26 of 100: Semicircular scatter 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 automate it a bit more.
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.
color_dict = {(2022,"Norway"): "#9194A3", (2004,"Norway"): "#2B314D",
(2022,"Denmark"): "#E2AFA5", (2004,"Denmark"): "#A54836",
(2022,"Sweden"): "#C4D6F8", (2004,"Sweden"): "#5375D4",
}
xlabel_color, title_color, datalabels_color ="#101628","#969CA3", "#2B314D"
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)
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 the year labels and then sort the data.
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1, }
df = df.sort_values(by=['countries',], key=lambda x: x.map(sort_order_dict))
#Add the color based on the color dictionary
df['color'] = df.set_index(['year', 'countries']).index.map(color_dict.get)
df['diff'] = df.groupby(['countries'])['sites'].diff()
df['diff']= np.where(df['diff'].isnull(), df.sites, df['diff']).astype(int)
year | countries | sites | year_lbl | color | diff | |
---|---|---|---|---|---|---|
4 | 2004 | Norway | 5 | ’04 | #2B314D | 5 |
5 | 2022 | Norway | 8 | ’22 | #9194A3 | 3 |
2 | 2004 | Denmark | 4 | ’04 | #A54836 | 4 |
3 | 2022 | Denmark | 10 | ’22 | #E2AFA5 | 6 |
0 | 2004 | Sweden | 13 | ’04 | #5375D4 | 13 |
1 | 2022 | Sweden | 15 | ’22 | #C4D6F8 | 2 |
Define the variables
countries = df.countries.unique()
years = df.year.unique()
directions = [-1,1,-1]
codes = df.year_lbl.unique()
positions = [["left"]*2 , ["right"]*2 , ["left"]*2]
ctry_positions = [["right"], ["left"], ["right"]]
x_label_positions = [[0.4], [0.7], [0.4]]
x_site_positions = [[0.2], [0.6], [0.2]]
img = [plt.imread("flags/no-rd.png"),plt.imread("flags/de-rd.png"), plt.imread("flags/sw-rd.png")]
#create a list of list for coloring the radial grid lines
grid_colors = [['#E1E5E7']*g + ['w']*g for g in df.sites[1::2]]
#create the list of colors for the site label (list of lists of colors)
site_label_colors = [[i] for i in df.color[0::2].to_list()]
Plot the chart
fig, axes = plt.subplots(nrows=3, ncols=1,figsize=(5,7), facecolor = "#FFFFFF",subplot_kw=dict(polar=True),)
fig.tight_layout(pad=-4.8)
for ax, xpos , xsitepos, site_colors, country, line_color, ctry_position, position, direction, im in zip(axes.ravel(),x_label_positions, x_site_positions, site_label_colors, countries,grid_colors,ctry_positions, positions, directions,img):
#add flag
image_box = OffsetImage(im, zoom = 0.04) #container for the image
ab = AnnotationBbox(image_box, (0, 0), frameon = False)
ax.add_artist(ab)
#plit the radial lines
sites_max = df[df.countries==country]['sites'].max()
r= [sites_max]*(sites_max*2)
angles = np.arange(0,2*np.pi,2*np.pi/(sites_max*2) )
ax.plot([angles, angles],[0,sites_max],'-',lw=1, zorder =0)
#change the color of the radial grid lines
for i, j in enumerate(ax.lines):
j.set_color(line_color[i])
#plot the bubbles
diff = df[df.countries == country]['diff'].to_numpy()
color = df[df.countries == country]['color'].to_numpy()
bubble_color = [color[0]]*diff[0] +[color[1]]*diff[1] + ['w']*sites_max
ax.scatter(angles, r,s=80,c=bubble_color,ec="w",zorder =1)
for x, xs, site_color, pos in zip(xpos , xsitepos, site_colors, ctry_position):
#add site label
ax.text (xs, 0.5, f'{sites_max} ', transform = ax.transAxes, size=11, weight ="bold",color = site_color, ha= pos, va="center",)
#add country label
ax.text (x, 0.5, f'{country}', transform = ax.transAxes, color= title_color, ha= pos, va="center")
site = df[df.countries==country]['sites']
for s,pos,xy_color, code in zip(site,position, color, codes):
ax.text( angles[s-1], r[s-1], f' {code} ', color =xy_color, ha = pos, va = "top", zorder =1000)
ax.set_theta_zero_location("N")
ax.set_theta_direction(direction)
ax.set_axis_off()
ax.set_ylim(0,sites_max+1)
The result:

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