Visualizing Proportions

Get to know your mushrooms 🍄

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

import matplotlib.pyplot as plt
import pandas as pd

mushrooms = pd.read_csv("../../data/mushrooms.csv")
mushrooms.head()

	class	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	...	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
0	Poisonous	Convex	Smooth	Brown	Bruises	Pungent	Free	Close	Narrow	Black	...	Smooth	White	White	Partial	White	One	Pendant	Black	Scattered	Urban
1	Edible	Convex	Smooth	Yellow	Bruises	Almond	Free	Close	Broad	Black	...	Smooth	White	White	Partial	White	One	Pendant	Brown	Numerous	Grasses
2	Edible	Bell	Smooth	White	Bruises	Anise	Free	Close	Broad	Brown	...	Smooth	White	White	Partial	White	One	Pendant	Brown	Numerous	Meadows
3	Poisonous	Convex	Scaly	White	Bruises	Pungent	Free	Close	Narrow	Brown	...	Smooth	White	White	Partial	White	One	Pendant	Black	Scattered	Urban
4	Edible	Convex	Smooth	Green	No Bruises	NaN	Free	Crowded	Broad	Black	...	Smooth	White	White	Partial	White	One	Evanescent	Brown	Abundant	Grasses

5 rows × 23 columns

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)

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

/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/2186401341.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  edibleclass = mushrooms.groupby(["class"]).count()

	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	800	4208	4208	4208	4208	4208	...	4208	4208	4208	4208	4208	4208	4208	4208	4208	4208
Poisonous	3916	3916	3916	3916	3796	3916	3916	3916	3916	3916	...	3916	3916	3916	3916	3916	3880	3880	3916	3916	3916

2 rows × 22 columns

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

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

/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/3512452478.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  habitat = mushrooms.groupby(["habitat"]).count()

	class	cap-shape	cap-surface	cap-color	bruises	odor	gill-attachment	gill-spacing	gill-size	gill-color	...	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
Grasses	2148	2148	2148	2148	2148	1056	2148	2148	2148	2148	...	2148	2148	2148	2148	2148	2148	2148	2148	2148	2148
Leaves	832	832	832	832	832	576	832	832	832	832	...	832	832	832	832	832	832	832	832	832	832
Meadows	292	292	292	292	292	256	292	292	292	292	...	292	292	292	292	292	292	292	292	292	292
Paths	1144	1144	1144	1144	1144	1104	1144	1144	1144	1144	...	1144	1144	1144	1144	1144	1144	1144	1144	1144	1144
Urban	368	368	368	368	368	272	368	368	368	368	...	368	368	368	368	368	368	368	368	368	368
Waste	192	192	192	192	192	0	192	192	192	192	...	192	192	192	192	192	192	192	192	192	192
Wood	3148	3148	3148	3148	3148	1332	3148	3148	3148	3148	...	3148	3148	3148	3148	3148	3148	3112	3112	3148	3148

7 rows × 22 columns

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.m

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

/var/folders/7w/fv5n0x414253d7dv5g2wwmb40000gn/T/ipykernel_93696/1230659777.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  capcolor = mushrooms.groupby(["cap-color"]).count()

	class	cap-shape	cap-surface	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
cap-color
Brown	2284	2284	2284	2284	1100	2284	2284	2284	2284	2284	...	2284	2284	2284	2284	2284	2272	2272	2284	2284	2284
Buff	168	168	168	168	96	168	168	168	168	168	...	168	168	168	168	168	168	168	168	168	168
Cinnamon	44	44	44	44	12	44	44	44	44	44	...	44	44	44	44	44	32	32	44	44	44
Green	1856	1856	1856	1856	808	1856	1856	1856	1856	1856	...	1856	1856	1856	1856	1856	1856	1856	1856	1856	1856
Pink	144	144	144	144	64	144	144	144	144	144	...	144	144	144	144	144	144	144	144	144	144
Purple	16	16	16	16	0	16	16	16	16	16	...	16	16	16	16	16	16	16	16	16	16
Red	1500	1500	1500	1500	876	1500	1500	1500	1500	1500	...	1500	1500	1500	1500	1500	1488	1488	1500	1500	1500
White	1040	1040	1040	1040	592	1040	1040	1040	1040	1040	...	1040	1040	1040	1040	1040	1040	1040	1040	1040	1040
Yellow	1072	1072	1072	1072	1048	1072	1072	1072	1072	1072	...	1072	1072	1072	1072	1072	1072	1072	1072	1072	1072

9 rows × 22 columns

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

import matplotlib.pyplot as plt
import pandas as pd
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.