PYTHON — Creating Colored Matrix in Python

Summary

The web content provides a tutorial on creating a colored matrix in Python using matplotlib and numpy to visualize data as a heatmap.

Abstract

The article titled "PYTHON — Creating Colored Matrix in Python" demonstrates how to generate visually appealing heatmaps from two-dimensional ndarray data. It guides the reader through the process of importing necessary functions, creating sample data arrays, and customizing the visualization by disabling tick marks and labels. The tutorial emphasizes the use of the matshow function from matplotlib to plot the arrays as color-coded matrices and includes the addition of text labels and a color bar for better data interpretation. The final output is a clean and clear grid of colored squares that represents the data in a meaningful way.

Opinions

The author suggests that digital design is an ongoing process, akin to painting with never-drying paint, implying the dynamic nature of data visualization.
Heatmaps are described as one of the most visually appealing types of visualizations, indicating a preference for this method of data representation.
The author's choice to disable tick marks and labels, and to add text labels to the diagonal color strips, reflects a preference for minimalistic and contextually informative visualizations.
The inclusion of a color bar is seen as essential for interpreting the color-coded data values, highlighting the importance of clear data mapping in visualizations.

PYTHON — Creating Colored Matrix in Python

Digital design is like painting, except the paint never dries. — Neville Brody

# Creating a Colored Matrix in Python

One of the most visually appealing types of visualizations is a heatmap. Although matplotlib does not have a direct method for creating heatmaps, we can still generate a grid of colored squares from a two-dimensional ndarray. To get started, we need to import the make_axes_locatable function from mpl_toolkits.

from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable

We’ll create two separate two-dimensional ndarrays, similar to matrices, to demonstrate the process of creating a colored matrix.

import matplotlib.pyplot as plt
import numpy as np

# Create two 10x10 arrays
x = np.diag(np.arange(2, 12))[::-1]
x2 = np.arange(100).reshape(10, 10)

# Disable tick marks and numeric labels
nolabels = {i: j for i, j in zip('xy', ['top', 'right'])}
nolabels.update({i: False for i in ['top', 'bottom', 'left', 'right']})

# Plot the data
with plt.rc_context({'axes.grid': False}):
    fig, (ax1, ax2) = plt.subplots(1, 2)
    cax = ax1.matshow(x, cmap='viridis')
    ax2.matshow(x2, cmap='RdYlGn_r')

    for ax in [ax1, ax2]:
        ax.tick_params(axis='both', which='both', **nolabels)

    for i, j in zip(*x.nonzero()):
        ax1.text(j, i, x[i, j], color='white', ha='center', va='center')

    divider = make_axes_locatable(ax2)
    cax = divider.append_axes("right", size="5%", pad=0.1)
    plt.colorbar(cax=cax)

    fig.suptitle('Heatmaps with Axes.matshow', fontsize=16)
    plt.show()

In this example, we first create two 10x10 arrays, x and x2, to represent the data that we want to visualize. We then use the matplotlib and numpy libraries to plot these arrays as color-coded matrices using the matshow function.

Additionally, we disable the tick marks and labels on the axes to create a cleaner visualization, and we add text labels to the diagonal color strips to provide more context to the visualization. Lastly, we include a color bar to indicate the color mapping of the data values.

The end result is a grid of colored squares representing the provided data, and the code provides a clear and detailed explanation of each step involved in creating the colored matrix.