66 of 100: Proportional Area 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: Location of things.

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 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.

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)

We need to sort the data.

 df = df.sort_values(['year' ,'sites' ], ascending=False ).reset_index(drop=True)

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()


colors =[ "#5375D4","#A54836","#2B314D"]*2

X, Y = np.meshgrid(np.arange(3), np.arange(2))

Plot the chart

fig, ax = plt.subplots(figsize=(8,4), facecolor = "#FFFFFF")
fig.tight_layout(pad=1.0)

X, Y = np.meshgrid(np.arange(3), np.arange(2))

ax.scatter(X,Y,s=sites*400,marker=icon_marker, color = colors, zorder=1)

for i, site in enumerate(sites):
    ax.annotate(site, (X.flatten()[i], Y.flatten()[i]-0.4), ha= "center", va="center", color= "k")

ax.axhline(-0.5, -0 ,1, ls="dotted", color ="#D4D5D7", clip_on = False )
ax.axhline(0.4, 0 , 1, ls="dotted", color ="#D4D5D7" , clip_on = False )
ax.axhline(1.4, 0 , 1, ls="dotted", color ="#D4D5D7" , clip_on = False )

ax.set_xlim(-0.5,2.5)
ax.set_ylim(-0.5,1.5)

ax.tick_params(axis='both', which='major',labeltop=True,labelbottom=False,length=0,labelsize=12,colors= '#757C85',pad = 2.5)
ax.spines[['top', 'left','right' ,'bottom']].set_visible(False)
ax.yaxis.set_ticks(range(len(years)), labels =years)
ax.xaxis.set_ticks(range(len(countries)), labels = countries)   

The result: