---
title: "Country profile"
format: html
jupyter: python3
---

```{python}
#| tags: [parameters]
country = "Brazil"
```

```{python}
#| echo: false
import pandas as pd
import matplotlib.pyplot as plt

profiles = pd.read_csv("data/country_profiles.csv")
one = profiles[profiles["country"] == country].sort_values("year")
```

# `{python} country`

This report uses World Bank indicators for `{python} country` between
`{python} int(one["year"].min())` and `{python} int(one["year"].max())`.

## The last five years

```{python}
#| echo: false
(one.tail(5)
    .loc[:, ["year", "gdp_per_capita", "life_expectancy", "population"]]
    .rename(columns={
        "year": "Year",
        "gdp_per_capita": "GDP per capita (US$)",
        "life_expectancy": "Life expectancy (years)",
        "population": "Population",
    })
    .style.format({
        "GDP per capita (US$)": "{:,.0f}",
        "Life expectancy (years)": "{:.1f}",
        "Population": "{:,.0f}",
    }).hide(axis="index"))
```

## Life expectancy over time

```{python}
#| echo: false
#| fig-cap: "Life expectancy at birth"
fig, ax = plt.subplots(figsize=(7, 3.5))
ax.plot(one["year"], one["life_expectancy"], color="#1B3A6B", linewidth=2)
ax.set_xlabel("Year")
ax.set_ylabel("Life expectancy (years)")
ax.spines[["top", "right"]].set_visible(False)
plt.tight_layout()
plt.show()
```

Source: World Bank World Development Indicators, downloaded 7 August 2026.
