parameters-py.qmd

---
title: "Valueboxes"
format: dashboard
---

```{python}
from plotnine import ggplot, aes, geom_point, geom_bar
from plotnine.data import mpg
```

```{python}
#| tags: [parameters]

car_class = "compact"
```

```{python}
#| label: filter-for-class
filtered_mpg = mpg[mpg['class'] == car_class]
filtered_mpg.reset_index(drop=True, inplace=True)
```

## Value boxes {height="25%"}

::: {.valuebox icon="car-front-fill" color="info"}
Class

`{python} car_class`
:::

```{python}
#| label: calculate-values
lowest_mileage_index = filtered_mpg['cty'].idxmin()
lowest_mileage_car = filtered_mpg.iloc[lowest_mileage_index]
lowest_mileage_cty = filtered_mpg.loc[lowest_mileage_index, 'cty']

highest_mileage_index = filtered_mpg['cty'].idxmax()
highest_mileage_car = filtered_mpg.iloc[highest_mileage_index]
highest_mileage_cty = filtered_mpg.loc[highest_mileage_index, 'cty']

mean_city_mileage = filtered_mpg['cty'].mean()
rounded_mean_city_mileage = round(mean_city_mileage, 2)
```

```{python}
#| content: valuebox
#| title: "Least efficient"
#| icon: fuel-pump-fill
#| color: danger
dict(
  value = str(f"{lowest_mileage_cty} mpg")
)
```

```{python}
#| content: valuebox
#| title: "Most efficient"
dict(
  icon = "fuel-pump",
  color = "success",
  value = str(f"{highest_mileage_cty} mpg")
)
```

::: {.valuebox icon="fuel-pump" color="secondary"}
Average city mileage

`{python} str(rounded_mean_city_mileage)` mpg
:::

## Plots {height="75%"}

```{python}
#| title: Highway vs. city mileage
(
    ggplot(filtered_mpg, aes(x = "cty", y = "hwy"))
    + geom_point()
)
```

```{python}
#| title: Drive types
(
    ggplot(filtered_mpg, aes(x = "drv"))
    + geom_bar()
)
```