53  Exercise 3: Double Dash!!(boards)

You’re now going to create a dashboard in Streamlit.

Here is a link to the dataset. Link

You can use the starter code from https://github.com/hsma-programme/h6_7b_web_apps_1/blob/main/exercises/exercise_3/starter_code.py

Click here to view the starter code

import pandas as pd
import plotly.express as px
import geopandas
import matplotlib.pyplot as plt

###########################################################
# Display a plot of the medals won by a country over time
###########################################################

# ========================== #
chosen_country = "UK"
# ========================== #

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

# Turn the dataframe into a 'long' format for plotting - don't worry about what that means!
# What we'll end up with is a dataframe with columns for Year, Country, Medal Type (called 'variable')
# and number of medals (called 'value')
medals_per_country_per_year_long = medals_per_country_per_year.melt(id_vars=["Year", "Country", "NOC"])

# Create a line plot using the plotly express library
fig = px.line(
    # Filter our dataframe to just our 'chosen_country'
    medals_per_country_per_year_long[medals_per_country_per_year_long["Country"] == chosen_country],
    # The value to plot on the vertical axis - this corresponds to the number of medals
    y="value",
    # Plot the year on the x axis
    x="Year",
    # Plot the number of medals on the y axis
    color="variable",
    # Colour the medals as orange for bronze, silver for silver, gold for gold, and blue for total
    color_discrete_sequence=["orange", "silver", "gold", "blue"],
    # Add a title, using an f-string to include the name of the chosen country
    title=f"Medals Won over Time - {chosen_country}"
    )

# Display the plotly plot in an interactive window
fig.show()

##################################################################
# Display a table of just the medal winners in Athletics in 2012
##################################################################

# ========================== #
year = 2012
# ========================== #

# ========================== #
sport = "Athletics"
# ========================== #

country_medals_by_event = pd.read_csv("country_medals_by_event.csv")

country_medals_by_event_filtered = (
    country_medals_by_event[
        (country_medals_by_event["Sport"] == sport) &
        (country_medals_by_event["Year"] == year)
    ])

print(country_medals_by_event_filtered[['Event', 'Bronze', 'Silver', 'Gold']])

############################################################################
# Print out the country that won the most gold medals in the selected year
############################################################################
# Hint - this might make a nice metric card!

# ========================== #
year = 2012
# ========================== #

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

# Filter the dataframe to just the rows for the selected year
medals_per_country_per_year_filtered = (
    medals_per_country_per_year[medals_per_country_per_year["Year"] == year]
    )

# Sort by number of gold medals won in descending order (so the most medals is at the top of the table)
# Then just keep the first row (i.e. the one for the country with the most gold medals in the
# selected year)
top_gold_medal_winner_row = (
    medals_per_country_per_year
    .sort_values(["Gold"], ascending=False)
    .head(1)
    )

# Using an f string, populate the text with the number of gold medals this country won
print(f"The country that won the most medals in 2012 was" +
      f" {top_gold_medal_winner_row['Country'].values[0]}" +
      f" with {top_gold_medal_winner_row['Gold'].values[0]:.0f} gold medals.")


###########################################################################
# Print out the number of gold medals won by the selected country in 2012
###########################################################################
# Hint - this might make a nice metric card!

# ========================== #
year = 2012
# ========================== #

# ========================== #
chosen_country = "UK"
# ========================== #

# Filter the medals table down to the selected year and country
medals_chosen_country = (
    medals_per_country_per_year[
        (medals_per_country_per_year["Year"] == year) &
        (medals_per_country_per_year["Country"] == chosen_country)
        ]
    )

# Print out how many medals the country won
print(f"The {chosen_country} won {medals_chosen_country['Gold'].values[0]:.0f}" +
      f" gold medals in {year}.")

# Calculate the difference between this country and the top scorer
# Hint - this might be an interesting value to display as part of a metric card...
difference = medals_chosen_country['Gold'].values[0] - top_gold_medal_winner_row['Gold'].values[0]

# Print out a message with the difference
if difference < 0:
    print(f"This was {abs(difference):.0f} fewer" +
          f" than {top_gold_medal_winner_row['Country'].values[0]}")
# Unless the difference = 0, in which case the selected country is also the most successful country
else:
    print("They were the top country for gold medals in that year.")


#########################################
# Print a map of medal winners in 2012
#########################################

# ========================== #
year = 2012
# ========================== #

# ========================== #
selected_medal_type="Gold"
# ========================== #

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

country_outlines = geopandas.read_file("countries_outlines.geojson")

medals_per_country_per_year_gdf = pd.concat([
    pd.merge(country_outlines, medals_per_country_per_year, left_on="id", right_on="NOC", how="inner"),
    pd.merge(country_outlines, medals_per_country_per_year, left_on="name", right_on="Country", how="inner")
]).drop_duplicates()

fig, ax = plt.subplots(figsize=(15, 8))

ax = medals_per_country_per_year_gdf[medals_per_country_per_year_gdf["Year"] == year].plot(
    selected_medal_type, legend=True, ax=ax
    )

ax.axis('off')

plt.title(f"Number of {selected_medal_type} Medals in {year}")

fig.show()


###################################################
# Print out possible countries to select from for
# the next run of the script
###################################################

print("The countries you could choose from are: " +
      f"{medals_per_country_per_year['Country'].unique().tolist()}")

print(f"Choose a year between {medals_per_country_per_year['Year'].min()}" +
      f" and {medals_per_country_per_year['Year'].max()}")

print("The sports you could choose from are: " +
      f"{country_medals_by_event['Sport'].unique().tolist()}")

