Waffle chart for time series

This post explains how to reproduce a waffle chart that is used in the context of time series. It describe the evolution of number of storms, splitted by storm type throughout time.

It explains step-by-step how to replicate it with reproducible code and explanations

About

This page showcases the work of Benjamin Nowak who originally made the chart with R. This post is a translation to Python by Joseph Barbier. Thanks to him for accepting sharing his work here!

Let's see what the final picture will look like:

small multiple waffle chart

Libraries

First, you need to install the following librairies:

matplotlib is used for creating the chart and add customization features
pandas for data loading and manipulation
highlight_text for custom annotations
pywaffle for creating the waffle chart
pyfonts for loading the fonts

And that's it!

# Libraries
import matplotlib.pyplot as plt
import pandas as pd
from pywaffle import Waffle
from highlight_text import fig_text, ax_text
from pyfonts import load_font, load_exact_font

Dataset

For this reproduction, we're going to retrieve the data directly from the gallery's Github repo. This means we just need to give the right url as an argument to pandas' read_csv() function to retrieve the data.

path = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/storms.csv'
path = '../../static/data/storms.csv'
df = pd.read_csv(path)
df.head()

	year	status	n
0	2010	hurricane	163.0
1	2010	tropical depression	72.0
2	2010	tropical storm	212.0
3	2010	tropical wave	0.0
4	2011	hurricane	106.0

Basic waffle chart

In order to create the following chart, we:

Sum 2020 Values: Calculate the total of storms for the year 2020 since it's the year with the most number of storms.
Prepare Subplots: Create a subplot for each unique year in the DataFrame, setting the figure size and resolution (dpi).
Generate Waffle Charts:
- Iterate over each unique year.
- Extract values for the current year.
- Ensure the sum of values matches the total for 2020 by adding a balancing value.
- Use Waffle.make_waffle to create the waffle chart for each year, with 100 rows and 10 columns, displayed vertically.

max_year_value = df[df['year']==2020]['n'].sum()

ncols = df['year'].nunique()
fig, axs = plt.subplots(ncols=ncols, figsize=(16,8), dpi=200)

for year, ax in zip(df['year'].unique(), axs):

    values = list(df[df['year']==year]['n'].values)
    values.append(max_year_value - sum(values))

    Waffle.make_waffle(
        ax=ax,
        rows=100, 
        columns=10, 
        values=values,
        vertical=True,
    )

plt.show()

Custom colors

The colours are changed using the colors argument to Waffle.make_waffle(). We specify that the last colour is 'white' so that it is confused with the background.

max_year_value = df[df['year']==2020]['n'].sum()

ncols = df['year'].nunique()
fig, axs = plt.subplots(ncols=ncols, figsize=(16,8), dpi=200)

for year, ax in zip(df['year'].unique(), axs):

    values = list(df[df['year']==year]['n'].values)
    values.append(max_year_value - sum(values))

    Waffle.make_waffle(
        ax=ax,
        rows=100, 
        columns=10, 
        values=values,
        vertical=True,
        colors=["#f08c44", "#ffd46c", "#78bcd4", "#386494", 'white']
    )

plt.show()

Title and credit

In order to add annotation, we need 2 new tools:

pyfonts to load the fonts
highlight_text to custom the text style

highlight_text provides the fig_text() and ax_text() functions (you can learn in this post that extensively describes how they work.)

outfit_font = load_exact_font(
   font_url="https://github.com/Outfitio/Outfit-Fonts/raw/main/fonts/ttf/Outfit-Bold.ttf"
)
cabin_font = load_exact_font(
   font_url="https://github.com/google/fonts/raw/main/ofl/cabin/Cabin[wdth,wght].ttf"
)

max_year_value = df[df['year']==2020]['n'].sum()

ncols = df['year'].nunique()
fig, axs = plt.subplots(ncols=ncols, figsize=(16,8), dpi=200)

for year, ax in zip(df['year'].unique(), axs):

    values = list(df[df['year']==year]['n'].values)
    values.append(max_year_value - sum(values))

    Waffle.make_waffle(
        ax=ax,
        rows=100, 
        columns=10, 
        values=values,
        vertical=True,
        colors=["#f08c44", "#ffd46c", "#78bcd4", "#386494", 'white']
    )

    ax.text(
        x=0.03, y=-0.03, s=str(year), font=cabin_font, fontsize=9, zorder=3
    )

title_text = "In the Atlantic Ocean, tropical storms are more common than other cyclones."
subtitle_text = "Tropical Storm is a cyclone with maximum sustained winds of 39 to 73 mph (34 to 63 knots)\nshowing number of cyclones for each year since 2010"
caption_text = "Graphic: Muhammad Azhar | #30DayChartChallenge | Data: NOAA"

fig_text(
    x=0.12, y=1.02, s=title_text, ha='left', va='top',
    fontsize=24, font=outfit_font
)
fig_text(
    x=0.12, y=0.97, s=subtitle_text, ha='left', va='top',
    fontsize=16, font=cabin_font
)
fig_text(
    x=0.5, y=0.06, s=caption_text, ha='center', color='grey',
    fontsize=13, font=cabin_font
)

plt.show()

