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Olympic Weightlifting Medals with Stacked Bar Charts

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You can access this notebook and more by getting the e-book, Data is Beautiful.

Preamble

In [1]:
import numpy as np                   # for multi-dimensional containers 
import pandas as pd                  # for DataFrames
import plotly.graph_objects as go    # for data visualisation

Introduction

In this section, we're going to use 120 years of Olympic history to create a visualisation. Let's set our sights on something that illustrates the distribution of Olympic medals awarded for the weightlifting sport.

Weightlifting cats

The Dataset

We'll use the 120 years of Olympic history: athletes and results dataset, which we'll download and load with pandas. You're also welcome to use the mirrored that has been used in the following cell.

In [2]:
data_url = 'https://shahinrostami.com/datasets/athlete_events.csv'
raw_data = pd.read_csv(data_url)
raw_data.head()
Out[2]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
0 1 A Dijiang M 24.0 180.0 80.0 China CHN 1992 Summer 1992 Summer Barcelona Basketball Basketball Men's Basketball NaN
1 2 A Lamusi M 23.0 170.0 60.0 China CHN 2012 Summer 2012 Summer London Judo Judo Men's Extra-Lightweight NaN
2 3 Gunnar Nielsen Aaby M 24.0 NaN NaN Denmark DEN 1920 Summer 1920 Summer Antwerpen Football Football Men's Football NaN
3 4 Edgar Lindenau Aabye M 34.0 NaN NaN Denmark/Sweden DEN 1900 Summer 1900 Summer Paris Tug-Of-War Tug-Of-War Men's Tug-Of-War Gold
4 5 Christine Jacoba Aaftink F 21.0 185.0 82.0 Netherlands NED 1988 Winter 1988 Winter Calgary Speed Skating Speed Skating Women's 500 metres NaN

It looks like the data was loaded without any issues. Let's have a quick look at the available features.

In [3]:
pd.DataFrame(raw_data.columns)
Out[3]:
0
0 ID
1 Name
2 Sex
3 Age
4 Height
5 Weight
6 Team
7 NOC
8 Games
9 Year
10 Season
11 City
12 Sport
13 Event
14 Medal

Data Wrangling

We're only interested in Olympic weightlifting data for our visualisation, so we'll filter by selecting all rows where the Sport is set to Weightlifting.

In [4]:
data = raw_data[raw_data.Sport =="Weightlifting"]
data.head()
Out[4]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
80 22 Andreea Aanei F 22.0 170.0 125.0 Romania ROU 2016 Summer 2016 Summer Rio de Janeiro Weightlifting Weightlifting Women's Super-Heavyweight NaN
154 59 Ivan Nikolov Abadzhiev M 24.0 164.0 71.0 Bulgaria BUL 1956 Summer 1956 Summer Melbourne Weightlifting Weightlifting Men's Lightweight NaN
155 59 Ivan Nikolov Abadzhiev M 28.0 164.0 71.0 Bulgaria BUL 1960 Summer 1960 Summer Roma Weightlifting Weightlifting Men's Middleweight NaN
156 60 Mikhail Abadzhiev M 24.0 172.0 75.0 Bulgaria BUL 1960 Summer 1960 Summer Roma Weightlifting Weightlifting Men's Middleweight NaN
234 112 Aziz Abbas M 21.0 169.0 67.0 Iraq IRQ 1964 Summer 1964 Summer Tokyo Weightlifting Weightlifting Men's Lightweight NaN

If we look at the Medal column in the table above, we can see NaN values for when an athlete was not awarded a medal. As we're only interested Olympic medalists for this visualisation, let's drop all the rows where no medal was awarded.

In [5]:
data = data[data.Medal.notna()]
data.head()
Out[5]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
2331 1301 Sri Wahyuni Agustiani F 21.0 147.0 47.0 Indonesia INA 2016 Summer 2016 Summer Rio de Janeiro Weightlifting Weightlifting Women's Flyweight Silver
2637 1480 Franz Aigner M 32.0 NaN 107.0 Austria AUT 1924 Summer 1924 Summer Paris Weightlifting Weightlifting Men's Heavyweight Silver
3045 1698 Khadzhimurat Magomedovich Akkayev M 19.0 178.0 105.0 Russia RUS 2004 Summer 2004 Summer Athina Weightlifting Weightlifting Men's Middle-Heavyweight Silver
3046 1698 Khadzhimurat Magomedovich Akkayev M 23.0 178.0 105.0 Russia RUS 2008 Summer 2008 Summer Beijing Weightlifting Weightlifting Men's Middle-Heavyweight Bronze
3067 1713 Artur Vladimirovich Akoyev M 26.0 NaN 109.0 Unified Team EUN 1992 Summer 1992 Summer Barcelona Weightlifting Weightlifting Men's Heavyweight II Silver

If we're interested, we can take a peek at how many medals have been awarded in total for bronze, silver, and gold.

