41 of 100: Dot plot 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!
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 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, xy_label_color, ='#101628',"#101628",
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 |
We need to create the country codes, colors and then sort the data.
df['ctry_code'] = df.countries.astype(str).str[:2].astype(str).str.upper()
df = df.sort_values([ 'countries'], ascending=True ).reset_index(drop=True)
#map the colors of a dict to a dataframe
df['color']= df.countries.map(color_dict)
index | year | countries | sites | ctry_code | color |
---|---|---|---|---|---|
0 | 2004 | Denmark | 4 | DE | #A54836 |
1 | 2022 | Denmark | 10 | DE | #A54836 |
2 | 2004 | Norway | 5 | NO | #2B314D |
3 | 2022 | Norway | 8 | NO | #2B314D |
4 | 2004 | Sweden | 13 | SW | #5375D4 |
5 | 2022 | Sweden | 15 | SW | #5375D4 |
Define the variables
nr_years = df.year.unique()
colors = df.color
Plot the chart
fig, axes = plt.subplots(ncols = df.year.nunique(), nrows = 1, figsize=(6,5),sharex=True, sharey=True, facecolor = "#FFFFFF", zorder= 1)
fig.tight_layout(pad=6.0)
for yr, ax in zip(nr_years, axes.ravel()):
temp_df = df[df.year ==yr]
x = temp_df.ctry_code
y = temp_df.sites
for value, color, count in zip(x, colors, y):
ax.plot([value]*count, list(range(count)), 'co', ms=10, linestyle='', cfig, axes = plt.subplots(ncols = df.year.nunique(), nrows = 1, figsize=(6,5),sharex=True, sharey=True, facecolor = "#FFFFFF", zorder= 1)
fig.tight_layout(pad=6.0)
for yr, ax in zip(nr_years, axes.ravel()):
temp_df = df[df.year ==yr]
x = temp_df.ctry_code
y = temp_df.sites
for value, color, count in zip(x, colors[1::2], y):
ax.plot([value]*count, list(range(count)), 'o', ms=10, linestyle='', color=color)
ax.set_yticklabels([])
ax.tick_params(axis='both', which='both',labelsize=12,length=0,color= xy_ticklabel_color, pad =13)
ax.spines[['top', 'left','bottom', 'right']].set_visible(False)
ax.set_xlabel(yr,color = xy_label_color, size = 14, weight="bold")
ax.xaxis.set_label_coords(0.5, -0.2)
The result:

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