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37 of 100: Tabular circle 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.

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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!

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

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: "#CC5A43", 2004: "#5375D4", }

xy_ticklabel_color,  datalabels_color ='#757C85',"#FFFFFF"

data = {
    "year": [2004, 2022, 2004, 2022, 2004, 2022],
    "countries" : [ "Denmark", "Denmark", "Norway", "Norway","Sweden", "Sweden",],
    "sites": [4,10,5,8,13,15]
}
df= pd.DataFrame(data)
indexyearcountriessites
02004Sweden13
12022Sweden15
22004Denmark4
32022Denmark10
42004Norway5
52022Norway8

We need to create the country code and year labels and then sort the data.

df['ctry_code'] = df.countries.astype(str).str[:2].astype(str).str.upper()
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)

sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1, 2004:4, 2022:5}
df = df.sort_values(by=['countries','year',], key=lambda x: x.map(sort_order_dict))
df['color']= df.year.map(color_dict)
yearcountriessitesctry_codeyear_lblcolor
22004Norway5NO’04#5375D4
32022Norway8NO’22#CC5A43
02004Denmark4DE’04#5375D4
12022Denmark10DE’22#CC5A43
42004Sweden13SW’04#5375D4
52022Sweden15SW’22#CC5A43

Define the variables

years = df.year.unique()
countries = df.countries.unique()
sites = df.sites
colors = df.color

Plot the chart

fig, axes = plt.subplots(ncols=2,nrows=3,figsize=(5,5),sharex=True, sharey=True, facecolor = "#FFFFFF")


for ax, color, site in zip(axes.ravel(), colors*3, sites):
    x=list(np.arange(0,site,1))
    y=list(reversed(np.arange(16- site,16,1)))

    ax.plot(x, y, '-', lw = 30, color = color, solid_capstyle="round",zorder=0 )    
    ax.scatter(0,15,s = 860,color= '#2C324F', zorder=2) 
    ax.annotate(site, (0,15),size= 12, color = datalabels_color, ha ="center", va ="center")
    ax.set_yticklabels([])
    ax.set_xticklabels([])
    ax.spines[['left','right','bottom','top']].set_visible(False)
    ax.tick_params(length=0)

ax.set_xlim(-4,20)
ax.set_ylim(-4,22)


for ax, year in zip(axes[:,0], countries):
    ax.set_ylabel(year, rotation=0,size= 12, color = xy_ticklabel_color)
    ax.yaxis.set_label_coords(-0.5, 0.5)

for ax, col in zip(axes[0], years):
    ax.set_title(col, x=0.5, y=1.2, color = xy_ticklabel_color) 

The result:

37 of 100: Tabular circle chart in matplotlib
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