In [6]:
pd.DataFrame(data.Medal.value_counts())
Out[6]:
Medal
Gold 217
Bronze 216
Silver 213

Now that we have our filtered and relevant data, let's build a list of participating countries. At first glance, it looks like Team may be the feature we're interested in, and for the Weightlifting sport, it is indeed a good selection. However, in other sports in the same dataset, we will see Teams such as Japan-1 and Japan-2.

In [7]:
pd.DataFrame(raw_data[raw_data.Team.str.contains("Japan")].Team.unique())
Out[7]:
0
0 Japan
1 Japan-1
2 Japan-2
3 Japan-3

For now, we'll continue with the NOC feature, which holds the name of the National Olympic Committee for each athlete.

In [8]:
noc = data.NOC.unique().tolist()
print(noc)
['INA', 'AUT', 'RUS', 'EUN', 'BUL', 'URS', 'LUX', 'USA', 'JPN', 'IRI', 'TUR', 'FRA', 'BLR', 'GEO', 'IRQ', 'HUN', 'AUS', 'POL', 'ROU', 'GER', 'SWE', 'ITA', 'CUB', 'GDR', 'CHN', 'NED', 'TPE', 'KAZ', 'PRK', 'MDA', 'GBR', 'ARM', 'UKR', 'BEL', 'CAN', 'PHI', 'LTU', 'GRE', 'TCH', 'EGY', 'COL', 'FIN', 'VIE', 'SUI', 'FRG', 'KOR', 'THA', 'NOR', 'DEN', 'MEX', 'EST', 'TTO', 'IND', 'UZB', 'NGR', 'CRO', 'VEN', 'QAT', 'LAT', 'ARG', 'SGP', 'LIB', 'ESP', 'AZE']

Visualising the Data

Now that we have prepared our data, let's create a few visualisations. Instead of just showing you the final visualisation, we will develop our visualisation incrementally, where each subsequent visualisation improves on the last.

Stacked Bar Chart - Iteration 1

When we started this notebook, we had the idea of creating a stacked bar chart to visualise the medals awarded to each country in the weightlifting sport. Our first visualisation may look something like the following.

In [9]:
fig = go.Figure(layout=dict(barmode='stack'))

fig.add_bar(name="Bronze", x=noc, y=data[data.Medal == "Bronze"].NOC
            .value_counts().reindex(noc), marker_color="brown")

fig.add_bar(name="Silver", x=noc, y=data[data.Medal == "Silver"].NOC
            .value_counts().reindex(noc), marker_color="silver")

fig.add_bar(name="Gold", x=noc, y=data[data.Medal == "Gold"].NOC
            .value_counts().reindex(noc), marker_color="gold")

fig.show()

It's not a bad start! We have our bars stacked in the right order, from bronze up to gold, and our colours were selected to be gold, silver, and brown (as no colour parameter exists for bronze).

Stacked Bar Chart - Iteration 2

However, we can make some improvements to enhance the usefulness and beauty of the visualisation. Let's try the following:

  • Assign some specific HEX colour codes for our bar colours,
  • Order the bars in descending order by total medals awarded,
  • and Angle the bar (tick) labels at -45 degrees.
In [10]:
fig = go.Figure(layout=dict(
    barmode='stack', 
    xaxis= dict(categoryorder='total descending', tickangle=-45)))

fig.add_bar(name="Bronze", x=noc, y=data[data.Medal == "Bronze"].NOC
            .value_counts().reindex(noc), marker_color="#A57164")

fig.add_bar(name="Silver", x=noc, y=data[data.Medal == "Silver"].NOC
            .value_counts().reindex(noc), marker_color="#C0C0C0")

fig.add_bar(name="Gold", x=noc, y=data[data.Medal == "Gold"].NOC
            .value_counts().reindex(noc), marker_color="#FFD700")

fig.show()

Great! It's already looking easier to navigate, and the colours are more suitable for the data they're representing.

Stacked Bar Chart - Iteration 3

Let's continue to make improvements, this time we'll try the following:

  • Reduce the font-size of the bar (tick) labels, as some currently disappear if the width of the plot is too small (e.g., when shrinking the browser width),
  • Change the font-colour of our bar (tick) labels,
  • Add an outline and some transparency to our bars,
  • Reduce the gaps between our bars,
  • Hide the y-axis ticks,
  • and Add a thick line at the bottom of the x-axis.
In [11]:
fig = go.Figure(layout=dict(
    barmode='stack', bargap = 0.1,
    xaxis= dict(categoryorder='total descending', tickangle=-45,
                showline=True, linewidth=2, linecolor='black',ticks='',
                tickfont=dict(size=8, color='black')),
    yaxis=dict(showticklabels=False)))

fig.add_bar(name="Bronze", x=noc, y=data[data.Medal == "Bronze"].NOC
            .value_counts().reindex(noc), marker_color="#A57164")

fig.add_bar(name="Silver", x=noc, y=data[data.Medal == "Silver"].NOC
            .value_counts().reindex(noc), marker_color="#C0C0C0")

fig.add_bar(name="Gold", x=noc, y=data[data.Medal == "Gold"].NOC
            .value_counts().reindex(noc), marker_color="#FFD700")

fig.update_traces(marker_line_color='#003366',
                  marker_line_width=1, opacity=0.7)

fig.show()

Looking good!