                                            Event          Bronze  \
4612                 Athletics Men's 1,500 metres         Morocco   
4613                Athletics Men's 10,000 metres        Ethiopia   
4614                   Athletics Men's 100 metres             USA   
4615           Athletics Men's 110 metres Hurdles         Jamaica   
4616           Athletics Men's 20 kilometres Walk           China   
4617                   Athletics Men's 200 metres         Jamaica   
4618    Athletics Men's 3,000 metres Steeplechase           Kenya   
4619         Athletics Men's 4 x 100 metres Relay          France   
4620         Athletics Men's 4 x 400 metres Relay        Trinidad   
4621                   Athletics Men's 400 metres        Trinidad   
4622           Athletics Men's 400 metres Hurdles     Puerto Rico   
4623                 Athletics Men's 5,000 metres           Kenya   
4624           Athletics Men's 50 kilometres Walk           China   
4625                   Athletics Men's 800 metres           Kenya   
4626                    Athletics Men's Decathlon            Cuba   
4627                 Athletics Men's Discus Throw         Estonia   
4628                 Athletics Men's Hammer Throw           Japan   
4629                    Athletics Men's High Jump          Canada   
4630                Athletics Men's Javelin Throw         Finland   
4631                    Athletics Men's Long Jump             USA   
4632                     Athletics Men's Marathon           Kenya   
4633                   Athletics Men's Pole Vault         Germany   
4634                     Athletics Men's Shot Put             USA   
4635                  Athletics Men's Triple Jump           Italy   
4636               Athletics Women's 1,500 metres         Bahrain   
4637              Athletics Women's 10,000 metres           Kenya   
4638                 Athletics Women's 100 metres         Jamaica   
4639         Athletics Women's 100 metres Hurdles             USA   
4640         Athletics Women's 20 kilometres Walk           China   
4641                 Athletics Women's 200 metres             USA   
4642  Athletics Women's 3,000 metres Steeplechase        Ethiopia   
4643       Athletics Women's 4 x 100 metres Relay         Ukraine   
4644       Athletics Women's 4 x 400 metres Relay         Jamaica   
4645                 Athletics Women's 400 metres             USA   
4646         Athletics Women's 400 metres Hurdles  Czech Republic   
4647               Athletics Women's 5,000 metres        Ethiopia   
4648                 Athletics Women's 800 metres          Russia   
4649               Athletics Women's Discus Throw           China   
4650               Athletics Women's Hammer Throw         Germany   
4651                 Athletics Women's Heptathlon          Russia   
4652                  Athletics Women's High Jump          Russia   
4653              Athletics Women's Javelin Throw         Germany   
4654                  Athletics Women's Long Jump             USA   
4655                   Athletics Women's Marathon          Russia   
4656                 Athletics Women's Pole Vault          Russia   
4657                   Athletics Women's Shot Put           China   
4658                Athletics Women's Triple Jump         Ukraine   

                  Silver                Gold  
4612                 USA             Algeria  
4613                 USA                  UK  
4614             Jamaica             Jamaica  
4615                 USA                 USA  
4616           Guatemala               China  
4617             Jamaica             Jamaica  
4618              France               Kenya  
4619            Trinidad             Jamaica  
4620                 USA             Bahamas  
4621  Dominican Republic             Grenada  
4622                 USA  Dominican Republic  
4623            Ethiopia                  UK  
4624           Australia              Russia  
4625            Botswana               Kenya  
4626                 USA                 USA  
4627                Iran             Germany  
4628            Slovenia             Hungary  
4629                 USA              Russia  
4630             Ukraine            Trinidad  
4631           Australia                  UK  
4632               Kenya              Uganda  
4633             Germany              France  
4634             Germany              Poland  
4635                 USA                 USA  
4636              Turkey              Turkey  
4637               Kenya            Ethiopia  
4638                 USA             Jamaica  
4639                 USA           Australia  
4640              Russia              Russia  
4641             Jamaica                 USA  
4642             Tunisia              Russia  
4643             Jamaica                 USA  
4644              Russia                 USA  
4645                  UK                 USA  
4646                 USA              Russia  
4647               Kenya            Ethiopia  
4648        South Africa              Russia  
4649              Russia             Croatia  
4650              Poland              Russia  
4651             Germany                  UK  
4652                 USA              Russia  
4653             Germany      Czech Republic  
4654              Russia                 USA  
4655               Kenya            Ethiopia  
4656                Cuba                 USA  
4657              Russia         New Zealand  
4658            Colombia          Kazakhstan  
The country that won the most medals in 2012 was USA with 82 gold medals.
The UK won 29 gold medals in 2012.
This was 53 fewer than USA
The countries you could choose from are: ['Australia', 'Austria', 'Denmark', 'France', 'Germany', 'Greece', 'Hungary', 'Switzerland', 'UK', 'USA', 'Belgium', 'Canada', 'Colombia', 'Cuba', 'Czech Republic', 'Haiti', 'India', 'Italy', 'Luxembourg', 'Mexico', 'Netherlands', 'New Zealand', 'Norway', 'Spain', 'Sweden', 'Finland', 'Russia', 'South Africa', 'Brazil', 'Estonia', 'Japan', 'Argentina', 'Ireland', 'Monaco', 'Poland', 'Portugal', 'Romania', 'Serbia', 'Uruguay', 'Chile', 'Egypt', 'Philippines', 'Latvia', 'Turkey', 'Iran', 'Jamaica', 'Panama', 'Peru', 'Puerto Rico', 'South Korea', 'Sri Lanka', 'Trinidad', 'Bulgaria', 'Lebanon', 'Venezuela', 'Bahamas', 'Iceland', 'Pakistan', 'Ethiopia', 'Ghana', 'Iraq', 'Morocco', 'Syria', 'Taiwan', 'Kenya', 'Nigeria', 'Tunisia', 'Cameroon', 'Mongolia', 'Uganda', 'Niger', 'North Korea', 'Bermuda', 'Thailand', 'Guyana', 'Tanzania', 'Zimbabwe', 'Algeria', 'China', 'Dominican Republic', 'Ivory Coast', 'Zambia', 'Costa Rica', 'Curacao', 'Djibouti', 'Indonesia', 'Senegal', 'Suriname', 'Virgin Islands, US', 'Croatia', 'Individual Olympic Athletes', 'Israel', 'Lithuania', 'Malaysia', 'Namibia', 'Qatar', 'Slovenia', 'Armenia', 'Azerbaijan', 'Belarus', 'Burundi', 'Ecuador', 'Georgia', 'Kazakhstan', 'Moldova', 'Mozambique', 'Slovakia', 'Tonga', 'Ukraine', 'Uzbekistan', 'Barbados', 'Kuwait', 'Kyrgyzstan', 'Macedonia', 'Saudi Arabia', 'Vietnam', 'Eritrea', 'Paraguay', 'United Arab Emirates', 'Afghanistan', 'Mauritius', 'Sudan', 'Tajikistan', 'Togo', 'Bahrain', 'Botswana', 'Cyprus', 'Gabon', 'Grenada', 'Guatemala', 'Montenegro', 'Fiji', 'Jordan', 'Kosovo']
Choose a year between 1896 and 2016
The sports you could choose from are: ['Athletics', 'Cycling', 'Fencing', 'Gymnastics', 'Shooting', 'Swimming', 'Tennis', 'Weightlifting', 'Wrestling', 'Archery', 'Basque Pelota', 'Cricket', 'Croquet', 'Equestrianism', 'Football', 'Golf', 'Polo', 'Rowing', 'Rugby', 'Sailing', 'Tug-Of-War', 'Water Polo', 'Boxing', 'Diving', 'Lacrosse', 'Roque', 'Figure Skating', 'Hockey', 'Jeu De Paume', 'Motorboating', 'Racquets', 'Art Competitions', 'Modern Pentathlon', 'Ice Hockey', 'Alpinism', 'Aeronautics', 'Basketball', 'Canoeing', 'Handball', 'Judo', 'Volleyball', 'Rhythmic Gymnastics', 'Synchronized Swimming', 'Table Tennis', 'Badminton', 'Baseball', 'Beach Volleyball', 'Softball', 'Taekwondo', 'Trampolining', 'Triathlon', 'Rugby Sevens']

C:\Users\Sammi\AppData\Local\Temp\ipykernel_48536\2287783435.py:162: UserWarning:

FigureCanvasAgg is non-interactive, and thus cannot be shown

Try to include at least

• a dataframe
• a graph or map
• a metric card
• a layout element (tabs, sidebar, columns)
• a user input for filtering the outputs (selectbox, slider, etc.)

