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 incorporates occasion to Athlete stage Olympic Video games Outcomes from Athens 1896 to Beijing 2022. After an EDA (Exploratory Knowledge 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 developed throughout time.
My plotting information consists of two totally different datasets, one for annually: 2020 and 1996. For every dataset I’ve computed the full sum of athletes that participated to every occasion (athlete_sum) and the way a lot that sum represents in comparison with the variety of complete athletes (male + feminine) (distinction). See a screenshot of the info under:
That is my method to visualise it:
- Measurement proportion. Utilizing radius of bubbles to match quantity athletes per sport. Larger bubbles will characterize extremely aggressive occasions, corresponding to Athletics
- Multi variable interpretation. Making use of colors to characterize feminine illustration. Mild inexperienced bubbles will characterize occasions with a 50/50 break up, corresponding to Hockey.
Right here is my place to begin (utilizing the code and method from above):
Some straightforward fixes: rising determine measurement and altering labels to empty if the dimensions isn’t over 250 to keep away from having phrases outdoors bubbles.
fig, ax = plt.subplots(figsize=(12,8),subplot_kw=dict(facet="equal"))#Labels edited instantly in dataset
Effectively, now a minimum of it’s readable. However, why is Athletics pink and Boxing blue? Let’s add a legend for example 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 are able to create rectangles (or circles) to point out that 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) can 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", measurement=14,fontweight='daring'))
# Mix rectangle and textual content
vbox = HPacker(youngsters=[da, text], align="high", 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='information',
boxcoords=("axes fraction", "information"),
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 below 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 fair 50/50 feminine to male break 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 by way of athlete quantity (Swimming, Athletics and Gymnastics) are very near having a 50/50 break up
