48 of 100: 3D bar 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:
This is not quite right, I am publishing it anyway in case you need a stacked 3D bar, so you can see how I did it, but as you can see the perspective is not right, the labels are hardcoded, the labels orientation is not right eithter…yeah, needs rework.
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
from matplotlib.lines import Line2D
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" : ["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 year labels, the percentage and then sort the data.
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
df['sub_total'] = df.groupby('year')['sites'].transform('sum')
df['pct_group'] = 100 * df['sites'] / df.sub_total
Define the variables
nr_countries = df.countries.nunique()
nr_years = df.year.nunique()
years = df.year.unique()
colors = ["#A54836","#2B314D", "#5375D4", ]
# width/depth of bars
dx = 100
dz = np.sort(df.sub_total)*10
# seperation between the two bars
separation = 3 * dx
# x and y anchor points of all the bars
xs = [0, 0, 0, separation, separation, separation]
zs = 0
# bar y-positions and heights
ys = []
dy = []
for year in years:
sites = df[df["year"] == year].sort_values("countries")["pct_group"].values
zp = np.cumsum(sites).tolist()
ys.extend([0] + zp[: len(zp) - 1])
dy.extend(sites.tolist())
print(sites, zp, ys, dy)
Plot the chart
fig = plt.figure(figsize=(15,10))
ax = fig.add_subplot( projection="3d")
ax.bar3d(xs, ys, zs, dx, dy, dz, color=colors + colors)
# add year labels
for i, year in enumerate(years):
ax.text(xs[i * nr_countries]+100, ys[i * nr_countries]-50, z=3, s=f"{year}", fontweight="bold", fontsize="large")
ax.set_aspect("equal")
ax.set_axis_off()
#add legend
lines = [Line2D([0], [0], color=c, marker='o',linestyle='', markersize=10,) for c in colors]
labels = ["Norway", "Denmark", "Sweden"]
plt.legend(lines, labels,
bbox_to_anchor=(0.5, -0.05), loc="lower center",
ncols = 3,frameon=False, fontsize= 14)
#add data labels
ax.text(40, 10, 210, "18%", "x", color = "w", size = 8, zorder=3)
ax.text(40, 30, 210, "23%", "x", color = "w", size = 8, zorder=3)
ax.text(40, 70, 210, "59%", "x", color = "w", size = 8, zorder=3)
ax.text(330, 30, 310, "30%", "x", color = "w", size = 8, zorder=3)
ax.text(330, 60, 310, "24%", "x", color = "w", size = 8, zorder=3)
ax.text(330, 100, 310, "46%", "x", color = "w", size = 8, zorder=3)
ax.text(-10, 100, 230, "22", "x", color = "w", size = 12, zorder=3,
bbox=dict(facecolor='black', edgecolor='black', boxstyle='round,pad=0.3'))
ax.text(290, 100, 350, "33", "x", color = "w", size = 12, zorder=3,
bbox=dict(facecolor='black', edgecolor='black', boxstyle='round,pad=0.3'))
The result:

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