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.

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.

Support this work

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