2 of 100: Gauge 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!
Left to do: I need to curve the text legends.
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 = {"Norway": "#2B314D", "Denmark": "#A54836", "Sweden": "#5375D4", }
xy_ticklabel_color, grand_totals_color, grid_color, datalabels_color ='#757C85',"#101628", "#C8C9C9", "#FFFFFF"
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)
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 year labels and then sort the data.
df = df.sort_values([ 'sites'], ascending=True ).reset_index(drop=True)
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
year | countries | sites | year_lbl | color | |
---|---|---|---|---|---|
0 | 2004 | Denmark | 4 | ’04 | #A54836 |
1 | 2004 | Norway | 5 | ’04 | #2B314D |
2 | 2022 | Norway | 8 | ’22 | #2B314D |
3 | 2022 | Denmark | 10 | ’22 | #A54836 |
4 | 2004 | Sweden | 13 | ’04 | #5375D4 |
5 | 2022 | Sweden | 15 | ’22 | #5375D4 |
Define the variables
First we split the semi arc into 20 bars, offset it by 2 and we will have a bar height of 0.5
nr_bars = 20
bar_height = 0.5 #height of the bars
offset = 2 # offset of the bars
Then we define the angles for the bars and find what the angles are for each site (rad_x[df.site]) and define the location of the bubbles and arcs. Once we defined everything, we will add it to the dataframe so we slice it based on country.
#divide 180 degrees into 20 bars
rad_x = np.deg2rad(np.linspace(0,180,nr_bars, endpoint= False))
#add the angle for each site to the dataframe
df['angles'] = rad_x[df.sites]
#get the radius for each circle
r= [2+bar_height/3,2+bar_height/3*2,2+bar_height/3*2,2+bar_height/3,2+bar_height/2,2+bar_height/2]
#add the radius to the dataframe
df['radius'] = r
year | countries | sites | year_lbl | color | angles | radius | |
---|---|---|---|---|---|---|---|
0 | 2004 | Denmark | 4 | ’04 | #A54836 | 0.628319 | 2.166667 |
1 | 2004 | Norway | 5 | ’04 | #2B314D | 0.785398 | 2.333333 |
2 | 2022 | Norway | 8 | ’22 | #2B314D | 1.256637 | 2.333333 |
3 | 2022 | Denmark | 10 | ’22 | #A54836 | 1.570796 | 2.166667 |
4 | 2004 | Sweden | 13 | ’04 | #5375D4 | 2.042035 | 2.250000 |
5 | 2022 | Sweden | 15 | ’22 | #5375D4 | 2.356194 | 2.250000 |
Next, we define the rest of the variables, the connection style for the arcs, as well as defining the axis labels and slice the angles and radius by country so we can then place the arcs using them.
year_labels= df.year_lbl
colors = df.color
legend = df.countries
theta = df.angles
radius = df.radius
sites = df.sites
#arch for the arrows
connectionstyle = ["arc3,rad=0.24","arc3,rad=0.15","arc3,rad=0.15"]
#slice angles and radius by country
angles_bycountry = df.groupby('countries')['angles'].apply(lambda x: x.values)
r_bycountry = df.groupby('countries')['radius'].apply(lambda x: x.values)
#add axis labels
axis_labels = list(range(0,25,5))
theta_labels = np.array([rad_x[i] for i in list(range(0,20,5))])
label_pos = np.append(theta_labels,np.pi) #add the last coordinate of the label
Plot the chart
With everything in place it is time to plot the chart:
fig, ax = plt.subplots(figsize=(10, 7), subplot_kw=dict(polar=True))
ax.set_theta_zero_location("W") # theta=0 at the top
ax.set_theta_direction(-1) # theta increasing clockwise
ax.set_thetamax(180) # stop at 180 degrees
ax.bar(rad_x, width=np.deg2rad(180/nr_bars), height=bar_height, bottom=offset,
linewidth=1, edgecolor="white",color=grid_color,
align="edge")
ax.scatter(theta, radius, color=colors, s = 305, zorder=2)
#add arcs
for angle,r, color,connectionstyle in zip(angles_bycountry,r_bycountry, colors.unique(),connectionstyle):
ax.annotate("", xy=(angle[0],r[0]), xytext=(angle[1],r[1]), zorder = 1,
arrowprops=dict(arrowstyle='-', connectionstyle = connectionstyle,
color = color, alpha= 0.5, linewidth=2, linestyle='-', antialiased=True))
#bubble data labels
for t, r,lb in zip(theta, radius, year_labels):
ax.annotate(lb, xy=(t,r), color ="w", size= 8, weight= "bold", ha="center", va="center")
# axis labels
for loc, val, r in zip( label_pos, axis_labels, radius):
ax.annotate(val, xy=(loc, offset-0.2), size= 12, va= 'center', ha= 'center', color=xy_ticklabel_color)
ax.text(0.5, 0, "World Heritage \nSites", size=12, ha="center", va="center")
ax.set_axis_off()
The result:

Reader Interactions