If you have time, try adding in

• a download button for a dataframe
• a download button for a graph or map

We’ll work on this until 4:05.

Don’t forget to refer to the Streamlit book - all of the above are covered in there.

I’ll be asking each group to share their code afterwards - we will upload your code to a repository and I’ll publish each app on Streamlit Community Cloud so you can see each other’s dashboards easily.

53.1 Dataset Overview

Several dataframes are provided, as well as a geojson file of country outlines for mapping purposes.

athlete_details_eventwise.csv

import pandas as pd

athlete_details_eventwise = pd.read_csv("athlete_details_eventwise.csv")

athlete_details_eventwise.head(5)

	Name	Sex	Age	Height	Weight	Country	NOC	Year	Sport	Event	Medal
0	Ahmad Shah Abouwi	M	NaN	NaN	NaN	Afghanistan	AFG	1956	Hockey	Hockey Men's Hockey	NaN
1	Jammal-ud-Din Affendi	M	28.0	NaN	NaN	Afghanistan	AFG	1936	Hockey	Hockey Men's Hockey	NaN
2	Mohammad Anwar Afzal	M	NaN	NaN	NaN	Afghanistan	AFG	1948	Football	Football Men's Football	NaN
3	Mohammad Aktar	M	17.0	156.0	48.0	Afghanistan	AFG	1980	Wrestling	Wrestling Men's Light-Flyweight, Freestyle	NaN
4	Mohammad Daoud Anwary	M	22.0	NaN	NaN	Afghanistan	AFG	1964	Wrestling	Wrestling Men's Bantamweight, Freestyle	NaN

country_medals_by_event.csv

import pandas as pd

country_medals_by_event = pd.read_csv("country_medals_by_event.csv")

country_medals_by_event.head()

	Year	Sport	Event	Bronze	Silver	Gold
0	1896	Athletics	Athletics Men's 1,500 metres	France	USA	Australia
1	1896	Athletics	Athletics Men's 100 metres	Hungary	Germany	USA
2	1896	Athletics	Athletics Men's 110 metres Hurdles	Data Missing	UK	USA
3	1896	Athletics	Athletics Men's 400 metres	UK	USA	USA
4	1896	Athletics	Athletics Men's 800 metres	Greece	Hungary	Australia

games_cities.csv

import pandas as pd

games_cities = pd.read_csv("games_cities.csv")

games_cities.head()

	Year	City
0	1896	Athina
1	1900	Paris
2	1904	St. Louis
3	1906	Athina
4	1908	London

medals_per_country_per_year.csv

import pandas as pd

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

medals_per_country_per_year.head()

	Year	Country	NOC	Bronze	Silver	Gold	Total
0	1896	Australia	AUS	1.0	0.0	2.0	3.0
1	1896	Austria	AUT	2.0	1.0	2.0	5.0
2	1896	Denmark	DEN	3.0	2.0	1.0	6.0
3	1896	France	FRA	2.0	4.0	5.0	11.0
4	1896	Germany	GER	2.0	5.0	7.0	14.0

countries_outlines.geojson

import geopandas

country_outlines = geopandas.read_file("countries_outlines.geojson")

country_outlines.plot()

53.2 Code Snippets

So you don’t have to spend too much time focussing on data manipulation and visualisation, here are a range of premade dataframes, graphs and maps you could choose to display in your app.

53.2.1 Dataframes

Medals per Country by Year

import pandas as pd

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

medals_per_country_per_year.sort_values("Total", ascending=False).head(15)

	Year	Country	NOC	Bronze	Silver	Gold	Total
45	1904	USA	USA	75.0	79.0	76.0	230.0
611	1980	Russia	URS	46.0	69.0	80.0	195.0
665	1984	USA	USA	30.0	61.0	82.0	173.0
81	1908	UK	GBR	37.0	50.0	56.0	143.0
707	1988	Russia	URS	46.0	31.0	54.0	131.0
596	1980	Germany	GDR	42.0	37.0	47.0	126.0
574	1976	Russia	URS	35.0	41.0	49.0	125.0
1265	2016	USA	USA	38.0	37.0	46.0	121.0
771	1992	Russia	EUN	29.0	38.0	45.0	112.0
1096	2008	USA	USA	35.0	39.0	36.0	110.0
215	1932	USA	USA	30.0	36.0	44.0	110.0
783	1992	USA	USA	37.0	34.0	37.0	108.0
493	1968	USA	USA	34.0	28.0	45.0	107.0
1181	2012	USA	USA	29.0	28.0	46.0	103.0
398	1960	Russia	URS	31.0	29.0	43.0	103.0

Medals per Country by Year - Sorted by Multiple Columns

import pandas as pd

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

medals_per_country_per_year.sort_values(["Gold", "Total"], ascending=True).head(15)

	Year	Country	NOC	Bronze	Silver	Gold	Total
14	1900	Colombia	COL	0.0	1.0	0.0	1.0
25	1900	Mexico	MEX	1.0	0.0	0.0	1.0
65	1908	Austria	AUT	1.0	0.0	0.0	1.0
111	1920	Greece	GRE	0.0	1.0	0.0	1.0
114	1920	Luxembourg	LUX	0.0	1.0	0.0	1.0
116	1920	New Zealand	NZL	1.0	0.0	0.0	1.0
135	1924	Haiti	HAI	1.0	0.0	0.0	1.0
139	1924	Japan	JPN	1.0	0.0	0.0	1.0
141	1924	Monaco	MON	1.0	0.0	0.0	1.0
143	1924	New Zealand	NZL	1.0	0.0	0.0	1.0
146	1924	Portugal	POR	1.0	0.0	0.0	1.0
147	1924	Romania	ROU	1.0	0.0	0.0	1.0
160	1928	Chile	CHI	0.0	1.0	0.0	1.0
168	1928	Haiti	HAI	0.0	1.0	0.0	1.0
178	1928	Philippines	PHI	1.0	0.0	0.0	1.0

Total Medals per Country - All Time - Sorted by Single Column

import pandas as pd

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

total_medals_all_time = (
    medals_per_country_per_year.drop(columns=["Year", "NOC"])
    .groupby('Country').sum()
    .reset_index()
    .sort_values('Total', ascending=False)
    )

total_medals_all_time.head(15)

