Python Pandas - scatter Plot

A scatter plot, also known as a scatter chart or scatter diagram, represents data as a collection of points plotted on an X-Y grid. The x axis represents one variable, while the y axis represents another. Additional visual variables like point size, color, or shape can represent a third variable. Scatter plots are helpful for visualizing the relationship or correlation between variables.

For example, imagine you have a dataset that records temperatures, and the corresponding amount of road traffic. A scatter plot visualizes the relationship between Temperature and Traffic range, where each dot represents a specific data point. Here the x-axis is the temperature in degrees Celsius, and the y-axis is the corresponding traffic range.

In this tutorial, we will learn about how to use the Pandas method for creating and customizing scatter plots with different examples.

Scatter Plot in Pandas

Pandas provides theDataFrame.plot.scatter() method to create scatter plots. This method internally use Matplotlib and return either amatplotlib.axes.Axes object or NumPy arraynp.ndarray object.

Syntax

Following is the syntax of the plot.scatter() Method −

DataFrame.plot.scatter(x, y, s=None, c=None, **kwargs)

Where,

• x: Specifies the column name or position for the horizontal axis.

• y: Specifies the column name or position for the vertical axis.

• s: Optional parameter specifies the marker size of each point. Options include, a string, a single scalar, or A sequence of scalars.

• c: It is also an optional parameter specifies the color of each point. Options include, a single color string, a sequence of color strings, or an array of colors.

• **kwargs: Additional arguments to customize the plot.

Example

Here is a basic example of creating a scatter plot using theDataFrame.plot.scatter() method.

import pandas as pdimport matplotlib.pyplot as pltplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {    'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],    'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Plot the scatter plotax = df.plot.scatter(x='Temperature', y='Ice_cream_sales')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Temperature vs. Ice Cream Sales')# Display the plotplt.show()

After executing the above code, we get the following output −

Customizing Scatter Plot

You can customize the appearance of the scatter plot by modifying parameters like size, color, marker style, and more.

Example: Customizing Scatter Plot Color

This example changes the color of the scatter points using thec parameter with a single color string.

import pandas as pdimport matplotlib.pyplot as pltplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Plot the scatter plotdf.plot.scatter(x='Temperature', y='Ice_cream_sales', c='red')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Customizing Scatter Plot Color')# Display the plotplt.show()

Following is the output of the above code −

Example: Changing Scatter Marker

The following example changes the scatter plot marker style using themarker parameter.

import pandas as pdimport matplotlib.pyplot as pltplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Plot the scatter plotdf.plot.scatter(x='Temperature', y='Ice_cream_sales', marker='D', c='darkgreen')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Changing Scatter Plot Marker')# Display the plotplt.show()

After executing the above code, we get the following output −

Example: Customizing Marker Size

You can control the size of the markers using thes parameter.

import pandas as pdimport matplotlib.pyplot as pltplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Plot the scatter plotdf.plot.scatter(x='Temperature', y='Ice_cream_sales', s=100, marker='*', c='darkgreen')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Customizing Marker Size')# Display the plotplt.show()

Following is the output of the above code −

Plotting Multiple Columns on the Same Axes

To plot multiple datasets on the same axes, specify theax parameter while reusing the previous plot's axes. And differentiate each dataset by specify colors and labels.

Example

This example demonstrates plotting multiple columns on the same axes with different colors and labels.

import pandas as pdimport matplotlib.pyplot as pltplt.rcParams["figure.figsize"] = [7, 4]# Sample dataset creationdata = {    'Temperature': [20, 20, 25, 28, 30, 32, 32, 35],    'Traffic': [4, 5, 5, 2, 2, 2, 6, 1],    'ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Plot the scatter plotax = df.plot.scatter(x='Temperature', y='Traffic', color='red', alpha=0.7, label="Traffic")df.plot.scatter(x="Temperature", y="ice_cream_sales", color="DarkGreen", label="Ice Cream Sales", ax=ax)# Add labels, title, and legendplt.xlabel('Temperature (C)')plt.ylabel('Traffic & Ice Cream Sales values')plt.title('Traffic and Ice Cream Sales vs. Temperature')plt.legend(title="Legend")# Display the plotplt.show()

After executing the above code, we get the following output −

Coloring Scatter Points by Column Values

You can use a DataFrame column to dynamically assign colors to the scatter plot points.

Example

This example demonstrates coloring scatter plot markers by DaatFrame column Values.

import pandas as pdimport matplotlib.pyplot as pltimport numpy as npplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Add a dynamic color columndf['Color'] = np.random.rand(len(df))# Plot the scatter plotdf.plot.scatter(x='Temperature', y='Ice_cream_sales', c='Color', cmap='viridis', s=100, marker='*')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Coloring Scatter Points by Column Values')# Display the plotplt.show()

On executing the above code we will get the following output −

Categorical Coloring

If you provide a categorical column to thec parameter, then a discrete colorbar will be generated.

Example

This example applies the categorical column to thec parameter of theplot.scatter() method to get the discrete colorbar.

import pandas as pdimport matplotlib.pyplot as pltimport numpy as npplt.rcParams["figure.figsize"] = [7, 4]# Create a DataFramedata = {'Temperature': [20, 20, 25, 28, 30, 32, 22, 35],'Ice_cream_sales': [15, 10, 18, 20, 22, 18, 22, 25]}df = pd.DataFrame(data)# Add a columndf['Season'] = pd.Categorical(['Summer', 'Winter', 'Summer', 'Spring', 'Spring', 'Summer', 'Winter', 'Spring'])# Plot the scatter plotdf.plot.scatter(x='Temperature', y='Ice_cream_sales', c='Season', cmap='plasma', s=100, marker='*')plt.xlabel('Temperature (C)')plt.ylabel('Ice Cream Sales')plt.title('Scatter Plot Categorical Coloring')# Display the plotplt.show()

Following is the output of the above code −