3 of 100: Lollipoll 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: In the future, I will try to add the colors to a dictionary and feed them to matplotlib, rather than having them in the dataframe.
This is the original viz that we are trying to recreate in matplotlib:

Import the packages
We will need matplotlib, and pandas.
import matplotlib.pyplot as plt
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 = {(2004,"Norway"): "#9194A3", (2022,"Norway"): "#2B314D",
(2004,"Denmark"): "#E2AFA5", (2022,"Denmark"): "#A54836",
(2004,"Sweden"): "#C4D6F8", (2022,"Sweden"): "#5375D4",
}
xy_ticklabel_color, xlabel_color, grand_totals_color, grid_color, datalabels_color ='#C8C9C9',"#101628","#101628", "#C8C9C9", "#2B314D"
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 |
Then we need to add: the x labels (year_lbl), the x axis title (Pct_change) and the color for each bar or lolipoll.
#custom sort
sort_order_dict = {"Denmark":2, "Sweden":3, "Norway":1, 2004:4, 2022:5}
df = df.sort_values(by=['year','countries',], key=lambda x: x.map(sort_order_dict))
# Add the x-axis labels
df['year_lbl'] ="'"+df['year'].astype(str).str[-2:].astype(str)
df['pct_change'] = df.groupby('countries', sort=False)['sites'].apply(
lambda x: x.pct_change()).to_numpy()
#Add the color based on the color dictionary
df['color'] = df.set_index(['year', 'countries']).index.map(color_dict.get)
year | countries | sites | year_lbl | pct_change | color | |
---|---|---|---|---|---|---|
4 | 2004 | Norway | 5 | ’04 | NaN | #9194A3 |
2 | 2004 | Denmark | 4 | ’04 | NaN | #E2AFA5 |
0 | 2004 | Sweden | 13 | ’04 | NaN | #C4D6F8 |
5 | 2022 | Norway | 8 | ’22 | 0.600000 | #2B314D |
3 | 2022 | Denmark | 10 | ’22 | 1.500000 | #A54836 |
1 | 2022 | Sweden | 15 | ’22 | 0.153846 | #5375D4 |
Define the variables:
#number of countries to loop over
countries = df.countries.unique()
Plot the lolipoll chart
Loop over countries to get one plot per country:
fig, axes = plt.subplots(ncols=len(countries), nrows=1, figsize=(8,6), sharex=True, sharey=True, facecolor= "white")
fig.tight_layout(pad=3.0)
#loop over the countries
for ctry , ax in zip(countries, axes.ravel()):
temp_df = df[df.countries==ctry]
pct = temp_df['pct_change'].max()
#format the plots
ax.set_ylim(0,df.sites.max()+5)
ax.set_xlim(-0.5,len(countries)-1.5)
ax.set_yticks([])
ax.tick_params(axis='both', which='both',length=0, labelsize=12,colors =xy_ticklabel_color)
ax.spines[['top', 'left', 'right']].set_visible(False)
ax.spines['bottom'].set_color(grid_color)
#add the circles at the end of the lolipoll bars
ax.scatter(temp_df.year_lbl, temp_df.sites, s=150, c= temp_df.color , edgecolors="w", zorder=2)
#add the vertical lines of the lolipoll
ax.vlines(list(range(len(countries)-1)), 0,temp_df.sites, color = temp_df.color, lw=4,zorder=1)
#add the data labels
for i,lb in enumerate(temp_df.sites):
ax.annotate(lb, xy=(i,lb+1), size=13,color =datalabels_color, weight= "bold", ha="center", va="center")
#add the x-axis titles
ax.set_xlabel(f'\u25B2\n{pct:.0%}\n\n{ctry}', color = xlabel_color, size = 12, weight= "bold")
ax.xaxis.set_label_coords(0.5, -0.1)
The result:

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