6 of 100: Horizontal Stacked bar chart in matplotlib
At the beginning of the year I challenged myself to create all 100 visualizations using python and matplotlib from the 1 dataset,100 visualizations project and I am sharing with you the code for all the visualizations.
Note: Data Viz Project is copyright Ferdio and available under a Creative Commons Attribution – Non Commercial – No Derivatives 4.0 International license. I asked Ferdio and they told me they used a Design tool to create all the plots.
Collaborate
There are a ton of improvements that can be made on the code, so let me know in the comments any improvements you make and I will update the post accordingly!
To be improved: The colors and annotations can be also automated. Will revisit later.
This is the original viz that we are trying to recreate in matplotlib:

Import the packages
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
Generate the data
We could actually go from numpy to matplotlib, but most data projects use pandas to transform the data, so I am using a pandas dataframe as the starting point.
color_dict_bar = {(2022,"Norway"): "#9194A3", (2004,"Norway"): "#2B314D",
(2022,"Denmark"): "#E2AFA5", (2004,"Denmark"): "#CE5A43",
(2022,"Sweden"): "#C4D6F8", (2004,"Sweden"): "#5375D4",
}
color_dict_edges = {(2022,"Norway"): "#2C324F", (2004,"Norway"): "#1B203A",
(2022,"Denmark"): "#CE5A43", (2004,"Denmark"): "#B6493F",
(2022,"Sweden"): "#5375D4", (2004,"Sweden"): "#4562C5",
}
xy_ticklabel_color, grand_totals_color, grid_color, datalabels_color ='#757C85',"#101628", "#C8C9C9", "#FFFFFF"
data = {
"year": [2004, 2022, 2004, 2022, 2004, 2022],
"countries" : ["Sweden", "Sweden", "Denmark", "Denmark", "Norway", "Norway"],
"sites": [13,15,4,10,5,8]
}
df= pd.DataFrame(data)
year | countries | sites | |
---|---|---|---|
0 | 2004 | Sweden | 13 |
1 | 2022 | Sweden | 15 |
2 | 2004 | Denmark | 4 |
3 | 2022 | Denmark | 10 |
4 | 2004 | Norway | 5 |
5 | 2022 | Norway | 8 |
We need to create the subtotals for each year so we use pandas groupby and then sort the data.
#Add the color based on the color dictionary
df['color_bars'] = df.set_index(['year', 'countries']).index.map(color_dict_bar.get)
df['color_edges'] = df.set_index(['year', 'countries']).index.map(color_dict_edges.get)
df['sub_total'] = df.groupby('countries')['sites'].transform('sum')
df = df.sort_values([ 'year', 'sub_total'], ascending = False).reset_index(drop=True)
index | year | countries | sites | color_bars | color_edges | sub_total |
---|---|---|---|---|---|---|
0 | 2022 | Sweden | 15 | #C4D6F8 | #5375D4 | 28 |
1 | 2022 | Denmark | 10 | #E2AFA5 | #CE5A43 | 14 |
2 | 2022 | Norway | 8 | #9194A3 | #2C324F | 13 |
3 | 2004 | Sweden | 13 | #5375D4 | #4562C5 | 28 |
4 | 2004 | Denmark | 4 | #CE5A43 | #B6493F | 14 |
5 | 2004 | Norway | 5 | #2B314D | #1B203A | 13 |
Define the variables:
The x and y axis have been hardcoded, but it can be automated. Will revisit later.
year = df.year.unique()
sites = df.sites
img = [plt.imread("flags/sw-rd.png"),plt.imread("flags/de-rd.png"), plt.imread("flags/no-rd.png")]
#define the colors
colors_bars = df.color_bars
color_bar_end_circles = df.color_bars[:3].tolist() + df.color_bars[-3:].to_list()*2
color_edge_end_circles = ['w'] + df.color_edges[1:3].to_list() + ['w'] + df.color_edges[4:6].to_list()
edge_colors = df.color_edges
#define the x and y axes - the position of the circles
x = df.sites.tolist() + [0]*len(countries)
y=list(range(len(countries)))*len(countries)
Plot the stacked bar chart
This can be further improved with the annotations and the end of the bars. Will check it out in the next round.
fig, ax = plt.subplots(figsize=(10,4), facecolor = "w")
#plot all the circles at the end of the bars
ax.scatter(x,y, marker="o",s=620, color=color_bar_end_circles, zorder=2)
#plot the data label circles
ax.scatter(x[:6],y[:6], marker="o",s=450, color=edge_colors, ec =color_edge_end_circles, zorder=2)
#plot the bars
for i, col in enumerate(year):
temp_df = df[df.year==col]
ax.barh(temp_df.countries, temp_df.sites, height=0.3, zorder=1,)
#plot the color of the bars and the data labels
for bar,color in zip(ax.patches,colors_bars):
bar.set_facecolor(color)
ax.text(
bar.get_x() + bar.get_width() ,
bar.get_height()/2 + bar.get_y(),
round(bar.get_width()),
ha='center',va="center", color='w', size=10 )
#format the plot
ax.set_xlim(-1)
ax.set_xticks([])
ax.spines[['left', 'right','top','bottom']].set_visible(False)
#set flags
ax.yaxis.set_ticks(range(3), countries,weight = "bold", ha="left")
ax.tick_params(axis='y', which='major', labelsize=14, colors= '#101628',length=0,pad=65)
tick_labels = ax.yaxis.get_ticklabels()
for i,im in enumerate(img):
ib = OffsetImage(im, zoom=.04)
ab = AnnotationBbox(ib ,
tick_labels[i].get_position(),
frameon=False,
box_alignment=(+7, +0.5)
)
ax.add_artist(ab)
for site, year in zip(sites[0::3], year):
#add the annotations at the end of the first bar chart.
ax.annotate(year, xy = (site, -0.13), xytext=(site, -.55),
color='black', ha= "center", va="center",
bbox=dict(facecolor='none', edgecolor='#DEE2E4', boxstyle='round,pad=0.5'),
arrowprops=dict( arrowstyle='-',color = "#DEE2E4" ) ,
annotation_clip=False)
The result:

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