17 of 100: Bump 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: Automate the chart limits and ticks.
This is the original viz that we are trying to recreate in matplotlib:

Import the 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 ='#757C85', "#C8C9C9", "#FFFFFF"
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)
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 |
Then we need to add the rank and sort:
df = df.sort_values(['year','sites' ], ascending=False ).reset_index(drop=True)
df["rank"] = df.groupby("year")["sites"].rank().astype(int)
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
index | year | countries | sites | rank | color |
---|---|---|---|---|---|
0 | 2022 | Sweden | 15 | 3 | #5375D4 |
1 | 2022 | Denmark | 10 | 2 | #A54836 |
2 | 2022 | Norway | 8 | 1 | #2B314D |
3 | 2004 | Sweden | 13 | 3 | #5375D4 |
4 | 2004 | Norway | 5 | 2 | #2B314D |
5 | 2004 | Denmark | 4 | 1 | #A54836 |
Define the variables
unique_countries = df.countries.unique()
countries = df.countries
sites = df.sites
x = df.year
y = df['rank']
colors = df.color.unique()
Plot the chart
unique_countries = df.countries.unique()
countries = df.countries
sites = df.sites
x = df.year
y = df['rank']
colors = df.color.unique()
fig, ax = plt.subplots(figsize=(4,5), facecolor = "#FFFFFF")
for color, country in zip(colors,unique_countries):
temp_df= df[df.countries ==country]
ax.plot(temp_df.year, temp_df['rank'], '-o', markersize = 28, mec = "w", mew=4, color = color, zorder=1)
# Get any single marker radius
marker_radius = np.sqrt(ax.lines[0].get_markersize() / np.pi)
for i, site in enumerate(sites):
ax.annotate(site, (x[i], y[i]), ha= "center", va="center", color= datalabels_color)
#add country legend at the end bar only
offset_text = df['year'].max() + marker_radius * 1.4
for y, country, color in zip(range(1,4), reversed(countries[:3]), reversed(colors)):
ax.annotate(country, (offset_text, y), ha= "left", va="center", color= color, annotation_clip=False)
ax.yaxis.set_ticks(np.arange(1, len(unique_countries)+1, 1))
ax.tick_params(axis='both', which='major',labeltop=True,labelbottom=False,length=0,labelsize=16, colors= xy_ticklabel_color)
ax.spines[['top','bottom','left','right']].set_visible(False)
ax.xaxis.set_ticks(x, labels =x)
#draws in the data coordinates hlines
ax.hlines(np.linspace(0.5, len(unique_countries)+.5, num=len(unique_countries)+1), 1995, 2034, lw=0.5, color=grid_color, clip_on=False , zorder =0)
The result:

Based on the current “To be improved: Automate the chart limits and ticks.”
1. Remove the set_xlims() and set_ylims()
2. Get any single marker radius (at line 6):
marker_radius = np.sqrt(ax.lines[0].get_markersize() / np.pi)
3. Change the offset_text variable:
offset_text = df[‘year’].max() + marker_radius * 1.1
Here, I am keeping 10% of the marker as space
4. Set horizontal alignment to left (at line 13) and remove 2022:
ax.annotate(country, (offset_text, y), ha= “left”, va=”center”, color= color, annotation_clip=False)
I loved how you get the marker size, clever!