How to Create Scatter Plot with Seaborn in Python

To create a scatter plot in Python using seaborn, use the sns.scatterplot() function by passing your data and specifying the x and y variables. This function plots points on a graph to show relationships between two numeric variables.

Syntax

The basic syntax for creating a scatter plot with Seaborn is:

• sns.scatterplot(x='x_column', y='y_column', data=dataframe): Plots points using columns from a DataFrame.
• x: The name of the column for the horizontal axis.
• y: The name of the column for the vertical axis.
• data: The DataFrame containing the data.

python

sns.scatterplot(x='x_column', y='y_column', data=dataframe)

Example

This example shows how to create a scatter plot using Seaborn with sample data. It plots the relationship between two numeric columns.

python

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd

# Sample data
data = pd.DataFrame({
    'height': [150, 160, 170, 180, 190],
    'weight': [50, 60, 65, 80, 90]
})

# Create scatter plot
sns.scatterplot(x='height', y='weight', data=data)

# Show the plot
plt.show()

Output

A scatter plot window showing points with height on the x-axis and weight on the y-axis.

Common Pitfalls

Common mistakes when creating scatter plots with Seaborn include:

• Not importing seaborn or matplotlib.pyplot, which are needed to plot and show the graph.
• Passing incorrect column names or data types that are not numeric.
• Forgetting to call plt.show() to display the plot in some environments.

Example of a wrong call and the correct way:

python

# Wrong: missing data argument or wrong column names
# sns.scatterplot(x='wrong_x', y='wrong_y')

# Correct:
sns.scatterplot(x='height', y='weight', data=data)

Quick Reference

Here is a quick summary of key parameters for sns.scatterplot():

Parameter	Description
x	Name of the column for x-axis values
y	Name of the column for y-axis values
data	DataFrame containing the data
hue	Column name for color grouping (optional)
style	Column name for marker style grouping (optional)
size	Column name for marker size grouping (optional)

Key Takeaways

Use sns.scatterplot() with x, y, and data parameters to create scatter plots.

Ensure your data columns are numeric and correctly named.

Always import seaborn and matplotlib.pyplot before plotting.

Call plt.show() to display the plot when needed.

Use optional parameters like hue and style to add more information visually.