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75 of 100: Icon 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 matplotlib as mpl
from matplotlib.lines import Line2D

from svgpathtools import svg2paths
from svgpath2mpl import parse_path

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: "#A54836", 2004: "#5375D4", }

xy_ticklabel_color, legend_color, grid_color, datalabels_color ='#101628',"#101628", "#C8C9C9", "#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 sort the data.

 df['diff']=df.groupby('countries')['sites'].diff().fillna(df.sites).astype(int)
df['sub_total'] = df.groupby('countries')['diff'].transform('sum')
#custom sort
sort_order_dict = {"Denmark":2, "Sweden":1, "Norway":3, 2004:5, 2022:4}
df = df.sort_values(by=['countries','year',], key=lambda x: x.map(sort_order_dict))
#map the colors of a dict to a dataframe
df['color']= df.year.map(color_dict)
yearcountriessitesdiffsub_totalcolor
52022Sweden15215#A54836
42004Sweden131315#5375D4
12022Denmark10610#A54836
02004Denmark4410#5375D4
32022Norway838#A54836
22004Norway558#5375D4

Create the heritage symbol

icon_path, attributes = svg2paths('flags/Unesco_World_Heritage_logo_notext_transparent.svg')
#matplotlib path object of the icon
icon_marker  = parse_path(attributes[0]['d'])

icon_marker.vertices -= icon_marker.vertices.mean(axis=0)
icon_marker = icon_marker.transformed(mpl.transforms.Affine2D().rotate_deg(180))
icon_marker = icon_marker.transformed(mpl.transforms.Affine2D().scale(-1,1))

Define the variables

countries = df.countries.unique()
sites = df.sites
years = df.year.unique().tolist()
sub_total = df.sub_total.unique()

#create the matrix
X= np.repeat(np.arange(1,6),3)
Y = np.tile(np.arange(1,4),5)

Plot the chart

fig, axes = plt.subplots(nrows=1, ncols=3,figsize=(8,4))

for ax,country, total in zip(axes.ravel(),  countries, sub_total):
    
    #add the colors for the symbol
    diff= df[df.countries==country]['diff']
    colors= df[df.countries==country]['color']
    symbol_color = np.insert(np.repeat(colors,diff).to_numpy(), 0,  ["#FFFFFF"]*(15-total))

    ax.scatter(Y,X, marker=icon_marker, s=800, color= symbol_color)
    ax.set_xlim(0,4)
    ax.set_ylim(0,6)
    ax.invert_xaxis()
    ax.set_title(country, weight= "bold", color= xy_ticklabel_color)
    ax.axis('off')

#add legend
text_legends = ["Before", "After"]
lines = [Line2D([0], [0], color=c,  marker=icon_marker,linestyle='', markersize=20,) for c in colors]
labels  = [f'{text_legend} 2004' for year, text_legend in zip(years, text_legends)]
for year in years:
    plt.figlegend( lines,labels,   
                  labelcolor=legend_color,
            bbox_to_anchor=(0.5, -0.05), loc="lower center",
                ncols = 2,frameon=False, fontsize= 12)

The result:

75 of 100: Icon chart in matplotlib
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