	Country	Bronze	Silver	Gold	Total
123	USA	707.0	802.0	1035.0	2544.0
98	Russia	487.0	498.0	592.0	1577.0
41	Germany	490.0	457.0	442.0	1389.0
122	UK	298.0	316.0	278.0	892.0
38	France	282.0	255.0	233.0	770.0
57	Italy	198.0	191.0	219.0	608.0
20	China	154.0	163.0	228.0	545.0
4	Australia	197.0	171.0	150.0	518.0
110	Sweden	188.0	175.0	150.0	513.0
48	Hungary	172.0	154.0	178.0	504.0
60	Japan	161.0	134.0	142.0	437.0
37	Finland	120.0	86.0	104.0	310.0
18	Canada	137.0	104.0	64.0	305.0
97	Romania	120.0	95.0	88.0	303.0
82	Netherlands	114.0	95.0	88.0	297.0

Medal Winners per event per year

import pandas as pd

medals_by_event = pd.read_csv("country_medals_by_event.csv")
medals_by_event

	Year	Sport	Event	Bronze	Silver	Gold
0	1896	Athletics	Athletics Men's 1,500 metres	France	USA	Australia
1	1896	Athletics	Athletics Men's 100 metres	Hungary	Germany	USA
2	1896	Athletics	Athletics Men's 110 metres Hurdles	Data Missing	UK	USA
3	1896	Athletics	Athletics Men's 400 metres	UK	USA	USA
4	1896	Athletics	Athletics Men's 800 metres	Greece	Hungary	Australia
...	...	...	...	...	...	...
5211	2016	Wrestling	Wrestling Women's Flyweight, Freestyle	Bulgaria	Azerbaijan	Japan
5212	2016	Wrestling	Wrestling Women's Heavyweight, Freestyle	China	Kazakhstan	Canada
5213	2016	Wrestling	Wrestling Women's Light-Heavyweight, Freestyle	Kazakhstan	Russia	Japan
5214	2016	Wrestling	Wrestling Women's Lightweight, Freestyle	India	Russia	Japan
5215	2016	Wrestling	Wrestling Women's Middleweight, Freestyle	Kazakhstan	Belarus	Japan

5216 rows × 6 columns

Medal Winners per event per year - single sport and year

import pandas as pd

selected_year = 2012
selected_sport = "Judo"

medals_by_event = pd.read_csv("country_medals_by_event.csv")
medals_by_event

	Year	Sport	Event	Bronze	Silver	Gold
0	1896	Athletics	Athletics Men's 1,500 metres	France	USA	Australia
1	1896	Athletics	Athletics Men's 100 metres	Hungary	Germany	USA
2	1896	Athletics	Athletics Men's 110 metres Hurdles	Data Missing	UK	USA
3	1896	Athletics	Athletics Men's 400 metres	UK	USA	USA
4	1896	Athletics	Athletics Men's 800 metres	Greece	Hungary	Australia
...	...	...	...	...	...	...
5211	2016	Wrestling	Wrestling Women's Flyweight, Freestyle	Bulgaria	Azerbaijan	Japan
5212	2016	Wrestling	Wrestling Women's Heavyweight, Freestyle	China	Kazakhstan	Canada
5213	2016	Wrestling	Wrestling Women's Light-Heavyweight, Freestyle	Kazakhstan	Russia	Japan
5214	2016	Wrestling	Wrestling Women's Lightweight, Freestyle	India	Russia	Japan
5215	2016	Wrestling	Wrestling Women's Middleweight, Freestyle	Kazakhstan	Belarus	Japan

5216 rows × 6 columns

Athletes in most events per year

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

most_events_per_year_by_athlete = athlete_statistics.value_counts(['Name', 'Country', 'Year']).reset_index()

most_events_per_year_by_athlete.head(15)

	Name	Country	Year	count
0	Robert Tait McKenzie	Canada	1932	44
1	Alfrd (Arnold-) Hajs (Guttmann-)	Hungary	1928	28
2	Miltiades Manno	Hungary	1932	27
3	Alfred James Munnings	UK	1948	25
4	Wilhelm (William) Hunt Diederich	USA	1932	19
5	Acee Blue Eagle	USA	1932	18
6	Jean Lucien Nicolas Jacoby	Luxembourg	1936	17
7	Stanisaw Noakowski	Poland	1928	17
8	Mahonri Mackintosh Young	USA	1932	16
9	ngel Zrraga Argelles	Mexico	1928	16
10	Georges-mile Fauvelle	France	1928	16
11	Pierre De Soete	Belgium	1928	16
12	Willis Augustus Lee, Jr.	USA	1920	15
13	Charles Zeller Klauder	USA	1928	14
14	Georg Vilhelm Lagerstedt	Sweden	1936	13

Athletes in most events per year - Details on Athlete with Most Events

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

most_events_per_year_by_athlete = athlete_statistics.value_counts(['Name', 'Country', 'Year']).reset_index()

athlete_name_most_events = most_events_per_year_by_athlete.head(1)['Name'].values[0]
athlete_name_most_events_year = most_events_per_year_by_athlete.head(1)['Year'].values[0]

athlete_statistics[(athlete_statistics["Name"] == athlete_name_most_events) &
                    (athlete_statistics["Year"] == athlete_name_most_events_year)].head(15)

	Name	Sex	Age	Height	Weight	Country	NOC	Year	Sport	Event	Medal
32262	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Medals And...	Bronze
32263	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32264	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32265	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32266	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32267	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32268	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32269	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32270	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32271	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32272	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32273	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32274	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32275	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN
32276	Robert Tait McKenzie	M	65.0	NaN	NaN	Canada	CAN	1932	Art Competitions	Art Competitions Mixed Sculpturing, Unknown Event	NaN

Athlete with most total events competed in

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

athlete_statistics.value_counts(['Name', 'Sex', 'Country']).reset_index()

	Name	Sex	Country	count
0	Robert Tait McKenzie	M	Canada	58
1	Heikki Ilmari Savolainen	M	Finland	39
2	Joseph "Josy" Stoffel	M	Luxembourg	38
3	Ioannis Theofilakis	M	Greece	36
4	Takashi Ono	M	Japan	33
...	...	...	...	...
116737	Jan Wijnants	M	Belgium	1
116738	Jan Wallisch	M	Czech Republic	1
116739	Jan Wagenaar	M	Netherlands	1
116740	Jan Wadas	M	Poland	1
116741	Karen Rayr van der Veen (-Wilkinson)	F	South Africa	1

116742 rows × 4 columns

Filtering to a single athlete - Exact Match

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

chosen_athlete = "Simone Arianne Biles"

athlete_statistics[athlete_statistics["Name"] == chosen_athlete]

	Name	Sex	Age	Height	Weight	Country	NOC	Year	Sport	Event	Medal
203896	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Individual All-Around	Gold
203897	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Team All-Around	Gold
203898	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Floor Exercise	Gold
203899	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Horse Vault	Gold
203900	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Uneven Bars	NaN
203901	Simone Arianne Biles	F	19.0	143.0	47.0	USA	USA	2016	Gymnastics	Gymnastics Women's Balance Beam	Bronze