Stacked Bar Chart - Final Iteration

Now to wrap things up, we may be interested in just selecting the "top 15" medal earning countries for weightlifting. We'll also start using the Team feature instead of working with the NOC. This will require some additional preparation. First, we'll determine the top 15 medal earners.

In [12]:
top_15 = data.Team.value_counts()[:15]
pd.DataFrame(top_15)
Out[12]:
Team
Soviet Union 62
China 57
United States 42
Bulgaria 36
Poland 32
Russia 26
Germany 25
Hungary 20
Iran 18
North Korea 17
Kazakhstan 16
Greece 16
France 16
Italy 15
Japan 14

Next, we'll filter our data to only include rows from these teams.

In [13]:
data =  data[data.Team.isin(list(top_15.index.values))]
teams = data.Team.unique().tolist()
data.head()
Out[13]:
ID Name Sex Age Height Weight Team NOC Games Year Season City Sport Event Medal
3045 1698 Khadzhimurat Magomedovich Akkayev M 19.0 178.0 105.0 Russia RUS 2004 Summer 2004 Summer Athina Weightlifting Weightlifting Men's Middle-Heavyweight Silver
3046 1698 Khadzhimurat Magomedovich Akkayev M 23.0 178.0 105.0 Russia RUS 2008 Summer 2008 Summer Beijing Weightlifting Weightlifting Men's Middle-Heavyweight Bronze
4001 2306 Ruslan Vladimirovich Albegov M 24.0 192.0 156.0 Russia RUS 2012 Summer 2012 Summer London Weightlifting Weightlifting Men's Super-Heavyweight Bronze
4360 2483 Rumen Aleksandrov M 20.0 176.0 89.0 Bulgaria BUL 1980 Summer 1980 Summer Moskva Weightlifting Weightlifting Men's Middle-Heavyweight Silver
4404 2511 Vasily Ivanovich Alekseyev M 30.0 185.0 160.0 Soviet Union URS 1972 Summer 1972 Summer Munich Weightlifting Weightlifting Men's Super-Heavyweight Gold

Finally, we'll produce our final visualisation that will display the top 15 medal earning countries (or teams) for the weightlifting sport. We'll also try the following improvements to our visualisation:

  • Changing the fonts to use Muli (if it's available),
  • Hide the legend as the bar colours are all we need,
  • Adding a title (and some top-margin to give it space),
  • Adding text above our bars indicating the total medals per country,
  • Increasing the thickness of our bar outlines (as we have fewer bars now),
  • and Changing the angle of the bar (tick) labels to 60 degrees, so they stay within the boundaries of our visualisation.
In [14]:
fig = go.Figure(layout=dict(
    title="Top 15 Olympic weightlifting medal earners between {}-{}"
        .format(data.Year.min(),data.Year.max()),
    barmode='stack', bargap = 0.1, margin=dict(t=40, r=0, b=0, l=0),
    font=dict(family="Muli", size=14, color="#212529",), showlegend=False,
    xaxis= dict(categoryorder='total descending', tickangle=60,
                showline=True, linewidth=2, linecolor='black',ticks='',
                tickfont=dict(family="Muli", size=16, color="#212529")),
    yaxis=dict(showticklabels=False)),
)

fig.add_bar(name="Bronze", x=teams, y=data[data.Medal == "Bronze"].Team
            .value_counts().reindex(teams), marker_color="#A57164")

fig.add_bar(name="Silver", x=teams, y=data[data.Medal == "Silver"].Team
            .value_counts().reindex(teams), marker_color="#C0C0C0")

fig.add_bar(name="Gold", x=teams, y=data[data.Medal == "Gold"].Team
            .value_counts().reindex(teams), marker_color="#FFD700",
            text=data.Team.value_counts().reindex(teams), textposition="outside")

fig.update_traces(marker_line_color='#003366',
                  marker_line_width=1.5, opacity=0.7, textfont_size=14)

fig.show()

Conclusion

In this section, we went through a few improvement cycles to produce a visualisation illustrating the top Olympic weightlifting medal earners in the 120 years of Olympic history: athletes and results dataset.

The visualisation ended up looking great, but a few plotly limitations prevented one final improvement - changing the bar colours to be gradients.

Weightlifting cats

Support this work

You can access this notebook and more by getting the e-book, Data is Beautiful.