84 of 100: 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: I need to round the corners of the bars. Let’s see how I manage to do that!
This is the original viz that we are trying to recreate in matplotlib:

Import the packages
We will need the following packages:
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
import pandas as pd
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.
data = {
"year": [2004, 2022, 2004, 2022, 2004, 2022, 2004,2022],
"countries" : [ "Denmark", "Denmark","Sweden", "Sweden", "Norway", "Norway","Sweden", "Sweden"],
"sites": [4,10,13,15,5,8,0,0]
}
df= pd.DataFrame(data)
index | 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 two groups (group1 and group2) to aggregate the data to stack it. We also need to group the data by year and and subgroup to calculate the sub totals. After that is just to add the colors, and the country codes.
df['group'] = ['group1']*4+['group2']*4
df['colors'] = ["#CC5A43"]*2+["#5375D4"]*2+["#2C324F"]*2+["#5375D4"]*2
df['ctry_code'] = df.countries.astype(str).str[:2].astype(str).str.upper()
df['sub_total_group'] = np.where(df.countries=="Sweden", "Subtotal1", "Subtotal2")
df['sub_total'] = df.groupby(['sub_total_group', 'year'])['sites'].transform('sum')
#custom sort
sort_order_dict = {"Denmark":2, "Sweden":1, "Norway":3, 2004:4, 2022:5, 'group1':6, 'group2':7}
df = df.sort_values(by=['group','countries','year'], key=lambda x: x.map(sort_order_dict))
year | countries | sites | group | colors | ctry_code | sub_total_group | sub_total | |
---|---|---|---|---|---|---|---|---|
2 | 2004 | Sweden | 13 | group1 | #5375D4 | SW | Subtotal1 | 13 |
3 | 2022 | Sweden | 15 | group1 | #5375D4 | SW | Subtotal1 | 15 |
0 | 2004 | Denmark | 4 | group1 | #CC5A43 | DE | Subtotal2 | 9 |
1 | 2022 | Denmark | 10 | group1 | #CC5A43 | DE | Subtotal2 | 18 |
6 | 2004 | Sweden | 0 | group2 | #5375D4 | SW | Subtotal1 | 13 |
7 | 2022 | Sweden | 0 | group2 | #5375D4 | SW | Subtotal1 | 15 |
4 | 2004 | Norway | 5 | group2 | #2C324F | NO | Subtotal2 | 9 |
5 | 2022 | Norway | 8 | group2 | #2C324F | NO | Subtotal2 | 18 |
Define the variables
nr_years = df.year.unique()
groups = df.group.unique()
sub_total_groups = df.sub_total_group.unique()
colors = ["#CC5A43","#2C324F","#5375D4"]
img = [plt.imread("flags/sw-rd.png"),plt.imread("flags/de-rd.png")]
no = plt.imread("flags/no-rd.png")
Plot the chart
fig, axes = plt.subplots(ncols = df.year.nunique(), nrows = 1, figsize=(7,5),sharex=True, sharey=True, facecolor = "#FFFFFF", zorder= 1)
fig.tight_layout(pad=3.0)
#one subplot per year
for yr, ax in zip(nr_years, axes.ravel()):
temp_df = df[df.year ==yr]
countries = temp_df.ctry_code
bottom= np.zeros(2)
for i,g in enumerate(groups):
temp_df2= temp_df[temp_df.group ==g]
#print( temp_df2)
x = temp_df2.group
y = temp_df2.sites
color = temp_df2.colors
ax.bar(range(2),y, bottom = bottom, color= color)
bottom +=y
#add data labels
for i,sg in enumerate(sub_total_groups):
temp_df3 = temp_df[temp_df.sub_total_group ==sg]
total = temp_df3.sub_total.max()
ax.text(i, total+1 , total, ha='center', color = "#171D2F", size = 20,) #weight='bold')
#add grand subtotals
for bar, country in zip(ax.patches, countries):
#print(bar)
ax.text(
bar.get_x() + bar.get_width() / 2, # Put the text in the middle of each bar. get_x returns the start, so we add half the width to get to the middle.
1 + bar.get_y(), # Vertically, add the height of the bar to the start of the bar, along with the offset.
round(bar.get_height()), # This is actual value we'll show.
ha='center', color='w', weight= "bold", size=16 )
ax.text(
bar.get_x() + bar.get_width() / 2, # Put the text in the middle of each bar. get_x returns the start, so we add half the width to get to the middle.
2.2 + bar.get_y(), # Vertically, add the height of the bar to the start of the bar, along with the offset.
country, # This is actual value we'll show.
ha='center', color='w', weight= "light", size=12)
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.tick_params(axis='both', which='both',length=0)
ax.spines[['top', 'left','bottom', 'right']].set_visible(False)
ax.set_title(yr,color = "#7C8091", weight= "bold", size = 14, x= 0.5, y = 1.2)
#set flags and tick labels
ax.xaxis.set_ticks(range(2), [ 'SE','DK+NO',])
tick_labels = ax.xaxis.get_ticklabels()
ax.tick_params(axis='both', which='major', length=0, labelsize=12,colors= '#171D2F',pad =50)
box_alignment_x= [0.5,0.5]
for (i,im), bx in zip(enumerate(img),box_alignment_x):
#print(tick_labels[i].get_position())
ib = OffsetImage(im, zoom=.04)
ab = AnnotationBbox(ib,
tick_labels[i].get_position(),
frameon=False,
box_alignment=(bx, 2)
)
ax.add_artist(ab)
#add overlapping flag 2022
image_box = OffsetImage(no, zoom = 0.04) #container for the image
ab = AnnotationBbox(image_box, (0,0), xybox= (1.15,-2.05), frameon = False)
ax.add_artist(ab)
#add overlapping flag 2004
image_box = OffsetImage(no, zoom = 0.04) #container for the image
ab = AnnotationBbox(image_box, (0,0), xybox= (-1.35,-2.05), frameon = False)
ax.add_artist(ab)
#add dividing line
line = plt.Line2D((.5,.5),(0,1), color="#E1E5E7", linewidth=1)
fig.add_artist(line)
The result:

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