Filtering to a single athlete - Partial Match

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

search_string = "Ben"

athlete_statistics[athlete_statistics["Name"].str.contains(search_string)]

	Name	Sex	Age	Height	Weight	Country	NOC	Year	Sport	Event	Medal
271	Benaissa Abed	M	23.0	170.0	51.0	Algeria	ALG	1988	Boxing	Boxing Men's Flyweight	NaN
327	Ben Ali Beghouach	M	21.0	186.0	89.0	Algeria	ALG	1988	Handball	Handball Men's Handball	NaN
328	Ben Ali Beghouach	M	29.0	186.0	89.0	Algeria	ALG	1996	Handball	Handball Men's Handball	NaN
348	Djillali Ben Brahim	M	27.0	178.0	71.0	Algeria	ALG	1980	Judo	Judo Men's Lightweight	NaN
349	Reda Ben Chehima	M	26.0	175.0	75.0	Algeria	ALG	2004	Fencing	Fencing Men's Sabre, Individual	NaN
...	...	...	...	...	...	...	...	...	...	...	...
219770	Lidija Benedeti-Lapajne	F	25.0	175.0	64.0	Serbia	YUG	1984	Athletics	Athletics Women's High Jump	NaN
219771	Marijan Bene	M	25.0	170.0	68.0	Serbia	YUG	1976	Boxing	Boxing Men's Welterweight	NaN
221821	Benson Mulomba	M	22.0	177.0	54.0	Zambia	ZAM	1972	Athletics	Athletics Men's 800 metres	NaN
221843	Benny Muziyo	M	23.0	NaN	NaN	Zambia	ZAM	2016	Boxing	Boxing Men's Middleweight	NaN
222158	Gavin Ben Sutherland	M	37.0	186.0	78.0	Zimbabwe	ZIM	2016	Archery	Archery Men's Individual	NaN

1617 rows × 11 columns

List of Events in Year

import pandas as pd

selected_year = 2012

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

athlete_statistics[athlete_statistics["Year"] == selected_year][['Sport', 'Event']].drop_duplicates().reset_index(drop=True).head(15)

	Sport	Event
0	Athletics	Athletics Men's 100 metres
1	Taekwondo	Taekwondo Men's Welterweight
2	Boxing	Boxing Men's Flyweight
3	Judo	Judo Men's Half-Lightweight
4	Athletics	Athletics Women's 100 metres
5	Taekwondo	Taekwondo Men's Featherweight
6	Weightlifting	Weightlifting Women's Lightweight
7	Swimming	Swimming Women's 100 metres Butterfly
8	Weightlifting	Weightlifting Men's Lightweight
9	Athletics	Athletics Men's Shot Put
10	Swimming	Swimming Men's 100 metres Freestyle
11	Weightlifting	Weightlifting Men's Middle-Heavyweight
12	Judo	Judo Women's Half-Lightweight
13	Shooting	Shooting Men's Air Pistol, 10 metres
14	Shooting	Shooting Men's Free Pistol, 50 metres

Count of Events in Year

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

athlete_statistics[athlete_statistics["Year"] == selected_year][['Sport', 'Event']].drop_duplicates().value_counts('Sport').reset_index().head(15)

	Sport	count
0	Athletics	47
1	Swimming	34
2	Wrestling	18
3	Cycling	18
4	Canoeing	16
5	Shooting	15
6	Weightlifting	15
7	Gymnastics	14
8	Judo	14
9	Rowing	14
10	Boxing	13
11	Fencing	10
12	Sailing	10
13	Taekwondo	8
14	Diving	8

53.2.2 Useful Lists and Info From Datasets

List of Sports

import pandas as pd

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

all_sports = athlete_statistics["Sport"].drop_duplicates().tolist()

print(all_sports)

['Hockey', 'Football', 'Wrestling', 'Athletics', 'Taekwondo', 'Judo', 'Boxing', 'Weightlifting', 'Shooting', 'Fencing', 'Swimming', 'Sailing', 'Synchronized Swimming', 'Triathlon', 'Equestrianism', 'Cycling', 'Volleyball', 'Handball', 'Rowing', 'Table Tennis', 'Tennis', 'Gymnastics', 'Trampolining', 'Badminton', 'Canoeing', 'Beach Volleyball', 'Basketball', 'Diving', 'Rugby', 'Rugby Sevens', 'Polo', 'Modern Pentathlon', 'Archery', 'Water Polo', 'Art Competitions', 'Golf', 'Figure Skating', 'Softball', 'Baseball', 'Rhythmic Gymnastics', 'Tug-Of-War', 'Ice Hockey', 'Lacrosse', 'Basque Pelota', 'Cricket', 'Croquet', 'Motorboating', 'Racquets', 'Jeu De Paume', 'Alpinism', 'Aeronautics', 'Roque']

List of Events in a particular year

import pandas as pd

selected_year = 2012

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

athlete_statistics_year = athlete_statistics[athlete_statistics["Year"] == selected_year]

all_events = athlete_statistics_year["Event"].drop_duplicates().tolist()

print(all_events)

