For my chart, I’m utilizing an Olympic Historic Dataset from Olympedia.org which Joseph Cheng shared in Kaggle with a public area license.
It comprises occasion to Athlete degree Olympic Video games Outcomes from Athens 1896 to Beijing 2022. After an EDA (Exploratory Information Evaluation) I remodeled it right into a dataset that particulars the variety of feminine athletes in every sport/occasion per 12 months. My bubble chart concept is to point out which sports activities have a 50/50 feminine to male ratio athletes and the way it has advanced throughout time.
My plotting knowledge consists of two completely different datasets, one for annually: 2020 and 1996. For every dataset I’ve computed the whole sum of athletes that participated to every occasion (athlete_sum) and the way a lot that sum represents in comparison with the variety of whole athletes (male + feminine) (distinction). See a screenshot of the info under:
That is my method to visualise it:
- Dimension proportion. Utilizing radius of bubbles to check quantity athletes per sport. Larger bubbles will symbolize extremely aggressive occasions, akin to Athletics
- Multi variable interpretation. Making use of colors to symbolize feminine illustration. Mild inexperienced bubbles will symbolize occasions with a 50/50 cut up, akin to Hockey.
Right here is my start line (utilizing the code and method from above):
Some simple fixes: growing determine dimension and altering labels to empty if the scale isn’t over 250 to keep away from having phrases exterior bubbles.
fig, ax = plt.subplots(figsize=(12,8),subplot_kw=dict(side="equal"))#Labels edited instantly in dataset
Properly, now not less than it’s readable. However, why is Athletics pink and Boxing blue? Let’s add a legend for instance the connection between colors and feminine illustration.
As a result of it’s not your common barplot chart, plt.legend() doesn’t do the trick right here.
Utilizing matplotlib Annotation Bbox we will create rectangles (or circles) to point out which means behind every color. We are able to additionally do the identical factor to point out a bubble scale.
import matplotlib.pyplot as plt
from matplotlib.offsetbox import (AnnotationBbox, DrawingArea,
TextArea,HPacker)
from matplotlib.patches import Circle,Rectangle# That is an instance for one part of the legend
# Outline the place the annotation (legend) shall be
xy = [50, 128]
# Create your coloured rectangle or circle
da = DrawingArea(20, 20, 0, 0)
p = Rectangle((10 ,10),10,10,shade="#fc8d62ff")
da.add_artist(p)
# Add textual content
textual content = TextArea("20%", textprops=dict(shade="#fc8d62ff", dimension=14,fontweight='daring'))
# Mix rectangle and textual content
vbox = HPacker(kids=[da, text], align="prime", pad=0, sep=3)
# Annotate each in a field (change alpha if you wish to see the field)
ab = AnnotationBbox(vbox, xy,
xybox=(1.005, xy[1]),
xycoords='knowledge',
boxcoords=("axes fraction", "knowledge"),
box_alignment=(0.2, 0.5),
bboxprops=dict(alpha=0)
)
#Add to your bubble chart
ax.add_artist(ab)
I’ve additionally added a subtitle and a textual content description underneath the chart simply through the use of plt.textual content()
Simple and consumer pleasant interpretations of the graph:
- Majority of bubbles are mild inexperienced → inexperienced means 50% females → majority of Olympic competitions have a good 50/50 feminine to male cut up (yay🙌)
- Just one sport (Baseball), in darkish inexperienced color, has no feminine participation.
- 3 sports activities have solely feminine participation however the variety of athletes is pretty low.
- The largest sports activities when it comes to athlete quantity (Swimming, Athletics and Gymnastics) are very near having a 50/50 cut up
