Matplotlib Ribbon Box

Introduction

Matplotlib is a popular data visualization library for Python. This tutorial will guide you through creating a ribbon box chart, a unique way to visualize data. A ribbon box chart is a stacked bar chart where each bar is "ribbon-like" and has a gradient color scheme.

VM Tips

After the VM startup is done, click the top left corner to switch to the Notebook tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.

Skills Graph

Importing libraries and loading data

In this step, we will import the necessary libraries and load the data.

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cbook
from matplotlib import colors as mcolors
from matplotlib.image import AxesImage
from matplotlib.transforms import Bbox, BboxTransformTo, TransformedBbox

Creating the RibbonBox class

In this step, we will create the RibbonBox class that will be used to create the gradient colors for the ribbon boxes.

class RibbonBox:
    ## Load the ribbon box image
    original_image = plt.imread(cbook.get_sample_data("Minduka_Present_Blue_Pack.png"))
    cut_location = 70
    b_and_h = original_image[:, :, 2:3]
    color = original_image[:, :, 2:3] - original_image[:, :, 0:1]
    alpha = original_image[:, :, 3:4]
    nx = original_image.shape[1]

    def __init__(self, color):
        rgb = mcolors.to_rgb(color)
        self.im = np.dstack([self.b_and_h - self.color * (1 - np.array(rgb)), self.alpha])

    def get_stretched_image(self, stretch_factor):
        stretch_factor = max(stretch_factor, 1)
        ny, nx, nch = self.im.shape
        ny2 = int(ny*stretch_factor)
        return np.vstack([self.im[:self.cut_location],
                          np.broadcast_to(self.im[self.cut_location], (ny2 - ny, nx, nch)),
                          self.im[self.cut_location:]])

Creating the RibbonBoxImage class

In this step, we will create the RibbonBoxImage class that will be used to create the actual ribbon boxes.

class RibbonBoxImage(AxesImage):
    zorder = 1

    def __init__(self, ax, bbox, color, *, extent=(0, 1, 0, 1), **kwargs):
        super().__init__(ax, extent=extent, **kwargs)
        self._bbox = bbox
        self._ribbonbox = RibbonBox(color)
        self.set_transform(BboxTransformTo(bbox))

    def draw(self, renderer, *args, **kwargs):
        stretch_factor = self._bbox.height / self._bbox.width

        ny = int(stretch_factor*self._ribbonbox.nx)
        if self.get_array() is None or self.get_array().shape[0] != ny:
            arr = self._ribbonbox.get_stretched_image(stretch_factor)
            self.set_array(arr)

        super().draw(renderer, *args, **kwargs)

Creating the chart

In this step, we will create the actual chart by using the RibbonBoxImage class to create the ribbon boxes.

def main():
    fig, ax = plt.subplots()

    years = np.arange(2004, 2009)
    heights = [7900, 8100, 7900, 6900, 2800]
    box_colors = [(0.8, 0.2, 0.2),
                  (0.2, 0.8, 0.2),
                  (0.2, 0.2, 0.8),
                  (0.7, 0.5, 0.8),
                  (0.3, 0.8, 0.7)]

    for year, h, bc in zip(years, heights, box_colors):
        bbox0 = Bbox.from_extents(year - 0.4, 0., year + 0.4, h)
        bbox = TransformedBbox(bbox0, ax.transData)
        ax.add_artist(RibbonBoxImage(ax, bbox, bc, interpolation="bicubic"))
        ax.annotate(str(h), (year, h), va="bottom", ha="center")

    ax.set_xlim(years[0] - 0.5, years[-1] + 0.5)
    ax.set_ylim(0, 10000)

    ## Create a background gradient
    background_gradient = np.zeros((2, 2, 4))
    background_gradient[:, :, :3] = [1, 1, 0]
    background_gradient[:, :, 3] = [[0.1, 0.3], [0.3, 0.5]]
    ax.imshow(background_gradient, interpolation="bicubic", zorder=0.1,
              extent=(0, 1, 0, 1), transform=ax.transAxes, aspect="auto")

    plt.show()

main()

Understanding the chart

In this step, we will discuss the chart that we have created. The chart consists of ribbon boxes stacked on top of each other to form a bar chart. The height of each ribbon box corresponds to a value in the dataset. The ribbon boxes have a gradient color scheme that makes them visually appealing.

Summary

In this tutorial, we learned how to create a ribbon box chart using Matplotlib. We created the RibbonBox and RibbonBoxImage classes to create the gradient colors and the actual ribbon boxes, respectively. We then used these classes to create the chart, which consists of ribbon boxes stacked on top of each other to form a bar chart.