Minimalist area chart

This post guides you through reproducing an area chart with two colors for positive and negative values, and clean annotations. The process starts with a basic example and ends with a polished, refined chart. Every step provides explanations and reproducible code.

About

An area chart is a kind of line chart where the area between the x-axis and the line if filled in a given color. This chart shows the evolution of Japan's population change between 1952 and 2024.

This chart has been created by Joseph Barbier. Thanks to him for accepting sharing its work here!

As a teaser, here is the plot we’re gonna try building:

area plot

Libraries

First, we need to load the following libraries:

matplotlib: for creating the plot
pandas: for data manipulation
highlight_text: for annotations
pyfonts for the font
drawarrow for the arrows

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pyfonts import load_font
from drawarrow import ax_arrow
from highlight_text import fig_text, ax_text

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.

url = "https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/japan-population.csv"
df = pd.read_csv(url)
df.head()

	date	pop_var	flag
0	1952	1238290.0	False
1	1953	1135376.0	False
2	1954	1035879.0	False
3	1955	997262.0	False
4	1956	931279.0	False

Simple area chart

An area chart consists of 2 main elements:

the main line (added via the plot() function)
the filled area (added via the fill_between() function, with a lower opacity)

fig, ax = plt.subplots(dpi=300, figsize=(10,7))

ax.plot(df['date'], df['pop_var'])
ax.fill_between(df['date'], df['pop_var'], alpha=0.3)

plt.show()

2 colors for positive and negative values

Since there’s no built-in method to change the color based on a value, so we create two area charts:

First chart: spans from the start date to year_index (the year is when the value drops below 0, and we find this info with the flag column).
Second chart: spans from year_index to the end date.

Apply different colors to each chart, and done!

fig, ax = plt.subplots(dpi=300, figsize=(10,7))

year_index = df[df['flag']].date.values[0]

# before
color = '#335c67'
before_df = df[df['date']<=year_index]
ax.plot(before_df['date'], before_df['pop_var'], color=color)
ax.fill_between(before_df['date'], before_df['pop_var'], alpha=0.3, color=color)

# after
color = '#9e2a2b'
after_df = df[df['date']>=year_index]
ax.plot(after_df['date'], after_df['pop_var'], color=color)
ax.fill_between(after_df['date'], after_df['pop_var'], alpha=0.3, color=color)

plt.show()

Customize axes

Enhance the plot with the following steps:

Use ax.set_axis_off() to remove all axes.
Load two fonts: one regular, one bold.
Highlight the highest and lowest values with scatter() and text().
Use a for loop and text() to label all dates in the chart's center.
Add a horizontal black line along the x-axis.

font = load_font(
   'https://github.com/impallari/Libre-Baskerville/blob/master/LibreBaskerville-Regular.ttf?raw=true'
)
boldfont = load_font(
   'https://github.com/impallari/Libre-Baskerville/blob/master/LibreBaskerville-Bold.ttf?raw=true'
)
digit_font = load_font(
   "https://github.com/google/fonts/blob/main/ofl/lato/Lato-Bold.ttf?raw=true"
)

fig, ax = plt.subplots(dpi=300, figsize=(10,7))
ax.set_axis_off()

year_index = df[df['flag']].date.values[0]

# before
color = '#335c67'
year_index = df[df['flag']].date.values[0]
before_df = df[df['date']<=year_index]
ax.plot(before_df['date'], before_df['pop_var'], color=color)
ax.fill_between(before_df['date'], before_df['pop_var'], alpha=0.3, color=color)
max_year = df[df['pop_var']==df['pop_var'].max()].date.values[0]
max_value = df[df['pop_var']==df['pop_var'].max()].pop_var.values[0]
ax.scatter(x=max_year, y=max_value, color=color, s=20)
ax.text(x=max_year-7, y=max_value, s=f'+{max_value:.0f}', font=digit_font, size=8, color=color)

# after
color = '#9e2a2b'
after_df = df[df['date']>=year_index]
ax.plot(after_df['date'], after_df['pop_var'], color=color)
ax.fill_between(after_df['date'], after_df['pop_var'], alpha=0.3, color=color)
min_year = df[df['pop_var']==df['pop_var'].min()].date.values[0]
min_value = df[df['pop_var']==df['pop_var'].min()].pop_var.values[0]
ax.scatter(x=min_year, y=min_value, color=color, s=20)
ax.text(x=min_year-5, y=min_value-70000, s=f'{min_value:.0f}', font=digit_font, size=8, color=color)

ax.plot([1952, 2024], [0,0], color='black', linewidth=0.6)
year_range = range(1960, 2021, 10)
for year in year_range:
   ax.text(x=year+1, y=40000, s=f'{year}', font=boldfont, size=8, ha='center')

plt.show()

Final chart with annotations

All the part is done, now we just have to:

add title, subtitle and credit/source using highlight_text
add a specific annotation about the year 2010 with an arrow from drawarrow

# style parameters
font = load_font(
   'https://github.com/impallari/Libre-Baskerville/blob/master/LibreBaskerville-Regular.ttf?raw=true'
)
boldfont = load_font(
   'https://github.com/impallari/Libre-Baskerville/blob/master/LibreBaskerville-Bold.ttf?raw=true'
)
digit_font = load_font(
   "https://github.com/google/fonts/blob/main/ofl/lato/Lato-Bold.ttf?raw=true"
)
arrow_props = dict(color='black', tail_width=0.05, linewidth=0.5, head_width=3, head_length=5, radius=0.2)

fig, ax = plt.subplots(dpi=300, figsize=(10,7))
ax.set_axis_off()

# before
color = '#335c67'
year_index = df[df['flag']].date.values[0]
before_df = df[df['date']<=year_index]
ax.plot(before_df['date'], before_df['pop_var'], color=color)
ax.fill_between(before_df['date'], before_df['pop_var'], alpha=0.3, color=color)
max_year = df[df['pop_var']==df['pop_var'].max()].date.values[0]
max_value = df[df['pop_var']==df['pop_var'].max()].pop_var.values[0]
ax.scatter(x=max_year, y=max_value, color=color, s=20)
ax.text(x=max_year-7, y=max_value, s=f'+{max_value:.0f}', font=digit_font, size=8, color=color)

# after
color = '#9e2a2b'
after_df = df[df['date']>=year_index]
ax.plot(after_df['date'], after_df['pop_var'], color=color)
ax.fill_between(after_df['date'], after_df['pop_var'], alpha=0.3, color=color)
min_year = df[df['pop_var']==df['pop_var'].min()].date.values[0]
min_value = df[df['pop_var']==df['pop_var'].min()].pop_var.values[0]
ax.scatter(x=min_year, y=min_value, color=color, s=20)
ax.text(x=min_year-5, y=min_value-70000, s=f'{min_value:.0f}', font=digit_font, size=8, color=color)

ax.plot([1952, 2024], [0,0], color='black', linewidth=0.6)
year_range = range(1960, 2021, 10)
for year in year_range:
   ax.text(x=year+1, y=40000, s=f'{year}', font=boldfont, size=8, ha='center')

s = "Japan's population is\nin <dramatic decline>."
fig_text(x=0.45, y=0.8, s=s, font=font, highlight_textprops=[{'font':boldfont}], fontsize=25, ha='left', va='top')

s = "Population variation per year between 1952 and 2024"
fig_text(x=0.45, y=0.68, s=s, font=font, fontsize=9.8, ha='left', va='top', alpha=0.5)

s = "<Graph>: barbierjoseph.com\n<Data>: macrotrends.net"
ax_text(x=1952, y=-30000, s=s, font=font, fontsize=6, ha='left', highlight_textprops=[{'font':boldfont}]*2)

s = "2010 is the first year to record\na <fall> in the population"
ax_text(x=1978, y=-210000, s=s, font=font, fontsize=8, ha='left', highlight_textprops=[{'font':boldfont}])
ax_arrow(tail_position=(1995, -300000), head_position=(2009, -50000), ax=ax, **arrow_props)

plt.savefig('../../static/graph/web-area-chart-with-different-colors-for-positive-and-negative-values.png', dpi=300, bbox_inches='tight')
plt.show()

Going further

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! 🔥

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