In this post you will learn How to create a Scatter Plot in Seaborn.
Scatter Plot in Seaborn –
Let’s read a dataset to work with.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
url = "https://raw.githubusercontent.com/bprasad26/lwd/master/data/batting.csv"
df = pd.read_csv(url)
df.head()
sns.relplot() –
There are two ways to create a scatter plot in seaborn. One using relplot() and another using scatterplot() . The relplot() is a figure level function for visualizing statistical relationships using two common approaches: scatter plots and line plots.
By Default relplot() plots a scatter plot.
scatterplot() (with kind=’scatter’, the default)
lineplot() (with kind=’line’)
Later we will see how to use scatterplot to create a scatter plot. Right now we will focus on the relplot().
To create a scatter plot, you have to define the x, y and the data in the relplot. Let’s plot strike rate (SR) on the x-axis and Runs on the y-axis.
sns.relplot(x="SR", y="Runs", data=df);
Right now, in this plot we have added two dimension. We can also add a third dimension to this plot using the hue parameter. Let’s add the Nationality of a player to the hue parameter.
sns.relplot(x="SR", y="Runs", hue="Nationality", data=df);
We can also use the style parameter to emphasize the difference between the classes.
sns.relplot(x="SR", y="Runs", hue="Nationality", style="Nationality", data=df);
In the example above the variable to the hue was categorical so seaborn uses the qualitative palette. If the hue variable is numeric then sequential palette is used.
sns.relplot(x="SR", y="Runs", hue="50", data=df);
You can also change the size of each points using the size parameter.
sns.relplot(x="SR", y="Runs", size="50", data=df);
We can also increase the size of these points using the sizes parameter.
sns.relplot(x="SR", y="Runs", size="50", sizes=(20, 200), data=df);
There is also a col parameter that create a faceted figure with multiple subplots arranged across the columns of the grid. Let’s create a separate plot for each teams.
sns.relplot(x="SR", y="Runs", col="Team",col_wrap=3, data=df);
There is also a row parameter for the rows. Let’s use a different dataset to show it.
tips = sns.load_dataset('tips')
sns.relplot(data=tips, x="total_bill", y="tip", hue="day", col="time", row="sex");
scatterplot() –
As I said before you can also use the scatterplot() function to create a scatter plot in seaborn. It is very similar to the relplot(). Let’s use it now.
sns.scatterplot(x="SR", y="Runs", data=df);
You can also use the hue parameter.
sns.scatterplot(x="SR", y="Runs", hue="Nationality", data=df)
You can also use the size and sizes parameters.
sns.scatterplot(x="SR", y="Runs", hue="Nationality",size="50", sizes=(20,200), data=df);