Matplotlib Table Function

Introduction

In this lab, we will learn how to use the Matplotlib table function to display a table within a plot. We will use a sample dataset to visualize the loss incurred by different natural disasters over the years.

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

Import Required Libraries

We will begin by importing the necessary libraries for the project. We will use the Matplotlib library to create the table.

import matplotlib.pyplot as plt
import numpy as np

Create the Dataset

Next, we will create a sample dataset to visualize the loss incurred by different natural disasters over the years. We will use a two-dimensional list to store the data and a tuple to store the column names.

data = [[ 66386, 174296,  75131, 577908,  32015],
        [ 58230, 381139,  78045,  99308, 160454],
        [ 89135,  80552, 152558, 497981, 603535],
        [ 78415,  81858, 150656, 193263,  69638],
        [139361, 331509, 343164, 781380,  52269]]

columns = ('Freeze', 'Wind', 'Flood', 'Quake', 'Hail')

Create Row Labels

We will create row labels for the dataset to represent the number of years for which the loss has been recorded. We will use a list comprehension to create the row labels.

rows = ['%d year' % x for x in (100, 50, 20, 10, 5)]

Create Color Scheme

We will create a color scheme for the table using the plt.cm.BuPu function. We will use a pastel shade of blue and purple colors for the rows.

colors = plt.cm.BuPu(np.linspace(0, 0.5, len(rows)))

Create Vertical Stacked Bar Chart

We will create a vertical stacked bar chart using the plt.bar function to represent the loss incurred by different natural disasters over the years. We will use a for loop to iterate over each row of data and plot the bars.

n_rows = len(data)

index = np.arange(len(columns)) + 0.3
bar_width = 0.4

y_offset = np.zeros(len(columns))

cell_text = []
for row in range(n_rows):
    plt.bar(index, data[row], bar_width, bottom=y_offset, color=colors[row])
    y_offset = y_offset + data[row]
    cell_text.append(['%1.1f' % (x / 1000.0) for x in y_offset])

Reverse Colors and Text Labels

We will reverse the colors and text labels of the table to display the last value at the top using the [::-1] function.

colors = colors[::-1]
cell_text.reverse()

Add Table to the Plot

We will add a table to the bottom of the plot using the plt.table function. We will pass the cell text, row labels, row colors, and column labels as parameters to the function.

the_table = plt.table(cellText=cell_text,
                      rowLabels=rows,
                      rowColours=colors,
                      colLabels=columns,
                      loc='bottom')

Adjust Plot Layout

We will adjust the layout of the plot to make room for the table using the plt.subplots_adjust function.

plt.subplots_adjust(left=0.2, bottom=0.2)

Add Axis Labels and Title

We will add axis labels and a title to the plot using the plt.ylabel, plt.yticks, plt.xticks, and plt.title functions.

values = np.arange(0, 2500, 500)
value_increment = 1000

plt.ylabel(f"Loss in ${value_increment}'s")
plt.yticks(values * value_increment, ['%d' % val for val in values])
plt.xticks([])
plt.title('Loss by Disaster')

Show Plot

We will display the plot using the plt.show function.

plt.show()

Summary

In this lab, we learned how to use the Matplotlib table function to display a table within a plot. We used a sample dataset to visualize the loss incurred by different natural disasters over the years. We followed the following steps:

1. Imported Required Libraries
2. Created the Dataset
3. Created Row Labels
4. Created Color Scheme
5. Created Vertical Stacked Bar Chart
6. Reversed Colors and Text Labels
7. Added Table to the Plot
8. Adjusted Plot Layout
9. Added Axis Labels and Title
10. Showed Plot.