"""
Built-in datasets for demonstration, educational and test purposes.
"""


def gapminder():
    """
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']`.
"""
    return _get_dataset("gapminder")


def tips():
    """
Each row represents a restaurant bill.

https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html

Returns:
    A `pandas.DataFrame` with 244 rows and the following columns:
    `['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`.
"""
    return _get_dataset("tips")


def iris():
    """
Each row represents a flower.

https://en.wikipedia.org/wiki/Iris_flower_data_set

Returns:
    A `pandas.DataFrame` with 150 rows and the following columns:
    `['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`.
"""
    return _get_dataset("iris")


def wind():
    """
Each row represents a level of wind intensity in a cardinal direction, and its frequency.

Returns:
    A `pandas.DataFrame` with 128 rows and the following columns:
    `['direction', 'strength', 'frequency']`.
"""
    return _get_dataset("wind")


def election():
    """
Each row represents voting results for an electoral district in the 2013 Montreal
mayoral election.

Returns:
    A `pandas.DataFrame` with 58 rows and the following columns:
    `['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`.
"""
    return _get_dataset("election")


def election_geojson():
    """
Each feature represents an electoral district in the 2013 Montreal mayoral election.

Returns:
    A GeoJSON-formatted `dict` with 58 polygon or multi-polygon features whose `id`
    is an electoral district numerical ID and whose `district` property is the ID and
    district name.
"""
    import gzip
    import json
    import os

    path = os.path.join(
        os.path.dirname(os.path.dirname(__file__)),
        "package_data",
        "datasets",
        "election.geojson.gz",
    )
    with gzip.GzipFile(path, "r") as f:
        result = json.loads(f.read().decode("utf-8"))
    return result


def carshare():
    """
Each row represents the availability of car-sharing services near the centroid of a zone
in Montreal.

Returns:
    A `pandas.DataFrame` with 249 rows and the following columns:
    `['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`.
"""
    return _get_dataset("carshare")


def _get_dataset(d):
    import pandas
    import os

    return pandas.read_csv(
        os.path.join(
            os.path.dirname(os.path.dirname(__file__)),
            "package_data",
            "datasets",
            d + ".csv.gz",
        )
    )
