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39 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.

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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!

To be improved: I am missing one y axis bar and the cutting of the bars. The hardcoding can be removed.

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,  grid_color, datalabels_color ='#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)
indexyearcountriessites
02004Sweden13
12022Sweden15
22004Denmark4
32022Denmark10
42004Norway5
52022Norway8

We need to sort the data.

sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1}
df = df.sort_values(by=['countries',], key=lambda x: x.map(sort_order_dict))
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
df['ctry_code'] = df.countries.astype(str).str[:2].astype(str).str.upper()
indexyearcountriessitescolorctry_code
42004Norway5#2B314DNO
52022Norway8#2B314DNO
22004Denmark4#A54836DE
32022Denmark10#A54836DE
02004Sweden13#5375D4SW
12022Sweden15#5375D4SW

Define the variables

dz=[]
for country in countries:
    sites = df[df.countries == country].sort_values("year", ascending =False)["sites"].values
    dz.extend(sites.tolist())

    print( dz)

xs = [x*2 for x in range(1, len(years)+1)]*len(countries)
ys = np.repeat([x*2 for x in range(1, len(countries)+1)], len(years))
zs = 0

dx = 0.8
dy = 1.2
colors_bar = df.color
colors_ticks = df.color.unique()
code = df.ctry_code.unique()
years =df.year.unique()

Plot the chart

fig = plt.figure(figsize=(6,6))
ax = fig.add_subplot(1, 1, 1, projection="3d")

#move the z axis to the the left
#https://stackoverflow.com/questions/48442713/move-spines-in-matplotlib-3d-plot
ax.zaxis._axinfo['juggled'] = (1,2,0)

ax.bar3d(xs, ys, zs, dx, dy, dz, color=colors_bar)

ax.set(xlim = [1,6], ylim = [1,8], zlim = [0,16])

ax.yaxis.set_ticks(np.arange(3, 8, 2), labels = code)
ax.xaxis.set_ticks(np.arange(2.5, 6, 2), labels = reversed(years))
ax.zaxis.set_ticks(np.arange(0, 16, 5), )

for axis in [ax.xaxis, ax.yaxis, ax.zaxis]:
    axis._axinfo['tick']['inward_factor'] = 0
    axis._axinfo['tick']['outward_factor'] = 0      #remove ticks
    axis.set_pane_color("w")                        # Make panes transparent


ax.xaxis._axinfo["grid"].update({"linewidth":0, "color" : "w"}) #color gridline
ax.yaxis._axinfo["grid"].update({"linewidth":0, "color" : "w"})

# Transparent spines
ax.xaxis.line.set_color(grid_color)   
ax.yaxis.line.set_color(grid_color)
ax.zaxis.line.set_color(grid_color)

ax.plot([6.1,6.1], [8.1,8.1],zs=[0,15], color = grid_color)
ax.plot([0.9,0.9], [8.1,8.1],zs=[0,15], color = grid_color)

ax.tick_params(axis='x', which='major',length=0, labelsize=12,colors= xy_ticklabel_color)
ax.tick_params(axis='z', which='major',length=0, labelsize=12,colors= grid_color)
ax.tick_params(axis='y', which='major',length=0, labelsize=12)



for ytick, color in zip(ax.get_yticklabels(), colors_ticks):
    ytick.set_color(color)
  

The result:

39 of 100: 3D bar chart in matplotlib
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