Data-Science-For-Beginners/3-Data-Visualization/11-visualization-proportions

Visualizing Proportions

Visualizing Proportions - Sketchnote by @nitya

In this lesson, you will use a different nature-focused dataset to visualize proportions, such as how many different types of fungi populate a given dataset about mushrooms. Let's explore these fascinating fungi using a dataset sourced from Audubon listing details about 23 species of gilled mushrooms in the Agaricus and Lepiota families. You will experiment with tasty visualizations such as:

• Pie charts 🥧
• Donut charts 🍩
• Waffle charts 🧇

💡 A very interesting project called Charticulator by Microsoft Research offers a free drag and drop interface for data visualizations. In one of their tutorials they also use this mushroom dataset! So you can explore the data and learn the library at the same time: Charticulator tutorial.

Pre-lecture quiz

Get to know your mushrooms 🍄

Mushrooms are very interesting. Let's import a dataset to study them:

import pandas as pd
import matplotlib.pyplot as plt
mushrooms = pd.read_csv('../../data/mushrooms.csv')
mushrooms.head()

A table is printed out with some great data for analysis:

class	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	stalk-root	stalk-surface-above-ring	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
Poisonous	Convex	Smooth	Brown	Bruises	Pungent	Free	Close	Narrow	Black	Enlarging	Equal	Smooth	Smooth	White	White	Partial	White	One	Pendant	Black	Scattered	Urban
Edible	Convex	Smooth	Yellow	Bruises	Almond	Free	Close	Broad	Black	Enlarging	Club	Smooth	Smooth	White	White	Partial	White	One	Pendant	Brown	Numerous	Grasses
Edible	Bell	Smooth	White	Bruises	Anise	Free	Close	Broad	Brown	Enlarging	Club	Smooth	Smooth	White	White	Partial	White	One	Pendant	Brown	Numerous	Meadows
Poisonous	Convex	Scaly	White	Bruises	Pungent	Free	Close	Narrow	Brown	Enlarging	Equal	Smooth	Smooth	White	White	Partial	White	One	Pendant	Black	Scattered	Urban

Right away, you notice that all the data is textual. You will have to convert this data to be able to use it in a chart. Most of the data, in fact, is represented as an object:

print(mushrooms.select_dtypes(["object"]).columns)

The output is:

Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
       'stalk-surface-below-ring', 'stalk-color-above-ring',
       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
       'ring-type', 'spore-print-color', 'population', 'habitat'],
      dtype='object')

Take this data and convert the 'class' column to a category:

cols = mushrooms.select_dtypes(["object"]).columns
mushrooms[cols] = mushrooms[cols].astype('category')

edibleclass=mushrooms.groupby(['class']).count()
edibleclass

Now, if you print out the mushrooms data, you can see that it has been grouped into categories according to the poisonous/edible class:

	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	stalk-shape	...	stalk-surface-below-ring	stalk-color-above-ring	stalk-color-below-ring	veil-type	veil-color	ring-number	ring-type	spore-print-color	population	habitat
class
Edible	4208	4208	4208	4208	4208	4208	4208	4208	4208	4208	...	4208	4208	4208	4208	4208	4208	4208	4208	4208	4208
Poisonous	3916	3916	3916	3916	3916	3916	3916	3916	3916	3916	...	3916	3916	3916	3916	3916	3916	3916	3916	3916	3916

If you follow the order presented in this table to create your class category labels, you can build a pie chart:

Pie!

labels=['Edible','Poisonous']
plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')
plt.title('Edible?')
plt.show()

Voila, a pie chart showing the proportions of this data according to these two classes of mushrooms. It's quite important to get the order of the labels correct, especially here, so be sure to verify the order with which the label array is built!

Donuts!

A somewhat more visually interesting pie chart is a donut chart, which is a pie chart with a hole in the middle. Let's look at our data using this method.

Take a look at the various habitats where mushrooms grow:

habitat=mushrooms.groupby(['habitat']).count()
habitat

Here, you are grouping your data by habitat. There are 7 listed, so use those as labels for your donut chart:

labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood']

plt.pie(habitat['class'], labels=labels,
        autopct='%1.1f%%', pctdistance=0.85)
  
center_circle = plt.Circle((0, 0), 0.40, fc='white')
fig = plt.gcf()

fig.gca().add_artist(center_circle)
  
plt.title('Mushroom Habitats')
  
plt.show()

This code draws a chart and a center circle, then adds that center circle in the chart. Edit the width of the center circle by changing 0.40 to another value.

Donut charts can be tweaked in several ways to change the labels. The labels in particular can be highlighted for readability. Learn more in the docs.

Now that you know how to group your data and then display it as a pie or donut, you can explore other types of charts. Try a waffle chart, which is just a different way of exploring quantity.

Waffles!

A 'waffle' type chart is a different way to visualize quantities as a 2D array of squares. Try visualizing the different quantities of mushroom cap colors in this dataset. To do this, you need to install a helper library called PyWaffle and use Matplotlib:

pip install pywaffle

Select a segment of your data to group:

capcolor=mushrooms.groupby(['cap-color']).count()
capcolor

Create a waffle chart by creating labels and then grouping your data:

import pandas as pd
import matplotlib.pyplot as plt
from pywaffle import Waffle
  
data ={'color': ['brown', 'buff', 'cinnamon', 'green', 'pink', 'purple', 'red', 'white', 'yellow'],
    'amount': capcolor['class']
     }
  
df = pd.DataFrame(data)
  
fig = plt.figure(
    FigureClass = Waffle,
    rows = 100,
    values = df.amount,
    labels = list(df.color),
    figsize = (30,30),
    colors=["brown", "tan", "maroon", "green", "pink", "purple", "red", "whitesmoke", "yellow"],
)

Using a waffle chart, you can plainly see the proportions of cap colors of this mushrooms dataset. Interestingly, there are many green-capped mushrooms!

✅ Pywaffle supports icons within the charts that use any icon available in Font Awesome. Do some experiments to create an even more interesting waffle chart using icons instead of squares.

In this lesson, you learned three ways to visualize proportions. First, you need to group your data into categories and then decide which is the best way to display the data - pie, donut, or waffle. All are delicious and gratify the user with an instant snapshot of a dataset.

🚀 Challenge

Try recreating these tasty charts in Charticulator.

Post-lecture quiz

Review & Self Study

Sometimes it's not obvious when to use a pie, donut, or waffle chart. Here are some articles to read on this topic:

https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart

https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce

https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm

https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402

Do some research to find more information on this sticky decision.

Assignment

Try it in Excel