Combine a choropleth map and a barplot in Python

This post demonstrates how to integrate a choropleth map with a barplot. You'll learn to create each chart individually and then seamlessly combine them.

It offers clear explanations with reproducible, step-by-step code examples.

About

This plot is a choropleth map combined with a barplot for the legend.

It has been originally designed by Vinicius Oike Reginatto in R. Here is a reproduction in Python by Joseph Barbier.

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

Libraries

For creating this chart, we will need a whole bunch of libraries!

matplotlib: to customize the appearance of the chart
geopandas: to handle make the map
pandas: to handle the data
seaborn: for the barplot
pypalettes: for the colors
highlight_text: for the annotations
pyfonts: for customizing the font

import geopandas as gpd
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
from pypalettes import load_cmap
from highlight_text import fig_text, ax_text
from pyfonts import load_font

Dataset

We need two datasets:

one for the map with the polygons and the measure of interest (in this case the HDI)
one for the bar chart, which we will deduce from the first by doing a few calculations.

path = "https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/saopaulo.geojson"
path = "../../static/data/saopaulo.geojson"
df = gpd.read_file(path)
df.head()

	code_udh	HDI	pop	geometry
0	1355030801001	0.866	20388.0	POLYGON ((-46.53789 -23.56245, -46.53799 -23.5...
1	1355030801002	0.870	21937.0	POLYGON ((-46.54193 -23.54086, -46.54212 -23.5...
2	1355030801003	0.790	10536.0	POLYGON ((-46.51514 -23.55757, -46.51534 -23.5...
3	1355030801004	0.816	27925.0	POLYGON ((-46.53260 -23.57370, -46.53276 -23.5...
4	1355030801005	0.820	6817.0	POLYGON ((-46.50676 -23.57095, -46.50697 -23.5...

Now we create the dataset for the barplot:

atlas = df[["HDI", "pop"]].copy()

bins = [0.0, 0.65, 0.699, 0.749, 0.799, 0.849, 0.899, 0.949, 1.0]
labels = [
    "0.650 or less",
    "0.650 to 0.699",
    "0.700 to 0.749",
    "0.750 to 0.799",
    "0.800 to 0.849",
    "0.850 to 0.899",
    "0.900 to 0.949",
    "0.950 or more",
]

atlas["group_hdi"] = pd.cut(atlas["HDI"], bins=bins, include_lowest=True, labels=labels)

pop_hdi = atlas.groupby("group_hdi", observed=True)["pop"].sum().reset_index()
pop_hdi["share"] = (pop_hdi["pop"] / pop_hdi["pop"].sum()) * 100

pop_hdi["y_text"] = pop_hdi["share"] / 2
pop_hdi["label"] = pop_hdi["share"].round(1).astype(str) + "%"

pop_hdi

	group_hdi	pop	share	y_text	label
0	0.650 or less	415953.0	3.710661	1.855331	3.7%
1	0.650 to 0.699	1620623.0	14.457362	7.228681	14.5%
2	0.700 to 0.749	2106520.0	18.791984	9.395992	18.8%
3	0.750 to 0.799	2460948.0	21.953789	10.976895	22.0%
4	0.800 to 0.849	1576054.0	14.059768	7.029884	14.1%
5	0.850 to 0.899	1417249.0	12.643090	6.321545	12.6%
6	0.900 to 0.949	1409668.0	12.575460	6.287730	12.6%
7	0.950 or more	202658.0	1.807885	0.903943	1.8%

Choropleth map

Creating the choropleth map is the easiest part:

load the YlGnBu colormap and specify that we want a continuous colormap
create a matplotlib figure with plt.subplots().
remove the axis
use the plot() method on the df geodataframe and specify the column to represent for the colors (here it's HDI)

palette_name = "YlGnBu"
cmap = load_cmap(palette_name, cmap_type="continuous")

fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
ax.axis("off")
df.plot(ax=ax, column="HDI", cmap=cmap, edgecolor="lightgrey", linewidth=0.2, alpha=0.9)
plt.show()

Barplot

Adding the bar chart may seem a little tricky, but it's not.