["Athletics Men's 100 metres", "Taekwondo Men's Welterweight", "Boxing Men's Flyweight", "Judo Men's Half-Lightweight", "Athletics Women's 100 metres", "Taekwondo Men's Featherweight", "Weightlifting Women's Lightweight", "Swimming Women's 100 metres Butterfly", "Weightlifting Men's Lightweight", "Athletics Men's Shot Put", "Swimming Men's 100 metres Freestyle", "Weightlifting Men's Middle-Heavyweight", "Judo Women's Half-Lightweight", "Shooting Men's Air Pistol, 10 metres", "Shooting Men's Free Pistol, 50 metres", "Boxing Men's Welterweight", "Volleyball Women's Volleyball", "Athletics Men's 5,000 metres", "Athletics Women's Marathon", "Judo Women's Heavyweight", "Athletics Men's 3,000 metres Steeplechase", "Wrestling Men's Lightweight, Greco-Roman", "Boxing Men's Light-Heavyweight", "Weightlifting Men's Heavyweight", "Boxing Men's Heavyweight", "Boxing Men's Lightweight", "Taekwondo Men's Flyweight", "Athletics Men's Marathon", "Boxing Men's Light-Flyweight", "Fencing Women's Foil, Individual", "Cycling Men's Road Race, Individual", "Wrestling Men's Light-Heavyweight, Freestyle", "Athletics Men's 800 metres", "Athletics Men's 1,500 metres", "Fencing Women's Sabre, Individual", "Athletics Men's Triple Jump", "Boxing Men's Bantamweight", "Boxing Men's Middleweight", "Rowing Women's Single Sculls", "Wrestling Men's Welterweight, Greco-Roman", "Swimming Men's 200 metres Butterfly", "Swimming Women's 100 metres Backstroke", "Shooting Men's Trap", "Handball Women's Handball", "Basketball Women's Basketball", "Athletics Women's 800 metres", "Swimming Women's 50 metres Freestyle", "Canoeing Men's Canadian Singles, 200 metres", "Canoeing Men's Canadian Doubles, 1,000 metres", "Judo Women's Middleweight", "Canoeing Men's Canadian Singles, 1,000 metres", "Swimming Men's 400 metres Individual Medley", "Athletics Women's 400 metres", "Athletics Men's 200 metres", "Rowing Women's Coxless Pairs", "Sailing Men's One Person Dinghy", "Shooting Men's Small-Bore Rifle, Prone, 50 metres", "Volleyball Men's Volleyball", "Hockey Women's Hockey", "Swimming Women's 400 metres Individual Medley", "Hockey Men's Hockey", "Tennis Men's Singles", "Wrestling Women's Flyweight, Freestyle", "Swimming Women's 800 metres Freestyle", "Swimming Women's 10 kilometres Open Water", "Sailing Men's Two Person Dinghy", "Basketball Men's Basketball", "Judo Men's Middleweight", "Handball Men's Handball", "Athletics Men's 20 kilometres Walk", "Sailing Women's One Person Dinghy", "Rowing Men's Lightweight Double Sculls", "Athletics Men's Hammer Throw", "Athletics Women's Discus Throw", "Canoeing Men's Kayak Doubles, 200 metres", "Athletics Women's Hammer Throw", 'Tennis Mixed Doubles', "Tennis Women's Doubles", "Rowing Men's Single Sculls", "Beach Volleyball Women's Beach Volleyball", "Swimming Men's 50 metres Freestyle", "Swimming Men's 100 metres Backstroke", "Rowing Women's Lightweight Double Sculls", 'Equestrianism Mixed Jumping, Individual', "Athletics Men's Discus Throw", "Table Tennis Men's Singles", "Taekwondo Women's Flyweight", "Sailing Women's Windsurfer", "Judo Men's Half-Middleweight", "Gymnastics Men's Floor Exercise", "Gymnastics Men's Rings", "Sailing Women's Two Person Dinghy", "Tennis Men's Doubles", "Judo Women's Extra-Lightweight", "Swimming Men's 400 metres Freestyle", "Swimming Men's 1,500 metres Freestyle", "Gymnastics Women's Individual All-Around", "Gymnastics Women's Floor Exercise", "Gymnastics Women's Uneven Bars", "Gymnastics Women's Balance Beam", "Cycling Men's Omnium", "Cycling Men's BMX", "Sailing Men's Windsurfer", "Canoeing Men's Canadian Singles, Slalom", "Rowing Men's Double Sculls", "Synchronized Swimming Women's Duet", "Judo Men's Half-Heavyweight", "Cycling Men's Mountainbike, Cross-Country", "Triathlon Men's Olympic Distance", "Athletics Men's Javelin Throw", "Wrestling Men's Heavyweight, Greco-Roman", "Weightlifting Women's Light-Heavyweight", "Gymnastics Men's Individual All-Around", "Gymnastics Men's Horse Vault", "Gymnastics Men's Parallel Bars", "Gymnastics Men's Horizontal Bar", "Gymnastics Men's Pommelled Horse", "Judo Men's Extra-Lightweight", "Athletics Women's Javelin Throw", "Wrestling Men's Featherweight, Freestyle", "Wrestling Men's Middleweight, Greco-Roman", "Weightlifting Men's Light-Heavyweight", "Weightlifting Women's Super-Heavyweight", "Athletics Men's Long Jump", "Wrestling Men's Super-Heavyweight, Greco-Roman", "Wrestling Men's Welterweight, Freestyle", "Weightlifting Men's Super-Heavyweight", "Swimming Women's 100 metres Freestyle", "Athletics Men's 50 kilometres Walk", "Athletics Men's 4 x 100 metres Relay", "Synchronized Swimming Women's Team", "Judo Men's Heavyweight", "Shooting Women's Air Pistol, 10 metres", "Swimming Women's 4 x 200 metres Freestyle Relay", "Shooting Women's Trap", "Archery Women's Individual", "Swimming Women's 200 metres Freestyle", "Swimming Women's 400 metres Freestyle", "Shooting Men's Skeet", "Water Polo Women's Water Polo", "Water Polo Men's Water Polo", "Rowing Men's Lightweight Coxless Fours", "Cycling Men's Team Pursuit, 4,000 metres", "Athletics Women's 400 metres Hurdles", "Gymnastics Women's Team All-Around", "Rowing Men's Coxed Eights", "Athletics Women's Pole Vault", "Canoeing Women's Kayak Fours, 500 metres", "Diving Women's Platform", "Cycling Women's BMX", "Rowing Men's Coxless Pairs", "Athletics Women's 1,500 metres", "Diving Women's Synchronized Platform", 'Equestrianism Mixed Three-Day