Creating a scatter plot matrix with Plotly Express in Python

Plotly Express is a Python library that allows us to create line plots quickly and easily, with customizable parameters and an interactive interface.

A scatter plot matrix, also known as a pairs plot or a scatterplot matrix, is a grid of scatter plots that allows you to visualize the relationships between multiple variables simultaneously. It is a useful tool for exploring the correlations, patterns, and distributions among different variables in a dataset.

Features of the scatter plot matrix

Some of the key features of a scatter plot matrix include:

Grid layout: The scatter plot matrix is presented as a grid of plots, with each cell representing the relationship between two variables. The layout is organized in a matrix form, allowing for easy comparison and identification of patterns.

Scatter plots: Each cell in the matrix contains a scatter plot representing the relationship between a pair of variables. The scatter plots help visualize the data's distribution, correlation, and potential outliers.

Diagonal plots: The diagonal cells of the matrix typically display histograms or density plots of the individual variables. These plots provide insights into the distribution of each variable separately, helping to identify skewness, central tendency, or multimodality.

Interactive features: Plotly Express creates interactive visualizations by default. The scatter plot matrix allows you to hover over data points to view their specific values and labels. You can also use zooming and panning functionalities to explore the plots at different scales.

Customization: Plotly Express provides a range of customization options to tailor the scatter plot matrix to your needs. You can modify axis labels, titles, color schemes, marker styles, and sizes. Additional features like trend lines, error bars, or marginal distribution plots can be added to individual scatter plots within the matrix.

Faceting: Plotly Express supports faceting, which means you can split the scatter plot matrix based on a categorical variable. This feature creates multiple scatter plot matrices, each corresponding to a different category, allowing for further comparisons and insights.

Integration with Plotly ecosystem: Plotly Express is part of the Plotly ecosystem, which includes other libraries such as Plotly.py and Dash. This integration enables you to seamlessly incorporate the scatter plot matrix into interactive dashboards or web applications.

Syntax

The scatter_matrix function syntax typically follows this structure:

Parameters

The followings are the parameters of scatter_matrix function:

• data_frame: The DataFrame or data source containing your data.

• dimensions: A list of column names or indices representing the variables to include in the scatter plot matrix. For example, dimensions=['x', 'y', 'z'].

• color: Specifies the column name or index to assign different colors to points based on their values in that column. For example, color='category'.

• symbol: Specifies the column name or index to assign different marker symbols to points based on their values in that column. For example, symbol='group'.

• title: The title of the scatter plot matrix. For example, title='Scatter Plot Matrix'.

• labels: Maps column names or indices to custom axis labels. For example, labels={'x': 'X-axis', 'y': 'Y-axis'}.

• range_color: Specifies the color range for the color mapping. It can be set to a tuple or list with two values representing the minimum and maximum values. For example, range_color=[0, 10].

• color_continuous_scale: Specifies the color scale to be used for continuous color mapping. You can choose from a variety of built-in color scales or define your custom color scale. For example, color_continuous_scale='Viridis'.

• symbol_sequence: Specifies the sequence of marker symbols to be used when the symbol parameter is not provided. It can be a list of symbol names or a string representing a symbol sequence. For example, symbol_sequence=['circle', 'square', 'cross'].

• hover_name: Specifies the column name or index to be displayed as the hover label for each point in the scatter plot matrix. For example, hover_name='label'.

• hover_data: A list of additional columns or indices from the data_frame to include in the hover label. For example, hover_data=['column1', 'column2'].

• marginal_x and marginal_y: Boolean values indicating whether to include marginal distribution plots (histograms or density plots) along the x-axis and y-axis, respectively. For example, marginal_x=True.

• height and width: The height and width of the scatter plot matrix in pixels. For example, height=600 and width=800.

• template: Specifies the template to be used for the scatter plot matrix. Plotly Express provides various built-in templates, such as 'plotly', 'seaborn', 'ggplot2', etc. For example, template='plotly_dark'.

Return type

The px.scatter_matrix() function returns a Plotly figure object that can be displayed with fig.show(). The figure object contains all the information required to produce the 3d line plot, including the data, layout, and style.

Implementation

In the following playground, we create a scatter matrix using a sample dataset called iris provided by Plotly Express. Used attributes (sepal_width, sepal_length, petal_width, petal_length , and species) defined as follows:

• sepal_width: This feature represents the width of the sepal, which is the outermost whorl of a flower. It is measured in centimeters.

• sepal_length: This feature represents the length of the sepal. It is also measured in centimeters.

• petal_width: This feature represents the width of the petal, which is the inner whorl of a flower. It is measured in centimeters.

• petal_length: This feature represents the length of the petal. It is also measured in centimeters.

• species: This feature represents the species of iris flowers. There are three possible species in the dataset: setosa, versicolor, and virginica. The species column assigns different colors or markers to the data points in the scatter plot matrix, allowing for visual differentiation of the iris flower species.

Explanation

The code above is explained in detail below:

• Lines 2–3: Import the required libraries for the code: plotly.express as px for creating the violin plot, and pandas as pd for handling data in a DataFrame.

• Line 6: Load the iris dataset provided by Plotly Express into a pandas DataFrame called df. The px.data.iris() function retrieves the dataset.

• Line 9: Print the first five rows of the loaded dataset. The head() function retrieves the top rows of the DataFrame and print() displays the result in the console. It helps to quickly inspect the data and verify its structure.

• Line 12: The scatter_matrix function uses the DataFrame df as the data source and specifies the dimensions to include in the scatter plot matrix. In this case, the dimensions are sepal_width, sepal_length, petal_width, and petal_length. The species column assigns different colors to the data points based on the iris flower species.

• Line 15: Display the plot using the fig.show() method, which shows the interactive plot.

Conclusion

The scatter plot matrix feature of Plotly Express provides a powerful and intuitive way to visualize multivariate relationships in a dataset. Creating a grid of scatter plots enables the simultaneous exploration of the relationships between multiple variables. The scatter plot matrix allows for easy identification of patterns, trends, and correlations, aiding in data analysis and decision-making.

Interactive features and customization options, such as color coding and symbol mapping, further enhance the ability to differentiate and understand complex datasets. Whether for exploratory data analysis or communication of insights, the scatter plot matrix in Plotly Express is a valuable tool for understanding the relationships between variables comprehensively.