46 of 100: Dot plot on a map 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!
This is the original viz that we are trying to recreate in matplotlib:

Import the packages
We will need the following packages:
import geopandas as gpd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.lines import Line2D
from svgpathtools import svg2paths
from svgpath2mpl import parse_path
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,color_map ='#101628','#D3D3D3'
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 subtotals for each year, the difference in sites between the years and then 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":3, "Norway":1, 2004:5, 2022:4}
df = df.sort_values(by=['countries','year',], key=lambda x: x.map(sort_order_dict))
df['color']= df.year.map(color_dict)
year | countries | sites | diff | sub_total | color | |
---|---|---|---|---|---|---|
5 | 2022 | Norway | 8 | 3 | 8 | #CC5A43 |
4 | 2004 | Norway | 5 | 5 | 8 | #5375D4 |
3 | 2022 | Denmark | 10 | 6 | 10 | #CC5A43 |
2 | 2004 | Denmark | 4 | 4 | 10 | #5375D4 |
1 | 2022 | Sweden | 15 | 2 | 15 | #CC5A43 |
0 | 2004 | Sweden | 13 | 13 | 15 | #5375D4 |
Generate the heritage icon
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))
Get the map data
map_df = gpd.read_file("https://raw.githubusercontent.com/eurostat/Nuts2json/master/pub/v2/2021/3035/20M/0.json")
map_df['country'] = map_df['id'].astype(str).str[:2]
map_df = map_df[map_df.country.isin(['NO','SE', 'DK']) ]
Define the variables
lat= [0.32,0.22,0.68]
lon =[0.5,0.14,0.28]
x = len(df.year.unique())
countries = df.countries.unique()
totals = df.sub_total.unique()
years = df.year.unique()
#create the matrix
X= np.repeat(np.arange(1,6),3)
Y = np.tile(np.arange(1,4),5)
xmin =[1,1,0]
xmax =[2,2.5,-1]
Plot the chart
for lt,ln,country, total, xmi,xma in zip(lat,lon,countries, totals,xmin,xmax):
ax_matrix= fig.add_axes([lt ,ln , 0.1, 0.2])
#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_matrix.scatter(Y,X, marker=icon_marker, s=800, color= symbol_color)
ax_matrix.set_xlim(0,4)
ax_matrix.set_ylim(0,6)
ax_matrix.set_xlabel(f'{country} {total}', color=xy_ticklabel_color, size = 14)
ax_matrix.set_yticklabels([])
ax_matrix.set_xticklabels([])
ax_matrix.grid(False)
ax_matrix.set_frame_on(False)
ax_matrix.tick_params(axis='both', which='both',length = 0)
ax_matrix.axhline( y= -0.4, xmin =xmi, xmax=xma,lw=1, clip_on = False, color="#939AA1")
#add legend
text_legends = ["Before", "After"]
colors = ["#5475D6","r",]
lines = [Line2D([0], [0], color=c, marker=icon_marker,linestyle='', markersize=20,) for c in colors]
labels = [f'{text_legend} 2004' for text_legend in text_legends]
for year in years:
plt.figlegend( lines,labels,
labelcolor=xy_ticklabel_color,
bbox_to_anchor=(0.5, -0.05), loc="lower center",
ncols = 2,frameon=False, fontsize= 12)
The result:

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