Event, Individual', 'Equestrianism Mixed Three-Day Event, Team', "Swimming Women's 4 x 100 metres Freestyle Relay", "Sailing Men's One Person Heavyweight Dinghy", "Shooting Men's Rapid-Fire Pistol, 25 metres", "Shooting Women's Sporting Pistol, 25 metres", "Rowing Men's Coxless Fours", "Rowing Women's Coxed Eights", "Badminton Women's Doubles", "Canoeing Men's Kayak Fours, 1,000 metres", "Athletics Men's 400 metres Hurdles", "Athletics Men's 4 x 400 metres Relay", "Diving Men's Platform", "Swimming Women's 200 metres Individual Medley", "Swimming Women's 4 x 100 metres Medley Relay", "Rowing Women's Double Sculls", "Sailing Women's Three Person Keelboat", "Triathlon Women's Olympic Distance", "Swimming Men's 4 x 100 metres Freestyle Relay", "Swimming Men's 4 x 100 metres Medley Relay", "Canoeing Men's Kayak Singles, Slalom", "Cycling Women's Team Pursuit", "Cycling Women's Omnium", "Modern Pentathlon Women's Individual", "Rowing Women's Quadruple Sculls", "Modern Pentathlon Men's Individual", "Boxing Women's Middleweight", "Canoeing Women's Kayak Doubles, 500 metres", "Rowing Men's Quadruple Sculls", "Swimming Women's 200 metres Breaststroke", "Canoeing Women's Kayak Singles, Slalom", "Table Tennis Men's Team", "Swimming Men's 200 metres Freestyle", "Swimming Men's 4 x 200 metres Freestyle Relay", "Trampolining Men's Individual", "Cycling Women's Road Race, Individual", "Cycling Women's Individual Time Trial", "Cycling Men's Team Sprint", "Shooting Men's Air Rifle, 10 metres", "Swimming Men's 100 metres Butterfly", "Swimming Men's 200 metres Individual Medley", "Swimming Women's 200 metres Butterfly", 'Equestrianism Mixed Dressage, Individual', 'Equestrianism Mixed Dressage, Team', 'Equestrianism Mixed Jumping, Team', "Cycling Women's Mountainbike, Cross-Country", "Swimming Women's 200 metres Backstroke", "Athletics Men's Pole Vault", "Boxing Men's Light-Welterweight", "Swimming Men's 10 kilometres Open Water", "Canoeing Men's Canadian Doubles, Slalom", "Sailing Men's Skiff", "Swimming Women's 100 metres Breaststroke", "Athletics Women's 3,000 metres Steeplechase", "Athletics Women's 20 kilometres Walk", "Swimming Men's 200 metres Backstroke", "Table Tennis Women's Singles", "Table Tennis Women's Team", "Weightlifting Women's Middleweight", "Boxing Men's Super-Heavyweight", "Shooting Women's Skeet", "Shooting Men's Double Trap", "Taekwondo Women's Welterweight", "Cycling Women's Team Sprint", "Athletics Women's 100 metres Hurdles", "Cycling Women's Sprint", "Cycling Women's Keirin", "Rhythmic Gymnastics Women's Individual", "Badminton Women's Singles", "Canoeing Women's Kayak Singles, 200 metres", "Canoeing Women's Kayak Singles, 500 metres", "Diving Women's Springboard", "Cycling Men's Sprint", "Cycling Men's Keirin", "Judo Women's Lightweight", "Swimming Men's 100 metres Breaststroke", "Swimming Men's 200 metres Breaststroke", "Cycling Men's Individual Time Trial", "Shooting Men's Small-Bore Rifle, Three Positions, 50 metres", "Shooting Women's Air Rifle, 10 metres", "Diving Women's Synchronized Springboard", "Canoeing Men's Kayak Doubles, 1,000 metres", "Badminton Men's Doubles", "Athletics Men's 400 metres", "Athletics Men's 10,000 metres", "Canoeing Men's Kayak Singles, 200 metres", "Canoeing Men's Kayak Singles, 1,000 metres", "Tennis Women's Singles", "Wrestling Men's Lightweight, Freestyle", "Shooting Women's Small-Bore Rifle, Three Positions, 50 metres", "Diving Men's Springboard", "Athletics Women's 5,000 metres", "Athletics Women's 10,000 metres", "Archery Men's Individual", "Athletics Women's Heptathlon", "Beach Volleyball Men's Beach Volleyball", "Judo Women's Half-Middleweight", "Wrestling Men's Light-Heavyweight, Greco-Roman", "Badminton Men's Singles", "Fencing Men's Foil, Individual", "Wrestling Men's Featherweight, Greco-Roman", "Wrestling Men's Middleweight, Freestyle", "Wrestling Men's Super-Heavyweight, Freestyle", "Judo Men's Lightweight", "Wrestling Men's Heavyweight, Freestyle", "Wrestling Women's Lightweight, Freestyle", "Weightlifting Men's Bantamweight", "Athletics Women's 4 x 100 metres Relay", "Athletics Men's High Jump", "Athletics Women's 200 metres", "Athletics Men's 110 metres Hurdles", "Athletics Women's Long Jump", "Judo Women's Half-Heavyweight", "Athletics Women's Triple Jump", "Athletics Women's High Jump", "Athletics Men's Decathlon", "Football Men's Football", "Fencing Men's Sabre, Individual", "Fencing Men's Sabre, Team", "Rhythmic Gymnastics Women's Group", "Athletics Women's 4 x 400 metres Relay", "Athletics Women's Shot Put", "Weightlifting Women's Heavyweight", "Gymnastics Women's Horse Vault", "Wrestling Women's Heavyweight, Freestyle", "Trampolining Women's Individual", "Football Women's Football", "Boxing Women's Lightweight", "Taekwondo Women's Heavyweight", "Boxing Women's Flyweight", "Sailing Men's Two Person Keelboat", "Fencing Men's epee, Individual", "Wrestling Women's Middleweight, Freestyle", "Taekwondo Men's Heavyweight", "Diving Men's Synchronized Springboard", 'Badminton Mixed Doubles', "Fencing Women's epee, Individual", "Taekwondo Women's Featherweight", "Diving Men's Synchronized Platform", "Gymnastics Men's Team All-Around", "Archery Women's Team", "Archery Men's Team", "Fencing Men's Foil, Team", "Fencing Women's epee, Team", "Weightlifting Men's Middleweight", "Weightlifting Women's Flyweight", "Weightlifting Men's Featherweight", "Weightlifting Women's Featherweight", "Fencing Women's Foil, Team"]