The main thing is to add a mini graph inside the choropleth. To do this, we create it with :

subax = ax.inset_axes(bounds=(0.58, 0.22, 0.15, 0.25), transform=fig.transFigure)

bounds designates the location of this minichart
transform specifies that the coordinates of bounds are relative to the figure. You can read more about this in this article

Once we've done that, we use the barplot() function from seaborn and use a for loop to add labels with specific positions/colours to the barplot.

palette_name = "YlGnBu"
cmap = load_cmap(palette_name, cmap_type="continuous")

fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
ax.axis("off")

# choropleth
df.plot(ax=ax, column="HDI", cmap=cmap, edgecolor="lightgrey", linewidth=0.2, alpha=0.9)

# barplot
mini_ax = ax.inset_axes(bounds=(0.58, 0.22, 0.15, 0.25), transform=fig.transFigure)
mini_ax.axis("off")
sns.barplot(
    data=pop_hdi,
    x="share",
    y="group_hdi",
    palette=palette_name,
    ax=mini_ax,
    hue="group_hdi",
    legend=False,
)
mini_ax.axvline(x=0, color="black")
mini_ax.invert_yaxis()

for index, row in pop_hdi.iterrows():
    x_pos = row["share"] - 6 if row["share"] > 4 else row["share"] + 1
    y_pos = index
    color = "white" if index in [5, 6] else "black"
    mini_ax.text(x_pos, y_pos, row["label"], color=color, size=7, va="center")
    mini_ax.text(-10, y_pos, row["group_hdi"], color="black", size=6, va="center")
plt.show()

Annotations

The only thing missing here is some good annotations with a nice font.

For this we'll use the Roboto font and load it with pyfonts. Then we'll basically use the fig.text() function to add the text we want.

font = load_font(
    "https://github.com/akella/wb/blob/master/css/fonts/Roboto-Medium.otf?raw=true"
)
boldfont = load_font(
    "https://github.com/akella/wb/blob/master/css/fonts/Roboto-Bold.otf?raw=true"
)

fig, ax = plt.subplots(figsize=(10, 10), dpi=300)
ax.axis("off")

df.plot(ax=ax, column="HDI", cmap=cmap, edgecolor="lightgrey", linewidth=0.2, alpha=0.9)

subax = ax.inset_axes(bounds=(0.58, 0.22, 0.15, 0.25), transform=fig.transFigure)
subax.set_axis_off()
sns.barplot(
    data=pop_hdi,
    x="share",
    y="group_hdi",
    palette=palette_name,
    ax=subax,
    hue="group_hdi",
    legend=False,
)
subax.axvline(x=0, color="black")
subax.invert_yaxis()

for index, row in pop_hdi.iterrows():
    x_pos = row["share"] - 6 if row["share"] > 4 else row["share"] + 1
    y_pos = index
    color = "white" if index in [5, 6] else "black"
    subax.text(x_pos, y_pos, row["label"], color=color, size=7, va="center", font=font)
    subax.text(
        -10, y_pos, row["group_hdi"], color="black", size=6, va="center", font=font
    )

fig.text(
    x=0.5, y=0.87, s="HDI in Sao Paulo, BR (2010)", ha="center", size=25, font=font
)
fig.text(x=0.55, y=0.48, s="Population share by HDI group", size=9, font=font)
fig_text(
    x=0.6,
    y=0.14,
    s="<Source>: Atlas Brasil",
    size=10,
    font=font,
    highlight_textprops=[{"font": boldfont}],
)

plt.savefig(
    "../../static/graph/web-combine-choropleth-map-with-barplot.png",
    bbox_inches="tight",
    dpi=300,
)
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! 🔥

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