Legend

The legend is entirely manually defined, which implies some workaround. In practice, we:

Categories and Colors:
- Extract unique categories from the 'status' column.
- Define colors for each category.
Rectangle Dimensions and Positioning:
- Set dimensions and initial positions for rectangles.
Create Legend:
- Iterate over categories and colors.
- For each category, create a colored rectangle and place it on the figure.
- Add the category name next to the corresponding rectangle.

outfit_font = load_exact_font(
   font_url="https://github.com/Outfitio/Outfit-Fonts/raw/main/fonts/ttf/Outfit-Bold.ttf"
)
cabin_font = load_exact_font(
   font_url="https://github.com/google/fonts/raw/main/ofl/cabin/Cabin[wdth,wght].ttf"
)

max_year_value = df[df['year']==2020]['n'].sum()

ncols = df['year'].nunique()
fig, axs = plt.subplots(ncols=ncols, figsize=(16,8), dpi=200)

for year, ax in zip(df['year'].unique(), axs):

    values = list(df[df['year']==year]['n'].values)
    values.append(max_year_value - sum(values))

    Waffle.make_waffle(
        ax=ax,
        rows=100, 
        columns=10, 
        values=values,
        vertical=True,
        colors=["#f08c44", "#ffd46c", "#78bcd4", "#386494", 'white']
    )

    ax.text(
        x=0.03, y=-0.03, s=str(year), font=cabin_font, fontsize=9, zorder=3
    )

title_text = "In the Atlantic Ocean, tropical storms are more common than other cyclones."
subtitle_text = "Tropical Storm is a cyclone with maximum sustained winds of 39 to 73 mph (34 to 63 knots)\nshowing number of cyclones for each year since 2010"
caption_text = "Graphic: Muhammad Azhar | #30DayChartChallenge | Data: NOAA"

fig_text(
    x=0.12, y=1.02, s=title_text, ha='left', va='top',
    fontsize=24, font=outfit_font
)
fig_text(
    x=0.12, y=0.97, s=subtitle_text, ha='left', va='top',
    fontsize=16, font=cabin_font
)
fig_text(
    x=0.5, y=0.06, s=caption_text, ha='center', color='grey',
    fontsize=13, font=cabin_font
)

total_blocks = 100 * 10  # 100 rows, 10 columns
line_block_positions = [200, 400, 600]
line_positions = [1 - (pos / total_blocks) for pos in line_block_positions]
for y, block_pos in zip(line_positions, line_block_positions[::-1]):
    line = plt.Line2D([0.125, 0.9], [y, y], transform=fig.transFigure, color='grey', linestyle='-', linewidth=0.2, alpha=0.5)
    fig.add_artist(line)
    fig.text(0.11, y, str(block_pos), va='center', ha='right', transform=fig.transFigure, fontsize=10, font=cabin_font)

# Add rectangles and labels
categories = list(df['status'].unique())
colors = ["#f08c44", "#ffd46c", "#78bcd4", "#386494"]
rect_height = 0.02
rect_width = 0.01
spacing = 0.08
start_x = 0.2
start_y = 0.85
for i, (category, color) in enumerate(zip(categories, colors)):
    rect = plt.Rectangle((start_x + i * (rect_width + spacing), start_y), rect_width, rect_height, 
                         facecolor=color, edgecolor='none', transform=fig.transFigure)
    fig.add_artist(rect)
    fig.text(start_x + i * (rect_width + spacing) + rect_width + 0.005, start_y + rect_height / 2, 
             category, va='center', ha='left', transform=fig.transFigure, fontsize=10, font=cabin_font)

plt.savefig('../../static/graph/web-waffle-chart-for-time-series.png', dpi=300, bbox_inches='tight')
plt.show()

Going further

This article explains how to reproduce a waffle chart with custom colors and fonts.

Contact & Edit

👋 This document is a work by Yan Holtz. You can contribute on github, send me a feedback on twitter or subscribe to the newsletter to know when new examples are published! 🔥

This page is just a jupyter notebook, you can edit it here. Please help me making this website better 🙏!