.

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()

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