59 of 100: Linked circular 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: A bit of hardcoding for the arrows and missing the curved text.
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
import numpy as np
import pandas as pd
from matplotlib.lines import Line2D
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 = {"Norway": "#2B314D", "Denmark": "#A54836", "Sweden": "#5375D4", }
xy_ticklabel_color, grid_color ='#929EA7',"#929EA7"
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 sort the data.
df = df.sort_values([ 'countries', 'year'], ascending=True ).reset_index(drop=True)
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
year | countries | sites | color | |
---|---|---|---|---|
0 | 2004 | Denmark | 4 | #A54836 |
1 | 2022 | Denmark | 10 | #A54836 |
2 | 2004 | Norway | 5 | #2B314D |
3 | 2022 | Norway | 8 | #2B314D |
4 | 2004 | Sweden | 13 | #5375D4 |
5 | 2022 | Sweden | 15 | #5375D4 |
Define the variables
countries = df.countries.unique()
years = df.year.unique()
sites =df.sites
max_sites = df.sites.max()+1
colors = df.color.unique()
edge_colors = df.color
face_colors = [["w"]*3, colors]
Plot the chart
fig, ax = plt.subplots(figsize=(5,7), facecolor = "#FFFFFF",subplot_kw=dict(polar=True) )
for year,face_colors in zip(years,face_colors):
temp_df = df[df.year==year]
for site,face_color,edge_color in zip(temp_df.sites,face_colors,colors):
ax.scatter((2 *np.pi)/ max_sites *site, 1,s=100,lw= 2,fc= face_color, ec=edge_color,zorder =1, clip_on=False)
ax.axvline( 0, 0.9, 1.1, color=grid_color, lw=1,zorder =0,clip_on= False )
ax.axvline( (2 *np.pi)/max_sites * (max_sites-1) , 0.9, 1.1, color=grid_color, lw=1,zorder =0,clip_on= False )
one_widget_angle= 360/max_sites
ax.set_theta_zero_location("N")
ax.set_theta_offset(np.deg2rad(90-one_widget_angle/2)) #rotate 0 position
ax.set(thetamin=0, thetamax=360-one_widget_angle)
thetatick_locs = np.arange(0,360,360/max_sites)
thetatick_labels = range(0,max_sites,1)
ax.set_thetagrids(thetatick_locs, thetatick_labels ,fontsize=12, zorder =0,color = "#929EA7" )
#add the polar labels
for label, angle in zip(ax.get_xticklabels(), thetatick_locs):
x, y = label.get_position()
lab = ax.text(x, y-0.05, label.get_text(), transform=label.get_transform(),
ha=label.get_ha(), va=label.get_va(),color = xy_ticklabel_color)
if angle <= 180:
lab.set_rotation(0-angle)
else:
lab.set_rotation(360-angle)
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_theta_direction(-1)
ax.set_rmax(1)
ax.grid(False,)
ax.spines[['start','end']].set_color('w')
ax.spines[['polar']].set_color(grid_color)
for k, spine in ax.spines.items(): #ax.spines to the back
spine.set_zorder(0)
sites2022 = df[df.year==df.year.max()]['sites']
sites2004 = df[df.year==df.year.min()]['sites']
for s22,s04, color in zip(sites2022,sites2004,colors):
#create the arrows
ax.annotate("",
xy=((2 *np.pi)/ max_sites *s22,0.95), # theta, radius
xytext=((2 *np.pi)/ max_sites *s04,0.95),
arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.5',
color=color, linewidth=2, linestyle='-', antialiased=True))
#add legend
color_legend = ["w","#838B93"]
marker_edge_color = ["#838B93","#838B93"]
lines = [Line2D([0], [0], color=c, marker='o',linestyle='',markeredgecolor=ec, markersize=10,) for c, ec in zip(color_legend, marker_edge_color)]
plt.figlegend( lines,years,
bbox_to_anchor=(0.5, 0), loc="lower center",
ncols = 2,frameon=False, fontsize= 10)
The result:

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