Creating Custom Colormaps in Python Scatter Plots: A Guide

When it comes to creating scatter plots in Python using Plotly Express or graph_objects, one of the most important aspects is choosing the right colormap for your data. The good news is that there are many ways to customize the color scales in your scatter plots, giving you a high degree of control over how your data is visualized.

In this article, we'll explore the different ways to specify custom colormaps in Python scatter plots using Plotly Express and graph_objects. We'll also discuss some best practices for choosing the right colormap for your data and provide examples of how to create custom colormaps using code.

Understanding Color Scales

A color scale is a mapping between the range 0 to 1 and a specific color domain, which is used to interpolate colors in your scatter plot. Unlike discrete color sequences, color scales are designed to be continuous, allowing for smooth transitions between different colors.

Specifying Custom Colormaps

There are several ways to specify custom colormaps in Python scatter plots using Plotly Express and graph_objects. Here are a few examples:

* Color Continuous Scale: You can use the `color_continuous_scale` argument in many Plotly Express functions or the `colorscale` argument in various graph_objects, such as `loraxis` or `lorscale`, to specify a custom colormap.

* Discrete Color Sequences: If you want to use a discrete color sequence instead of a continuous colormap, you can use the `colors` argument in Plotly Express functions or the `color_discrete_map` argument in graph_objects.

Best Practices for Choosing Colormaps

When choosing a colormap for your scatter plot, there are a few best practices to keep in mind:

* Choose colors that are easy to distinguish: Make sure that the colors you choose are easily distinguishable from one another, especially if you're plotting multiple classes or categories.

* Consider the data being plotted: Think about the type of data you're plotting and choose a colormap that is suitable for that data. For example, if you're plotting categorical data, a discrete color sequence may be more appropriate than a continuous colormap.

Creating Custom Colormaps

In some cases, you may want to create a custom colormap from scratch. This can be done using a variety of techniques, including creating a custom color scale or using a pre-existing colormap as a starting point.

Here is an example of how you might create a custom colormap using code:

```

import numpy as np

import matplotlib.pyplot as plt

# Create a custom colormap

cmap = plt.get_cmap('viridis')

# Define the colors for each class

colors = ''red', 'blue', 'green''

# Create a scatter plot with the custom colormap

plt.scatter(x, y, c=y, cmap=cmap)

```

In this example, we're using the `viridis` colormap as a starting point and defining three custom colors for our scatter plot. We then use these colors to create a scatter plot with the custom colormap.

In conclusion, creating custom colormaps in Python scatter plots is a powerful way to visualize your data and make it more easily interpretable. By understanding how color scales work and specifying custom colormaps using Plotly Express and graph_objects, you can create scatter plots that are both visually appealing and informative. Remember to choose colors that are easy to distinguish and consider the type of data you're plotting when selecting a colormap. With practice and patience, you'll be able to create stunning scatter plots that effectively communicate your message.

Python scatter plot colormap