olympicdash-py-1.qmd

---
title: "Olympic Games"
format: html
---

```{python}
#| label: load-packages
from plotnine import *
import great_tables as gt
import pandas as pd
```

```{python}
#| label: load-data
olympics_full = pd.read_csv("data/olympics.csv", low_memory = False)
```

```{python}
#| label: prep-data
# Filter for Summer season and non-NA medals
olympics = olympics_full[(olympics_full["season"] == "Summer") & (olympics_full["medal"].notna())]

# Split the team column at "-" into two columns
split_data = olympics["team"].str.split("-", n = 1, expand = True)
olympics.loc[:, "team"] = split_data[0]

# Reorder the medal column categories
olympics.loc[:, "medal"] = pd.Categorical(olympics["medal"], categories = ["Bronze", "Silver", "Gold"])
```

## Medals by sport

```{python}
#| warning: false
# Lump the sport column to top 15 categories, grouping others as Other
top_15_sports = olympics["sport"].value_counts().nlargest(15).index
olympics["sport"] = olympics["sport"].apply(lambda x: x if x in top_15_sports else "Other")

# Convert the sport column to a categorical type with order based on frequency, and reverse the order
olympics["sport"] = pd.Categorical(olympics["sport"], categories = olympics["sport"].value_counts().index[::-1])

# Move the Other category of the sport column to the beginning
new_order = ["Other"] + [cat for cat in olympics["sport"].cat.categories if cat != "Other"]
olympics["sport"] = olympics["sport"].cat.reorder_categories(new_order)

# Plot
(
    ggplot(olympics, aes(x = "sport", fill = "medal"))
    + geom_bar()
    + coord_flip()
    + guides(fill = guide_legend(reverse = True)) 
    + labs(x = "", y = "", fill = "Medal") 
    + theme_minimal() 
    + theme(
        legend_position = "inside",
        legend_position_inside = (0.9, 0.2),
        legend_direction = "horizontal",
        legend_background = element_rect(fill = "white", color = "gray"),
        figure_size = (10, 5)
    )
)
```

## Medals by year

```{python}
# Count the occurrences of each medal per year
olympics_count = olympics.groupby(["year", "medal"], observed=True).size().reset_index(name = "n")

# Plot
(
    ggplot(olympics_count, aes(x = "year", y = "n", color = "medal")) 
    + geom_point(size = 0.5) 
    + geom_line() 
    + guides(color = guide_legend(reverse = True)) 
    + scale_x_continuous(breaks = range(1896, 2020, 8)) 
    + labs(x = "Year", y = "", color = "Medal") 
    + theme_minimal() 
    + theme(
        legend_position = "inside",
        legend_position_inside = (0.9, 0.2),
        legend_direction = "horizontal",
        legend_background = element_rect(fill = "white", color = "gray"),
        figure_size = (10, 2.5)
    )
)
```

## Medals by country

```{python}
# Count the occurrences of each medal per team
olympics_count = olympics.groupby(["team", "medal"]).size().reset_index(name="n")

# Pivot olympics_count to get medals as columns
olympics_pivot = olympics_count.pivot_table(index = "team", columns = "medal", values = "n", fill_value = 0)

# Calculate the total number of medals
olympics_pivot["Total"] = olympics_pivot[["Bronze", "Gold", "Silver"]].sum(axis=1)

# Reset the index and rearrange columns
olympics_pivot = olympics_pivot.reset_index()
olympics_pivot = olympics_pivot[["team", "Gold", "Silver", "Bronze", "Total"]]

# Sort by Total medals, then team
olympics_pivot = olympics_pivot.sort_values(by=["Total", "team"], ascending=[False, True])

# Rename the team column to Team
olympics_pivot.rename(columns={"team": "Team"}, inplace=True)

(
    gt.GT(olympics_pivot)
)
```