README.md

Visualizing Proportions

Visualizing Proportions - Sketchnote by @nitya

For dis lesson, you go use one dataset wey dey focus on nature to show proportions, like how many different types of fungi dey inside one dataset about mushrooms. Make we check dis fungi wey dey interesting well-well using one dataset wey Audubon provide, wey get details about 23 species of gilled mushrooms for Agaricus and Lepiota families. You go try some sweet visualizations like:

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

💡 One project wey dey very interesting na Charticulator wey Microsoft Research do. E dey free and e get drag and drop interface for data visualizations. For one of their tutorials, dem use dis mushroom dataset! So you fit check the data and learn the library at the same time: Charticulator tutorial.

Pre-lecture quiz

Know your mushrooms 🍄

Mushrooms dey very interesting. Make we import one dataset to study dem:

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

One table go show with some correct 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

As you dey look am, you go notice say all the data na text. You go need change dis data so you fit use am for chart. Most of the data sef dey as object:

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

The output na:

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 dis data and change the 'class' column to category:

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

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

Now, if you print the mushrooms data, you go see say e don group into categories based on 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 wey dey for dis table to create your class category labels, you fit build one pie chart:

Pie!

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

Voila, one pie chart wey dey show the proportions of dis data based on dis two classes of mushrooms. E dey very important to arrange the labels well, especially for here, so make sure say you check the order wey you take build the label array!

Donuts!

One pie chart wey dey look more interesting na donut chart, wey be pie chart wey get hole for middle. Make we check our data with dis method.

Look the different habitats wey mushrooms dey grow:

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

For here, you dey group your data by habitat. Dem get 7 listed, so use dem 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()

Dis code dey draw one chart and one center circle, then e go add dat center circle for the chart. You fit change the width of the center circle by editing 0.40 to another value.

Donut charts fit change in different ways to make the labels dey clear. The labels fit dey highlighted to make dem easy to read. Learn more for the docs.

Now wey you sabi how to group your data and show am as pie or donut, you fit try other types of charts. Try waffle chart, wey be another way to show quantity.

Waffles!

Waffle chart na another way to show quantities as 2D array of squares. Try show the different quantities of mushroom cap colors for dis dataset. To do dis, you go need install one helper library wey dem call PyWaffle and use Matplotlib:

pip install pywaffle

Select one part of your data to group:

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

Create one 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"],
)

With waffle chart, you fit see the proportions of cap colors for dis mushrooms dataset. E dey interesting say plenty green-capped mushrooms dey!

✅ Pywaffle dey support icons inside the charts wey fit use any icon wey dey Font Awesome. Try experiment to create one more interesting waffle chart wey go use icons instead of squares.

For dis lesson, you don learn three ways to show proportions. First, you go group your data into categories, then decide the best way to show the data - pie, donut, or waffle. All of dem dey sweet and dey give the user quick snapshot of the dataset.

🚀 Challenge

Try recreate dis sweet charts for Charticulator.

Post-lecture quiz

Review & Self Study

Sometimes e no dey clear when to use pie, donut, or waffle chart. Check dis articles to read more about dis matter:

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 about dis decision wey dey sticky.

Assignment

Try am for Excel

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Disclaimer:
Dis dokyument don translate wit AI translation service Co-op Translator. Even as we dey try make am accurate, abeg sabi say machine translation fit get mistake or no correct well. Di original dokyument for im native language na di main source wey you go trust. For important mata, na beta make you use professional human translation. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis translation.