def METHOD_NAME(datetimes=False, centroids=False, year=None, pretty_names=False):
    """
    Each row represents a country on a given year.
    https://www.gapminder.org/data/
    Returns:
        A `pandas.DataFrame` with 1704 rows and the following columns:
        `['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
        'iso_alpha', 'iso_num']`.
        If `datetimes` is True, the 'year' column will be a datetime column
        If `centroids` is True, two new columns are added: ['centroid_lat', 'centroid_lon']
        If `year` is an integer, the dataset will be filtered for that year
    """
    df = _get_dataset("gapminder")
    if year:
        df = df[df["year"] == year]
    if datetimes:
        df["year"] = (df["year"].astype(str) + "-01-01").astype("datetime64[ns]")
    if not centroids:
        df = df.drop(["centroid_lat", "centroid_lon"], axis=1)
    if pretty_names:
        df.rename(
            mapper=dict(
                country="Country",
                continent="Continent",
                year="Year",
                lifeExp="Life Expectancy",
                gdpPercap="GDP per Capita",
                pop="Population",
                iso_alpha="ISO Alpha Country Code",
                iso_num="ISO Numeric Country Code",
                centroid_lat="Centroid Latitude",
                centroid_lon="Centroid Longitude",
            ),
            axis="columns",
            inplace=True,
        )
    return df