Earliest and Latest Years in Datasets

import pandas as pd

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

earliest_year = medals_per_country_per_year["Year"].min()

latest_year = medals_per_country_per_year["Year"].max()

print(f"The earliest year in the dataset is {earliest_year}")

print(f"The latest year in the dataset is {latest_year}")

The earliest year in the dataset is 1896
The latest year in the dataset is 2016

Oldest and Youngest Athletes in given year in given event

import pandas as pd

selected_year = 2012
selected_sport = "Gymnastics"

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

athlete_statistics_filtered = athlete_statistics[(athlete_statistics["Year"] == selected_year) & (athlete_statistics["Sport"] == selected_sport)]

youngest_athlete = athlete_statistics_filtered["Age"].min()

oldest_athlete = athlete_statistics_filtered["Age"].max()

print(f"The youngest athlete who competed in {selected_sport} in {selected_year} was {youngest_athlete:.0f} years old.")

print(f"The oldest athlete who competed in {selected_sport} in {selected_year} was{oldest_athlete:.0f} years old.")

The youngest athlete who competed in Gymnastics in 2012 was 15 years old.
The oldest athlete who competed in Gymnastics in 2012 was39 years old.

53.2.3 Plots

Single Medal Type - per Country by Year

import pandas as pd
import plotly.express as px

selected_countries = ["UK", "USA", "Canada", "Australia", "China", "Russia", "France", "Germany", "Italy"]

selected_medal_type = "Bronze"

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

px.line(medals_per_country_per_year[medals_per_country_per_year["Country"].isin(selected_countries)],
        x="Year", y=selected_medal_type, color="Country",
        title=f"{selected_medal_type} medals over time - subset of countries")

Single Medal Type - per Country by Year - Single Country

import pandas as pd
import plotly.express as px

chosen_country = "UK"
selected_medal_type = "Bronze"

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

px.line(medals_per_country_per_year[medals_per_country_per_year["Country"] == chosen_country],
        y=selected_medal_type, x="Year",
        title=f"{selected_medal_type} medals for {chosen_country} over time")

All Medal Types - Single Country

import pandas as pd
import plotly.express as px

chosen_country = "UK"

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

medals_per_country_per_year_long = medals_per_country_per_year.melt(id_vars=["Year", "Country", "NOC"])

px.line(medals_per_country_per_year_long[medals_per_country_per_year_long["Country"] == chosen_country],
        y="value", x="Year", color="variable",
        color_discrete_sequence=["orange", "silver", "gold", "blue"],
        title=f"Medals Won over Time - {chosen_country}")

Total Medals per Country - Bar Chart of Summed Values - Limiting Number of Countries

import pandas as pd
import plotly.express as px

rows_to_display = 10

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

total_medals_all_time = (
    medals_per_country_per_year.drop(columns=["Year", "NOC"])
    .groupby('Country').sum()
    .reset_index()
    .sort_values('Total', ascending=False)
    )

px.bar(total_medals_all_time.head(rows_to_display), x='Country', y='Total',
       title=f"Total Number of Medals since 1896 - Top {rows_to_display} Countries")

Total Medals per Country - Stacked Bar Chart

import pandas as pd
import plotly.express as px

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

total_medals_all_time = (
    medals_per_country_per_year.drop(columns=["Year", "NOC"])
    .groupby('Country').sum()
    .reset_index()
    .sort_values('Total', ascending=False)
    )

total_medals_all_time_long = total_medals_all_time.drop(columns='Total').melt(id_vars="Country")

px.bar(total_medals_all_time_long,
        x="Country", y="value", color="variable",
        color_discrete_sequence=["orange", "silver", "gold"]
        )

Total Medals per Country - Stacked Bar Chart as Percentage of Total

import pandas as pd
import plotly.express as px

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

total_medals_all_time = (
    medals_per_country_per_year.drop(columns=["Year", "NOC"])
    .groupby('Country').sum()
    .reset_index()
    .sort_values('Total', ascending=False)
    )

total_medals_all_time_long = total_medals_all_time.drop(columns='Total').melt(id_vars="Country")

total_medals_all_time_long['Total'] = total_medals_all_time_long['value'].groupby(total_medals_all_time_long['Country']).transform('sum')

total_medals_all_time_long['Percentage of Total'] = total_medals_all_time_long['value'] / total_medals_all_time_long['Total']

px.bar(total_medals_all_time_long[total_medals_all_time_long['Country'].isin(total_medals_all_time_long.head(rows_to_display)['Country'].values)],
       x="Country", y="Percentage of Total", color="variable",
       color_discrete_sequence=["orange", "silver", "gold"],
       title=f"Breakdown of Medals by type - Top {rows_to_display} countries by total all-time medal count")

Athlete Gender Balance by Year - Stacked Bar Chart (Parts of a Whole)

import pandas as pd
import plotly.express as px

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

distinct_athletes_per_year = athlete_statistics[['Name', 'Year','Country','Sex', 'Age', 'Height', 'Weight']].drop_duplicates()

athlete_counts_by_sex_by_year = distinct_athletes_per_year.value_counts(['Sex', 'Year']).reset_index()

athlete_counts_by_sex_by_year['total_athletes_both_sex_in_year'] = athlete_counts_by_sex_by_year['count'].groupby(athlete_counts_by_sex_by_year['Year']).transform('sum')

athlete_counts_by_sex_by_year['Percentage of Athletes'] = athlete_counts_by_sex_by_year['count'] / athlete_counts_by_sex_by_year['total_athletes_both_sex_in_year']

px.bar(athlete_counts_by_sex_by_year,
       x="Year", y="Percentage of Athletes", color="Sex",
       title=f"Change in Gender Split Over Time")

Events per Sport Per Year

import pandas as pd
import plotly.express as px

athlete_statistics = pd.read_csv("athlete_details_eventwise.csv")

selected_sport = "Gymnastics"

athlete_statistics_sport = athlete_statistics[athlete_statistics["Sport"] == selected_sport]

events_per_year_by_sport = (
    athlete_statistics_sport[['Year', 'Event']]
    .drop_duplicates()
    .value_counts(['Year'])
    .reset_index()
)

px.line(events_per_year_by_sport.sort_values("Year"), x="Year", y="count",
            markers=True, title=f"{selected_sport} Events Per Olympics",
            range_x=[athlete_statistics.Year.min(), athlete_statistics.Year.max()])

53.2.4 Maps

The provided maps are imperfect due to the complexity of matching up country names across the olympic dataset and an unexplained bug affecting the colouring of certain countries in the interactive map.

For the purpose of the exercise, don’t worry about the slight issues with the map - the focus is just on displaying a map successfully within your dashboard.

Number of Medals by Location - Static Map

import pandas as pd
import geopandas
import matplotlib.pyplot as plt

selected_year = 2012
selected_medal_type="Bronze"

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

country_outlines = geopandas.read_file("countries_outlines.geojson")

medals_per_country_per_year_gdf = pd.concat([
    pd.merge(country_outlines, medals_per_country_per_year, left_on="id", right_on="NOC", how="inner"),
    pd.merge(country_outlines, medals_per_country_per_year, left_on="name", right_on="Country", how="inner")
]).drop_duplicates()

fig, ax = plt.subplots(figsize=(15, 8))
ax = medals_per_country_per_year_gdf[medals_per_country_per_year_gdf["Year"] == selected_year].plot(
    "Gold", legend=True, ax=ax
    )
ax.axis('off')
plt.title(f"Number of {selected_medal_type} Medals in {selected_year}")

Text(0.5, 1.0, 'Number of Bronze Medals in 2012')

Number of Medals by Location - Interactive Map

import pandas as pd
import geopandas
import folium

selected_year = 2012
selected_medal_type="Bronze"

medals_per_country_per_year = pd.read_csv("medals_per_country_per_year.csv")

country_outlines = geopandas.read_file("countries_outlines.geojson")

medals_per_country_per_year_gdf = pd.concat([
    pd.merge(country_outlines, medals_per_country_per_year, left_on="id", right_on="NOC", how="inner"),
    pd.merge(country_outlines, medals_per_country_per_year, left_on="name", right_on="Country", how="inner")
]).drop_duplicates()

#create base map
world_map_medals = folium.Map(
    location=[50.71671, -3.50668],
    zoom_start=2,
    tiles='cartodbpositron'
    )

# create and add choropleth map
choropleth = folium.Choropleth(
    geo_data=medals_per_country_per_year_gdf[medals_per_country_per_year_gdf["Year"] == selected_year], # dataframe with geometry in it
    data=medals_per_country_per_year_gdf[medals_per_country_per_year_gdf["Year"] == selected_year], # dataframe with data in - may be the same dataframe or a different one
    columns=['name', 'Gold'], # [key (field for geometry), field to plot]
    key_on='feature.properties.Country'
    )

choropleth = choropleth.add_to(world_map_medals)

choropleth = choropleth.geojson.add_child(
    folium.features.GeoJsonTooltip(
        ['name', 'Gold'],
        labels=True
        )
)

world_map_medals

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